The Molecular Correlates of 
Sleep and Sleep Deprivation 
in vivo and in vitro 
 
 
 
 
 
 
William Gee 
Selwyn College 
This dissertation is submitted for the degree of Doctor of Philosophy 
October 2018
i 
 
The Molecular Correlates of Sleep and Sleep 
Deprivation in vivo and in vitro 
 
William Gee 
 
Summary 
 
This thesis describes the use of in vivo and in vitro models to better understand the molecular 
correlates of sleep and sleep deprivation. Unlike previous studies, we utilise a timecourse based 
experimental design throughout, which has the advantage of identifying how the abundance of 
molecules return to baseline following sleep deprivation. Chapter 3 outlines the transcriptome of 
mouse cortex collected over 54 hours from mice subjected to varied durations of sleep deprivation. 
The timecourse experimental design aids in the identification of genes that are induced during both 
spontaneous and enforced wakefulness, and facilitates the dissociation of genes whose expression is 
tightly linked to the current wake state of the animal from those whose expression is linked to the 
total amount of wakefulness recently experienced by the animal. Like previous studies, we identify 
several genes involved in the unfolded protein response and synaptic function that are upregulated 
by sleep deprivation. We also find that increasing durations of sleep deprivation progressively reduces 
the total number of rhythmically expressed genes in mouse cortex, with only a handful of transcripts 
identified as diurnal following 12 hour sleep deprivation.  
Chapter 4 outlines the proteomic and metabolomic effects of 12 hour sleep deprivation. Proteomic 
analyses indicate that the abundance of ribosomal and nucleosomal proteins is suppressed for at least 
24 hours following sleep deprivation, whilst the abundance of several phosphodiesterases are acutely 
increased following sleep deprivation. Metabolomic analyses of sleep deprived mouse cortex 
identified 3 molecular species whose abundance profile implicate them as sleep homeostats.  
Finally, we also set out to develop an in vitro model of sleep deprivation based on the optogenetic 
activation of a neuroblastoma cell line, which is outlined in Chapter 5. Following several rounds of 
optimisation, the stable expression of an opsin was found to induce intracellular calcium spikes and 
immediate early gene expression during illumination. Transcriptomic profiling of illuminated SH-SY5Y 
cells induced large scale transcriptomic changes, and modulated the expression of genes involved in 
synapses, cholesterol synthesis, the molecular clock and the unfolded protein response. Although 
these functional classes are reminiscent of those modulated by in vivo sleep deprivation, there was 
only a slight enrichment of individual genes modulated by in vivo sleep deprivation amongst the blue 
light sensitive genes, indicating further work is required to more closely model in vivo sleep 
deprivation.   
ii 
 
Declaration 
This dissertation is the result of my own work and includes nothing which is the 
outcome of work done in collaboration except as declared in the Preface and 
specified in the text. 
It is not substantially the same as any that I have submitted, or, is being 
concurrently submitted for a degree or diploma or other qualification at the 
University of Cambridge or any other University or similar institution except as 
declared in the Preface and specified in the text. I further state that no substantial 
part of my dissertation has already been submitted, or, is being concurrently 
submitted for any such degree, diploma or other qualification at the University of 
Cambridge or any other University or similar institution except as declared in the 
Preface and specified in the text.  
It does not exceed the prescribed word limit for the relevant Degree Committee. 
 
 
 
 
 
 
 
 
Signed: ______________________________ (William Gee) 
 
iii 
 
Acknowledgements 
 
First and foremost, I would like to thank Ak Reddy for giving me the opportunity to carry out a PhD in 
his lab. Very few students have simultaneously had the opportunity to carry out large scale studies 
and had the opportunity to use many recent innovations during their PhD, and so I’d like to thank Ak 
further for his technologically minded approach to research.  
I would like to thank all of the Reddy Lab members, past and present, who helped me with my project. 
Particular thanks is due to Laura Bollepalli and David Pritchett, who helped me extensively with the 
basic handling, set up of experiments and even sleep deprivations involved in the in vivo work 
presented here. Alessandra Stangherlin, the cloning queen, taught me as many cloning and cell culture 
tricks as I could learn, which were invaluable for the in vitro work presented here. Whenever I had a 
question on how to handle data, I could always count on Guillaume Rey and Utham Valekunja for 
sound advice. I’d like to thank Sandipan Ray for his help with my proteomics project. I’d also like to 
thank all the members of the Reddy lab for the day to day discussions and jokes that made the workday 
more enjoyable. 
I’d like to thank Greg Strachan for his confocal microscopy training, ordering and interesting lunchtime 
discussions about new techniques. At CBS, which at times felt like my home away from home, I’m 
indebted to Ian Purvis, John Mcquillan, and especially Charley Beresford and Laura McKinven for all 
their help with preparation of rooms, cabinet checks and general husbandry, without which I would 
have only seen dim red light for several months straight!  
Away from work, I’m tremendously grateful to my friends Claire Bromley, Nick Jamieson and 
Christopher Lim, who, through their own experiences of PhDs, all shared the highs and pointed out 
the silver linings of the lows.  
The final thanks must go to my partner, and now wife, Rachel. Her indomitable happiness and support 
has been the biggest constant throughout my studies, maintaining a good outlook on things, whatever 
the problem I was facing. Although I suspect she wasn’t particularly happy when I started my PhD, I’m 
certain she’ll be overjoyed when I hand it in, and, as such, it is dedicated to her. 
 
 
iv 
 
Contents 
 
LIST OF FIGURES IX 
LIST OF TABLES XIV 
ABBREVIATIONS XV 
1. GENERAL INTRODUCTION 1 
1.1. What is Sleep? 2 
1.2. Functions of Sleep 4 
1.3. The Neurological Control of Sleep 9 
1.4. The Molecular and Cellular Basis of Sleep and Sleep Homeostasis 16 
1.5. Models to Investigate Sleep at the Molecular and Cellular Level 22 
1.6. Tools to Interrogate Molecular Changes Associated with Sleep and Sleep Deprivation 25 
1.7. Aims and Approach of this PhD Project 30 
2. GENERAL METHODS 32 
2.1. Mice Used 33 
2.2. Sleep Deprivation Protocol 33 
2.3. Timecourse Tissue Sampling Protocol 34 
2.4. Preparation of Libraries for RNA-Seq Analysis 34 
2.5. Analysis of RNA-Seq Data 36 
2.6. Proteomics Analyses 38 
2.7. Metabolomic Analyses 40 
v 
 
2.8. Statistical Analysis of Datasets 41 
2.9. Media used for Cell Experiments 42 
2.10. Cell Maintenance 42 
2.11. Generation of Stable Cell Lines 42 
2.12. Stimulation of Cells with Neurotransmitter Cocktail 43 
2.13. Stimulation of Cells with Light 43 
2.14. Live Cell Luminescence 45 
2.15. Generation of Plasmids for Stable Expression of Opsins 47 
2.16. Quantitative Real Time PCR (qPCR) 51 
2.17. Live Cell Microscopy of RCAMP Expressing Cells 52 
3. THE EFFECT OF WAKE AND SLEEP ON THE MOUSE CORTEX TRANSCRIPTOME 53 
3.1.1. Typical Sleep and Activity Patterns in Mice 54 
3.1.2. Methods for Sleep Restriction of Rodents 57 
3.1.3. Tissues Affected by Sleep Deprivation 62 
3.1.4. Research Aims 63 
3.2.1. Design of Cabinets for Housing Mice for Sleep Deprivation 64 
3.2.2. Validation of Automated Sleep Deprivation 65 
3.2.3. Experimental Design and Possible Molecular Profiles 66 
3.2.4. Transcriptional Profiling of Sleep Deprived Mouse Cortex 69 
3.3.1. Comparison to Previous Studies 80 
3.3.2. Heatshock proteins 80 
3.3.3. Cholesterol Synthesis 82 
3.3.4. Circadian Genes 84 
vi 
 
3.3.5. Homeostatic Profile Genes 88 
3.3.6. Stress Profile Genes 93 
3.3.7. Attributes of this Experimental Design 96 
3.3.8. Comparison to Adrenalectomized Mice 99 
3.3.9. Suggested Subsequent Experiments 100 
4. THE PROTEOMIC AND METABOLOMIC IMPACT OF SLEEP DEPRIVATION ON MOUSE CORTEX
 102 
4.1. Proteomic Profiling of Sleep Deprived Mouse Cortex 103 
4.2. Proteomic Changes consistent with Reduced Neuronal Excitability following Long Term Sleep 
Deprivation 111 
4.3. Proteomic Changes Consistent with Reduced Protein Synthesis and Cell Replication following Sleep 
Deprivation 112 
4.4. Poor Overlap Between Proteomic and Transcriptomic Changes 112 
4.5. Suggested Further Experiments to Interrogate the Proteomic Effect of Sleep Deprivation 113 
4.6. Metabolomic Profiling of Sleep Deprived Mouse Cortex 114 
4.7. Metabolomic Profiling Implicates 3 Molecular Peaks as Potential Homeostats 119 
4.8. The Global Effect of Sleep Deprivation on Cortex Metabolites appears modest 122 
4.9. Further Experiments to Understand the Metabolomic Impact of Sleep Deprivation 123 
5. MODELLING SLEEP DEPRIVATION IN VITRO 124 
5.1.1. Previous use of in vitro models in sleep research 125 
5.1.2. SH-SY5Y cells provide a source of Human derived Neuronal like cells 126 
5.1.3. Optogenetic Tools Available to Researchers 128 
5.1.4. In vitro Research Aims 135 
5.2.1. Excitation of SH-SY5Y Cells Stimulates Gene Expression 136 
vii 
 
5.2.2. Activation of Channelrhodopsin Stimulates Gene Expression in SH-SY5Y Cells 137 
5.2.3. Blue Light Activation of SH-SY5Y Cells in vitro Induces Global Transcription Changes Similar to Sleep 
Deprivation in vivo 139 
5.2.4. Refinement of the Optogenetic Protocol 142 
5.2.5. Opsin Activation Modulates the Molecular Clock 145 
5.2.6. c-Fos Activation by Channelrhodopsin is Highly Sensitive to Medium Components and Conditions 146 
5.2.7. Expression of a High Conductivity Opsin Confers Sensitivity to Light at Neutral pH 150 
5.2.8. Fluorescent Calcium Imaging Reveals Intracellular Calcium Increases in Response to Stimulation with 
Light 152 
5.2.9. Transcriptomic Analyses Reveals an Acute Response to CoChR Activation in SH-SY5Y Cells 156 
5.3. Light Activation of SH-SY5Y cells induces similar functional Gene Groups, but not the same genes, as in 
vivo Sleep Deprivation 160 
5.3.1. Direct gene comparison 160 
5.3.2. Cholesterol Biosynthesis is Induced in SH-SY5Y Cells, but not Sleep Deprivation 161 
5.3.3. Clock Genes are Modulated in SH-SY5Y Cells and by Sleep Deprivation 163 
5.3.4. Chaperone Genes are Induced in both SH-SY5Y cells by Sleep Deprivation 165 
5.3.5. SH-SY5Y Transcriptomic Response may be Orchestrated by Creb 3 167 
5.3.6. Experimental Design Considerations 168 
5.3.7. Perspectives on Future Cell Studies 170 
5.3.8. Future in vitro experiments 171 
6. GENERAL DISCUSSION 174 
6.1. What is Sleep Deprivation? 174 
6.2. What is the Aim of Sleep Deprivation? 175 
6.3. Sleep Architecture during Automated Sleep Deprivation. 176 
viii 
 
6.4. Limitations of the Techniques Used in this Thesis 177 
6.5. Value of Global Omic Approaches 179 
6.6. Perspectives on the Molecular Study of Sleep 181 
REFERENCES 183 
APPENDIX 217 
 
  
ix 
 
List of Figures 
 
Figure Name Section Page 
Figure 1.1. Representative EEG and EMG Spectra of Wake and Sleep in Mice 1.1.2. 3 
Figure 1.2. The Two Process Model proposes an Interaction between a 
Circadian and Homeostatic Drive for Sleep 
1.3.1. 9 
Figure 1.3. Sleep and Wake Circuitry of the Mammalian Brain  1.3.2. 11 
Figure 1.4. The Two Process Model predicts that Sleep Deprivation Increases 
Sleep Drive the following Day 
1.3.3. 13 
Figure 1.5. Next Generation Sequencing Pipeline 1.6.1. 27 
Figure 1.6. Experimental Approach within the Lab 1.7. 30 
   
Figure 2.1. RNA for RNA-Seq was extracted from the left cortex between 
0.5mm anterior to 2.5mm posterior to Bregma 
2.4. 35 
Figure 2.2. Typical Input RNA and Final Library Electropherogram 2.4. 36 
Figure 2.3. Tophat Pipeline Example 2.5. 37 
Figure 2.4. Cuffdiff Command Example 2.5. 38 
Figure 2.5. Protein for TMT-based Proteomics was extracted from the cortex 
between 2.5mm to 0.5mm anterior to Bregma 
2.6. 39 
Figure 2.6. Schematic of LED Illumination Pattern 2.13. 44 
Figure 2.7. Schematic of Final LED based Illumination System 2.13. 45 
Figure 2.8. Experimental layout for U20S Light Stimulation and Luciferase 
based Readout of the Cellular Clock 
2.14. 46 
Figure 2.9. Experimental layout for Gibson Assembly of Plasmids 2.15. 50 
   
Figure 3.1. Typical Sleep and Activity Patterns of two Strains of Mice 3.1.1. 54 
Figure 3.2. 6-Hour Sleep Deprivation of Mice Increases Sleep Duration during 
the subsequent Dark Phase 
3.1.1. 56 
Figure 3.3. The Lafayette Sleep Fragmentation Chamber disrupts Sleep in mice 
using a motor driven bar 
3.1.2. 61 
Figure 3.4. Customisation of the Sleep Deprivation Cabinet 3.2.1. 64 
Figure 3.5. Sleep Deprivation Markers are Induced in Mouse Cortex by 
Automated Sleep Deprivation 
3.2.2. 65 
x 
 
Figure 3.6. Experimental Design for Transcriptomic Characterisation of Sleep 
Deprivation in Mice 
3.2.3. 67 
Figure 3.7. Hypothetical Effects of Sleep Deprivation on Molecular Abundance 
Profiles in Mouse Cortex 
3.2.3. 68 
Figure 3.8. Sleep Deprivation Induces changes in the Abundance of Thousands 
of Genes in Mouse Cortex 
3.2.4. 69 
Figure 3.9. 16% of Expressed Cortical Transcripts in non-Sleep Deprived Mice 
Exhibited Significant 24 hour Oscillations during the Timecourse 
3.2.4. 73 
Figure 3.10. Enriched Gene Classes amongst Diurnal Genes 3.2.4. 74 
Figure 3.11. Enriched Gene Classes amongst Sleep Deprivation Dependent 
Genes 
3.2.4. 75 
Figure 3.12. 17 Genes Matching the Hypothetical Homeostatic Profile were 
Identified 
3.2.4. 76 
Figure 3.12. 15 Genes Matching the Hypothetical Stress Gene Profile were 
Identified 
3.2.4. 77 
Figure 3.13. Homer1a demonstrates a different Expression Pattern to the 
total Homer1 in Response to Sleep Deprivation  
3.2.4. 78 
Figure 3.14. 17 Isoforms Matching the Hypothetical Homeostatic Profile and 
21 Matching the Stress Profile were Identified 
3.2.4. 79 
Figure 3.15. Chaperone Genes are Induced by both Spontaneous and 
Enforced Wakefulness 
3.3.2. 81 
Figure 3.16. Further Chaperone Genes are Induced by both Spontaneous and 
Enforced Wakefulness 
3.3.2. 82 
Figure 3.17. Some Cholesterol Genes are Modulated by Sleep Deprivation, but 
none are Rhythmic in Undisturbed Mice 
3.3.3. 82 
Figure 3.18. The Expression of Clock Genes is only Modestly Affected during 
Sleep Deprivation, but Severely Perturbed during Recovery from 12 hour 
Sleep Deprivation  
3.3.4. 85 
Figure 3.19. The Number of Rhythmic Transcripts in Mouse Cortex is 
Progressively Reduced by Increasing Duration of Sleep Deprivation  
3.3.4. 87 
Figure 3.20. Representative Homeostatic Gene Expression Profiles 3.3.5. 88 
Figure 3.21. Idealised Homeostatic Gene Expression Profiles 3.3.5. 89 
Figure 3.22. Idealised Binary Gene Expression Profiles 3.3.5. 90 
Figure 3.23. The Expression of Crh Reflects an Idealised Homeostatic Gene 
Profile 
3.3.5. 91 
Figure 3.24. Idealised Stress Gene Expression Profiles 3.3.6. 93 
Figure 3.25. Rasd1 and Vip Expression Fit Different Stress Gene Profiles 3.3.6. 94 
xi 
 
Figure 3.26. Genes Previously Identified as Induced by Sleep Deprivation have 
Markedly different Expression Profiles during Spontaneous Wake Cycles and 
during Recovery Sleep 
3.3.7. 97 
Figure 3.27. Genes Previously Identified as Modulated by Sleep Deprivation in 
Adrenalectomized mice demonstrate Diurnal Expression and an acute 
response to Sleep Deprivation 
3.3.8. 99 
   
Figure 4.1. Experimental Design for Proteomic Characterisation of Sleep 
Deprivation in Mice 
4.1. 103 
Figure 4.2. 12-hour Sleep Deprivation Induces changes in the Abundance of 
Hundreds of Proteins in Mouse Cortex 
4.1. 104 
Figure 4.3. TMT-based Proteomics reveals Abundance Changes in Proteins 
relating to Microtubules, Synapses, Calcium Binding Proteins, Histones, 
Mitochondrial Function, Phosphodiesterases, Ribosomes and Transport  
4.1. 105 
Figure 4.4. Protein Classes Differentially Expressed in Mouse Cortex following 
12-hour Sleep Deprivation Compared to Mice Sacrificed without Sleep 
Deprivation 
4.1. 107 
Figure 4.5. Protein Classes Differentially Expressed in Mouse Cortex following 
12-hour Sleep Deprivation Compared to Mice Sacrificed following 24 hour 
Recovery from 12-hour Sleep Deprivation 
4.1. 108 
Figure 4.6. Protein Classes Differentially Expressed in Mouse Cortex following 
12-hour Sleep Deprivation and 24-hour Recovery Compared to Mice 
Sacrificed without Sleep Deprivation  
4.1. 109 
Figure 4.7. Heatmap of Proteins whose Transcript was Identified as Exhibiting 
a Homeostatic or Stress Profile  
4.1. 110 
Figure 4.8. Experimental Design for Metabolic Characterisation of Sleep 
Deprivation in Mice  
4.2. 114 
Figure 4.9. No Statistical Change in Metabolite Abundance was Identified 
through Targeted Metabolite Analysis 
4.2. 115 
Figure 4.10. Individual Abundance Plots of Adenosine, Arginine, Aspartate, 
Methionine, Tryptophan and Tyrosine 
4.2. 116 
Figure 4.11. Individual Abundance Plots of Peaks Identified as Sleep-Wake 
dependent  
4.2. 118 
Figure 4.12. Individual Abundance Plots of Peaks Resembling a Homeostatic 
Abundance Profile  
4.2. 120 
Figure 4.13. Sleep Deprivation may Reduce the Breakdown of Nicotinic Acid 4.2. 121 
   
Figure 5.1. Genes Induced by Sleep Deprivation are Induced in Primary 
Neurones Treated with an Excitatory Cocktail 
5.1.1. 126 
Figure 5.2. Rhodopsin based Optogenetic tools operate through Secondary 
Signal Molecules 
5.1.3. 129 
xii 
 
Figure 5.3. Microbial Opsins are Ionophoric 5.1.3. 130 
Figure 5.4. Repeated Stimulation reduces Channelrhodopsin 2 mediated 
Photocurrents  
5.1.3. 131 
Figure 5.5. Point Mutations Alter the Magnitude and Kinetics of Photocurrents 5.1.3. 132 
Figure 5.6. Genome Mining and Chimeric Opsins provide Superior 
Optogenetic Tools 
5.1.3. 133 
Figure 5.7. Sleep Deprivation Markers are Induced by Excitatory 
Neurotransmitters in SH-SY5Y Cells 
5.2.1. 136 
Figure 5.8. Production of Light Sensitive Cells  5.2.2. 138 
Figure 5.9. Sleep Deprivation Markers Induced by Light Exposure in SH-SY5Y 
cells 
5.2.2. 139 
Figure 5.10. Blue Light Exposure Modulates the Expression of Chaperone, 
Biological Rhythm and Replication Genes 
5.2.3. 140 
Figure 5.11. Optogenetic Activation of SH-SY5Y Cells produces a similar 
Expression Trend as that previously observed following the Pharmacological 
Activation of Primary Neurones 
5.2.3. 141 
Figure 5.12. Refined Optogenetic Plasmids 5.2.4. 142 
Figure 5.13. Refined Optogenetic Plasmids Induce Fos Expression at Low Light 
Levels 
5.2.4. 143 
Figure 5.14. Halorhodopsin Activation is not Sufficient to Prevent 
Channelrhodopsin Mediated Fos Expression 
5.2.4. 144 
Figure 5.15. Circadian Effects of Photocurrents in U20S Osteosarcoma Cells 5.2.5. 146 
Figure 5.16. HEPES Buffered Medium Removes Sensitivity of Cells to Blue 
Light 
5.2.6. 144 
Figure 5.17. Medium Buffered at Neutral pH Removes Sensitivity of Cells to 
Blue Light 
5.2.6. 147 
Figure 5.18. Varying pH or Introducing Excitatory Drugs to the Medium Fails to 
Restore Sensitivity of Cells to Blue Light 
5.2.6. 149 
Figure 5.19. Expression of CoChR Confers Light Sensitivity to SH-SY5Y cells at 
Neutral pH  
5.2.7. 151 
Figure 5.20. SH-SY5Y Cells do not Exhibit Spontaneous Calcium Spikes 5.2.8. 152 
Figure 5.21. Excitatory Drugs Induces Intracellular Increases in Calcium in 
SH-SY5Y Cells 
5.2.8. 153 
Figure 5.22. Stimulation with Blue Light Increases Intracellular Calcium in 
SH-SY5Y Cells Stably Expressing CoChR in an Intensity and Extracellular 
Calcium Dependent Manner 
5.2.8. 154 
Figure 5.23. Blue Light Stimulation of SH-SY5Y cells expressing CoChR induces 
the Differential Expression of more than 3000 Transcripts 
5.2.9. 156 
xiii 
 
Figure 5.24. Blue Light Illumination Modulates the Expression of Genes 
Related to Specific Functions 
5.2.9. 159 
Figure 5.25. Limited Overlap of Blue Light Inducible and Sleep Associated 
Mouse Genes 
5.3.1. 160 
Figure 5.26. Blue Light Activation of SH-SY5Y Cells Induces the Expression of 
Cholesterol and Fatty Acid Synthetic Pathways 
5.3.2. 161 
Figure 5.27. The Expression of Core Clockwork Genes is Transiently Induced 
by Blue Light, but does not Oscillate in Untreated Cells 
5.3.3. 163 
Figure 5.28. The Expression of Chaperone Genes is Induced only in 
Illuminated Opsin Expressing SH-SY5Y cells 
5.3.4. 165 
Figure 5.29. Comparison of Neuronal Model Systems 5.3.7. 170 
 
  
xiv 
 
List of Tables 
 
Table Name Section Page 
Table 1: Phenotypes of Sleep Deprivation 1.2.5. 7 
 
Table 2: Media Compositions used for in vitro Experiments 2.9. 42 
Table 3: Primers used for the Gibson Assembly of Plasmids CRIP and CHALIP 2.15. 48 
Table 4: Primers used for the Gibson Assembly of Plasmids Chronos-, CoChR-, 
and CHIEF-RIP 
2.15. 48 
Table 5: Primers and Probe Combinations used in qPCR 2.16. 52 
   
Table 6: Techniques for Applying Sleep Deprivation to Rodents 3.1.2. 57 
Table 7: Gene Classes Upregulated in Mouse Cortex following 6-hour Sleep 
Deprivation Compared to non-sleep deprived mice sacrificed at the same 
timepoint. 
3.2.4. 70 
Table 8: Gene Classes Downregulated in Mouse Cortex following 6-hour Sleep 
Deprivation Compared to non-sleep deprived mice sacrificed at the same 
timepoint. 
3.2.4. 71 
Table 9: Previously Implicated Chaperone Gene Expression Data 3.3.2. 80 
Table 10: Further Chaperone Gene Expression Linked to Wakefulness 3.3.2. 81 
Table 11: Gene Expression of Genes in Cholesterol Metabolising Pathway 3.3.3. 83 
Table 12: Gene Expression of Clock Machinery Genes 3.3.4. 84 
Table 13: Gene Expression of Arc, Cdkn1a, Sult1a1 and Rasd1  3.3.7. 97 
   
Table 14: Predicted Molecular Identities of Peaks Modified by Sleep 
Deprivation 
4.6. 117 
Table 15: Predicted Molecular Identities of Potential Small Molecule 
Homeostats 
4.7. 119 
   
Table 16: Genes Upregulated following Blue Light Exposure 5.2.9. 157 
Table 17: Genes Downregulated following Blue Light Exposure 5.2.9. 158 
  
xv 
 
Abbreviations 
 
µg Microgram 
µl Microlitre 
2D Two-dimensional 
3' 3-prime (of nucleic acids) 
3D Three-dimensional 
5' 5-prime (of nucleic acids) 
5-HT 5-hydroxytryptamine, serotonin 
AC Adenylate Cyclase 
aCSF Artificial Cerebral Spinal Fluid 
AMP Adenosine Monophosphate 
AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid 
AMPK Adenosine Monophosphate-activated Protein Kinase 
ANOVA Analysis of Variance 
ARAS Ascending Reticular Activating System 
ATP Adenosine triphosphate 
BCA Bicinchoninic Acid 
BDNF Brain Derived Neurotrophic Factor 
BL Blue Light 
bp Base Pair 
bPAC Bacterial Photoactivated Adenylyl Cyclase 
cAMP Cyclic Adenosine Monophosphate 
xvi 
 
CCD Charge Coupled Device 
cDNA Complementary DNA 
cGPDE cGMP Phosphodiesterase  
CHALIP ChR2(C128T)-2A-Halorhodopsin-IRES-PuroR 
CHES Cyclohexyl-2-aminoethanesulfonic acid 
ChIP-Seq Chromatin Immunoprecipitation- Sequencing 
ChR2 Channelrhodopsin 2 
CLOCK Circadian Locomotor Output Cycles Kaput 
cm centimetre 
CMV Cytomegalovirus 
CO2 Carbon Dioxide 
CoA Coenzyme A 
CRF Corticotropin Releasing Factor 
CRIP ChR2(C128T)-RCAMP-IRES-PuroR 
CRY Cryptochrome 
CSF Cerebral Spinal Fluid 
DMEM Dulbecco's Modified Eagle's Medium 
DMH Dorsomedial Hypothalamus 
DNA Deoxyribonucleic Acid 
DREADD Designer Receptor Exclusively Activated by Designer Drugs 
DRN Dorsal Raphe Nucleus 
dscDNA Double-stranded Complementary DNA 
EDTA EthyleneDiamine Tetraacetic Acid 
EEG Electroencephalography 
xvii 
 
EGFP Enhanced Green Fluorescent Protein 
EMG Electromyography 
eNpHR Enhanced Natronomonas Halorhodopsin  
EOG Electro-Oculography 
ER Endoplasmic Reticulum 
EVH1 Enabled/Vasodilator-stimulated phosphoprotein Homology 1 
FBS Foetal Bovine Serum 
FDR False Discovery Rate 
FRAP Fluorescence Recovery after Photobleaching 
GABA gamma-Aminobutyric acid 
GAPDH Glyceraldehyde 3-Phosphate Dehydrogenase  
GPCR G-Protein Coupled Receptor 
GTP Guanosine Triphosphate 
HEK Human Embryonic Kidney 
HEPES  4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid 
IP3 Inositol triphosphate 
IRES Internal Ribosome Entry Site 
LB Luria-Bertani  
LC Liquid Chromatography 
LC-MS Liquid Chromatography Mass Spectrometry 
LDT Laterodorsal Tegmental nucleus 
LED Light Emitting Diode 
LHA Lateral Hypothalamus Area 
LTP Long Term Potentiation 
xviii 
 
MEM Minimal Eagle's Medium 
mm millimetre 
MnPO Median Preoptic Nucleus 
MOPS (3-(N-morpholino)propanesulfonic acid) 
MOSFET Metal-Oxide-Semiconductor Field-Effect Transistor  
mRNA Messenger Ribonucleic Acid 
ms millisecond 
mW milliwatt 
mW/cm2 milliwatt per square centimetre 
NADH Nicotinamide Adenine Dinucleotide 
NADPH Nicotinamide Adenine Dinucleotide Phosphate 
NEB New England Biolabs 
nm nanometre 
NMDA N-methyl-D-aspartate 
NP40 Nonionic Polyoxyethylene-40 
NREM Non Rapid Eye Movement 
NSAID Non-Steroidal Anti-Inflammatory Drug 
NTRK2 Neurotrophic Tyrosine Receptor Kinase 2 
PCR Polymerase Chain Reaction 
PDE Phosphodiesterase 
PEG  Polyethylene Glycol 
PEI Polyethylene Imine 
PGD2 Prostaglandin D2 
PGH2 Prostaglandin H2 
xix 
 
PLC Phospholipase C 
PPT Pedunculopontine Tegmentum 
PuroR Puromycin Resistance 
qPCR Quantitative Polymerase Chain Reaction 
REM Rapid Eye Movement 
RIP RCAMP-IRES-PuroR 
RNA Ribonucleic Acid 
RNA-Seq Ribonucleic Acid Sequencing 
SCN Suprachiasmic Nucleus 
SD Sleep Deprivation 
SLC Solute Carrier Family 
SREBP Sterol regulatory element-binding protein 
SV40 Simian Virus 40 
SWS Slow Wave Sleep 
TAPSO  N-[Tris(hydroxymethyl)methyl]-3-amino-2-hydroxypropanesulfonic acid 
TBP TATA-Box Binding Protein 
TCEP Tris(2-Carboxyethyl)Phosphine 
TEAB Triethylammonium Bicarbonate 
TMN Tuberomammillary Nucleus 
TMT Tandem Mass Tag 
TNF-α Tumour Necrosis Factor Alpha 
UPL Universal Probe Library 
UPR Unfolded Protein Response 
VEGF Vascular Endothelial Growth Factor  
xx 
 
VLPO Ventrolateral Preoptic Hypothalamus 
WLL White Light Laser 
Xbp1 X-Box Binding Protein 1 
  
1 
 
 
 
 
 
 
 
 
 
1. General Introduction  
 
This section summarises the current understanding of the function and control of sleep, the molecular 
signatures of wakefulness, and the experimental models and tools available to researchers. This 
chapter concludes with an outline of the aim and approach of this thesis. 
  
2 
 
1.1. What is Sleep? 
1.1.1. Behavioural Definition of Sleep 
We spend approximately one third of our lives asleep, and we intuitively know that it is a prolonged 
period of night-time rest during which we become much less aware of the environment. Common 
experience also teaches us that we will wake if there is a particularly loud noise, and that a poor night's 
sleep will usually lead to sleeping longer the next evening. Together, these simple observations form 
the basis of the formal definition of sleep across the animal kingdom.  
Sleep is thus generally defined as a rapidly reversible state of immobility and reduced responsiveness 
to external stimuli, into which the animal enters during specific times of the day in a characteristic 
posture, and the duration of which is under homeostatic control (Foster & Lockley 2012). Of these 
criteria, reduced responsiveness, the rapid reversibility of the state (which differentiates sleep from 
hibernation or coma), and the presence of homeostatic control are considered to be the core 
behavioural characteristics of sleep.  
1.1.2. Electroencephalographic Definition of Sleep 
In animals with well-developed brains, electroencephalography (EEG) measurements can be used to 
more precisely define sleep in real time. EEG uses electrodes, implanted either onto the scalp or 
directly into the brain, to monitor voltage fluctuations due to ion flux associated with neuronal activity 
in the cortex. EEG can be complemented with electromyography (EMG), which uses electrodes to 
monitor muscle tone, or electrooculography (EOG), which monitors eye movement.  
EEG studies during the 1950s showed that as mammals fall asleep, the EEG measurements gradually 
change from the high frequency (>12Hz), low amplitude pattern characteristic of wakefulness to a low 
frequency (<5Hz), high amplitude pattern, termed delta waves, characteristic of deep, slow wave sleep 
(SWS) (Hess et al. 1953). Concomitant with the reduced frequency pattern, there is a decrease in 
muscle tone as measured by EMG. In humans, after approximately 70 minutes, the EEG profile returns 
to a pattern characteristic of wakefulness, despite the person still being asleep. This stage of sleep is 
accompanied by rapid eye movements, and so is known as Rapid Eye Movement (REM) Sleep (Dement 
& Kleitman 1957). Although EEG measurements struggle to differentiate between wakefulness and 
REM sleep, the rapid eye movement and complete lack of muscle tone associated with REM sleep can 
be detected by EOG and EMG, respectively. After the initial bout of REM sleep, the person returns to 
non-REM (NREM) sleep which gradually deepens to SWS. This cycle of NREM to REM sleep happens 
every 90 minutes throughout the night, punctuated by occasional awakenings (Foster & Lockley 2012). 
Over the course of the night, the proportion of SWS decreases, whereas the proportion of REM sleep 
increases. The daily duration and architecture of sleep varies across species, with mammals subject to 
3 
 
predation and those with small brains relative to body size demonstrating a lower abundance of REM 
sleep  (Lesku et al. 2006). 
 
 
 
The power of delta waves in an EEG spectrum is negatively correlated with the amount of sleep 
previously obtained. For example, the proportion of time spent in SWS is highest at the beginning of 
normal sleep (Feinberg et al. 1967), and is increased following sleep deprivation (Borbély et al. 1981) 
but decreased by daytime naps (Feinberg et al. 1985). Therefore the delta power is considered an 
indicator of the need for sleep, or “sleep debt” (Feinberg 1974).  
 
  
NREM 
Sleep
Wake
REM 
Sleep
EEG
EMG
EEG
EMG
EEG
EMG
Figure 1.1. Representative EEG and EMG Spectra of Wake and Sleep in Mice: Sleep can be 
tracked in real time using electroencephalography (EEG) which tracks cortical voltage fluctuations. 
The EEG spectrum of an awake mouse is characterised by small amplitude high frequency 
oscillations, as is the EEG spectrum of a mouse in rapid eye movement (REM) sleep. In contrast 
the EEG spectrum of mice in non-REM (NREM) sleep is characterised by high amplitude low 
frequency oscillations. Electromyography (EMG), which monitors muscle tone, can be used to 
complement EEG sleep scoring. The EMG spectrum of an awake mouse exhibits significant 
fluctuations, whereas mice in NREM, and especially REM sleep, exhibit almost static EMG spectra.  
Figure modified, with permission, from one provided by D.Pritchett.  
 
4 
 
1.2. Functions of Sleep 
1.2.1. The Cost of Sleep 
 
“If sleep doesn’t serve an absolutely vital function, it is the greatest mistake evolution ever made.”  
 
This quote by sleep research pioneer Allan Rechtschaffen succinctly identifies that there are great 
costs attached to sleep that are presumably offset by some fundamental purpose. All animals studied 
to date display a sleep like state, characterised by prolonged inactivity with a reversibly increased 
arousal threshold and a requirement for additional recovery sleep following sleep deprivation. During 
this period, animals are unable to forage, search for mates and are vulnerable to predators, and yet 
some animals, like the brown bat, spend as much as 80% of their life asleep (Campbell & Tobler 1984). 
Although animals can postpone sleep in response to a threat, the homeostatic drive to recuperate 
sleep later leaves them vulnerable. Severe evolutionary pressure has even failed to eliminate sleep in 
dolphins and other marine mammals, which have instead evolved a uni-hemispheric sleep pattern to 
allow them to continuously surface for air (Mukhametov 1987; Mukhametov et al. 1977, 1985). 
However, despite the costs of sleep being clear, why sleep exists at all remains enigmatic.  
 
1.2.2. The Necessity of Sleep 
To date, the function of sleep has been mostly investigated experimentally by studying the effects of 
sleep deprivation. Early studies found that rats can survive an average of 19 days of total sleep 
deprivation, approximately the same period as they can survive total starvation (Everson et al. 1989). 
No common cause of death is associated with total sleep deprivation. Instead, rats show oedema, skin 
lesions, impaired immune responses and metabolic abnormalities, indicating the collapse of multiple 
systems. For example, Koban et al. found that persistently sleep deprived rats had double the caloric 
intake of control rats, yet were still in negative energy balance due to massive energy expenditure 
(Koban et al. 2008; Koban & Swinson 2005), whilst Everson found opportunistic invasion of the 
bloodstream by normally harmless bacteria without a febrile response (Everson 1993). Remarkably, 
allowing rats to sleep usually reverses most of the adverse effects of prolonged sleep deprivation 
within two days. 
Although sleep deprivation has also been shown to induce death in cats after two weeks (McGinty & 
Sterman 1968), and even within twelve hours in Drosophila cyc01 mutants (Shaw et al. 2002), whether 
sleep deprivation is lethal to humans is unknown. In 1965, Randy Gardner voluntarily stayed awake 
for 11 consecutive days without any apparent long-term damage. However, both acute and chronic 
sleep loss have been linked to several disorders in humans (Colten et al. 2006). 
5 
 
1.2.3. Central Effects of Sleep Deprivation 
The most obvious immediate effects of sleep loss include cognitive defects and reduced reaction 
speed. For example, decision making during a market based game was severely compromised after 
one night's sleep deprivation (Harrison & Horne 1999), whilst reaction speeds in psychomotor 
vigilance tests are also reduced (Christie et al. 2008; Roca et al. 2012; Rupp 2013). Outside of the 
laboratory, these deficiencies can have significant effects: the rate of clinical mistakes has been 
correlated to sleep loss (Philibert 2005; Ruggiero et al. 2012), and even 20 hours of prolonged 
wakefulness has the same effect on reaction time as consuming the legal alcohol limit for driving 
(Williamson & Feyer 2000), and is associated with an increased risk of car accidents (Garbarino et al. 
2004; Liu et al. 2003; Maycock 1997). 
In addition to reducing cognitive performance, sleep deprivation also impairs memory formation and 
consolidation. In humans, sleep has been shown to be beneficial for declarative memory (i.e. recalling 
facts and events) (Gais & Born 2004), procedural memory (i.e. learning tasks) (Backhaus & Junghanns 
2006; Plihal & Born 1997), and emotional memory (Groch et al. 2013). Even six-minute naps were 
found to be sufficient to increase the recall of word lists 60 minutes after learning them (Lahl et al. 
2008). Decreased learning has also been shown in both mice and rats after acute and chronic partial 
sleep deprivation, and flies fail to associate an odour with an aversive outcome after sleep deprivation 
(Li et al. 2009). Therefore, memory consolidation has been proposed as a fundamental function of 
sleep conserved across species. 
Sleep has also been proposed to have evolved to facilitate central metabolic homeostasis. The 
metabolic theory of sleep was first based on the concept that during wakefulness the brain operates 
in a way that is not sustainable over long periods, and must eventually rest in a manner analogous to 
muscles after strenuous exercise, in order to clear waste products and prepare for the next session of 
activity. Consistent with this theory, early studies showed that brain glycogen content is depleted by 
prolonged wakefulness in rats and flies (Kong et al. 2002; Zimmerman et al. 2004) and is increased 
after sleep onset and during anaesthesia (Karnovsky et al. 1983), with lactate showing the opposite 
pattern (Naylor et al. 2012). AMPK activity is increased by sleep deprivation, indicating an increased 
demand for ATP during wakefulness (Chikahisa et al. 2009), whilst there is a large increase in ATP 
following sleep onset, which is independent of the time of day (Dworak et al. 2010). More recent 
studies have shown through two-photon in vivo microscopy that flow through the brain's "glymphatic" 
system greatly increases during sleep and anaesthesia in mice, contributing to the clearance of waste 
metabolites and β-amyloid protein (Xie et al. 2013).  
6 
 
Sleep deprivation also induces the Unfolded Protein Response (UPR) and endoplasmic reticulum (ER) 
stress response in the brain, characterised by the upregulation of several heat-shock chaperone 
proteins (Maret et al. 2007; Terao et al. 2003). The induction of ER stress and the UPR by sleep 
deprivation has been directly linked to reduced protein synthesis in sleep deprived mouse cortex 
(Naidoo et al. 2005), which appears to have a protective role. Indeed increasing the temperature, 
which induces heat-shock proteins independently of sleep, protects Drosophila from the lethal effects 
of sleep deprivation (Shaw et al. 2002). In summary, at the cellular level it appears that prolonged 
wakefulness is characterised by increased energy expenditure, and decreased synthesis of proteins. 
In contrast, sleep is associated with the upregulation of multiple anabolic pathways, including haem 
and porphyrin synthesis, translation and cholesterol biosynthesis (Mackiewicz et al. 2007). Therefore 
at the cellular level, falling asleep appears to correspond to a shift from the catabolic state of 
wakefulness to an anabolic state of sleep. 
 
1.2.4. System-wide Effects of Sleep Deprivation 
Cognitive performance, memory consolidation, central metabolism and cellular housekeeping are all 
dependent on sleep, indicating that sleep plays a large part in the function of the brain. However, 
more subtle purposes of sleep have been proposed based on changes that occur during sleep 
deprivation and affect the entire body. Interplay between sleep and the immune system has been 
inferred from the tendency for infections and toxins to increase sleep duration, and because 
prolonged sleep deprivation of rats can lead to opportunistic bacterial infections (Everson 1993). Since 
then, the effectiveness of Influenza, Hepatitis A and Hepatitis B vaccinations has been shown to be 
reduced by sleep restriction (Lange et al. 2003; Prather et al. 2012; Spiegel et al. 2002). Similarly, 48 
hours of sleep deprivation was shown to reduce the phytohemagglutinin induced activation of human 
lymphocytes for five days after sleep loss (Palmblad et al. 1979). Accordingly, prolonged wakefulness 
has been shown to induce the expression of several cytokines within the brain, which appear to 
decrease neuronal activity (Besedovsky et al. 2012). The use of common signalling molecules by both 
immune cells and sleep deprived brain tissue may provide a mechanistic insight into the crosstalk 
between immune function and sleep. 
Sleep has also been tightly linked to whole body metabolic homeostasis in humans. Several 
epidemiological studies have found sleep duration to be inversely linked to BMI in both adults (Ko et 
al. 2007; Kripke et al. 2002; Moreno et al. 2006) and children (Padez et al. 2005; Reilly et al. 2005; von 
Kries et al. 2002). A meta-analysis using data from a total of 630,000 participants found that the Odds 
Ratio for short sleep duration and obesity for children was 1.89, and 1.55 for adults (Cappuccio et al. 
2008). Remarkably, other studies have found a U-shaped relationship between weight and sleep 
7 
 
duration, with those sleeping fewer than 7 hours, or more than 9 hours a day, having an increased risk 
of obesity (Gottlieb et al. 2006; Patel et al. 2006). Consistent with epidemiological studies, acute sleep 
deprivation in human volunteers has been shown to increase food intake (Brondel et al. 2010; 
Markwald et al. 2013), with a change in preference toward more energy dense foods, and decreased 
glucose tolerance (Beebe et al. 2013; Benedict et al. 2012; Greer et al. 2013). Although there are 
conflicting reports on whether activity is increased (Jung et al. 2011) or decreased (Benedict et al. 
2011) after sleep loss, those studies that find increased activity usually report that food intake is still 
in excess of energy expenditure (Spaeth et al. 2013; St-Onge et al. 2011). 
 
1.2.5. Societal Costs of Poor Sleep 
With the advent of electrical lighting and increased caffeine availability, the average duration of daily 
sleep of both adults and children has potentially been decreasing in the past century (Depner et al. 
2014; Matricciani et al. 2012). A population wide chronic sleep restriction may have significant 
implications for the health of modern day societies. Whilst chronic sleep restriction has been linked 
to reduced immune function and increased incidence of diabetes and obesity, acute sleep deprivation 
is linked to an increased prevalence of car accidents and clinical mistakes. The burden of poor sleep 
on the healthcare system could be reduced by an increased understanding of the mechanisms through 
which sleep maintains overall health. Understanding sleep at the molecular level may be especially 
promising, since that would reveal targets amenable to rational drug design that may provide novel 
treatments for sleep associated disorders. 
 
Table 1: Phenotypes of Sleep Deprivation 
 Phenotype References 
 
Central Phenotype of 
Sleep Deprivation 
 
Cognitive and Memory Deficits 
Impaired Reaction Speed 
Impaired β-amyloid clearance 
 
 
 (Backhaus & Junghanns 2006; Gais & 
Born 2004; Groch et al. 2013; 
Harrison & Horne 1999; Li et al. 2009; 
Plihal & Born 1997) 
 (Christie et al. 2008; Roca et al. 2012; 
Rupp 2013) 
 (Di Meco et al. 2014; Xie et al. 2013) 
8 
 
 
Whole Animal 
Phenotypes of Sleep 
Deprivation 
 
Impaired Immune Function 
Impaired Metabolic 
Homeostasis 
 
 
 (Everson 1993; Lange et al. 2003; 
Palmblad et al. 1979; Prather et al. 
2012; Spiegel et al. 2002) 
 (Cappuccio et al. 2008; Gottlieb et al. 
2006; Ko et al. 2007; Kripke et al. 
2002; Moreno et al. 2006; Padez et al. 
2005; Patel et al. 2006; Reilly et al. 
2005; von Kries et al. 2002) 
 
Societal Costs of Sleep 
Deprivation. 
 
 
Increased Incidence of Clinical 
Errors 
Increased Incidence of Car 
Accidents 
 
 (Philibert 2005; Ruggiero et al. 2012) 
 (Garbarino et al. 2004; Liu et al. 
2003; Maycock 1997; Williamson & 
Feyer 2000) 
  
9 
 
1.3. The Neurological Control of Sleep 
1.3.1. Overall Output of Sleep Circuitry 
The regulation of sleep can be well described by the two-process regulation model, proposed by 
Borbély (Borbély 1982). This model proposes that there are two separate processes that affect sleep 
timing and duration: Process S, the homeostatic drive to sleep, and Process C, the circadian drive to 
sleep. The homeostatic need for sleep increases with prolonged wakefulness and is dissipated by 
sleep, whilst the circadian sleep drive oscillates during the day. In humans, the two processes work 
together to maintain sleep pressure below a threshold throughout the day and promote a 
consolidated bout of sleep through the night (see Fig 1.2.). 
 
 
  
0:00 8:00 16:00 0:00 8:00 16:00 0:00 8:00
Time of Day
Sleep 
Pressure
Process S – Homeostatic Sleep DriveProcess C – Circadian Sleep Drive
Figure 1.2. The Two Process Model proposes an Interaction between a Circadian and 
Homeostatic Drive for Sleep: This schematic represents an idealised two process model for a 
human, who is only awake between 8am until midnight each day. Process S, the homeostatic drive 
for sleep, progressively increases during continued wakefulness, and decreases during 
subsequent sleep. In contrast, Process C, the circadian sleep drive, oscillates over a 24-hour 
period, peaking just before habitual wake time and reaching its minimum a few hours before 
habitual sleep time. The two processes interact, and the net effect is that sleep pressure is 
maintained below a theoretical threshold (blue line) during the day, and above that threshold 
during the night. Therefore, the processes interact to generate consolidated periods of wake and 
sleep, despite mounting and dissipating sleep pressure, respectively. 
 
10 
 
1.3.2. Circuitry that Controls Sleep 
Wakefulness and sleep are coordinated by several excitatory and inhibitory neural networks that 
extend throughout the brain. Regions of the brain responsible for sleep were first identified in the 
1920s by autopsies of encephalitis sufferers exhibiting sleep disorders (Von Economo 1930). Patients 
who had suffered from excess sleep (hypersomnia) often had lesions in the brainstem and posterior 
hypothalamus, whilst those suffering from insomnia typically had lesions in the anterior 
hypothalamus. Since then, the projections and neurotransmitter profile of these regions have been 
characterised. 
Several of the wake-promoting centres are part of the ascending reticular activating system, including 
the noradrenergic locus ceruleus (LC), the serotonin (5-HT) producing dorsal raphe nucleus (DRN), 
histaminergic neurons of the tuberomammillary nucleus (TMN), and the cholinergic neurons of the 
pedunculopontine tegmental (PPT) and lateral dorsal tegmental (LDT) nuclei (Moruzzi & Magoun 
1949). The ascending reticular system projects from these centres in the brainstem and posterior 
hypothalamus to the hypothalamus, thalamus, basal forebrain and the cortex (see Fig 1.3.). 
Accordingly, lesioning several of the nuclei induces hypersomnia, as does histamine or 5-HT blockade 
(Landolt et al. 1999; Monti et al. 1991), whilst optogenetic activation of the LC is sufficient to induce 
awakening (Carter et al. 2010). Further wake promoting neurons are found in the basal forebrain, 
where the optogenetic activation of glutamatergic and cholinergic signalling has been shown to induce 
wakefulness (Xu et al. 2015).  
The firing of the individual wake-promoting nuclei is coordinated by a cluster of excitatory neurons in 
the lateral hypothalamic area (LHA), promoting prolonged sessions of wakefulness. The neurones 
release orexin (also known as hypocretin), a neuropeptide first named for its induction of feeding 
following intracerebroventricular injection (Sakurai et al. 1998). Later, however, it was found that 
orexin overexpression induces fragmented sleep, whilst orexin deficiency is linked to narcolepsy and 
cataplexy in humans, mice and dogs (Chemelli et al. 1999; Hara et al. 2001; Lin et al. 1999; Peyron et 
al. 2000; Thannickal et al. 2000). As well as projecting to the DRN, LC, TMN, LDT and PPT (Bayer et al. 
2001; Horvath et al. 1999; Liu et al. 2002; Yamanaka et al. 2003b), orexinergic neurons also extend 
throughout the forebrain, including to the feeding and reward centres of the hypothalamus 
(Nakamura et al. 2000; Yamanaka et al. 2000). In mice, overexpression of orexin protects against diet 
induced obesity and hyperglycaemia (Funato et al. 2009), whilst orexin antagonism decreases self-
administration of cocaine, ethanol and high fat food (Harris et al. 2005; Moorman & Aston-Jones 2009; 
Valdivia et al. 2014). Therefore, orexinergic neurons both directly and indirectly regulate several 
aspects of complex behaviours in addition to the sleep-wake cycle. 
11 
 
 
Opposing the wake-promoting system of the ascending reticular activating system and orexinergic 
systems are a population of wake inactive, sleep active GABAergic and galaninergic neurons in the 
median preoptic (MnPO) and ventrolateral preoptic (VLPO) areas of the hypothalamus (Gaus et al. 
2002; Sherin et al. 1996). Interestingly, neurons in the VLPO are also activated by several anaesthetics 
(Moore et al. 2012), further suggesting a role in sleep promotion. The preoptic hypothalamus extends 
inhibitory projections to the wake-promoting centres of the brainstem (Uschakov et al. 2007) and 
lateral and posterior hypothalamus (Saito et al. 2013; Sherin et al. 1998), and so induce sleep by 
inhibiting several wake-promoting pathways simultaneously. Severe lesioning of the VLPO in cats and 
rats causes over 50% reduction in NREM sleep time (McGinty & Sterman 1968; Szymusiak & McGinty 
1986), with the number of sleep active neurons remaining after lesioning correlating with sleep time 
(Lu et al. 2000). Furthermore, the ability of the VLPO to rapidly induce sleep has recently been shown 
by DREADD (Designer Receptors Exclusively Activated by Designer Drugs) and optogenetic based 
approaches (Chung et al. 2017; Zhang et al. 2015).  
Conversely, the wake-promoting centres extend inhibitory projections to the VLPO (Chou et al. 2002; 
Deurveilher et al. 2002; Gallopin et al. 2000). The mutual inhibition of the wake and sleep-promoting 
Figure 1.3. Sleep and Wake Circuitry of the Mammalian Brain: The wake promoting ascending 
reticular activating system includes noradrenergic locus coeruleus (LC), the serotonergic (5-HT) 
dorsal raphe nucleus (DRN), histaminergic tuberomammillary nucleus (TMN), and the cholinergic 
pedunculopontine tegmental (PPT) and lateral dorsal tegmental (LDT). It extends throughout the 
brain, directly activating the cortex and thalamus, and inhibiting the sleep promoting, GABA-ergic 
ventrolateral preoptic nucleus (VLPO). The sleep promoting nuclei are activated by orexin expressing 
neurons of the lateral hypothalamus (LHA), and inhibited by the VLPO. 
12 
 
centres acts as a "Flip-Flop Switch" to ensure stability of wakefulness and sleep, as well as facilitating 
rapid state transitions between the two states, which can occur within 1 second (Takahashi et al. 
2010). The extensive communication between the centres also facilitates the integration of the signals 
that determine whether an animal is awake or asleep. 
 
1.3.3. Circadian Inputs to the Sleep Circuitry 
The probability of an animal being awake is affected by the time of day - Process C in the two-process 
model- and the amount of time spent awake- Process S. In humans, circadian drive to sleep decreases 
during the day, offsetting an increasing homeostatic drive to maintain nearly constant alertness. 
Shortly before habitual sleep time, the circadian drive begins to increase, normally resulting in sleep, 
which in turn decreases the homeostatic drive. However, if wakefulness is prolonged, the drive to 
sleep increases rapidly due to the combination of ever increasing circadian and homeostatic sleep 
pressure. The circadian component of sleep timing has been well demonstrated in humans, where 
total sleep deprivation is characterised by a drastic and progressive decrease in alertness and cognitive 
performance until the habitual waking time, followed by a partial recovery of alertness the next 
morning when the circadian component again begins to favour wakefulness (see Fig 1.4.) (Dijk et al. 
1992). However, alertness and performance are still reduced compared to the day before sleep 
deprivation, reflecting an increased homeostatic drive for sleep.  
Information about the time of day is relayed to the rest of the body from the suprachiasmatic nucleus 
(SCN) of the hypothalamus (Moore & Eichler 1972). The circadian oscillation of SCN activity acts as a 
master pacemaker that synchronises the internal clock of peripheral cells (Buijs et al. 1999; Cailotto et 
al. 2009). The activity of neurons in the SCN is dependent on their own cellular clock, which is sensitive 
to several environmental stimuli, most notably light (Do & Yau 2010). Although the SCN only has sparse 
direct innervations of the sleep circuitry, circadian input is relayed to the sleep circuitry via the 
dorsomedial hypothalamus (DMH), which in turn extends GABAergic projections to the VLPO, and 
excitatory glutamatergic projections to orexinergic neurons of the lateral hypothalamus (Chou et al. 
2003). Indeed, blinding rats or maintaining them in constant light gradually dampens rhythms in sleep 
(Eastman & Rechtschaffen 1983; Ibuka & Kawamura 1975), whereas lesioning the DMH or the SCN in 
rats causes sleep to permanently become evenly distributed across the light-dark cycle (Chou et al. 
2003; Eastman et al. 1984; Ibuka & Kawamura 1975). Although lesioning the SCN in squirrel monkeys 
also results in a marked increase in daily sleep duration (Edgar et al. 1993), total sleep duration in rats 
is not significantly affected by lesioning of the SCN or DMH. Ablation in mice has been shown to both 
increase (Easton et al. 2004) and to have no effect on (Ibuka et al. 1980) sleep duration, which may be 
linked to strain differences.  
13 
 
  
Similar to the ablation of the SCN, transgenic mice lacking core circadian genes such as Bmal1 or both 
Cry1 and Cry2 have less rhythmic sleep patterns (Laposky et al. 2005; Wisor et al. 2002). However, 
genetic disruption of the clock also significantly affects total sleep duration, with the Bmal1-/- and 
cry1-/-/cry2 -/- mice sleeping 90 and 110 minutes longer per day than wild-type controls, respectively, 
even under rhythmic light dark cycles. EEG analysis showed that the increased sleep duration of these 
clock mutants is associated with an increased NREM delta power, indicative of an elevated 
requirement for sleep despite sleeping longer than wild type controls. In contrast, knockout of Clock 
in mice results in a 2 hour reduction of daily sleep time with reduced daily delta energy compared to 
wild-type controls (Naylor et al. 2000), indicating an reduced intrinsic requirement for sleep. Although 
0:00 8:00 16:00 0:00 8:00 16:00 0:00 8:00
Time of Day
Sleep 
Pressure
Figure 1.4. The Two Process Model predicts that Sleep Deprivation Increases Sleep Drive the 
following Day: This schematic represents an idealised two process model for a human, who is 
normally only awake between 8am until midnight each day, but is subjected to 6 hour additional 
sleep deprivation (red bar) before waking at 8am. Process S, the homeostatic drive for sleep, 
progressively increases during the day and continues to rise during the sleep deprivation. During 
the two hour sleep opportunity, Process S decreases, but then rises again following waking at 
8am. Compared to the day before, the homeostatic drive is much higher following sleep 
deprivation. In contrast, Process C is here modelled as being unaffected by sleep deprivation. 
Interaction of the two processes indicates that if able the person would have remained asleep 
past 8am or gone to sleep earlier than midnight following sleep deprivation, because the 
theoretical threshold had been surpassed. However, the model also predicts that the person 
would struggle to fall asleep around 4pm, despite exceptionally high homeostatic sleep pressure.  
 
14 
 
the effect of SCN ablation on sleep duration in mice is unclear, the observation that the global 
knockout of core clock genes can have opposing effects on sleep duration indicates that these genes 
can modulate sleep timing and duration through a mechanism distinct from a non-functional SCN.  
1.3.4. Homeostatic Inputs to Timing of Sleep 
The timing and duration of sleep is also under homeostatic control (Process S), with the propensity to 
sleep being dependent on the total duration of recent wakefulness. This central feature of sleep 
remains much less understood than the circadian control of sleep. The presence of homeostatic 
control necessitates a mechanism by which animals can assess the length of sleep and wakefulness. 
Unlike the circadian input, which is associated with a well-defined group of neurons that rhythmically 
signals to sleep and wake controlling centres, there does not seem to be an analogous group of 
neurons whose activity is directly related to the proportion of time spent awake. Another possibility 
may be the coupling of excitatory wake-promoting signals with a slow, long-term inhibitory signal. 
Indeed, both cholinergic neurons of the basal forebrain (Saunders et al. 2015) and histaminergic 
neurons of the TMN have been shown to co-transmit GABA, with genetic knockdown of GABA function 
in TMN neurons decreasing sleep time in mice (Yu et al. 2015). Recently, sleep-active cortical 
interneurons expressing nitric oxide synthase have been suggested to be homeostatic neurons, based 
on the observation that their firing appears to positively correlate to NREM delta power, a measure 
of sleep debt (Gerashchenko et al. 2008; Zielinski et al. 2013). However, the activity of these neurons 
is strongly suppressed during wake, even after 6 hours of sleep deprivation, indicating that these 
neurons do not signal homeostatic sleep pressure in the awake animal (Morairty et al. 2013).  
An alternative to a cluster of specialised neurons being responsible for the homeostatic sleep drive is 
a molecular homeostat, the abundance of which is linked to the sleep-wake cycle. Indeed, evidence 
for endogenous, sleep-deprivation induced molecules was first found over a century ago (Kubota 
1989). The ideal homeostat would either promote sleep, accumulate during wakefulness and be 
removed during sleep; or show the inverse pattern and inhibit sleep. Indeed, it is clear that several 
metabolites and signal molecules which may act as potential homeostats accumulate during 
prolonged wakefulness in the extracellular space in the brain (see Section 1.4.) (Krueger & Obal 2003). 
The best characterised of these molecules is adenosine, the concentration of which increases during 
wakefulness and decreases during sleep (Porkka-Heiskanen et al. 1997). Adenosine was first 
implicated as important for sleep induction when the stimulant effects of caffeine and other 
methylxanthines was attributed to the antagonism of adenosine receptors (Landolt et al. 1995; 
Schwierin et al. 1996), a trait that is conserved from humans to flies (Shaw et al. 2000). Conversely, 
adenosine receptor agonists promote sleep with an EEG pattern resembling the deep sleep that 
follows sleep deprivation (Benington et al. 1995). The most widespread adenosine receptor (A1) 
15 
 
couples to Gi3 which inhibits adenylate cyclase, leading to the silencing of the neuron (Dunwiddie & 
Fredholm 1989; LaMonica et al. 1985). The less ubiquitous excitatory A2A receptor is expressed in sleep 
active neurons of the preoptic area of the hypothalamus (Scammell et al. 2001). Therefore, much of 
the sleep-inducing effect of adenosine has been attributed to the A1 receptor repression of wake-
promoting neurons, and the A2a receptor mediated activation of sleep-promoting centres (Porkka-
Heiskanen et al. 2000).  
1.3.5. The Timing of Sleep is Sensitive to Environmental Cues 
The two-process model is an idealised representation of sleep regulation; however the in vivo control 
of sleep transitions is not rigid, but instead flexible and sensitive to the environment. Hunger, thirst 
and the possibility of a threat are all stressors able to increase wakefulness, despite mounting sleep 
pressure. For example, male rats placed in a cage previously occupied by another male for 1 week 
show reduced total sleep and higher sleep fragmentation in the following 6 hours compared to control 
rats transferred to clean cages (Cano et al. 2008; Revel et al. 2009), whilst rats subjected to total 
starvation progressively decrease daily sleep time, and have almost no sleep at all in the final 24 hours 
before death (Jacobs & McGinty 1971). Similarly, introducing a novel object or tapping on a cage can 
induce transient wakefulness in mice. At the neuronal level, these stressors appear to be integrated 
into the sleep-wake cycle by orexin neurons. The lateral hypothalamus receives projections from the 
amygdala, which activates orexin neurons in response to fear stimuli such as foot shock (Winsky-
Sommerer et al. 2004). Orexin neurons are also directly activated by ghrelin, a gut hormone which 
signals hunger, and inhibited by leptin, an adipokine which signals a positive energy balance 
(Yamanaka et al. 2003a). Therefore, the timing of sleep is also dependent on environmental cues, 
allowing animals to postpone sleep to better cope with threats and opportunities. 
  
16 
 
1.4. The Molecular and Cellular Basis of Sleep and Sleep Homeostasis  
In contrast to the well understood neural pathways involved in the control of wakefulness, the 
molecular and cellular changes that occur during sleep and wakefulness are poorly understood. 
Several molecules accumulate during prolonged wakefulness, and some (e.g. adenosine) appear to be 
involved in sleep induction and homeostasis. Other molecules are elevated during wakefulness 
compared to sleep, but, rather than acting as a signal, appear to have a functional role in meeting the 
demands of the awake state. Conversely, some molecules elevated during sleep appear to have roles 
that hint at the function of sleep. Finally, there are a group of molecules modulated by sleep that 
appear to mediate the pathophysiological effects of acute and chronic sleep loss. 
 
1.4.1. Small Molecule Changes that Accompany Sleep and Sleep Deprivation 
Adenosine is the best characterised of the small molecules that accumulate during sleep, yet even its 
functions are unclear. A major obstacle to identifying its involvement in sleep is that the adenosine 
moiety is a component of several essential cofactors (e.g. ATP, NADH, Coenzyme A), and so can be 
produced and consumed by several independent pathways. During wakefulness, extracellular 
adenosine is increased by both neuronal and glial contributions (Schmitt et al. 2012). Neuronal ATP is 
packaged into vesicles and released with several neurotransmitters (Burnstock 1999; Richardson & 
Brown 1987), whilst the glial ATP containing vesicles released by glutamate stimulation do not appear 
to contain other transmitters (Newman 2003). Once in the extracellular space, ATP is rapidly degraded 
by progressive dephosphorylation to adenosine (Dunwiddie et al. 1997). Intracellular adenosine can 
cross the membrane through channels, and during periods of metabolic stress, intracellular 
conversion of ATP to adenosine has been hypothesised to increase extracellular adenosine (Brundege 
& Dunwiddie 1998). Consistent with this, infusion of the mitochondrial uncoupler dinitrophenol into 
the brain has been shown to increase extracellular adenosine (Kalinchuk et al. 2003), whereas 
supplementing the diet of rats with creatine attenuates sleep deprivation induced adenosine 
accumulation (Dworak et al. 2017).  
The rate of adenosine released increases with the duration of previous activity, with brain slices 
prepared from sleep deprived animals releasing more adenosine in response to glutamatergic 
stimulation (Sims et al. 2013). Removal of extracellular adenosine is mediated by both adenosine 
deaminase, which converts adenosine to inosine, and astrocytic adenosine kinase, which produces 
AMP (Porkka-Heiskanen et al. 2002). During prolonged activity, adenosine accumulates more quickly 
than it is cleared, whilst inactivity favours clearance of adenosine. 
17 
 
Extracellular adenosine activates four known G-protein coupled receptors (GPCR), which utilise cAMP 
as a secondary messenger. Inhibitory A1 and A3 receptors decrease cAMP levels, whereas excitatory 
A2a and A2b receptors increase intracellular cAMP (Haas & Selbach 2000). A1 agonism has also been 
shown to activate inwardly rectifying potassium channels, providing a second inhibitory mechanism 
(Andoh et al. 2006; Kirsch et al. 1990). The most widespread receptor is the inhibitory A1 receptor, 
whereas excitatory A2a receptors are present in sleep promoting regions of the preoptic area of the 
hypothalamus. Therefore, adenosine mediated activation and inhibition of sleep- and wake-
promoting centres, respectively, is a plausible mechanism by which sleep pressure is induced by 
prolonged wakefulness. Remarkably, however, genetic knockout of the A1 receptor is not only viable, 
but also has no significant effect on sleep duration or homeostasis, indicating that other mechanisms 
exist for sleep induction and homeostasis (Stenberg et al. 2003).  
Adenosine and synaptic activity also locally stimulate the release of tumour necrosis factor-alpha 
(TNF-α) and other cytokines from glia (Hide et al. 2000), which have been shown to modulate neuronal 
activity, increase local blood flow and induce sleep (Imeri & Opp 2009). Therefore, release of 
adenosine from cells appears to signal prolonged activity and the possibility of metabolic stress, and 
acts to reduce activity, boost cell survival and increase phagocytosis and waste clearance. Consistent 
with a neuroprotective role of adenosine, inhibition or knockout of A1 receptors exacerbates the 
excitatory neurotoxicity of kainic acid (Matsuoka et al. 1999).  
Untargeted metabolomic studies have since shown that several other small molecules increase during 
sleep deprivation, although several of these have examined serum or urine, and so whether these 
changes reflect those occuring in the brain is unclear. Plasma metabolites most affected by sleep 
deprivation are typically lipids, acylcarnitines and amino acids (Bell et al. 2013; Davies et al. 2014; 
Weljie et al. 2015). Serum phenylalanine and tryptophan, which are precursors to several 
neurotransmitters, and urine indoxyl-sulfate are all increased following sleep deprivation 
(Giskeødegård et al. 2015), which may indicate an increased rate of central neurotransmitter turnover. 
The changes in lipid concentrations are more difficult to interpret, as lipids act as both an energy 
source and as signalling molecules. Elevated acylcarnitines suggests a role of lipid breakdown in sleep 
deprivation, whereas elevated lysophospholipids may point toward prostaglandin synthesis. Indeed, 
the abundance of prostaglandins D2 and E2 in rat cerebral spinal fluid is progressively increased by 
sleep deprivation, where they promote sleep (Ram et al. 1997).  
Recently, a role of simple metal cations in the control of wakefulness states has been identified (Ding 
et al. 2016). The concentration of potassium ions rapidly increases in the CSF of mice at the transition 
from sleep to wakefulness, accompanied by a slower decrease in magnesium and calcium ion 
18 
 
concentrations. At the onset of spontaneous sleep and isoflurane induced anaesthesia, the opposite 
pattern is observed. Manipulation of these cations through infusion of modified aCSF into the cisterna 
magna is sufficient to trigger state changes in both spontaneously awake and asleep mice. 
Remarkably, local administration of sleep inducing aCSF to the left hemisphere of awake mice induces 
increases in delta power in the left hemisphere comparable to that seen in asleep mice, without 
causing a concominant change in the right hemisphere. Conversely, the reverse pattern was also 
shown in asleep mice with local application of wake inducing aCSF.  
In contrast to the activity induced inhibition mediated by adenosine and prostaglandins, however, it 
appears that the activity of extracellular cations should form a positive feedback cycle. Neural activity 
elevates extracellular potassium, partially depolarising nearby neurons, whereas reduced magnesium 
during wake lessens the magnesium lock on NMDA receptors, further promoting excitatory 
glutamatergic signalling. Furthermore, in vivo extracellular cation concentrations are relatively stable 
except at state transitions, indicating that their concentration carries little information about how long 
the animal has recently spent in either state. Therefore, it appears that the changing ion 
concentrations may mechanistically induce and maintain state transitions, but not provide the 
homeostatic drive for sleep. 
1.4.2. Macromolecule Abundance Changes that Accompany Sleep and Sleep Deprivation 
The transcriptomic effects of sleep deprivation on mouse and rat cortex and whole brain have been 
investigated through microarray based studies, which have identified several transcripts that are 
upregulated following sleep deprivation (Cirelli et al. 2004; Mackiewicz et al. 2007; Maret et al. 2007). 
A group of these genes, including Arc and Homer1a, had previously been characterised as immediate 
early genes and are expressed in neurons in response to recent activity (Lyford et al. 1995; Sato et al. 
2001), indicating that sleep deprivation is associated with increased neural activity in the cortex. Arc 
mRNA and protein is localised to dendrites of neurons, where it promotes internalisation of AMPA 
glutamate receptors (Chowdhury et al. 2006). Primary neurons either lacking Arc or constitutively 
overexpressing Arc indicate that the proportion of AMPA receptors on the cell surface negatively 
correlates with the abundance of Arc protein (Shepherd et al. 2006), suggesting that Arc is involved in 
activity induced inhibition of neuronal activity.  
Homer1a is an activity induced splice variant of the constitutively expressed long form Homer1. Long 
form Homer1 is a tetrameric scaffold protein that contains an EVH1 domain, which binds cell surface 
metabotropic glutamate receptors and endoplasmic reticulum IP3 receptors. As metabotropic 
glutamate receptors induce the production of IP3, Homer1 facilitates glutamate induced calcium 
release from intracellular stores. Homer1a contains the same EVH1 domain, but lacks the coiled coil 
19 
 
domain responsible for oligomerisation, reducing the proportion of metabotropic glutamate receptors 
involved in a functional scaffold (Tu et al. 1998). It therefore appears that Homer1a, like Arc, is 
involved in activity induced attenuation of excitatory signals. Unexpectedly, mice specifically lacking 
the Homer1a variant exhibit increased daily sleep time that is more fragmented than wild type 
controls and have an intact homeostatic response to sleep deprivation (Naidoo et al. 2012).  
Brain-derived neurotrophic factor (BDNF) and its receptor, NTRK2, are also transcriptionally 
upregulated by sleep deprivation (Cirelli & Tononi 2000). BDNF decreases the intrinsic excitability of 
cortical neurons (Desai et al. 1999), whereas central injection of BDNF in rats induces a 15% increase 
in sleep time over the following day (Kushikata et al. 1999) and TrkB antagonism decreases sleep 
duration (Faraguna et al. 2008), indicating that BDNF may act as a molecular homeostat. However, 
BDNF, together with Arc, is also extensively involved in synaptic plasticity and homeostatic scaling of 
synapses. During wake, long-term potentiation (LTP) of individual synapses within the brain is linked 
with learning and the formation of memories. Left unchecked however, net increases in synaptic 
strength would increase global firing rates, not only increasing the metabolic demands of the brain, 
but also decreasing the signal-to-noise ratio of firing patterns within neural networks. The “Synaptic 
Homeostasis Hypothesis” postulates that a function of sleep is to allow a balancing homeostatic net 
decrease in synaptic strength to occur (Tononi & Cirelli 2003). This hypothesis is consistent with 
apparent increases in central energy demand during wakefulness and the disruption of learning 
caused by sleep deprivation. 
Microarray studies of sleep deprived mouse and rat brain have identified several genes involved in 
oxidative phosphorylation, most notably subunits of NADH dehydrogenase, cytochrome-C oxidase and 
ATP synthase, are upregulated at the mRNA level after only three hours of sleep deprivation, 
consistent with an increased demand for ATP (Cirelli & Tononi 1998, 1999; Nikonova et al. 2010). 
Conversely, one study showed that sleep deprivation decreases the mRNA expression of haem and 
porphyrin synthesis, translation and cholesterol biosynthesis (Mackiewicz et al. 2007). In addition to 
revealing an increase in catabolic gene expression, studies have also shown that the unfolded protein 
response (UPR) and endoplasmic reticulum (ER) stress response is induced by sleep deprivation, 
characterised by the upregulation of several heat-shock proteins and X-box binding protein-1 (Xbp1) 
(Maret et al. 2007; Terao et al. 2003). Unfolded protein and ER stress responses reduce the translation 
of protein, whereas NREM sleep is associated with centrally increased incorporation of isotopic 
leucine into protein, suggesting that sleep is associated with an upregulation protein synthesis (Ramm 
& Smith 1990). Therefore, at the transcript level and protein level, sleep is associated with anabolic 
processes whilst wake is associated with catabolic pathways. 
20 
 
Although most studies have focussed on global transcript changes within the brain, Bellesi et al. 
interrogated how sleep deprivation alters the glial transcriptome (or more specifically, the glial mRNA 
undergoing translation), by precipitating RNA associated with genetically tagged ribosomes (Bellesi et 
al. 2015). Amongst the genes most upregulated by sleep deprivation were several cytoskeletal and 
extracellular matrix proteins, suggesting astrocytic changes in shape in response to neuronal activity.  
In contrast to the well-studied transcriptional correlates of sleep and wake, changes in protein levels 
between wake and sleep are comparatively poorly characterised, reflecting the technological 
limitations of proteomic analysis. Early studies used 2D gel electrophoresis to identify spots that 
change following sleep deprivation, coupled with mass-spectrometry based identification of the 
proteins in those spots. The utility of 2D gel based proteomics is limited by the low proportion of spots 
being identified, and also by post-translational modifications affecting migration within the gel, such 
that phosphorylation of a protein may be misinterpreted as a global decrease in its abundance, as 
both events would decrease the parent spot intensity. However, based on 2D gel studies, authors 
concluded that the abundance of proteins involved in mitochondrial energy production is modulated 
by sleep deprivation (Pawlyk et al. 2007), a finding that has recently been supported by a small scale 
mass-spectrometry based study (Ren et al. 2016).  
1.4.3. Evidence of Localised Sleep 
The link between neuronal activity and local release of sleep inducing molecules, and the direct activity 
of those molecules on neurons, present an interesting question: can individual regions of the brain 
"sleep" independently of other regions? Although counterintuitive, sleepwalking and other 
parasomnias could be described as some regions of the brain being "awake" despite other regions 
being "asleep" (Mahowald & Schenck 2005). This is shown, for example, by the ability to avoid objects 
or to open doors, despite the absence of consciousness. In humans, the low frequency waves 
characteristic of SWS predominate in the frontal cortex before more posterior regions, and similarly, 
blood flow during sleep is not the same across the brain, but instead shows regional dependence 
(Braun et al. 1997; Maquet 2001; Werth et al. 1997). Therefore behavioural, electrical and blood flow 
markers of sleep throughout the brain appear to be location dependent. 
The cortex of the brain is organised into columns, with individual layers of the column involved in their 
own function, such as input or output (Krueger et al. 2008). In the organisation of the cortex, the 
different layers of a column are thought to represent a basic unit, responsible for a specific process, 
like responding to stimulation of a whisker. Local field potentials from individual columns can be 
measured, and show different patterns during whole animal wake and sleep (Rector et al. 2005). 
Periods of sleep-like local field potentials in a cortical column can also appear during whole animal 
21 
 
wakefulness. Remarkably, rats trained to respond to the stimulation of a specific whisker show a 
greater incidence of error of when the local field potential of the cortical column receiving the input 
from that whisker is in a sleep like state. Consistent with the possibility of local activity dependent 
regulation of sleep, the rate of whisker stimulation was positively associated with the probability of 
the corresponding column entering a sleep like state (Krueger et al. 2008).  
 
1.4.4. Implications of Local Sleep 
If individual collections of cells are able to sleep independently of other cells in the same animal, the 
sleep and wake-promoting nuclei may have evolved not to induce sleep itself, but instead to 
coordinate the rest phase of different neuronal assemblies across the brain. This effect would be 
analogous to the SCN acting as a master-pacemaker, synchronising otherwise cell-autonomous 
circadian rhythms. By synchronising the sleep states of the brain, the sleep controlling networks may 
maximise the regions of brain in an awake state during the active phase of the animal, facilitating the 
interaction of multiple regions required for higher level tasks and processing. Cognitive defects during 
sleep deprivation may therefore be mechanistically linked to several regions of the cortex entering 
into a sleep-like state, despite the animal remaining awake (Vyazovskiy et al. 2011). Indeed, fluoro-
deoxyglucose uptake in the cortex of awake humans previously subjected to 24-hour sleep deprivation 
is reduced compared to rested awake controls, indicating wake-induced local inactivation of networks 
(Thomas et al. 2000).  
The ability of individual regions to enter a sleep-like state, despite the action of wake promoting 
centres and other nearby regions remaining in an awake state, indicates the presence of 
highly-localised state-controlling signals, with important practical implications for investigating the 
molecular and cellular aspects of sleep function and homeostasis. Homeostatic signals may not diffuse 
as far as wake-controlling centres, and may even remain within the cell and exclusively act in an 
autocrine manner. Removing the obligation for homeostats to diffuse long distances within the brain, 
or even leave the cell, extends the list of possible molecular homeostats to include the abundance, or 
even localisation, of proteins, transcripts, ions and other small molecules. Indeed, almost any aspect 
of cellular composition or architecture may be co-opted to record the duration of recent activity.  
The phenomenon of local sleep may also allow sleep to be modelled ex vivo, separate from wake and 
sleep promoting centres. Indeed, transcriptomic analysis of primary mouse neurones stimulated with 
a cocktail of excitatory and wake associated neurotransmitters induces similar changes at the 
transcript level as found in mouse cortex after six hours of sleep deprivation (Hinard et al. 2012). An 
ex vivo model could ultimately allow a reductionist approach to sleep research. Although the in vivo 
22 
 
validity of conclusions based on in vitro data is often questionable, an in vitro model of sleep would 
allow the rapid and cost-effective screening of pharmacological or genetic interventions designed to 
perturb molecular correlates of sleep.  
 
1.5. Models to Investigate Sleep at the Molecular and Cellular Level 
The molecular study of sleep is hindered by the absence of a good model. Although behavioural 
aspects of sleep can be investigated well using human volunteers, collecting samples, other than blood 
and urine, for molecular analysis is not usually possible in human studies. Therefore, alternative 
systems must be used to model human sleep, each with their own advantages and disadvantages.  
 
1.5.1. Rodent Models of Sleep 
The molecular study of sleep has been based almost entirely on in vivo rat and mouse models. There 
are several benefits of mammalian in vivo models. Mammals have a clear readout for sleep and 
wakefulness (EEG), exhibit both REM and NREM sleep stages, and have similar brain architecture and 
neurochemistry to humans. More generally, working with animals also allows interactions between 
different tissues to be interrogated. Drugs and other interventions, such as lesions or varied light 
cycles, can be introduced in the adult or developing animal. Rodents, especially mice, are also 
genetically tractable, and several knock-in and knock-out mice as well as viral vectors are available, 
facilitating the interrogation of individual gene functions. More recently, genetically encoded 
fluorescent reporters allow for the non-destructive, in vivo real-time readout of calcium spikes and 
other cellular events. Finally, being a well-established model, a wealth of knowledge surrounding sleep 
in rodents already exists, which facilitates experiment design and formation of further conclusions.  
There are, however, several biological differences between human and rodent sleep patterns. Sleep 
is far less consolidated in rodents than in humans, with sleep bouts averaging only a few minutes in 
duration, and unlike humans they spend around a third of their active phase asleep (Franken et al. 
1999). Rats and mice are both nocturnal, which may complicate interpretations about the interaction 
between the homeostatic and circadian drive for sleep.  
Rodents such as Arvicanthis ansorgei have recently been introduced as a diurnal model for sleep 
research. However, despite being more active during the day than night, the sleep patterns of 
Arvicanthis follow a crepuscular pattern, with wake predominating at dawn and dusk, and sleep 
periods being evenly distributed across the light and dark phases (Hubbard et al. 2015). As the 
23 
 
Arvicanthis model is still being developed, it lacks the availability of previous studies, transgenic lines 
or whole genome sequences that are available in other rodent models.  
There are also experimental difficulties in using rodents to model human sleep. Unlike human sleep 
deprivation studies, where the subjects can be asked to remain awake, animals must be kept awake 
by constant stimulation, which can be stressful. Initial total sleep deprivation protocols were based on 
the "disk over water technique", where a rat is placed onto a rotating platform surrounded by water 
(Rechtschaffen & Bergmann 1995). When the rat falls asleep, the platform begins to rotate, and forces 
the rat to either move to counteract the rotation, or be carried into the water. This technique can be 
used to impose wakefulness for up to 30 days in rats, but is associated with a massive increase in 
corticosteroid levels, indicating the rats are highly stressed (Everson et al. 1989). Although the animal 
is nevertheless still sleep deprived, the stress associated with the induction of sleep deprivation may 
significantly affect its subsequent response to the increased sleep drive. Indeed, the stress associated 
with single housing mice appears sufficient to fragment recovery sleep after sleep deprivation. The 
disk over water technique has also been proposed to induce "learned helplessness", which can induce 
several systemic symptoms (Rial et al. 2007). These significant confounders reduce the reliability of 
results obtained by this protocol.  
To reduce the stressfulness of sleep deprivation, the "gentle handling" protocol has been employed 
(Franken et al. 1991). Gentle handling involves continuously introducing novel stimuli, tapping the 
cage or handling the mice to maintain wakefulness. Compared to the disk over water technique, 
corticosteroid levels remain low. However, gentle handling requires many researchers to maintain 
prolonged wakefulness in even a few mice, and is only effective for approximately 8 hours. Therefore, 
gentle handling protocols have the disadvantage of being highly laborious, leading to underpowered 
studies. 
  
1.5.2. Fly Models of Sleep 
Drosophila melanogaster has become a useful model organism for investigating several aspects of 
sleep, especially at the cellular level. The night-time consolidated rest period of flies has several 
parallels with mammalian sleep (Cirelli & Bushey 2008), including a reversibly increased arousal 
threshold, a requirement for additional recovery sleep following sleep deprivation (Shaw et al. 2000), 
and similar changes in gene expression profiles induced by sleep deprivation (Cirelli et al. 2005). 
Drosophila are readily manipulated genetically (Elliott & Brand 2008), are low cost, and procedures on 
flies are unregulated. Combined with highly accurate activity based readouts of wakefulness, 
24 
 
Drosophila allow for a high throughput analysis of sleep through environmental, genetic and 
pharmacological interventions.  
However, differences in brain structure, intercellular signalling pathways and gene homology between 
flies and humans reduce the relevance of findings in flies to that of human sleep (Sehgal et al. 2007). 
Pharmacological interventions are usually carried out by addition of drugs to the food, severely 
limiting temporal resolution and obligating the exposure of peripheral tissue to the drug. Dosage of 
drugs is difficult to control, especially if the drug is susceptible to degradation in the food or is not 
absorbed well, and any drug that induces sleep will also reduce its own dosage by reducing available 
feeding time. Although flies facilitate high-powered, high throughput behavioural experiments, the 
amount of tissue available from each individual fly is comparatively very low compared to mammalian 
models. Therefore collecting sufficient tissue for molecular analyses is very laborious, especially if the 
brain is dissected from the rest of the fly head, leading to underpowered molecular studies.  
1.5.3. In vitro Models of Sleep 
Cell lines, primary cell cultures and tissue explants have been used to model the molecular aspects of 
several diseases, including diabetes, cancer and Alzheimer's disease. Nevertheless, in vitro models 
have yet to be extensively employed in sleep research, presumably because it is unclear what the 
behaviour based definitions of sleep and wakefulness actually correlate to at the cellular level. 
However, the observation that sleep deprivation modifies transcription shows that cells within the 
cortex have signalling pathways that link wakefulness with intracellular changes. If these same 
pathways can be modulated pharmacologically or genetically, then it may be possible to model 
molecular changes associated with wake and sleep in vitro.  
Tissue explants maintained ex vivo sever long range neuronal connections whilst preserving local 
cellular connections and organisation. Organotypic brain slices have been used to experimentally 
demonstrate cellular changes in neuronal activity following in vivo sleep deprivation (Campbell et al. 
2002; Liu et al. 2010), and to investigate whether sleep associated observations are conserved ex vivo 
(Ding et al. 2016; Han et al. 2014; Hu et al. 2010; Sims et al. 2013). Unlike slice cultures, preparation 
of primary neurons involves the disruption of intercellular connections followed by the selection of 
neuronal cells. Although new connections form between cultured primary neurons, the networks 
formed will not be identical to those found in vivo. Hinard et al. showed that primary cortical neurons 
treated with a cocktail of wake associated neurotransmitters show changes at the transcriptomic level 
that are very similar to those induced in mouse brain by six hours of sleep deprivation in mice (Hinard 
et al. 2012), indicating that sleep and wakefulness can be modelled in vitro, despite the reduced 
number of glial cells and native neuronal networks.  
25 
 
Slice cultures and primary neurons allow the researcher to control the extracellular environment of 
mature neurons. Slices and primary neurons are both genetically tractable, either by the use of 
transgenic mouse lines or viral vectors. However, organotypic slice cultures and primary neurons are 
laborious and costly to prepare and maintain, and can introduce variability between experiments. 
Therefore, a neuronal cell line based model would be a promising addition to sleep research. Cell lines 
allow for a high throughput, low variability approach to research, and also enable samples of human 
origin to be generated. Cell lines stably expressing transgenes can readily be generated, which not 
only allows the manipulation of cell dynamics, but also facilitates the real time monitoring of cellular 
parameters through fluorescent or bioluminescent based reporters.  
 
1.6. Tools to Interrogate Molecular Changes Associated with Sleep and Sleep 
Deprivation 
The availability of multiple systems to model sleep is complemented by recent technological advances 
that facilitate the molecular interrogation of samples. Advantages of newer techniques include higher 
accuracy, reproducibility, throughput and cost-effectiveness. However, many have yet to be applied 
to sleep research. 
1.6.1. Next Generation Sequencing of DNA 
Perhaps the most drastic advance has been in the field of sequencing small sections of DNA, which, 
driven by innovations associated with the Human Genome Project, has now become much more high-
throughput and cost effective. Several approaches to sequencing have been developed, but one of 
the most commonly used is Illumina sequencing. The library preparation for sequencing includes a 
fragmentation step that results in approximately 200bp lengths of DNA. The DNA fragments produced 
are subsequently fused to short adaptors, which are then used to prime the sequencing reaction. 
During the sequencing reaction, nucleotides with a fluorophore fused to the 3’ hydroxyl group are 
used, with a different fluorophore for each nucleotide. Sequencing is achieved by stepwise template 
directed synthesis to add a single nucleotide at a time, followed by fluorescent microscopy to 
determine which fluorophore, and hence nucleotide, had been added. After imaging, the 3’ hydroxyl 
group is restored by cleavage of the fluorophore, allowing the next nucleotide to be added. Illumina 
platforms allow for the parallel sequencing of approximately 2 billion 100bp DNA sequences at a time, 
which can then be compiled to form a genome, or aligned to a reference genome in order to quantify 
how many reads from each region are present in the sample.  
Compared to microarrays, next generation sequencing technology offers several advantages. 
Although microarrays may have several thousand probes, each probe is designed based on expected 
26 
 
sequences, excluding the possibility of discovering novel exon junctions and splice variants. 
Sequencing of DNA is better able to distinguish between similar sequences that may both hybridise 
with the same bait molecules in a microarray, improving specificity and sensitivity to less abundant 
reads. In turn, sequencing offers a greater dynamic range of detection, as there is no background 
signal nor upper saturation limit.  
By varying the original source for the DNA fragments being sequenced, multiple common uses for next 
generation sequencing have been developed, including exome-sequencing, the quantitative 
characterisation of protein DNA binding patterns through chromatin immunoprecipitation (ChIP-Seq) 
and the transcriptome wide quantification of gene expression (RNA-Seq). Genomic sequencing of 
patients or animals with specific phenotypes is able to link individual point mutations to pathologies. 
Regions of the genome that are bound by specific transcription factors or histones can be enriched by 
co-immunoprecipitation with that factor before sequencing in order to characterise the binding 
pattern of that factor. Alternatively, RNA-Seq involves the production of DNA fragments from the 
reverse-transcription of RNA, which are then prepared for sequencing. 
Sequencing DNA fragments originating from mouse genomic DNA, coupled with phenotypic 
characterisation of ethylnitrosourea mutated mice, has been used in sleep research to identify 
mutations associated with altered sleep duration (Funato et al. 2016). RNA-Seq can be used to identify 
differences in gene expression or splicing associated with a treatment, such as sleep-deprivation, 
whereas ChIP-Seq can be used to link expression patterns to histone modifications or the activity of 
specific transcription factor binding sites. However, to date, there is very little RNA-Seq or ChIP-Seq 
based data available relating to sleep.  
27 
 
 
1.6.2. Tandem Mass Tag based Proteomics 
Similar in concept to Next Generation Sequencing, Tandem Mass Tag (TMT) Mass Spectrometry 
quantifies proteins by fusing short peptide fragments, produced by enzymatic digestion of proteins, 
to small molecular tags. Each tag is composed of a reporter region, a cleavable linker region, a mass 
normalisation region and a protein reactive region. Using isotopic nitrogen and carbon, the mass of 
the reporter region of different tags is varied, offset by an equivalent change in mass in the mass 
normalisation region. Therefore, the total mass and chemical structure of each tag is identical, only 
the distribution of mass across the reporter and normalisation regions vary between tags. Therefore, 
these tags allow up to eleven samples to be multiplexed and analysed at the same time during liquid 
chromatography and subsequent mass spectrometry, greatly reducing the technical variation 
between samples. 
H
3
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Immuno-
precipitation
Sonication and 
DNA Digestion
Purification of 
ChIP’ed DNA
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dscDNA
Reverse 
Transcription
RNAseH
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DNA Library 
Preparation
Sequencing 
30m reads and 
alignment to 
genome
Sox9
Atp5f1
Adora3
Wdr77
Gabra2
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Gpa33
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Acvr1b
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Acvrl1
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Egfl6
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Tpd52l1
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Th
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Ccnd2
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Klf6
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Apoh
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Figure 1.5. Next Generation Sequencing Pipeline: Next generation sequencing (NGS) allows for the 
sequencing of hundreds of millions of small (25-200bp) DNA fragments. These sequences can then 
be aligned to a genome to identify point mutations. Alternatively, the number of fragments 
originating from every known coding or promotor region within that genome can be quantified, 
and the abundance compared between samples. By using chromatin immunoprecipitation (ChIP), 
the abundance of specific chromatin marks at promotors can be inferred. By using RNA as a source 
for double stranded cDNA (dscDNA) fragments, the abundance of several thousand known 
transcripts can be inferred.  
 
28 
 
The precise mass of peptides is determined using mass spectrometry and then the possible amino acid 
compositions that match that molecular weight are compared to proteomic or genomic references 
and assigned to a specific protein. The abundance of the peptide in each sample is inferred from the 
relative amounts of the different reporter regions, which is cleaved from the rest of the tag during 
ionisation. Similar to ChIP-Seq, the pool of proteins examined by mass-spectrometry can first be 
filtered on the basis of phosphorylation status, subcellular localisation or binding partners by 
techniques such as titanium dioxide columns, subcellular fractionation or co-immunoprecipitation, 
respectively (Possemato et al. 2017; Schwertman et al. 2013; Simor et al. 2017).  
1.6.3. Genetically Encoded Real Time Molecular Reporters 
One pitfall of the destructive sampling techniques used for the molecular characterisation of sleep is 
that treatment groups within most experiments are made up of distinct individuals, and therefore 
treatment effects may be confounded by individual biological variation. Although blood may be taken 
from the same person for RNA-Seq or proteomic analyses before and after sleep deprivation, the same 
cannot be done with mouse cortex. Similarly, destructive techniques place practical limits on the 
sampling frequency that is possible during lengthy treatments such as sleep deprivation and sleep 
recovery, which in turn reduces the temporal resolution of experiments.  
In contrast, non-destructive techniques such as video tracking or EEG analysis allow the same 
individual animal to be monitored repeatedly and continuously. By non-destructively monitoring the 
same individual, baseline values can be generated for each individual before treatment. Therefore 
non-destructive techniques allow individuals to act as their own control, increasing the power of the 
experiment. The temporal resolution of continuous recordings also tends to be very high- video 
tracking based behavioural experiments may collect data at 50 frames per second- allowing short lived 
or specifically timed events to be detected.  
Some molecular events can be directly and non-destructively monitored. For example, one study 
monitored the autofluorescence of the brain across the sleep wake cycle, from which the authors 
concluded that central NADH levels are state dependent (Mottin et al. 1997). Other events can be 
monitored by taking advantage of small molecules whose fluorescence depends on their molecular 
interactions. For example, using the calcium sensitive BAPTA dye, one study found that cortical 
intracellular calcium waves of newly born mouse pups were more prevalent in vivo during the rest 
phase than the active phase (Adelsberger et al. 2005).  
As an alternative to small molecule sensors, the genetic introduction of the calcium binding calmodulin 
to green-fluorescent protein (GFP) has enabled a genetically modified protein to be used whose 
fluorescence is dependent on the local calcium concentration (Nakai et al. 2001). Since then, 
29 
 
genetically encoded fluorescent sensors have been developed that are sensitive to several molecular 
parameters, such as NADPH concentration (Tao et al. 2017), reactive oxygen species (Bilan et al. 2013), 
pH (Tantama et al. 2011), and membrane potential (St-Pierre et al. 2014). Further developments have 
allowed fluorescent imaging at red-shifted wavelengths, enabling simultaneous monitoring of two 
separate sensors within the same cell (Akerboom et al. 2013). The localisation of the sensor can be 
genetically controlled, either by the addition of a short signal peptide to direct the sensor to specific 
organelles within the cell, or by using a promotor to limit expression of the construct to a specific cell 
type. The use of circadian promotors, such as Bmal or Per2 promotors, to drive fluorescent or 
bioluminescent constructs has been extensively used as a readout of the cellular clock (Noguchi et al. 
2010), facilitating high-throughput drug screens (Lee et al. 2016). The combinatorial use of promotors, 
subcellular localisation signals and multicolour sensors therefore offers a very powerful toolbox for 
the investigation of sleep at the molecular level.  
  
30 
 
1.7. Aims and Approach of this PhD Project 
The aim of this PhD project is to better understand the fundamental changes that occur at the 
molecular and cellular level during sleep and wakefulness, and specifically to address the absence of 
data about the abundance of sleep dependent molecules during recovery from sleep deprivation. In 
the first two bodies of work presented in this thesis, we take advantage of recent technological 
advances in automated sleep deprivation and molecular analytical methods to identify the 
transcriptomic, proteomic and metabolic correlates of sleep deprivation and recovery sleep in mouse 
cortex. In the final set of experiments presented in this thesis, we attempt to create an in vitro model 
of sleep deprivation using a human neuroblastoma cell line and optogenetic tools, and discuss to what 
extent the transcriptional correlates are conserved between the two models. Within the lab, but not 
discussed in this thesis, these findings are compared to data generated using Drosophila by other 
members in the lab. By using several models, each with their own advantages and confounders, we 
hope to identify fundamental functions of sleep and the molecular mechanisms of sleep homeostasis. 
 
Transcriptomic, 
Proteomic and 
Metabolic Analysis
Identify 
Conserved 
Changes
Multiple 
Models
Similar 
Experimental 
Design
Figure 1.6. Experimental Approach within the Lab: Although sleep is conserved across species, the 
duration, timing and architecture of sleep bouts vary significantly. Our hypothesis is that sleep is 
conserved across species due to a fundamental function that is also conserved. Using any single 
model in isolation to understand sleep risks confounding fundamental functions of sleep with 
species specific changes. By using similar experimental designs to characterise molecular changes 
in in vitro, mouse and fly models of sleep deprivation, we hope to identify conserved and 
presumably fundamental molecular signatures of sleep and wakefulness. In this thesis, data from 
the mouse and in vitro models are presented and compared. Work relating to the Drosophila model 
was carried out exclusively by other members of the lab, and so will not be discussed in this thesis. 
 
31 
 
The experiments upon which this thesis is based contribute novel data. Although numerous studies 
have previously characterised transcriptomic and proteomic changes associated with sleep 
deprivation, the vast majority have used now outdated technology and have been limited in scope to 
a handful of timepoints. In this thesis, we collect tissue at ten timepoints distributed across a total of 
54 hours, and used next-generation sequencing based transcriptomics to characterise the effects of 
3,6 and 12 hours sleep deprivation. The timecourse style experiment places sleep dependent changes 
into a circadian perspective and allows the kinetics of recovery to be inferred. We complement these 
transcriptomic analyses with similar proteomic and metabolomic timecourses. Although other groups 
have attempted to model sleep in vitro, previous studies have pharmacologically activated tissue 
explants or primary neurons. Here we present our work in generating a cell-line model of sleep 
deprivation that can be optogenetically stimulated in a large scale and easily controlled manner.  
The overall emphasis of this thesis is on the molecular rather than anatomical or behavioural aspects 
of sleep deprivation. An improved understanding of sleep at a basic molecular level may ultimately 
facilitate the discovery of pharmacological interventions to alter sleep patterns and treat sleep related 
pathologies.  
  
32 
 
 
 
 
 
 
 
 
2. General Methods 
 
This section details the experimental procedures used to gather data for this thesis. 
  
33 
 
2.1. Mice Used 
Wild type, male C57/Bl6J mice were purchased from Charles River Laboratories and allowed to 
acclimatise in the new institute for at least 2 weeks after arrival. During acclimatisation, mice were 
group housed in individually ventilated cages, with ad libitum access to standard chow and water and 
a 12h light, 12h dark cycle (lights on at 06:00). Mice were all aged 9-10 weeks at the time of 
experiments. 
2.2. Sleep Deprivation Protocol 
Sleep deprivation was applied using sleep fragmentation chambers (Campden Instruments, 
Model 80391), which have been previously demonstrated to effectively deprive mice (Kaushal et al. 
2012). The chambers resemble a typical laboratory mouse cage, except that there is a movable “L” 
shaped bar descending from the cage lid, the horizontal section of which spans the width of the cage. 
The bar couples to a frame containing a worm gear motor, which causes the bar to sweep across the 
cage every 7.5 seconds. When the bar reaches one edge of the cage, the direction of the motor is 
reversed and the bar sweeps back again. Although the speed of the motor is fixed, it is possible to 
delay the switching of the motor direction, such that the bar pauses at one edge of the cage. During 
this time, mice still have access to food and water. 
The chambers allowed ad libitum access to food and water, and the chambers placed inside a 
customised cabinet with a controlled light cycle (12h light, 12h dark, lights on at 06:00). The chambers 
had woodchipping, but did not have nesting material or environmental enrichments such as chew 
toys, as these block the movement of the bar.  
Mice were transferred into sleep fragmentation chambers at 15:00, Day 0. Mice used for 
transcriptomic and proteomic analyses were pair housed, whilst those used for metabolomic analyses 
were trio housed. Sleep deprivation was initiated at lights on (06:00) on Day 2, by switching on the 
motors. The motors were set to pause for 120 seconds between sweeps for the first 30 minutes of 
sleep deprivation, and the duration of the pause was gradually decreased (through pauses of 60s, 30s 
and 15s duration) to 0s during the first 90 minutes of sleep deprivation, such that by 07:30, the bar 
was moving continuously. During this time, the novelty of the moving bar was sufficient to maintain 
constant activity of the mice, despite the bar only moving a fraction of the time. The mice were 
continuously monitored during the first 2 hours of sleep deprivation to ensure the mice were avoiding 
the bar, and to remove anything blocking movement of the bar (e.g. food pellets, piles of 
woodchipping).  
During 12 hour sleep deprivation protocols, wakefulness was maintained between 12:00-18:00 by 
occasional tapping on the cage, moving the cage or gentle touches with a brush, in addition to the 
34 
 
sweeping bar. At the end of sleep deprivation, motors were switched off when the sweeping bar was 
at the edge of the cage furthest from the food hopper and water bottle.  
2.3. Timecourse Tissue Sampling Protocol 
All tissue sampling timecourses had 10 timepoints, spaced 6 hours apart, beginning 12 hours before 
sleep deprivation and ending 42 hours after the start of sleep deprivation (see Fig 3.6.). The capacity 
of the cabinet was limited to 8 cages, and therefore to generate the number of samples required, 
multiple rounds of tissue collections were pooled to generate an entire timecourse.  
For transcriptomic and proteomic analyses, mice were pair housed, and 2 cages were taken per 
timepoint (to give a final n=4). Therefore, each individual treatment group was composed of 3 
separate tissue collection timecourses. For metabolomic analyses, mice were trio housed, and 1 cage 
was taken per timepoint (to give a final n=3). Therefore, each individual treatment group was 
composed of 2 separate tissue collection timecourses. 
For transcriptomic and proteomic analyses, pair-housed mice were simultaneously sacrificed using a 
rising CO2 concentration. Whole brains were quickly removed from each animal and frozen on dry ice. 
After both brains had been removed, both livers were removed, before other peripheral tissues (heart, 
kidney, epidydimal fat and femoral muscle) were harvested and frozen on dry ice. For metabolomic 
analyses, mice from one cage were sequentially sacrificed by cervical dislocation and their brain and 
liver harvested. Cerebral cortex was isolated from the rest of the brain using a flat blunt instrument 
before freezing on dry ice. Tissues were then transferred to long term storage at -80oC. 
 
2.4. Preparation of Libraries for RNA-Seq Analysis 
For RNA-Seq analysis of mouse cortex, a 3mm thick section of cortex was isolated from the left side of 
the frozen brains, spanning between 0.5mm anterior to bregma to 2.5mm posterior to bregma 
(see Fig 2.1.). This region samples cingulate, motor, somatosensory, parietal, auditory and visual 
regions of the cortex. The dissection was carried out on dry ice to maintain RNA quality by preventing 
the tissue from thawing. The frozen sections were returned to the freezer. To extract RNA, 700µl of 
TRIzol® was added to the tissue chunk and the tissue homogenised using a disposable pestle. 700µl of 
ethanol was then added, and RNA extracted using Direct-zol™ RNA MiniPrep Kit (Zymo Research), 
following the manufacturer’s instructions.  
35 
 
 
The integrity of the extracted RNA was confirmed using a QIAxcel system (QIAGEN), before total RNA 
libraries were prepped for sequencing using the KAPA-RiboErase Hyper Prep, using manufacturer’s 
instructions. Briefly, the library prep was carried out on 1µg total RNA. Ribosomal RNA (rRNA) was 
depleted using DNA oligomers that are complementary to rRNA and subsequent RNAse H degradation 
of rRNA-rDNA hybrids. The rDNA oligomers were subsequently degraded by DNAse treatment. rRNA 
depleted RNA was then fragmented to approximately 200bp sized fragments by incubation at 94oC for 
6 minutes in 1x KAPA Fragment, Prime and Elute Buffer. This RNA was then used as a template for 
double stranded DNA synthesis, before adaptors were ligated onto the 5’ end of the fragments. The 
library was then amplified by PCR, but to minimise the effect of PCR bias on subsequent sequencing, 
the rounds of PCR were kept to a minimum (3-5 rounds).  
Bregma 0.5mm Bregma -2.5mm
Figure 2.1. RNA for RNA-Seq was extracted from the left cortex between 0.5mm anterior to 
2.5mm posterior to Bregma: The cortex sample collected for RNA-Seq is highlighted in red, from a 
sagittal perspective (upper tile) and coronal perspective (lower tiles) 
36 
 
 
After each step of the protocol, a bead based clean-up was carried out using KAPA Pure Beads to 
remove reagents from the previous step. Briefly, KAPA Pure Beads is a suspension of magnetic nucleic 
acid binding beads in a solution containing sodium chloride and polyethylene glycol (PEG). The higher 
the concentration of PEG and NaCl, the higher the proportion of nucleic acid bound to the beads, and 
so addition of the suspension to RNA or DNA containing solutions results in the nucleic acid binding to 
the beads. The solution, containing reagents, was removed from the beads using a magnetic stand, 
and the beads twice washed in 80% ethanol to remove any residual solution. When the beads are 
resuspended in an aqueous solution without high PEG and salt conditions, the nucleic acids elute from 
the beads and can be used for the next step of the protocol. 
2.5. Analysis of RNA-Seq Data 
Libraries were sequenced using an Illumina HiSeq 2500 or HiSeq 4000, generating approximately 30 
million 100bp pair-ended reads per sample. Reads were aligned using the Tophat Cufflinks pipeline, 
described below. Reads were initially trimmed on the basis of their sequencing quality score using 
Trimmomatic, such that any 4 nucleotide region with an average Phred score of less than 15 (equating 
to a predicted sequencing accuracy of 97%) was removed from that read. Any pair of reads where one 
of the reads were trimmed to a length shorter than 36nt was discarded (see Fig 2.3., line 13). Trimmed 
paired-end reads were then aligned to the appropriate genome (GRCm38/mm10 and GRCh38/hg38 
for mouse and human RNA sources, respectively) using TopHat2, using the “no-novel-juncs” option to 
instruct TopHat2 to only consider previously identified exon junctions (see Fig 2.3., line 20). Alignment 
for RNA-Seq using TopHat2 was typically 85%. Samtools was then used to discard any reads aligning 
to multiple loci in the genome and sort the remainder by chromosome coordinate (see Fig 2.3., 
line 22).  
Figure 2.2. Typical Input RNA and Final Library Electropherogram: RNA integrity is estimated on the 
basis of electrophoresis (left panel). The relative abundance of 28S and 18S peaks, centred at 
4.7kb and 1.9kb, respectively, is used as an indicator of quality. During library preparation, RNA is 
fragmented to approximately 200bp fragments. The average size of fragments is increased to 
approximately 300bp following ligation of adapters (see right panel).  
37 
 
 
The number of reads aligning to each transcript was then determined using the Cuffquant command 
of the Cufflinks package (see Fig 2.3., line 25), and then all the Cuffquant outputs were subsequently 
compared in the same cuffdiff command. Cuffdiff normalises the number of reads per transcript on 
the basis of the size of that transcript followed by the number of reads in that library to produce a 
value quoted in fragments per kilobase per million fragments (FPKM). Cuffdiff was run using the 
geometric library normalisation method (which normalises across libraries based on the number of 
reads of the median expressed gene in each library rather than total number of reads), and to account 
for variable rRNA depletion during library preparation, was instructed not to consider any reads 
aligning to rRNA genes (see Fig 2.4., line 13 and 14).  
 
Figure 2.3. Tophat Pipeline Example: Above is a representative example of a cuffdiff command 
submission script. Lines 1-7 outlines parameters about the job, such as memory to be allocated, 
whereas lines 11,16,17 and 18 specify which software packages are required. Paired end sequencing 
files (INPUT_FILE_1 and INPUT_FILE_2) are first trimmed based on sequencing quality using 
Trimmomatic. The trimmed reads are then used as an input for tophat2, which exports aligned reads 
to accepted_hits.bam. Samtools uses this file as an input and is used to sort the reads and remove 
multiply aligned reads. The sorted, filtered reads are then used as an input to cuffquant, which 
quantifies the abundance of each individual transcript. 
38 
 
 
Differential expression analysis between single timepoints is carried out within cuffdiff, and the 
statistical values for isoform and gene differential analysis extracted from isoform_exp.diff and 
gene_exp.diff, respectively, whilst the FPKM values of individual biological replicates were extracted 
from isoform.read_group_tracking and genes.read_group_tracking and analysed using R to identify 
genes with a rhythmic and sleep deprivation dependent expression profile. 
2.6. Proteomics Analyses 
For Proteomic analysis, a separate 2mm section of cortex was taken from both the left and right side 
of the brain, spanning from approximately 2.5mm anterior to bregma to 0.5mm anterior to bregma 
(see Fig 2.5.). This region therefore encompasses cingulate, motor, somatosensory, orbital and insular 
agranular regions of the cortex. The tissue was chopped on dry ice to prevent thawing, before being 
homogenised by a mini-pestle in protein lysis buffer (9M Urea, 0.5% NP40, 50mM HEPES pH=8.5), 
containing 0.25µg/ml benzonase nuclease, 1x Halt™ Protease and Phosphatase Inhibitors 
(ThermoScientific #78429 and ThermoScientific #78420). Proteins were prepared for proteomic 
quantification using the TMT10plex™ Isobaric Label Reagent Set (ThermoScientific #90110), which 
allows for the simultaneous quantification of proteins from up to 10 samples during the same mass 
spectrometry run. 4 ten-plex experiments were carried out: one pair of ten-plex experiments was 
carried out on the control and the sleep deprived mice, using protein pooled from 4 biological 
replicates at each time point, such that each TMT-label was associated protein from a different 
timepoint. A second pair of tenplex experiments was carried out using unpooled protein from 
3 individual biological replicates collected at the end of the light phase the day before and after sleep 
deprivation and a further 4 biological replicates collected immediately following 12 hour sleep 
deprivation. 
Figure 2.4. Cuffdiff Command Example: Above is a representative example of a cuffdiff command 
submission script. Lines 1-7 outlines parameters about the job, such as memory to be allocated, 
whereas lines 9-11 specify which software packages are required, whilst a representative cuffdiff 
command is outlined on lines 13-18. 
39 
 
 
 
Briefly, protein was precipitated by the addition of 6 volumes of ice cold acetone followed by 
overnight incubation at 4oC. The supernatant was discarded and the pellet resuspended by sonication 
in 100mM Triethylammonium bicarbonate (TEAB) buffer (SIGMA #T7408), and quantified using a 
bicinchoninic acid (BCA) assay kit (ThermoScientific #23225). A total of 100µg protein in 100µl 
100mM TEAB buffer was reduced by incubation with 5µl of 200mM tris(2-carboxyethyl)phosphine 
(TCEP) solution at 55oC for 60 minutes. The sample was then incubated with 5µl of 
375mM iodoacetamide at room temperature for 30 minutes, to alkylate thiol groups that would 
otherwise react with the TMT-labels. The reduced, alkylated protein was then digested by overnight 
incubation at 37oC with 2.5µg trypsin. The next day, the ten TMT Label Reagents were resuspended in 
40µl acetonitrile and added to the protein digest fragments, such that different samples ultimately 
run in the same tenplex are labelled with different reagents. Following one-hour incubation, the 
labelling reaction was quenched by addition of 8µL of 5% hydroxylamine. The labelled fragments 
originating from different samples were then pooled in equimolar amounts, and subjected to mass 
spectrometry analysis. Peptide and parent protein identification and quantification was carried out 
using MaxQuant and statistical analysis was performed using R. Maxquant based identification and 
quantification was carried out by another member of the lab (Dr S. Ray), but subsequent R based 
analyses was carried out by the author of this thesis. 
Bregma 2.5mm Bregma 0.5mm
Figure 2.5. Protein for TMT-based Proteomics was extracted from the cortex between 2.5mm to 
0.5mm anterior to Bregma: The cortex sample collected for proteomic analysis is highlighted in 
red, from a sagittal perspective (upper tile) and coronal perspective (lower tiles) 
40 
 
2.7. Metabolomic Analyses 
For Metabolomic analyses, the left side of the whole mouse cortex was lyophilised overnight using a 
freeze-drier. The dried tissue was then finely ground using an apothecary’s pestle and mortar, its dry 
weight measured and was then placed into a 2ml Eppendorf microfuge tube and returned to a -80oC 
freezer.  
Once all the samples had been ground, 400µl HPLC grade Chloroform (Sigma, 650498-1L) and 
200µl LC-MS grade Methanol (Sigma, 000000001060351000) was added to each tube and the samples 
vortexed to suspend the dried tissue. The samples were incubated at 4oC for 60 minutes, during which 
time they were subjected to 3x8minute sonication in a water bath sonicator. The samples were then 
centrifuged at 0oC at 16,000g for 10 minutes, and the supernatant removed into a new Eppendorf 
tube. 400µl Methanol and 200µl LC-MS grade water (Sigma, 000000001153331000) containing 
3 nmol 13C5,15N1-valine was added to each sample, and the samples vortexed to resuspend the pellet. 
The tubes were then incubated at 4oC for 60 minutes, during which time they were subjected to 
another 3x8minute sonication in a water bath sonicator. After sonication, the samples were 
centrifuged at 0oC at 16,000g for 10 minutes and the supernatant added to the previously removed 
supernatant. The isolated supernatants were then dried in a speed vac and resuspended into 50µl 
chloroform, 150µl methanol and 150µl water. This solvent mixture partitions into two phases: the 
upper aqueous layer contains polar metabolites, the lower apolar phase contains apolar metabolites, 
whilst cell debris and proteins accumulate at the interphase.  
The equivalent of 1mg of dried tissue of the polar phase from each sample was subjected to liquid 
chromatography mass spectrometry (LC-MS), in addition to a pooled sample that was run multiple 
times. The LC-MS spectra were analysed using both a targeted and untargeted approach. All identified 
peaks were quantified using Progenesis QI, and the identities of peaks of interest were predicted using 
the ChemSpider database. Any peaks whose size exhibited a coefficient of variance of greater than 0.3 
between the repeated sampling of the pooled sample were removed from downstream analysis. 
Additionally, the abundance of specific compounds was determined through a targeted approach. 
Solutions containing individual compounds of interest purchased from Sigma were subjected to 
LC-MS, to identify precise elution windows, mass-to-charge ratio and fragmentation pattern returned 
by the same machine as used for cortex samples. Fragmentation of ions from the pooled sample 
matching the mass and elution time of target compounds was used to further confirm that the peak 
corresponds to the compound of interest, and that the compound of interest was extracted in 
detectable quantities from the cortex. The area of these peaks were then quantified using TraceFinder, 
and the data exported for statistical analysis in R.   
41 
 
2.8. Statistical Analysis of Datasets 
Omic datasets were analysed through a combination of Cuffdiff, JTK analyses, Analysis of variance 
(ANOVA) and Student t-tests. The resulting p-values were then corrected for multiple testing to 
produce q-values using the “p.adjust” function in R, using the Benjamini-Hochberg method  (Benjamini 
& Hochberg 1995). Tests returning q-values lower than 0.05 were considered for further analysis.  
RNA-Seq data alignment and quantification utilised the Cufflinks pipeline, the final stage of which, 
Cuffdiff, performs statistical analysis of differential gene expression for each gene in each sample pair 
based on raw reads.  
The final output values for each replicate (FPKM normalised RNA expression, protein abundance, 
metabolite peak area) were also subjected to JTK analysis for the identification of rhythmic molecules  
(Hughes et al. 2010), setting the number of replicates to 4, time between timepoints as 6 hours and 
the cycle period to 24 hours. For metabolomic and proteomic and the cellular RNA-Seq timecourses, 
all the timepoints were considered in JTK analysis. For the RNA-Seq of mouse cortex, only 
timepoints 1-8 were considered, due to timepoints 9 and 10 of the control and SD6 groups suffering 
from batch effects. Notably, inclusion of the additional timepoints in JTK analysis does not alter the 
ultimate conclusions about the global effect of sleep deprivation on rhythmic genes.  
Identification of sleep dependent genes was performed between pairs of conditions 
(e.g. Control vs SD6) using ANOVA analysis using the “aov” function in R, with the format 
aov(value ~ Group * Time, data=(Control&SD6)). This function produces 3 p-values, indicating the 
significance of the effects of Group, Timepoint and whether there is an interaction between the two 
(i.e. a differently shaped abundance profile). Only timepoints 3-8 were considered in the ANOVA 
analysis, with the interaction p-value being exported for further use.  
Two-tailed, unpaired student t-tests were used to compare differential metabolite and protein 
abundances at specific timepoints, and for comparing qPCR expression data.  
  
42 
 
2.9. Media used for Cell Experiments 
 
2.10. Cell Maintenance 
Cells were maintained in Corning T75 flasks, and were passaged when they were approximately 70-
90% confluent. The medium was aspirated, the cells washed with 10ml pre-warmed phosphate 
buffered saline (PBS), and then 3ml of trypsin-EDTA solution added. The cells remained in the trypsin 
solution with gentle tapping until almost all of the cells had detached, before the trypsin was 
neutralised by addition of 10ml of medium. A proportion (HEK: 5%, U20S: 10%, SH-SY5Y: 25%) of the 
suspension of detached cells were transferred to a new T75 flask, and medium added to make the 
total volume to 15ml, and the new flask returned to the incubator (37oC, 5% CO2). The remaining cells 
were either discarded, or seeded into plates for transfection or assays. 
2.11. Generation of Stable Cell Lines 
For production of stable cell lines, cells were plated into a 6-well plate. One day after plating, cells 
were transfected with the relevant plasmid using 25kDa Linear Polyethyleneimine (PEI) 
(Alfa Aesar, #43896). The next day, cells were trypsinised and transferred to a 10cm cell culture dish, 
and the appropriate antibiotic (e.g. puromycin or Geneticin) added 48 hours after transfection at the 
lowest concentration previously determined to eliminate untransfected cells of that type (typically 
Table 2: Media Compositions used for in vitro Experiments 
Name Use Cell Lines  Contents 
Normal 
Base 
General 
Growth and 
Culture of 
Cell lines 
U20S, 
HEK293T 
Dulbecco's Modified Eagle's Medium (DMEM) (Sigma 
Aldrich D5671), 
Supplemented with 10% FBS (HyClone, SV30180.03), 1% 
Glutamax (Gibco, 35050-038), 1% Non-Essential Amino Acid 
Mixture (Sigma, M7145), 1% Penicillin / Streptomycin mixture 
(Sigma, P0781), 0.2% Mycozap Plus (Lonza, VZA-2022) 
OptiSHY General 
Growth and 
Culture of 
Cell lines 
SH-SY5Y 50% OptiMEM (Gibco, 31985-047), 50% F12 Ham 
Mixture (Sigma, N6658), 
Supplemented with 12% FBS, 1% Glutamax 1% Non-Essential 
Amino Acid Mixture, 1% Penicillin / Streptomycin 
mixture,0.2% Mycozap 
AIR 
Medium 
Assays in 
atmospheric 
CO2  
U20S, 
HEK293T, 
SH-SY5Y 
DMEM Powder (without NaHCO3 or Phenol Red) 
(Sigma D5030),  
Reconstituted with 10% FBS, 1% Glutamax, 1% Non-Essential 
Amino Acid Mixture, 1% Penicillin / Streptomycin mixture, 
0.2% Mycozap, 5g/L glucose, 40mM MOPS (Sigma, M3183) or 
HEPES (Sigma, H4034). 
43 
 
2µg/ml Puromycin (Invivogen, ant-pr-1), or 100-200 µg/ml Geneticin (Gibco, 10131-035). Resistant 
colonies appeared on the plate after 2-6 weeks, depending on cell type and selection antibiotics. 
Monoclonal lines were produced by placing sterile filter paper disks soaked in trypsin solution on top 
of individual colonies, and transferring them to their own well in a cell culture plate. Polyclonal lines 
were produced by the pooling of multiple colonies.  
For faster and more reliable production of polyclonal SH-SY5Y puromycin resistant stable cell lines, 
cells were plated into 10cm dishes and transfected using PEI when 50% confluent. 2 days later, cells 
were treated with puromycin for two days, and then allowed to recover in puromycin free medium 
for 2 days. The cycling treatment of antibiotic continued for 2 weeks, before the cells were 
continuously grown in puromycin containing medium.  
2.12. Stimulation of Cells with Neurotransmitter Cocktail 
A Neurotransmitter Cocktail containing multiple neurotransmitters associated with wakefulness was 
produced, based on Hinard et al. A 100x stock concentration containing 1mM carbachol (Sigma, 
C4382), 100 µM NMDA (Cambridge Bioscience, 14581-50 mg-CAY), 100 µM AMPA (Sigma, A0326), 
100 µM kainic acid (Sigma, K0250), 100 µM ibotenic acid (Sigma, I2765), 100 µM serotonin (Sigma, 
H9523), 100 µM histamine (Sigma, 53300), 100 µM noradrenaline (Sigma, 74480), 100 µM dopamine 
(Sigma, PHR1090), and 1µM orexin (Sigma, O6012) was dissolved in water, aliquoted, and frozen. This 
stock concentration was added to cells and the cells returned to the incubator. After 4 hours of cocktail 
treatment, cells were lysed in TriReagent and the RNA purified. 
2.13. Stimulation of Cells with Light 
The stimulation of cells with light was developed during this project, and the outlined protocol below 
represents the final product of this development. In the course of the project, preliminary data were 
generated with similar protocols, but without some of the method refinements. Method details 
specific to individual experiments are listed in the appropriate section of Section 5.2..  
Cells expressing opsin containing constructs were illuminated with blue light 
(wavelength 468.5±1.5nm), green light (523.5±1.5nm) or red light (622.5±2.5nm) using a 
programmable LED array, based on the NeoPixel Shield LED array (Adafruit) controlled by an 
Arduino Uno. Each cell containing plate was illuminated by two arrays at a distance of 3cm, held in 
place by a custom printed part. The maximum light intensity able to be supplied to cells at this distance 
was calculated to be 1.6mW/cm2, 0.42mW/cm2, and 0.38mW/cm2 for the blue, green and red LEDs, 
respectively, and measured to be 1.2mW/cm2, 1.0mW/cm2 and 0.95mW/cm2, respectively.  
44 
 
The Arduino is programmable in such a way that when powered up, it runs a set of commands once 
and then operates a command loop continuously until power is removed. The programmed single run 
commands consisted of a pause of 20 seconds, whilst the loop specifies which colour LEDs are 
activated, intensity of light emitted, number of flashes per second, duration of each flash, and how 
many flashes occur in a row before a pause in flashing. During the sample preparation for the 
transcriptomic analyses of CoChR-RCAMP expressing cells, green and blue LEDs were both active at 
maximum light intensity, at a frequency of 8Hz. Each flash consisted of 20ms of both blue and green 
light, followed by a further 5ms of green light only. After ten seconds (i.e. 80 flashes), there was a 
10 second pause before the next train of flashes. 
 
 
Because each plate was illuminated by two arrays controlled by separate Arduino boards, small 
differences in timekeeping between pairs of Arduinos caused the flashes of the arrays to become 
desynchronised. Therefore, to re-synchronise each individual array, the power supplies to all the 
Arduino boards were controlled by a master Arduino through a MOSFET module. This master Arduino 
was programmed to remove the power for 10 seconds every 10 minutes, causing each Arduino to 
restart its set of commands, thereby synchronising all of the arrays before significant drift could occur. 
 
0 30Time(s)
0 1Time(s)
Figure 2.6. Schematic of LED Illumination Pattern: Arduino controlled LED shields containing RGB 
LEDs were used to illuminate cells. Each flash of light was composed of blue and green light, with 
simultaneous onset. The blue light flash lasts 20ms whereas the green flash lasts 25ms. 8 flashes 
per second took place during the on phase, which lasted 10 seconds, followed by complete 
darkness for 10 seconds. Every 10 minutes, the arrays were switched off for 30 seconds.  
45 
 
Illumination took place in a light tight incubator with atmospheric CO2 at 37oC. Arrays were placed 
inside individual compartments of light tight boxes, such that light from an array could only reach the 
cells placed directly above it and not others in the same incubator. At the rear of each compartment 
was a 10cm diameter fan that ran continuously to reduce any local heating due to the LEDs. Cells could 
be placed into and removed from their compartment for sampling without exposing plates in other 
compartments to environmental light. Cells had their medium replaced with AIR medium 
(see Section 2.8.) and their plates sealed before being placed into the incubator. The cells were then 
maintained in darkness for 36 hours before onset of light exposure.  
 
2.14. Live Cell Luminescence 
U2OS cells, expressing luciferase under the control of the Per2 promoter were transfected with the 
CRIP or CHALIP plasmid (see Section 2.14.), and stable expression of the construct was selected for 
using 2µg/ml puromycin and 200µg/ml Geneticin. 
Figure 2.7. Schematic of Final LED based Illumination System: Arduino controlled LED shields 
containing RGB LEDs were used to illuminate cells. Cells were positioned 3cm above the light 
arrays using a printed scaffold. Behind each system was a 10cm fan, which was on whenever cells 
were in the incubator to maintain a constant temperature during illumination. The cells, LED 
arrays and fan were placed inside individual compartments of light tight boxes in a 37 oC incubator, 
such that each plate could be individually accessed without exposing nearby plates to ambient 
light. The LED arrays were powered through a master Arduino, which removed the power supply 
briefly every 10 minutes, thereby resynchronising the two individual shields of each array.  
46 
 
Six 96-Well Assay Plates were seeded with the channelrhodopsin-expressing U2OS luciferase cell lines 
produced above, and the original luciferase cell lines. The cell lines were all cultured for 1 week before 
this experiment in Normal Base Medium supplemented with 1µM retinyl acetate and 1µM all-trans 
retinal. The day after seeding, plates had their circadian clocks synchronised by treatment with 100nM 
dexamethasone for 20 minutes, before having the medium replaced with luciferin containing AIR 
Medium, supplemented with 1µM retinyl acetate (Sigma, R4632) and 1µM all-trans retinal (Insight 
Biotechnology sc-210778A) and placed into a dark incubator.  
Between 12-32 hours after dexamethasone treatment, cells were illuminated with flashing blue light 
(Intensity = 20/255, at 20Hz, 5 seconds on, 5 seconds off) for four hours using the custom LED arrays 
described above. The cells were then placed into an incubator with a deep cooled CCD camera, to 
quantify light emission by luciferase in 30 minute bins. After 5 days, images were analysed using an 
ImageJ script, and the bioluminescence of each well quantified. An R script was used to plot and 
detrend (baseline-correct) the raw data. 
 
1. Clocks Entrained with 
Dexamethasone 2. Cells exposed to 
Blue Light at 
Different Times
3. Luciferase Expression 
Tracked for 5 days
Time
B
io
lu
m
in
es
ce
n
ce
Figure 2.8. Experimental layout for U20S Light Stimulation and Luciferase based Readout of the 
Cellular Clock: Human Osteosarcoma cells (n=24) expressing an opsin construct and luciferase 
under the Per2 promotor were plated in 96 well plates and allowed to grow to confluency. Half 
the cells had their clocks synchronised by dexamethasone at 9am, the other half at 9pm. 12-32 
hours after dexamethasone treatment, the cells were treated with flashing light for 4 hours, and 
then placed into a dark incubator, under a CCD camera. Photos were taken with a 25 minute 
exposure every 30 minute, and the luminescence of each well quantified for each timepoint using 
ImageJ. The data were subsequently analysed using an R script to fit sine waves to the raw data,  
and parameters such as phase, period and amplitude determined. 
47 
 
2.15. Generation of Plasmids for Stable Expression of Opsins 
Plasmids to be used to generate light sensitive cells were produced by Gibson assembly, and 
incorporated into the pEGFP N1 plasmid backbone, to form products which lacked GFP. The pEGFP N1 
plasmid contains a CMV promoter, pBR322 origin, Neomycin/Kanamycin resistance cassette, and an 
SV40 origin, which allows plasmid replication independent of cell division in cell lines expressing SV40 
large T-antigen. 
Gibson assembly involves amplifying coding regions from multiple constructs by PCR, using the high 
fidelity Phusion Hot Start II DNA polymerase (Thermo Scientific, F-549S). The primers are >40bp, and 
are designed such that the 3' end is complementary to the sequence being amplified, whilst the 5' end 
is complementary to the sequence to which that amplicon will be fused. The design of primers was 
facilitated by the NEBuilder Assembly Tool (nebuilder.neb.com). The PCR products were all mixed in 
stoichiometric ratios with the cleaved backbone, and NEB Gibson Master Mix (2x) (NEB, E2611S). The 
product of the assembly was transformed into Stbl3 competent cells (Thermoscientific, C7373-03), 
using manufacturer's instructions, and plated onto Kanamycin plates. Colonies were picked, amplified, 
plasmid DNA extracted using Qiagen MaxiPreps (#12163) and subsequently sequenced. The 
expression of the construct and puromycin resistance was initially checked in HEK 293T cells before 
stable expression in SH-SY5Y cells.  
Four sets of plasmid production occurred, with some constructs from the first two batches being used 
as templates for later rounds of cloning. The first two plasmids created were ChR2(C128T)-RCAMP-
IRES-PuroR (CRIP) and ChR2(C128T)-2A-eNpHR2.0-IRES-PuroR. 
The ChR2(C128T) coding sequence was amplified from Addgene Plasmid 20295, deposited by Karl 
Deisseroth. RCAMP1h was amplified from Addgene Plasmid 42874, deposited by Loren Looger. The 
IRES-Puro sequence was amplified from Addgene plasmid 30205, deposited by Darrell Kotton. The 2A 
sequence-N-terminus of eNpHR2.0 was amplified from Addgene Plasmid 22047, deposited by Edward 
Boyden, whilst the C-terminal of eNpHR2.0 was amplified from Addgene Plasmid 26966, deposited by 
Karl Deisseroth. The plasmid backbone was produced by gel purification of the restriction digestion 
products of Addgene Plasmid 22047 by enzymes EcoRI, KpnI and EcoRV. 
  
48 
 
Table 3: Primers used for the Gibson Assembly of Plasmids CRIP and CHALIP 
Plasmid 
being 
Assembled 
Fragment 
Being 
Generated 
Primer 1 
 
Primer 2 Donor 
Plasmid 
CHALIP ChR2  
(C128T) 
ACCGGTGCCACCATGGGTACCATGG
ACTATGGCGGCGCTTTG 
CAATTTTCTGTTTGCTCACCATGGT
GGCGGC 
(Addgene 
# 20295) 
CHALIP 2A-eNpHR  
(N-terminus) 
CATGGTGAGCAAACAGAAAATTGTG
GCAC 
CGACAGGCACCAGAATTGTGCTCAC
TGC 
(Addgene 
# 22047) 
CHALIP eNpHR2.0  
(C-terminus) 
CACAATTCTGGTGCCTGTCGTCAGC
ATTG 
TAGGGGGGGGGGTTACACCACGTTG
ATGTCGATC 
(Addgene 
# 26966) 
CHALIP IRES-PuroR CAACGTGGTGTAACCCCCCCCCCTA
ACGTTAC 
TCGCGGCCGCTCAACATGTGAATTC
TTAGGCACCGGGCTTGCG 
(Addgene 
# 30205) 
CRIP ChR2  
(C128T) 
ACCGGTGCCACCATGGGTACCATGG
ACTATGGCGGCGCTTTG 
GATGAGAACCGCTCACCATGGTGGC
GGC 
(Addgene 
# 20295) 
CRIP RCAMP1h CATGGTGAGCGGTTCTCATCATCAT
CATCATC 
TTAGGGGGGGGGGTTACTTCGCTGT
CATCATTTG 
(Addgene 
# 42874) 
CRIP IRES-PuroR AGCGAAGTAACCCCCCCCCCTAACG
TTAC 
TCGCGGCCGCTCAACATGTGAATTC
TTAGGCACCGGGCTTGCG 
(Addgene 
# 30205) 
A second batch of plasmids were created which substituted the ChR(C128T) opsin of CRIP for one of 
ChRonos, CoChR or CHIEF (E162A/T198C). The ChRonos fragment was amplified from Addgene 
Plasmid 62726, deposited by Edward Boyden. The CoChR fragment was amplified from Addgene 
Plasmid 59070, deposited by Edward Boyden, whilst the CHIEF (E162A/T198C) fragment was amplified 
from Addgene Plasmid 51095, deposited by Jonathan Ting. These fragments were fused to RCAMP-
IRES-PuroR, amplified from CRIP. The plasmid backbone was produced by gel purification of the 
restriction digestion products of Addgene Plasmid 22047 by enzymes EcoRI, KpnI and EcoRV. 
 
Table 4: Primers used for the Gibson Assembly of Plasmids Chronos-, CoChR-, and CHIEF-RIP 
Plasmid 
being 
Assembled 
Fragment Being 
Generated 
Primer 1 Primer 2 Donor 
Plasmid 
Chronos-
RIP  
Chronos 
Opsin 
ACCGGTGCCACCATGGGTACGGA
AACAGCCGCCACAAT 
TAGCCATACCCGCCACTCCTCCCTCCT
C 
(Addgene 
# 62726) 
Chronos-
RIP 
RCAMP-IRES-
PuroR 
AGGAGTGGCGGGTATGGCTAGCA
TGACTG 
TCGCGGCCGCTCAACATGTGAATTCTT
AGGCACCGGGCTTGCG 
CRIP 
CoChR- 
RIP 
CoChR 
Opsin 
ACCGGTGCCACCATGGGTACGCT
GGGAAACGGCAGCGC 
TAGCCATACCTGCTACTACCGGTGCCG
CC 
(Addgene 
# 59070) 
CoChR- 
RIP 
RCAMP-IRES-
PuroR 
GGTAGTAGCAGGTATGGCTAGCA
TGACTG 
TCGCGGCCGCTCAACATGTGAATTCTT
AGGCACCGGGCTTGCG 
CRIP 
CHIEF- 
RIP 
CHIEF 
(E162A/T198C) 
ACCGGTGCCACCATGGGTACGTC
GCGGAGGCCATGGCT 
TAGCCATACCGGCTCCGCTTCCGTTAA
CG 
(Addgene 
# 51095) 
CHIEF- 
RIP 
RCAMP-IRES-
PuroR 
AAGCGGAGCCGGTATGGCTAGCA
TGACTG 
TCGCGGCCGCTCAACATGTGAATTCTT
AGGCACCGGGCTTGCG 
CRIP 
 
49 
 
Constructed plasmids from Gibson assembly were amplified by transforming competent E. coli cells 
(New England Biolabs, #C2527I) with the Gibson assembly reaction products, following manufacturer’s 
instructions, followed by plating onto agar plates containing 50 µg/g kanamycin. Following overnight 
incubation at 37oC, colonies were picked with a sterile tip. A portion of the colony was transferred into 
sterile a 96-well plate, containing Luria-Bertani (LB) broth, containing 50 µg/ml kanamycin, whilst the 
remainder was transferred into a PCR reaction tube containing Taq Polymerase master-mix 
(NEB, #M0270L), and primers flanking the whole insert. Following PCR amplification, following 
manufacturer’s instructions, the PCR products were subjected to gel electrophoresis in a 1% agarose 
gel containing SyberSafe, and imaged. Colonies which resulted in amplification of the correct sized 
fragments were amplified overnight in LB-broth, and the plasmids purified using the Qiagen Maxiprep 
protocol. All plasmids were sequenced to ensure no errors had been incorporated, which was carried 
out by Source Bioscience.  
50 
 
 
Digestion of Plasmid Backbone and 
PCR amplification of Insert Fragments
Gibson 
Assembly
Colony 
PCR
Amplification 
and Sequencing
Figure 2.9. Experimental layout for Gibson Assembly of Plasmids: Primers were designed whose 3’ 
end was complementary to the fragment being amplified, whilst the 5’ end was complementary 
to the 5’ adjacent fragment of the final plasmid. PCR using these primers and suitable donor 
plasmids yielded fragments flanked by regions complementary to the flanks of other fragments. 
The backbone was produced by enzymatic digestion of a donor plasmid. The fragments and 
cleaved backbone were gel purified and subsequently mixed and incubated together in the 
presence of Gibson Assembly Master Mix.  
The final reaction mixture was used to transform competent E. coli which were then plated onto 
antibiotic containing agar plates. Colonies were picked the next morning. Some of the colony was 
used to inoculate LB broth, the rest of the colony was placed into a PCR reaction mixture 
containing primers flanking the full expected insert. PCR was carried out, and the products 
subjected to gel electrophoresis. Colonies yielding fragments of the expected size were amplified 
and the plasmids purified. Finally, the plasmids were sequenced to ensure no mutations or errors 
had occurred during the cloning process. 
51 
 
2.16. Quantitative Real Time PCR (qPCR) 
For quantification of expression of genes of interest, cells were lysed in TRI Reagent (Zymo, R2050), 
and RNA Extraction was performed using the Zymo RNA extraction kit (R2052), or Zymo 96 well RNA 
extraction kit (R2054), using the manufacturer's instructions, including the DNase digestion step. The 
isolated RNA had its concentration determined using a NanoDrop 1000 Spectrophotometer machine 
(Thermo Scientific). 1µg per sample of extracted RNA was Reverse Transcribed using the RT High 
Capacity Kit (Life Technologies, 4368814), following manufacturer's instructions, in a total reaction 
volume of 20µl. Following inactivation of the Reverse Transcriptase, the solution was diluted by 
addition of 80µl water. 2µl of the cDNA solution was used per reaction for quantitative PCR, using the 
Universal Probe Library (UPL) Technique (Roche).  
Expression of genes was quantified from ct values using the delta CT method. The quantity of RNA of 
a given gene in a sample was expressed relative to the expression of a housekeeping gene (GAPDH, 
for SH-SY5Y and TBP for mouse derived samples) in the same sample, using Equation 1.  
Equation 1. 
𝑬𝑮𝒊 =  𝒏 × 𝟐 
−(𝑪𝑻𝒊−𝑪𝑻𝒉𝒌) 
 
                       𝐸𝐺𝑖 −  𝐸𝑥𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑜𝑓 𝐺𝑒𝑛𝑒            𝑛 − 𝐴𝑛𝑦 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 𝑛𝑢𝑚𝑏𝑒𝑟 
                       𝐶𝑇𝑖 −  𝐶𝑇 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝐺𝑒𝑛𝑒                𝐶𝑇ℎ𝑘 −  𝐶𝑇 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝐻𝑜𝑢𝑠𝑒𝑘𝑒𝑒𝑝𝑖𝑛𝑔 𝐺𝑒𝑛𝑒 
  
52 
 
Table 5: Primers and Probe Combinations used in qPCR 
 Gene Name Primer 1 Primer 2 UPL Probe 
 Arc CTCCCAGGGGAGAGTAGAAGTC GAAGCACCGGGACATCAG 84 
 Dusp1 TGGGTACATCAAGTCCATCTG GCAAAAAGAAACCGGATCAC 29 
 Dusp4 TGCATCCCAGTGGAAGATAA GCAGTCCTTCACGGCATC 17 
 Fos 
 
ACTACCACTCACCCGCAGAC CCAGGTCCGTGCAGAAGT 67 
 GAPDH AGCCACATCGCTCAGACAC AATACGACCAAATCCGTTGACT 60 
 Homer1a TTTGGTTGCTCGCTCCAC TAAGGCTGCGGGTTCAAA 22 
 Npas4 GCACTCGTGCAAGCACAC AGAGACGCTACGTTCCTTTCC 8 
 Nr4a1 TCCTGGTGTAAGCTTTGGTAT GGATCCCTGCCCTCTAACAG 49 
 Nr4a3 TGCCTGTATTTATTGCAAGAC GTCACTGTGAACACCCCATTTA 17 
 P4HA1 AAGATCTAACAGGACTAGATGTTTCCA TCCTCCAACTCCATAATTTGC 6 
 Xbp1 GGAGTTAAGACAGCGCTTGG CACTGGCCTCACTTCATTCC 37 
 
2.17. Live Cell Microscopy of RCAMP Expressing Cells 
Live cells expressing RCAMP containing constructs were imaged using a Leica SP8 confocal microscope. 
Cells in AIR medium were imaged at 37oC under atmospheric CO2 every 200ms using the White Light 
Laser (WLL) for excitement of the RCAMP fluorophore at 570nm, whilst emission was monitored 
between 580-620nm using a hybrid detector. Drugs and salts were added by addition of between 
2-20µl of a stock solution. Cells were imaged during blue light treatment using the FRAP (fluorescence 
recovery after photobleaching) module. Whilst constantly imaging RCAMP at 570nm using low level 
WLL light, cells were exposed to high levels of blue light for 400ms using the 488nm and 476nm argon 
laser lines. The frames imaged during exposure to argon laser light were deleted and not included in 
subsequent data analysis. Data from the traces were normalised using the Leica software default 
normalisation, which defines the highest intensity recorded as 1 and scales other frames linearly, and 
exported for plotting using R scripts. The half-life of decay for CoChR mediated calcium spikes was 
determined using an R script that isolated each individual peak from multiple FRAP traces and 
determined the time taken for the magnitude of the peak to halve and quarter. 
  
53 
 
 
 
 
 
 
 
3. The Effect of Wake and 
Sleep on the Mouse Cortex 
Transcriptome 
 
This section details our transcriptomic profiling of sleep deprived mouse cortex. Using a timecourse 
style experimental design, we find several genes whose expression is diurnal in undisturbed animals 
and increases during experimentally imposed sleep deprivation, indicating that these genes are 
dependent on the wake state of the animal rather than time of day. Additionally, we find a group of 
genes whose expression is induced specifically during sleep deprivation, but do not show oscillations 
in control animals. By characterising the expression of genes during recovery from sleep deprivation, 
we speculate one whether genes may play a homeostatic function. Finally, global rhythms in 
transcription are progressively blunted following increasing durations of sleep deprivation, 
underscoring the bidirectional relationship between sleep patterns and biological rhythms. 
  
54 
 
3.1.1. Typical Sleep and Activity Patterns in Mice 
Mice display several differences in key sleep parameters compared to humans, including the duration, 
timing and degree of consolidation of sleep. Under constant 12:12 light dark conditions, single housed 
lab mice sleep approximately 10.5 hours per day, with the majority of the light phase being spent 
asleep (Franken et al. 1999).  
 
Locomotor activity is greatly suppressed during the light phase, but begins to rise around the start of 
the dark period (Kopp 2001). Activity peaks shortly after the onset of darkness (Welsh et al. 1986) and 
remains high for the first half of the dark phase before reducing to a level significantly above daytime 
activity. However, despite this elevated locomotor activity, approximately 30% of the total sleep of 
C57/BL6 BALB/cByJ
0
50
100
-12 0 12 24
0
50
100
-12 0 12 24
%
T
im
e
A
sl
ee
p
0
50
100
-12 0 12 24
0
50
100
-12 0 12 24
A
ct
iv
it
y 
0
10
20
30
4 8
1
6
3
2
6
4
1
2
8
2
5
6
5
1
2
1
0
2
4
+
0
10
20
30
4 8 1
6
3
2
6
4
1
2
8
2
5
6
5
1
2
1
0
2
4
+
%
 T
o
ta
l S
W
S 
A
B
C
Figure 3.1. Typical Sleep and Activity Patterns of two Strains of Mice: Previously recorded locomotor 
activity (A) and sleep (B) patterns of C57/BL6 (left column) and BALB/cByJ mice (right column) are 
plotted, relative to the 12:12 light dark cycle indicated above the plots. Although both strains are 
predominantly active during the dark phase and register most of their sleep during the light phase, 
the pattern of sleep and activity plots vary. The bout distribution of slow wave sleep (SWS), 
expressed as a proportion of total SWS accrued in that bout length, is also plotted (C).  
Locomotor activity plots (A) are plotted using data from Kopp (2001), whilst sleep and bout 
duration plots (B,C) are plotted using data from Franken et al. (1999).  
 
55 
 
mice is nevertheless obtained during the dark phase (Franken et al. 1999). Unlike humans, mice also 
exhibit highly fragmented sleep patterns with over 20 episodes of wake per hour being typical. Due to 
the high number of wake intervals, the average bout duration of slow wave sleep (SWS) is short in 
mice, with the majority of SWS being achieved in bouts lasting less than 4 minutes (Franken et al. 
1999). 
Depriving mice of the opportunity to sleep after the onset of light modulates both the timing and 
consolidation of subsequent sleep. Sleep deprivation (SD) during the first 6 hours of the light phase, 
during which period mice typically sleep 200 minutes, induces C57BL/6J mice to spend a greater 
proportion of the subsequent 6 hours asleep compared to previous day and in more consolidated 
bouts (Franken et al. 1999). However, the additional time spent asleep during the remainder of the 
dark phase only totals approximately 40 minutes, indicating that the sleep deficit has not been 
balanced before the onset of darkness. Consistent with this expectation, the proportion of time spent 
asleep remains elevated compared to undisturbed controls over the following dark period, sleeping 
an additional 80 minutes compared to controls. Therefore, only approximately 60% of the sleep lost 
during SD is recouped over the following 18 hours of recovery opportunity. The incomplete recovery 
of total sleep time may be compensated by a twofold increase in delta power observed during the 
initial stages of recovery sleep, which is thought to reflect a deeper sleep and increased sleep pressure. 
Indeed the recovery of lost delta energy, the integral of delta power, is close to complete following 18 
hours of ad libitum sleep (Franken et al. 1999). 
Importantly, there are significant differences in the major sleep parameters of different strains of 
mice, with AKR/J mice sleeping 3 hours longer per day than DBA/2J mice (Franken et al. 1999). Some 
strains exhibit a short rest period during the dark phase, whilst the BALB/cByJ strain demonstrates an 
extended rest period that begins in the latter half of the dark phase and continues until the end of the 
light phase. The routine sleep-wake patterns of mice have important experimental implications. If 
sleep deprivation is experimentally imposed at the beginning of the light phase, then mice whose rest 
phase begins midway through the dark period will have likely already dissipated a substantial 
proportion of the homeostatic sleep pressure from the previous active phase before sleep deprivation 
is applied. In contrast, mice whose rest phase usually commences at the onset of light will enter sleep 
deprivation with a high homeostatic drive for sleep. At the cessation of sleep deprivation, these mice 
would therefore experience a homeostatic sleep pressure that is significantly higher than any sleep 
pressure normally experienced over the course of a day. At the molecular level, the especially high 
homeostatic drive may be required to trigger cellular pathways to cope with sleep deprivation.  
56 
 
 
 
A further consideration is that any candidate gene or pathway identified as a result of sleep 
deprivation experiments may be further investigated by the use of transgenic mice. Because of the 
significant effect that genetic background can have on sleep patterns, and the considerable time and 
expense required for the typical 10 backcrosses to produce congenic backgrounds, performing 
experiments on strains of mice with several transgenic lines already available, such as C57/BL6, can 
greatly facilitate downstream experiments involving transgenic lines (Eisener-Dorman et al. 2009; 
Seong et al. 2004).   
0
50
100
-12 0 12 24 36 48
0
50
100
-12 0 12 24 36 48
%
Ti
m
e
A
sl
ee
p
%
T
im
e
A
sl
ee
p
A
B
Figure 3.2. 6-Hour Sleep Deprivation of Mice Increases Sleep Duration during the subsequent Dark 
Phase: Previously recorded sleep patterns of C57/BL6 (A) and BALB/cByJ mice (B) following 6-hour 
sleep deprivation are plotted. The 12:12 light dark cycle is indicated above the plots by the white 
and black bars, whilst the red bar indicates sleep deprivation. Imposition of sleep deprivation 
almost totally removes sleep, and the lost duration of sleep is partially recovered by an increase 
of sleep duration over the next 24 hours  
Both plots are plotted using data from Franken et al. (1999).  
57 
 
3.1.2. Methods for Sleep Restriction of Rodents 
One of the defining characteristics of sleep is a reversibly reduced sensitivity to the environment. An 
asleep animal will typically not be aware of small variations in the environment, but will wake in 
response to a loud noise or sudden pain. Sleep deprivation can therefore be imposed onto animals by 
repeatedly introducing environmental or sensory events.  
Sleep deprivation has been experimentally achieved by taking advantage of several sensory 
approaches, including touch, temperature, pain, smell and noise. Some approaches, such as placing a 
mouse into a cage containing the smell of a predator or forced immobility (Papale et al. 2005), rely on 
the induced stress to maintain wakefulness. Other techniques, such as the disk over water technique 
(Rechtschaffen & Bergmann 1995), introduce significant stress to the animal, as indicated by elevated 
corticosterone levels (Ramesh et al. 2008). Although stressful sleep deprivation techniques may be 
effective at maintaining wakefulness, stress can be a significant confounder in sleep deprivation 
studies, and can also disrupt subsequent recovery sleep following the cessation of imposed sleep 
deprivation.  
 
Table 6: Techniques for Applying Sleep Deprivation to Rodents 
Technique Methodology Advantages and 
Disadvantages 
Reference 
Gentle 
Handling 
 
Introduction of novel stimuli, e.g. 
items into cage, tapping on cage, 
gentle touching, movement of cage. 
 
 
+ Relatively Low 
Stress 
+ Commonly Used 
Protocol 
+ Little forced 
increase in activity 
-  Laborious and Low 
Throughput 
 
 (Franken et al. 
1991) 
Cage Change 
 
Mice are placed into a clean cage, or 
one previously occupied by another 
mouse or rat. 
 
+ Simple 
- Can only impose 
sleep loss for short 
periods 
 (Febinger et al. 
2014) 
58 
 
Forced 
Restraint 
 
Animals are placed into a small 
cylinder only just big enough to fit 
them.  
 
 
+ Simple 
- Locomotion, eating, 
drinking, socialising 
and grooming 
behaviours are 
severely disrupted 
 
 (Papale et al. 
2005) 
Forced 
Locomotion 
Mice are placed onto rotating 
treadmills or onto one of two 
adjacent platforms that are 
alternately submerged.  
 
+ Simple 
+ Can maintain 
wakefulness for long 
periods 
- Forces large increase 
in activity and may 
disrupt other 
behaviours 
  
 (Borbély & 
Neuhaus 1979; 
Piérard et al. 
2007) 
Disk Over 
Water 
Technique 
 
Mouse is placed on a platform above 
water and its sleep patterns 
monitored by EEG. When the mouse 
falls asleep, the platform rotates, 
forcing the mouse to move or be 
plunged into the water.  
 
+ Can maintain 
wakefulness for long 
periods (upto 4 weeks 
in rats) 
+ Not as laborious as 
gentle handling. 
 
- Highly stressful 
 
 (Rechtschaffen 
& Bergmann 
1995) 
Environmental 
Noise 
 
Speakers are placed near the cage 
and loud noises (e.g. traffic, sirens, 
bell) played at random intervals. 
 
+ Simple and High 
throughput 
- Only achieves partial 
sleep deprivation 
 (Mavanji et al. 
2013; Rabat et 
al. 2005) 
59 
 
Lafayette 
Sleep 
Fragmentation 
Chamber 
 
A motor drives the continuous 
sweeping of a bar along the floor of 
the home cage. Mice must step over 
the bar or be hit by it.  
 
+ High Throughput 
+ Low Stress 
- Food or bedding can 
block the movement 
of the bar, so requires 
supervision by the 
researcher. 
 (Ramesh et al. 
2008) 
 
Therefore “gentle handling” has emerged as the gold-standard of sleep deprivation. Gentle handling 
relies on the introduction of novel objects to the cage, small noises and gentle touches to impose 
wakefulness in animals. Corticosterone levels of mice sleep deprived by gentle handling are lower than 
those sleep deprived by techniques such as the disk over water, indicating that it imposes sleep 
deprivation in a less stressful manner. However, prolonged sleep deprivation is difficult to achieve 
with gentle handling, and its laborious nature greatly reduces the scale of experiments that can be 
carried out. For this reason, higher throughput sleep deprivation approaches that apply sleep 
deprivation in a reliable and low stress manner have been developed in recent years.  
Recently, multiple automated cages have been developed which employ noise (Mavanji et al. 2013), 
airpuffs (Gross et al. 2015) and moving bars to maintain wakefulness (Kaushal et al. 2012). The 
Lafayette Sleep Fragmentation Chamber is similar in size to a typical cage, and multiple mice may be 
housed inside with ad libitum access to food and water. Sleep disruption is applied through an “L” 
shaped bar that descends from the top of the cage and spans the width of the living area. The bar 
connects to a worm motor driven module, which when switched on causes the bar to repeatedly 
sweep across the base of the living area. The mouse must climb over the bar, thus imposing sleep 
restriction. This chamber has been used to impose both total sleep deprivation (Kaushal et al. 2012) 
and sleep fragmentation (Nair et al. 2011) through continuous or intermittent sweeping of the bar, 
respectively. Mice subjected to 6 hours of continuous sweeping of the bar at the onset of the active 
phase spend 95% of that period in the wake state (Kaushal et al. 2012), without a significant increase 
in plasma corticosterone levels at the end of the procedure (Ramesh et al. 2008). In contrast, mice 
subjected to sleep deprivation through the disk-over-water technique demonstrated a two-fold 
increase in plasma corticosterone, indicating that this procedure is significantly more stressful 
(Ramesh et al. 2008). Some of the novel sleep deprivation protocols have already questioned some of 
the conclusions of more stressful sleep deprivation studies (Wang et al. 2013). 
60 
 
Other approaches to manipulating sleep patterns in rodents include lesioning sleep-promoting or 
inhibiting centres and genetic approaches, such as the overexpression or knockout of orexin. Although 
both approaches can modulate sleep duration, the pleitropic effects of orexin and the likelihood of 
lesions destroying long range connections and other neurons unrelated to sleep makes the 
interpretation of the results difficult (Revel et al. 2009). Recent advances in pharmacogenetics and 
optogenetics have now facilitated the activation and silencing of specific neurons with millisecond 
precision (Fenno et al. 2011; Urban & Roth 2015). The activation of wakefulness controlling centres 
has already been shown to modulate sleep duration (Carter et al. 2010). However, the emphasis of 
studies to date has been on demonstrating a neural pathway participates in sleep regulation or 
anaesthesia, rather than characterising the effects of sleep deprivation induced in this manner. 
Although more challenging, laborious and perhaps more variable than automated sleep deprivation 
approaches, specific activation of sleep promoting centres through optogenetic or pharmacogenetic 
approaches would allow the researcher to carry out sleep elongation studies (Zhang et al. 2015), which 
may yield fascinating data that complement sleep deprivation studies.  
61 
 
 
  
A
B
Figure 3.3. The Lafayette Sleep Fragmentation Chamber disrupts Sleep in mice using a motor driven 
bar: The Lafayette Sleep Fragmentation Chamber consists of a cage and stand (A). The cage 
contains living quarters, with access to food and water, and an “L”-shaped bar which descends 
from the cage top and spans the width of the cage. The stand houses a motor which, when the 
cage is docked to the stand, couples to the “L”-shaped bar, and therefore drives its movement. 
(B) shows six photos taken across a total of 10 seconds during sleep deprivation of a single mouse. 
As the bar approaches, the mouse must climb over it. Once the bar is at the end of the cage, the 
bar changes direction, and the mouse must climb over it once again after which it feeds from the 
food hopper. The bar makes a full circuit approximately every 15 seconds. 
Photos in (A) are provided, with permission, by Lafayette Instrument Company, Inc. 
 
62 
 
3.1.3. Tissues Affected by Sleep Deprivation 
It is generally assumed that sleep is a phenomenon that primarily affects the function of the brain, 
presumably because the most striking consequence of falling asleep is a loss of consciousness. 
Nevertheless, there is significant evidence that sleep affects the function of several peripheral tissues. 
For example, following sleep deprivation, humans have reduced glucose tolerance (Beebe et al. 2013; 
Benedict et al. 2012; Greer et al. 2013), an impaired immune response to vaccination (Lange et al. 
2003; Prather et al. 2012; Spiegel et al. 2002) and increased blood pressure and variable heart rate 
(Dettoni et al. 2012; Sauvet et al. 2010). Consistent with functional disruption, sleep deprivation has 
also been shown to induce molecular changes in peripheral tissues. Sleep deprivation of mice 
modulates the expression of 500 genes in the lungs and 1000 genes in the heart (Anafi et al. 2013), 
induces the unfolded protein response in the pancreas (Naidoo et al. 2014) and causes oxidative 
damage and cell death in the intestinal lining (Everson et al. 2014). To what extent these peripheral 
phenotypes may be secondary effects of disrupted brain function, elevated corticosterone levels, or 
the additional activity and opportunity for food intake associated with sleep deprivation is unclear. 
Intriguingly, it was recently demonstrated that peripheral tissues contribute toward the control of 
sleep duration and timing. Genetic knockout of Bmal1 in mice increases the daily duration of sleep by 
2.5 hours and ablates circadian rhythmicity (Laposky et al. 2005). Restoring Bmal1 expression 
specifically in the brain, but not in skeletal muscle, restores circadian locomotor rhythms (McDearmon 
et al. 2006). Remarkably, although the increase in sleep duration is not rescued by central Bmal1 
expression, expression of Bmal1 in skeletal muscle is sufficient to restore wild type sleep duration 
(Ehlen et al. 2017). This surprising finding supports the theory that the control of sleep timing does 
not reside solely within the brain, and the contribution of peripheral tissue to the control of sleep 
suggests that sleep may play a role in the normal function of tissues outside the brain. 
Whether or not peripheral tissues play a major role, it is clear that the brain is responsible for a 
considerable proportion of the control of sleep. Optogenetic activation of the locus coeruleus is 
sufficient to wake sleeping mice within one second (Carter et al. 2010), whereas central perfusion of 
adenosine agonists induces sleep (Benington et al. 1995). Both spontaneous and pharmacologically 
induced sleep is also associated with large scale and reproducible changes in EEG spectra (Dement & 
Kleitman 1957), extracellular fluid volume and flux (Xie et al. 2013), and localised neuronal activity 
(Moore et al. 2012), indicating that sleep plays a significant role in several aspects of brain physiology 
and function.  
 
63 
 
However, due to the heterogeneity in brain structure and function, the role and effects of sleep 
certainly vary across even small distances. For example, the sleep active, sleep promoting 
ventrolateral preoptic region of the hypothalamus is situated only 2mm from the wake promoting 
histaminergic tuberomammillary nucleus, whilst the lateral hypothalamus contains both wake 
promoting orexin expressing and wake inhibiting melanin concentrating hormone expressing neurons 
(Konadhode et al. 2013). Unsurprisingly, sleep dependent molecular changes have also been 
determined to be region specific. For example, glycogen and glucose content of C57/BL6 mice is 
modulated by 6 hours sleep deprivation in the cortex, but not the cerebellum or the brain stem 
(Franken et al. 2003). Similarly, a microarray study comparing transcription in cortex and cerebellum 
found that of the 220 genes identified as being modulated by sleep deprivation in either region, only 
55 (25%) were differentially expressed following sleep deprivation in both cortex and cerebellum 
(Cirelli et al. 2004).  
Molecular analysis of whole brain homogenate is therefore likely to reduce the apparent magnitude 
of molecular changes following sleep deprivation, which in turn may cause several effects to be 
overlooked. Instead, the majority of studies investigating the molecular correlates of sleep have to 
date focussed on the cerebral cortex, because the cortex generates EEG signatures that are 
characteristic of the current and recent wake state of the animal (Franken et al. 1999; Steriade & 
Hobson 1976), and exhibits reduced function following sleep deprivation (Vyazovskiy et al. 2011).  
 
3.1.4. Research Aims 
In the body of work presented below, we outline our efforts in carrying out a screen of molecular 
changes associated with sleep deprivation in mouse cortex. Utilising a recently developed, semi-
automated sleep deprivation technique, we produced tissue samples from mice that had been sleep 
deprived for varying durations. Unlike previous studies, we allowed significant recovery sleep and 
sampled repeatedly during this period, and therefore our studies provide insight into the kinetics with 
which changes associated with acute sleep deprivation return to baseline levels. We couple this semi-
automated sleep deprivation approach with RNA-sequencing to produce a large scale, timecourse 
style transcriptomic screen of sleep deprived cortex.  
  
64 
 
3.2.1. Design of Cabinets for Housing Mice for Sleep Deprivation 
To generate sleep deprived tissue for molecular analyses, mice were housed inside Lafayette Sleep 
Fragmentation Chambers placed inside a ventilated Techniplast cabinet. The cabinets required 
extensive customisation in order to be suitable for automated sleep deprivation. In order to maximise 
the capacity of the cabinet, the rear metal panel of the cabinet was replaced by a second set of doors, 
which also aided vision and accessibility of the cages. Barriers were constructed and placed between 
cages to prevent mice seeing mice in adjacent cages. The interior lights were stripped and replaced 
with dimmable, cool white LED strips above each cage and a single red LED strip in the centre of the 
shelf. The cycle of these LEDs were controlled by a timer plug and set to a 12:12 light dark cycle. 
 
 
 
Figure 3.4. Customisation of the Sleep Deprivation Cabinet: To house the animals during sleep 
deprivation experiments, we customised a Techniplast Cabinet. Red tinted transparent doors 
were added to both sides of the cabinet to utilise the maximum space available and to facilitate 
access and observation of the mice. The interior lights were replaced with dimmable cool white 
LED strips placed above each cage and a central red LED strip, controlled by separate timer plugs. 
Barriers were constructed to prevent mice in one cage seeing mice in another. The cabinet itself 
was housed in a room illuminated only by dim red light, to minimise the impact of opening the 
doors during sampling.  
 
65 
 
3.2.2. Validation of Automated Sleep Deprivation 
The Lafayette Sleep Fragmentation Chamber had previously been validated as an effective sleep 
deprivation tool in an EEG based study (Kaushal et al. 2012). As an initial trial experiment to determine 
whether the chamber was also able to induce previously reported transcriptional changes, male, 
young adult (8-10 weeks old) C57/Bl6 mice (n=8) were pair-housed in the sleep fragmentation 
chambers with access to food and water overnight. The motors were switched on intermittently (every 
30 seconds) the following morning. Initially, the bar sweeping across the cage every 30s was sufficient 
to maintain continuous wakefulness, as mice investigated the bar during the periods when the bar 
was stationary. After 2 hours however, the novelty of the moving bar was insufficient to prevent the 
mice falling asleep, so the motor was switched on continuously. Mice quickly learnt how to climb over 
the bar, and did not appear to be stressed by the moving bar. Sleep deprivation was applied for a total 
of 8 hours, during which time the mice were monitored constantly. By the end of the procedure, mice 
were visibly sleep deprived and entering a sleep posture almost immediately after the bar had passed. 
In contrast, control mice appeared to have spent the majority of the time asleep.  
 
 
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Figure 3.5. Sleep Deprivation Markers are Induced in Mouse Cortex by Automated Sleep 
Deprivation: Male mice aged 8-10 weeks were sleep deprived for 8 hours  (data are mean ± SEM). 
Cortex was harvested and the expression of genes previously related to sleep deprivation 
quantified. The expression of genes was normalised to the housekeeping gene TATA-binding 
protein (TBP), and the fold change of expression compared to ab libitum sleep controls plotted. 
p-values were determined using a Student t-test, comparing the treated to untreated group for 
each gene, and the p-value indicated by the number of asterisks above the relevant gene.   
 * - p<0.05, **-p<0.01, ***-p<0.001.  
66 
 
Immediately following sleep deprivation, the brain from each mouse was harvested and RNA 
extracted from the cortex. qPCR was carried out to quantify the expression of some genes previously 
indicated as being upregulated during sleep deprivation in rat or mouse. Several of the target genes 
were upregulated after the sleep deprivation, indicating that the automated system is sufficient to 
maintain sleep deprivation. 
3.2.3. Experimental Design and Possible Molecular Profiles 
Having become satisfied that the automated sleep deprivation protocol was able to maintain 
prolonged wakefulness in mice, we designed an experimental plan that would allow the identification 
of sleep related molecules and to characterise how the concentration of these molecules is dependent 
on the sleep wake status of the animal.  
We hypothesised that during sleep deprivation the concentration of molecules would fall into one of 
four patterns, “independent”, “binary”, “homeostatic” or “stress response” (see Fig 3.7.). A molecule 
that was independent of sleep deprivation was expected to exhibit no change during or following sleep 
deprivation compared to control animals. Binary molecules were expected to exhibit stepwise high 
and low expression over the course of a normal day, linked to the sleep wake cycle, and for sleep 
deprivation to prolong the high level without affecting the absolute concentration. Therefore, the 
concentration of a binary molecule would indicate the current state of the animal but give no 
information about the amount of sleep in the preceding 24 hours. In contrast, homeostatic molecules 
were expected to oscillate over the course of a normal day with a cosine pattern, with the change in 
concentration of the molecule being dependent on the sleep wake cycle. Therefore, the concentration 
of a homeostatic molecule would not indicate the current wake state of the animal, but instead give 
information about the total amount of sleep in the recent past. Importantly, the concentration of 
these molecules is not expected to return to baseline levels until excess homeostatic sleep pressure, 
evidenced by the presence of recovery sleep, has been dissipated. In Figure 3.7., dissipation of excess 
homeostatic sleep pressure following 12 hour sleep deprivation is modelled as taking exactly 12 hours. 
Finally, we expected some molecules to demonstrate a stress response profile, where expression was 
constitutively low but strongly activated after a prolonged period of wake. These genes may exhibit a 
spike during the course of a normal day or only be induced following sleep deprivation. Therefore the 
concentration of these molecules would indicate whether or not the animal was exhibiting particularly 
high homeostatic sleep pressure. 
To identify sleep related molecules and which pattern best characterises their expression, we 
designed a timecourse style experiment. Mice were placed into the sleep deprivation cabinet 3 hours 
before the onset of dark on Day 0 with a 12:12 light dark cycle. At the beginning of the light phase on 
67 
 
Day 2, mice were sleep deprived for 0, 3, 6 or 12 hours, after which time they were allowed ad libitum 
sleep. Mice were sacrificed at 10 timepoints separated by 6 hours across the course of 54 hours, with 
the sampling being synchronised to the beginning and middle of the light and dark phases. Sampling 
began 12 hours before the onset of sleep deprivation and continued as long as 39 hours following 
cessation of sleep deprivation. The 10-timepoint design of this experiment reflects the limit of 
multiplexing in TMT-based proteomics, as there were only 10 isobaric TMT tags available at that time 
(since then an eleventh tag has become available). For consistency of analysis and to ease comparison 
across different data sets, this same 10-point timecourse design was used for characterising the 
transcriptomic, proteomic and metabolomic associated with sleep deprivation in mouse cortex.  
 
 
Figure 3.6. Experimental Design for Transcriptomic Characterisation of Sleep Deprivation in Mice: 
To identify which genes are modulated by sleep deprivation, and then to determine the rate at 
which their expression returns to baseline levels, we designed a timecourse style experiment. 
Mice were sampled every 6 hours for 54 hours, beginning at the beginning of the dark phase 
before sleep deprivation, and extending to the middle of the second dark phase following sleep 
deprivation. Sleep deprivation (in red) took place from the beginning of the light phase on Day 2, 
and lasted either 3, 6 or 12 hours. The black and white bar at the bottom of the figure represents 
the light dark cycle to which the mice were exposed. SD- Sleep Deprivation. 
68 
 
 
Sleep 
Deprivation
Sleep 
Deprivation
A
Sleep 
Deprivation
Sleep 
Deprivation
B
C
Sleep 
Deprivation
Sleep 
Deprivation
D
Figure 3.7. Hypothetical Effects of Sleep Deprivation on Molecular Abundance Profiles in Mouse 
Cortex: We hypothesised that individual molecular abundance profiles would fit into one of four 
broad and idealised classes. Class A is characterised by molecules with either constant or 
oscillatory concentration that is unaffected by sleep deprivation. Class B is characterised by 
molecules that rapidly change their abundance in response to state changes. Class C is 
characterised by molecules whose concentration is dependent on the proportion of time recently 
spent awake or asleep. Class D is characterised by molecules whose concentrations rapidly change 
in response to exceptionally high sleep pressure. The light dark cycle is indicated by the shaded 
grey bars, whilst sleep deprivation is indicated by the red shaded region. 
 
69 
 
3.2.4. Transcriptional Profiling of Sleep Deprived Mouse Cortex 
To identify to what extent sleep deprivation affects the transcriptomic profile of mouse cortex, we 
carried out the timecourse described in Section 3.2.3.. 9-10 week old, male, C57/Bl6J mice were 
pairhoused and subjected to sleep deprivation. Four animals were sacrificed, and the cortex collected 
every 6 hours for 54 hours. Therefore each group required a total of 40 animals and the whole 
experiment made use of tissue from a total of 160 animals. Total RNA was collected from the cortex 
and each replicate was individually subjected to RNA-Seq analysis. Due to the size of the experiment, 
the samples were interrogated in two separate batches; the first containing the Control and 6 hour SD 
groups, the second containing the 3 hour and 12 hour SD groups. Following alignment, data were 
analysed both through the Cufflinks package differential expression function, to compare a treated 
group at an individual timepoint to the control group at that same timepoint. 
 
 
Following 6 hours of sleep deprivation, approximately two thousand genes were significantly 
modulated compared to control mice with uninterrupted sleep. The number of significantly 
modulated genes remained high for approximately 24 hours following sleep deprivation before 
returning to baseline levels. Of the 1499 genes that were significantly increased following 6 hours 
sleep deprivation, there was an enrichment in genes coding for ribosomal proteins, oxidative 
Timepoint Timepoint
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Figure 3.8. Sleep Deprivation Induces changes in the Abundance of Thousands of Genes in Mouse 
Cortex: RNA from mouse cortex was collected at timepoints separated by 6 hours over the course of 
54 hours. Control mice were allowed ad libitum sleep, whereas the sleep deprived groups were 
subjected to 3, 6 or 12-hour sleep deprivation beginning at the onset of the light phase on Day 2. 
Plotted above are the number of genes differentially expressed between 6-hour sleep deprived and 
control mice (SD6), and the number of genes differentially expressed between mice subjected to 
3 hours and 12 hour sleep deprivation (SD12). The total height of the bar indicates the total number 
of differentially expressed genes, whilst the blue portion indicates the number of genes differentially 
expressed at that timepoint that are also differentially expressed in that timecourse after 6 hours 
sleep deprivation.  
70 
 
phosphorylation, mRNA splicing, GTP binding proteins, the endoplasmic reticulum, nucleosome 
assembly and cell-cell adherens junctions. Following 6 hours recovery, genes coding for ribosomal 
proteins remained enriched amongst upregulated genes, as did genes associated with oxidative 
phosphorylation, nucleosome assembly and cell-cell adherens junctions. Additionally, genes 
associated with the proteasome complex, translation elongation factors and antioxidant proteins 
were enriched. Similar gene functions were enriched among upregulated genes following up to 24 
hours recovery from sleep deprivation. 
 
Table 7: Gene Classes Upregulated in Mouse Cortex following 6-hour Sleep Deprivation Compared 
to non-sleep deprived mice sacrificed at the same timepoint. 
Timepoint Functional Cluster q-value 
12:00, Day 2 
6 Hour Sleep Deprivation 
Ribosomal Proteins 
Oxidative Phosphorylation 
mRNA Splicing 
GTP Binding Proteins 
Endoplasmic Reticulum 
Nucleosome Assembly 
Cell-cell Adherens Junction Genes 
1.0x10-39 
5.7x10-13 
4.1x10-10 
2.5x10-2 
3.9x10-3 
2.0x10-2 
3.6x10-2 
18:00, Day 2 
6 Hour Sleep Deprivation, 
6 Hour Recovery 
Ribosomal Proteins 
Oxidative Phosphorylation 
Nucleosome Assembly 
Cell-cell Adherens Junction Genes 
Proteasome Complex 
Translation Elongation Factors 
Antioxidant Genes 
2.9x10-67 
5.1x10-36 
1.5x10-6 
4.8x10-3 
1.6x10-7 
9.8x10-4 
2.7x10-3 
00:00, Day 3 
6 Hour Sleep Deprivation, 
12 Hour Recovery 
Ribosomal Proteins 
Oxidative Phosphorylation 
Proteasome Complex 
Endoplasmic Reticulum 
Antioxidant Genes 
2.3x10-46 
2.7x10-25 
3.4x10-7 
2.1x10-5 
2.4x10-2 
71 
 
06:00, Day 3 
6 Hour Sleep Deprivation, 
18 Hour Recovery 
Ribosomal Proteins 
Oxidative Phosphorylation 
Nucleosome Assembly 
Proteasome Complex 
Cell-cell Adherens Junction Genes 
5.5x10-57 
4.8x10-34 
1.2x10-6 
5.8x10-3 
1.4x10-2 
12:00, Day 3 
6 Hour Sleep Deprivation, 
24 Hour Recovery 
Ribosomal Proteins 
Oxidative Phosphorylation 
Nucleosome Assembly 
Proteasome Complex 
2.9x10-47 
8.2x10-19 
3.0x10-6 
3.3x10-2 
  
In contrast to upregulated genes, far fewer genes were statistically downregulated following sleep 
deprivation. 524 genes were significantly downregulated immediately following 6 hour sleep 
deprivation, but these genes were not statistically enriched in any ontology group following 
adjustment for multiple testing other than membrane proteins. Following 6 hours recovery from sleep 
deprivation, 485 genes were significantly reduced compared to mice that had not been sleep 
deprived, which were enriched in genes relating to ion transport, including calcium ion transport, 
postsynaptic density genes, and plexin genes. Similar groups were enriched amongst downregulated 
genes following a total of 12 hours recovery, however there was little functional enrichment amongst 
downregulated genes following more than 12 hour recovery from sleep deprivation. 
Table 8: Gene Classes Downregulated in Mouse Cortex following 6-hour Sleep Deprivation 
Compared to non-sleep deprived mice sacrificed at the same timepoint. 
Timepoint Functional Cluster q-value 
12:00, Day 2 
6 Hour Sleep Deprivation 
Membrane Proteins 
Glycoproteins 
8.0x10-6 
6.1x10-4 
18:00, Day 2 
6 Hour Sleep Deprivation, 
6 Hour Recovery 
Ion Transport 
Calcium Transport 
Postsynaptic Density  
Plexin Genes 
8.4x10-4 
4.4x10-2 
6.3x10-5 
2.4x10-2 
00:00, Day 3 
6 Hour Sleep Deprivation, 
12 Hour Recovery 
Postsynaptic Density 
Ion Transport 
Calcium Transport 
cAMP Signalling Pathways 
1.0x10-9 
7.5x10-5 
1.7x10-3 
9.5x10-3 
 
72 
 
After 6 hours sleep deprivation was found to induce the differential expression of thousands of genes, 
we repeated the timecourse with mice sleep deprived for 3 or 12 hours to determine to what extent 
the change in expression of wake related genes was dependent on the duration of sleep deprivation. 
However, comparison between the two pairs of timecourses identified several thousand genes as 
differentially expressed at pre-sleep deprivation timepoints, indicating that technical variation 
between the two batches would hinder comparison of samples between batches. Therefore, for the 
purposes of differential expression analyses, mice that had been sleep deprived for 12 hours were 
compared to mice that had been sleep deprived for 3 hours rather than ab libitum sleep controls. 
Following the first 6 hours of sleep deprivation, compared to mice that had only been sleep deprived 
for 3 hours (and so had already had 3 hours of recovery sleep opportunity), 402 genes were 
significantly upregulated in mouse cortex. These genes were enriched in ribosomal protein genes, 
chaperone genes, dual specificity phosphatases and genes associated with oxidative phosphorylation. 
Remarkably, fewer genes were significantly increased following 12 hour sleep deprivation, and 
amongst the 353 genes that were upregulated, there was surprisingly little functional enrichment. 
Upregulated genes were enriched in genes associated with the extracellular matrix, positive regulation 
of transcription and developmental proteins. In contrast, genes associated with synapses and 
neurogenesis were decreased immediately following 6 hours sleep deprivation, whereas glycoprotein 
genes were downregulated following 12 hour sleep deprivation.  
Genes that were significantly upregulated in both pairs of timecourses immediately following sleep 
deprivation were enriched in genes coding for ribosomal proteins and genes associated with oxidative 
phosphorylation, whereas genes that were significantly decreased immediately following sleep 
deprivation were enriched in genes associated with glycoproteins, the postsynaptic membrane and 
kinases.  
Transcriptomic data were also analysed through a custom R script that carries out JTK and ANOVA 
analyses on the entire timecourse, to identify genes that have circadian and statistically different 
expression patterns. Following alignment, a total of 17875 transcripts were identified as being 
expressed in the cortex, on the basis of having an average abundance greater than 0.5 FPKM and being 
detectable in every sample. The expression of 16% (2914) of these expressed transcripts was identified 
through JTK analysis as rhythmic with a period of 24 hours in animals not subjected to any sleep 
deprivation. It is noteworthy that genes cycling with a 24-hour rhythm cannot firmly be identified as 
circadian on the basis of this experiment, due to the 12:12 light dark cycle imposed on these animals. 
Strictly, circadian genes are those that show oscillations in abundance even in the absence of 
73 
 
environmental cues. With this experimental design, it is impossible to determine whether a given gene 
is “anticipating” or “responding to” the environment.  
 
 
Genes identified as rhythmic were enriched in genes associated with a broad array of processes, 
including Biological Rhythms, the regulation of transcription, Synapses, DNA damage, Kinases, the ER, 
Chaperones, Ubiquitin conjugation and AMPK signalling. 38% (1120) of rhythmic genes were identified 
as peaking within 3 hours of the end of the dark phase, and were enriched in genes related to 
Figure 3.9. 16% of Expressed Cortical Transcripts in non-Sleep Deprived Mice Exhibited Significant 
24 hour Oscillations during the Timecourse: RNA collected from individual biological replicates was 
subjected to RNA-Seq analyses. Genes with 24 hour cycles of expression were identified using JTK 
analysis. The expression of 25 genes from each of the 8 identified phases (timing of peak) are 
plotted, with red cells representing high expression and blue cells representing low expression.  
74 
 
chaperone function, synapses, and biological rhythms. In contrast, genes that peak at the end of the 
light phase were enriched in genes relating DNA damage repair and ubiquitin conjugation. 
 
 
To identify which genes are affected by sleep deprivation, we carried out ANOVA analyses followed 
by FDR based correction for multiple testing to determine the genes whose abundance show an 
interaction between time and sleep deprivation duration. Of the 2914 genes oscillating with a 24 hour 
rhythm in control mice, 505 genes (17%) exhibited a significantly different pattern of expression in 
both mice that had been sleep deprived for 6 and 12 hours. These genes were enriched in genes 
relating to synapses, including cholinergic synapses, protein processing in the endoplasmic reticulum, 
biological rhythms and chaperone functions. Of these 505 genes, 176 also demonstrated a 
significantly different expression pattern between mice subjected to 3 hour or 12 hour sleep 
deprivation. These genes were enriched in synapse proteins. 
Figure 3.10. Enriched Gene Classes amongst Diurnal Genes: Genes whose expression in mouse 
cortex oscillated with a 24-hour rhythm were subjected to functional annotation, and enriched 
gene classes displayed above. The enrichment of genes is indicated by the width of each bar, 
whilst the q-value is indicated by the colour of the bar.  
 
75 
 
 
In contrast, only 5% of non-circadian genes (790 genes) showed a significantly different pattern 
between control mice and both mice that had been sleep deprived for 6 or 12 hours. The functional 
classes enriched amongst these genes were ribosomal proteins, transport, oxidative phosphorylation, 
the proteasome complex and glycolysis. Of these, only 6% (47 genes) showed a significantly different 
expression pattern between mice subjected to 3 hour or 12 hour sleep deprivation. 
 
To determine which genes show a homeostatic gene profile (see Fig 3.7.C.) genes were filtered to 
identify those which were rhythmic and peaked at the end of the dark (active) phase in control mice, 
showed a consistently significantly different expression profile, and whose abundance remained 
elevated during 6 and 12 hour sleep deprivation. 17 genes were identified whose expression matched 
this homeostatic profile, and a further 3 genes matched the reverse pattern (i.e. have their minimum 
daily expression at the onset of the rest phase and remain suppressed during sleep deprivation). 
         
                         
                  
                 
          
       
                   
                     
    
         
          
  
  
  
  
  
     
       
Figure 3.11. Enriched Gene Classes amongst Sleep Deprivation Dependent Genes: Genes whose 
expression profile in mouse cortex was modulated by sleep deprivation were subjected to 
functional annotation, and enriched gene classes displayed above. The enrichment of genes is 
indicated by the width of each bar, whilst the q-value is indicated by the colour of the bar.  
 
76 
 
 
To identify genes that may match the stress gene profile, genes were filtered to identify those which 
were not rhythmic in control mice, or were rhythmic but whose peaks were not synchronised with the 
onset of the rest phase, but nevertheless showed a consistently elevated expression during sleep 
deprivation. A total of 15 genes were identified, made up of 10 genes which were identified as 
exhibiting rhythmic oscillations and 5 genes that did not exhibit oscillations in control mice.  
Figure 3.12. 17 Genes Matching the Hypothetical Homeostatic Profile were Identified: The expression 
profiles of genes were filtered to identify rhythmic genes that peaked at the end of the active phase 
and remained elevated during sleep deprivation but not during sleep, or showing the opposite 
pattern. Plotted on the same axes is the expression data from individual timecourses, normalised to 
the average of the first 3 (pre-treatment) timepoints, with error bars indicating SEM. The black line 
represents expression data from mice with uninterrupted sleep, whilst the blue, purple and red lines 
represent the data from mice subjected to 3, 6 and 12-hour sleep deprivation, respectively. The 
vertical grey bars indicate the timing of the dark phase, whereas the horizontal blue, purple and red 
bars situated between Zeitgeber time 0-12 represents the duration of 3, 6 and 12- hour sleep 
deprivation, respectively.  
77 
 
 
 
RNA-sequencing data can also be used to estimate the abundance of a specific isoform of a gene, 
rather than the sum abundance of all the isoforms of that gene. Splice variants of the same gene can 
have dramatically different functions and expression patterns. For example, the isoform Homer1a 
competes for binding partners with full length scaffold protein Homer1, and so acts to uncouple 
excitatory signalling in neurones. In line with previous studies, we found that the expression of 
Homer1a, but not the total expression of all isoforms of Homer1, was significantly upregulated 
following 12-hour sleep deprivation. 
Figure 3.12. 15 Genes Matching the Hypothetical Stress Gene Profile were Identified: The expression 
profiles of genes were filtered to identify genes that were elevated during sleep deprivation, but 
whose expression is not synced to the sleep-wake cycle in control animals. Plotted on the same axes 
is the expression data from individual timecourses, normalised to the average of the first 3 (pre-
treatment) timepoints, with error bars indicating SEM. The black line represents expression data 
from mice with uninterrupted sleep, whilst the blue, purple and red lines represent the data from 
mice subjected to 3, 6 and 12-hour sleep deprivation, respectively. The vertical grey bars indicate 
the timing of the dark phase, whereas the horizontal blue, purple and red bars situated between 
Zeitgeber time 0-12 represents the duration of 3, 6 and 12- hour sleep deprivation, respectively.  
78 
 
 
 
The output from Cuffdiff based quantification of isoform expression, which only considers genes with 
at least two known isoforms, revealed that rhythmically expressed isoforms were enriched in 
transcripts relating to biological rhythms, mRNA processing, the regulation of transcription, Synapses, 
ubiquitin conjugation, the cell cycle and lipid biosynthesis. Isoforms peaking at the end of the dark 
phase were enriched in transcripts relating to synapses, unfolded protein binding and biological 
rhythms, whilst transcripts peaking at the end of the light phase were enriched in transcripts relating 
to mRNA processing, transcription regulation, DNA replication and lipid metabolism. 426 of these 
circadian isoforms had statistically different expression profiles following 6 and 12 hour sleep 
deprivation, compared to undisturbed mice, which were enriched in transcripts relating to 
chaperones, synapses, biological rhythms. Of these, 171 genes also showed a different expression 
pattern between mice subjected to 3 and 12 hour sleep deprivation, which were enriched in 
transcripts relating to kinases and synapses. A further 84 non-rhythmic isoforms were identified as 
exhibiting a dose dependent change in expression profile in response to sleep deprivation, which were 
enriched in transcripts relating to the cytosolic large ribosomal subunit and mitochondrial function.  
   
To determine which genes show a homeostatic gene profile, isoforms were filtered to identify those 
which were rhythmic and peaked at the end of the dark (active) phase in control mice, showed a 
consistently significantly different expression profile, and whose abundance remained elevated 
during 6 and 12 hour sleep deprivation. 17 transcripts were identified whose expression matched 
this homeostatic profile, and a further 4 transcripts matched the reverse pattern. Isoform analysis 
identified some homeostatic isoforms that were previously identified at the whole gene level, but 
Homer1 Homer1a
Figure 3.13. Homer1a demonstrates a different Expression Pattern to the total Homer1 in Response 
to Sleep Deprivation: The expression profiles of Homer1 and Homer1a are plotted to demonstrate 
how isoform expression can vary from whole gene expression.  
79 
 
also identified isoforms of Homer1, Wisp1, Mical2, Ezr, Pdzd2, Syne1 and Rasgef1b as exhibiting 
homeostatic expression profiles.   
A total of 21 transcripts were identified that matched the stress gene profile, made up of 12 transcripts 
which were identified as exhibiting rhythmic oscillations and 9 transcripts that did not exhibit 
oscillations in control mice. Again, there was significant overlap with stress genes identified at the 
whole gene level, but also include Hif3a, Sgk1 and Fkbp5. 
 
 
 
Figure 3.14. 17 Isoforms Matching the Hypothetical Homeostatic Profile and 21 Matching the Stress 
Profile were Identified: The expression profiles of isoforms were filtered to identify homeostatic and 
stress expression profile isoforms, as outlined above. Plotted on the same axes is the expression 
data from individual timecourses, normalised to the average of the first 3 (pre-
treatment) timepoints, with error bars indicating SEM. The black line represents expression data 
from mice with uninterrupted sleep, whilst the blue, purple and red lines represent the data from 
mice subjected to 3, 6 and 12-hour sleep deprivation, respectively. The vertical grey bars indicate 
the timing of the dark phase, whereas the horizontal blue, purple and red bars situated between 
Zeitgeber time 0-12 represents the duration of 3, 6 and 12- hour sleep deprivation, respectively 
80 
 
3.3.1. Comparison to Previous Studies  
The transcriptomic effects of sleep deprivation have previously been examined using microarray 
technology in rats (Cirelli et al. 2004), mice (Mackiewicz et al. 2007; Maret et al. 2007), sparrows (Jones 
et al. 2008), fish (Sigurgeirsson et al. 2013) and flies (Cirelli et al. 2005; Zimmerman et al. 2006). This 
work is the first large scale sequencing-based profiling of sleep deprived mammalian cortex that we 
are aware of. Because of the timecourse style experimental design and biological replication, we have 
been able to carry out statistical tests not only comparing individual timepoints, but the expression 
profile as a whole. Previous studies have implicated a broad range of genes as being subject to 
regulation by wakefulness, and several studies have concluded that wake modulated genes fall into 
one of three broad functional groups: response to cellular stress, synaptic plasticity, and metabolism 
(Mackiewicz et al. 2009). To what extent do the data presented here overlap with previously published 
data?  
3.3.2. Heatshock proteins 
Previous studies have repeatedly identified the induction of heatshock proteins and genes involved in 
the unfolded protein response as a molecular correlate of sleep deprivation across several species. 
Although body temperature is elevated during wakefulness, the induction of heat shock proteins 
through cellular stress pathways is thought to represent a mechanism by which protein synthesis is 
reduced during prolonged wakefulness. A previous microarray study in mice identified 8 heatshock 
genes as being upregulated during sleep deprivation (Mackiewicz et al. 2007). Of the 7 detected in this 
study (Hsp105 did not reach the expression threshold to be considered for analysis), 3 had significantly 
different expression profiles following 6 hour sleep deprivation compared to non-sleep deprived mice, 
whilst Hspa5 trended toward significance. Consistent with these genes performing a function during 
spontaneous wakefulness, rather than only being induced during particularly high homeostatic sleep 
pressure, all 7 genes exhibited 24-hour rhythms in expression in control animals.  
Table 9: Previously Implicated Chaperone Gene Expression Data 
Gene  
Rhythmic 
q-value 
Fold Change  
6hr SD 
Fold Change  
12hr SD 
SD6 ANOVA 
q-value 
Dnajb11 1.72 x10-4 1.24  * 1.08 7.21 x10-3 
Dnajb5 7.80 x10-6 1.16 1.24 3.50 x10-1 
Dnajc1 1.16 x10-2 1.22 1.19 2.97 x10-3 
Dnajc3 5.24 x10-4 1.46  * 1.1 1.22 x10-3 
Hspa1a 1.16 x10-2 1.21 0.99 1.18 x10-1 
Hspa1b 1.06 x10-2 1.96* 1.09 1.42 x10-1 
Hspa5 1.27 x10-3 1.55  * 1.28  * 6.45 x10-2 
81 
 
 
Several other chaperones and mediators of the unfolded protein response were implicated in our 
transcriptomic screen. Therefore the data presented in this chapter supports the role of chaperone 
proteins in both the regulation of spontaneous and prolonged wakefulness. 
Table 10: Further Chaperone Gene Expression Linked to Wakefulness 
Gene  
Rhythmic 
q-value 
Fold Change 
6hr SD 
Fold Change 
12hr SD 
SD6 ANOVA 
q-value 
Atf6 5.85x10-1 1.35  * 1.14 4.77x10-2 
Calr 1.57x10-5 1.22 1.18 1.65x10-3 
Chordc1 4.83x10-3 1.8  * 1.22  * 7.18x10-5 
Derl1 8.68x10-2 1.24  * 1.03 4.32x10-2 
Hsp90aa1 1.34x10-1 1.35  * 0.99 4.74x10-3 
Hsp90b1 3.14x10-4 1.39  * 1.09 9.45x10-4 
Hspd1 3.70x10-2 1.39  * 1.08 2.03x10-3 
Pdcl 3.70x10-2 1.21 1.2 1.18x10-3 
Pdia6 2.06x10-3 1.22  * 1.14 2.04x10-2 
Stt3b 1.45x10-3 1.25  * 1.14 1.13x10-3 
Yod1 1 1.49  * 0.99 3.90x10-3 
 
Hspa5 Dnajb5 Dnajc3
Figure 3.15. Chaperone Genes are Induced by both Spontaneous and Enforced Wakefulness: The 
cortex expression of chaperone genes previously identified as dependent on the wake state of the 
animal was found to be dependent on both time of day in control animals and duration of sleep 
deprivation. The table outlines the JTK derived q-value for rhythmic gene expression in control 
mice, the fold change in expression immediately following 6 hour and 12 hour sleep deprivation 
(compared to control animals and mice subjected to only 3 hour sleep deprivation, respectively), 
and the q-value comparing the expression profile of 6 hour sleep deprived mice to control mice. 
The asterisks in the fold change columns denote whether cuffdiff identified that comparison as 
significantly different (i.e. q-value < 0.05). The expression of individual genes are plotted in the 
graphs, where the grey bars indicate the light-dark cycle, whilst the blue, purple and red bars and 
lines indicate the timing and expression of 3-, 6-, and 12-hour sleep deprived mice, respectively. 
The expression of control animals is plotted in black.  
 
82 
 
 
3.3.3. Cholesterol Synthesis 
A microarray study published by Mackiewicz et al concluded that a key function of sleep is the 
biosynthesis of lipids, and identified the transcription of the majority of the cholesterol biosynthetic 
enzymes as sleep dependent (Mackiewicz et al. 2007). The authors hypothesised that cholesterol 
biosynthesis and uptake may play an important role in membrane homeostasis and promote the 
formation of lipid rafts at synapses during sleep. It is worth remarking here that that study was more 
powerful than previous studies, and so was able to statistically identify smaller magnitude changes in 
gene expression. Indeed, the authors found that the expression of many of the cholesterol 
biosynthetic genes was statistically downregulated by approximately 25% or less following 12 hour 
sleep deprivation, significantly lower than the magnitude of changes reported in immediate early 
genes and other gene classes. However, a later study found that cholesterol biosynthetic genes were 
no longer affected by sleep deprivation following adrenalectomy, indicating that their expression is 
under regulation by glucocorticoid signalling (Mongrain et al. 2010).  
Our data suggests that although some cholesterol metabolising genes are affected by sleep 
deprivation, the expression of the pathway is not tightly linked to either the time of day or the state 
of wakefulness of the animal. Genes involved in general cholesterol metabolism, but not synthesis, 
trended toward being enriched amongst transcripts identified as oscillating with 24-hour rhythms 
Figure 3.16. Further Chaperone Genes are Induced by both Spontaneous and Enforced 
Wakefulness: The cortex expression of other chaperone genes were identified as dependent on 
the wake state of the animal. The table outlines the JTK derived q-value for rhythmic gene 
expression in control mice, the fold change in expression immediately following 6 hour and 
12 hour sleep deprivation (compared to control animals and mice subjected to only 3 hour sleep 
deprivation, respectively), and the q-value comparing the expression profile of 6 hour sleep 
deprived mice to control mice. The asterisks in the fold change columns denote whether cuffdiff 
identified that comparison as significantly different (i.e. q-value < 0.05). The heatmap indicates 
the z-score normalised expression of individual genes in the cortex of mice subjected to 6 hour 
sleep deprivation, compared to undisturbed mice at the same timepoint, where blue indicates 
low expression and red indicates elevated expression. 
 
83 
 
(q-value = 0.08 and 0.37, respectively), whilst cholesterol biosynthetic genes were not statistically 
overrepresented amongst genes identified as being downregulated by either 6 or 12-hour sleep 
deprivation. Of the 10 cholesterol biosynthesis genes identified as upregulated during sleep by 
Mackiewicz, we found a total of 4 were significantly downregulated immediately following either 
6 or 12-hour sleep deprivation (Dhcr24, Dhcr7, Hmgcs1, Mvk), whilst the expression profile of 
1 enzyme (Fdps) was statistically different following 6 hours sleep deprivation. None of these genes 
demonstrated rhythmic expression in control animals, which is unexpected for genes postulated to 
perform a key function of sleep. In the context of the modest sized changes induced by 6 and 12-hour 
sleep deprivation, it may be the case that the expression is indeed rhythmic, but sufficiently low 
amplitude to not be detected in our study. Therefore, although cholesterol biosynthesis being linked 
to the rest phase is an attractive conclusion that is consistent with the theoretical anabolic and 
membrane homeostasis roles of sleep, the transcriptomic data obtained in this work does not support 
cholesterol synthesis as a core function of sleep.  
Table 11: Gene Expression of Genes in Cholesterol Metabolising Pathway 
Gene  
Rhythmic  
q-value 
Fold Change 
6hr SD 
Fold Change 
12hr SD 
SD6 ANOVA 
q-value 
Dhcr24 1 0.79  * 0.74  * 5.97x10-2 
Dhcr7 1 0.66  * 0.77 1.32x10-1 
Fdft1 1 1.06 0.93 1.04x10-1 
Fdps 8.89x10-1 1.21 0.73 2.09x10-2 
Hmgcr 7.79x10-1 1 0.97 6.80x10-1 
Hmgcs1 8.89x10-1 1.12 0.83  * 3.49x10-1 
Lss 7.54x10-2 0.72 0.94 5.62x10-1 
Mvd 1 0.76 0.57 7.37x10-2 
Mvk 1 0.86 0.73  * 3.03x10-1 
Nsdhl 3.08x10-1 0.81 0.86 3.14x10-1 
 
 
  
Figure 3.17. Some Cholesterol Genes are Modulated by Sleep Deprivation, but none are Rhythmic 
in Undisturbed Mice: A previous study indicated that the expression of several cholesterol 
biosynthetic genes are downregulated in mouse cortex by sleep deprivation. This table presents 
data from this screen for the genes previously identified as repressed during sleep deprivation. 
The table outlines the JTK derived q-value for rhythmic gene expression in control mice, the fold 
change in expression immediately following 6 hour and 12 hour sleep deprivation (compared to 
control animals and mice subjected to only 3 hour sleep deprivation, respectively), and the q-value 
comparing the expression profile of 6 hour sleep deprived mice to control mice. The asterisks in 
the fold change columns denote whether that comparison was significantly different 
(i.e. q-value < 0.05).  
 
84 
 
3.3.4. Circadian Genes 
Biological rhythms play a large role in determining both the duration and timing of sleep, with core 
circadian genes implicated in influencing both Process C and Process S of the Two Process Model 
proposed by Borbély. The interplay between circadian rhythms and the sleep wake cycle is further 
complicated by the presence of both local cellular clocks and a central pacemaker (the SCN), whilst 
experimental sleep deprivation in mice disrupts several behavioural patterns, by the introduction of 
stress, modulated neuronal excitability, and increased opportunity for eating, drinking and social 
behaviour. Since sleep deprivation induces recovery sleep during the subsequent habitual active 
phase, further disrupting the typical behaviour of the animal, it has previously been proposed that 
sleep deprivation may perturb circadian rhythms (Challet et al. 2001; Deboer et al. 2003). Indeed, 
expression of core clock components has previously been linked to sleep deprivation, whereas the 
DNA binding activity of core clock components CLOCK, Arntl and NPAS2 is reduced following sleep 
deprivation (Mongrain et al. 2011), suggesting that the expression of a considerable proportion of 
rhythmic transcripts is affected by sleep deprivation.  
Immediately following sleep deprivation, expression changes in core clock genes were relatively 
modest, with a significant increase in CLOCK and Npas2, and decrease of Dbp expression being 
identified. Similarly, the expression profile of only a handful of core clock genes is significantly 
different following 6 hours of sleep deprivation, indicating that short term sleep deprivation induced 
disruption to the canonical transcription based molecular clock in the cortex may overall only be slight. 
However, during the recovery phase from 12-hour sleep deprivation, the expression of core clock 
genes such as Arntl, Clock and Dbp remains perturbed for 24 hours following cessation of sleep 
deprivation, suggesting that longer term sleep deprivation and the subsequent recovery sleep may 
greatly disrupt rhythmic gene expression.  
Table 12: Gene Expression of Clock Machinery Genes 
Gene  
Rhythmic  
q-value 
Fold Change 
6hr SD 
Fold Change 
12hr SD 
SD6 ANOVA 
q-value 
Arntl 1.08x10-6 1.08 1.18 4.34x10-1 
Clock 8.08x10-2 1.30  * 1.22 4.79x10-2 
Cry1 9.52x10-5 1.07 1.02 6.86x10-2 
Cry2 2.63x10-2 0.84 1.01 1.02x10-1 
Dbp 1.28x10-5 0.69  * 1.03 2.55x10-2 
Npas2 7.93x10-3 0.91 1.38  * 2.52x10-1 
Nr1d1 5.83x10-3 0.75 0.92 2.83x10-1 
Nr1d2 5.69x10-6 1.16 0.97 9.42x10-3 
Per2 2.17x10-5 1.29 1.21 1.60x10-2 
Per3 8.15x10-6 0.94 1.10 7.63x10-2 
 
85 
 
 
 
 
Figure 3.18. The Expression of Clock Genes is only Modestly Affected during Sleep Deprivation, but 
Severely Perturbed during Recovery from 12 hour Sleep Deprivation: Previous studies have 
indicated clock genes as involved in the response to sleep deprivation in mouse cortex. This table 
presents data from this screen for the a subset of core clock genes. The table outlines the JTK 
derived q-value for rhythmic gene expression in control mice, the fold change in expression 
immediately following 6 hour and 12 hour sleep deprivation (compared to control animals and 
mice subjected to only 3 hour sleep deprivation, respectively), and the q-value comparing the 
expression profile of 6 hour sleep deprived mice to control mice. The asterisks in the fold change 
columns denote whether that comparison was significantly different (i.e. q-value < 0.05).  The 
expression of individual genes are plotted in the graphs, where the grey bars indicate the light-
dark cycle, whilst the purple and red bars and lines indicate the timing and expression of 6-, and 
12-hour sleep deprived mice, respectively. The expression of control animals is plotted in black. 
 
 
86 
 
Indeed, transcriptome wide analysis reveals that increasing durations of sleep deprivation 
progressively dampen rhythmic transcript expression in the cortex. Whereas 2917 genes were 
identified as rhythmic in animals with unperturbed sleep, 1248 (43%) transcripts were rhythmic 
following 3-hour sleep deprivation and 769 (26%) following 6 hour sleep deprivation. Remarkably, 
following 12 hour sleep deprivation only 63 genes (2%) were still identified as rhythmic. The dramatic 
reduction in rhythmic transcript number may indicate that sampling every 6 hours results in a study 
that is underpowered to find rhythmic transcripts following small perturbations in expression, rather 
than a complete ablation of the clock. However, the near absence of rhythmic transcripts following 
12 hour sleep deprivation appears to indicate that almost every gene whose expression habitually 
oscillates with a 24 hour rhythm in the cortex is subject to modulation by the sleep wake cycle. There 
are several possible molecular mechanisms through which the expression of individual rhythmic genes 
could be disrupted, including through altered homeostatic sleep pressure, resetting of the molecular 
clock and altered glucocorticoid rhythms. Based on the data in this thesis, it is difficult to conclude 
which mechanism is responsible for specific gene perturbations, however on the basis of expression 
patterns following progressively increasing durations of sleep deprivation, it is possible to speculate 
and to identify possible candidates. 
87 
 
 
  
Figure 3.19. The Number of Rhythmic Transcripts in Mouse Cortex is Progressively Reduced by 
Increasing Duration of Sleep Deprivation: The number of rhythmic transcripts in each dataset 
identified by JTK analysis is indicated by the area of blue, whilst the Zeitgeber time of day that 
those transcripts peak is indicated by the angle from the centre (where 0 is the onset of the light 
phase and 12 is the onset of the dark phase). The black concentric circles are guides that indicate 
the total number of genes present. The total area of the circles, from inner to outer, represents 
200, 800, 2400,4000 and 6000 genes. Since segments are binned into 3-hour phase intervals, a 
segment touching the inner circle will therefore contain 25 genes, whilst a segment touching the 
outermost circle will contain 750 genes.  
 
 
88 
 
3.3.5. Homeostatic Profile Genes 
Our hypothesis before this experiment was that there is a subgroup of sleep dependent molecules 
that exhibit a “homeostatic profile”, the abundance of which would be related to the homeostatic 
sleep pressure the animal was experiencing. To identify homeostatic candidates, we searched for 
genes demonstrating increasing abundance during the active phase and declining abundance during 
the rest phase in control animals, but an increased abundance following sleep deprivation in a dose 
dependent manner. When we applied these filters to our dataset, we found several genes previously 
implicated in sleep homeostasis met those criteria, and some that matched the reverse profile.  
 
  
Figure 3.20. Representative Homeostatic Gene Expression Profiles: The expression pattern of genes 
were filtered for homeostatic genes on the basis of rhythmic expression in control mice that 
peaked at the end of the active phase, and were induced by sleep deprivation. Plotted here are 
3 genes that demonstrate that profile, and one that demonstrates the reverse profile. 
 
 
89 
 
However, once plotted, it became clear that genes selected on this basis (e.g. Bdnf, Cbln4, Cdkn1a, 
Bace2) typically rebounded to baseline expression very quickly following 3 or 6-hour sleep deprivation, 
whereas following 12-hour sleep deprivation, expression was even reduced compared to non-sleep 
deprived controls following only 6-hour recovery sleep opportunity. These findings were unexpected 
for genes that are postulated to act as homeostatic markers of sleep deprivation, as we expected the 
abundance to remain elevated above that of control animals until the sleep deprived animal had had 
sufficient opportunity for recovery sleep.  
 
Instead, the expression profile of these genes appears to be more consistent with a “binary gene”, 
i.e. a gene whose expression is tightly linked to the very recent wake state of the animal, but carries 
little information about historic sleep deprivation or the homeostatic sleep pressure the animal is 
currently experiencing. A binary gene expression would be consistent with a rapid recovery following 
6-hour sleep deprivation, as both sleep deprived and control animals are predominantly asleep during 
this period. A binary pattern also predicts that 12-hour sleep deprivation should result in lower 
expression in the subsequent dark phase than control animals, because the proportion of time spent 
awake in this period is considerably reduced due to high homeostatic sleep pressure.   
Sleep 
Deprivation
Homeostatic Profile
Figure 3.21. Idealised Homeostatic Gene Expression Profiles: Ideally, homeostatic genes should 
oscillate during the normal wake-sleep cycle but continue to increase during sleep deprivation 
and remain high until the animal has had sufficient time for complete recovery sleep. These 
idealised graphs show the expected expression of a homeostatic gene in a mouse, where the dark 
bars represent the habitual active phase of the mouse, and the pink section represents enforced 
wakefulness.  
 
 
90 
 
 
However, transcripts that demonstrate a binary expression profile should not necessarily be discarded 
when searching for homeostatic molecules. Indeed, it could be argued that a binary pattern would be 
exactly what would be expected for a transcript that ultimately codes for a protein with a homeostatic 
function. Assuming a similar translation efficiency of binary transcripts across the sleep-wake cycle, 
the rate of synthesis of the corresponding proteins would be high during wake and low during sleep. 
If degradation of these proteins is also unaffected by the wake status of the animal, an appropriate 
rate of degradation would result in the accumulation of the protein during wake and the net removal 
of that protein during sleep. Therefore, homeostatic sleep pressure may be signalled through the 
transcriptionally regulated increase of extracellular signalling proteins like BDNF, CBLN2, CBLN4, or 
enzymes responsible for the production of extracellular signal molecules, such as PTGS2 or DIO2, 
whilst proteins such as HOMER1a or CDKN1a may coordinate the intracellular response to elevated 
sleep drive. A reduced expression of BACE2, implicated in the removal of amyloid plaques which 
accumulate during sleep deprivation (Abdul-Hay et al. 2012; Kang et al. 2009; Xie et al. 2013), may 
play a role in the pathogenesis of sleep deprivation. 
Do previous studies support a role of these transcripts in homeostasis? Intracerebral injection of BDNF 
protein increases sleep in rats (Kushikata et al. 1999), whilst unilateral injections of BDNF induce 
increases in local slow wave activity but not the contralateral hemisphere, with BDNF antagonists 
eliciting the reverse effect (Faraguna et al. 2008). In humans, a polymorphism in the coding region of 
BDNF has been linked to an increased time spent in deep NREM sleep in humans (Bachmann et al. 
2012). Similarly, Ptgs2, identified in our screen, has also been implicated in control of sleep induction. 
Ptgs2 encodes cyclooxygenase-2, which carries out the committed step in prostaglandin synthesis, 
producing prostaglandin H2 (PGH2) from arachidonic acid (Tetsuya et al. 2005). PH2 itself acts as a 
Sleep 
Deprivation
Binary Profile
Figure 3.22. Idealised Binary Gene Expression Profiles: Ideally, the expression of “binary” genes 
should be high during wakefulness and low during sleep, and not be affected by the duration of 
prior bouts of wakefulness. These idealised graphs show the expected expression of a binary gene 
in a mouse, where the dark bars represent the habitual active phase of the mouse, and the pink 
section represents enforced wakefulness. For simplicity, the fragmented nature of rodent sleep is 
not reflected here, but recovery sleep during the habitual active phase of mice is indicated. 
 
 
91 
 
precursor for prostaglandin D2 (PGD2), which strongly induces sleep when introduced centrally in rats 
and monkeys (Hayaishi 1991), whilst levels of PGD2 and other products from PGH2 are elevated in 
rats during both spontaneous sleep and during sleep deprivation (Ram et al. 1997). Interesingly, 
prostaglandin D synthase (Ptgds) expression is also upregulated following both 6- and 12-hour sleep 
deprivation. The increased expression of Ptgs2 and Ptgds may induce an increase in PGD2 levels 
through an increase in the availability of PGH2 precursor and prostaglandin D synthase activity. 
Consistent with a sleep inducing role of prostaglandin synthesis pathways, inhibition of 
cyclooxygenase-2 through the consumption of non-steroidal anti-inflammatory drugs (NSAID) at night 
time disrupts sleep in humans (Murphy et al. 1994), whilst intracerebroventricular injection of specific 
cyclooxygenase-2 inhibitors reduces both spontaneous and TNF-α induced sleep duration in rats 
(Terao et al. 1998; Yoshida et al. 2003).  
 
One transcript, Crh, did demonstrate a homeostatic expression pattern, whereby expression remained 
high during the initial portion of the recovery phase and did not fall below the expression seen in 
control animals during recovery. The half-life of Crh mRNA has been previously identified as being 
short (t ½ < 15 minutes) (Ma et al. 2001), and therefore the elevated levels during the recovery phase 
are not due to a slow degradation of the mRNA. A homeostatic profile for Crh mRNA may reflect that 
its protein product, corticotropin releasing factor (CRF), also has a short half-life (Schulte et al. 1982; 
Schürmeyer et al. 1984) and therefore requires a sustained elevation of synthesis to maintain a high 
abundance. Therefore the upstream signalling pathways inducing Crh expression in response to sleep 
loss may be distinct from those involved in the control of seemingly current state dependent genes. 
Figure 3.23. The Expression of Crh Reflects an Idealised Homeostatic Gene Profile: The expression 
profile of Corticotropin Releasing Hormone (Crh) is of special interest because it matches the 
idealised homeostatic gene expression profile. 
 
 
92 
 
Crh, which codes for corticotropin releasing factor (CRF), appears to play a different role in the control 
of sleep. Although CRF is strongly linked to stress induction, and therefore may appear to be a 
technical artefact of experimental sleep deprivation, its rhythmic expression which peaks at the end 
of the active phase in control animals suggests that it may also be involved in the homeostasis of 
spontaneous wake and sleep. However, intracerebral injection of CRF in mice appears to increase 
wakefulness and decrease both NREM and REM sleep, which is unexpected for a molecule signalling 
high homeostatic sleep pressure (Sanford et al. 2008). The effect on wakefulness and NREM sleep 
appears to be mediated through Crh receptors expressed in the brain, as central knockout of Crhr1 
abrogates the Crh mediated decrease in NREM sleep duration, despite the corticosterone induction 
remaining intact (Romanowski et al. 2010). The induction of Crh may therefore be a stress induced 
consequence of experimental sleep deprivation, or a molecular mechanism through which 
wakefulness is maintained, despite rising homeostatic and circadian sleep pressure. However, it is 
important to remember that this experiment isolated RNA from the cortex, whilst the stress related 
functions of Crh are typically associated with the hypothalamus  (Füzesi et al. 2016). Crh signalling in 
the cortex may therefore serve a function distinct from the stress response.  
  
93 
 
3.3.6. Stress Profile Genes 
We also predicted that there may be a subset of genes that demonstrate “stress” expression profiles, 
which would be switched on during periods of particularly high homeostatic sleep pressure to either 
induce sleep or cope with the demands of extended wakefulness. We hypothesised that such genes 
would be upregulated during sleep deprivation, but either not be rhythmic in control animals, or show 
a peak just before habitual sleep time. 
 
 
 
Amongst the genes identified as exhibiting a “stress” expression pattern were Rasd1 and Vip. Rasd1, 
also known as ras-related dexamethasone induced or Dexras1, has previously been identified as a 
sleep deprivation dependent transcript in mouse (Thompson et al. 2010) and playing a role in circadian 
entrainment to light (Cheng et al. 2004), whilst a SNP near Rasd1 is associated with habitual wake time 
in humans (Hu et al. 2016). At the molecular level, Rasd1 is a monomeric G-protein that is activated 
by NMDA-receptor activity and nitric oxide (Fang et al. 2000). Activation of Rasd1 is associated with 
Sleep 
Deprivation
Sleep 
Deprivation
Stress Profile
Figure 3.24. Idealised Stress Gene Expression Profiles: Ideally, the expression of “stress” genes 
should be induced during particularly high sleep pressure. The expression of these genes may 
therefore be triggered only following sleep deprivation, or may be triggered at the end of the 
habitual wake phase. These idealised graphs show the expected expression of a stress gene in a 
mouse, where the dark bars represent the habitual active phase of the mouse, and the pink 
section represents enforced wakefulness.  
 
 
94 
 
efflux of iron from lysosomes, which in turn leads to the inhibition of neuronal firing (White et al. 
2016). Therefore Rasd1 may act in a negative feedback loop, which limits neuronal firing in response 
to neuronal activity.  
Intriguingly, Vip, which codes for vasoactive intestinal peptide, also demonstrates a non-rhythmic 
expression pattern in control animals and is activated following sleep deprivation, but does not return 
to baseline levels within 6 hours of recovery sleep opportunity like Rasd1. Vip has previously been 
associated with sleep and sleep deprivation studies, with VIP abundance increasing in cerebral spinal 
fluid during sleep deprivation, whilst intracerebral injection of VIP promotes sleep  (Bourgin et al. 
1997; Jime´nez-Anguiano et al. 1993; Prospe´ro-Garcia et al. 1986). Conversely, knockout of Vip in 
mice results in reduced sleep duration, altered distribution of sleep across the day and a blunted 
rebound in response to sleep deprivation (Hu et al. 2011). Interestingly, these sleep effects of VIP 
appear to predominantly be associated with REM sleep rather than NREM sleep, as VIP knockout and 
injection induce greater changes to REM sleep duration, whilst specifically depriving REM sleep is also 
sufficient to induce Vip expression. Therefore, based on previous studies, it appears as though VIP 
may play a role in sleep homeostasis, specifically REM sleep homeostasis. An elevated expression for 
several hours following the cessation of sleep deprivation is consistent with a homeostatic transcript, 
however it is unexpected that the expression of Vip does not oscillate during the day in control 
animals.  
 
Figure 3.25. Rasd1 and Vip Expression Fit Different Stress Gene Profiles: Rasd1 and Vip expression 
is induced during sleep deprivation, but does not display rhythmic expression in control animals, 
suggesting that their induction is specifically linked to prolonged wakefulness. Whereas Rasd1 
expression rapidly returns to baseline following cessation of sleep deprivation, Vip expression 
remains elevated for prolonged periods.  
 
 
95 
 
When we expanded our criteria of stress genes to include genes that are ordinarily rhythmic in control 
animals, but do not peak during spontaneous waking, we identified some genes that are strongly 
induced by sleep deprivation but typically peak at the end of the rest phase in control animals 
(e.g. Xdh, Plin4, Tsc22d3). It is difficult to immediately reconcile in terms of sleep homeostasis how 
genes that normally have highest expression at the end of the rest phase can be strongly induced by 
sleep deprivation. Instead it seems more plausible that these genes are induced not by sleep 
deprivation but by an increase in corticosterone associated with sleep deprivation. Plasma 
corticosterone levels in the mouse usually oscillate during the day, with the peak abundance occurring 
near the end of the light phase (Ottenweller et al. 1979; Yoshida et al. 2005), whereas experimental 
sleep deprivation may induce plasma corticosterone increases. Genes induced by corticosterone 
would therefore be expected to usually demonstrate a peak at the end of the rest phase (coinciding 
with the peak of plasma corticosterone), and possibly demonstrate an elevated expression following 
6 hour sleep deprivation. Consistent with this finding, Plin4 and Tsc22d3 have previously been shown 
to be induced in the brain by dexamethasone (Juszczak & Stankiewicz 2018), whilst the sleep 
deprivation mediated induction of Xdh and Tsc22d3 was absent in mice which had undergone 
adrenalectomy (Mongrain et al. 2010).  
Therefore, genes with a “stress” expression pattern may truly be induced by a stress associated 
increase in corticosterone during experimental sleep deprivation. However, it is difficult to determine 
whether the induction of corticosteroids is a physiological signalling pathway utilised in response to 
extended wakefulness, or an experimental confounder of imposing sleep deprivation. Surgical 
removal of the adrenal glands may help delineate the individual contributions of stress and sleep 
deprivation: indeed intact adrenal glands are required for the induction of an estimated 70% of wake 
dependent transcripts in response to sleep deprivation (Mongrain et al. 2010). However, 
adrenalectomy also disrupts feedback pathways between corticosteroids and Crh, a wake promoting 
transcript, perhaps introducing further confounding effects (Ma et al. 2001).  
Utilising different model systems or sleep deprivation techniques may indicate to what extent 
corticosteroid modulated gene expression is an artefact of experimental sleep deprivation.  (Friess et 
al. 2004). Administration of wake promoting stimulants, including caffeine, methamphetamine and 
methylphenidate, raises plasma corticosterone in rodents  (L. et al. 2006; Petit et al. 2010; Spindel & 
Wurtman 1984). Sleep deprivation in humans has been demonstrated to induce increases in plasma 
cortisol (Spiegel et al. 1999), which in turn has been demonstrated to influence sleep architecture and 
increase delta power. Therefore, corticosteroid induction may be tightly linked with prolonged 
wakefulness, rather than an experimental artefact. 
96 
 
3.3.7. Attributes of this Experimental Design 
In addition to the use of next-generation sequencing technology, the timecourse style of our 
experiment, coupled with varied durations of sleep deprivation is what is novel about the data 
presented in this chapter. This approach has the advantage that it can identify not only the acute gene 
expression changes occurring during sleep deprivation, but also the rate at which these genes return 
to baseline levels and how this recovery depends on the amount of sleep debt accrued. To illustrate 
the value of such timecourse datasets, data and expression profiles are presented below of 4 genes 
identified in previous studies as increasing during sleep deprivation. In our study, the expression of all 
of them are identified as increasing at least twofold following 6 hour sleep deprivation compared to 
non-sleep deprived mice, however data from the additional timepoints reveal marked differences in 
habitual expression in undisturbed mice as well as differences in recovery from sleep deprivation.  
3 of the genes show significant 24 hour rhythms in undisturbed mice, with the expression of Rasd1 
being approximately flat during the day. Of the 3 rhythmic genes, Sult1a1 peaks at the beginning of 
the habitual active phase, whereas Arc and Cdkn1a both peak just before the end of the active phase. 
Whereas 6 hour sleep deprivation increases the expression of Cdkn1a, Sult1a1 and Rasd1 to levels 
higher than occurring during the course of the normal day, sleep deprivation appears only to lessen 
the reduction in Arc expression. Compared to 6 hours, 12 hour sleep deprivation further increases the 
expression of Sult1a1, in contrast to the expression of Cdkn1a and Rasd1 which appear to reach a 
plateau. Finally, following cessation of sleep deprivation, the expression of Arc, Cdkn1a and Rasd1 
quickly rebound to baseline levels, whilst the expression of Sult1a1 remains high for at least 12 hours. 
  
97 
 
 
Table 13: Gene Expression of Arc, Cdkn1a, Sult1a1 and Rasd1  
Gene  
Rhythmic  
q-value 
Fold Change 
6hr SD 
Fold Change 
12hr SD 
SD6 ANOVA 
q-value 
Arc 9.54E-03 2.05  * 1.63  * 5.99E-01 
Cdkn1a 7.80E-06 2.59  * 2.16  * 5.31E-03 
Sult1a1 3.70E-02 2.39  * 2.08  * 9.47E-03 
Rasd1 3.87E-01 3.58  * 2.06  * 2.62E-04 
 
Figure 3.26. Genes Previously Identified as Induced by Sleep Deprivation have Markedly different 
Expression Profiles during Spontaneous Wake Cycles and during Recovery Sleep: The expression of 
Arc, Cdkna1, Sult1a1 and Rasd1 have all been previously identified as strongly induced during 
sleep deprivation. However, previous studies did not indicate that the expression of each of these 
genes demonstrate distinct expression patterns during spontaneous wake, sleep deprivation and 
recovery, emphasising the power of a timecourse style experimental design. The table outlines 
the JTK derived q-value for rhythmic gene expression in control mice, the fold change in 
expression immediately following 6 hour and 12 hour sleep deprivation (compared to control 
animals and mice subjected to only 3 hour sleep deprivation, respectively), and the q-value 
comparing the expression profile of 6 hour sleep deprived mice to control mice. The asterisks in 
the fold change columns denote whether that comparison was significantly different 
(i.e. q-value < 0.05).  The expression of individual genes are plotted in the graphs, where the grey 
bars indicate the light-dark cycle, whilst the blue, purple and red bars and lines indicate the timing 
and expression of 3-, 6-, and 12-hour sleep deprived mice, respectively. The expression of control 
animals is plotted in black. 
 
98 
 
Naturally, the power of this experiment to determine the kinetics of recovery could have been greatly 
increased by decreasing the time between timepoints. However, increasing the resolution necessarily 
results in either an increase in total samples, or reduction in total timecourse duration or replicates, 
and therefore we sought to strike a balance between these factors. One option may have been to 
include additional sampling timepoints during the 12 hour sleep deprivation period, whilst 
maintaining a 6 hour resolution between other timepoints. In this way, the acute effects of short term 
sleep deprivation as well as the rate of recovery could be better characterised.  
The biggest practical problem encountered during this project was the number of sleep deprived mice 
that were necessary. Due to the costs involved in both the generation of tissue and subsequent 
transcriptomic analyses, samples were generated and processed in two batches, the first containing 
the control and 6 hour sleep deprived mice, and the second containing the 3 and 12 hour sleep 
deprived mice. This introduced an undesirable batch effect which complicated downstream data 
analyses- whilst the overall shape of the expression profiles were similar between batches, direct 
comparison of absolute expression values between batches was problematic. Therefore, we found 
ourselves in the unenviable position of comparing the 12 hour deprivation group to the 3 hour sleep 
deprivation group for absolute gene expression. Even in hindsight, it is difficult to conclude whether 
it would have been better to process all the samples simultaneously to aid the subsequent data 
analysis, or to have progressed with caution as we chose.  
  
99 
 
3.3.8. Comparison to Adrenalectomized Mice 
Mongrain et al previously showed that a significant proportion of the transcriptional response to sleep 
deprivation in the cortex is in part coordinated by glucocorticoid signalling (Mongrain et al. 2010). By 
observing the effects of sleep deprivation on transcription in adrenalectomized mice, they identified 
a core subset of genes whose expression in the cortex is linked to sleep deprivation independent of 
glucocorticoid induction, which may be an experimental confounder or true physiological response.  
Comparing the work presented here with a list of 78 genes identified by Mongrain et al as being 
modulated by sleep deprivation and time of day, we find that 65% of those genes are also modulated 
by sleep deprivation in our study, whilst 89% of those genes showed a diurnal rhythm in expression in 
control animals (Fig 3.27.A.). Interestingly, 6 hour sleep deprivation induced only a transient change 
in expression of these genes (Fig 3.27.B.), indicating that these core genes typically demonstrate a 
binary expression profile. Since sleep homeostasis appears unaffected following adrenalectomy in 
mice, sleep homeostasis may be encoded by the expression of binary genes. It is noteworthy, 
however, that the elicited delta power increase in different strains following sleep deprivation is 
positively correlated with the magnitude of glucocorticoid induction. Whether this link is due to an 
important signalling role of glucocorticoids during sleep deprivation or due to the extra interaction 
required to maintain wakefulness at higher sleep pressure is still unclear, however.  
 
 
A B
Figure 3.27. Genes Previously Identified as Modulated by Sleep Deprivation in Adrenalectomized mice 
demonstrate Diurnal Expression and an acute response to Sleep Deprivation: The cortex expression of 
glucocorticoid independent genes identified by Mongrain et al are plotted here. The heatmaps 
indicate the z-score normalised expression of individual genes in the cortex of mice in undisturbed 
mice (A) and those subjected to 6 hour sleep deprivation, compared to undisturbed mice at the same 
timepoint (B), where blue indicates low expression and red indicates elevated expression. 
 
100 
 
3.3.9. Suggested Subsequent Experiments 
Several downstream studies are possible following this transcriptomic screen. Perhaps the most 
pressing is the EEG-characterisation of sleep patterns during and following sleep deprivation. Although 
6 hour sleep deprivation in C57/Bl6 mice is well characterised (Franken et al. 1999), the sleep rebound 
following 12 hour sleep deprivation is less understood. Similarly, although 6 hour sleep deprivation 
using the automated system that we used has been previously validated (Kaushal et al. 2012), sleep 
deprivation was difficult to maintain during the subsequent 6 hours, and was therefore applied 
through a mixture of gentle handling and automated sleep deprivation. Although supervision of the 
mice ensured that they were deprived of sleep, we have no quantitative estimate of the proportion 
of time spent asleep by the mice. Confirmation of the efficacy of sleep deprivation, as well as the time 
required for the sleep patterns of mice to recover to baseline levels, would inform the analysis of the 
transcriptomic dataset.  
This experiment extracted total RNA from homogenised mouse cortex, and so the RNA interrogated 
included contributions from different cell types and different cellular localisations. Therefore, any cell 
or location specific changes in RNA abundance are overlooked by this experimental approach. Local 
translation of mRNA occurs at dendrites and axons, and is thought to be important in axonal 
maintenance and synaptic potentiation (Verma et al. 2005; Zhang & Poo 2002). Therefore, it would 
be interesting to understand which RNA molecules accumulate specifically near synapses and in the 
soma, or in specific cell types. However, the presence of mRNA at a location does not necessarily show 
that that gene is undergoing translation. Indeed, recently it was shown that Arc mRNA is encapsulated 
in viral like particles, and released by donor neurons for translation in recipient neurons (Pastuzyn et 
al. 2018). Isolating RNA that is being actively translated in a given cell type can be achieved through a 
translating-ribosome affinity-purification approach (Heiman et al. 2014), which has previously been 
performed to characterise the effect of sleep deprivation on oligodendrocytes (Bellesi et al. 2013). 
Combining this technique with subcellular fractionation may reveal synaptic specific translation 
changes.  
During the tissue collection for this timecourse, peripheral tissues were also collected. Since the brain 
has been the major focus of studies investigating the molecular consequences of sleep deprivation, a 
similar transcriptomic screen of these peripheral tissues would provide entirely novel data. Previous 
studies indicate that gene expression in peripheral tissues is modulated by sleep deprivation (Anafi et 
al. 2013; Maret et al. 2007), whilst muscle has recently been implicated in the control of sleep duration 
(Ehlen et al. 2017). Understanding which pathways are activated during sleep deprivation in these 
tissues may therefore provide clues about the extent the periphery controls sleep timing and duration, 
101 
 
and possibly identify pathological pathways that are activated during sleep deprivation. Whether 
peripheral tissues experience similar disruption to rhythmic expression as found in the cortex may be 
particularly interesting. Tissues exhibit different rates of recovery from jetlag (Yamazaki et al. 2000), 
and so sleep deprivation may also induce the desynchrony that is thought to underlie the negative 
effects of jetlag (Vosko et al. 2010).  
The different shaped expression profiles of wake dependent genes surely reflect differences in control 
of transcription and degradation. Having identified genes with a homeostatic and binary expression 
profile, one particularly interesting downstream experiment may be to investigate what transcription 
factors bind to the promotor regions of those genes, and whether a handful of transcription factors 
are enriched amongst those that bind those sites. The involvement of those transcription factors 
during sleep deprivation could be confirmed through either identifying their localisation or 
phosphorylation status through immunofluorescence or western blotting, or by directly quantifying 
the DNA-binding patterns through chromatin immunoprecipitation sequencing (ChIP-Seq). Any 
transcription factor implicated as binding in response to sleep deprivation could be further 
investigated by characterising the effect on sleep of the conditional knockout or the overexpression 
of a constitutively active form of that factor.  
The intuitive experiment to follow transcriptomic profiling, however, is the proteomic profiling of 
similar samples. Because the most apparent function of mRNA molecules is to code for the 
corresponding protein, it could be argued that changes occurring at the protein level are far more 
relevant to the ultimate cellular processes occurring within the cell.  
  
102 
 
 
 
 
 
 
 
4. The Proteomic and 
Metabolomic Impact of Sleep 
Deprivation on Mouse Cortex 
 
This section details our experiments to better understand the proteomic and metabolomic effects of 
sleep deprivation in mouse cortex. Using a similar experimental design as our transcriptomic 
experiments, we find that the abundance of hundreds of proteins is modulated by 12-hour sleep 
deprivation, including several synaptic proteins. We also find a prolonged decrease in abundance of 
many ribosomal protein subunits, suggesting global protein synthesis may be depressed following 
sleep deprivation. In contrast, we identify relatively few metabolites whose abundance are dependent 
on time of day or sleep deprivation. 
  
103 
 
4.1. Proteomic Profiling of Sleep Deprived Mouse Cortex 
To identify to what extent transcriptomic changes are reflected at the protein level, we carried out 
TMT-based proteomic analysis of cortex from mice that had been sleep-deprived for 12 hours. At the 
time of running the experiment, 10 TMT tags were available, and therefore the maximum number of 
samples able to be compared within one multiplex was 10. Due to the technical difficulties of 
comparing between different ten-plex experiments, four separate ten-plex experiments were carried 
out. The first involved comparing individual biological replicates, using 3 replicates from 12 hours 
before the onset of sleep-deprivation, 4 replicates from immediately after 12 hours sleep deprivation, 
and 3 replicates from 24 hours after the cessation of sleep deprivation. These three timepoints were 
separated by 24 hours (Fig 4.1.A). The same ten-plex was carried out on non-sleep deprived animals. 
A second pair of ten-plex experiments was carried out where protein from 4 individual biological 
replicates at 10 timepoints spaced 6 hours apart was pooled to a single technical replicate (Fig 4.1.B).  
 
Control
12hr SD
18:00 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00
Day             
1
Day             
2
Day             
3
TMT
1,2,3
TMT
4,5,6,7
TMT
8,9,10
Control
12hr SD
18:00 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00
Day             
1
Day             
2
Day             
3
TMT
1
TMT
2
TMT
3
TMT
4
TMT
5
TMT
6
TMT
7
TMT
8
TMT
9
TMT
10
A
B
Figure 4.1. Experimental Design for Proteomic Characterisation of Sleep Deprivation in Mice: 
Protein from individual timepoints were subjected to proteomic based analyses. In one pair of 
experiments, protein from tissue collected at 18:00 on Day 1, Day 2 or Day 3 formed the ten-plex. 
In a second pair of experiments, protein was pooled from biological replicates at each of the 
10 timepoints, spaced 6 hours apart. The red section represents sleep deprivation, whilst the 
black and white bar represents the light-dark cycle imposed on the mice. 
104 
 
5357 proteins were identified in mice with uninterrupted sleep from the ten-plex designed as 
Figure 4.1.A. There were relatively few proteins that were statistically differentially expressed 
between the samples collected at the onset of the dark phase on Day 1, Day 2 and Day 3. Pairwise 
comparison between Day 1 and Day 2, Day 1 and Day 3, and Day 2 and Day 3 revealed a total of 27, 0 
and 6 proteins, respectively, were statistically differentially expressed. 7 of the 9 proteins whose 
abundance was significantly higher in samples collected on Day 2 than Day 1 were serum proteins 
(bikunin, Complement C3, haptoglobin, hemopexin, inter alpha-trypsin inhibitor, orosomucoid 1, 
serum amyloid A), indicating that the samples collected on Day 2 had more blood contamination than 
those collected on Day 1. Consistent with this conclusion, the 5 proteins whose abundance was higher 
on Day 2 compared to Day 3 were all serum proteins (Complement C3, fibrinogen alpha, fibrinogen 
beta, hemopexin and orosomucoid 1).  
 
 
Figure 4.2. 12-hour Sleep Deprivation Induces changes in the Abundance of Hundreds of Proteins in 
Mouse Cortex: Protein from mouse cortex was collected at the end of the light phase over 3 days 
and subjected to TMT-based proteomic analyses. Control mice were allowed ad libitum sleep, 
whereas the sleep deprived group were subjected to 12-hour sleep deprivation during the entirety 
of the light phase on Day 2. FDR adjusted student t-tests between groups identified proteins 
whose abundance significantly changed. The blue bars plotted above represent the number of 
proteins that significantly differed in the control group in the respective comparison, whereas the 
red bars represent the number of proteins that differed in the 12-hour sleep deprived group.  
105 
 
In contrast to non-sleep deprived samples, of the 5600 proteins identified, the abundance of a total 
of 177, 130 and 260 proteins was statistically different between cortex samples collected from sleep 
deprived mice on Day 1 and Day 2, Day 1 and Day 3, and Day 2 and Day 3, respectively. Among the 
total of 425 proteins that exhibited a significant change in abundance across at least one of these 
three comparisons, there was an enrichment in proteins involved with synapses, the cytosolic large 
ribosomal subunit, microtubules, the mitochondrial inner membrane, motor proteins, symport, 
phosphodiesterase function, proteins with C2 domains or EF-hand domains, and complement and 
coagulation cascades. 
 
Microtubule
Synapse 
Proteins
C2 Domain 
Proteins
Control SD
106 
 
EF Hand 
Proteins
Mitochondrial 
Inner 
Membrane
Histones
Ribosomal 
Proteins
Phospho-
diesterase
Symport 
Proteins
SDControl
Figure 4.3. TMT-based Proteomics reveals Abundance Changes in Proteins relating to Microtubules, 
Synapses, Calcium Binding Proteins, Histones, Mitochondrial Function, Phosphodiesterases, 
Ribosomes and Transport : Protein from mouse cortex was collected at the end of the light phase 
over 3 days and subjected to TMT-based proteomic analyses. Control mice were allowed ad libitum 
sleep, whereas the sleep deprived group were subjected to 12-hour sleep deprivation during the 
entirety of the light phase on Day 2. FDR adjusted student t-tests between groups identified proteins 
whose abundance significantly changed, and enriched functional  groups identified using DAVID 
functional annotation. Functional clusters are plotted above, with the functional group indicated. 
The Z-score normalised abundance of a protein in individual biological replicates is indicated by the 
colour, where red indicates a high abundance and blue indicates a low abundance. The left heatmap 
shows replicate data from 3 timepoints. The leftmost section indicates samples collected Day 1 (no 
107 
 
 
Of the 66 proteins upregulated following 12 hours sleep deprivation, there was a statistical 
enrichment of proteins involved containing the calcium binding EFh domain, proteins associated with 
phosphodiesterase activity and proteins associated with dopaminergic synapses. In contrast, the 110 
proteins that were downregulated following 12 hour sleep deprivation were enriched in synapse 
proteins, cytosolic large ribosomal subunits, and proteins associated with symport and nucleosomes. 
The abundance of proteins associated with blood microparticles was also significantly reduced, 
indicating that the samples collected on Day 1 had more blood contamination than those collected 
immediately following sleep deprivation.  
 
 
 
       
          
       
                  
                                 
                    
              
                                    
     
         
          
  
  
  
  
  
     
       
sleep deprivation), the middle section indicates samples collected Day 2 (immediately following 
12-hour sleep deprivation), and the rightmost section indicates samples collected Day 3 (following 
12-hour sleep deprivation and 24 hour recovery). The right heatmaps show pooled data from the 
control and sleep deprived timecourses. The black and white bars represent the light dark cycle, 
whilst the red bar indicates the timing of sleep deprivation. Proteins that fall into multiple group are 
re-plotted in each group. Note that not all proteins detected in the replicate dataset were detected 
in the timecourse datasets.  
 
Figure 4.4. Protein Classes Differentially Expressed in Mouse Cortex following 12-hour Sleep 
Deprivation Compared to Mice Sacrificed without Sleep Deprivation: Proteins identified as 
modulated immediately following 12 hour sleep deprivation compared to non-sleep deprived 
mice were subjected to functional annotation, and enriched gene classes displayed above. The 
enrichment of genes is indicated by the width of each bar, whilst the q-value is indicated by the 
colour of the bar. 
108 
 
The 140 proteins that exhibited significantly lower abundance following 24 hours recovery from sleep 
deprivation were enriched in proteins associated with proteins with EF hand domains, 
phosphodiesterase activity, and those associated with dopaminergic synapses. Therefore it appears 
that those classes that had been upregulated during sleep deprivation reduced toward baseline levels 
during subsequent recovery. Remarkably, following 24-hour recovery, downregulated proteins are 
enriched with ribosomal proteins, including the cytosolic large ribosomal subunit proteins. Proteins 
that were downregulated following 24 hours recovery were also enriched in mitochondrial inner 
membrane proteins, including Complex I subunits and ATP Synthase components. Proteins 
upregulated following 24-hour recovery were enriched in proteins associated with blood, indicating 
that there was more blood contamination in the samples collected following 24-hour recovery than 
after 12-hour sleep deprivation.  
 
Comparison between mice sacrificed on Day 1 and those that had been sacrificed on Day 3 revealed 
that 73 proteins exhibit reduced abundance following 12-hour sleep deprivation and 24-hour 
recovery. The downregulated proteins were enriched in ribosomal proteins, especially those 
associated with the large ribosomal subunit; synapse proteins, including those associated with 
excitatory synapses, and genes associated with nucleosome function.  The 57 proteins whose 
       
                           
               
                 
                 
                 
                  
    
         
          
  
  
  
  
  
     
       
Figure 4.5. Protein Classes Differentially Expressed in Mouse Cortex following 12-hour Sleep 
Deprivation Compared to Mice Sacrificed following 24 hour Recovery from 12-hour Sleep 
Deprivation: Proteins identified as modulated immediately following 12 hour sleep deprivation 
compared to mice allowed 24 hours recovery were subjected to functional annotation, and 
enriched gene classes displayed above. The enrichment of genes is indicated by the width of each 
bar, whilst the q-value is indicated by the colour of the bar. 
109 
 
abundance was increased following 12 hour sleep deprivation and 24 hour recovery compared to 
pre-sleep deprivation mice were enriched only in proteins associated with the cytoskeleton. 
 
 
 
To determine to what extent changes at the transcript level are reflected by changes in protein 
abundance, the abundance of proteins encoded by genes identified as exhibiting a homeostatic or 
stress profile were plotted. Approximately 40% of the proteins encoded by the transcripts of 
interested were detected in our proteomics screen. Of the 9 proteins detected from homeostatic 
transcripts, only Homer1 showed a significant difference in abundance following sleep deprivation, 
specifically between the pre-sleep deprivation timepoint and following 24 hour recovery 
(q-value = 0.047). Remarkably, despite the Homer1a transcript increasing during sleep deprivation 
and recovering to baseline within 24 hours, Homer1 protein abundance trends downward 
immediately following sleep deprivation (q-value = 0.07) and remains lower over the following 
24 hours. A similar abundance profile is suggested by the circadian ten-plex. Of the stress profile 
genes, only LSAMP protein was significantly different following sleep deprivation, which showed a 
significant decrease on Day 3 as compared to Day 2 (q-value=0.029).  
 
          
       
                                 
                  
            
   
         
          
  
  
  
  
  
     
       
Figure 4.6. Protein Classes Differentially Expressed in Mouse Cortex following 12-hour Sleep 
Deprivation and 24-hour Recovery Compared to Mice Sacrificed without Sleep Deprivation: Proteins 
identified as modulated following 24 hour recovery from 12 hour sleep deprivation compared to 
non-sleep deprived mice were subjected to functional annotation, and enriched gene classes 
displayed above. The enrichment of genes is indicated by the width of each bar, whilst the q-value 
is indicated by the colour of the bar. 
110 
 
 
  
Day
1
Day
2
Day
3
Day
1
Day
2
Day
3
Control SD
Control SD
Figure 4.7. Heatmap of Proteins whose Transcript was Identified as Exhibiting a Homeostatic or 
Stress Profile: The abundance of detected proteins coded for by sleep dependent transcripts are 
plotted above, with homeostatic genes plotted in the upper set of heatmaps, and stress profile 
genes plotted in the lower set. The Z-score normalised abundance of protein is indicated by the 
colour, where red indicates a high abundance and blue indicates a low abundance. The leftmost 
tiles represent protein replicate data, taken from mice on Day 1 at 18:00 (no sleep deprivation), 
Day 2 (immediately following 12-hour sleep deprivation), and Day 3 (following 12-hour sleep 
deprivation). The rightmost maps show protein abundance from Control and Sleep Deprived mice 
collected 6 hours apart, where sleep deprivation takes place immediately following the third 
timepoint and ends immediately following the fifth timepoint.  
111 
 
4.2. Proteomic Changes consistent with Reduced Neuronal Excitability 
following Long Term Sleep Deprivation 
Previous publications have extensively investigated the transcriptomic changes associated with sleep 
deprivation. In contrast, the proteomic changes associated with the state of wakefulness of animals is 
very poorly characterised. In this work we present two sets of TMT-based proteomic experiments: one 
set examined only three timepoints with biological replicates, whilst the second generated a 
timecourse style dataset, with only single replicate data. In this way we were able to statistically 
identify proteins whose abundance was affected by sleep deprivation, and then to qualitatively 
understand how their abundance profile changes over time. 
The proteins whose abundance was significantly increased immediately following sleep deprivation 
were enriched in proteins whose function with phosphodiesterase function, whilst a reduced 
abundance of several synaptic proteins was also observed. An increased activity of 
phosphodiesterases such as PDE1a, PDE1b, PDE2a and PDE10a would be expected to reduce the 
intracellular abundance of cAMP and cGMP, dampening excitatory signalling pathways. Synaptic 
proteins downregulated included subunits of the NMDA (Gria2), AMPA (Grin1) and metabotropic 
glutamate receptor (Grm2) classes, the scaffold protein Homer1, as well as subunits of GABA 
symporters (SLC6A1, SLC6A11). Gria2 and Grin1 function in excitatory ionotropic glutamate signalling, 
whereas Homer1 promotes the release of intracellular calcium stores in response to metabotropic 
glutamate receptor activation. In contrast, GABA symporters act to rapidly reduce the concentration 
of extracellular GABA, leading to the cessation of inhibitory signalling. Therefore, immediately 
following sleep deprivation, it appears that the abundance changes of several proteins would be 
expected to dampen excitatory signalling pathways and enhance inhibitory pathways in the cortex. 
Although these protein changes appear consistent with the concept of local sleep and activity induced 
inhibition of neuronal signalling, sleep deprivation followed by subsequent experimental 
measurement of cortical excitability in humans and rats indicate that neurones become more 
excitable following sleep deprivation, not less (Huber et al. 2013; Vyazovskiy et al. 2009; Yan et al. 
2011). One possible explanation for this apparent contradiction is that changes in localisation or 
phosphorylation status of excitatory pathway constituents may offset the increase in abundance of 
inhibitory proteins. Another explanation may account the differences to differing experimental design, 
and that short-term sleep deprivation (e.g. 4 hours) has markedly different consequences for neuronal 
excitability than the longer term 12-hour sleep deprivation applied here. Consistent with this concept, 
spontaneous wakefulness in flies leads to increased neuronal activity, whilst prolonged sleep 
deprivation (29 hours wakefulness) eventually reduces neuronal activity and responsiveness (Bushey 
et al. 2015).  
112 
 
4.3. Proteomic Changes Consistent with Reduced Protein Synthesis and Cell 
Replication following Sleep Deprivation  
As well as proteins related to neuronal firing being modulated by sleep deprivation, histones and 
protein ribosomal subunits are downregulated immediately following 12 hour sleep deprivation. 
Reduced histone abundance may indicate a reduction in cell division following sleep deprivation, 
which has previously been reported for cells in the hippocampus and cortical oligodendrocytes (Bellesi 
et al. 2013; Guzmán-Marín et al. 2003; Murata et al. 2017). Reduced ribosomal protein abundance 
may indicate a general suppression in protein synthesis, consistent with the hypothesis that sleep 
promotes anabolic pathways within the brain. Indeed, sleep deprivation has previously been shown 
to inhibit central protein synthesis in rodents (Naidoo et al. 2005; Ramm & Smith 1990; Tudor et al. 
2016).  
Intriguingly, the abundance of ribosomal and nucleosomal proteins remains reduced even following 
24 hours recovery sleep opportunity, whilst the timecourse style dataset indicates that the abundance 
of these proteins remains lower than that of non-sleep deprived animals at all timepoints sampled 
following sleep deprivation. Therefore, total sleep deprivation may impair protein synthesis and cell 
division for up to two days or more, which may in turn have consequences for plasticity and learning.  
4.4. Poor Overlap Between Proteomic and Transcriptomic Changes 
Because we had access to a transcriptomic dataset of similar experimental design, we were able 
compare the abundance profile of detected proteins to the expression of the associated transcript. 
However, of the detected proteins identified as exhibiting a homeostatic or stress profile at the 
transcript level, only two (Homer1 and LSAMP) exhibited significantly different protein abundance 
following sleep deprivation. Remarkably, although sleep deprivation induces both Homer1a and 
LSAMP transcript expression, the abundance of the corresponding proteins appears to fall following 
sleep deprivation. In the case of Homer1, this may be driven by a decrease in the full length Homer1 
protein, whose transcript abundance is comparatively unaffected by sleep deprivation. Overall 
therefore, the correlation between protein abundance and transcript profile is poor. The general lack 
of strong correlation between transcriptomic and proteomic responses has been previously reported 
(Ghazalpour et al. 2011), and may implicate post-transcriptional processes as an important regulator 
of protein abundance. Similarly, in the context of reduced ribosomal protein availability, an increase 
in transcript abundance may be required to maintain a constant level of short half-life proteins.  
113 
 
4.5. Suggested Further Experiments to Interrogate the Proteomic Effect of 
Sleep Deprivation 
There is currently a relative absence of published proteomic studies investigating the effects of sleep 
deprivation in mammalian brain, and so there are several avenues for future research. Proteins are 
subject to several post-translational modifications, which can drastically change their function, 
localisation or target them for degradation. Because the synthesis of new proteins is suppressed 
during wakefulness compared to sleep (Naidoo et al. 2005), modulating the activity of proteins already 
present in the cell through phosphorylation may be the major pathway through which cells adapt to 
sleep deprivation. A phosphoproteomic screen of sleep deprived mouse brain, which can be achieved 
through the titanium dioxide enrichment of phosphopeptides before TMT-tag labelling (Possemato et 
al. 2017), may therefore provide insight into which signalling pathways are activated during prolonged 
wakefulness. Immunoaffinity based approaches can similarly be used to enrich proteins tagged with 
ubiquitin prior to mass-spectrometry based quantification (Schwertman et al. 2013), which may 
indicate proteins targeted for degradation during sleep deprivation. Local accumulation or depletion 
of proteins may play an important role in the regulation of sleep-wake cycles, yet be overlooked by 
global proteomics approaches. Subcellular fractionation of tissue homogenate, followed by proteomic 
interrogation could reveal important cellular events such as translocation of transcription factors to 
the nucleus, release of neuropeptides or neurotrophic factors into the extracellular space, or 
translocation of receptors to synapses.  
  
114 
 
4.6. Metabolomic Profiling of Sleep Deprived Mouse Cortex 
The diverse transcriptomic and proteomic changes induced during sleep deprivation included several 
synaptic genes and proteins, indicating that cell-to-cell communication is modulated by sleep 
deprivation. We therefore hypothesised that the abundance of small molecule messengers released 
at synapses, such as glutamate, acetylcholine and histamine, may too be modified by sleep 
deprivation. Similarly, both transcriptomic and proteomic profiling of sleep deprived mice indicated 
an effect of sleep deprivation on ribosomes and mitochondrial oxidative phosphorylation, which is 
consistent with the hypothesis that the sleep-wake cycle may represent different stages of anabolism 
and catabolism within the brain. We therefore hypothesised that the abundance of molecules relating 
to energy production and macromolecule synthesis would be modulated by sleep deprivation and give 
insight into the metabolic state of the cell during wakefulness and sleep deprivation. We therefore 
carried out metabolomic analyses of sleep deprived mouse cortex using liquid-chromatography mass 
spectrometry (LC-MS). Using this technology, we were able to characterise the abundance of polar 
molecules, but not hydrophobic molecules such as long chain fatty acids. To also characterise the 
effects of recovery, we carried out a timecourse to collect tissue from mice that had been sleep 
deprived for 12 hours, with tissue from 3 mice being collected at each timepoint.  
 
Initially we carried out targeted metabolomic analysis to quantify the abundance of approximately 
60 specific molecules in mouse cortex, however we were only able to identify and quantify 47 of these 
metabolites. Amongst the metabolites that we could not reliably quantify in our targeted analyses 
were neurotransmitters (e.g. dopamine, histamine) and pentose-phosphate pathway intermediates 
(e.g. ribulose 5-phosphate, ribose 5-phosphate). The abundance of molecules involved in glucose 
metabolism (e.g. glycolytic, pentose phosphate pathway and citric acid cycle intermediates), carnitine 
Control
12hr SD
18:00 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00
Day             
1
Day             
2
Day             
3
TMT
1,2,3
TMT
4,5,6,7
TMT
8,9,10
Figure 4.8. Experimental Design for Metabolic Characterisation of Sleep Deprivation in Mice: Cortex 
was collected from two groups of mice every 6 hours for a total of 54 hours. One group of mice 
had ad libitum sleep, whilst the other group was sleep deprived during the entire light phase of 
Day 2 (indicated by red boxes). Mice were trio housed and a single cage taken per timepoint to 
provide n=3 per timepoint. Whole cortex was removed whilst fresh, frozen on dry ice, and 
subsequently polar molecules were extracted and quantified.  
115 
 
conjugates and amino acids were quantified in order to identify whether there were changes in 
glucose utilisation, fatty acid oxidation or protein synthesis, respectively. ANOVA analysis was carried 
out on the abundance profile of molecules, and student t-tests were performed to compare individual 
timepoints between treatment groups. Remarkably however, no molecule exhibited statistically 
significant differences (i.e. adjusted p value < 0.05) in abundance as a result of 12-hour sleep 
deprivation, following correction for multiple testing.  
 
Figure 4.9. No Statistical Change in Metabolite Abundance was Identified through Targeted 
Metabolite Analysis: Cortex was collected from two groups of mice every 6 hours for a total of 
54 hours. The black and white bars represent the light dark cycle. One group of mice had ad 
libitum sleep, whilst the other group was sleep deprived during the entire light phase of Day 2 
(indicated by red boxes). Polar molecules were extracted and quantified through liquid 
chromatography-mass spectrometry, normalised to the number of ions detected from that 
sample, and the Z-score normalised replicates plotted above, where red indicates high abundance 
and blue represents low abundance. Control mice are plotted on the left plot, whilst mice 
subjected to sleep deprivation are plotted on the right plot. Molecules are grouped by their 
functional class and then alphabetically. Metabolite names are indicated to the right of the figure, 
whilst ESI (electrospray ionisation) indicates whether the detected ion was positively or negatively 
charged. 
116 
 
Aspartate was visually identified as a molecule fitting the idealised homeostatic profile, as it appeared 
to peak at the end of the dark phase in control mice, but continued to rise during sleep deprivation 
(see Fig 4.10.). Similarly, some other amino acids (arginine, methionine, tryptophan and tyrosine) 
appeared to show relatively large changes following 12 hour sleep deprivation, but were not 
statistically significant following correction for multiple testing. Similarly, adenosine did not show a 
statistical difference in expression profile or abundance following 12 hour sleep deprivation. Overall, 
visual inspection of individual molecular abundance profiles appeared to indicate that the variation in 
abundance of molecules between timepoints and treatment groups was usually low, which combined 
with a small replicate number (n=3) and multiple testing, likely severely limited the ability of this 
experiment to identify molecules whose abundance was dependent on the sleep wake cycle. 
 
 
  
Figure 4.10. Individual Abundance Plots of Adenosine, Arginine, Aspartate, Methionine, Tryptophan 
and Tyrosine: The abundance of adenosine, arginine, aspartate, methionine, tryptophan and 
tyrosine in samples extracted from mouse cortex are plotted above. Data from mice with access 
to ad libitum sleep are plotted in blue, whilst those subjected to sleep deprivation are plotted in 
red. The grey bars represent the dark phase, whilst the pink section indicates the sleep deprivation 
period.  
117 
 
We also carried out untargeted analyses, whereby individual peaks corresponding to different ions 
are quantified in each sample, allowing a hypothesis free approach. Following quantification of peaks, 
identification of the molecules that those peaks correspond to can be attempted based on the 
molecular mass and charge of the ion. Of the 624 peaks that were assigned at least one molecular 
identity and was found in each sample, JTK analysis identified 3 peaks whose abundance oscillated 
with a 24-hour rhythm. A total of 8 peaks were identified as having a significantly different abundance 
profile following adjustment for multiple tests, including all 3 of the rhythmic peaks. Identification of 
the peaks indicated that the rhythmic molecules were possibly nicotinic acid derivatives and inosine 
monophosphate (IMP).  
 
Table 14: Predicted Molecular Identities of Peaks Modified by Sleep Deprivation 
Peak 
m/z Ratio 
Rhythmic 
q-value 
ANOVA 
q-value 
Possible Molecular 
Identities 
153.066 0.017 0.0048 
N-methyl-pyridone-carboxamide,  
N-(Hydroxymethyl)nicotinamide 
154.050 0.00049 0.028 
Amino-hydroxybenzoic acid, 
Hydroxy-methylnicotinic acid, 
3-Hydroxyanthranilic acid 
347.039 0.024 0.028 
Inosinic acid (IMP), 
5-Formamidoimidazole-4-carboxamide ribotide (FAICAR), 
Mannopyranosyloxy-phosphonooxy-propanoic acid 
141.066 0.72         0.024 
Isonicotinic acid, 
Imidazolepropionic acid, 
Methylimidazoleacetic acid, 
1,3-dimethyluracil, 
Niacin, 
Picolinic Acid 
148.044 1.0 0.044 Methionine 
165.077 1.0 0.0037 Deoxy -mannitol 
169.062 1.0 0.031 
Glycyl-4-hydroxyproline, 
N-Acetylglutamine 
296.082 0.96 0.0080 Methylthioadenosine 
118 
 
 
   
  
     
     
     
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
          
     
     
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
           
     
     
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
           
     
     
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
           
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
           
     
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
           
       
       
       
       
       
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
           
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
           
Figure 4.11. Individual Abundance Plots of Peaks Identified as Sleep-Wake dependent: The 
abundance of peaks identified as significantly disrupted by sleep deprivation by ANOVA analysis 
of untargeted metabolomic profiling of mouse cortex are plotted above. Data from mice with 
access to ad libitum sleep are plotted in blue, whilst those subjected to sleep deprivation are 
plotted in red. The grey bars represent the dark phase, whilst the pink section indicates the sleep 
deprivation period. 
119 
 
4.7. Metabolomic Profiling Implicates 3 Molecular Peaks as Potential 
Homeostats 
Our metabolomic analyses of polar molecules within the cortex identified a total of 8 molecular peaks 
whose abundance profile was significantly affected by 12-hour sleep deprivation. Remarkably, the 
mass to charge ratio of three of these peaks match the expected masses of different nicotinamide 
molecules. Furthermore, the abundance of the molecules tentatively identified as hydroxymethyl-
nicotinamide and hydroxymethyl-nicotinic acid demonstrated significant 24 hour oscillations in 
control animals, peaking at the end of the rest phase. In contrast, sleep deprived mice demonstrate a 
blunted increase of these same molecules during sleep deprivation, followed by an increase, rather 
than decrease, in abundance during the second half of the subsequent dark phase. Therefore, these 
molecules appear to demonstrate an abundance profile expected from a homeostatic molecule. 
Intriguingly, the molecule assigned as nicotinic acid demonstrates an approximately opposite pattern. 
Although the abundance of the molecule was not statistically rhythmic in control mice, visually the 
abundance appears to be rhythmic which peaks at the end of the active phase. Remarkably, the 
abundance of this molecule remains elevated during sleep deprivation, and so would appear to also 
demonstrate a homeostatic profile, but one which mirrors the profile of the hydroxymethyl-
nicotinamide derivatives.  
Table 15: Predicted Molecular Identities of Potential Small Molecule Homeostats 
Peak 
m/z Ratio 
Rhythmic 
q-value 
ANOVA 
q-value 
Possible Molecular 
Identities 
141.066 0.72         0.024 
Isonicotinic acid, 
Methylimidazoleacetic acid, 
Imidazolepropionic acid, 
1,3-dimethyluracil, 
Niacin, 
Picolinic Acid 
153.066 0.017 0.0048 
N-methyl-pyridone-carboxamide,  
N-(Hydroxymethyl)nicotinamide 
154.050 0.00049 0.028 
Amino-hydroxybenzoic acid, 
Hydroxy-methylnicotinic acid, 
3-Hydroxyanthranilic acid 
120 
 
 
Since methyl-pyridone-carboxamides are breakdown products of nicotinic acid, one interpretation of 
these mirrored profiles may be that the degradation of nicotinic acid is linked to sleep, and is inhibited 
during normal wake and sleep deprivation. However, the biological implications of this would be 
unclear, because nicotinic acid derivatives include both nicotinamide adenine dinucleotide (NAD) and 
nicotinamide adenine dinucleotide-phosphate (NADPH). Whilst NADH is involved in a range of 
catabolic processes (e.g. glycolysis, citric acid cycle, β-oxidation of fatty acids), the NADPH pool is 
typically participates in anabolic pathways and other pathways requiring strong reducing equivalents 
(e.g. fatty acid synthesis, reduction of glutathione).  
     
     
     
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
          
     
     
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
           
     
     
     
                   
             
 
 
  
 
   
 
 
  
 
 
 
 
 
 
 
 
           
Figure 4.12. Individual Abundance Plots of Peaks Resembling a Homeostatic Abundance Profile: The 
abundance of 3 peaks appearing rhythmic in control animals and significantly disrupted by sleep 
deprivation are plotted above. Data from mice with access to ad libitum sleep are plotted in blue, 
whilst those subjected to sleep deprivation are plotted in red. The grey bars represent the dark 
phase, whilst the pink section indicates the sleep deprivation period. Information about their mass 
to charge (m/z) ratio, whether the abundance oscillates with a 24 hour rhythm in control mice 
and possible molecular identities, are outlined in the table.  
121 
 
 
However, it is important to recall that these molecular assignments have been performed on the basis 
of the mass to charge ratio only, and therefore several chemical isomers are equally valid assignments. 
Indeed, the peak tentatively assigned as nicotinic acid may instead be methylimidazoleacetate, 
imidazolepropionate, dimethyluracil or picolinic acid, the products of histamine, histidine, 
methylxanthine and tryptophan breakdown, respectively. Similarly, the peak at (m/z=154.050) may 
equally be assigned as 3-hydroxyanthranilic acid, another product of tryptophan breakdown, and a 
precursor to picolinic acid. Therefore, another equally valid interpretation of the mirrored peak 
abundance profiles may be that the kynurenine pathway, through which both 3-hydroxyanthranilic 
acid and picolinic acid are produced, is dependent on the sleep-wake cycle. This possibility is intriguing, 
not least because another product of the kynurenine pathway is the potent NMDA-receptor agonist, 
quinolinic acid, which has previously been linked to sleep and to reduce sleep duration following 
intracerebroventricular injection (Cho et al. 2018; Milaśius et al. 1990). Further work is therefore 
required to determine which molecular assignments, if any, are correct.  
  
Nicotinic 
Acid
Nicotinamide
N-methyl-
Nicotinamide
N1-Methyl-4-
pyridone-3-
carboxamide
N1-Methyl-2-
pyridone-5-
carboxamide
Figure 4.13. Sleep Deprivation may Reduce the Breakdown of Nicotinic Acid: Molecules whose 
abundance demonstrated a homeostatic profile may be involved in the breakdown of nicotinic 
acid. One possible identification for the peak at m/z=141.066 is nicotinic acid, which is high during 
the habitual active phase, low during the habitual rest phase, and elevated during sleep 
deprivation. Intriguingly, one possible identification of the peak at m/z=153.066 is 
N-methyl-pyridone-carboxamide, which is a breakdown product of nicotinic acid.  
122 
 
4.8. The Global Effect of Sleep Deprivation on Cortex Metabolites appears 
modest 
Overall however, the number of molecules demonstrating significant oscillations in control animals or 
that were identified as dependent on the wake state of the animal was low, especially when compared 
to the transcriptomic and proteomic datasets presented in this thesis. One possible explanation is 
simply that these findings reflect the biological reality, and that the abundance of only a handful of 
polar metabolites show variations large enough to be statistically identified with three replicates, 
following correction for multiple testing. A second possibility is that an artefact of the experimental 
design masked real changes occurring at the metabolite level. For example, animals are inevitably 
awake during the period leading up to tissue collection, independent of the time of day or previous 
sleep deprivation, due to the transfer and handling of them and their cagemates. Therefore, any 
molecule whose abundance rapidly changes in response to the current sleep-wake state of the animal 
would likely not be identified as wake dependent, because at the time of sacrifice those molecules 
would all be in their “wake” concentration. 
Comparison of previous work to the data presented here is hindered by the almost complete absence 
of published metabolomic data originating from sleep deprived brain tissue. The lack of published 
studies may be consistent with attempted studies yielding no positive results. The only metabolomic 
data of 6 hour sleep deprived mouse cortex we are aware of is presented in Hinard et al (Hinard et al. 
2012). In this publication, the authors conclude a role for sleep is membrane homeostasis, because 
the majority of the handful of metabolites identified as increased during sleep deprivation were 
lysolipids. However, because our approach only quantified polar metabolites, we were unable to 
determine the abundance of the hydrophobic lysolipids. Of the polar molecules detected, Hinard et al 
found increases in alanine and lactate, aspartate, methylimidazoleacetate and 4-hydroxybutyrate, and 
decreases in the abundance of methylthioadenosine and pantothenate, although the fold-changes 
observed was not reported. Our targeted metabolomics quantified alanine, but found very little 
variation between time points or sleep deprivation, whilst we were unable to quantify lactate through 
targeted metabolomics. Intriguingly, our targeted approach identified aspartate as trending toward a 
wake dependent abundance profile, whereas methylimidazoleacetate is a potential identity of the 
wake dependent peak at m/z=141.066. Methylthioadenosine was also statistically implicated as a 
potential wake dependent metabolite in this study, however the abundance profile in control animals 
is unusual, with great variation between timepoints in the latter half of the timecourse but almost 
none in first half.  
Adenosine is perhaps the best characterised small molecule somnogen, and infusion of adenosine 
receptor agonists and antagonists into wake controlling centres has previously been shown to induce 
123 
 
and reduce sleep, respectively (Porkka-Heiskanen et al. 1997; Scammell et al. 2001; Schwierin et al. 
1996; Zong‐Yuan et al. 2005). Release of adenosine in an activity dependent manner has been 
reported from in vitro studies (J. & Nicholas 2007), whilst a microdialysis study carried out on cats 
indicated that extracellular adenosine in the cortex increases by approximately 20% during sleep 
deprivation (Porkka-Heiskanen et al. 2000). Our experiments indicate that adenosine levels show 
relatively little variation with time or sleep deprivation. Similarly, adenosine is not identified as a sleep 
dependent molecule by Hinard et al (Hinard et al. 2012). This discrepancy may reflect biological 
variation between rodents and cats. Another possibility is that whilst microdialysis based studies 
strictly measure the abundance of extracellular metabolites, mass spectrometry based analysis of 
tissue homogenate measures the combined abundance of both intracellular and extracellular 
metabolites. Therefore, discrepancies between microdialysis and mass spectrometry based studies 
may reflect changes in the localisation rather than the abundance of metabolites.  
 
4.9. Further Experiments to Understand the Metabolomic Impact of Sleep 
Deprivation 
Further metabolomic studies include confirming the identification of these molecules through the 
targeted tandem mass spectrometry of these peaks and comparison of the molecular fragments with 
the fragmentation spectrum derived from purchased standards. Following a firm identification of a 
sleep related molecule, that molecule could be intracerebrally injected and its effects on sleep 
determined through EEG analyses. Alternatively, the effect of the infusion of inhibitors of the relevant 
pathway could be determined.  
An alternative approach to determining what aspects of metabolism are important in the regulation 
of sleep is to screen the effect of several small molecule inhibitors of metabolic pathways for a sleep 
phenotype. Having identified a small molecule inhibitor that affects sleep duration or timing, the 
implicated pathway could then be investigated. Although an untargeted screen is possible in mice, the 
cost of housing, EEG implantation and surgery required for infusion of small molecules would hinder 
the discovery of sleep modulating drugs. One possibility is to perform a drug screen in Drosophila by 
introducing drugs into their food, however controlling the precise timing of ingestion and the dosage 
of drug is difficult. Ideally, a large scale drugs screen would involve the use of a mammalian cell line 
model, because cell lines not only offer a mammalian system whose environment is easily controlled, 
but also are far less costly and more easily scaled than mouse colonies. However, no widely available 
cell line based model of sleep deprivation currently exists. 
  
124 
 
 
 
 
 
 
 
5. Modelling Sleep 
Deprivation in vitro 
 
This section details our efforts to create an in vitro model of sleep deprivation using a human derived 
neuroblastoma cell line. In vitro models are often lower cost, higher throughput and  less regulated 
than mammalian models, facilitating high-throughput studies or small molecule screens. We first 
recreate experiments carried out on primary neurones, and find that neuroblastoma cell lines retain 
a transcriptional response to excitatory neurotransmitters. We then use optogenetic tools to create 
cell lines that can be subjected to the high throughput and straightforward application and withdrawal 
of stimulation. Ultimately, we perform a timecourse style experiment to identify the transcriptomic 
effects of 12 hour activation of this cell line. We find several functional gene groups that have 
previously been associated with sleep deprivation are upregulated following the prolonged activation 
of SH-SY5Y cells, indicating that it is possible to model sleep deprivation in vitro in a high throughput 
manner. 
  
125 
 
5.1.1. Previous use of in vitro models in sleep research 
Sleep deprivation driven changes in transcription and intracellular protein expression indicate that 
cells are sensitive to the wake state of the animal. Furthermore, the observation that regions of the 
cortex can enter a sleep like state despite the animal remaining awake (Vyazovskiy et al. 2011) 
suggests that groups of neurons may possess an intrinsic ability and drive for sleep. In trying to 
understand what is the minimum assembly that exhibits sleep like behaviour, researchers have turned 
to in vitro studies, predominantly using tissue explants and primary neuron cultures.  
In a seminal paper linking the in vitro properties of neurons to the in vivo effects of sleep deprivation, 
Hinard et al. identified that dissociated cortical neurons treated with a cocktail of excitatory drugs 
exhibit changes that closely resemble those induced by wakefulness in vivo (Hinard et al. 2012). After 
7 days in culture, primary mouse neurons developed synchronous, low frequency firing patterns, 
similar to those of sleeping mice. Exposure to excitatory neurotransmitters caused a dose dependent 
decrease in synchrony, similar to that seen in waking mice. Excitatory stimulation of primary neurons 
also induced similar transcriptomic changes as seen in sleep deprived mice, and both the cocktail and 
sleep deprivation led to a reversible phosphorylation of metabotropic glutamate receptors. 
Stimulation of primary neurons also led to a 40% increase in oxygen consumption and the production 
of several lysolipids, which are elevated in the serum of sleep deprived humans (Davies et al. 2014). 
Therefore, Hinard et al. demonstrated several molecular correlates of sleep and wake are preserved 
in primary neuron cultures, introducing the possibility of a reductionist approach to understanding 
sleep.  
126 
 
 
5.1.2. SH-SY5Y cells provide a source of Human derived Neuronal like cells 
A reductionist approach to understanding sleep would carry several experimental benefits. Being able 
to control the environment that cells are exposed to much more precisely than in vivo allows the 
researcher to more easily characterise the effects of small molecules on cell biology. The 
comparatively low cost of in vitro systems also facilitates high throughput studies, whilst having a 
homogenous cell population in vitro removes uncertainty surrounding which cells in a tissue sample 
are responsible for the molecular changes identified. However, the choice of which cell type to use as 
a model for in vitro studies is crucial for the validity and potential uses of that model. Organotypic 
brain slices or primary neurones derived from animals represent one approach to culture cells with a 
neuronal phenotype, but suffer from being laborious to generate and being derived from rodents. 
Alternatively, human stem cell culture offers the possibility of generating human neurones for in vitro 
studies, but is very expensive and laborious, limiting its use in high-throughput studies. In contrast, 
transformed cell lines offer a comparatively inexpensive approach to readily generate large numbers 
of human derived samples. A fundamental limitation of cell lines in research, however, is the 
uncertainty surrounding how closely transformed cells recapitulate the functions and phenotypes 
Sleep 
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Figure 5.1. Genes Induced by Sleep Deprivation are Induced in Primary Neurones Treated with an 
Excitatory Cocktail: Hinard et al showed that primary mouse neurones treated with a cocktail of 
excitatory neurotransmitters induces several genes also induced in mouse cortex during sleep 
deprivation. Data for these plots is taken from Hinard et al, and represents the expression of genes 
relative to unperturbed mice or cells.  
127 
 
demonstrated in vivo. Therefore, the choice of cell line is an important consideration that determines 
the ultimate validity and limitations of the final model.  
Several human neuronal model cell lines have been generated, but one of the most commonly used 
and best characterised are SH-SY5Y cells. SH-SY5Y cells are of neuroblastoma origin isolated from a 
bone marrow metastasis (Biedler et al. 1978) and is a thrice subcloned semi-adherent derivative of 
the human SK-S-SH neuroblastoma cell line, selected for on the basis of neuroblast morphology. 
SH-SY5Y cells were originally shown to synthesise acetylcholine, dopamine and GABA (Biedler et al. 
1978), and since have also been shown to produce norepinephrine through dopamine hydroxylase 
(Kume et al. 2008). The dopaminergic phenotype of the SH-SY5Y cell line has led to its widespread use 
as a Parkinson’s Disease model and also in general neurotoxicity research, leading to its in-depth 
characterisation.  
Typical for a cancer derived cell line, SH-SY5Y cells exhibit genetic abnormalities compared to healthy 
human cells, including a complete trisomy of chromosome 7. However a whole exome sequencing 
study suggested that the genetic defects seen in SH-SY5Y cells are expected to only have a small effect 
on Parkinson’s disease and Huntington’s disease related pathways (Krishna et al. 2014), indicating that 
genetic abnormalities of this cell line does not preclude SH-SY5Y cells from modelling some aspects of 
neuronal biology.  
Several differentiation protocols have been developed for SH-SY5Y cells, with the aim of creating a 
phenotype that more closely resembles mature neurons. Differentiation is characterised by the 
withdrawal from the cell cycle, the extension of long neurite structures and the expression of mature 
neuronal markers (Lopes et al. 2010). Retinoic acid is the most commonly used differentiation agent 
for SH-SY5Y cells (Påhlman et al. 1984), but other approaches use phorbol esters (Påhlman et al. 1981), 
dibutyryl cAMP (Kume et al. 2008) or serum deprivation (Shipley et al. 2016). Some studies further 
supplement the differentiation medium with cholesterol (Sarkanen et al. 2007), brain derived 
neurotrophic factor (BDNF) (Encinas et al. 2000) or Vitamin D (Celli et al. 1999), and the length of 
published differentiation protocols range from 24 hours to three weeks. The variety of differentiation 
protocols can make findings from separate studies using SH-SY5Y cell culture difficult to reconcile. 
Indeed, the precise method of differentiation can drive SH-SY5Y cells toward distinct neuronal 
phenotypes. For example, phorbol ester treated SH-SY5Y cells produce 50 fold higher levels of 
norepinephrine compared to retinoic acid treated cells (Påhlman et al. 1984), whereas retinoic acid 
treatment drives higher levels of muscarinic receptor and acetylcholine synthesis (Adem et al. 1987). 
Therefore the differentiation protocol used in experiments, if any, has to be carefully chosen.  
128 
 
Previous studies have shown that SH-SY5Y cells resemble neurons in several aspects, including 
neurotransmitter synthesis and synaptic packaging (Sarkanen et al. 2007), having an electrically active 
membrane (Forsythe et al. 1992), and expression of neuron specific markers, such as NeuN (Agholme 
et al. 2010). SH-SY5Y cells have also been shown to exhibit neuronal characteristics linked to sleep, 
such as sensitivity to anaesthetic (Zhang et al. 2009) and induction of cytokines following intense 
stimulation, which remarkably is attenuated by treatment with the sleep associated melatonin 
(Parameyong et al. 2013). Therefore, SH-SY5Y cells may prove a useful system to model molecular and 
cellular aspects of wake and sleep. 
 
5.1.3. Optogenetic Tools Available to Researchers 
In recent years, light sensitive proteins have been exploited by researchers to trigger cellular signalling 
pathways with extraordinary temporal precision. Like neurotransmitter receptors, the detection of 
light is typically coupled to the production of secondary signalling molecules or the movement of ions 
across cellular membranes. Different domains of life have independently evolved sensors that 
incorporate retinoid cofactors, however functional and mechanistic differences between prokaryotic 
and eukaryotic sensors introduce important practical considerations.  
5.1.3.1. Rhodopsin Based Light Sensors 
Rhodopsin is the G-protein coupled receptor (GPCR) protein responsible for light detection within rod 
cells of mammalian eyes. Rhodopsin is structurally similar to ligand gated GPCRs, but is instead 
sensitive to light. Light absorption by its 11-cis retinal cofactor moiety induces a change in 
configuration to all-trans retinal, in turn inducing conformational changes within rhodopsin that 
activates its associated G-protein. The all-trans retinal cofactor is then removed from the protein and 
replaced with a new 11-cis cofactor, restoring light sensitivity, whilst the all-trans retinal is recycled by 
nearby cells. Rhodopsin activates a G-protein which stimulates cGMP hydrolysis and subsequent 
hyperpolarisation of the neuron. However chimeric receptors have been engineered that combine the 
light sensitivity of rhodopsin and the protein-protein interaction partners of adrenergic receptors 
(Airan et al. 2009; Kim et al. 2005), adenosine 2A receptor (Li et al. 2015) and mu opioid receptor 
(Siuda et al. 2015). These engineered opsins can be used not only to interrogate intracellular signalling 
pathways of specific receptors, but also as a tool to trigger a variety of intracellular signalling pathways 
in response to light.  
129 
 
 
5.1.3.2. Microbial Opsins 
In contrast to the secondary signalling molecule mediated activation or inhibition of neurons triggered 
by light sensitive rhodopsin based tools, microbial retinal based receptors are typically ionotropic. The 
best characterised microbial opsin is Channelrhodopsin 2 (ChR2), a blue light sensitive proton and 
cation channel isolated from the alga C. reinhardtii (Nagel et al. 2003). Expression of ChR2 in HEK293 
cells, Xenopus oocytes and mouse neurons is sufficient to generate light dependent currents across 
the membrane and trigger neuronal firing in vivo (Boyden et al. 2005). Like rhodopsin, light absorption 
drives conformational changes in ChR2 through a change in configuration of a retinal cofactor. 
However, unlike rhodopsin, the dark acclimatised ChR2 contains all-trans retinal, and the 13-cis retinal 
produced upon light exposure spontaneously returns to an all-trans configuration in situ.  
 
cGPDE AC PLC
+ + +-
Figure 5.2. Rhodopsin based Optogenetic tools operate through Secondary Signal Molecules: 
Mammalian rhodopsin actvates cGMP phosphodiesterases (cGPDE) in response to light. Chimeric 
receptors combining rhodopsin with the intracellular domains of other receptors create light 
sensitive tools that activate distinct signalling pathways. A rhodopsin A2A receptor chimera 
activates adenylate cyclase (AC) in response to light, leading to an increase in cyclic AMP, whereas 
a mu opiod receptor chimera inactivates AC. Similarly, a beta-adrenergic receptor based chimera 
stimulates phospholipase C (PLC) in response to light, inducing inositol triphosphate and 
diacylglycerol based signalling cascades. 
130 
 
 
Exposure to blue light induces ChR2 mediated currents to start within 2ms, which reduce with a half-
life of 14ms after cessation of light exposure (Lin et al. 2009). Because of the almost instantaneous 
induction of electrical activity within neurons, ChR2 expression in neurons can drive rapid and 
extraordinary changes in behaviour. For example, optogenetic activation of neurons controlling 
aggression in mice is sufficient to switch males from attacking to grooming pups (Wu et al. 2014). The 
clear potential of ChR2 as a tool in neuroscience has motivated several studies that examine the 
biophysical characteristics of ChR2, in turn facilitating the optimisation of ChR2 through mutagenesis.  
ChR2 is 50% activated by 470nm wavelength light at an intensity of 1mW/mm2, which is significantly 
higher than the intensity of direct sunlight (Benedetti et al. 2001). Once opened, ChR2 facilitates the 
passive diffusion of protons and cations across the membrane, showing the highest conductivity for 
protons, followed by sodium, potassium and calcium. Therefore, under physiological conditions, 
photocurrents elicited by ChR2 expressed in neurons are dominated by comparably sized sodium and 
proton currents (Schneider et al. 2013). Initial studies of ChR2 photocurrents revealed that its 
photocycle is not a simple binary switch between open and closed. Instead, at the beginning of a flash, 
the elicited photocurrent transiently peaks (I0) before decaying to a stationary conductance (IS) for the 
remainder of the flash. This process is termed desensitisation. For wild-type ChR2, the peak 
hv
Cl-
hv
Na+
K+
Figure 5.3. Microbial Opsins are Ionophoric: In contrast to mammalian rhodopsin, microbial opsins 
directly mediate the movement of ions. Halorhodopsin couples light absorption to the inward 
pumping of chloride ions, hyperpolarising the membrane. Channelrhodopsin 2 is a light gated 
channel, which once opened in the presence of light facilitates the passive diffusion of protons and 
cations across the membrane. 
131 
 
photocurrent is approximately fivefold higher than its stationary photocurrent (Nagel et al. 2003). 
Recent exposure to light reduces the peak photocurrent elicited by subsequent flashes, in a process 
termed inactivation, reducing the probability of triggering an action potential. ChR2 requires 
approximately 30 seconds of darkness to recover from inactivation, which can be accelerated by 
exposure to 570nm light (Lin et al. 2009). The large decrease in conductivity following previous light 
exposure and relatively slow recovery presents practical problems for researchers wishing to drive 
repeated or rapid depolarisation within the same cells. 
 
 
5.1.3.3. Refinement of Channelrhodopsin as an Optogenetic Tool 
The clear potential but practical limitations of wild type channelrhodopsin inspired the search for 
better performing opsins through mutagenesis and genome mining. Today there is an extensive 
toolbox of microbial opsins, and there is no longer any scenario where wild-type ChR2 is the best 
suited experimental tool available (Klapoetke et al. 2014).  
ChR2 (H134R) was identified based on the homology of Channelrhodopsin with the well characterised 
Bacteriorhodopsin, and exhibits increased photocurrents but slower kinetics (Nagel et al. 2005). 
Similarly, ChR2 (T159C) was found to produce even greater photocurrents with even slower closing 
kinetics (Berndt et al. 2011). Conversely, ChR2 (E123T) produces much faster kinetics and is able to 
drive a much higher frequency of action potentials in vivo, but suffers from reduced photocurrents 
C
u
rr
en
t
Time
I0
IS
Figure 5.4. Repeated Stimulation reduces Channelrhodopsin 2 mediated Photocurrents: 
Channelrhodopsin 2 (ChR2) opens in response to blue light (represented by the blue rectangles 
above the plot), however the current elicited is not constant. After illumination, a peak current (I0) 
is achieved that rapidly decays to a much reduced stationary current (Is). The difference between 
I0 and Is is a measure of the desensitisation of ChR2. Repeated flashes also reduce the elicited I0, in 
a process termed inactivation. However, ChR2 gradually recovers from inactivation in darkness, 
with I0 being almost fully restored after 30 seconds of darkness. 
132 
 
(Gunaydin et al. 2010), reflecting that high photocurrents are associated with slow kinetics (Mattis et 
al. 2012). Step function opsins are produced by the mutation of C128, characterised by a channel 
closing rate orders of magnitude greater than wild type ChR2 (Berndt et al. 2009). The slow closing 
rate of step function opsins has the effect of increasing apparent light sensitivity by a few orders of 
magnitude. However, the maximum photocurrent and the associated change in membrane potential 
is significantly reduced.  
 
 
A second strategy employed to improve ChR2 as an optogenetic tool has been to engineer a chimera 
channel by combining ChR2 with ChR1, a second opsin isolated from C. reinhardtii (Sineshchekov et 
al. 2002). Unlike ChR2, ChR1 produces very small photocurrents when expressed in vivo, however does 
not appear to have significant desensitisation. Lin et al. reasoned that combining regions of ChR1 and 
ChR2 may produce a chimera with sizeable photocurrents but with reduced desensitisation. Indeed 
the chimeric opsin produced, termed ChIEF, has similar kinetics and similar peak photocurrent as 
ChR2, however displays only 20% desensitisation, and therefore has stationary photocurrents 
approximately fourfold higher than wild-type ChR2 (Lin et al. 2009), allowing ChIEF to drive more rapid 
chains of action potentials in vivo.  
More recently, genome mining has uncovered 61 ChR2 related opsins in microbial algae (Klapoetke et 
al. 2014). Three opsins in particular were found to have characteristics that make them attractive for 
use as an optogenetic tool. The opsin Chrimson exhibits a greatly red-shifted absorbance spectrum, 
C
u
rr
en
t
Time
I0
IS
ChR2 
(WT)
ChR2
(H134R)
ChR2
(E123T)
ChR2
(C128T)
Figure 5.5. Point Mutations Alter the Magnitude and Kinetics of Photocurrents: Mutation of H134 or 
T159 increases the photocurrent of Channelrhodopsin 2, but also results in slower kinetics. In 
contrast, E123 mutants generate smaller photocurrents but faster kinetics. Mutation of C128 
greatly reduces the peak photocurrent and channel closing rate, conferring an apparent increase 
in light sensitivity. 
133 
 
with a peak absorbance of 600nm. Its red shifted spectrum allows the independent activation of 
distinct neural populations in close proximity, by expressing Chrimson in one population and a blue 
light sensitive opsin in the other. However, the kinetics and photocurrents of Chrimson are otherwise 
comparable to ChR2, limiting its use outside of multiwavelength control of neuronal activity. In 
contrast, the slightly green-shifted Chronos exhibits very fast kinetics and is able to drive action 
potentials at rates exceeding 50 Hz, but nevertheless exhibits much larger photocurrents than ChR2 
with reduced desensitisation and is able to drive action potentials at much lower light intensity. 
Therefore, the generally superior parameters of Chronos compared to ChR2 make Chronos an 
attractive tool for optogenetic activation of cells in a wide variety of experimental designs. Finally, the 
opsin CoChR has the largest photocurrent generated by any identified microbial opsin by blue or green 
light, due to a combination of very high single channel conductance and excellent trafficking to the 
cell membrane. Coupled with its low desensitisation and rapid recovery from inactivation, CoChR can 
drive repeated activation of neurons at low light levels (Schild & Glauser 2015). However, CoChR does 
exhibit slightly slower closing kinetics than ChR2, which may limit its general use.  
 
 
 
 
 
ChR2 ChrimsonChIEF Chronos CoChR
Time
C
u
rr
en
t
Figure 5.6. Genome Mining and Chimeric Opsins provide Superior Optogenetic Tools: ChIEF, an 
engineered chimera of Channelrhodopsin 1 and Channelrhodopsin 2 (ChR2), shows similar peak 
photocurrents as ChR2, but significantly reduced desensitisation and inactivation. Chrimson is a 
naturally occurring red light sensitive opsin, with comparable kinetics and photocurrent as ChR2. 
Chronos has rapid kinetics and sizeable photocurrents, whereas CoChR has very large 
photocurrents and demonstrates excellent trafficking to the cellular membrane in mammalian 
cells. 
134 
 
5.1.3.4. Inhibitory Microbial Opsins 
Complementary to microbial light-gated cation channels are a group of microbial light driven pumps 
that hyperpolarise cells. The first of these pumps to be expressed in vivo was the chloride pumping 
halorhodopsin isolated from N. pharaonis (Schobert & Lanyi 1982), which couples absorption of yellow 
light to the inward movement of chloride ions, thereby hyperpolarising and silencing neurons 
(Gradinaru et al. 2008). The distinct absorption spectra and functions of ChR2 and halorhodopsin 
made it an attractive tool for the bidirectional control of the same cell (Han et al. 2009). However, 
halorhodopsin has a tendency to aggregate in the endoplasmic reticulum when expressed at high 
levels, and even when fused with motifs to enhance trafficking can fail to entirely silence neurons. In 
contrast, the light driven proton pump archaerhodopsin, isolated from H. sodomense, was found to 
almost totally silence neurons in response to light (Chow et al. 2010; Han et al. 2011). However, the 
comparatively high light intensity required for pumps and the use of protons to carry charge across 
the membrane may result in local heat and pH changes following long term light exposure.  
Microbial light-gated anion channels provide an alternative to pump based inhibition of neurons. 
Through the mutagenesis of pore residues of ChR2, the normally excitatory cation conducting opsin 
can be converted to an inhibitory chloride permeable channel, allowing the temporally precise 
silencing of neurons in vivo (Berndt et al. 2014; Wietek et al. 2014, 2015). Similarly, genome mining 
has uncovered naturally occurring microbial anion channels in Guillardia theta, which causes rapid 
silencing of primary neurons at lower light levels than anion permeable ChR2 variants (Govorunova et 
al. 2015).  
5.1.3.5. Comparison of Eukaryotic and Prokaryotic Optogenetic Tools 
The optogenetic toolbox available to researchers wishing to control neuronal activity includes both 
ion mediated tools derived from prokaryotes and secondary signalling molecule mediated tools, 
largely derived from eukaryotes. The most appropriate tool depends on several aspects of the 
experimental design, including the system, duration of stimulation and the temporal resolution 
required. Whilst microbial opsins offer extremely precise control of action potential firing, driving 
inappropriate firing patterns in vivo may induce a supraphysiological response and result in unusual 
behavioural outputs. Under these circumstances, the activation of cAMP signalling pathways by 
rhodopsin based tools to generally elevate neuronal activity may produce more physiologically 
relevant results. In contrast, modified rhodopsin based tools are susceptible to arrestin mediated 
inhibition and require recycling of the retinal cofactor. Therefore microbial opsins may be better suited 
for repeated stimulation or in systems where the cofactor may not be effectively recycled. 
135 
 
5.1.4. In vitro Research Aims 
In the body of work presented below, we outline our efforts to generate an in vitro model of sleep 
deprivation using SH-SY5Y cells and optogenetic tools. The development of the model is guided by 
transcriptomic correlates of sleep deprivation in mice. Although primary neurons have previously 
been identified as a promising system with which to study molecular aspects of sleep, extraction and 
culture of primary neurons is laborious, and do not allow many advantages over established in vivo 
models for large scale studies. In contrast, a cell line based model may provide a useful system in 
which preliminary drug screens could be carried out in a high throughput manner.  
  
136 
 
5.2.1. Excitation of SH-SY5Y Cells Stimulates Gene Expression 
Hinard et al. (2012) showed that treating primary mouse neurones with a cocktail of wake associated 
neurotransmitters for six hours causes changes at the transcriptomic level that resemble changes that 
occur after in vivo sleep deprivation in mouse brain. As the first stage in determining whether cell lines 
could be used as an appropriate model for sleep deprivation, we treated human neuroblastoma 
SH-SY5Y cells with a cocktail of wake-associated neurotransmitters, (containing noradrenalin, 
histamine, carbachol, dopamine, serotonin, kainic acid, ibotenic acid, AMPA and orexin) for 4 hours, 
and quantified the gene expression of 9 genes previously associated with sleep deprivation. We found 
that, normalised to GAPDH expression, 8 of the 9 target genes significantly increased after treatment 
with the cocktail (Fig 5.7.). 
 
 
  
*
*
*
*
*
*
* **
Figure 5.7. Sleep Deprivation Markers are Induced by Excitatory Neurotransmitters in SH-SY5Y Cells: 
Human Neuroblastoma cells (n=6) were treated with a cocktail of waking neurotransmitters for 4 
hours. The expression of several sleep deprivation associated transcription factors was quantified 
relative to GAPDH by qPCR, and plotted relative to untreated controls (data are mean ± SEM).  
p-values were determined using a Student t-test, comparing the treated to untreated group for 
each gene, and the p-value indicated by the number of asterisks above the relevant gene.   
 * - p<0.05, **-p<0.01 
 
137 
 
5.2.2. Activation of Channelrhodopsin Stimulates Gene Expression in 
SH-SY5Y Cells 
The upregulation of sleep deprivation markers in SH-SY5Y cells after neurotransmitter cocktail 
treatment indicated that SH-SY5Y cells retain the cellular machinery required to respond to excitatory 
signals at the molecular level. We therefore decided that these cells could be used to model 
transcription and other molecular changes that occur during sleep deprivation.  
There are, however, three major disadvantages of using a stimulatory cocktail to model sleep 
deprivation. Firstly, during long term activation, as the neurotransmitters are degraded and their 
receptors are attenuated through arrestin mediated pathways, the stimulatory effect of the cocktail 
will vary significantly over time. Secondly, modelling recovery after prolonged activity necessitates a 
medium change to remove the excitatory neurotransmitters. A medium change would also remove 
any molecules released into the extracellular space during activity, which may otherwise play a role in 
the subsequent recovery phase, thereby confounding experiments attempting to model recovery 
sleep in this model. Thirdly, repeated medium changes would ultimately limit the use of the final 
model in high-throughput screens of drugs that modulate the molecular response to sleep 
deprivation.  
We reasoned, however, that optogenetic tools, which have been used in vivo to activate and silence 
neuronal activity with very precise temporal resolution, might offer several advantages over the 
neurotransmitter cocktail. By using light as a stimulus, channelrhodopsin would not only activate the 
cells in a convenient and high-throughput manner, but also allow rapid withdrawal of stimulation. In 
this way, we hoped to be able to model the molecular changes associated with prolonged neuronal 
firing and subsequent recovery. Since the ultimate end goal of modelling sleep deprivation in cells was 
using -omics tools to generate a full picture of the molecular intracellular changes during wakefulness, 
stable cell lines were produced, to eliminate the effects of variable construct expression, membrane 
instability and cell death that accompany transient transfection. 
 
 
138 
 
 
 
SH-SY5Y cells were transfected with ChR2-GFP-2A-Hal-YFP, a plasmid that utilises a viral 2A sequence 
that induces ribosomal skipping to induce approximately 1:1 expression of Channelrhodopsin 2 and 
Halorhodopsin. Stable insertion of the plasmid was selected for using G418, and GFP labelled colonies 
chosen to generate monoclonal cell lines (Fig 5.8.). Generation of SH-SY5Y cell lines stably expressing 
the transgene was complicated by the high tendency of SH-SY5Y cells to senesce at low confluency, 
however eventually stable clones were produced after approximately 90 days. 
  
Transfection
and Selection 
with G418
Figure 5.8. Production of Light Sensitive Cells: Monoclonal Human Neuroblastoma cells stably 
expressing Channelrhodopsin and Halorhodopsin were produced by selection of G418 resistant 
GFP expressing cells.  
139 
 
SH-SY5Y cells stably expressing channelrhodopsin were exposed to flashing blue light, using a custom 
programmable LED array. Cells were exposed to 20 flashes of blue light a second, each lasting 20ms, 
at an intensity of 0.5mW/cm2 in a cycle of 5 seconds on and then 5 seconds of darkness for 4 hours. 
RNA was harvested and the expression of the target genes examined in Section 3.2.1. determined. 
Compared to light exposed wild type cells and ChR2 expressing cells maintained in the dark, expression 
of the target genes were increased, but not by the same magnitude as seen with the neurotransmitter 
cocktail (Fig 5.9.). 
 
 
 
 
5.2.3. Blue Light Activation of SH-SY5Y Cells in vitro Induces Global 
Transcription Changes Similar to Sleep Deprivation in vivo 
Having determined that SH SY5Y cells respond to an optogenetic stimulus at the transcriptional level, 
we wished to determine to what extent the change in transcription resembles that seen in vivo. In 
particular, we were interested in potential homeostatic genes. Homeostatic genes are expected to 
either increase during prolonged wakefulness and then decline during subsequent sleep, or show the 
inverse pattern. To identify transcripts that fit either pattern, wild type SH-SY5Y cells and those 
expressing ChR2-2A-Hal were treated with blue light for 6 hours, and then allowed to recover in 
Figure 5.9. Sleep Deprivation Markers Induced by Light Exposure in SH-SY5Y cells: Human 
Neuroblastoma cells (n=6) stably expressing [ChR2-2A-Hal] were exposed to flashing blue light for 
4 hours at an intensity of 0.5mW/cm2. The expression of sleep deprivation associated genes was 
determined relative to GAPDH expression and plotted with plotted relative to controls maintained 
in darkness (data are mean ± SEM).  
* - p<0.05, **-p<0.01, ***-p<0.001 
 
140 
 
darkness for 3 hours. Cells were lysed at 3-hour intervals, and RNA extracted and prepared for 
RNA-Seq analysis.  
Transcripts were filtered to identify those modulated by blue light in Channelrhodopsin expressing 
cells but unaffected in wild-type SH-SY5Y cells. Because this experiment was carried out without 
replicates, a gene was identified as being of interest if its expression in ChR2 expressing cells a) was 
similar at baseline conditions between the two genotypes, and b) changed by at least a factor of 2 
after 6 hours of illumination, and c) differed by at least 4 standard deviations from the mean 
expression of that gene in the control group after 6 hours illumination, but not in wild type cells. In 
this way, a total of 464 genes were identified as having their expression modulated during 
illumination. 
285 genes were identified as increasing during blue light in ChR2 expressing but not wild-type cells, of 
which 265 recovered toward baseline levels during the subsequent recovery dark phase. Conversely, 
the expression of a further 179 genes decreased during blue light of which 176 rebounded during 
subsequent darkness. The genes that increased during illumination were enriched in genes relating to 
the regulation of transcription, chaperone function, and biological rhythms (Benjamini corrected 
q-values = 3.5x10-7, 1.9x10-3 and 6.1x10-3, respectively), whilst genes that decreased during 
illumination were enriched in genes relating to DNA replication (q-value = 3.1x10-3).  
 
Chaperone 
Genes
Biological
Rhythms
DNA 
Replication
Chaperone 
Genes
Biological
Rhythms
DNA 
Replication
DNA 
Replication
Chaperone 
Genes
Biological
Rhythms
-2 -1 0 1 2
Row Z-Score
Figure 5.10. Blue Light Exposure Modulates the Expression of Chaperone, Biological Rhythm and 
DNA Replication Genes: Human Neuroblastoma cells stably expressing [ChR2-2A-Hal] were 
exposed to flashing blue light for 6 hours at an intensity of 0.5mW/cm2 and allowed to recover for 
3 hours. Cells were sampled every 3 hours, and subjected to RNA-Seq analysis in single replicates. 
Gene clusters significantly modulated included Chaperone, Biological Rhythm and DNA replication 
genes. Plotted above are heatmaps of the Z-score normalised expression of genes within these 
clusters, where blue indicate low expression and red indicates high expression. The blue and grey 
bars represent the blue light exposure periods.  
 
141 
 
The expression data from this experiment was compared to the 72 genes from Hinard et al. that were 
identified as changing both during in vivo sleep deprivation and by treatment of primary neurones 
with an excitatory neurotransmitter cocktail. Of these 72 genes, 56 were expressed in SH-SY5Y cells, 
9 of which were “significantly” changed in SH-SY5Y cells, based on the criteria laid out above. However, 
plotting the remaining 47 genes indicated a good overlap between light stimulation and previous data 
collected by Hinard et al., as several genes appeared to fit the pattern, but did not reach our criteria 
because of differences in baseline gene expression or variation within the control group.  
 
-2 -1 0 1 2
Row Z-Score
Figure 5.11. Optogenetic Activation of SH-SY5Y Cells produces a similar Expression Trend as that 
previously observed following the Pharmacological Activation of Primary Neurones: Human 
Neuroblastoma cells stably expressing [ChR2-2A-Hal] were exposed to flashing blue light for 6 
hours at an intensity of 0.5mW/cm2 and allowed to recover for 3 hours. Cells were sampled every 
3 hours and subjected to RNA-Seq analysis in single replicates. The Z-score normalised expression 
of genes previously indicated as conserved between in vivo sleep deprivation and in vitro 
activation of primary neurons are plotted above, where blue represents low expression and red 
indicates high expression. Genes that met our significance criteria are annotated with an asterisk 
following their name. The blue and grey bars represent the blue light exposure periods. 
 
142 
 
5.2.4. Refinement of the Optogenetic Protocol 
RNA-seq analysis revealed several transcripts associated with neuronal firing and sleep deprivation 
were upregulated in SH-SY5Y cells following exposure to light. Heat shock proteins (HSP) were 
amongst the genes most upregulated, and although several HSPs are induced by sleep deprivation in 
vivo, they are also upregulated by heat and free radical production. Indeed, during illumination, the 
temperature of the plates increased to 39-40oC.  
We therefore introduced physical approaches to reduce increases in temperature associated with 
illumination. Cells were raised above the LED arrays by 3cm using custom printed parts. Additionally, 
a fan was placed behind each array, such that there was a constant air flow through the light tight 
compartment that each array was placed into. These measures had the combined effect of reducing 
increases in temperature to less than 0.3oC during illumination.  
 
We also redesigned the plasmids to encode for optogenetic constructs better suited for long term 
light exposure. To reduce heat and phototoxicity, we wanted to reduce the amount of light that the 
cells were exposed to. We therefore replaced the ChR2 (H134R) variant with ChR2(C128T). This "Step 
Function Opsin" has a greatly reduced channel closing time compared to ChR2 (H134R) (toff = 2s vs 
25ms), previously shown to increase light sensitivity at a population level by an order of magnitude. 
To reduce GFP mediated ROS production, we replaced fluorescent protein tags with an IRES-PuroR 
cassette, which directly couples antibiotic resistance to the expression of the construct. For 
a) b)
Figure 5.12. Refined Optogenetic Plasmids: CHalIP (a) and CRIP (b) plasmids were produced by Gibson 
Assembly to improve light sensitivity, remove blue light sensitive fluorescent tags, and directly 
couple antibiotic resistance to construct expression. CHalIP allows bidirectional optogenetic control, 
whilst CRIP allows for Calcium imaging at wavelengths independent of Channelrhodopsin2.  
143 
 
bidirectional optogenetic control, we fused 2A-eNpHR2.0 to the C-terminus of ChR2 (C128T) in one 
construct. In another construct, we fused the red shifted calcium sensor RCAMP1h to the C-terminus 
of ChR2 (C128T). The RCAMP1h fusion not only acts as a red shifted fluorescent tag to the opsin, but 
also allows calcium monitoring at wavelengths outside of the absorbance spectrum of 
Channelrhodopsin.  
SH-SY5Y cells stably expressing the constructs were produced. Both constructs were able to drive 
larger increases in Fos expression in response to a fivefold lower light intensity (0.1mW/cm2) than the 
previous construct (Fig 5.13.).  
 
 
 
Figure 5.13. Refined Optogenetic Plasmids Induce Fos Expression at Low Light Levels: SH-SY5Y cells, 
stably expressing CRIP, CHALIP or the original ChR2-2A-Hal construct (2A), were exposed to flashing 
blue light at intensity 0.1mW/cm2 for 6 hours, and the expression of Fos determined by qPCR (n=6). 
Fos expression is plotted relative to GAPDH expression (data are mean ±SEM).  
p-values were determined using a Student t-test, comparing the light exposed group to the dark 
treated cells of the same genotype, and the p-value indicated by the number of asterisks above the 
column. * - p<0.05, **-p<0.01, ***-p<0.001 
 
144 
 
Having established that the step-function opsins could drive Fos expression in response to lower levels 
of light than the ChR2(H134R) variant, we next wanted to know whether red light activation of 
halorhodopsin could prevent ChR2 mediated Fos expression in SH-SY5Y cells. We therefore exposed 
cells stably expressing the ChR2(C128T)-2A-eNpHR2.0 construct to both blue and red light and 
quantified expression of Fos through qPCR. Red light was unable to significantly reduce Fos induction 
by blue light, and alone was sufficient to induce a small but significant increase in Fos expression. We 
consequently discarded the concept of using halorhodopsin to hyperpolarise SH-SY5Y cells in our 
model.  
 
 
  
Figure 5.14. Halorhodopsin Activation is not Sufficient to Prevent Channelrhodopsin Mediated Fos 
Expression: SH-SY5Y cells stably expressing CHALIP were exposed to flashing blue light at intensity 
0.1mW/cm2 with or without constant red light at intensity 0.2mW/cm2 for 6 hours, and the 
expression of Fos determined by qPCR (n=6). Fos expression is plotted relative to GAPDH expression 
(data are mean ±SEM).  
 
p-values were determined using a Student t-test, comparing the light exposed groups to the dark 
treated cells, and the p-value indicated by the number of asterisks above the column.  
ns- p> 0.05, * - p<0.05, ***-p<0.001 
 
145 
 
5.2.5. Opsin Activation Modulates the Molecular Clock 
RNA-Seq revealed that the expression of core circadian clock genes was modulated by blue light 
exposure and recovery. To test the hypothesis that photocurrents through Channelrhodopsin affects 
the cellular clock, stable U20S cell lines were produced that express both luciferase under the control 
of the Per2 promoter, and either the CRIP or CHALIP construct. U20S cells were chosen as they are an 
established cellular model of circadian rhythms, and U20S cells stably expressing luciferase under the 
Per2 promotor present an easily accessible readout of the cellular clock.  
Cells had their internal circadian clocks synchronised, and were subsequently exposed to flashing blue 
light in HEPES buffered AIR medium between 12-32 hours after synchronisation. The bioluminescence 
of each well was tracked for over 4 days, plotted and detrended to determine the period and phase 
of their oscillations. The phase of U20S cells not expressing Optogenetic constructs was not greatly 
affected (<1 hour) by blue light treatment, although one-way analysis of variance (ANOVA) did reveal 
a statistical dependence of time of treatment. In contrast, the expression of the CHalIP or CRIP 
constructs leads to the phase of the cellular clock being much more sensitive to the timing of blue light 
exposure, indicated by a highly significant interaction of genotype and response to the timing of light 
treatment (p<0.0001), as shown by two-way ANOVA. 
 
0 4 8 12 16 20
16
18
20
22
24
26
WT
CRIP
CHALIP
CT Time of Treatment
C
ir
c
a
d
ia
n
 P
h
a
s
e
C
t 
T
im
e
 o
f 
T
re
a
tm
e
n
t
Phase
WT
Phase
CRIP
Phase
CHALIP
C
t 
T
im
e
 o
f 
T
re
a
tm
e
n
t
Figure 5.15. Circadian Effects of Photocurrents in U20S Osteosarcoma Cells: CHalIP or CRIP was 
stably expressed in U20S cells expressing luciferase under the control of the Per2 promoter. After 
the cellular clocks were synchronised, cells were exposed to flashing blue light for 4 hours, starting 
at cell time (CT) 0, 4, 8, 12, 16 or 20. The bioluminescence was tracked over several days, detrended 
and the data plotted as a heatmap. Blue represents a local minimum in luciferase activity, whilst 
red represents a maximum. Data are also plotted as Mean ±SEM in the adjacent scatter plot.  
146 
 
5.2.6. c-Fos Activation by Channelrhodopsin is Highly Sensitive to Medium 
Components and Conditions 
Prior to the luciferase experiments outlined in Section 5.2.5., all the characterisations of opsin 
expressing cells had been performed in bicarbonate buffered medium. However, exposure to light 
took place at atmospheric CO2 levels, and therefore the bicarbonate-based buffering had been 
ineffective. Indeed, the presence of bicarbonate at atmospheric CO2 caused the pH of the medium to 
increase to pH 9 within 1 hour of transfer. Despite this pH rise not appearing to cause any cell death 
or detachment of cells, we decided that the dramatic change in pH toward alkaline conditions was 
wholly unphysiological and would likely be a major confounder in our experiment. We therefore 
carried out further experiments in AIR Medium (see Section 2.8.), which replaced the sodium 
bicarbonate buffering system with HEPES.  
The first experiment carried out in HEPES buffered medium aimed to titrate light intensity against FOS 
expression in CRIP expressing SH-SY5Y cells, to minimise light exposure in future experiments. 
Previous experiments had indicated that flashes at 0.1mW/cm2 were sufficient to induce FOS 
expression in bicarbonate buffered medium under atmospheric CO2, however in HEPES buffered 
medium FOS induction by blue light was eliminated, even at higher light intensities (Fig 5.16). 
 
 
 
Figure 5.16. HEPES Buffered Medium Removes Sensitivity of Cells to Blue Light: Wild type SH-SY5Y 
cells and those stably expressing CRIP were exposed to flashing blue light at intensities ranging from 
0.0-0.3mW/cm2 for 6 hours, and the expression of Fos determined by qPCR (n=3). Fos expression is 
plotted relative to GAPDH expression (data are mean ±SEM).  
147 
 
HEPES has previously been demonstrated to cause phototoxicity in the presence of other medium 
components, and so we hypothesised that the response to light could be restored by replacing HEPES 
with another appropriate buffer, such as MOPS or TAPSO. Whereas TAPSO buffered medium was 
statistically associated with a decrease in FOS expression during exposure to blue light, expression of 
FOS in MOPS based medium had no dependence on blue light. In contrast, cells expressing opsins in 
bicarbonate containing medium showed induction of FOS in response to blue light (Fig 5.17.). Cells in 
bicarbonate containing medium also had an increased level of basal FOS expression compared to cells 
in HEPES, MOPS or TAPSO buffered media.  
 
 
  
Figure 5.17. Medium Buffered at Neutral pH Removes Sensitivity of Cells to Blue Light: SH-SY5Y cells, 
stably expressing CRIP, CHALIP or the original ChR2-2A-Hal construct (2A), were exposed to flashing 
blue light at intensity 0.1mW/cm2 for 6 hours in the presence of either HEPES, MOPS, TAPSO or 
bicarbonate (NaHCO3) buffered media at atmospheric CO2, and the expression of Fos determined 
by qPCR (n=3). Black bars represent dark treated controls, Blue bars represent blue light treated 
cells  
* - p<0.05, **-p<0.01, ***-p<0.001 
*** 
148 
 
We next determined that FOS induction in bicarbonate buffered medium was dependent on exposure 
to atmospheric CO2, as sealing the plates to prevent gas exchange prevented blue light induced FOS 
induction in bicarbonate containing medium. We therefore hypothesised that high pH, rather than 
specific medium components, was required for the opsin conferred light sensitivity. To test this 
hypothesis, we exposed cells expressing CRIP to blue light, having changed their media 18 hours prior 
to light exposure to media buffered either with 40mM MOPS at pH 7 or 8, or buffered with 40mM 
CHES at pH 9 or 10. Cells maintained at pH 10 died overnight, whereas those buffered at pH 7-9 
showed no sensitivity to blue light (Fig 5.18.).  
 
Our next hypothesis was that the change in pH was relevant, rather than the pH value itself. At the 
molecular level, we considered the possibility that alkaline conditions caused the non-enzymatic 
hydrolysis of glutamine present in the media to glutamate, which in turn may activate glutamatergic 
receptors on the surface of SH-SY5Y cells, with which channelrhodopsin activation was synergistic. 
This would explain the increase of Fos expression in the absence of light, as well as the light mediated 
increase in Fos expression. In contrast, after prolonged high pH, the rate of glutamate formation may 
have decreased, and extracellular glutamate become depleted through uptake mechanisms. We 
therefore tested the possibility that non-hydrolysable drugs added to the medium the day before light 
exposure may exhibit the same synergism with blue light. Cells expressing CRIP were exposed to blue 
light in medium buffered with MOPS at pH 7.4, containing the cocktail used in Section 5.2.1. at a 
concentration ranging from 10-6 - 1X. However, drug addition failed to produce any response to blue 
light (Fig 5.18.). 
 
149 
 
 
  
0
10
-6
10
-5
10
-4
10
-3
10
-2
10
-1 1
0
1
2
3
4
Drug Cocktail Concentration
F
o
s
 E
x
p
re
s
s
io
n
N
o
rm
a
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d
 t
o
 G
A
P
D
H
Dark
Light
Figure 5.18. Varying pH or Introducing Excitatory Drugs to the Medium Fails to Restore Sensitivity of 
Cells to Blue Light:  
a) SH-SY5Y stably expressing CRIP were exposed to flashing blue light at 0.1mW/cm2 for 6 hours, in 
medium buffered at pH 7,8 or 9, and the expression of Fos determined by qPCR (n=3). Fos expression 
is plotted relative to GAPDH expression (data are mean ±SEM).  
b) SH-SY5Y stably expressing CRIP were exposed to flashing blue light at 0.1mW/cm2 for 6 hours, in 
medium containing a range of concentration of excitatory neurotransmitter cocktail at pH 7.4, and 
the expression of Fos determined by qPCR (n=3). Fos expression is plotted relative to GAPDH 
expression (data are mean ±SEM). 
a) 
b) 
150 
 
5.2.7. Expression of a High Conductivity Opsin Confers Sensitivity to Light 
at Neutral pH 
Having ruled out several pH mediated effects on the cells and media composition, we then considered 
the effect of high pH on channelrhodopsin itself. Previous studies had shown that the kinetics, 
conductivity and ion selectivity of channelrhodopsin is highly dependent on pH. Although the majority 
of data available relates to neutral and acidic conditions, we speculated that the shift to basic 
conditions changed some basic parameters of channelrhodopsin to make it more favourable for 
activation of SH-SY5Y cells. There were three parameters of channelrhodopsin we thought important: 
the size of photocurrent, its photocycle kinetics, and the inactivation kinetics, relating to the decrease 
in photocurrent following repeated light exposure. We therefore chose to create new stable cell lines, 
expressing recently discovered or engineered opsins found to have improved conductivity and 
kinetics.  
CoChR is a naturally occurring green shifted algal opsin, found to have much higher conductivity and 
expression levels than wild type ChR2 with comparable photocycle kinetics and greatly reduced 
inactivation. CHIEF (E162A/T198C) is a synthetic chimera opsin with very fast kinetics and greatly 
reduced inactivation, whereas Chronos is a naturally occurring opsin with generally superior 
photocycle and inactivation kinetics, light sensitivity and conductivity.  
Plasmids encoding for the stable expression of Chronos, CoChR or CHIEF (E162A/T198C) fused to 
RCAMP were produced. Despite stably expressing in HEK 293T cells, expression of 
CHIEF (E162A/T198C) containing constructs was lethal in SH-SY5Y cells, and no puromycin resistant 
cells survived after transfection with CHIEF (E162A/T198C)-RCAMP. However, both Chronos-RCAMP 
and CoChR-RCAMP constructs were stably expressed. As an initial experiment, cells were exposed to 
between 2-32 pulses of light per second, with each pulse lasting 25ms and consisting of both blue and 
green light at a total light intensity of 2mW/cm2 in medium buffered at pH 7.4 with 40mM MOPS. After 
six hours of illumination, cells were lysed and the expression of Fos determined (see Fig 5.19.).  
Expression of Fos was statistically upregulated following light exposure to CoChR expressing cells at 
pulse frequencies of 8Hz and higher, whereas Chronos expressing cells only had a statistical increase 
in Fos expression at a pulse frequency of 32Hz. We therefore chose to pursue the use of CoChR to 
activate SH-SY5Y cells. Initially, the degree of Fos induction was found to be inconsistent and sensitive 
to several experimental design parameters, most notably days in culture, confluency and the presence 
of supplemental retinoids. However, guided in part by microscopy experiments outlined in 
Section 3.2.8., we eventually standardised light exposure to occur 7 days after plating at high 
confluency in the absence of supplementary retinoids. Under these conditions, we were able to 
151 
 
reliably induce a large (30-100X) increase in Fos expression by exposure to light, which rapidly recovers 
toward baseline levels after cessation of illumination (see Figure 3.19.).  
  
Figure 5.19. Expression of CoChR Confers Light Sensitivity to SH-SY5Y cells at Neutral pH:  
a) SH-SY5Y cells, stably expressing Chronos or CoChR supplemented with 1µM retinal were exposed 
to flashing blue and green light at a total intensity of 2mW/cm2 for 6 hours at pH 7.4 and the 
expression of Fos determined by qPCR and plotted relative to the expression of untreated cells of 
the same genotype (n=3). Asterisks indicate the statistical significance of a flash frequency 
compared to untreated cells of the same genotype. 
b) Undifferentiated SH-SY5Y cells stably expressing CoChR were exposed to 8 flashes of blue and 
green light per second at a total intensity of 2mW/cm2 for 3,6 or 9 hours at pH 7.4 without 
supplemental retinoids, and the expression of Fos determined by qPCR (n=4) and normalised to the 
first timepoint of the dark treated control. For clarity, recovery timepoints are joined by lines to their 
final illumination timepoint, but that illumination timepoint has not been repeated multiple times. 
Asterisks indicate the statistical significance of a treated timepoint compared to untreated cells at 
the same timepoint. 
* - p<0.05, **-p<0.01, ***-p<0.001 
 
 
 
 
a) 
b) 
152 
 
5.2.8. Fluorescent Calcium Imaging Reveals Intracellular Calcium Increases 
in Response to Stimulation with Light 
We took advantage of the RCAMP moiety fused to opsins to image intracellular calcium at red-shifted 
wavelengths whilst simultaneously stimulating cells with either ions, drugs or blue light activation of 
the opsin.  
Surprisingly, in the absence of any external stimulation, no spontaneous calcium signals were ever 
detected (Fig 5.20.), but intracellular calcium readily responded to addition of potassium to the 
medium. Intracellular calcium also increased in response to the neurotransmitter cocktail used in 
section 5.2.1., showing responses individually to dopamine, histamine, and carbachol (Fig 5.21.).  
In contrast to cells expressing ChR2(C128T) or Chronos, cells expressing CoChR showed transient 
calcium spikes in response to pulses of light, the magnitude of which was sensitive to light intensity 
and extracellular calcium levels (Fig 5.22.). The half life of the peaks was determined to be 
approximately 1 second, considerably larger than the 200ms toff previously measured for CoChR. 
 
 
Figure 5.20. SH-SY5Y Cells do not Exhibit Spontaneous Calcium Spikes: Live SH-SY5Y cells, stably 
expressing CoChR-RCAMP were imaged 5 times per second at 570nm to excite the calcium sensitive 
RCAMP fluorophore. No spontaneous changes in RCAMP fluorescence were detected, however 
addition of 20mM KCl induces a rise in intracellular calcium. Pixel values were normalised by defining 
the maximum intensity recorded in the recording as 1 and linearly scaling all other images, and no 
smoothing was applied. 
 
 
 
153 
 
 
Figure 5.21. Excitatory Drugs Induces Intracellular Increases in Calcium in SH-SY5Y Cells: Live 
undifferentiated SH-SY5Y cells stably expressing CoChR-RCAMP were imaged at 570nm to excite the 
calcium sensitive RCAMP fluorophore. Pixel values were normalised by defining the maximum 
intensity recorded in the timecourse as 1 and linearly scaling all other images, and were not 
subjected to any smoothing. a) After 90 seconds, a cocktail of excitatory drugs was added to the 
cells at a final concentration of 10µM carbachol, 1µM NMDA, 1µM AMPA, 1µM kainic acid, 1µM 
ibotenic acid, 1µM serotonin, 1µM histamine, 1µM noradrenaline, 1µM dopamine and 10nM 
orexin. b) After 30 seconds, Dopamine was added to a final concentration of 5µM. After 330 
seconds, Histamine was added to a final concentration of 5µM, and after 390 seconds, Carbachol 
was added to a final concentration of 50µM.  
a) 
b) 
154 
 
 
Figure 5.22. Stimulation with Blue Light Increases Intracellular Calcium in SH-SY5Y Cells Stably 
Expressing CoChR in an Intensity and Extracellular Calcium Dependent Manner: Live undifferentiated 
SH-SY5Y cells, stably expressing CoChR-RCAMP were imaged every 200ms in AIR medium at 570nm, 
and a) simultaneously excited using an argon laser at 488nm for 200ms every 30 seconds, or b) 
stimulated with either low or high light intensities (10% 488nm or 50% 476nm + 50% 488nm laser 
power, respectively) every 20 seconds, or c) stimulated at high light intensity every 20 seconds in 
medium containing either 0.5mM or 1.8mM calcium. Frames captured during blue light stimulation 
were discarded from subsequent data analysis, and lines were not subjected to any smoothing.  
 
 
a) 
b) 
c) 
155 
 
The fluorescent imaging of RCAMP therefore confirmed in real time that treatment with drugs and 
CoChR mediated light stimulation induced intracellular calcium responses in undifferentiated SH-SY5Y 
cells. In contrast, retinoic acid differentiated cells showed severely dampened responses to drugs and 
light and revealed considerable variation between the calcium responses of adjacent cells. The 
desensitising effect of retinoic acid was not rescued by prolonging the length of differentiation up to 
six weeks, nor was the expression of the construct abrogated by differentiation. Therefore, despite 
retinoic acid inducing morphological changes that cause SH-SY5Y cells to more closely resemble 
mature neurons, retinoic acid differentiation appeared to reduce the molecular response to excitatory 
signals.  
Remarkably, supplementing the medium with retinal or retinyl acetate was not necessary for light 
induced calcium responses, presumably because the serum component contains sufficient levels of 
retinoids. Indeed, supplementation with retinoids actually reduced the magnitude and reproducibility 
of the observed calcium responses, resembling the effect of retinoic acid. Removal of supplemental 
retinal from the medium of SH-SY5Y cells also led to an increased and reproducible level of light 
mediated Fos induction, as assayed by qPCR, consistent with a greater proportion of cells responding 
to light stimulation. SH-SY5Y cells have previously been shown to convert retinol and retinal to retinoic 
acid, and chronic exposure to low levels of retinyl acetate has been shown to induce a partial 
differentiation of SH-SY5Y cells. Although treatment with retinal does not induce the rapid extension 
of processes characteristic of retinoic acid differentiation, it appears that supplementation with retinal 
triggers a cellular response that leads to a dampened response to excitatory stimulation.  
 
  
156 
 
5.2.9. Transcriptomic Analyses Reveals an Acute Response to CoChR 
Activation in SH-SY5Y Cells 
To determine to what extent light activated SH-SY5Y cells recapitulate the transcriptomic changes 
observed during in vivo sleep deprivation, we carried out a timecourse experiment to produce samples 
for RNA-Seq. Undifferentiated, high confluency SH-SY5Y cells were exposed to flashing blue light for 
12 hours, and lysed every 6 hours. Sampling began 12 hours before the onset of blue light and finished 
24 hours following the end of stimulation. Therefore the timecourse spanned a total of 48 hours. A 
second set of SH-SY5Y cells expressing CoChR were maintained in the dark and sampled at the same 
timepoints to act as the control group. 
 
 
Differential expression analysis revealed 3082 transcripts were significantly differentially expressed 
immediately following 12 hour exposure to blue light (Fig 5.23.). Of these, 30% were also differentially 
expressed following only 6 hours blue light exposure. Remarkably, the expression of 85% of the 
transcripts differentially expressed following 12 hours blue light rebound to baseline levels following 
only 6 hours recovery.  
Figure 5.23. Blue Light Stimulation of SH-SY5Y cells expressing CoChR induces the Differential 
Expression of more than 3000 Transcripts: Live undifferentiated SH-SY5Y cells, stably expressing 
CoChR-RCAMP were exposed to flashing blue light for 12 hours, beginning at 0 hours, and allowed 
to recover for up to 24 hours. Cells were sampled in triplicate every 6 hours and subjected to 
sequencing based transcriptomic analyses and subsequent Cufflinks based differential expression 
analysis. The total height of each bar represents the number of genes differentially expressed in 
light exposed cells compared to cells maintained in darkness at that timepoint. The blue portion of 
each bar indicates the number of genes that are also differentially expressed following 12 hours 
exposure to blue light. 
 
 
157 
 
The 1740 transcripts significantly upregulated by 12 hours blue light exposure were statistically 
enriched in genes relating to the regulation of transcription, chaperone function, the endoplasmic 
reticulum, the cell cycle, mRNA splicing, and sterol biosynthesis. Genes relating to the regulation of 
transcription, chaperone function, the endoplasmic reticulum, the cell cycle and sterol biosynthesis 
were also significantly upregulated following only 6 hours blue light exposure. In contrast, none of 
these functional gene groups are statistically enriched amongst upregulated genes during the recovery 
phase, indicating that the expression of these genes is tightly linked to activity. Instead, genes 
upregulated during the recovery phase are enriched in membrane proteins and genes associated with 
Golgi transport following 6 hours recovery, and glycolysis and microtubule function following 12 hours 
recovery.  
 
Table 16: Genes Upregulated following Blue Light Exposure 
Timepoint Functional Cluster q-value 
6hr BL Regulation of Transcription 
Chaperones 
Endoplasmic Reticulum 
Cell Cycle 
Sterol Biosynthesis 
3x10-16 
4x10-9 
5x10-6 
9x10-4 
2x10-8 
12hr BL Regulation of Transcription 
Chaperones 
Endoplasmic Reticulum 
Cell Cycle 
mRNA Processing 
Sterol Biosynthesis 
2x10-21 
8x10-11 
2x10-10 
1x10-8 
3x10-7 
8x10-6 
12hr BL, 6hr R Membrane Proteins 
Golgi Trafficking 
1x10-6 
6x10-3 
12hr BL, 12hr R Glycolysis 
Microtubule 
3x10-7 
1x10-3 
 
Despite a similar number of transcripts being downregulated as upregulated following 12 hours blue 
light exposure, there was remarkably little functional enrichment observed in downregulated genes. 
Genes involved in cell junctions and mitochondrial function were statistically downregulated, 
following 12 hours blue light, whilst genes relating to mitochondrial function were also downregulated 
158 
 
following only 6 hours of blue light. Following 6 hours recovery, downregulated transcripts were 
enriched in histones and synapse, and following 12 hours recovery, the expression of histones, DNA 
replication genes, respiratory chain genes, and ribosomal proteins are repressed. 
Table 17: Genes Downregulated following Blue Light Exposure  
Timepoint Functional Cluster q-value 
6hr BL Mitochondria 3x10-2 
12hr BL Cell Junction  
Mitochondria 
3x10-2 
4x10-2 
12hr BL, 6hr R Histones 
Synapse 
4x10-16 
2x10-4 
12hr BL, 12hr R Histones 
DNA Replication  
Respiratory Chain  
Ribosomal Protein 
2x10-36 
3x10-9 
1x10-8 
1x10-7 
 
JTK analysis was carried out to identify which genes exhibit circadian oscillations in expression. 
Remarkably, no transcripts were identified as exhibiting statistically significant rhythmic expression in 
cells maintained in darkness. 37 transcripts were identified as rhythmic in the light treated group, 
however these transcripts may be false positives due to a modulation of expression induced by blue 
light. The absence of rhythmic transcripts indicates that the individual cellular clocks of the cell 
population were not synchronised. Therefore the criteria used in Section 3.2.3. to identify homeostatic 
genes in vivo (genes with a rhythmic expression under control conditions, whose expression is 
modulated by sleep deprivation in a dose dependent manner) cannot be applied in this in vitro 
experiment.  
ANOVA analyses identified that the expression of 373 genes increased at least 2 fold following 
12 hours illumination and had a significant interaction between treatment group and timepoint. The 
genes that increased were enriched in genes relating to the endoplasmic reticulum, cholesterol 
synthesis, chaperones and circadian regulators. In contrast, the 394 genes whose expression reduced 
by at least 50% and was identified as having a significant interaction between timepoint and treatment 
were significantly enriched only in postsynaptic genes, specifically cholinergic receptor subunits.  
 
159 
 
 
 
 
  
Endoplasmic 
Reticulum
Cholesterol 
Biosynthesis
Chaperone 
Genes
Circadian 
Regulation
Synapse 
Genes
Chaperon  
Genes
Circadian 
Regulation
Synapse 
Genes
Figure 5.24. Blue Light Illumination Modulates the Expression of Genes Related to Specific Functions: 
The transcripts modulated by blue light were enriched in genes related to the endoplasmic 
reticulum, cholesterol biosynthesis, chaperone proteins, circadian regulation and synapse genes. 
Genes that appear in multiple clusters are plotted in each individual region.  
 
160 
 
5.3. Light Activation of SH-SY5Y cells induces similar functional Gene 
Groups, but not the same genes, as in vivo Sleep Deprivation 
To what extent do the changes identified following blue light exposure of SH-SY5Y cells compare to 
those seen following sleep deprivation of mice? We can assess the similarity by determining the 
overlap of individual genes modulated following treatment and the overlap of overrepresented 
functional gene groups affected by treatment. 
5.3.1. Direct gene comparison 
At the level of individual gene comparison between mouse cortex and human SH-SY5Y cells, 
approximately 60-65% of genes identified as expressed in one model were also expressed in the other. 
Unlike mouse cortex, no genes were identified as undergoing rhythmic expression in untreated cells, 
and the rhythmic expression of no transcript was conserved across models. Of the 10148 genes 
expressed in both mouse cortex and SH-SY5Y cells, 1282 (13%) were identified as being modulated 
following 12-hour blue light exposure. Of the genes identified as rhythmic in mouse cortex, 17% were 
modulated by blue light, whilst 16% of genes modulated by sleep deprivation in mouse cortex were 
also modulated following opsin mediated activation of SH-SY5Y cells. Therefore, the enrichment of 
sleep deprivation associated genes amongst those modulated by blue light appears slight, suggesting 
that blue light activation of SH-SY5Y cells does not faithfully recapitulate the same gene expression 
changes induced in mouse cortex during sleep deprivation.  
 
 
 
B C DA
Figure 5.25. Limited Overlap of Blue Light Inducible and Sleep Associated Mouse Genes: Panel (A) 
demonstrates the overlap of total gene expression between the two models. 10148 genes were 
expressed in both mouse cortex (blue circle) and SH-SY5Y cells (red cirlce). Panel (B) shows the 
proportion of commonly expressed genes modified by blue light. Panel (C) shows the proportion of 
commonly expressed genes identified as rhythmic in mouse cortex modified by blue light, whilst 
Panel (D) shows the proportion of commonly expressed genes identified as sleep deprivation 
dependent in mouse cortex that are modified by blue light. In Panels (B-D), the blue section 
represents the number of genes modulated by blue light, whilst the grey section represents the 
number of genes in that category unaffected by blue light exposure.  
161 
 
In contrast, the enrichment amongst blue light sensitive transcripts of genes associated with the 
endoplasmic reticulum and synapse, chaperone function and circadian regulation is similar to the 
functional clusters identified in mouse cortex following sleep deprivation, whilst the enrichment of 
genes associated with cholesterol biosynthesis is reminiscent of sleep deprivation data published by 
other groups (Mackiewicz et al. 2007).  
 
5.3.2. Cholesterol Biosynthesis is Induced in SH-SY5Y Cells, but not Sleep 
Deprivation 
Previous studies have linked cholesterol synthesis to sleep in mouse brain, but specifically those 
studies found that sleep deprivation leads to a slight downregulation in cholesterol biosynthetic gene 
expression. In contrast, blue light activation of SH-SY5Y cells induces a marked upregulation of the 
pathway. Several fatty acid biosynthetic genes are also upregulated, indicative of sterol regulatory 
element-binding protein (SREBP) controlled transcription (Brown & Goldstein 1997).  
 
Acetyl
CoA
Acetoacetyl
CoA
Mevalonate Squalene Cholesterol
Malonyl
CoA
Saturated
Fatty Acids
Unsaturated
Fatty Acids
Acat2
Hmgcs
Hmgcr
Cyp51a1
Dhcr24
Dhcr7
Msmo1
Sc5d
Mvd
Idi1
Fdft1
Nshdl
Fasn
Elovl4
Elovl5
Hacd2
Hacd3
Scd
Figure 5.26. Blue Light Activation of SH-SY5Y Cells Induces the Expression of Cholesterol and Fatty Acid 
Synthetic Pathways: Selected metabolic intermediates in the cholesterol and fatty acid biosynthetic 
pathways are displayed in black. Displayed in blue are enzymes responsible for the catalysis of 
intermediate steps that were found to be upregulated at the transcript level in opsin expressing 
SH-SY5Y cells following blue light exposure. 
162 
 
Brain cholesterol is almost exclusively synthesised locally, and so net synthesis of cholesterol rich 
components such as myelin sheaths or lipid rafts requires the local induction of cholesterol synthesis 
(Helga & Pierre 2002; Jurevics et al. 1997). SREBP signalling is dependent on several cellular systems, 
including the molecular clock (Gilardi et al. 2014) and receptor tyrosine kinase or NMDA-receptor 
signalling pathways (Porstmann et al. 2005; Taghibiglou et al. 2009). The activation of cholesterol 
synthesis in response to excitatory NMDA-signalling is consistent with the induction seen in response 
to opsin mediated activation of cells. Cholesterol has previously been shown to influence neuronal 
activity and dendrite growth (Bukiya et al. 2017; Moutinho et al. 2016), whilst BDNF induces the de 
novo synthesis of cholesterol in cortical neurones, which is subsequently deposited in lipid rafts 
(Suzuki et al. 2007). The strong induction of cholesterol synthesis in response to activity in SH-SY5Y 
cells may therefore indicate the production of lipid rafts and the induction of synaptic homeostasis. 
Since BDNF is induced in response to sleep deprivation in vivo, it’s remarkable that cholesterol 
synthesis is either unaffected or downregulated by sleep deprivation. The contrasting response of 
SH-SY5Y cells may reflect a very low spontaneous electrical activity of the cell-line prior to optical 
stimulation, which may greatly decrease baseline expression of activity related genes, resulting in an 
inflated fold change of these genes following stimulation. Similarly, if cholesterol synthesis is activity 
dependent, the available pool of cholesterol in cells prior to blue light exposure may be very low, and 
so the production of lipid rafts would oblige de novo cholesterol synthesis. 
  
5.3.3. Clock Genes are Modulated in SH-SY5Y Cells and by Sleep 
Deprivation  
A major conclusion of the transcriptomic profiling of mouse cortex was that rhythmic expression of 
transcripts is progressively reduced with increasing durations of sleep deprivation, with 12-hour sleep 
deprivation reducing the number of rhythmic transcripts by 98%. The absence of any rhythmic 
transcription under control conditions in this SH-SY5Y dataset excludes the possibility of a similar 
comparison being made here. However, it is noteworthy that genes involved in circadian regulation 
were an overrepresented class amongst genes that were strongly induced by blue light exposure. 
Previous studies have demonstrated that the expression of clock genes in mammalian cell lines can be 
induced by raising intracellular calcium or cAMP (Balsalobre et al. 2000), resulting in subsequent 
rhythmic gene expression. Similarly, Per2 induction by neuronal activity has been shown both in vitro 
and in vivo, and is linked to the presence of cAMP/Ca2+ response elements in the promoter region of 
Per2 (Balsalobre et al. 2000; Koyanagi et al. 2011; Yan & Okamura 2002). Following cessation of 
illumination however, the expression of core clockwork genes returns to baseline within 6 hours, and 
moreover does not oscillate in the following 24 hours. Therefore, the elevated expression of clock 
163 
 
genes appears to be tightly linked to blue-light activation and does not appear sufficient to entrain the 
cellular clock of SH-SY5Y cells.  
 
  
 
  
A
B
Figure 5.27. The Expression of Core Clockwork Genes is Transiently Induced by Blue Light, but does 
not Oscillate in Untreated Cells: Core clockwork genes are induced by blue light exposure compared 
to untreated controls but rapidly return to baseline following cessation of light exposure. The 
heatmap plots the z-score normalised expression of genes compared to untreated controls, with 
red indicating higher expression and blue indicating lower expression. The timepoint is indicated 
below, with blue light exposure occurring between 0-12 hours (A). Individual gene expression 
profiles are plotted in black for untreated cells, and in red for treated cells. The blue bar represents 
the timing of blue light exposure. Expression is plotted in FPKM (fragments per kilobase per million 
reads).  
164 
 
5.3.4. Chaperone Genes are Induced in both SH-SY5Y cells by Sleep 
Deprivation 
Similar to in vivo sleep deprivation, several chaperone genes are induced following blue light 
illumination of opsin expressing SH-SY5Y cells. Genes induced compared to dark-maintained cells 
include CHORDC1, DNAJC3, HSP90AA1 and HSPA5, all of which are similarly upregulated in mouse 
cortex during sleep deprivation. Like the overwhelming majority of blue-light modulated genes, the 
induction of chaperone genes is limited to within the illumination period and rapidly return to baseline 
expression levels in darkness.  
Several chaperone genes are named heat shock proteins due to their involvement with the cellular 
response to elevated temperature (Richter et al. 2010). One unavoidable consequence of illuminating 
cells is that absorption of light by the cell medium or any part of the dish will result in a local 
temperature increase. Because elevated temperatures quickly induce chaperone expression, it is 
appropriate to be cautious before concluding that light exposure induces chaperone expression 
through a photocurrent induced increase in activity, rather than through an unwanted heating effect. 
However, introducing distance between the light source and cells, placement of fans and pauses 
between cycles of illumination reduced temperature increases during illumination to approximately 
0.3oC, which is comparable to the variation of temperature measured at different levels within the 
incubator. Conversely, our preliminary transcriptomic analysis of SH-SY5Y cells found that chaperone 
genes were induced particularly in opsin expressing cells. Although conditions varied greatly between 
the two experiments, it is noteworthy that cells involved in preliminary experiment underwent a 
tenfold greater increase in temperature than those in the final transcriptomic timecourse experiment, 
and yet the expression of chaperone genes in wild type cells was largely unaffected. Whilst it is 
possible that the expression of opsin containing constructs sensitises cells to heat stress, this data is 
consistent with the expression of chaperone proteins being linked to neuronal activity. Indeed, 
exposure of primary neurones to glutamate induces HSPA5 expression in the absence of a 
temperature increase, and is associated with the local activation of the unfolded protein response at 
postsynaptic dendrites (Atsushi et al. 2017).  
165 
 
 
 
The expression of chaperone genes has previously been linked to sleep deprivation in both flies (Shaw 
et al. 2000) and rodents, including following adrenalectomy (Maret et al. 2007; Mongrain et al. 2010; 
Terao et al. 2003). It appears that chaperone induction has a protective function, with temperature 
mediated increases in chaperone expression protecting against the lethality of subsequent sleep 
deprivation in flies and glutamate excitotoxicity in primary neurones (Rordorf et al. 1991; Shaw et al. 
2002). Since chaperone proteins are conserved across kingdoms, they may be a conserved feature of 
sleep deprivation across species and might hint at some of the fundamental functions of sleep. It is 
also intriguing to note that chaperone genes are induced in peripheral tissues such as the liver during 
A
B
 
  
  
  
    
        
 
 
 
 
       
 
 
  
  
  
    
        
 
 
 
 
      
 
   
    
    
    
    
    
        
 
 
 
 
        
 
  
   
   
   
    
        
 
 
 
 
     
C
 
   
   
    
        
 
 
 
 
     
 
   
   
   
    
        
 
 
 
 
      
 
   
   
   
    
        
 
 
 
 
      
 
   
   
   
    
        
 
 
 
 
      
Figure 5.28. The Expression of Chaperone Genes is Induced only in Illuminated Opsin Expressing 
SH-SY5Y cells: Panel A shows the expression of sleep deprivation induced chaperone genes during 
12 hour illumination in CoChR expressing SH-SY5Y cells (red line), compared to dark maintained 
CoChR expressing cells (black line). Panels B and C shows the expression of genes during 6 hour 
illumination in ChR2-2A-Hal expressing SH-SY5Y cells (red line), compared to illuminated wild-type 
cells (black line). Note that timepoints are spaced by 6 hours in Panel A and by 3 hours in Panel B. 
Illumination of opsin expressing cells induces chaperone expression, but opsin expression or 
illumination alone does not. The timing of blue light exposure is indicated by the blue shading.  
166 
 
sleep deprivation, and so their sleep-related function may not be limited to only within the brain. 
Moreover, our study found that chaperone expression is associated with both spontaneous and 
enforced wakefulness, indicating that they play a role in physiological wakefulness, rather than 
responding only to a stress specifically associated with exceptionally prolonged periods of waking.  
Although mammalian body temperature is closely linked with the state of wakefulness, it is likely that 
chaperones perform a temperature-independent function during sleep deprivation. The commonly 
proposed function of chaperone protein induction during sleep deprivation is to reduce translation of 
proteins, which is expected to decrease additional cellular stress (Harding et al. 2000). Indeed, ageing 
reduces the induction of chaperone proteins and increases the abundance of pro-apoptotic markers 
in mouse cortex following sleep deprivation (Naidoo et al. 2008). Since protein synthesis is 
energetically costly, the induction of chaperones may act to limit the anabolic load occurring during 
wakefulness.  
5.3.5. SH-SY5Y Transcriptomic Response may be Orchestrated by Creb 3 
Intriguingly, cholesterol biosynthetic enzymes and the unfolded protein response are both induced by 
the transcription factor Creb3, also known as Luman (Ying et al. 2015). Luman mRNA is locally 
translated at sites of axonal damage and is subsequently transported to the nucleus where it 
stimulates transcription (Ying et al. 2014). Luman mRNA knockdown disrupts axonal regeneration in 
response to injury, whilst previously it has been shown that components of the unfolded protein 
response are involved in BDNF neurite outgrowth (Hayashi et al. 2007). Therefore both chaperone and 
cholesterol biosynthesis induction during blue light exposure may support neurite outgrowth in 
SH-SY5Y cells. Consistent with this, Luman expression is elevated in SH-SY5Y cells following 12 hour 
blue light exposure. Although our in vivo transcriptomic screen did not find Luman to be regulated by 
sleep deprivation, a previous study found that Luman demonstrated markedly increased DNA-
hydroxymethylation in mouse cortex following 6 hour sleep deprivation (Massart et al. 2014), 
indicating that Luman may play a role in sleep in vivo.  
  
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5.3.6. Experimental Design Considerations 
The ultimate aim of this project was to produce a low cost, human derived in vitro model of sleep and 
sleep deprivation. Outlined in the results section is our approach and experimental findings whilst we 
were developing this model. The expression of light-gated channels in SH-SY5Y cells was used to 
promote membrane depolarisation in response to light, and therefore treated cells were actually 
subjected to two interventions: expression of an opsin and exposure to light. Exposure to high light 
levels likely has opsin-independent effects on the transcriptome of cells, but similarly, the expression 
of the opsin itself almost certainly has profound effects, including on the growth rate of the cells. The 
two intuitive controls used to determine the effects of blue light exposure on opsin expressing SH-SY5Y 
cells were either cells of the same genotype maintained in darkness, or cells of a different genotype 
maintained in the same light routine. Ideally, both sets of controls would be carried out in each 
experiment, but this would not only impose considerable additional cost, but also complicate the 
design and execution of the experiment. During the development and characterisation of this model, 
the control group chosen to compare the treated group against was therefore an important 
consideration, and the best choice of control groups varied between experiments.  
Initially it was important to determine whether the opsin was functionally able to modify transcription, 
and because cells were subjected to significant heating during preliminary experiments we were also 
expecting large scale changes in expression during illumination through opsin-independent 
mechanisms. In the early stages of the development of the model, the control group was therefore 
chosen to be wild-type cells treated with the same light exposure as opsin expressing cells. In this way, 
the effect of the opsin itself was identified, allowing us to choose a suitable opsin to activate SH-SY5Y 
cells with. 
However, following the introduction of physical refinements such as the elevation of the cells above 
the light source and the installation of fans to reduce heating and promote cooling during illumination, 
our concerns about the heating effects of illumination were reduced. Subsequently, we had also 
confirmed through microscopy and qPCR that CoChR was active in SH-SY5Y cells and able to trigger 
intracellular events in response to light. We had also found that the response to light was modulated 
by several environmental cues, including cell density, time between plating and light exposure, 
and pH. Therefore, in later experiments we were less concerned about the effects of heating (because 
the increase in temperature during illumination had been decreased by 90%), and more concerned 
about other aspects of the environment that cells were exposed to during illumination. We noticed 
that SH-SY5Y lines expressing different opsins demonstrated differences in growth rate. Opsin 
expressing cells demonstrated longer doubling time in culture compared to wild-type cells, 
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presumably due to the presence of puromycin in the growth medium and the burden imposed by the 
high expression of the opsin and antibiotic resistance genes. Therefore, during preparation for 
experiments, it was difficult to match cell density between different genotypes, which also led to 
different rates of acidification of the medium following plating out the cells. These secondary effects 
of differing genotypes, coupled with the expected changes in gene expression in response to the highly 
expressed opsin construct and the presence of antibiotic, would possibly change the baseline 
expression of several genes and also introduce variation between experiments. Therefore, for later 
experiments, we opted for dark maintained, opsin expressing cells as a control for light exposed cells.  
A third possible control group would have been a cell line expressing a non-functional opsin which is 
then exposed to the same light regime as CoChR expressing cells. We explored this possibility but 
could not identify a suitable non-functional construct. Introduction of the R120A and E107A mutations 
has previously been shown to reduce channelrhodopsin photocurrents by up to 90% (Kato et al. 2012), 
but whether the corresponding mutations in CoChR would be sufficient to remove all opsin mediated 
effects was unknown, and a partial effect of the opsin would greatly complicate subsequent data 
analysis. 
It should also be noted that the possible experimental designs for downstream experiments are 
limited if the chosen control group cannot respond to light through opsin mediated pathways. 
Ultimately, our transcriptomic profiling experimental design only exposed cells to light for 12 hours, 
whilst control cells were maintained in constant darkness. Since RCAMP recording of cells did not 
identify any spontaneous intracellular calcium events, it could be argued that the default state of cells 
is a low activity state. Given the complete absence of identified rhythmic genes in the dark maintained 
cells, it appears this default state does not rhythmically transition into a more active state that may 
reflect the sleep-wake cycle. Therefore, this experiment might not have modelled sleep deprivation 
per se, but instead a single period of waking, the duration of which is less than the typical wake bout 
for humans. In hindsight, another experimental design may have been to expose cells to a series of 
12 hour light-dark cycles before the start of the experiment, to recreate a normal sleep-wake cycle. 
Sleep deprivation could then be modelled by extending the illumination whilst the controls would be 
exposed to darkness. Importantly, a prerequisite for such experimental designs is that both the control 
and “sleep deprivation” group respond to blue light in the same manner during the acclimatisation 
period that mimics the normal day.  
  
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5.3.7. Perspectives on Future Cell Studies 
We have undertaken a reductionist approach to investigating sleep by characterising the effect of 
prolonged activity on SH-SY5Y neuroblastoma cells. Reductionist approaches have been extensively 
used to model intracellular changes in complex processes, such as diabetes, but have yet to be widely 
used in the sleep field. The premise for this model is based on the work of Hinard et al, who showed 
that the transcriptional effects of sleep deprivation can be recapitulated in vitro by pharmacological 
activation of primary neurons derived from mouse cortex (Hinard et al. 2012). As a trial, we treated 
SH-SY5Y cells with the same cocktail and showed that they still respond at the transcript level to 
pharmacological activation. However, the temporal precision of optogenetic tools allows the rapid 
and complete removal of stimulus, offering the possibility of modelling the subsequent recovery phase 
or inducing intermittent activity. Stable expression of opsin based constructs in neuroblastoma cells 
has provided access to an effectively unlimited quantity of low cost, low variability neuron like cells 
whose activity can be easily manipulated. These characteristics are some of the primary advantages 
of taking a reductionist, in vitro approach.  
A key consideration for future in vitro experiments is whether to continue using SH-SY5Y cells as a 
model system or to use another cell line, primary neuron or explant-based culture. Primary neuron or 
brain explant-based approaches would facilitate the comparison with in vivo mouse data, and so 
would be especially useful in determining which molecules modulated by sleep deprivation are 
dependent on local neuronal activity, rather than associated with wake controlling nuclei within the 
brain or circulating hormones such as corticosterone. However, such an approach would not be likely 
to offer any more insight than in vivo studies into human specific sleep modulated molecules, nor 
would it facilitate a low cost, high-throughput screening platform for potential wake modulating 
compounds.  
Stem cell culture offers an intermediate solution, whereby human cells very closely matching a 
neuronal phenotype, or even organoids closely matching brain tissue (Lancaster & Knoblich 2014), can 
be produced. Indeed, opsin mediated activity was recently demonstrated in human stem cell-derived 
neurones (Klapper et al. 2017). Modulating the activity of human derived cerebral organoid cultures 
could provide an extraordinary level of insight into the molecular effects of sleep deprivation in 
humans. However, the high cost and labour intensity of stem cell culture would hinder the 
development of a stem cell-based model and its eventual use as a drug screening platform.  
The low cost and scalability of cell lines makes them the most suitable model for high-throughput 
screens. However, the fundamental disadvantage of cell line based approaches is the question of their 
validity; specifically whether an immortalised adrenal neuroblastoma cell line can be used to model 
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senescent, long lived neurons in the brain. Although several previous studies had outlined similarities 
between SH-SY5Y cells and neurones, those studies used a range of differentiation protocols, which 
appeared to interfere with opsin mediated gene expression. Replacing SH-SY5Y cells with another cell 
line is one option for future experiments. NG108-15 cells, which are known to have an entrainable 
cellular clock (Hampp et al. 2008), had been considered for this project, but were rejected because 
they are rat-mouse hybridoma cell line, which was expected to complicate sequencing based 
transcriptomic experiments. Other cell lines commonly used to model neurons include the human 
derived Ntera2 cells (Pleasure & Lee 1993), rat derived PC12 cells (Greene & Tischler 1976) and mouse 
derived Neuro 2a cells (Tremblay et al. 2010). Although these cell lines may resemble mature neurons 
in several aspects, whether they would provide more suitable sleep-deprivation models than SH-SY5Y 
cells is unclear.  
Model  
System 
Cost and Labour 
Intensity 
Validity as 
 Neuronal Model 
Insight into  
Human Biology 
Use in Drug 
Screens 
In vivo Sleep 
Deprivation 
+++ ++++ + ++ 
Primary Neurons 
and Explant Culture 
+++ +++ + + 
Stem Cell derived 
Neuronal Culture 
++++ +++ +++ + 
Immortalised  
Cell Line 
+ + ++ ++++ 
 
5.3.8. Future in vitro experiments 
Further characterisation of the opsin expressing cells is required for SH-SY5Y cells to continue to be 
used as an in vitro model of sleep deprivation. Electrophysiological characterisation of the SH-SY5Y 
cells used in this experiment would quantify the electrical activity of the cells more directly than the 
RCAMP-based calcium imaging experiments performed in this thesis. Combining electrophysiological 
measurements with specific inhibitors of ion channels would also identify which ion channels are 
operating during illumination, and also whether prolonged illumination modulates the activity of 
specific ion channels. To some extent, these experiments could be carried out using genetically 
encoded fluorescent voltage sensors (Perron et al. 2009), however bleaching of the sensors during 
Figure 5.29. Comparison of Neuronal Model Systems: Several model systems are available to 
researchers investigating neuronal biology. The choice of which system to use depends on the 
ultimate end goal of the experiment, as well as the available resources.   
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opsin activation may complicate the precise quantification of membrane electrical gradients in this 
manner.  
A major failing of the experimental design used in this study is the lack of 24 hour rhythms exhibited 
in the transcriptome of control cells. Efforts to entrain the molecular clock of SH-SY5Y cells using serum 
shock failed, whilst pre-treatment of SH-SY5Y cells with dexamethasone, which is routinely used to 
synchronise the cellular clocks of other cell lines (Rey et al. 2016), abrogated subsequent c-FOS 
induction in response to blue light. However, a cell line with a functional molecular clock may not 
actually be desirable. Core clock genes were robustly induced by illumination in SH-SY5Y cells, whilst 
opsin expressing U20S cells demonstrated a phase shift following illumination, indicating that the 
resetting of the molecular clock would confound transcriptomic studies in cells with functional 
rhythms.  
One approach to recreate rhythmic expression in SH-SY5Y cells may be to subject both the control and 
“sleep deprived” cells to 12:12 light-dark cycles prior to the beginning of the timecourse. The control 
group would continue on this 12:12 cycle throughout the time course, whilst the treated group would 
have the illumination extended into the habitual rest phase. However, how to suitably model the 
subsequent recovery phase following sleep deprivation is not readily apparent. One option would be 
to continue the light dark cycles, such that the treated group would be exposed to 36 hour continuous 
illumination. This may be appropriate if we were trying to model the effects of 36 hour enforced 
wakefulness, but is flawed when comparing to mice that have the opportunity to sleep during their 
habitual active phase following sleep deprivation. A second, more novel option is to model the 
illumination pattern during the recovery phase, or perhaps even the entire experimental design, on 
EEG determined wake-sleep patterns of mice. Indeed, one experimental advantage of optogenetic 
tools over pharmacological agents is that stimuli can be introduced and withdrawn with millisecond 
precision, and so even the fragmented sleep-wake patterns of rodents could be closely mimicked with 
illumination patterns. Basing the illumination pattern on EEG data of undisturbed and sleep deprived 
mice might aid in directly comparing the subsequent cell-line expression data to that found in vivo. 
If SH-SY5Y cells are capable of accumulating homeostatic sleep pressure, it is feasible that the cells 
would release a somnogenic signal molecule into the extracellular space during illumination. The 
release of signal molecules could be investigated using a perfusion system to continuously sample cell 
medium during constant illumination, and the molecules identified using mass spectrometry based 
proteomic or metabolomic approaches. Any molecules identified in this screen could be intracranially 
perfused into mice, and the in vivo effect on sleep-wake cycles monitored either through electro-
encephalography or video tracking. Whether SH-SY5Y cells release any neuroactive compounds during 
172 
 
illumination could be more directly tested in vitro. The cell medium of light treated cells could be 
transferred to a second well containing SH-SY5Y cells, primary neurones or an explant culture. 
Whether the donor medium is sufficient to modulate neuronal activity in the recipient cell culture can 
be determined by electrophysiological parameters, fluorescent reporter intensity or c-Fos expression.  
It is worth remarking that prolonged wakefulness in vivo eventually leads to the failure of neurones to 
electrically respond to stimuli (Bushey et al. 2015; Vyazovskiy et al. 2011). In contrast, CoChR directly 
and reliably depolarises the membrane, even following repeated stimulation (Klapoetke et al. 2014). 
Because CoChR is a microbial opsin, it is likely insensitive to mammalian pathways, such as the arrestin 
pathway, that limit the activity of receptor signalling. Therefore the expression and activation of 
CoChR in a neuronal cell may drive a supraphysiological duration of activity that bypasses the 
mechanisms through which neurons would usually enter an activity induced sleep-like state. 
Experimentally, this may be beneficial and aid in the identification of stress responses. However, 
enforcing activity in neurons past the physiological limits may induce non-physiological pathways or 
even cell death. Depending on the aims of the experiment therefore, the use of a step-function-opsin 
may be more appropriate to modulate activity of in vitro cells. The low conductivity and slow kinetics 
of step-function opsins promote a sustained subthreshold increase in excitability of neurons (Berndt 
et al. 2009). Therefore step-function opsins increases the spontaneous electrical activity of neurons 
without directly invoking action potentials, and so may be more suitable for experiments that aim to 
preserve the entry of neurons into a sleep-like state.  
 
  
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6. General Discussion 
 
6.1. What is Sleep Deprivation? 
This thesis aimed to outline the changes that occur at the molecular level during wake, sleep and sleep 
deprivation. The importance of the homeostatic and circadian control of sleep timing encouraged us 
to carry out a timecourse style experiment throughout, with tissue production being facilitated by a 
semi-automated sleep deprivation approach. We then characterised the effects of sleep deprivation 
at a system-wide level using omics approaches. The abundance of molecules during the course of a 
normal day and during sleep deprivation and subsequent recovery then allowed us to speculate on 
the involvement of these molecules in the response to sleep deprivation.  
Therefore, this thesis centred on the effect of two separate variables on the molecular biology of the 
brain: time and sleep deprivation. But what sleep deprivation represents is not straightforward, nor is 
deciding what is desirable from experimentally imposed sleep deprivation. In addition to prolonged 
wakefulness, sleep deprivation also represents a period of increased opportunity for eating, activity, 
social behaviour and light exposure. Since the experiments presented in this thesis then consider later 
timepoints, during which period mice are undergoing recovery sleep, the effect of these confounders 
is later reversed. However, it is important to recall that mice have fragmented sleep and spend about 
30% of their rest phase awake, and so already have opportunity for these activities throughout the 
day in between bouts of sleep. Perhaps larger confounders are that experimental sleep deprivation 
also incorporates a potential stress response, which may drive gene expression changes through 
glucocorticoid induction  (Mongrain et al. 2010), and a learning or exploratory response, which itself 
has been shown to induce sleep deprivation related genes such as Homer1a and Arc  (Vazdarjanova 
et al. 2002).  
During experimental design, the researcher is able to tailor conditions to precisely identify the effect 
of the exact variable that he is interested in. Experimentally, the confounders listed above, except the 
learning response to the application of sleep deprivation itself, could potentially be controlled. For 
example, mice can be adrenalectomized and single housed in darkness without access to food. 
Arguably, if the researcher is solely interested in the homeostatic drive for sleep, the experiment could 
also be performed on mice whose circadian rhythms have been ablated through either constant light 
exposure, genetic disruption of the molecular clock, or ablation of the SCN. Under such conditions, 
any changes seen could be reliably attributed to prolonged wakefulness, rather than a confounder of 
experimental sleep deprivation.  
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However, applying all of these interventions before sleep deprivation not only imposes significant 
labour and ethical constraints on the experiment, but also drastically changes the model from which 
the data are gathered. Although this model may more precisely identify wake dependent genes, and 
so may be interesting from a basic science perspective, the model is several steps further removed 
from humans, limiting the translational uses of the data. Furthermore, in trying to remove 
confounders during an experiment examining the homeostatic response to sleep deprivation, a 
researcher may unwittingly begin to unpick important components of the homeostatic machinery 
itself. Therefore, by imposing several experimental interventions to generate a better controlled 
experiment, the resulting data may indicate wholly unphysiological responses. Based on this 
reasoning, the experiments in this thesis involved sleep deprivation that was performed on wild type 
mice under conventional housing conditions, resulting in potentially more confounded data but from 
a more biologically relevant source.  
 
6.2. What is the Aim of Sleep Deprivation? 
Considering what is the most desirable outcome from sleep deprivation is often overlooked. Generally 
it seems 100% wakefulness is the aim of sleep deprivation protocols, with total sleep deprivation being 
obtainable for the initial stages and then ranging from 80-95% wakefulness during longer term sleep 
deprivations. Total sleep deprivation has the advantage of being a clearly defined behavioural state, 
however, as well as being impracticable, is again unphysiological for mice. Perhaps, sleep deprivation 
should instead aim to achieve 75% wakefulness, similar to the proportion of time spent awake by mice 
during their active phase. However, any stress profile molecules that are suddenly induced during 
particularly high homeostatic pressure to immediately induce sleep may be overlooked by less severe 
sleep deprivation protocols. On balance therefore, it may be best to aim for total sleep deprivation 
but not be concerned if mice nevertheless spend about 10% of the time asleep.  
However, the practical difficulties surrounding applying 100% sleep deprivation introduces a further 
confounder that may vary between protocols and experimenters. During manual sleep deprivation, a 
mouse may be asleep for a couple of minutes whilst the experimenter is engaged with other cages. 
Because of the preceding sleep deprivation, that mouse will have accrued significant sleep pressure 
and therefore have a deeper sleep during this period, characterised by increased delta power and 
periods of slow wave sleep. Therefore, this mouse, over a period of 12 hours, will have prolonged 
periods of wake followed by prolonged periods of deep sleep, and so experience a more consolidated 
wake-sleep pattern. In contrast, a mouse undergoing automated sleep deprivation, such as that used 
in this thesis, may have a markedly different sleep pattern, despite spending a similar period of time 
175 
 
asleep. Once acclimatised to the movement of the bar, it was observed that mice typically gravitated 
to one corner of the cage and remained there for extended periods of time. Whilst in that corner, mice 
would have to only interact with the bar every 15 seconds, and so may have been able to sleep whilst 
the bar moved to the other end of the cage and returned back again. These mice therefore would 
never have a sleep opportunity longer than 15 seconds, but that opportunity would occur four times 
every minute. Entering into this experiment, the author of this thesis was uneasy that, depending on 
how long an individual mouse required to fall asleep, the total proportion of time spent awake during 
automated sleep deprivation could vary drastically. It was for this reason that manual sleep 
deprivation was applied in addition to the automated bar for the second half of 12 hour sleep 
deprivations performed in this thesis. Any sleep achieved by the mouse during automated sleep 
deprivation would be expected to be limited to light stage 1 sleep, with a near complete absence of 
deeper slow wave sleep or REM sleep. Therefore, even though gentle handling and automated sleep 
deprivation may achieve similar sleep deprivation efficiency in terms of duration of wakefulness, the 
stages of sleep achieved and the distribution of those stages may vary drastically with difficult to 
foresee effects. The existence and poorly understood effects of localised sleep further complicates 
differences between protocols. Total sleep deprivation, if achievable, may result in an increased 
incidence of local sleep, whilst consolidated opportunities for sleep may lower the occurrence of local 
sleep. Since the effects of altered sleep architectures is still poorly understood, the only practical 
solution may be to publish as much information about sleep architecture alongside molecular data, 
such that data from different deprivation techniques can be retrospectively reconciled.  
 
6.3. Sleep Architecture during Automated Sleep Deprivation.  
The architecture of sleep during sleep deprivation is therefore a significant experimental design 
feature, and so a noticeable omission from this thesis is the measurement of sleep and sleep 
architecture, including under typical conditions, during sleep deprivation and during subsequent 
recovery sleep. Steps were taken before submission of this thesis to quantify sleep patterns of mice 
subjected to our experimental sleep deprivation protocol, which centred on EEG and video tracking 
based methods. EEG is the gold standard of sleep quantification, and also has the distinction of being 
able to define different sleep stages in mice. However, the requirement for the surgical implantation 
of electrodes imposes ethical and training limitations that caused significant delays in generating this 
data. In contrast, video tracking represents a non-invasive approach to quantify sleep, using prolonged 
lack of movement as a proxy for sleep. Although not described in this thesis, video tracking was 
performed on single housed mice subjected to sleep deprivation, but the subsequent data proved 
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difficult to analyse and limited in its applicability. Significant video editing was required to account for 
the background movement of the woodchippings and to remove unequal light densities that 
interfered strongly with tracking software, and several assumptions were imposed when the mouse 
disappeared from view (usually due to climbing the sleep deprivation bar). Because tracking uses 
prolonged inactivity as a proxy for sleep, sleep during the movement of the bar was inevitably 
“quantified” as totally absent. Video tracking of the dark phase indicated that mice had been 
successfully sleep restricted, evidenced by an increase of inactivity, but without any indication of the 
architecture during recovery sleep or the percentage wakefulness during sleep deprivation. Due to 
the practical difficulties, the data yielded from this experiment was qualitative at best and offered no 
more insight than the experimenter’s observations, and as such was omitted from this thesis.  
Because of this, EEG experiments remained planned. During the corrections of this thesis, preliminary 
EEG data from the protocol used here was generated on single housed males. In line with the author’s 
expectations, sleep deprivation during the first 3 hours was total. Around 4 hours into sleep 
deprivation, an unusual pattern emerged, whereby mice would experience one epoch (4 seconds) of 
stage 1 sleep every 4 epochs. These sleep bouts were concentrated into small bursts, whereby mice 
would sleep 4 seconds every 16 seconds for approximately 20 minutes before exhibiting 20 minutes 
of total wakefulness. This pattern was extended throughout the remainder of the 12-hour automated 
sleep deprivation protocol, with total wakefulness averaging approximately 90%, despite the lack of 
additional manual sleep deprivation in this protocol. Importantly, there was not a single epoch of REM 
sleep, which may indicate why the expression of the REM sleep related VIP transcript was affected 
during sleep deprivation but was flat during the course of a normal day.  
 
6.4. Limitations of the Techniques Used in this Thesis 
Transcriptomic profiling of sleep deprived mouse cortex identified several genes previously implicated 
in sleep deprivation, and provided novel data tracking their recovery over the following 36 hours. 
Particularly striking was the observation that several genes previously implicated as demonstrating a 
homeostatic profile actually appeared to be dependent on the current state of the animal, rather than 
the total duration of recent wakefulness, with only Crh resembling the ideal homeostatic profile. Other 
genes (e.g. Gjb6, Mfsd2a, Sdc4, Sult1a1 and Vip) were induced in a seemingly dose-dependent manner 
for extended periods following sleep deprivation, and yet the expression of these genes does not 
appear to be associated with homeostatic sleep pressure in non-sleep deprived controls. Finally, the 
rhythmic amplitude of global transcription was found to be progressively dampened following 
increasing durations of sleep deprivation. 
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The large scale nature of this experiment was greatly facilitated by the comparable robustness and 
reproducibility of RNA-extraction, library preparation, sequencing and bioinformatic pipelines 
associated with RNA-Seq based transcriptomics. The limited technical variability, coupled with the 
large fold-changes in transcript expression that can be elicited by sleep deprivation or blue-light 
illumination of cells, allowed the author of this thesis to identify several sleep dependent genes with 
high confidence. However, multiple approaches are used to make alignment computationally less 
demanding, and during quantification of reads, several assumptions and approximations are built into 
software packages about how to normalise libraries, deal with duplicate reads and reads that align to 
multiple regions in the transcriptome. Different pipelines may deal with these problems in markedly 
different ways, whilst the researcher may impose further choices. Therefore, using different settings 
or pipelines to analyse the same raw transcriptomic dataset may yield different conclusions. In some 
respects, this observation emphasises the importance that different research groups perform similar 
experiments. Through the course of previous microarray studies, some sleep deprivation related 
genes have been repeatedly identified, whilst others are unique to specific screens. It is only through 
repeated experimentation that the “core” sleep deprivation genes are converged upon. 
However, the major drawback of transcriptomic studies is not their reliability, but their utility. 
Knowing that a transcript is upregulated during sleep deprivation is initially only of limited use for 
translational research. Indeed, a recently published study examining the effect of mouse strain on the 
transcriptomic correlates of sleep deprivation found that some wake dependent genes are not even 
conserved to other strains of mouse  (Diessler et al. 2018)! Although the expression of that gene can 
be experimentally modified in rodents through the use of transgenic lines or viral vectors, the 
therapeutic use of viral vectors in the human brain is a very invasive tool with which to treat sleep 
related disorders. Potentially, if the RNA codes for an extracellular signalling protein, infusion of the 
protein product or a peptide mimetic may play a therapeutic role. However, in the future, as the 
transcriptomic effects of ever more interventions are characterised, it may be possible to mine the 
available datasets and identify an intervention that induces a transcriptional response that mirrors 
that of sleep deprivation. This approach may identify novel treatments that counteract the effects of 
sleep deprivation.  
The rational design of drugs would perhaps be most greatly facilitated by understanding which 
proteins and especially which metabolites play a role in the biological response to sleep deprivation. 
The enzymatic activity or binding interactions of proteins can be disrupted or enhanced by small 
molecules, which if able to cross the blood-brain barrier may ultimately provide an orally available 
drug to combat sleep related disorders. Indeed, phosphodiesterase inhibitors have previously been 
identified as a potential therapeutic tool to combat the cognitive defects associated with restricted 
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sleep  (Guo et al. 2016; Vecsey et al. 2009). An even more direct approach would be to create mimetics 
of endogenous somnogenic small molecules to modulate sleep, similar to how caffeine interferes with 
the sleep-promoting effects of endogenous adenosine. Therefore, following the characterisation of 
the transcriptomic effects of sleep deprivation, we then sought to characterise the proteomic and 
metabolomic effects.  
Our proteomics dataset demonstrated that hundreds of proteins are affected by sleep deprivation, 
whilst very few were statistically altered in undisturbed animals. However, the overlap between the 
proteomic and transcriptomic datasets was disappointingly small, a surprisingly common observation 
(Maier et al. 2009). Recently, a phosphoproteomic screen following 6 hour sleep deprivation indicated 
that there are large scale phosphorylation events following sleep deprivation  (Wang et al. 2018). That 
same study identified only a handful of proteins whose abundance were significantly changed 
following sleep deprivation, indicating that the proteomic effect of short term sleep deprivation may 
be very small. This general trend is consistent with the concept that anabolism predominates during 
sleep, and therefore during wake, post-translationally modifying existing proteins rather than de novo 
synthesis may the mechanism through which protein activities are modulated in response to 
prolonged wakefulness. It may be intriguing to perform sleep elongation studies to determine 
whether the effects of excessive sleep are also mediated through modifications or through additional 
protein synthesis.  
Although the abundance of proteins from different samples within the same multiplexed experiment 
can be compared with high precision, normalisation and comparison between different multiplexes is 
very difficult. Because there were only 10 isobaric tags available at the time of experiment, 
simultaneously interrogating the effects of different treatments at multiple timepoints was not 
technically feasible. In contrast, our metabolomic approach was able to simultaneously compare 
several timepoints in replicate. However, the precision of the metabolomic approach was not great 
enough to identify small fold changes in metabolite abundance. In hindsight, perhaps we should have 
instead examined a lower number of timepoints with many more replicates, in order to maximise our 
statistical power to identify sleep dependent molecules.  
 
6.5. Value of Global Omic Approaches 
The major advantages of examining biological processes at the system wide level are the possibilities 
to identify previously unimplicated pathways and the value of raw data for retrospective analysis, 
either by other groups with interests in different pathways, or by the same researcher following some 
further experimentation. With the ever decreasing cost of sequencing, transcriptome level profiling is 
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becoming a financially viable alternative to performing multiple quantitative PCRs, and as more 
transcriptomic datasets are accumulated and made publicly available, unexpected parallels may be 
drawn between seemingly very different conditions, potentially revealing novel treatment strategies.  
A major downside of bulk sequencing, proteomics and liquid-chromatography mass-spectrometry is 
that information is lost regarding which cells or tissue regions are responsible for the observed 
changes. Potentially, a gene may be upregulated in every single cell during sleep deprivation, but a 
more likely scenario would be that each gene is upregulated in specific subsets of cells. One solution 
may be to compare genes of interest with the Allen Brain Atlas  (Lein et al. 2006) or the recently 
published single cell sequencing based Tabula Muris tool  (Schaum et al. 2018). However, these atlases 
are typically performed on mice under standard conditions, and so a gene whose expression is acutely 
induced at a specific time of day or by a treatment may be absent from these references. Potentially, 
a treatment may induce genes specifically in cell types that otherwise do not express that gene, and 
as such the atlas may be completely misleading. Under these circumstances, performing fluorescence 
in situ hybrisation (FISH) following transcriptomic studies may yield useful spatial data, and has 
previously been performed on sleep deprived mouse brain  (Thompson et al. 2010). Equivalent 
reference atlases for proteomic and metabolomic studies would be very valuable tools 
However, whilst studying heterogenous tissue or cell types, such as neurones, important molecules 
may be overlooked by bulk omic approaches. For example, if a small number of cortical neurons were 
entirely responsible for the homeostatic sleep drive, which they signalled through expression of a 
single gene, bulk sequencing would likely fail to identify this gene. If the gene is unique to that cell 
type, the resultant low number of reads may not reach the expression cut-off value necessary to be 
considered. Conversely, if that gene were expressed by all cell types and undergoes a tenfold increase 
in only a single cell type during sleep deprivation, that fold increase is greatly diluted by contributions 
from other cell types and likely not reach statistical significance.  
Ideally, information about which cells are responsible for gene expression changes would therefore 
be preserved during transcriptomic experiments. Single cell sequencing technology now makes this 
possible, with cell transcriptomes subsequently being clustered. This approach not only allows the 
researcher to directly determine which cells are responsible for changes in specific gene expression 
and to identify expression changes in unique markers, but also allows the researcher to determine 
which genes are co-expressed. The transcriptomic response to sleep deprivation may be coordinated 
by a handful of transcription factors and so single cell sequencing may identify a transcriptional 
program in specific cell-subsets that is indicative of a specific transcription factor.  
180 
 
However, it is worth considering that preparation of samples for single cell sequencing requires 
dissociation and isolation of live cells immediately following tissue collection. Whilst carrying out a 
timecourse style experiment, especially one that is preceded by a protracted technique such as 
12-hour sleep deprivation, reliably performing an involved protocol can be technically very 
challenging. Therefore, single cell-sequencing may not have been feasible for this experimental 
design.  
 
6.6. Perspectives on the Molecular Study of Sleep  
A major limitation of researching the molecular effects of sleep deprivation is that isolating cortex RNA 
or cellular protein necessarily involves the sacrifice of the subject. Therefore, unlike EEG or video-
based assays, timecourse experiments require distinct individuals for each timepoint. This 
requirement slows the collection of samples by imposing the necessity to sleep deprive multiple 
animals, as well as by reducing the statistical power of the experiment through the introduction of 
biological variation between conditions. An in vitro model would therefore be a valuable tool for the 
investigation of molecular changes occurring during sleep deprivation, as it would simultaneously 
reduce biological variation between samples and allow the rapid production of large numbers of 
samples. Preferably, the in vitro assay for whether a drug may affect sleep patterns would not rely on 
the direct quantification of transcript abundance (which would involve RNA-extraction and 
subsequent qPCR). Instead, fluorometric assays of adenosine concentration, or ideally a live cell 
bioluminescence-based assay of gene expression or cellular parameter, would minimise the time and 
cost required to quantify the effects of drugs. Once optimised, several molecules can be screened in 
a low-cost, high throughput fashion, with promising candidates progressing to in vivo trials.  
A particularly interesting aspect of in vitro models is the absence of wake coordinating centres, such 
as the VLPO or the orexinergic system, and so they have the potential to specifically reveal the 
mechanisms through which neuronal assemblies enter into local sleep. Understanding which 
molecules are involved in this transition may inform our understanding of the fundamental roles of 
sleep and its control. Small molecules that override the entry into local sleep, although they may be 
useful in combating the cognitive deficits associated with sleep deprivation, may ultimately prove to 
be unsafe. Speculatively, the activity dependent entry of networks into a sleep-like state may be a 
protective measure to prevent neuronal death, and so disrupting that process may cause sleep 
deprivation mediated cell death.  
Perhaps the most exciting characteristic of in vitro models however is the potential to use human 
samples in research. Although EEG spectra and behavioural assays, or the molecular quantification of 
181 
 
hormones and metabolites in plasma and urine can be performed using humans, the quantification of 
gene expression or protein abundance from cortex following sleep deprivation is only possible on 
rodents or other lab animals. Differences in human and mouse physiologies complicate the general 
translation of rodent studies to humans. Similarly, the use of SH-SY5Y cells in this thesis replaces the 
question as to what extent mouse and human responses are similar with the question as to what 
extent a transformed neuroblastoma cell line resembles brain tissue. Clearly, neither mouse cortex 
nor SH-SY5Y cells are an ideal model with which to model the molecular changes occurring in the 
human brain during sleep deprivation. However, with great strides being taken in improving stem-cell 
technology, both in the creation of stem cells from adult humans and the differentiation protocols, it 
is easy to imagine that there will soon be powerful in vitro models based on stem cells created from 
naturally short- and long- sleepers. Such an approach would simultaneously remove the species and 
cell type differences between the experimental model and humans, maximising the translational 
impact of any findings. 
 
 
  
182 
 
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App. Table of Contents:  App. 1 
Appendix 
  
App. Table of Contents 
 
App.1. Genes Modulated by 6 Hour Sleep Deprivation ............................................................................ App.2 
App.2. Genes Modulated by 12 Hour Sleep Deprivation ........................................................................ App.23 
App.3. Diurnal Genes in Mouse Cortex .................................................................................................. App.30 
App.4. Diurnal Genes Affected by Sleep Deprivation ............................................................................. App.84 
App.5. Non-Diurnal Genes Affected by Sleep Deprivation ..................................................................... App.85 
App.6. Genes showing a Homeostatic Expression Profile ....................................................................... App.86 
App.7. Genes showing a Stress Expression Profile ................................................................................. App.87 
App.8. Diurnal Transcripts Affected by Sleep Deprivation...................................................................... App.88 
App.9. Non-Diurnal Transcripts Affected by Sleep Deprivation .............................................................. App.89 
App.10. Isoforms showing a Homeostatic Expression Profile ................................................................. App.90 
App.11. Isoform showing a Stress Expression Profiles ........................................................................... App.92 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation ................................................. App.94 
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells .......................................................... App.104 
App.14. Genes affected by 12 hour illumination .................................................................................. App.112 
 
 
 
 
 
 
 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 2 
App.1. Genes Modulated by 6 Hour Sleep Deprivation  
Genes modulated immediately following 6-hour sleep deprivation by at least 1.5 fold, compared to 
mice with uninterrupted sleep opportunity. Data presented is the Gene ID and name, the Log2 fold 
change, and the Benjamini adjusted q-value for significance. Data derived from cuffdiff command. 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000065126 Snord104 6.78 0.000521 
ENSMUSG00000098343 Mir6240 4.41 0.000521 
ENSMUSG00000095676 Gm25099 3.82 0.000521 
ENSMUSG00000064352 mt-Ts1 3.61 0.0191 
ENSMUSG00000088856 Gm24727 3.52 0.044 
ENSMUSG00000092805 Gm26461 3.41 0.000521 
ENSMUSG00000064382 Gm26447 3.41 0.00412 
ENSMUSG00000024175 Tekt4 3.38 0.0447 
ENSMUSG00000065087 Snord22 3.2 0.000521 
ENSMUSG00000097312 Gm26870 3.17 0.000521 
ENSMUSG00000064344 mt-Tm 3.15 0.000521 
ENSMUSG00000089542 Gm25835 3.15 0.00686 
ENSMUSG00000064981 Snora70 3.09 0.00448 
ENSMUSG00000002831 Plin4 3.08 0.000521 
ENSMUSG00000080365 Gm25776 3.07 0.000521 
ENSMUSG00000080465 Gm22486 3.04 0.000521 
ENSMUSG00000064360 mt-Nd3 2.96 0.000521 
ENSMUSG00000064941 Gm23238 2.96 0.000521 
ENSMUSG00000077563 Snora68 2.93 0.000521 
ENSMUSG00000065778 Gm22154 2.92 0.000521 
ENSMUSG00000091957 Rps2-ps10 2.88 0.000521 
ENSMUSG00000077505 Gm24233 2.85 0.0136 
ENSMUSG00000064994 Gm22422 2.84 0.000521 
ENSMUSG00000075015 Gm10801 2.79 0.000521 
ENSMUSG00000064634 Gm22620 2.66 0.0172 
ENSMUSG00000078886 Gm2026 2.65 0.000521 
ENSMUSG00000062933 Gm10123 2.63 0.000521 
ENSMUSG00000075014 Gm10800 2.61 0.000521 
ENSMUSG00000093355 Snora26 2.5 0.000521 
ENSMUSG00000088252 Snord13 2.42 0.0109 
ENSMUSG00000027654 Fam83d 2.42 0.000521 
ENSMUSG00000088990 Gm22767 2.4 0.000521 
ENSMUSG00000056054 S100a8 2.4 0.00224 
ENSMUSG00000035694 Caps2 2.37 0.000521 
ENSMUSG00000065820 Gm26316 2.37 0.000521 
ENSMUSG00000097052 Snhg7 2.33 0.0121 
ENSMUSG00000078887 Gm6710 2.33 0.000521 
ENSMUSG00000064655 Gm25788 2.3 0.000521 
ENSMUSG00000064427 Gm22748 2.27 0.00878 
ENSMUSG00000044285 Gm1821 2.27 0.000521 
ENSMUSG00000106746 2900064F13Rik 2.27 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 3 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000064966 Snord15b 2.25 0.000521 
ENSMUSG00000078898 Gm4723 2.2 0.000521 
ENSMUSG00000064604 Snora44 2.17 0.00878 
ENSMUSG00000105843 Gm42644 2.17 0.000521 
ENSMUSG00000092819 Gm23639 2.16 0.000521 
ENSMUSG00000099440 Gm29593 2.14 0.041 
ENSMUSG00000065637 Gm26397 2.11 0.00339 
ENSMUSG00000026822 Lcn2 2.03 0.00263 
ENSMUSG00000035202 Lars2 2.03 0.000521 
ENSMUSG00000064350 mt-Ty 1.99 0.0222 
ENSMUSG00000044522 A730020M07Rik 1.99 0.0118 
ENSMUSG00000077254 Gm26079 1.97 0.00376 
ENSMUSG00000064853 Gm23442 1.95 0.000521 
ENSMUSG00000086859 Snhg20 1.95 0.000521 
ENSMUSG00000084421 Gm25107 1.95 0.000521 
ENSMUSG00000078875 Gm14419 1.9 0.000521 
ENSMUSG00000089235 Gm23119 1.86 0.0155 
ENSMUSG00000070392 Gm20634 1.85 0.0167 
ENSMUSG00000049892 Rasd1 1.84 0.000521 
ENSMUSG00000096684 Gm25989 1.83 0.000521 
ENSMUSG00000109332 RP23-189G24.4 1.83 0.000521 
ENSMUSG00000098973 Mir6236 1.78 0.000521 
ENSMUSG00000065226 Gm25791 1.74 0.000521 
ENSMUSG00000100755 Rps23-ps1 1.72 0.0235 
ENSMUSG00000080888 Gm14387 1.72 0.00224 
ENSMUSG00000080538 Gm25541 1.7 0.000521 
ENSMUSG00000059835 Rpl13-ps3 1.7 0.0464 
ENSMUSG00000092746 Rn7s6 1.69 0.000521 
ENSMUSG00000084744 Gm25291 1.69 0.000521 
ENSMUSG00000084708 Gm22988 1.66 0.000521 
ENSMUSG00000056071 S100a9 1.66 0.000985 
ENSMUSG00000065686 Snora5c 1.65 0.0233 
ENSMUSG00000071637 Cebpd 1.64 0.00376 
ENSMUSG00000094655 Gm25360 1.63 0.000521 
ENSMUSG00000088008 Gm25492 1.61 0.00142 
ENSMUSG00000069305 Hist1h4n 1.61 0.00518 
ENSMUSG00000064387 Snora73a 1.6 0.000521 
ENSMUSG00000058385 Hist1h2bg 1.6 0.000521 
ENSMUSG00000017778 Cox7c 1.59 0.000521 
ENSMUSG00000065353 Snora73b 1.58 0.000521 
ENSMUSG00000077192 Snora17 1.58 0.00586 
ENSMUSG00000056313 1810011O10Rik 1.58 0.000521 
ENSMUSG00000032845 Alpk2 1.58 0.000521 
ENSMUSG00000074521 Gm14327 1.54 0.000521 
ENSMUSG00000024778 Fas 1.54 0.000985 
ENSMUSG00000069306 Hist1h4m 1.54 0.00878 
ENSMUSG00000064945 Rny3 1.5 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 4 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000066315 Gm12918 1.5 0.000521 
ENSMUSG00000106147 Rnu3a 1.5 0.000521 
ENSMUSG00000088088 Rmrp 1.49 0.000521 
ENSMUSG00000064348 mt-Tn 1.49 0.0396 
ENSMUSG00000064513 Gm22457 1.48 0.000521 
ENSMUSG00000042737 Dpm3 1.46 0.000521 
ENSMUSG00000064347 mt-Ta 1.46 0.0403 
ENSMUSG00000097551 Gm7976 1.45 0.000521 
ENSMUSG00000078872 Gm14401 1.44 0.000521 
ENSMUSG00000039405 Prss23 1.44 0.000521 
ENSMUSG00000087057 Gm11730 1.44 0.0425 
ENSMUSG00000047259 Mc4r 1.44 0.000521 
ENSMUSG00000087963 Gm25394 1.43 0.000521 
ENSMUSG00000074874 Ctla2b 1.43 0.00847 
ENSMUSG00000065336 Snora34 1.43 0.0399 
ENSMUSG00000024066 Xdh 1.43 0.000521 
ENSMUSG00000107272 Gm42730 1.4 0.000521 
ENSMUSG00000079641 Rpl39 1.4 0.000521 
ENSMUSG00000098557 Kctd12 1.39 0.0328 
ENSMUSG00000065251 Gm23971 1.39 0.000521 
ENSMUSG00000090671 Gm5067 1.38 0.0103 
ENSMUSG00000041957 Pkp2 1.38 0.000521 
ENSMUSG00000023067 Cdkn1a 1.37 0.000521 
ENSMUSG00000023232 Serinc2 1.34 0.000521 
ENSMUSG00000090691 Gm3667 1.33 0.00142 
ENSMUSG00000066170 E230001N04Rik 1.33 0.00142 
ENSMUSG00000102426 Kantr 1.33 0.000521 
ENSMUSG00000058443 Rpl10-ps3 1.32 0.000521 
ENSMUSG00000065037 Rn7sk 1.31 0.000521 
ENSMUSG00000034892 Rps29 1.3 0.000521 
ENSMUSG00000084350 Znf41-ps 1.3 0.00263 
ENSMUSG00000054944 5330416C01Rik 1.3 0.00586 
ENSMUSG00000077611 Gm23946 1.3 0.0139 
ENSMUSG00000086429 Gt(ROSA)26Sor 1.29 0.000521 
ENSMUSG00000073877 Gm13306 1.27 0.000521 
ENSMUSG00000106219 5830416I19Rik 1.27 0.00971 
ENSMUSG00000097340 Gm26617 1.26 0.000521 
ENSMUSG00000008822 Acyp1 1.26 0.000521 
ENSMUSG00000045996 Polr2k 1.26 0.000521 
ENSMUSG00000030711 Sult1a1 1.26 0.000521 
ENSMUSG00000089756 Gm8898 1.25 0.000521 
ENSMUSG00000101939 Gm28438 1.25 0.000521 
ENSMUSG00000071796 6820431F20Rik 1.25 0.000521 
ENSMUSG00000060962 Dmkn 1.24 0.00184 
ENSMUSG00000090546 Cdr1 1.24 0.000521 
ENSMUSG00000024222 Fkbp5 1.23 0.000521 
ENSMUSG00000038059 Smim3 1.23 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 5 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000104033 Gm37773 1.22 0.00815 
ENSMUSG00000106628 Gm43558 1.22 0.000521 
ENSMUSG00000039634 Zfp189 1.21 0.000521 
ENSMUSG00000094856 Gm21962 1.2 0.000521 
ENSMUSG00000108473 RP23-32A8.10 1.2 0.000521 
ENSMUSG00000031431 Tsc22d3 1.19 0.000521 
ENSMUSG00000035606 Ky 1.18 0.00263 
ENSMUSG00000028578 Caap1 1.18 0.000521 
ENSMUSG00000072620 Slfn2 1.18 0.000521 
ENSMUSG00000083111 Gm14421 1.18 0.000521 
ENSMUSG00000070858 Gm1673 1.17 0.00302 
ENSMUSG00000064380 Gm26448 1.17 0.0209 
ENSMUSG00000038646 Fam103a1 1.17 0.000521 
ENSMUSG00000073062 Zxdb 1.17 0.000521 
ENSMUSG00000064899 Snord118 1.14 0.00815 
ENSMUSG00000089617 Scarna10 1.14 0.000521 
ENSMUSG00000048572 Tmem252 1.13 0.000521 
ENSMUSG00000089281 Scarna6 1.11 0.00184 
ENSMUSG00000008668 Rps18 1.1 0.000521 
ENSMUSG00000078453 Abracl 1.1 0.000521 
ENSMUSG00000067578 Cbln4 1.1 0.000521 
ENSMUSG00000060923 Acyp2 1.09 0.000521 
ENSMUSG00000065701 Rny1 1.08 0.000521 
ENSMUSG00000073940 Hbb-bt 1.08 0.000521 
ENSMUSG00000057863 Rpl36 1.08 0.000521 
ENSMUSG00000031762 Mt2 1.08 0.000521 
ENSMUSG00000010592 Dazl 1.08 0.000521 
ENSMUSG00000067288 Rps28 1.05 0.000521 
ENSMUSG00000097347 Gm17275 1.05 0.000521 
ENSMUSG00000020424 Gatsl3 1.05 0.00653 
ENSMUSG00000023004 Tuba1b 1.05 0.000521 
ENSMUSG00000050856 Atp5k 1.05 0.000521 
ENSMUSG00000049796 Crh 1.05 0.000521 
ENSMUSG00000028648 Ndufs5 1.05 0.000521 
ENSMUSG00000067212 H2-T23 1.04 0.000521 
ENSMUSG00000105366 Gm43719 1.04 0.0285 
ENSMUSG00000022602 Arc 1.04 0.000521 
ENSMUSG00000024480 Ap3s1 1.03 0.000521 
ENSMUSG00000104297 Gm38046 1.03 0.00971 
ENSMUSG00000030432 Rpl28 1.02 0.00448 
ENSMUSG00000079173 Zan 1.02 0.00483 
ENSMUSG00000039221 Rpl22l1 1.02 0.000521 
ENSMUSG00000020857 Nme2 1.02 0.00719 
ENSMUSG00000078878 Gm14305 1.02 0.000521 
ENSMUSG00000046727 Cystm1 1.02 0.000521 
ENSMUSG00000043498 9330132A10Rik 1.01 0.000521 
ENSMUSG00000074170 Plekhf1 1.01 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 6 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000087579 1500017E21Rik 1.01 0.00376 
ENSMUSG00000090733 Rps27 1 0.000521 
ENSMUSG00000045534 Kcna5 1 0.0136 
ENSMUSG00000105186 Gm43778 1 0.0462 
ENSMUSG00000094114 Gm21967 0.998 0.000521 
ENSMUSG00000084911 Gm16185 0.997 0.00518 
ENSMUSG00000079018 Ly6c1 0.997 0.000521 
ENSMUSG00000087968 Gm25395 0.996 0.000521 
ENSMUSG00000051319 1500011K16Rik 0.992 0.000521 
ENSMUSG00000053475 Tnfaip6 0.99 0.000521 
ENSMUSG00000038717 Atp5l 0.989 0.000521 
ENSMUSG00000037185 Krt80 0.986 0.0299 
ENSMUSG00000022528 Hes1 0.98 0.000521 
ENSMUSG00000089951 Gm14435 0.98 0.000521 
ENSMUSG00000026238 Ptma 0.971 0.000521 
ENSMUSG00000090877 Hspa1b 0.971 0.000521 
ENSMUSG00000024726 Carnmt1 0.964 0.000521 
ENSMUSG00000039001 Rps21 0.964 0.000521 
ENSMUSG00000103477 5930409G06Rik 0.964 0.000521 
ENSMUSG00000078867 Gm14418 0.957 0.000521 
ENSMUSG00000063316 Rpl27 0.957 0.000521 
ENSMUSG00000051243 Islr2 0.954 0.000521 
ENSMUSG00000073131 Vma21 0.953 0.000521 
ENSMUSG00000014313 Cox6c 0.953 0.000521 
ENSMUSG00000032360 Hcrtr2 0.95 0.000521 
ENSMUSG00000037653 Kctd8 0.944 0.000521 
ENSMUSG00000096768 Erdr1 0.943 0.000521 
ENSMUSG00000109536 RP24-439I22.3 0.942 0.000521 
ENSMUSG00000095590 Gm24305 0.937 0.000985 
ENSMUSG00000034936 Arl4d 0.937 0.000521 
ENSMUSG00000042712 Wbp5 0.933 0.000521 
ENSMUSG00000099583 Hist1h3d 0.931 0.0258 
ENSMUSG00000071083 Gm10311 0.92 0.00184 
ENSMUSG00000108456 RP24-143K11.2 0.918 0.027 
ENSMUSG00000074575 Kcng1 0.917 0.0337 
ENSMUSG00000090291 Lrrc10b 0.912 0.0115 
ENSMUSG00000103322 Gm37404 0.91 0.000521 
ENSMUSG00000074971 Fibin 0.909 0.00302 
ENSMUSG00000072704 Smim10l1 0.908 0.000521 
ENSMUSG00000040113 Mettl11b 0.908 0.0141 
ENSMUSG00000054091 1810037I17Rik 0.906 0.000521 
ENSMUSG00000002289 Angptl4 0.904 0.00448 
ENSMUSG00000030208 Emp1 0.903 0.018 
ENSMUSG00000105285 Gm43238 0.903 0.000521 
ENSMUSG00000005124 Wisp1 0.901 0.000521 
ENSMUSG00000079494 Cml5 0.899 0.0383 
ENSMUSG00000036781 Rps27l 0.899 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 7 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000066637 Ttc32 0.894 0.013 
ENSMUSG00000001707 Eef1e1 0.894 0.000521 
ENSMUSG00000078784 1810022K09Rik 0.893 0.000521 
ENSMUSG00000063889 Crem 0.893 0.000521 
ENSMUSG00000028211 Trp53inp1 0.893 0.000521 
ENSMUSG00000067860 Zic3 0.892 0.0374 
ENSMUSG00000048758 Rpl29 0.892 0.000521 
ENSMUSG00000024766 Lipo1 0.889 0.0112 
ENSMUSG00000078861 Zfp931 0.889 0.000521 
ENSMUSG00000102854 C130023A14Rik 0.889 0.00339 
ENSMUSG00000060636 Rpl35a 0.887 0.000521 
ENSMUSG00000090223 Pcp4 0.886 0.000521 
ENSMUSG00000020108 Ddit4 0.884 0.000521 
ENSMUSG00000027306 Nusap1 0.882 0.00751 
ENSMUSG00000072568 Fam84b 0.88 0.00142 
ENSMUSG00000047215 Rpl9 0.879 0.000521 
ENSMUSG00000074754 Gm561 0.879 0.000521 
ENSMUSG00000100750 Gm29084 0.877 0.000521 
ENSMUSG00000064356 mt-Atp8 0.875 0.000521 
ENSMUSG00000037573 Tob1 0.875 0.000521 
ENSMUSG00000002910 Arrdc2 0.872 0.000521 
ENSMUSG00000026315 Serpinb8 0.87 0.000521 
ENSMUSG00000035595 1600002K03Rik 0.867 0.0183 
ENSMUSG00000078862 Gm14326 0.866 0.000521 
ENSMUSG00000062691 Cebpzos 0.866 0.0144 
ENSMUSG00000029084 Cd38 0.863 0.00339 
ENSMUSG00000044155 Lsm8 0.863 0.000521 
ENSMUSG00000021520 Uqcrb 0.859 0.000521 
ENSMUSG00000063253 Scoc 0.858 0.000521 
ENSMUSG00000033186 Mzt1 0.858 0.000521 
ENSMUSG00000030188 Magohb 0.856 0.00184 
ENSMUSG00000045954 Sdpr 0.851 0.000521 
ENSMUSG00000001774 Chordc1 0.849 0.000521 
ENSMUSG00000062328 Rpl17 0.847 0.000521 
ENSMUSG00000079480 Pin4 0.846 0.000521 
ENSMUSG00000055373 Fut9 0.845 0.000521 
ENSMUSG00000075266 Cenpw 0.845 0.000521 
ENSMUSG00000021676 Iqgap2 0.843 0.000521 
ENSMUSG00000065911 Gm24447 0.841 0.000521 
ENSMUSG00000106990 Gm42547 0.84 0.000521 
ENSMUSG00000073374 C030034I22Rik 0.84 0.0133 
ENSMUSG00000062006 Rpl34 0.837 0.000521 
ENSMUSG00000010797 Wnt2 0.835 0.00302 
ENSMUSG00000090963 Gm17655 0.835 0.0458 
ENSMUSG00000048482 Bdnf 0.835 0.000521 
ENSMUSG00000068184 Ndufaf2 0.831 0.000521 
ENSMUSG00000096349 Gm22513 0.829 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 8 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000032373 Car12 0.826 0.00263 
ENSMUSG00000048222 Mfap1b 0.825 0.000521 
ENSMUSG00000090110 Cmc4 0.825 0.013 
ENSMUSG00000094377 Gm24407 0.823 0.000521 
ENSMUSG00000030413 Pglyrp1 0.823 0.00783 
ENSMUSG00000048355 Arxes1 0.821 0.000521 
ENSMUSG00000078427 Sarnp 0.82 0.000521 
ENSMUSG00000065145 Vaultrc5 0.817 0.0188 
ENSMUSG00000032807 Alox12b 0.814 0.000521 
ENSMUSG00000049751 Rpl36al 0.81 0.000521 
ENSMUSG00000020738 Sumo2 0.809 0.000521 
ENSMUSG00000028179 Cth 0.808 0.00339 
ENSMUSG00000032487 Ptgs2 0.807 0.000521 
ENSMUSG00000041841 Rpl37 0.803 0.000521 
ENSMUSG00000049511 Htr1b 0.803 0.000521 
ENSMUSG00000021025 Nfkbia 0.801 0.000521 
ENSMUSG00000029817 Tra2a 0.801 0.000521 
ENSMUSG00000048616 Nog 0.799 0.000521 
ENSMUSG00000033316 Galnt9 0.798 0.000521 
ENSMUSG00000018239 Zcchc10 0.797 0.00376 
ENSMUSG00000051671 Coa6 0.796 0.000521 
ENSMUSG00000028676 Srsf10 0.794 0.000521 
ENSMUSG00000060935 Tmem263 0.791 0.000521 
ENSMUSG00000028480 Glipr2 0.79 0.0106 
ENSMUSG00000007682 Dio2 0.787 0.000521 
ENSMUSG00000031758 Cdyl2 0.786 0.000521 
ENSMUSG00000015672 Mrpl32 0.786 0.000521 
ENSMUSG00000102504 Gm21955 0.785 0.000521 
ENSMUSG00000024521 Pmaip1 0.785 0.000985 
ENSMUSG00000074715 Ccl28 0.784 0.000985 
ENSMUSG00000051451 Crebzf 0.78 0.000521 
ENSMUSG00000048251 Bcl11b 0.778 0.000521 
ENSMUSG00000008682 Rpl10 0.775 0.000521 
ENSMUSG00000097695 Gm26905 0.775 0.000521 
ENSMUSG00000034701 Neurod1 0.773 0.00552 
ENSMUSG00000051185 Fam174a 0.772 0.000521 
ENSMUSG00000025290 Rps24 0.772 0.000521 
ENSMUSG00000026072 Il1r1 0.771 0.0144 
ENSMUSG00000020460 Rps27a 0.77 0.000521 
ENSMUSG00000021732 Fgf10 0.769 0.0199 
ENSMUSG00000030047 Arhgap25 0.767 0.00142 
ENSMUSG00000105549 Gm43540 0.765 0.000521 
ENSMUSG00000019997 Ctgf 0.765 0.000521 
ENSMUSG00000027765 P2ry1 0.765 0.0328 
ENSMUSG00000024883 Rin1 0.764 0.000521 
ENSMUSG00000046516 Cox17 0.763 0.000521 
ENSMUSG00000025362 Rps26 0.762 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 9 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000017009 Sdc4 0.76 0.000521 
ENSMUSG00000029265 Dr1 0.76 0.000521 
ENSMUSG00000012405 Rpl15 0.759 0.000521 
ENSMUSG00000021098 4930447C04Rik 0.757 0.00142 
ENSMUSG00000093909 Gm3883 0.756 0.00783 
ENSMUSG00000028410 Dnaja1 0.756 0.000521 
ENSMUSG00000020397 Med7 0.755 0.0139 
ENSMUSG00000028655 Mfsd2a 0.753 0.000521 
ENSMUSG00000020427 Igfbp3 0.753 0.000521 
ENSMUSG00000022820 Ndufb4 0.753 0.000521 
ENSMUSG00000031530 Dusp4 0.753 0.000521 
ENSMUSG00000050029 Rap2c 0.752 0.000521 
ENSMUSG00000074519 Etohi1 0.751 0.0115 
ENSMUSG00000028495 Rps6 0.751 0.000521 
ENSMUSG00000016179 Camk1g 0.75 0.000521 
ENSMUSG00000105353 Gm42428 0.75 0.000521 
ENSMUSG00000078864 Gm14322 0.75 0.0127 
ENSMUSG00000024317 Rnf138 0.749 0.000521 
ENSMUSG00000044224 Dnajc21 0.749 0.000521 
ENSMUSG00000104960 Snhg8 0.748 0.000521 
ENSMUSG00000043991 Pura 0.747 0.000521 
ENSMUSG00000060402 Chst8 0.746 0.0118 
ENSMUSG00000049744 Arhgap15 0.742 0.000521 
ENSMUSG00000034765 Dusp5 0.742 0.000521 
ENSMUSG00000088835 Gm23547 0.741 0.0351 
ENSMUSG00000021903 Galnt15 0.74 0.00376 
ENSMUSG00000046330 Rpl37a 0.739 0.000521 
ENSMUSG00000052305 Hbb-bs 0.738 0.000521 
ENSMUSG00000054766 Set 0.736 0.000521 
ENSMUSG00000102753 Gm37056 0.735 0.0172 
ENSMUSG00000042216 Sgsm1 0.73 0.000521 
ENSMUSG00000032757 Bet1 0.73 0.000521 
ENSMUSG00000089417 Gm22009 0.73 0.000521 
ENSMUSG00000030218 Mgp 0.729 0.000521 
ENSMUSG00000073295 Nudt11 0.728 0.000521 
ENSMUSG00000049517 Rps23 0.728 0.000521 
ENSMUSG00000031327 Chic1 0.727 0.000521 
ENSMUSG00000021203 Otub2 0.727 0.000521 
ENSMUSG00000063457 Rps15 0.726 0.000521 
ENSMUSG00000057322 Rpl38 0.726 0.000521 
ENSMUSG00000090125 Pou3f1 0.724 0.000521 
ENSMUSG00000017404 Rpl19 0.722 0.000521 
ENSMUSG00000073293 Nudt10 0.722 0.000521 
ENSMUSG00000041592 Sdk2 0.72 0.0136 
ENSMUSG00000066798 Zbtb6 0.719 0.000521 
ENSMUSG00000026361 Cdc73 0.718 0.0209 
ENSMUSG00000063364 3300002I08Rik 0.717 0.0207 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 10 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000098274 Rpl24 0.717 0.000985 
ENSMUSG00000062184 Hs6st2 0.715 0.000521 
ENSMUSG00000024614 Tmx3 0.713 0.000521 
ENSMUSG00000021765 Fst 0.713 0.00184 
ENSMUSG00000022021 Diaph3 0.712 0.00518 
ENSMUSG00000040693 Slco4c1 0.711 0.0112 
ENSMUSG00000042682 Selk 0.71 0.000521 
ENSMUSG00000025790 Slco3a1 0.71 0.000521 
ENSMUSG00000006360 Crip1 0.709 0.0062 
ENSMUSG00000061132 Blnk 0.708 0.000521 
ENSMUSG00000048706 Lurap1l 0.707 0.000521 
ENSMUSG00000031202 Rab39b 0.706 0.000521 
ENSMUSG00000042505 Sdhaf3 0.703 0.000521 
ENSMUSG00000047216 Cdh19 0.701 0.00483 
ENSMUSG00000028221 Tmem55a 0.701 0.000521 
ENSMUSG00000063632 Sox11 0.7 0.000521 
ENSMUSG00000024608 Rps14 0.7 0.000521 
ENSMUSG00000035686 Thrsp 0.698 0.000521 
ENSMUSG00000056260 Lrif1 0.697 0.000521 
ENSMUSG00000021556 Golm1 0.697 0.000521 
ENSMUSG00000063787 Chchd1 0.695 0.000521 
ENSMUSG00000090862 Rps13 0.695 0.000521 
ENSMUSG00000079283 2310009B15Rik 0.695 0.0109 
ENSMUSG00000039234 Sec24d 0.695 0.0412 
ENSMUSG00000037984 Neurod6 0.695 0.000521 
ENSMUSG00000071748 Gm14698 0.694 0.000521 
ENSMUSG00000104806 Gm42566 0.694 0.00302 
ENSMUSG00000044408 Sptssa 0.693 0.000521 
ENSMUSG00000074527 Gm14296 0.693 0.000521 
ENSMUSG00000001627 Ifrd1 0.693 0.000521 
ENSMUSG00000047675 Rps8 0.693 0.000521 
ENSMUSG00000016427 Ndufa1 0.691 0.000521 
ENSMUSG00000078866 Gm14420 0.69 0.000521 
ENSMUSG00000054717 Hmgb2 0.69 0.0201 
ENSMUSG00000042842 Serpinb6b 0.688 0.0318 
ENSMUSG00000048007 Timm8a1 0.688 0.0158 
ENSMUSG00000036777 Anln 0.687 0.000521 
ENSMUSG00000064345 mt-Nd2 0.687 0.000521 
ENSMUSG00000035104 Eva1a 0.686 0.000521 
ENSMUSG00000042595 Fam199x 0.686 0.000521 
ENSMUSG00000033849 B3galt2 0.686 0.000521 
ENSMUSG00000038007 Acer2 0.686 0.00552 
ENSMUSG00000071862 Lrrtm2 0.683 0.000521 
ENSMUSG00000087267 4933427J07Rik 0.683 0.000521 
ENSMUSG00000063531 Sema3e 0.682 0.000521 
ENSMUSG00000031609 Sap30 0.677 0.00686 
ENSMUSG00000047714 Ppp1r2 0.677 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 11 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000062101 Zfp119b 0.675 0.00184 
ENSMUSG00000079083 Jrkl 0.675 0.000521 
ENSMUSG00000056116 H2-T22 0.674 0.00184 
ENSMUSG00000046318 Ccbe1 0.673 0.00971 
ENSMUSG00000046999 1110032F04Rik 0.672 0.000521 
ENSMUSG00000095362 Gm14325 0.672 0.0314 
ENSMUSG00000033685 Ucp2 0.671 0.0458 
ENSMUSG00000061787 Rps17 0.671 0.000521 
ENSMUSG00000060594 Layn 0.669 0.00448 
ENSMUSG00000029551 Psmg3 0.668 0.0207 
ENSMUSG00000029304 Spp1 0.668 0.000521 
ENSMUSG00000019772 Vip 0.668 0.000521 
ENSMUSG00000105942 Gm43175 0.667 0.000985 
ENSMUSG00000068523 Gng5 0.667 0.000521 
ENSMUSG00000075271 Ttc30a1 0.665 0.00376 
ENSMUSG00000087260 Lamtor5 0.665 0.000521 
ENSMUSG00000090553 Snrpe 0.664 0.000521 
ENSMUSG00000078868 Gm14412 0.663 0.000521 
ENSMUSG00000051579 Tceal8 0.663 0.000521 
ENSMUSG00000042670 Immp1l 0.662 0.000521 
ENSMUSG00000044017 Adgrd1 0.659 0.00483 
ENSMUSG00000031246 Sh3bgrl 0.659 0.00686 
ENSMUSG00000018199 Trove2 0.658 0.000521 
ENSMUSG00000039208 Metrnl 0.658 0.0062 
ENSMUSG00000085328 Gm17131 0.657 0.000985 
ENSMUSG00000078974 Sec61g 0.657 0.000521 
ENSMUSG00000029838 Ptn 0.657 0.000521 
ENSMUSG00000044674 Fzd1 0.656 0.000521 
ENSMUSG00000022193 Psmb5 0.655 0.000521 
ENSMUSG00000066613 Zfp932 0.655 0.000521 
ENSMUSG00000047344 Lancl3 0.653 0.000521 
ENSMUSG00000085492 Trmt61b 0.653 0.00686 
ENSMUSG00000091625 Lsm5 0.653 0.0139 
ENSMUSG00000021091 Serpina3n 0.652 0.000521 
ENSMUSG00000042540 Acot5 0.65 0.0194 
ENSMUSG00000014813 Stc1 0.648 0.0115 
ENSMUSG00000021730 Hcn1 0.645 0.000521 
ENSMUSG00000023025 Larp4 0.644 0.000521 
ENSMUSG00000046567 4930430F08Rik 0.644 0.0141 
ENSMUSG00000066687 Zbtb16 0.641 0.000521 
ENSMUSG00000060708 Bloc1s4 0.641 0.000985 
ENSMUSG00000048040 Arxes2 0.64 0.000521 
ENSMUSG00000025894 Aasdhppt 0.639 0.000521 
ENSMUSG00000021290 2010107E04Rik 0.638 0.000521 
ENSMUSG00000022012 Enox1 0.638 0.000521 
ENSMUSG00000046215 Rprml 0.637 0.00518 
ENSMUSG00000038412 Higd1a 0.636 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 12 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000026864 Hspa5 0.636 0.000521 
ENSMUSG00000019146 Cacng2 0.636 0.000521 
ENSMUSG00000027351 Spred1 0.636 0.000521 
ENSMUSG00000035273 Hpse 0.634 0.025 
ENSMUSG00000038323 1700066M21Rik 0.634 0.000521 
ENSMUSG00000031245 Hmgn5 0.633 0.0186 
ENSMUSG00000029279 Brdt 0.633 0.000985 
ENSMUSG00000071014 Ndufb6 0.633 0.000521 
ENSMUSG00000014177 Tvp23b 0.632 0.000521 
ENSMUSG00000072761 Gm6712 0.631 0.0421 
ENSMUSG00000096025 Gm38400 0.631 0.000521 
ENSMUSG00000003031 Cdkn1b 0.63 0.000521 
ENSMUSG00000042541 Shfm1 0.629 0.000521 
ENSMUSG00000028165 Cisd2 0.628 0.000521 
ENSMUSG00000036902 Neto2 0.628 0.000521 
ENSMUSG00000050786 Ccdc126 0.627 0.000521 
ENSMUSG00000106918 Mrpl33 0.627 0.0147 
ENSMUSG00000019961 Tmpo 0.627 0.000521 
ENSMUSG00000028645 Slc2a1 0.626 0.000521 
ENSMUSG00000067336 Bmpr2 0.625 0.000521 
ENSMUSG00000034653 Ythdc2 0.625 0.000521 
ENSMUSG00000031765 Mt1 0.625 0.000521 
ENSMUSG00000033342 Plppr5 0.622 0.000521 
ENSMUSG00000066324 Impad1 0.622 0.000521 
ENSMUSG00000047415 Gpr68 0.621 0.00971 
ENSMUSG00000019851 Perp 0.62 0.00483 
ENSMUSG00000024072 Yipf4 0.619 0.000521 
ENSMUSG00000032328 Tmem30a 0.619 0.000521 
ENSMUSG00000032551 1110059G10Rik 0.618 0.00263 
ENSMUSG00000054162 Spock3 0.616 0.000521 
ENSMUSG00000050783 Htr1f 0.616 0.00142 
ENSMUSG00000098234 Snhg6 0.615 0.00518 
ENSMUSG00000042396 Rbm7 0.615 0.000521 
ENSMUSG00000060938 Rpl26 0.615 0.000521 
ENSMUSG00000071172 Srsf3 0.615 0.000521 
ENSMUSG00000026773 Pfkfb3 0.613 0.00142 
ENSMUSG00000073879 Gm5859 0.613 0.000985 
ENSMUSG00000020607 Fam84a 0.613 0.000521 
ENSMUSG00000048379 Socs4 0.612 0.000521 
ENSMUSG00000063406 Tmed5 0.612 0.00909 
ENSMUSG00000051920 Rspo2 0.612 0.00224 
ENSMUSG00000067925 Cxx1a 0.611 0.000521 
ENSMUSG00000031885 Cbfb 0.611 0.00586 
ENSMUSG00000024097 Srsf7 0.611 0.000521 
ENSMUSG00000000838 Fmr1 0.611 0.000521 
ENSMUSG00000028773 Fabp3 0.61 0.000985 
ENSMUSG00000060803 Gstp1 0.609 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 13 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000074892 B3galt5 0.608 0.0103 
ENSMUSG00000034738 Nostrin 0.608 0.0094 
ENSMUSG00000035840 Lysmd3 0.608 0.000521 
ENSMUSG00000032679 Cd59a 0.608 0.022 
ENSMUSG00000054477 Kcnn2 0.607 0.000521 
ENSMUSG00000053769 Lysmd1 0.606 0.00263 
ENSMUSG00000025324 Atp10a 0.606 0.000521 
ENSMUSG00000028936 Rpl22 0.606 0.000521 
ENSMUSG00000042742 B630005N14Rik 0.606 0.000521 
ENSMUSG00000019689 1110001J03Rik 0.605 0.00184 
ENSMUSG00000020224 Llph 0.605 0.000521 
ENSMUSG00000049420 Tmem200a 0.605 0.000521 
ENSMUSG00000029131 Dnajb6 0.604 0.000521 
ENSMUSG00000072949 Acot1 0.604 0.0204 
ENSMUSG00000031875 Cmtm3 0.603 0.0109 
ENSMUSG00000033307 Mif 0.602 0.000521 
ENSMUSG00000093674 Rpl41 0.601 0.000521 
ENSMUSG00000102917 Gm37724 0.601 0.00719 
ENSMUSG00000036934 4921524J17Rik 0.601 0.000521 
ENSMUSG00000026568 Mpc2 0.6 0.000521 
ENSMUSG00000057766 Ankrd29 0.599 0.00142 
ENSMUSG00000097392 D930016D06Rik 0.599 0.0351 
ENSMUSG00000067928 Zfp760 0.598 0.000521 
ENSMUSG00000051705 Senp8 0.598 0.000521 
ENSMUSG00000020163 Uqcr11 0.598 0.000521 
ENSMUSG00000020189 Osbpl8 0.597 0.000521 
ENSMUSG00000079317 Trappc2 0.596 0.000521 
ENSMUSG00000021930 Spryd7 0.596 0.000521 
ENSMUSG00000021226 Acot2 0.595 0.00751 
ENSMUSG00000020601 Trib2 0.595 0.000521 
ENSMUSG00000018102 Hist1h2bc 0.595 0.000521 
ENSMUSG00000021774 Ube2e1 0.595 0.0273 
ENSMUSG00000007836 Hnrnpa0 0.594 0.000521 
ENSMUSG00000020561 Twistnb 0.594 0.000521 
ENSMUSG00000045034 Ankrd34b 0.594 0.000521 
ENSMUSG00000028243 Ubxn2b 0.594 0.000521 
ENSMUSG00000053070 9230110C19Rik 0.592 0.00339 
ENSMUSG00000032381 Fam96a 0.591 0.000521 
ENSMUSG00000030869 Ndufab1 0.59 0.000521 
ENSMUSG00000050288 Fzd2 0.59 0.0109 
ENSMUSG00000038607 Gng10 0.589 0.000521 
ENSMUSG00000006333 Rps9 0.588 0.000521 
ENSMUSG00000031333 Abcb7 0.588 0.000521 
ENSMUSG00000075700 Selt 0.587 0.000521 
ENSMUSG00000050711 Scg2 0.586 0.000521 
ENSMUSG00000059291 Rpl11 0.586 0.000521 
ENSMUSG00000029836 Cbx3 0.585 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 14 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000042804 Gpr153 -0.585 0.000985 
ENSMUSG00000031853 BC021891 -0.587 0.0124 
ENSMUSG00000032540 Abhd5 -0.588 0.0299 
ENSMUSG00000026360 Rgs2 -0.59 0.00224 
ENSMUSG00000059540 Tcea2 -0.59 0.00184 
ENSMUSG00000020435 Osbp2 -0.59 0.0147 
ENSMUSG00000049907 Rasl11b -0.591 0.000521 
ENSMUSG00000058454 Dhcr7 -0.594 0.00878 
ENSMUSG00000023008 Fmnl3 -0.594 0.00719 
ENSMUSG00000009376 Met -0.595 0.000521 
ENSMUSG00000084416 Rpl10a-ps1 -0.595 0.047 
ENSMUSG00000068151 A230006K03Rik -0.596 0.0263 
ENSMUSG00000046352 Gjb2 -0.596 0.00184 
ENSMUSG00000050721 Plekho2 -0.598 0.000521 
ENSMUSG00000024186 Rgs11 -0.599 0.0316 
ENSMUSG00000062960 Kdr -0.6 0.000521 
ENSMUSG00000033809 Alg3 -0.601 0.0144 
ENSMUSG00000026925 Inpp5e -0.603 0.047 
ENSMUSG00000026344 Lypd1 -0.604 0.0169 
ENSMUSG00000061119 Prcp -0.606 0.000521 
ENSMUSG00000071477 Zfp777 -0.606 0.0342 
ENSMUSG00000036529 Sbf1 -0.607 0.0318 
ENSMUSG00000028766 Alpl -0.607 0.0238 
ENSMUSG00000042115 Klhdc8a -0.609 0.000985 
ENSMUSG00000020836 Coro6 -0.609 0.00142 
ENSMUSG00000052331 Ankrd44 -0.61 0.0112 
ENSMUSG00000020802 Ube2o -0.613 0.0304 
ENSMUSG00000036196 Slc26a8 -0.615 0.0112 
ENSMUSG00000060279 Ap2a1 -0.616 0.013 
ENSMUSG00000024948 Map4k2 -0.616 0.0212 
ENSMUSG00000055725 Paqr3 -0.622 0.0294 
ENSMUSG00000029073 Cptp -0.622 0.0304 
ENSMUSG00000029428 Stx2 -0.623 0.0201 
ENSMUSG00000002221 Paxip1 -0.623 0.0275 
ENSMUSG00000029594 Rbm19 -0.623 0.0451 
ENSMUSG00000055407 Map6 -0.625 0.000521 
ENSMUSG00000020067 Mypn -0.629 0.0346 
ENSMUSG00000028969 Cdk5 -0.641 0.000521 
ENSMUSG00000017724 Etv4 -0.641 0.0473 
ENSMUSG00000030315 Vgll4 -0.642 0.000521 
ENSMUSG00000029513 Prkab1 -0.642 0.00184 
ENSMUSG00000100153 Gm5601 -0.644 0.0447 
ENSMUSG00000030096 Slc6a6 -0.646 0.00815 
ENSMUSG00000086905 Gm13716 -0.647 0.00483 
ENSMUSG00000087396 4933407K13Rik -0.648 0.036 
ENSMUSG00000063446 Plppr1 -0.648 0.00653 
ENSMUSG00000006728 Cdk4 -0.654 0.0372 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 15 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000078624 Olfr613 -0.658 0.000521 
ENSMUSG00000002845 Tmem39a -0.659 0.0399 
ENSMUSG00000017764 Zswim1 -0.661 0.000521 
ENSMUSG00000051627 Hist1h1e -0.665 0.000521 
ENSMUSG00000033857 Engase -0.666 0.0458 
ENSMUSG00000029298 Gbp9 -0.666 0.0278 
ENSMUSG00000037649 H2-DMa -0.667 0.00518 
ENSMUSG00000030269 Mtmr14 -0.668 0.0207 
ENSMUSG00000020473 Aebp1 -0.669 0.000985 
ENSMUSG00000079139 Gm4204 -0.67 0.00483 
ENSMUSG00000032033 Barx2 -0.671 0.000521 
ENSMUSG00000058076 Sdhc -0.673 0.00142 
ENSMUSG00000060166 Zdhhc8 -0.674 0.000521 
ENSMUSG00000034432 Cops8 -0.674 0.000521 
ENSMUSG00000008036 Ap2s1 -0.674 0.00339 
ENSMUSG00000023030 Slc11a2 -0.674 0.015 
ENSMUSG00000064037 Gpn1 -0.676 0.0136 
ENSMUSG00000039176 Polg -0.679 0.0109 
ENSMUSG00000027890 Gstm4 -0.68 0.0191 
ENSMUSG00000053414 Hunk -0.681 0.000521 
ENSMUSG00000039057 Myo16 -0.684 0.000521 
ENSMUSG00000040165 Cd209c -0.685 0.0212 
ENSMUSG00000033453 Adamts15 -0.686 0.000985 
ENSMUSG00000063972 Nr6a1 -0.687 0.0248 
ENSMUSG00000040495 Chrm4 -0.688 0.000985 
ENSMUSG00000020805 Slc13a5 -0.69 0.00142 
ENSMUSG00000031392 Irak1 -0.691 0.00184 
ENSMUSG00000022216 Psme1 -0.694 0.00971 
ENSMUSG00000046463 5930403N24Rik -0.694 0.05 
ENSMUSG00000031170 Slc38a5 -0.695 0.0243 
ENSMUSG00000049470 Aff4 -0.7 0.0282 
ENSMUSG00000001930 Vwf -0.706 0.00586 
ENSMUSG00000006498 Ptbp1 -0.711 0.0263 
ENSMUSG00000026965 Anapc2 -0.712 0.000521 
ENSMUSG00000002007 Srpk3 -0.715 0.0344 
ENSMUSG00000025384 Faap100 -0.716 0.0133 
ENSMUSG00000107306 Gm42577 -0.716 0.000521 
ENSMUSG00000021009 Ptpn21 -0.718 0.000521 
ENSMUSG00000043587 Pxylp1 -0.718 0.00224 
ENSMUSG00000106948 Gm42785 -0.721 0.000521 
ENSMUSG00000005148 Klf5 -0.722 0.0238 
ENSMUSG00000100801 Gm15459 -0.724 0.000521 
ENSMUSG00000019370 Calm3 -0.731 0.00448 
ENSMUSG00000048826 Dact2 -0.733 0.000521 
ENSMUSG00000026814 Eng -0.736 0.00518 
ENSMUSG00000010122 Slc47a1 -0.737 0.00719 
ENSMUSG00000029090 Adgra3 -0.739 0.0141 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 16 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000020792 Exoc7 -0.74 0.000521 
ENSMUSG00000037791 Phf12 -0.74 0.0196 
ENSMUSG00000020101 Vsir -0.741 0.000521 
ENSMUSG00000041468 Gpr12 -0.744 0.000521 
ENSMUSG00000085811 Cep112it -0.745 0.000985 
ENSMUSG00000042857 Gm9776 -0.751 0.0153 
ENSMUSG00000089901 Gm8113 -0.753 0.00142 
ENSMUSG00000100725 Gm28062 -0.754 0.0412 
ENSMUSG00000027318 Adam33 -0.757 0.00971 
ENSMUSG00000014791 Elmo3 -0.757 0.05 
ENSMUSG00000103560 Gm38070 -0.758 0.05 
ENSMUSG00000020374 Rasgef1c -0.761 0.000521 
ENSMUSG00000020363 Gfpt2 -0.764 0.000521 
ENSMUSG00000107495 Gm44215 -0.769 0.000521 
ENSMUSG00000020330 Hmmr -0.771 0.0292 
ENSMUSG00000074863 Platr25 -0.773 0.0164 
ENSMUSG00000047155 Cyp4x1 -0.776 0.000521 
ENSMUSG00000027288 Zfp106 -0.776 0.0175 
ENSMUSG00000067276 Capn6 -0.776 0.0285 
ENSMUSG00000040524 Zfp609 -0.777 0.0139 
ENSMUSG00000092395 Gm20463 -0.777 0.0438 
ENSMUSG00000044364 Tmem74b -0.783 0.00815 
ENSMUSG00000060860 Ube2s -0.788 0.0144 
ENSMUSG00000026566 Mpzl1 -0.789 0.00552 
ENSMUSG00000040857 Erf -0.789 0.0094 
ENSMUSG00000035835 Plppr3 -0.792 0.00586 
ENSMUSG00000042807 Hecw2 -0.792 0.00376 
ENSMUSG00000035890 Rnf126 -0.795 0.000521 
ENSMUSG00000001870 Ltbp1 -0.795 0.024 
ENSMUSG00000091549 Gm6548 -0.798 0.0227 
ENSMUSG00000063730 Hsd3b2 -0.808 0.0299 
ENSMUSG00000029599 Ddx54 -0.811 0.00142 
ENSMUSG00000040938 Slc16a11 -0.812 0.015 
ENSMUSG00000032400 Zwilch -0.812 0.0421 
ENSMUSG00000002885 Adgre5 -0.812 0.00483 
ENSMUSG00000026383 Epb41l5 -0.814 0.0158 
ENSMUSG00000032590 Apeh -0.82 0.00224 
ENSMUSG00000066760 Psg16 -0.831 0.000521 
ENSMUSG00000028294 Cfap206 -0.842 0.0292 
ENSMUSG00000019876 Pkib -0.845 0.00586 
ENSMUSG00000043993 2900052L18Rik -0.845 0.0204 
ENSMUSG00000102813 Gm37795 -0.85 0.0191 
ENSMUSG00000028661 Epha8 -0.856 0.00686 
ENSMUSG00000097163 BC051077 -0.859 0.029 
ENSMUSG00000032431 Crtap -0.864 0.0209 
ENSMUSG00000074210 E130208F15Rik -0.866 0.0164 
ENSMUSG00000041731 Pgm5 -0.869 0.00142 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 17 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000035486 Plk5 -0.872 0.000521 
ENSMUSG00000086130 Gm16211 -0.872 0.000521 
ENSMUSG00000051354 Samd3 -0.872 0.00224 
ENSMUSG00000093910 Zfp853 -0.873 0.0183 
ENSMUSG00000053783 1700016K19Rik -0.874 0.0385 
ENSMUSG00000032122 Slc37a2 -0.878 0.00184 
ENSMUSG00000025795 Rassf3 -0.878 0.000521 
ENSMUSG00000034685 Fam171a2 -0.88 0.0381 
ENSMUSG00000104682 Gm42636 -0.883 0.028 
ENSMUSG00000020052 Ascl1 -0.888 0.0243 
ENSMUSG00000066797 Zfp648 -0.893 0.0356 
ENSMUSG00000050211 Pla2g4e -0.896 0.000521 
ENSMUSG00000043843 Tmem145 -0.896 0.000521 
ENSMUSG00000029505 Ep400 -0.901 0.0248 
ENSMUSG00000004415 Col26a1 -0.903 0.0273 
ENSMUSG00000102700 Gm38312 -0.903 0.000521 
ENSMUSG00000037321 Tap1 -0.905 0.0335 
ENSMUSG00000044254 Pcsk9 -0.907 0.00653 
ENSMUSG00000026970 Rbms1 -0.911 0.00263 
ENSMUSG00000081752 Gm14680 -0.917 0.0199 
ENSMUSG00000027004 Frzb -0.921 0.000521 
ENSMUSG00000035142 Nubpl -0.921 0.0225 
ENSMUSG00000015947 Fcgr1 -0.922 0.015 
ENSMUSG00000107383 Gm4366 -0.925 0.00224 
ENSMUSG00000030123 Plxnd1 -0.933 0.00847 
ENSMUSG00000054135 A430110L20Rik -0.939 0.0235 
ENSMUSG00000078139 AK157302 -0.94 0.0255 
ENSMUSG00000101188 Eif4a-ps4 -0.943 0.000521 
ENSMUSG00000050505 Pcdh20 -0.946 0.000521 
ENSMUSG00000038058 Nod1 -0.952 0.024 
ENSMUSG00000038742 Angptl6 -0.958 0.0217 
ENSMUSG00000027188 Pamr1 -0.959 0.000521 
ENSMUSG00000101906 Mrgprc2-ps -0.961 0.000521 
ENSMUSG00000000317 Bcl6b -0.962 0.015 
ENSMUSG00000041674 BC006965 -0.97 0.0191 
ENSMUSG00000090353 Gm17555 -0.977 0.000521 
ENSMUSG00000040605 Bace2 -0.987 0.000521 
ENSMUSG00000030735 Gm9755 -1.01 0.0191 
ENSMUSG00000106053 Gm20752 -1.01 0.0207 
ENSMUSG00000107881 Gm44250 -1.01 0.00142 
ENSMUSG00000034121 Mks1 -1.01 0.00412 
ENSMUSG00000029370 Rassf6 -1.01 0.0421 
ENSMUSG00000032010 Usp2 -1.01 0.000521 
ENSMUSG00000045238 A730035I17Rik -1.01 0.0183 
ENSMUSG00000105134 Gm42923 -1.02 0.0339 
ENSMUSG00000006931 P3h4 -1.02 0.033 
ENSMUSG00000069682 Gm10275 -1.02 0.000521 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 18 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000102705 4632432E15Rik -1.03 0.015 
ENSMUSG00000032565 Nudt16 -1.03 0.000521 
ENSMUSG00000035775 Krt20 -1.03 0.0153 
ENSMUSG00000028458 Tesk1 -1.03 0.00878 
ENSMUSG00000097657 Gm7389 -1.05 0.00783 
ENSMUSG00000001750 Tcirg1 -1.05 0.000521 
ENSMUSG00000081683 Fzd10 -1.06 0.00302 
ENSMUSG00000043671 Dpy19l3 -1.07 0.00518 
ENSMUSG00000023192 Grm2 -1.07 0.000521 
ENSMUSG00000074507 Gm14340 -1.08 0.0109 
ENSMUSG00000075514 Gm13375 -1.09 0.0248 
ENSMUSG00000062611 Rps3a2 -1.09 0.00815 
ENSMUSG00000103182 Gm37091 -1.09 0.0158 
ENSMUSG00000031558 Slit2 -1.09 0.000521 
ENSMUSG00000020131 Pcsk4 -1.09 0.0217 
ENSMUSG00000026956 Uap1l1 -1.1 0.00783 
ENSMUSG00000045251 Zfp688 -1.1 0.00483 
ENSMUSG00000005493 Msh4 -1.1 0.00142 
ENSMUSG00000031489 Adrb3 -1.1 0.00302 
ENSMUSG00000069892 9930111J21Rik2 -1.11 0.000521 
ENSMUSG00000048949 Gm6206 -1.11 0.000521 
ENSMUSG00000105476 Gm43740 -1.12 0.000521 
ENSMUSG00000019214 Chtf18 -1.13 0.000521 
ENSMUSG00000032572 Col6a4 -1.13 0.0275 
ENSMUSG00000049001 Ndnf -1.14 0.0447 
ENSMUSG00000104103 Gm9517 -1.14 0.00483 
ENSMUSG00000027613 Eif6 -1.15 0.000521 
ENSMUSG00000107215 Gm43197 -1.15 0.000985 
ENSMUSG00000108779 RP24-269A16.7 -1.16 0.0115 
ENSMUSG00000102685 Gm37373 -1.16 0.00847 
ENSMUSG00000066842 Hmcn1 -1.17 0.00719 
ENSMUSG00000109196 RP24-291N22.1 -1.18 0.000521 
ENSMUSG00000040296 Ddx58 -1.2 0.000521 
ENSMUSG00000104159 Gm38099 -1.2 0.00518 
ENSMUSG00000016756 Cmah -1.2 0.000521 
ENSMUSG00000086179 Gm14317 -1.21 0.0479 
ENSMUSG00000043801 Oaz1-ps -1.22 0.00224 
ENSMUSG00000073164 2410018L13Rik -1.22 0.000521 
ENSMUSG00000085881 Gm15912 -1.22 0.0204 
ENSMUSG00000102591 Gm38383 -1.23 0.000521 
ENSMUSG00000100837 1700063D05Rik -1.24 0.0449 
ENSMUSG00000097148 Gm3839 -1.24 0.000521 
ENSMUSG00000097797 Gm26901 -1.26 0.0419 
ENSMUSG00000075511 1700001L05Rik -1.26 0.00412 
ENSMUSG00000104060 Gm37954 -1.27 0.00483 
ENSMUSG00000097275 Gm26648 -1.27 0.0235 
ENSMUSG00000085828 Gm15612 -1.27 0.018 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 19 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000082519 Vamp7-ps -1.28 0.000521 
ENSMUSG00000101523 Gm10031 -1.28 0.000521 
ENSMUSG00000097327 E030030I06Rik -1.28 0.00552 
ENSMUSG00000019945 1700040L02Rik -1.29 0.0062 
ENSMUSG00000050621 Rps27rt -1.29 0.0396 
ENSMUSG00000002324 Rec8 -1.29 0.0155 
ENSMUSG00000098404 Mrip-ps -1.3 0.000521 
ENSMUSG00000049233 Apoo-ps -1.32 0.00552 
ENSMUSG00000091478 Gm10039 -1.33 0.0118 
ENSMUSG00000080242 Gm15487 -1.33 0.00184 
ENSMUSG00000105065 Gm42513 -1.33 0.000521 
ENSMUSG00000091102 5830462I19Rik -1.33 0.0115 
ENSMUSG00000004842 Pou1f1 -1.35 0.00224 
ENSMUSG00000002083 Bbc3 -1.35 0.0238 
ENSMUSG00000106001 Gm42826 -1.36 0.000521 
ENSMUSG00000086922 Gm13835 -1.37 0.0304 
ENSMUSG00000106860 1700008H02Rik -1.38 0.000521 
ENSMUSG00000103625 Gm37357 -1.39 0.000521 
ENSMUSG00000078808 Vmn1r58 -1.41 0.000521 
ENSMUSG00000052142 Rasal3 -1.41 0.0263 
ENSMUSG00000074219 Gm10644 -1.41 0.0238 
ENSMUSG00000087028 Gm13387 -1.43 0.0144 
ENSMUSG00000047773 Ankfn1 -1.43 0.000521 
ENSMUSG00000060019 Gm10073 -1.44 0.00448 
ENSMUSG00000071041 Gm15210 -1.45 0.00412 
ENSMUSG00000097445 Gm26631 -1.46 0.0287 
ENSMUSG00000035506 Slc12a8 -1.47 0.00263 
ENSMUSG00000108446 RP23-367H8.4 -1.48 0.01 
ENSMUSG00000070343 Gm10288 -1.49 0.00184 
ENSMUSG00000085783 Gm9816 -1.51 0.000521 
ENSMUSG00000080935 Got2-ps1 -1.52 0.000521 
ENSMUSG00000103630 Gm37242 -1.54 0.000521 
ENSMUSG00000097502 4930528D03Rik -1.57 0.0227 
ENSMUSG00000096528 G430049J08Rik -1.57 0.00376 
ENSMUSG00000081400 Gm13680 -1.57 0.00686 
ENSMUSG00000085830 Grin1os -1.58 0.00224 
ENSMUSG00000099608 4933411E06Rik -1.58 0.000521 
ENSMUSG00000028461 Ccdc107 -1.58 0.000521 
ENSMUSG00000015850 Adamtsl4 -1.58 0.0175 
ENSMUSG00000097979 Gm4691 -1.6 0.0311 
ENSMUSG00000023393 Slc17a9 -1.6 0.0332 
ENSMUSG00000026331 Slco6c1 -1.64 0.00263 
ENSMUSG00000103475 Gm37697 -1.66 0.000521 
ENSMUSG00000093385 A330044P14Rik -1.66 0.000521 
ENSMUSG00000102858 Gm37086 -1.68 0.000521 
ENSMUSG00000105255 Gm42413 -1.69 0.047 
ENSMUSG00000098183 Gm27010 -1.72 0.0121 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 20 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000062456 Rpl9-ps6 -1.73 0.000521 
ENSMUSG00000099764 Rps10-ps2 -1.74 0.00847 
ENSMUSG00000030048 Gkn3 -1.76 0.000521 
ENSMUSG00000042501 Cpa6 -1.77 0.000521 
ENSMUSG00000053740 Gm6457 -1.78 0.0278 
ENSMUSG00000050490 Gm8394 -1.79 0.0358 
ENSMUSG00000051650 B3gnt2 -1.79 0.0243 
ENSMUSG00000070610 Gm13127 -1.8 0.00586 
ENSMUSG00000062168 Ppef1 -1.81 0.0153 
ENSMUSG00000072692 Rpl37rt -1.81 0.000521 
ENSMUSG00000099615 Gm28362 -1.86 0.000521 
ENSMUSG00000097961 Gm27000 -1.86 0.00263 
ENSMUSG00000060419 Rps16-ps2 -1.87 0.0494 
ENSMUSG00000046341 Gm11223 -1.89 0.000521 
ENSMUSG00000023140 Reg2 -1.93 0.00448 
ENSMUSG00000058625 Gm17383 -1.93 0.00376 
ENSMUSG00000092702 Gm24514 -1.93 0.000521 
ENSMUSG00000078636 Gm7336 -1.94 0.000521 
ENSMUSG00000085950 Gm13589 -1.95 0.00448 
ENSMUSG00000075053 Vdac3-ps1 -1.96 0.000521 
ENSMUSG00000101316 Gm12663 -1.96 0.0186 
ENSMUSG00000063522 2010109I03Rik -1.97 0.000521 
ENSMUSG00000103539 Gm37834 -1.97 0.0133 
ENSMUSG00000058126 Tpm3-rs7 -1.98 0.000521 
ENSMUSG00000105440 Gm43673 -1.98 0.00878 
ENSMUSG00000109335 RP23-287I20.1 -2 0.000521 
ENSMUSG00000071035 Gm5499 -2.01 0.000521 
ENSMUSG00000103053 Gm38271 -2.02 0.00448 
ENSMUSG00000082809 Gm14150 -2.03 0.000521 
ENSMUSG00000068706 Gm10250 -2.08 0.000521 
ENSMUSG00000104837 Gm42520 -2.09 0.0299 
ENSMUSG00000093798 Gm8355 -2.1 0.000521 
ENSMUSG00000078134 Gm12355 -2.12 0.00586 
ENSMUSG00000082896 Gm5844 -2.12 0.000521 
ENSMUSG00000079311 Gm3222 -2.13 0.015 
ENSMUSG00000104051 Gm38128 -2.14 0.000521 
ENSMUSG00000105370 Gm42718 -2.15 0.000521 
ENSMUSG00000050122 Vwa3b -2.16 0.00751 
ENSMUSG00000089647 Gm2245 -2.2 0.00142 
ENSMUSG00000083621 Gm14586 -2.21 0.00184 
ENSMUSG00000079407 1700110I01Rik -2.23 0.000985 
ENSMUSG00000084817 Gm5526 -2.24 0.00376 
ENSMUSG00000081305 Gm12879 -2.26 0.033 
ENSMUSG00000108314 RP23-73F23.5 -2.27 0.000521 
ENSMUSG00000104496 Gm5837 -2.3 0.0311 
ENSMUSG00000097609 Gm26659 -2.31 0.000521 
ENSMUSG00000106988 Tsg101-ps -2.32 0.0209 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 21 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000058809 Hspd1-ps3 -2.32 0.000521 
ENSMUSG00000107176 Gm9794 -2.33 0.000521 
ENSMUSG00000095690 Rab11b-ps2 -2.34 0.00142 
ENSMUSG00000049124 Gm8186 -2.36 0.0466 
ENSMUSG00000100580 4933436I20Rik -2.36 0.00339 
ENSMUSG00000105471 A430073D23Rik -2.37 0.000521 
ENSMUSG00000063902 Gm7964 -2.39 0.000521 
ENSMUSG00000108500 RP23-335G1.5 -2.42 0.0248 
ENSMUSG00000037096 Gm9762 -2.43 0.00142 
ENSMUSG00000064193 Gm4735 -2.44 0.000521 
ENSMUSG00000104046 Gm37567 -2.44 0.000521 
ENSMUSG00000102972 Gm37348 -2.52 0.00847 
ENSMUSG00000074292 Gm10660 -2.59 0.000521 
ENSMUSG00000079941 Gm11273 -2.59 0.0445 
ENSMUSG00000066362 Rps13-ps1 -2.6 0.00751 
ENSMUSG00000102289 Gm31258 -2.61 0.0253 
ENSMUSG00000102470 Gm37244 -2.63 0.000985 
ENSMUSG00000105931 Gm43014 -2.63 0.000521 
ENSMUSG00000100033 Gm8337 -2.64 0.0124 
ENSMUSG00000083097 Gm14494 -2.67 0.0477 
ENSMUSG00000082274 Gm14026 -2.7 0.00909 
ENSMUSG00000050097 Ces2b -2.7 0.000521 
ENSMUSG00000082536 Gm13456 -2.73 0.000521 
ENSMUSG00000043889 Gm8399 -2.75 0.00412 
ENSMUSG00000083325 Gm14121 -2.78 0.000521 
ENSMUSG00000103922 Gm6123 -2.78 0.00302 
ENSMUSG00000093064 Gm23153 -2.8 0.000521 
ENSMUSG00000083563 Gm13340 -2.83 0.000521 
ENSMUSG00000098222 Gm8318 -2.85 0.0209 
ENSMUSG00000082894 Gm6480 -2.9 0.000521 
ENSMUSG00000059040 Eno1b -2.93 0.00184 
ENSMUSG00000083391 Gm14148 -2.99 0.000521 
ENSMUSG00000070729 Gm12966 -3 0.00142 
ENSMUSG00000052825 Gm9892 -3.02 0.01 
ENSMUSG00000108249 Gm43960 -3.02 0.000521 
ENSMUSG00000104913 Gm6560 -3.03 0.0121 
ENSMUSG00000046440 Gm5564 -3.11 0.000521 
ENSMUSG00000067869 Tcea1-ps1 -3.13 0.0161 
ENSMUSG00000098113 Gm2445 -3.22 0.0248 
ENSMUSG00000083863 Gm13341 -3.27 0.00184 
ENSMUSG00000041872 Il17f -3.28 0.0124 
ENSMUSG00000053038 Gm6180 -3.36 0.0332 
ENSMUSG00000064694 Gm24146 -3.38 0.000521 
ENSMUSG00000082454 Gm12183 -3.42 0.000521 
ENSMUSG00000046952 Gm5815 -3.44 0.0106 
ENSMUSG00000102718 Gm37761 -3.45 0.000521 
ENSMUSG00000071343 Gm10327 -3.5 0.00518 
App.1. Genes Modulated by 6 Hour Sleep Deprivation:  App. 22 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000080776 Gm12174 -3.51 0.00224 
ENSMUSG00000107951 Gm6210 -3.52 0.000521 
ENSMUSG00000083679 Gm12892 -3.61 0.000521 
ENSMUSG00000091045 Vmn2r55 -3.62 0.00376 
ENSMUSG00000078599 Skint8 -3.65 0.000521 
ENSMUSG00000044751 Gm12231 -3.66 0.000521 
ENSMUSG00000108857 RP24-297G19.6 -3.75 0.000521 
ENSMUSG00000108884 RP23-306P12.3 -3.77 0.000521 
ENSMUSG00000070443 Gm10291 -3.78 0.0273 
ENSMUSG00000104802 Gm5869 -3.79 0.00263 
ENSMUSG00000104095 Gm37315 -3.93 0.00909 
ENSMUSG00000092674 Gm24105 -4.05 0.000521 
ENSMUSG00000098111 Gm4654 -4.09 0.000521 
ENSMUSG00000032899 Styk1 -4.13 0.00339 
ENSMUSG00000082319 Gm8822 -4.24 0.00184 
ENSMUSG00000082035 Rpl17-ps8 -4.28 0.000521 
ENSMUSG00000085180 AI838599 -4.62 0.000521 
ENSMUSG00000100863 Gm12669 -4.69 0.0447 
ENSMUSG00000065254 Gm23973 -4.78 0.000521 
ENSMUSG00000064999 Gm26035 -4.84 0.000521 
ENSMUSG00000108255 Gm16499 -10.7 0.000521 
  
App.2. Genes Modulated by 12 Hour Sleep Deprivation:  App. 23 
App.2. Genes Modulated by 12 Hour Sleep Deprivation  
Genes immediately modulated by 12-hour sleep deprivation by at least 1.5 fold compared to mice 
sleep deprived for 3 hours with 9 hour recovery opportunity. Data presented is the Gene ID and name, 
the Log2 fold change, and the Benjamini adjusted q-value for significance. Data derived from cuffdiff 
command. 
 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000092137 Gcom1 12.5 0.015 
ENSMUSG00000006574 Slc4a1 4.7 0.000521 
ENSMUSG00000026822 Lcn2 3.64 0.000521 
ENSMUSG00000099907 Gm10421 2.97 0.0453 
ENSMUSG00000102059 Gm20257 2.91 0.000521 
ENSMUSG00000037095 Lrg1 2.29 0.000521 
ENSMUSG00000043556 Fbxl7 1.98 0.0282 
ENSMUSG00000051367 Six1 1.97 0.0136 
ENSMUSG00000056054 S100a8 1.93 0.0204 
ENSMUSG00000033386 Frrs1 1.9 0.0144 
ENSMUSG00000098306 Gm28040 1.85 0.0169 
ENSMUSG00000054582 Pabpc1l 1.83 0.0278 
ENSMUSG00000091956 C2cd4b 1.81 0.0356 
ENSMUSG00000023083 H2-M10.2 1.75 0.044 
ENSMUSG00000019773 Fbxo5 1.73 0.029 
ENSMUSG00000002289 Angptl4 1.7 0.000521 
ENSMUSG00000039476 Prrx2 1.68 0.00552 
ENSMUSG00000057378 Ryr3 1.68 0.000521 
ENSMUSG00000108569 RP23-44H21.1 1.67 0.0103 
ENSMUSG00000002831 Plin4 1.64 0.000521 
ENSMUSG00000086539 Gm16759 1.61 0.0183 
ENSMUSG00000041193 Pla2g5 1.6 0.000521 
ENSMUSG00000069265 Hist1h3a 1.58 0.015 
ENSMUSG00000093672 Gm20655 1.56 0.0414 
ENSMUSG00000068855 Hist2h2ac 1.53 0.00302 
ENSMUSG00000035692 Isg15 1.53 0.00878 
ENSMUSG00000104195 B230377A18Rik 1.49 0.0479 
ENSMUSG00000030431 Tmem238 1.47 0.0412 
ENSMUSG00000095098 Ccdc85b 1.46 0.000521 
ENSMUSG00000071637 Cebpd 1.45 0.000521 
ENSMUSG00000109447 RP24-75N6.1 1.45 0.0487 
ENSMUSG00000027555 Car13 1.43 0.000521 
ENSMUSG00000033227 Wnt6 1.4 0.0268 
ENSMUSG00000006219 Fblim1 1.39 0.0094 
ENSMUSG00000107747 Gm5881 1.38 0.0191 
ENSMUSG00000085022 Gm5860 1.34 0.0285 
ENSMUSG00000005268 Prlr 1.33 0.0282 
App.2. Genes Modulated by 12 Hour Sleep Deprivation:  App. 24 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000047907 Tshz2 1.33 0.000521 
ENSMUSG00000027868 Tbx15 1.32 0.0496 
ENSMUSG00000024175 Tekt4 1.32 0.0427 
ENSMUSG00000016024 Lbp 1.3 0.00653 
ENSMUSG00000100929 Gm28064 1.29 0.0153 
ENSMUSG00000103662 Gm34294 1.29 0.000985 
ENSMUSG00000027875 Hmgcs2 1.27 0.000521 
ENSMUSG00000049796 Crh 1.26 0.000521 
ENSMUSG00000066637 Ttc32 1.24 0.00483 
ENSMUSG00000093803 Ppp2r3d 1.24 0.00653 
ENSMUSG00000017897 Eya2 1.23 0.0167 
ENSMUSG00000033177 Tmprss7 1.23 0.0201 
ENSMUSG00000096768 Erdr1 1.21 0.000521 
ENSMUSG00000037166 Ppp1r14a 1.21 0.000521 
ENSMUSG00000090356 Teddm3 1.2 0.00815 
ENSMUSG00000039278 Pcsk1n 1.19 0.000521 
ENSMUSG00000083261 Gm7816 1.18 0.0405 
ENSMUSG00000061535 C1qtnf7 1.18 0.0304 
ENSMUSG00000044103 Il1f9 1.18 0.041 
ENSMUSG00000041012 Cmtm8 1.16 0.025 
ENSMUSG00000102578 Gm10576 1.13 0.0346 
ENSMUSG00000023067 Cdkn1a 1.11 0.000521 
ENSMUSG00000097312 Gm26870 1.1 0.00686 
ENSMUSG00000034686 Prr7 1.09 0.000521 
ENSMUSG00000096972 Gm26883 1.09 0.000521 
ENSMUSG00000102579 Gm37965 1.07 0.0094 
ENSMUSG00000030111 A2m 1.07 0.0155 
ENSMUSG00000063021 Hist1h2ak 1.07 0.0144 
ENSMUSG00000107173 Gm43266 1.07 0.024 
ENSMUSG00000030711 Sult1a1 1.05 0.000521 
ENSMUSG00000049892 Rasd1 1.04 0.000521 
ENSMUSG00000023140 Reg2 1.03 0.000521 
ENSMUSG00000022146 Osmr 1.02 0.000521 
ENSMUSG00000067578 Cbln4 1.01 0.000521 
ENSMUSG00000043659 Npsr1 0.992 0.0436 
ENSMUSG00000032487 Ptgs2 0.99 0.000521 
ENSMUSG00000038775 Vill 0.98 0.0419 
ENSMUSG00000036907 C1ql2 0.976 0.0304 
ENSMUSG00000008845 Cd163 0.976 0.000521 
ENSMUSG00000029343 Crybb1 0.966 0.0136 
ENSMUSG00000033730 Egr3 0.959 0.000521 
ENSMUSG00000042842 Serpinb6b 0.959 0.00847 
ENSMUSG00000044145 1810024B03Rik 0.955 0.0419 
ENSMUSG00000071341 Egr4 0.949 0.000521 
ENSMUSG00000056313 1810011O10Rik 0.945 0.0118 
ENSMUSG00000001948 Spa17 0.944 0.0376 
ENSMUSG00000016206 H2-M3 0.935 0.0372 
App.2. Genes Modulated by 12 Hour Sleep Deprivation:  App. 25 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000039891 Txlnb 0.934 0.0158 
ENSMUSG00000029070 Mxra8 0.931 0.0139 
ENSMUSG00000047963 Stbd1 0.93 0.0183 
ENSMUSG00000031530 Dusp4 0.923 0.000521 
ENSMUSG00000040212 Emp3 0.92 0.0365 
ENSMUSG00000051590 Map3k19 0.92 0.000521 
ENSMUSG00000050138 Kcnk12 0.909 0.000521 
ENSMUSG00000102891 Gm19114 0.907 0.000521 
ENSMUSG00000028607 Cpt2 0.907 0.041 
ENSMUSG00000036492 Rnf39 0.906 0.000985 
ENSMUSG00000026072 Il1r1 0.901 0.000521 
ENSMUSG00000048572 Tmem252 0.9 0.00412 
ENSMUSG00000040569 Slc26a7 0.895 0.00263 
ENSMUSG00000047632 Fgfbp3 0.887 0.000521 
ENSMUSG00000024222 Fkbp5 0.882 0.000521 
ENSMUSG00000029304 Spp1 0.881 0.000521 
ENSMUSG00000084904 Gm14827 0.876 0.000521 
ENSMUSG00000105022 Gm43537 0.869 0.0374 
ENSMUSG00000042622 Maff 0.868 0.0434 
ENSMUSG00000050288 Fzd2 0.865 0.000521 
ENSMUSG00000039903 Eva1c 0.865 0.0118 
ENSMUSG00000046561 Arsj 0.865 0.00184 
ENSMUSG00000032092 Mpzl2 0.86 0.000985 
ENSMUSG00000021390 Ogn 0.858 0.00302 
ENSMUSG00000034765 Dusp5 0.853 0.000521 
ENSMUSG00000032532 Cck 0.848 0.000521 
ENSMUSG00000085565 Gm15721 0.847 0.000521 
ENSMUSG00000037428 Vgf 0.842 0.000521 
ENSMUSG00000058488 Kl 0.841 0.00302 
ENSMUSG00000027765 P2ry1 0.838 0.0225 
ENSMUSG00000041203 2310036O22Rik 0.838 0.000521 
ENSMUSG00000097324 Mir143hg 0.828 0.0498 
ENSMUSG00000104586 4921539H07Rik 0.826 0.000521 
ENSMUSG00000019232 Etnppl 0.825 0.000521 
ENSMUSG00000023882 Zfp54 0.822 0.0475 
ENSMUSG00000025776 Crispld1 0.82 0.000521 
ENSMUSG00000051048 P4ha3 0.818 0.0172 
ENSMUSG00000047420 Fam180a 0.816 0.0136 
ENSMUSG00000100210 Hist1h3f 0.809 0.00815 
ENSMUSG00000043993 2900052L18Rik 0.809 0.00483 
ENSMUSG00000032265 Fam46a 0.808 0.000521 
ENSMUSG00000024134 Six2 0.798 0.000985 
ENSMUSG00000021702 Thbs4 0.796 0.0139 
ENSMUSG00000070858 Gm1673 0.777 0.0155 
ENSMUSG00000026062 Slc9a2 0.776 0.00719 
ENSMUSG00000040152 Thbs1 0.77 0.00142 
ENSMUSG00000016179 Camk1g 0.768 0.000521 
App.2. Genes Modulated by 12 Hour Sleep Deprivation:  App. 26 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000061524 Zic2 0.765 0.000521 
ENSMUSG00000038059 Smim3 0.765 0.000521 
ENSMUSG00000029718 Pcolce 0.764 0.00686 
ENSMUSG00000078640 Gm11627 0.764 0.0255 
ENSMUSG00000026315 Serpinb8 0.764 0.000521 
ENSMUSG00000024014 Pim1 0.76 0.0115 
ENSMUSG00000028655 Mfsd2a 0.76 0.000521 
ENSMUSG00000046470 Sox18 0.758 0.00224 
ENSMUSG00000063727 Tnfrsf11b 0.757 0.00224 
ENSMUSG00000017692 Rhbdl3 0.749 0.000521 
ENSMUSG00000026167 Wnt10a 0.749 0.000521 
ENSMUSG00000073418 C4b 0.748 0.0188 
ENSMUSG00000037239 Spred3 0.745 0.000521 
ENSMUSG00000105366 Gm43719 0.741 0.0225 
ENSMUSG00000102900 Gm37811 0.741 0.000985 
ENSMUSG00000067297 Ifit1bl2 0.737 0.0282 
ENSMUSG00000031762 Mt2 0.73 0.000521 
ENSMUSG00000035299 Mid1 0.73 0.000521 
ENSMUSG00000003032 Klf4 0.723 0.00339 
ENSMUSG00000022678 Nde1 0.721 0.000521 
ENSMUSG00000032289 Thsd4 0.712 0.0147 
ENSMUSG00000055148 Klf2 0.709 0.000521 
ENSMUSG00000028971 Cort 0.708 0.00686 
ENSMUSG00000056999 Ide 0.708 0.000521 
ENSMUSG00000022602 Arc 0.706 0.000521 
ENSMUSG00000060402 Chst8 0.705 0.0325 
ENSMUSG00000028487 Bnc2 0.7 0.0273 
ENSMUSG00000104919 Gm42617 0.695 0.00783 
ENSMUSG00000040565 Btaf1 0.69 0.000521 
ENSMUSG00000099583 Hist1h3d 0.687 0.0496 
ENSMUSG00000021379 Id4 0.686 0.000521 
ENSMUSG00000020614 Fam20a 0.683 0.00719 
ENSMUSG00000048482 Bdnf 0.682 0.000521 
ENSMUSG00000001943 Vsig2 0.679 0.0494 
ENSMUSG00000046618 Olfml2a 0.677 0.00224 
ENSMUSG00000097578 Gm26798 0.672 0.00376 
ENSMUSG00000073388 A330017A19Rik 0.671 0.0278 
ENSMUSG00000029135 Fosl2 0.668 0.000521 
ENSMUSG00000086040 Wipf3 0.666 0.0115 
ENSMUSG00000010122 Slc47a1 0.663 0.0155 
ENSMUSG00000060591 Ifitm2 0.66 0.0109 
ENSMUSG00000108137 Gm44053 0.655 0.0332 
ENSMUSG00000024039 Cbs 0.655 0.0285 
ENSMUSG00000039208 Metrnl 0.645 0.0183 
ENSMUSG00000024087 Cyp1b1 0.645 0.00339 
ENSMUSG00000041559 Fmod 0.639 0.000521 
ENSMUSG00000088185 Scarna2 0.637 0.000521 
App.2. Genes Modulated by 12 Hour Sleep Deprivation:  App. 27 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000032128 Robo3 0.634 0.000521 
ENSMUSG00000040740 Slc25a34 0.633 0.0118 
ENSMUSG00000064264 Zfp428 0.632 0.000521 
ENSMUSG00000044674 Fzd1 0.623 0.000521 
ENSMUSG00000098202 B830012L14Rik 0.62 0.000521 
ENSMUSG00000031765 Mt1 0.61 0.000521 
ENSMUSG00000043445 Pgp 0.61 0.000985 
ENSMUSG00000028128 F3 0.609 0.000521 
ENSMUSG00000019772 Vip 0.608 0.000521 
ENSMUSG00000033633 Clec18a 0.603 0.0196 
ENSMUSG00000021835 Bmp4 0.598 0.0299 
ENSMUSG00000029822 Osbpl3 0.597 0.00184 
ENSMUSG00000086320 Gm12840 0.595 0.0335 
ENSMUSG00000029695 Aass 0.592 0.0358 
ENSMUSG00000032807 Alox12b 0.591 0.00971 
ENSMUSG00000022150 Dab2 0.589 0.0141 
ENSMUSG00000009687 Fxyd5 0.588 0.0365 
ENSMUSG00000039109 F13a1 0.585 0.0161 
ENSMUSG00000063260 Syt10 -0.585 0.00751 
ENSMUSG00000031775 Pllp -0.586 0.000521 
ENSMUSG00000027577 Chrna4 -0.587 0.00339 
ENSMUSG00000032564 Cpne4 -0.591 0.0109 
ENSMUSG00000024670 Cd6 -0.591 0.0344 
ENSMUSG00000027004 Frzb -0.592 0.0139 
ENSMUSG00000025370 Cdh9 -0.598 0.000521 
ENSMUSG00000054931 Zkscan4 -0.601 0.0294 
ENSMUSG00000027188 Pamr1 -0.603 0.00518 
ENSMUSG00000067377 Tspan6 -0.605 0.0183 
ENSMUSG00000028654 Mycl -0.606 0.0401 
ENSMUSG00000021647 Cartpt -0.612 0.0481 
ENSMUSG00000062760 1810041L15Rik -0.612 0.0121 
ENSMUSG00000025171 Ubtd1 -0.613 0.023 
ENSMUSG00000040767 Snrnp25 -0.616 0.0243 
ENSMUSG00000003070 Efna2 -0.628 0.00653 
ENSMUSG00000035713 Usp35 -0.636 0.0496 
ENSMUSG00000026278 Bok -0.637 0.000521 
ENSMUSG00000035202 Lars2 -0.637 0.000521 
ENSMUSG00000026765 Lypd6b -0.641 0.00971 
ENSMUSG00000090223 Pcp4 -0.646 0.000521 
ENSMUSG00000047773 Ankfn1 -0.649 0.00412 
ENSMUSG00000025795 Rassf3 -0.651 0.000521 
ENSMUSG00000038550 Ciart -0.654 0.00878 
ENSMUSG00000034810 Scn7a -0.662 0.000521 
ENSMUSG00000018923 Med11 -0.671 0.00909 
ENSMUSG00000076617 Ighm -0.686 0.000521 
ENSMUSG00000044254 Pcsk9 -0.693 0.024 
ENSMUSG00000027849 Syt6 -0.702 0.000521 
App.2. Genes Modulated by 12 Hour Sleep Deprivation:  App. 28 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000037977 6430571L13Rik -0.708 0.0209 
ENSMUSG00000064360 mt-Nd3 -0.718 0.00224 
ENSMUSG00000040557 Wbscr27 -0.73 0.0144 
ENSMUSG00000042328 Hps4 -0.733 0.0396 
ENSMUSG00000000093 Tbx2 -0.738 0.0353 
ENSMUSG00000028950 Tas1r1 -0.744 0.0199 
ENSMUSG00000021223 Papln -0.745 0.0385 
ENSMUSG00000009376 Met -0.764 0.000521 
ENSMUSG00000039316 Rftn1 -0.783 0.0188 
ENSMUSG00000022468 Endou -0.795 0.00483 
ENSMUSG00000027669 Gnb4 -0.798 0.000521 
ENSMUSG00000097383 1500026H17Rik -0.799 0.00971 
ENSMUSG00000094655 Gm25360 -0.804 0.0235 
ENSMUSG00000044461 Shisa2 -0.815 0.0438 
ENSMUSG00000020374 Rasgef1c -0.823 0.000521 
ENSMUSG00000025905 Oprk1 -0.829 0.000521 
ENSMUSG00000061086 Myl4 -0.847 0.000521 
ENSMUSG00000028174 Rpe65 -0.864 0.044 
ENSMUSG00000002043 Trappc6a -0.871 0.0449 
ENSMUSG00000022061 Nkx3-1 -0.877 0.00719 
ENSMUSG00000044499 Hs3st5 -0.888 0.00142 
ENSMUSG00000044813 Shb -0.901 0.0199 
ENSMUSG00000025188 Hps1 -0.906 0.0372 
ENSMUSG00000007080 Pole -0.94 0.0432 
ENSMUSG00000106321 Gm43674 -0.941 0.0314 
ENSMUSG00000038173 Enpp6 -0.969 0.00142 
ENSMUSG00000062382 Gm10116 -0.971 0.0282 
ENSMUSG00000032400 Zwilch -0.986 0.0436 
ENSMUSG00000075511 1700001L05Rik -0.989 0.0302 
ENSMUSG00000042514 Klhl14 -0.991 0.0106 
ENSMUSG00000053216 Btn2a2 -0.995 0.027 
ENSMUSG00000044734 Serpinb1a -1 0.00263 
ENSMUSG00000035258 Abi3bp -1.01 0.000521 
ENSMUSG00000027496 Aurka -1.01 0.0356 
ENSMUSG00000020131 Pcsk4 -1.01 0.00224 
ENSMUSG00000042631 Xkr7 -1.04 0.00376 
ENSMUSG00000041674 BC006965 -1.04 0.00483 
ENSMUSG00000055602 Tcp10b -1.05 0.028 
ENSMUSG00000021541 Trpc7 -1.05 0.0062 
ENSMUSG00000078695 Cisd3 -1.07 0.000521 
ENSMUSG00000075334 Rprm -1.08 0.000521 
ENSMUSG00000048351 Coa7 -1.12 0.0109 
ENSMUSG00000040605 Bace2 -1.12 0.000521 
ENSMUSG00000039552 Rsph4a -1.12 0.00376 
ENSMUSG00000020908 Myh3 -1.13 0.000521 
ENSMUSG00000026826 Nr4a2 -1.19 0.000521 
ENSMUSG00000050121 Opalin -1.19 0.000521 
App.2. Genes Modulated by 12 Hour Sleep Deprivation:  App. 29 
Ensembl_ID Gene Log2_FC q_val 
ENSMUSG00000021281 Tnfaip2 -1.21 0.033 
ENSMUSG00000040258 Nxph4 -1.27 0.0282 
ENSMUSG00000076612 Ighg2c -1.28 0.000521 
ENSMUSG00000035486 Plk5 -1.31 0.000521 
ENSMUSG00000006269 Atp6v1b1 -1.37 0.0109 
ENSMUSG00000025946 Pth2r -1.41 0.0158 
ENSMUSG00000029134 Plb1 -1.43 0.00339 
ENSMUSG00000046491 C1qtnf2 -1.49 0.0177 
ENSMUSG00000039563 2210406O10Rik -1.53 0.000521 
ENSMUSG00000085783 Gm9816 -1.6 0.0144 
ENSMUSG00000039748 Exo1 -1.64 0.0201 
ENSMUSG00000020679 Hnf1b -1.66 0.00586 
ENSMUSG00000087107 AI662270 -1.77 0.0263 
ENSMUSG00000030048 Gkn3 -1.83 0.00376 
ENSMUSG00000010080 Epn3 -2.03 0.0337 
ENSMUSG00000070780 Rbm47 -2.07 0.0158 
ENSMUSG00000073530 Pappa2 -2.36 0.000521 
ENSMUSG00000070687 Htr1d -2.85 0.000985 
ENSMUSG00000063659 Zbtb18 -3.72 0.000521 
 
  
App.3. Diurnal Genes in Mouse Cortex:  App. 30 
 
App.3. Diurnal Genes in Mouse Cortex 
Genes identified as diurnal by JTK algorithm. Data presented is the Benjamini adjusted p-value for 
24-hour gene expression pattern for mice subjected to 0,3,6 or 12 hour sleep deprivation 
(C,SD3,SD6 or SD12, respectively). Also included is the timing of the peak expression in control animals 
(C_phase), where 0 is defined as 6pm (lights off) and 12 is defined as 6am (lights on). Amplitude is 
normalised to gene expression value. Ordered by genes that remain rhythmic through progressively 
increasing duration of sleep deprivation, followed by phase, followed by amplitude of oscillation.  
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000055560 Zfp459 7.00E-04 0.00018 0.00064 0.038 0 0.31 
ENSMUSG00000004328 Hif3a 0.0095 0.00018 0.0041 0.028 0 0.26 
ENSMUSG00000034858 Fam214a 6.30E-05 1.30E-07 0.0023 0.0077 0 0.22 
ENSMUSG00000051351 Zfp46 4.30E-05 0.00015 5.40E-05 0.027 0 0.21 
ENSMUSG00000021215 Net1 8.40E-05 7.10E-06 0.00022 0.027 0 0.2 
ENSMUSG00000038214 Bend3 0.029 0.067 0.35 0.027 0 0.12 
ENSMUSG00000047414 Flrt2 0.00047 0.16 0.046 0.038 0 0.11 
ENSMUSG00000035024 Ncapd3 0.047 0.0013 0.091 0.028 0 0.077 
ENSMUSG00000070565 Rasal2 0.011 0.00062 0.029 0.038 0 0.057 
ENSMUSG00000073761 4933427I04Rik 0.0032 0.007 0.3 0.043 3 0.31 
ENSMUSG00000021775 Nr1d2 5.70E-06 0.00011 0.071 0.047 3 0.18 
ENSMUSG00000036106 Prr5 0.047 0.015 0.11 0.038 3 0.18 
ENSMUSG00000020044 Timp3 0.0048 0.023 0.067 0.036 3 0.1 
ENSMUSG00000078117 Gm16485 0.026 0.58 0.75 0.027 3 0.084 
ENSMUSG00000004951 Hspb1 0.0044 0.0012 0.0029 0.027 9 0.46 
ENSMUSG00000090877 Hspa1b 0.011 0.0013 0.058 0.027 9 0.33 
ENSMUSG00000025823 Pdia4 0.0016 0.0023 0.00064 0.038 9 0.31 
ENSMUSG00000042116 Vwa1 0.0048 0.00017 0.26 0.03 9 0.23 
ENSMUSG00000036915 Kirrel2 0.015 0.086 0.097 0.027 9 0.17 
ENSMUSG00000056962 Jmjd6 0.022 0.98 0.04 0.038 9 0.13 
ENSMUSG00000022075 Rhobtb2 0.00015 0.0048 0.00043 0.027 9 0.1 
ENSMUSG00000045312 Lhfpl2 0.019 0.25 0.058 0.036 9 0.09 
ENSMUSG00000028634 Hivep3 0.043 0.0012 0.12 0.047 9 0.088 
ENSMUSG00000029648 Flt1 0.04 8.50E-05 0.062 0.0077 9 0.072 
ENSMUSG00000021250 Fos 0.0029 0.00082 0.00073 0.028 12 0.49 
ENSMUSG00000019916 P4ha1 7.00E-04 1.20E-05 1.80E-05 0.038 12 0.37 
ENSMUSG00000028341 Nr4a3 0.0018 2.90E-06 9.30E-05 0.027 12 0.33 
ENSMUSG00000056708 Ier5 0.0072 0.00012 4.60E-05 0.036 12 0.32 
ENSMUSG00000041378 Cldn5 0.02 0.00022 0.062 0.0076 12 0.29 
ENSMUSG00000030093 Wnt7a 0.0058 0.026 0.0095 0.027 12 0.27 
ENSMUSG00000062960 Kdr 1.90E-05 2.60E-08 1.80E-05 0.047 12 0.24 
ENSMUSG00000043415 Otud1 0.0048 0.00017 0.026 0.0077 12 0.19 
ENSMUSG00000027435 Cd93 0.0016 0.00017 6.30E-05 0.027 12 0.18 
ENSMUSG00000021224 Numb 0.001 0.0034 0.002 0.038 12 0.16 
App.3. Diurnal Genes in Mouse Cortex:  App. 31 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000029312 Klhl8 0.0072 0.46 0.091 0.016 12 0.1 
ENSMUSG00000034575 Papd7 0.017 4.70E-05 4.30E-05 0.038 12 0.079 
ENSMUSG00000055116 Arntl 1.10E-06 4.70E-05 2.10E-06 0.027 15 0.25 
ENSMUSG00000066829 Zfp810 0.004 6.70E-05 0.00073 0.036 21 0.26 
ENSMUSG00000052496 Pkdrej 0.0014 0.0019 0.015 0.027 21 0.25 
ENSMUSG00000060314 Zfp941 8.40E-05 0.0019 0.054 0.036 21 0.19 
ENSMUSG00000021010 Npas3 0.0018 0.19 0.015 0.027 21 0.073 
ENSMUSG00000031483 Erlin2 0.04 0.067 0.33 0.0077 21 0.062 
ENSMUSG00000050211 Pla2g4e 0.0044 0.00018 0.00058 0.3 0 0.45 
ENSMUSG00000021903 Galnt15 0.00035 7.10E-06 0.00043 0.3 0 0.44 
ENSMUSG00000038550 Ciart 5.20E-05 0.00022 0.00058 0.17 0 0.38 
ENSMUSG00000031853 BC021891 0.0044 0.0095 0.0033 0.34 0 0.36 
ENSMUSG00000098221 Gm27030 0.0065 0.3 0.012 1 0 0.33 
ENSMUSG00000032010 Usp2 0.014 0.25 0.0037 0.58 0 0.32 
ENSMUSG00000045414 1190002N15Rik 5.20E-05 2.50E-06 0.0095 0.17 0 0.3 
ENSMUSG00000096936 Gm3510 0.014 3.00E-04 0.026 0.48 0 0.3 
ENSMUSG00000097454 Gm26892 0.00047 0.00018 0.001 0.31 0 0.3 
ENSMUSG00000089737 Gm15688 0.0048 0.62 0.018 1 0 0.3 
ENSMUSG00000047606 Ankrd34c 7.00E-04 0.003 0.00019 0.44 0 0.29 
ENSMUSG00000074766 Ism1 0.0011 0.063 0.0023 0.25 0 0.26 
ENSMUSG00000074001 Klhl40 0.00035 1.30E-05 0.0079 0.079 0 0.26 
ENSMUSG00000019970 Sgk1 2.20E-05 0.018 0.015 0.26 0 0.25 
ENSMUSG00000025511 Tspan4 7.70E-05 0.37 0.022 1 0 0.24 
ENSMUSG00000045193 Cirbp 0.0053 4.10E-06 0.026 0.066 0 0.24 
ENSMUSG00000034161 Scx 0.024 0.04 0.024 1 0 0.23 
ENSMUSG00000031382 Asb11 0.0014 5.00E-04 0.024 0.5 0 0.22 
ENSMUSG00000025450 Gm9752 0.0053 1 0.043 0.48 0 0.2 
ENSMUSG00000071691 Gm960 0.024 0.33 0.031 0.88 0 0.2 
ENSMUSG00000034320 Slc26a2 0.0029 0.07 0.049 0.37 0 0.2 
ENSMUSG00000086555 Gm13446 0.0048 0.011 0.049 0.78 0 0.2 
ENSMUSG00000039629 Strip2 8.40E-05 0.00091 0.012 0.88 0 0.19 
ENSMUSG00000067872 Ccdc87 0.0072 0.5 0.043 1 0 0.19 
ENSMUSG00000097820 E530011L22Rik 0.011 0.033 0.026 1 0 0.19 
ENSMUSG00000020889 Nr1d1 0.0058 0.053 0.04 0.44 0 0.18 
ENSMUSG00000023206 Il15ra 0.019 0.51 0.046 0.6 0 0.18 
ENSMUSG00000042686 Jph1 0.0048 0.17 0.026 0.41 0 0.17 
ENSMUSG00000037355 Uvssa 0.00027 1 0.0018 1 0 0.17 
ENSMUSG00000039096 Rsad1 0.012 0.00032 0.00054 0.76 0 0.16 
ENSMUSG00000027796 Smad9 0.0048 0.026 0.002 1 0 0.16 
ENSMUSG00000030087 Klf15 0.013 0.00018 0.02 0.64 0 0.15 
ENSMUSG00000085084 4930570G19Rik 0.0013 1 0.031 1 0 0.15 
ENSMUSG00000055884 Fancm 0.0032 0.024 0.02 1 0 0.14 
ENSMUSG00000037224 Zfyve28 0.022 0.0025 0.024 1 0 0.14 
ENSMUSG00000036817 Sun1 0.0053 0.029 0.046 0.91 0 0.14 
ENSMUSG00000053914 Kdm4d 0.0058 0.17 0.00043 0.83 0 0.13 
ENSMUSG00000008305 Tle1 0.013 1 0.015 0.29 0 0.13 
ENSMUSG00000099478 Gm28370 0.0053 0.17 0.026 0.39 0 0.13 
App.3. Diurnal Genes in Mouse Cortex:  App. 32 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000020280 Pus10 0.022 0.93 0.049 1 0 0.13 
ENSMUSG00000038068 Rnf144b 0.014 0.042 0.0033 0.78 0 0.13 
ENSMUSG00000026843 Fubp3 0.017 0.71 0.026 1 0 0.13 
ENSMUSG00000034614 Pik3ip1 0.0095 1 0.00043 1 0 0.12 
ENSMUSG00000059149 Mfsd4 0.0036 2.00E-05 0.0079 1 0 0.12 
ENSMUSG00000095930 Nim1k 7.70E-05 0.045 0.0041 1 0 0.11 
ENSMUSG00000084947 Gm15594 0.014 0.13 0.037 0.39 0 0.11 
ENSMUSG00000042997 Nhlrc3 0.02 0.6 0.0041 1 0 0.11 
ENSMUSG00000059772 Slx1b 0.0087 0.0088 0.0033 0.83 0 0.11 
ENSMUSG00000097023 AI854517 0.0032 1 0.018 1 0 0.1 
ENSMUSG00000055296 Tmem245 0.043 0.32 0.043 0.95 0 0.1 
ENSMUSG00000026489 Adck3 0.031 0.063 0.037 1 0 0.1 
ENSMUSG00000030255 Sspn 0.0029 0.004 0.0029 1 0 0.1 
ENSMUSG00000010307 Tmem86a 0.047 6.00E-05 0.0047 0.74 0 0.1 
ENSMUSG00000026096 Osgepl1 0.047 1 0.015 1 0 0.099 
ENSMUSG00000040648 Ppip5k2 0.0065 0.0023 0.017 1 0 0.094 
ENSMUSG00000038215 Cep44 0.0065 0.15 0.031 1 0 0.093 
ENSMUSG00000069631 Strada 0.004 0.13 0.037 0.5 0 0.088 
ENSMUSG00000039307 Hexdc 0.031 0.096 0.043 1 0 0.085 
ENSMUSG00000030880 Polr3e 0.047 0.00032 0.0047 0.58 0 0.081 
ENSMUSG00000027242 Wdr76 0.014 0.06 0.043 0.29 0 0.073 
ENSMUSG00000058761 Rnf169 0.0036 1 0.037 1 0 0.072 
ENSMUSG00000029804 Herc3 0.043 0.39 0.049 1 0 0.051 
ENSMUSG00000003477 Inmt 4.30E-05 0.015 0.00085 0.68 3 0.39 
ENSMUSG00000040170 Fmo2 0.012 0.0015 0.00058 0.14 3 0.36 
ENSMUSG00000096257 Ccer2 0.00031 0.021 0.015 0.68 3 0.33 
ENSMUSG00000051777 Iqcj 0.013 0.24 0.018 0.88 3 0.29 
ENSMUSG00000020131 Pcsk4 0.0087 0.027 0.037 1 3 0.28 
ENSMUSG00000059824 Dbp 1.30E-05 0.00044 2.10E-06 0.76 3 0.27 
ENSMUSG00000020424 Gatsl3 0.0016 0.063 0.022 0.095 3 0.27 
ENSMUSG00000028957 Per3 8.20E-06 8.50E-05 0.0013 0.07 3 0.26 
ENSMUSG00000040675 Mthfd1l 1.90E-05 0.057 0.0087 0.56 3 0.25 
ENSMUSG00000070601 Vmn2r84 0.004 0.0027 0.04 1 3 0.23 
ENSMUSG00000074449 Gm15319 0.0013 0.026 0.0079 0.83 3 0.23 
ENSMUSG00000009378 Slc16a12 0.00047 0.0082 0.00064 0.13 3 0.23 
ENSMUSG00000046808 Atp10d 0.013 0.096 0.026 0.25 3 0.23 
ENSMUSG00000028661 Epha8 0.012 0.001 0.0015 0.39 3 0.22 
ENSMUSG00000028645 Slc2a1 0.00035 0.00066 0.046 0.075 3 0.22 
ENSMUSG00000038304 Cd160 0.00041 0.086 0.00043 0.78 3 0.22 
ENSMUSG00000039059 Hrh3 0.0013 6.70E-05 0.026 0.56 3 0.2 
ENSMUSG00000022389 Tef 0.00078 3.00E-04 0.043 0.5 3 0.16 
ENSMUSG00000049791 Fzd4 0.00052 0.0012 0.013 0.58 3 0.16 
ENSMUSG00000030256 Bhlhe41 8.40E-05 0.68 0.022 1 3 0.14 
ENSMUSG00000059602 Syn3 0.031 1 0.017 0.6 3 0.12 
ENSMUSG00000086544 Chn1os3 0.037 1 0.0095 1 3 0.12 
ENSMUSG00000048347 Pcdhb18 0.022 1 0.049 1 3 0.075 
ENSMUSG00000048756 Foxo3 0.024 0.02 0.034 1 3 0.074 
App.3. Diurnal Genes in Mouse Cortex:  App. 33 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000061751 Kalrn 0.022 0.0034 0.0047 1 3 0.072 
ENSMUSG00000026790 Odf2 0.034 0.6 0.037 0.76 3 0.068 
ENSMUSG00000018001 Cyth3 0.029 1 0.049 1 3 0.059 
ENSMUSG00000002847 Pla1a 0.0053 0.22 0.0095 0.52 6 0.33 
ENSMUSG00000025491 Ifitm1 0.0044 0.042 0.02 0.91 6 0.31 
ENSMUSG00000055866 Per2 2.20E-05 0.00032 0.0026 0.36 6 0.28 
ENSMUSG00000072572 Slc39a2 0.031 0.53 0.00073 1 6 0.17 
ENSMUSG00000070461 9230112E08Rik 0.0021 1 0.0071 0.86 6 0.11 
ENSMUSG00000004568 Arhgef18 0.0021 1 0.002 1 6 0.092 
ENSMUSG00000038026 Kcnj9 0.04 1 0.022 1 6 0.09 
ENSMUSG00000031770 Herpud1 0.043 0.5 0.046 1 6 0.086 
ENSMUSG00000041168 Lonp1 0.047 0.77 0.031 0.91 6 0.078 
ENSMUSG00000034807 Colgalt1 0.019 0.85 0.02 1 6 0.071 
ENSMUSG00000022602 Arc 0.0095 0.0016 0.0018 0.14 9 0.69 
ENSMUSG00000023232 Serinc2 0.00015 0.00028 0.00073 0.095 9 0.65 
ENSMUSG00000031530 Dusp4 1.90E-05 4.40E-08 0.00058 1 9 0.46 
ENSMUSG00000022769 Sdf2l1 1.90E-05 0.0038 0.0054 0.56 9 0.42 
ENSMUSG00000062563 Cys1 8.20E-06 0.00056 0.0015 0.3 9 0.42 
ENSMUSG00000087249 Gm16062 0.0095 0.37 0.0047 0.66 9 0.4 
ENSMUSG00000026864 Hspa5 0.0013 0.0016 0.00054 0.075 9 0.38 
ENSMUSG00000025316 Banp 0.001 0.01 0.013 0.2 9 0.37 
ENSMUSG00000024793 Tnfrsf25 0.014 1 0.018 1 9 0.37 
ENSMUSG00000072919 Noxred1 0.0032 0.17 0.02 0.52 9 0.35 
ENSMUSG00000032501 Trib1 0.022 0.00066 0.00054 0.3 9 0.34 
ENSMUSG00000060862 Zbtb40 7.80E-06 0.00032 0.00048 0.3 9 0.33 
ENSMUSG00000023272 Creld2 7.80E-06 0.04 0.0033 0.83 9 0.32 
ENSMUSG00000042978 Sbk1 0.00011 0.0027 0.0087 0.78 9 0.3 
ENSMUSG00000024042 Sik1 0.0014 2.00E-04 0.0087 0.46 9 0.3 
ENSMUSG00000021203 Otub2 0.0011 0.0025 0.0047 0.46 9 0.3 
ENSMUSG00000009092 Derl3 0.0018 1 0.034 1 9 0.3 
ENSMUSG00000049511 Htr1b 0.00023 0.0032 0.037 0.11 9 0.28 
ENSMUSG00000040183 Ankrd6 8.20E-06 0.017 0.037 0.2 9 0.27 
ENSMUSG00000051335 Gfod1 3.50E-05 3.30E-08 0.002 0.29 9 0.26 
ENSMUSG00000031758 Cdyl2 7.80E-06 0.0016 0.013 0.95 9 0.26 
ENSMUSG00000038418 Egr1 0.0029 2.30E-07 0.00064 0.23 9 0.26 
ENSMUSG00000026773 Pfkfb3 0.0011 0.0027 0.043 0.26 9 0.25 
ENSMUSG00000052229 Gpr17 1.00E-05 0.045 0.0087 0.059 9 0.24 
ENSMUSG00000063415 Cyp26b1 0.037 0.05 0.0023 0.31 9 0.24 
ENSMUSG00000096847 Tmem151b 2.60E-05 0.00015 0.037 0.6 9 0.23 
ENSMUSG00000041773 Enc1 0.00031 2.50E-06 0.024 1 9 0.22 
ENSMUSG00000021277 Traf3 7.80E-06 0.00066 0.012 1 9 0.22 
ENSMUSG00000046667 Rbm12b1 0.00041 1 0.0029 0.76 9 0.21 
ENSMUSG00000021587 Pcsk1 0.00061 0.13 0.015 1 9 0.21 
ENSMUSG00000025810 Nrp1 0.0036 0.001 0.0012 0.83 9 0.21 
ENSMUSG00000021990 Spata13 0.034 0.34 0.02 1 9 0.21 
ENSMUSG00000073838 Tufm 0.019 1 0.043 1 9 0.21 
ENSMUSG00000053004 Hrh1 0.00052 0.68 0.002 1 9 0.2 
App.3. Diurnal Genes in Mouse Cortex:  App. 34 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000017386 Traf4 0.04 1 0.037 0.22 9 0.2 
ENSMUSG00000037035 Inhbb 0.0011 0.43 0.0079 0.83 9 0.2 
ENSMUSG00000057329 Bcl2 1.80E-05 0.0014 0.018 0.44 9 0.2 
ENSMUSG00000013089 Etv5 0.00052 0.00066 0.0037 0.81 9 0.2 
ENSMUSG00000094626 Cecr6 0.00015 0.00091 2.10E-06 0.066 9 0.19 
ENSMUSG00000020038 Cry1 9.50E-05 0.13 0.0095 0.52 9 0.19 
ENSMUSG00000005483 Dnajb1 0.0016 0.98 0.0013 0.37 9 0.19 
ENSMUSG00000035007 Rundc1 9.50E-05 3.00E-04 0.0026 0.36 9 0.19 
ENSMUSG00000029817 Tra2a 0.0065 0.0048 0.043 0.14 9 0.19 
ENSMUSG00000020032 Nuak1 9.50E-05 2.10E-05 0.00011 0.087 9 0.19 
ENSMUSG00000030584 Dpf1 0.0018 0.015 0.0095 1 9 0.18 
ENSMUSG00000028527 Ak4 0.0016 0.074 0.00019 0.26 9 0.18 
ENSMUSG00000010554 Mettl16 0.0026 0.012 0.0013 0.25 9 0.18 
ENSMUSG00000033377 Palmd 0.0016 0.01 0.043 1 9 0.17 
ENSMUSG00000068114 Ccdc134 0.00052 0.24 0.0029 1 9 0.17 
ENSMUSG00000033594 Spata2l 0.011 0.074 0.043 0.13 9 0.17 
ENSMUSG00000032115 Hyou1 0.00031 0.029 0.017 0.88 9 0.17 
ENSMUSG00000051726 Kcnf1 9.50E-05 1 0.0029 1 9 0.16 
ENSMUSG00000023827 Agpat4 0.0053 1 0.0087 1 9 0.16 
ENSMUSG00000000184 Ccnd2 7.00E-04 0.074 0.017 0.83 9 0.16 
ENSMUSG00000061079 Zfp143 4.30E-05 0.011 0.04 0.34 9 0.16 
ENSMUSG00000049420 Tmem200a 0.0087 0.0013 0.04 1 9 0.16 
ENSMUSG00000071719 Tmem28 0.001 0.0015 0.0054 0.68 9 0.16 
ENSMUSG00000031168 Ebp 0.043 1 0.037 1 9 0.16 
ENSMUSG00000034265 Zdhhc14 0.0048 0.011 0.024 1 9 0.16 
ENSMUSG00000032290 Ptpn9 0.0048 0.004 0.00048 0.39 9 0.16 
ENSMUSG00000055652 Klhl25 0.0018 0.5 0.012 1 9 0.16 
ENSMUSG00000019055 Plod1 0.0048 0.37 0.049 1 9 0.15 
ENSMUSG00000024219 Anks1 0.001 1 0.043 0.46 9 0.15 
ENSMUSG00000038132 Rbm24 0.012 0.006 0.0033 0.62 9 0.15 
ENSMUSG00000073139 BC023829 0.0058 0.0027 0.0095 0.5 9 0.15 
ENSMUSG00000040711 Sh3pxd2b 0.0048 3.00E-04 0.013 0.76 9 0.15 
ENSMUSG00000030103 Bhlhe40 0.02 0.045 0.049 0.46 9 0.14 
ENSMUSG00000048578 Mlec 7.80E-06 0.1 0.00019 0.26 9 0.14 
ENSMUSG00000022556 Hsf1 0.0013 0.0027 0.043 1 9 0.14 
ENSMUSG00000025408 Ddit3 0.013 0.11 0.012 0.1 9 0.14 
ENSMUSG00000037239 Spred3 0.00061 2.00E-04 0.037 1 9 0.14 
ENSMUSG00000050530 Fam171a1 0.00047 0.026 0.012 1 9 0.14 
ENSMUSG00000042846 Lrrtm3 0.011 0.16 0.034 0.68 9 0.14 
ENSMUSG00000071757 Zhx2 0.0018 0.0052 0.015 0.22 9 0.14 
ENSMUSG00000097736 9530059O14Rik 0.026 0.078 0.031 0.41 9 0.14 
ENSMUSG00000036158 Prickle1 0.0011 0.048 0.0012 0.23 9 0.14 
ENSMUSG00000023915 Tnfrsf21 0.001 0.14 0.0041 1 9 0.13 
ENSMUSG00000072082 Ccnf 0.024 0.36 0.046 0.95 9 0.13 
ENSMUSG00000036964 Trim17 0.0072 0.23 0.04 0.97 9 0.13 
ENSMUSG00000001098 Kctd10 0.0029 1 0.049 1 9 0.13 
ENSMUSG00000042210 Abhd14a 0.02 0.96 5.40E-05 1 9 0.13 
App.3. Diurnal Genes in Mouse Cortex:  App. 35 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000045282 Tmem86b 0.024 0.029 0.022 1 9 0.13 
ENSMUSG00000019487 Trip10 0.024 0.014 0.037 1 9 0.12 
ENSMUSG00000051495 Irf2bp2 0.0036 3.80E-05 0.0087 0.18 9 0.12 
ENSMUSG00000046269 Usp27x 0.0048 0.0016 0.00064 0.48 9 0.12 
ENSMUSG00000020522 Mfap3 0.019 0.029 0.022 0.86 9 0.12 
ENSMUSG00000034958 Atcay 0.0044 1 0.026 0.5 9 0.12 
ENSMUSG00000021484 Lman2 7.80E-06 1 0.018 1 9 0.12 
ENSMUSG00000059796 Eif4a1 0.034 0.75 0.031 1 9 0.12 
ENSMUSG00000024781 Lipa 0.013 0.029 0.0033 0.6 9 0.12 
ENSMUSG00000040822 1700123O20Rik 0.024 0.37 0.022 1 9 0.12 
ENSMUSG00000031523 Dlc1 0.0087 0.83 0.043 0.66 9 0.12 
ENSMUSG00000020654 Adcy3 0.0014 1 0.034 1 9 0.12 
ENSMUSG00000073805 Fam196a 0.0087 0.015 0.00058 0.13 9 0.11 
ENSMUSG00000022814 Umps 0.0032 0.77 0.0087 1 9 0.11 
ENSMUSG00000042331 Specc1 3.00E-05 0.18 0.0012 0.71 9 0.11 
ENSMUSG00000038855 Itpkb 0.0014 0.0032 0.02 0.48 9 0.11 
ENSMUSG00000027940 Tpm3 0.029 0.31 0.034 1 9 0.11 
ENSMUSG00000037112 Sik2 0.0011 0.00062 0.0087 0.051 9 0.11 
ENSMUSG00000047824 Pygo2 0.0053 1 0.046 1 9 0.11 
ENSMUSG00000038485 Socs7 1.90E-05 0.033 0.0047 1 9 0.11 
ENSMUSG00000033658 Ddx19b 8.40E-05 0.45 0.02 1 9 0.11 
ENSMUSG00000052406 Rexo4 0.0032 0.027 0.04 0.39 9 0.11 
ENSMUSG00000052713 Zfp608 0.00035 0.00025 0.043 0.17 9 0.11 
ENSMUSG00000020250 Txnrd1 8.20E-06 0.004 0.013 0.13 9 0.11 
ENSMUSG00000050511 Oprd1 0.004 0.0044 0.049 0.66 9 0.1 
ENSMUSG00000017667 Zfp334 3.00E-05 0.00082 0.0015 0.44 9 0.1 
ENSMUSG00000039633 Lonrf1 7.00E-04 0.0076 0.002 0.21 9 0.1 
ENSMUSG00000019947 Arid5b 0.0048 0.0088 0.017 0.11 9 0.1 
ENSMUSG00000018209 Stk4 0.0029 0.51 0.00022 1 9 0.1 
ENSMUSG00000034075 Zdhhc5 0.029 0.023 0.043 1 9 0.1 
ENSMUSG00000021071 Trim9 9.50E-05 0.013 0.046 1 9 0.1 
ENSMUSG00000035226 Rims4 0.022 0.53 0.029 1 9 0.1 
ENSMUSG00000025871 4833439L19Rik 0.0036 0.31 0.02 1 9 0.096 
ENSMUSG00000003153 Slc2a3 0.00078 1 0.02 1 9 0.096 
ENSMUSG00000032064 Dixdc1 0.0087 0.057 0.049 0.48 9 0.095 
ENSMUSG00000070056 Mfhas1 0.0016 1 0.043 1 9 0.094 
ENSMUSG00000051510 Mafg 0.00031 1 0.049 1 9 0.094 
ENSMUSG00000039804 Ncoa5 0.0095 0.34 0.0041 1 9 0.093 
ENSMUSG00000036450 Hif1an 0.0048 0.22 0.0062 1 9 0.092 
ENSMUSG00000046562 Unc119b 0.0079 1 0.00085 0.14 9 0.091 
ENSMUSG00000034563 Ccpg1 0.001 0.045 0.029 0.14 9 0.088 
ENSMUSG00000014547 Wdfy2 0.0016 1 0.022 1 9 0.088 
ENSMUSG00000022185 Acin1 0.00013 0.082 0.00064 0.17 9 0.088 
ENSMUSG00000036006 Fam65b 0.0079 0.53 0.0033 1 9 0.087 
ENSMUSG00000041313 Slc7a1 0.0053 0.14 0.049 1 9 0.086 
ENSMUSG00000003585 Sec14l2 0.04 0.68 0.022 1 9 0.085 
ENSMUSG00000037896 Rcor1 0.026 0.096 0.011 0.68 9 0.085 
App.3. Diurnal Genes in Mouse Cortex:  App. 36 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000020780 Srp68 0.004 0.013 0.017 1 9 0.083 
ENSMUSG00000042426 Dhx29 0.024 0.063 0.046 0.5 9 0.08 
ENSMUSG00000017802 Fam134c 0.00017 1 0.04 1 9 0.078 
ENSMUSG00000029063 Nadk 0.0065 1 0.018 1 9 0.078 
ENSMUSG00000069227 Gprin1 0.0087 1 0.0012 0.78 9 0.078 
ENSMUSG00000031774 Fam192a 0.024 0.13 0.046 1 9 0.077 
ENSMUSG00000025209 Peo1 0.02 0.4 0.029 0.44 9 0.071 
ENSMUSG00000042650 Alkbh5 0.00047 0.93 0.015 1 9 0.07 
ENSMUSG00000031155 Pim2 0.0044 0.12 0.026 0.41 9 0.069 
ENSMUSG00000039936 Pik3cd 0.047 0.21 0.049 1 9 0.068 
ENSMUSG00000031078 Cttn 0.019 0.51 0.024 1 9 0.067 
ENSMUSG00000026024 Als2 0.022 1 0.017 1 9 0.066 
ENSMUSG00000001036 Epn2 0.043 0.4 0.037 0.37 9 0.065 
ENSMUSG00000025085 Ablim1 0.00013 0.029 0.022 1 9 0.063 
ENSMUSG00000032624 Eml4 0.0072 0.0023 0.049 0.52 9 0.062 
ENSMUSG00000055065 Ddx17 0.0023 1 0.0037 1 9 0.06 
ENSMUSG00000039982 Dtx4 0.04 0.021 0.049 0.31 9 0.057 
ENSMUSG00000018547 Pip4k2b 0.0087 0.06 0.015 1 9 0.056 
ENSMUSG00000022412 Mief1 0.012 1 0.024 1 9 0.055 
ENSMUSG00000068742 Cry2 0.026 1 0.043 0.54 9 0.052 
ENSMUSG00000004099 Dnmt1 0.0044 0.58 0.031 1 9 0.051 
ENSMUSG00000019969 Psen1 0.024 1 0.00054 1 9 0.051 
ENSMUSG00000034413 Neurl1b 0.0044 0.9 0.034 1 9 0.049 
ENSMUSG00000035629 Rubcn 0.019 1 0.024 1 9 0.048 
ENSMUSG00000029922 Mkrn1 0.034 0.8 0.034 0.42 9 0.042 
ENSMUSG00000031545 Gpat4 0.015 1 0.034 1 9 0.037 
ENSMUSG00000037868 Egr2 0.00078 4.20E-05 2.10E-05 0.22 12 0.68 
ENSMUSG00000036390 Gadd45a 0.0053 0.11 0.00048 1 12 0.44 
ENSMUSG00000054944 5330416C01Rik 0.019 0.36 0.0087 1 12 0.42 
ENSMUSG00000060962 Dmkn 0.0053 0.053 0.0062 1 12 0.4 
ENSMUSG00000023034 Nr4a1 0.0087 0.00018 0.00064 0.28 12 0.38 
ENSMUSG00000036052 Dnajb5 7.80E-06 2.60E-08 0.00019 0.14 12 0.38 
ENSMUSG00000029641 Rasl11a 0.031 0.053 0.034 0.5 12 0.38 
ENSMUSG00000037465 Klf10 0.0026 6.30E-06 1.80E-05 0.34 12 0.37 
ENSMUSG00000005124 Wisp1 0.00031 0.006 0.0062 1 12 0.35 
ENSMUSG00000023067 Cdkn1a 7.80E-06 0.00022 0.022 1 12 0.33 
ENSMUSG00000020601 Trib2 2.60E-05 0.00032 6.30E-05 0.26 12 0.32 
ENSMUSG00000023905 Tnfrsf12a 0.0072 0.3 0.0029 0.91 12 0.31 
ENSMUSG00000020571 Pdia6 0.0021 3.80E-05 5.40E-05 0.14 12 0.29 
ENSMUSG00000019960 Dusp6 0.0053 0.00022 0.00027 0.26 12 0.29 
ENSMUSG00000070348 Ccnd1 0.0016 0.0019 0.0041 1 12 0.29 
ENSMUSG00000059991 Nptx2 7.70E-05 0.0019 0.0087 0.97 12 0.28 
ENSMUSG00000002983 Relb 0.0032 0.45 0.029 1 12 0.28 
ENSMUSG00000030047 Arhgap25 0.017 0.11 0.018 0.64 12 0.28 
ENSMUSG00000025402 Nab2 0.0018 0.00022 0.0041 0.13 12 0.28 
ENSMUSG00000024427 Spry4 0.0032 0.00015 0.00014 0.41 12 0.27 
ENSMUSG00000019850 Tnfaip3 0.0036 0.029 0.018 0.21 12 0.27 
App.3. Diurnal Genes in Mouse Cortex:  App. 37 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000007617 Homer1 9.50E-05 4.20E-05 0.0013 0.26 12 0.26 
ENSMUSG00000089774 Slc5a3 0.0013 0.004 0.024 0.059 12 0.26 
ENSMUSG00000040363 Bcor 0.00052 6.60E-10 4.60E-05 0.1 12 0.25 
ENSMUSG00000049649 Gpr3 0.004 0.0014 0.011 0.31 12 0.25 
ENSMUSG00000032575 Manf 0.0014 0.0027 0.001 0.46 12 0.25 
ENSMUSG00000048482 Bdnf 0.00027 1.00E-05 0.00041 1 12 0.25 
ENSMUSG00000028680 Plk3 0.0053 0.0032 0.00073 0.91 12 0.24 
ENSMUSG00000023805 Synj2 3.50E-05 8.80E-06 0.0026 0.5 12 0.24 
ENSMUSG00000037984 Neurod6 0.013 0.0021 0.0087 0.41 12 0.24 
ENSMUSG00000032060 Cryab 0.015 1 0.037 1 12 0.23 
ENSMUSG00000002897 Il17ra 9.50E-05 0.1 0.00043 1 12 0.22 
ENSMUSG00000003814 Calr 1.60E-05 0.0088 9.30E-05 1 12 0.22 
ENSMUSG00000049225 Pdp1 1.60E-05 4.40E-08 0.0023 0.39 12 0.22 
ENSMUSG00000020484 Xbp1 0.00031 0.0056 0.0026 0.14 12 0.22 
ENSMUSG00000063889 Crem 0.0087 0.091 0.022 0.74 12 0.22 
ENSMUSG00000041324 Inhba 2.20E-05 0.0048 0.029 1 12 0.21 
ENSMUSG00000032487 Ptgs2 0.0029 0.006 0.0054 1 12 0.21 
ENSMUSG00000032058 Ppp2r1b 0.0044 0.22 0.0033 0.66 12 0.21 
ENSMUSG00000020513 Tubd1 0.0023 0.07 0.018 0.62 12 0.21 
ENSMUSG00000037010 Apln 0.00047 0.0088 0.0047 1 12 0.2 
ENSMUSG00000038244 Mical2 3.00E-05 0.0016 0.046 1 12 0.2 
ENSMUSG00000011256 Adam19 8.40E-05 0.027 0.02 1 12 0.2 
ENSMUSG00000042540 Acot5 0.04 0.067 0.04 1 12 0.2 
ENSMUSG00000053137 Mapk11 0.00013 0.00062 0.037 1 12 0.19 
ENSMUSG00000029135 Fosl2 0.00019 0.00056 0.00085 0.44 12 0.19 
ENSMUSG00000022269 Mar-11 0.015 0.18 0.034 0.56 12 0.18 
ENSMUSG00000078235 Fam43b 0.00078 5.50E-05 5.00E-05 0.11 12 0.18 
ENSMUSG00000055805 Fmnl1 0.0053 2.50E-06 0.0041 0.13 12 0.18 
ENSMUSG00000022893 Adamts1 0.0036 0.006 0.00043 0.14 12 0.18 
ENSMUSG00000037169 Mycn 0.011 0.17 0.0095 0.1 12 0.18 
ENSMUSG00000045314 Sowahb 0.00092 0.013 0.00048 0.2 12 0.18 
ENSMUSG00000004460 Dnajb11 0.00017 0.074 0.011 0.42 12 0.17 
ENSMUSG00000031217 Efnb1 0.037 0.13 0.024 1 12 0.17 
ENSMUSG00000021253 Tgfb3 0.0011 7.90E-06 0.0012 1 12 0.17 
ENSMUSG00000044548 Dact1 0.014 0.13 0.001 0.21 12 0.17 
ENSMUSG00000030748 Il4ra 0.0032 2.10E-05 0.043 0.78 12 0.17 
ENSMUSG00000004891 Nes 0.0026 0.17 0.00019 1 12 0.16 
ENSMUSG00000035898 Uba6 0.0044 2.10E-05 0.0062 0.11 12 0.16 
ENSMUSG00000024966 Stip1 1.80E-05 0.0021 5.40E-05 0.5 12 0.16 
ENSMUSG00000029657 Hsph1 6.30E-05 0.003 0.0037 0.095 12 0.15 
ENSMUSG00000006705 Pknox1 0.00019 0.37 0.031 1 12 0.15 
ENSMUSG00000041220 Elovl6 0.0058 0.023 0.012 0.46 12 0.15 
ENSMUSG00000030357 Fkbp4 0.0014 0.082 0.022 0.74 12 0.15 
ENSMUSG00000086118 Gm14169 0.015 0.06 0.043 1 12 0.15 
ENSMUSG00000057101 Zfp180 1.90E-05 0.035 0.00085 1 12 0.15 
ENSMUSG00000017144 Rnd3 0.00015 4.70E-05 0.0033 0.42 12 0.15 
ENSMUSG00000025232 Hexa 0.00052 0.024 5.40E-05 0.54 12 0.15 
App.3. Diurnal Genes in Mouse Cortex:  App. 38 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000069763 Tmem100 0.0021 0.067 0.0095 0.81 12 0.14 
ENSMUSG00000031068 Glrx3 0.031 0.3 0.026 1 12 0.14 
ENSMUSG00000063229 Ldha 0.031 1 0.00073 0.58 12 0.14 
ENSMUSG00000021037 Ahsa1 3.00E-05 0.0056 0.00064 0.16 12 0.14 
ENSMUSG00000027540 Ptpn1 0.0072 0.024 0.00058 1 12 0.14 
ENSMUSG00000021728 Emb 0.0058 0.006 0.012 0.48 12 0.14 
ENSMUSG00000035900 Gramd4 0.0053 0.07 0.013 0.26 12 0.14 
ENSMUSG00000029701 Rbm28 0.0018 0.045 0.049 0.37 12 0.14 
ENSMUSG00000021951 N6amt2 0.0048 1 0.029 1 12 0.14 
ENSMUSG00000026227 2810459M11Rik 0.04 0.042 0.046 0.71 12 0.14 
ENSMUSG00000024691 Fam111a 0.02 0.045 0.049 1 12 0.14 
ENSMUSG00000030898 Cckbr 0.022 1.40E-06 0.018 0.22 12 0.14 
ENSMUSG00000030956 Fam53b 0.0065 0.029 0.0037 0.13 12 0.14 
ENSMUSG00000079043 Fastkd5 0.014 0.063 0.013 0.41 12 0.14 
ENSMUSG00000044847 Lsm11 0.00013 1 0.0018 1 12 0.13 
ENSMUSG00000022765 Snap29 0.024 0.031 0.0037 0.13 12 0.13 
ENSMUSG00000067279 Ppp1r3c 0.00035 0.01 0.018 1 12 0.13 
ENSMUSG00000018648 Dusp14 0.0079 2.00E-05 0.046 0.83 12 0.13 
ENSMUSG00000025277 Abhd6 0.0079 0.00014 0.0062 0.5 12 0.13 
ENSMUSG00000022114 Spry2 0.004 0.18 5.40E-05 0.41 12 0.13 
ENSMUSG00000021365 Nedd9 0.0044 0.078 0.017 1 12 0.13 
ENSMUSG00000054893 Zfp667 0.043 0.02 0.0026 0.6 12 0.12 
ENSMUSG00000032316 Clk3 0.0021 0.0082 0.0079 0.58 12 0.12 
ENSMUSG00000006736 Tspan31 0.0016 0.46 0.017 1 12 0.12 
ENSMUSG00000003348 Mob3a 0.024 1 0.049 0.68 12 0.12 
ENSMUSG00000027286 Lrrc57 0.0032 0.1 0.037 0.54 12 0.12 
ENSMUSG00000018572 Phf23 0.0072 0.0082 0.0095 0.42 12 0.12 
ENSMUSG00000097417 Gm26669 0.0029 2.10E-05 0.02 0.1 12 0.12 
ENSMUSG00000030272 Camk1 0.001 0.07 0.0015 0.71 12 0.12 
ENSMUSG00000075327 Zbtb2 0.0095 0.15 0.017 0.39 12 0.11 
ENSMUSG00000049327 Setd8 0.034 0.037 0.02 0.81 12 0.11 
ENSMUSG00000027583 Zbtb46 0.026 0.25 0.046 1 12 0.11 
ENSMUSG00000019806 Aig1 0.001 0.074 0.029 1 12 0.11 
ENSMUSG00000046417 Lrrc75a 0.011 0.18 0.002 0.14 12 0.11 
ENSMUSG00000049532 Sall2 0.0032 0.0088 0.0054 0.31 12 0.11 
ENSMUSG00000001143 Lman2l 0.02 0.8 0.049 1 12 0.11 
ENSMUSG00000021890 Eaf1 0.029 1 0.034 1 12 0.1 
ENSMUSG00000044715 Gskip 0.024 0.14 0.0041 0.95 12 0.1 
ENSMUSG00000032285 Dnaja4 0.047 0.082 0.046 1 12 0.1 
ENSMUSG00000032437 Stt3b 0.0014 0.55 0.0037 1 12 0.1 
ENSMUSG00000020923 Ubtf 0.00047 0.0088 0.0054 1 12 0.1 
ENSMUSG00000026626 Ppp2r5a 0.02 0.0095 0.037 0.17 12 0.1 
ENSMUSG00000045969 Ing1 0.0087 0.063 5.40E-05 0.12 12 0.099 
ENSMUSG00000030521 Mphosph10 0.0014 0.23 0.022 0.29 12 0.097 
ENSMUSG00000046093 Hpcal4 0.013 1 0.0023 1 12 0.096 
ENSMUSG00000057469 E2f6 0.034 0.026 0.026 1 12 0.095 
ENSMUSG00000027562 Car2 0.043 1 0.046 1 12 0.094 
App.3. Diurnal Genes in Mouse Cortex:  App. 39 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000019804 Snx3 0.0023 0.013 0.012 1 12 0.093 
ENSMUSG00000035105 Egln3 0.04 0.93 0.0062 1 12 0.092 
ENSMUSG00000022607 Ptk2 0.0026 0.36 0.046 1 12 0.092 
ENSMUSG00000004056 Akt2 0.0058 0.0088 0.049 1 12 0.091 
ENSMUSG00000022505 Emp2 0.037 0.2 0.018 1 12 0.091 
ENSMUSG00000030134 Rasgef1a 0.0079 0.0021 0.0023 1 12 0.089 
ENSMUSG00000067586 S1pr3 0.024 1 0.022 0.54 12 0.087 
ENSMUSG00000035620 Ric8b 0.0079 0.013 0.013 0.066 12 0.085 
ENSMUSG00000043388 Tmem130 0.00019 0.14 0.034 0.62 12 0.085 
ENSMUSG00000044864 Ankrd50 0.0021 0.1 0.015 1 12 0.078 
ENSMUSG00000005936 Kctd20 0.031 1 0.0041 1 12 0.078 
ENSMUSG00000052214 Opa3 0.0058 0.11 0.0025 0.62 12 0.077 
ENSMUSG00000032249 Anp32a 0.034 0.62 0.026 1 12 0.075 
ENSMUSG00000028656 Cap1 0.00035 0.033 0.00019 1 12 0.073 
ENSMUSG00000023944 Hsp90ab1 0.001 0.16 0.00054 0.86 12 0.073 
ENSMUSG00000021360 Gcnt2 0.014 0.0014 0.024 1 12 0.071 
ENSMUSG00000020246 Hcfc2 0.029 0.56 0.022 1 12 0.07 
ENSMUSG00000030870 Ubfd1 0.0048 1 0.024 1 12 0.069 
ENSMUSG00000030286 Emc3 0.0044 0.43 0.026 1 12 0.069 
ENSMUSG00000022587 Ly6e 0.02 0.029 0.02 1 12 0.066 
ENSMUSG00000026080 Chst10 0.043 0.46 0.0026 0.36 12 0.065 
ENSMUSG00000032026 Rexo2 0.0018 0.13 0.0018 1 12 0.064 
ENSMUSG00000033732 Sf3b3 0.0095 0.05 0.022 0.95 12 0.06 
ENSMUSG00000025190 Got1 0.031 0.29 0.046 0.91 12 0.056 
ENSMUSG00000006763 Saal1 0.0095 1 0.022 1 12 0.054 
ENSMUSG00000052833 Sae1 0.029 1 0.034 1 12 0.053 
ENSMUSG00000043259 Fam13c 0.017 0.0018 0.031 1 12 0.053 
ENSMUSG00000031948 Kars 0.0029 0.42 0.046 1 12 0.053 
ENSMUSG00000020864 Ankrd40 0.0065 0.34 0.017 1 12 0.048 
ENSMUSG00000027184 Caprin1 0.029 0.11 0.04 0.6 12 0.047 
ENSMUSG00000020142 Slc1a4 0.037 1 0.043 1 12 0.025 
ENSMUSG00000056749 Nfil3 2.20E-05 7.90E-06 5.00E-05 0.087 15 0.35 
ENSMUSG00000027861 Casq2 3.50E-05 0.13 0.046 0.34 15 0.31 
ENSMUSG00000032373 Car12 0.047 0.24 0.024 0.52 15 0.25 
ENSMUSG00000037474 Dtl 0.0013 0.21 0.0087 0.34 15 0.24 
ENSMUSG00000039114 Nrn1 0.0011 0.46 0.018 1 15 0.17 
ENSMUSG00000020000 Moxd1 0.0095 0.078 0.026 0.81 15 0.16 
ENSMUSG00000070532 Ccdc190 0.0095 0.045 0.0029 0.83 15 0.16 
ENSMUSG00000041193 Pla2g5 0.0044 0.23 0.015 1 15 0.15 
ENSMUSG00000041959 S100a10 0.017 0.53 0.046 1 15 0.14 
ENSMUSG00000001700 Gramd3 0.00061 0.14 9.30E-05 1 15 0.12 
ENSMUSG00000042369 Rbm45 0.001 0.24 0.013 1 15 0.1 
ENSMUSG00000050711 Scg2 0.022 0.067 0.049 1 15 0.098 
ENSMUSG00000075012 Fjx1 0.013 0.64 0.043 0.26 15 0.091 
ENSMUSG00000029659 Medag 0.04 0.36 0.018 0.83 15 0.09 
ENSMUSG00000021373 Cap2 7.70E-05 0.00062 0.013 1 15 0.084 
ENSMUSG00000037712 Fermt2 0.00092 0.074 0.0026 1 15 0.083 
App.3. Diurnal Genes in Mouse Cortex:  App. 40 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000045092 S1pr1 0.031 0.048 0.046 1 15 0.05 
ENSMUSG00000019874 Fabp7 0.0032 0.0088 0.0079 0.055 18 0.3 
ENSMUSG00000027875 Hmgcs2 0.00013 0.0032 0.001 1 18 0.3 
ENSMUSG00000024256 Adcyap1 0.0087 0.88 0.029 0.91 18 0.15 
ENSMUSG00000038879 Nipal2 0.04 0.016 0.0095 1 18 0.092 
ENSMUSG00000021065 Fut8 0.02 0.39 0.049 0.91 18 0.09 
ENSMUSG00000079477 Rab7 0.014 0.75 0.012 1 18 0.043 
ENSMUSG00000051354 Samd3 0.00031 0.027 0.04 1 21 0.43 
ENSMUSG00000047363 Cstad 0.00017 0.057 0.034 0.1 21 0.34 
ENSMUSG00000052525 Spdya 0.034 0.37 0.018 1 21 0.33 
ENSMUSG00000042857 Gm9776 0.00023 0.12 0.0095 0.86 21 0.32 
ENSMUSG00000097321 1700028E10Rik 0.0079 0.33 0.026 1 21 0.29 
ENSMUSG00000053414 Hunk 0.00019 1.70E-05 0.0015 0.18 21 0.28 
ENSMUSG00000089889 0610040B10Rik 0.001 1 0.04 1 21 0.27 
ENSMUSG00000020363 Gfpt2 0.0029 0.0018 0.0023 1 21 0.27 
ENSMUSG00000032940 Rbm11 0.00019 0.027 0.002 0.26 21 0.26 
ENSMUSG00000100017 2410022M11Rik 1.80E-05 0.04 0.046 0.13 21 0.26 
ENSMUSG00000030089 Slc41a3 0.00041 0.33 0.0054 0.25 21 0.23 
ENSMUSG00000097675 1700101I11Rik 0.0048 0.033 0.022 1 21 0.23 
ENSMUSG00000097164 Cep83os 0.00035 0.0014 0.0095 0.087 21 0.23 
ENSMUSG00000087479 Gm16835 0.022 0.39 0.012 1 21 0.22 
ENSMUSG00000020653 Klf11 0.004 0.017 0.0029 0.26 21 0.22 
ENSMUSG00000000811 Txnrd3 0.0014 0.0023 0.0029 0.23 21 0.22 
ENSMUSG00000042851 Zc3h6 0.0011 0.17 0.034 0.079 21 0.21 
ENSMUSG00000006411 Pvrl4 0.0087 0.33 0.046 1 21 0.2 
ENSMUSG00000021902 Phf7 0.012 0.029 0.0062 1 21 0.19 
ENSMUSG00000073565 Prr16 0.0065 0.045 0.049 0.42 21 0.19 
ENSMUSG00000000126 Wnt9a 0.0053 0.0019 0.049 1 21 0.19 
ENSMUSG00000028133 Rwdd3 8.20E-06 0.19 0.026 0.6 21 0.18 
ENSMUSG00000097638 Carlr 0.015 0.06 0.0026 0.28 21 0.17 
ENSMUSG00000042213 Zfand4 0.0021 0.5 0.0087 0.39 21 0.17 
ENSMUSG00000050312 Nsun3 0.029 0.42 0.0033 0.71 21 0.17 
ENSMUSG00000028636 Ppcs 0.043 0.057 0.046 0.37 21 0.17 
ENSMUSG00000020930 Ccdc103 0.019 0.07 0.043 0.68 21 0.17 
ENSMUSG00000052395 Rft1 0.0029 0.05 0.0095 0.25 21 0.17 
ENSMUSG00000047446 Arl4a 0.00078 0.0082 0.037 0.3 21 0.16 
ENSMUSG00000020639 Pfn4 0.00015 5.00E-04 0.029 0.34 21 0.16 
ENSMUSG00000032397 Tipin 8.20E-06 0.0038 0.0079 0.11 21 0.16 
ENSMUSG00000022833 Ccdc14 0.00011 0.71 0.02 0.52 21 0.15 
ENSMUSG00000028653 Trit1 0.0053 0.45 0.0071 0.66 21 0.15 
ENSMUSG00000048920 Fkrp 0.017 0.0082 0.024 0.12 21 0.14 
ENSMUSG00000050796 B3galt6 0.00027 0.11 0.043 0.39 21 0.13 
ENSMUSG00000054723 Vmac 0.022 1 0.029 1 21 0.13 
ENSMUSG00000054770 Kctd18 0.015 1 0.029 1 21 0.13 
ENSMUSG00000034173 Zbed5 0.00092 0.096 0.026 0.74 21 0.13 
ENSMUSG00000041471 Fam35a 0.02 0.12 0.024 1 21 0.12 
ENSMUSG00000029802 Abcg2 0.0095 0.013 0.024 0.34 21 0.12 
App.3. Diurnal Genes in Mouse Cortex:  App. 41 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000052616 Ccdc79 0.026 1 0.046 1 21 0.11 
ENSMUSG00000072946 Ptgr2 0.00017 0.031 0.029 0.66 21 0.11 
ENSMUSG00000021646 Mccc2 0.00052 0.048 0.013 0.56 21 0.11 
ENSMUSG00000045140 Pigw 0.0048 0.37 0.0095 0.33 21 0.11 
ENSMUSG00000020311 Erlec1 0.0044 1 0.04 0.83 21 0.11 
ENSMUSG00000025086 Trub1 0.0011 0.18 0.04 1 21 0.1 
ENSMUSG00000024172 St6gal2 0.011 0.24 0.017 1 21 0.1 
ENSMUSG00000057147 Dph6 0.00041 1 0.04 1 21 0.095 
ENSMUSG00000033439 Trmt13 0.031 0.11 0.026 1 21 0.092 
ENSMUSG00000063200 Nol7 0.00047 1 0.046 1 21 0.091 
ENSMUSG00000046079 Lrrc8d 0.022 0.34 0.018 1 21 0.089 
ENSMUSG00000055553 Kxd1 0.012 0.091 0.0087 0.25 21 0.081 
ENSMUSG00000021007 Spata7 0.0029 1 0.043 1 21 0.077 
ENSMUSG00000060890 Arr3 0.0018 0.023 0.42 1 0 0.37 
ENSMUSG00000097797 Gm26901 0.043 0.013 0.15 0.76 0 0.3 
ENSMUSG00000076612 Ighg2c 0.019 0.0019 0.75 1 0 0.25 
ENSMUSG00000049580 Tsku 0.04 0.0019 0.067 0.52 0 0.23 
ENSMUSG00000031285 Dcx 0.0044 0.021 0.084 1 0 0.22 
ENSMUSG00000091272 Gm17641 0.014 0.014 0.071 1 0 0.21 
ENSMUSG00000031167 Rbm3 3.50E-05 0.00011 0.067 0.31 0 0.2 
ENSMUSG00000032245 Cln6 0.014 0.0016 0.11 1 0 0.18 
ENSMUSG00000038393 Txnip 0.00078 0.0018 0.1 0.13 0 0.17 
ENSMUSG00000015882 Lcorl 0.0013 0.039 0.33 1 0 0.17 
ENSMUSG00000042804 Gpr153 0.031 0.037 0.22 1 0 0.15 
ENSMUSG00000029208 Guf1 0.0065 0.006 0.17 0.66 0 0.15 
ENSMUSG00000035064 Eef2k 0.0032 3.00E-04 0.097 0.52 0 0.15 
ENSMUSG00000040044 Orc3 0.0032 0.004 0.067 0.12 0 0.14 
ENSMUSG00000035967 Ddx26b 0.0079 0.013 0.17 0.36 0 0.14 
ENSMUSG00000033060 Lmo7 0.0087 0.0038 0.44 1 0 0.13 
ENSMUSG00000042032 Mat2b 0.00092 0.006 0.55 1 0 0.13 
ENSMUSG00000035944 Ttc38 0.022 0.021 0.59 0.5 0 0.13 
ENSMUSG00000037890 Wdr19 0.014 2.10E-05 0.62 0.16 0 0.13 
ENSMUSG00000026484 Rnf2 0.0021 0.013 0.12 0.86 0 0.13 
ENSMUSG00000027346 Gpcpd1 0.00092 0.00035 0.17 0.64 0 0.13 
ENSMUSG00000028211 Trp53inp1 0.037 0.00062 0.097 0.14 0 0.13 
ENSMUSG00000036192 Rorb 0.026 0.04 0.32 1 0 0.12 
ENSMUSG00000022797 Tfrc 0.0044 0.048 0.29 1 0 0.11 
ENSMUSG00000000223 Drp2 0.043 0.006 0.19 1 0 0.11 
ENSMUSG00000037434 Slc30a1 0.00078 0.0038 0.11 0.11 0 0.11 
ENSMUSG00000055493 Epm2a 0.0011 0.0082 0.12 0.71 0 0.1 
ENSMUSG00000024835 Coro1b 0.034 0.018 0.9 0.95 0 0.095 
ENSMUSG00000074892 B3galt5 0.034 3.00E-04 0.14 0.18 0 0.093 
ENSMUSG00000066357 Wdr6 0.037 0.027 0.078 0.64 0 0.093 
ENSMUSG00000078161 Erich3 0.026 0.015 1 1 0 0.09 
ENSMUSG00000044308 Ubr3 0.00027 0.012 0.067 0.54 0 0.089 
ENSMUSG00000062373 Tmem65 0.024 0.014 0.9 1 0 0.088 
ENSMUSG00000078490 Cfap74 0.0065 0.027 0.071 0.74 0 0.087 
App.3. Diurnal Genes in Mouse Cortex:  App. 42 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000042272 Sestd1 0.047 0.029 0.26 0.66 0 0.086 
ENSMUSG00000015879 Fam184b 0.0072 0.031 1 1 0 0.084 
ENSMUSG00000045174 Amer3 0.047 0.0065 0.12 0.78 0 0.084 
ENSMUSG00000040850 Psme4 0.024 0.029 0.26 1 0 0.081 
ENSMUSG00000038332 Sesn1 0.013 0.0065 0.067 0.78 0 0.075 
ENSMUSG00000034295 Fhod3 0.012 3.00E-04 0.29 0.95 0 0.071 
ENSMUSG00000047037 Nipa1 0.0087 0.00035 0.29 1 0 0.067 
ENSMUSG00000044022 Pcdhb21 0.0013 0.0023 0.42 1 0 0.066 
ENSMUSG00000056367 Actr3b 0.019 0.00056 0.33 0.34 0 0.064 
ENSMUSG00000028098 Rnf115 0.0087 0.0095 0.27 0.059 0 0.06 
ENSMUSG00000005262 Ufd1l 0.04 0.0034 1 0.54 0 0.057 
ENSMUSG00000041483 Zfp281 0.017 0.0044 0.64 0.5 0 0.054 
ENSMUSG00000000276 Dgke 0.043 0.0048 0.62 1 0 0.052 
ENSMUSG00000020628 Trappc12 0.02 0.031 0.19 0.88 0 0.051 
ENSMUSG00000062190 Lancl2 0.04 0.0027 0.67 1 0 0.05 
ENSMUSG00000022010 Tsc22d1 0.011 0.048 0.054 1 0 0.046 
ENSMUSG00000002831 Plin4 8.40E-05 0.00062 0.078 0.26 3 0.6 
ENSMUSG00000007877 Tcap 0.017 0.0056 0.15 1 3 0.32 
ENSMUSG00000030711 Sult1a1 0.037 0.00012 0.1 0.97 3 0.27 
ENSMUSG00000025324 Atp10a 0.004 0.00066 0.067 0.079 3 0.26 
ENSMUSG00000024066 Xdh 0.0095 3.30E-05 0.13 0.07 3 0.26 
ENSMUSG00000032278 Paqr5 0.0044 0.00073 0.1 0.13 3 0.25 
ENSMUSG00000008845 Cd163 0.017 0.0088 0.071 1 3 0.24 
ENSMUSG00000021025 Nfkbia 0.029 1.50E-05 0.23 0.095 3 0.2 
ENSMUSG00000041957 Pkp2 0.031 0.014 0.26 0.83 3 0.2 
ENSMUSG00000033083 Tbc1d4 0.001 0.042 0.13 0.52 3 0.16 
ENSMUSG00000022610 Mapk12 0.00019 0.017 0.44 0.86 3 0.16 
ENSMUSG00000036856 Wnt4 0.031 0.00015 1 1 3 0.16 
ENSMUSG00000002910 Arrdc2 0.0095 0.016 0.24 1 3 0.15 
ENSMUSG00000039634 Zfp189 0.022 0.0015 0.42 0.42 3 0.14 
ENSMUSG00000045954 Sdpr 0.00031 0.0027 0.15 0.56 3 0.14 
ENSMUSG00000048706 Lurap1l 0.043 0.018 1 0.39 3 0.11 
ENSMUSG00000037235 Mxd4 0.037 0.021 0.11 1 3 0.1 
ENSMUSG00000005951 Shpk 0.02 0.0034 0.078 0.86 3 0.096 
ENSMUSG00000040010 Slc7a5 0.014 0.0019 0.21 0.23 3 0.095 
ENSMUSG00000030747 Dgat2 0.022 0.007 0.067 0.3 3 0.09 
ENSMUSG00000046999 1110032F04Rik 0.014 0.0082 0.22 1 3 0.086 
ENSMUSG00000020823 Sec14l1 0.012 0.007 0.1 0.5 3 0.083 
ENSMUSG00000030168 Adipor2 0.019 1.30E-05 0.071 0.21 3 0.079 
ENSMUSG00000019894 Slc6a15 0.022 0.029 0.87 1 3 0.058 
ENSMUSG00000030048 Gkn3 0.0023 0.00032 0.062 0.66 6 0.33 
ENSMUSG00000038059 Smim3 0.024 0.015 0.17 0.41 6 0.2 
ENSMUSG00000031431 Tsc22d3 0.0032 4.10E-07 0.062 0.075 6 0.2 
ENSMUSG00000069833 Ahnak 0.0014 0.0048 0.38 0.44 6 0.18 
ENSMUSG00000054280 Prr14l 0.0018 0.0095 0.75 1 6 0.13 
ENSMUSG00000034000 Neu4 0.012 0.048 0.48 0.56 6 0.12 
ENSMUSG00000019726 Lyst 0.0065 0.0021 0.2 0.31 6 0.12 
App.3. Diurnal Genes in Mouse Cortex:  App. 43 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000035067 Xkr6 0.047 0.0065 0.55 1 6 0.11 
ENSMUSG00000040584 Abcb1a 0.011 0.031 0.071 0.29 6 0.1 
ENSMUSG00000028452 Vcp 0.043 0.026 0.46 0.81 6 0.073 
ENSMUSG00000035851 Ythdc1 0.0014 0.011 1 1 6 0.064 
ENSMUSG00000033316 Galnt9 0.00013 0.004 0.097 1 9 0.4 
ENSMUSG00000042216 Sgsm1 8.20E-06 0.0044 0.15 1 9 0.33 
ENSMUSG00000067578 Cbln4 0.0023 0.0088 0.22 1 9 0.3 
ENSMUSG00000091971 Hspa1a 0.012 0.018 0.15 0.11 9 0.26 
ENSMUSG00000074575 Kcng1 0.013 0.0076 0.2 0.079 9 0.26 
ENSMUSG00000078684 5830417I10Rik 0.00011 0.006 0.37 0.41 9 0.24 
ENSMUSG00000003545 Fosb 0.013 3.30E-08 0.067 0.18 9 0.23 
ENSMUSG00000035711 Dok3 0.04 0.029 0.091 0.14 9 0.23 
ENSMUSG00000015656 Hspa8 0.015 0.0065 0.23 0.62 9 0.23 
ENSMUSG00000016179 Camk1g 2.20E-05 0.04 0.57 1 9 0.22 
ENSMUSG00000021676 Iqgap2 2.20E-05 0.0095 0.21 1 9 0.22 
ENSMUSG00000035671 Zswim4 0.0032 0.004 0.084 0.28 9 0.21 
ENSMUSG00000038894 Irs2 8.40E-05 1.00E-05 0.38 1 9 0.21 
ENSMUSG00000025372 Baiap2 0.0053 0.0014 0.16 0.5 9 0.2 
ENSMUSG00000001247 Lsr 0.015 0.004 0.27 0.5 9 0.19 
ENSMUSG00000061132 Blnk 0.026 0.015 0.15 1 9 0.19 
ENSMUSG00000027520 Zdbf2 0.02 0.012 0.81 0.3 9 0.19 
ENSMUSG00000034275 Igsf9b 0.00017 0.00011 0.62 0.48 9 0.19 
ENSMUSG00000039813 Tbc1d2 0.0032 0.0038 0.27 0.25 9 0.19 
ENSMUSG00000037014 Sstr4 0.02 0.048 0.48 1 9 0.18 
ENSMUSG00000048878 Hexim1 0.0065 5.50E-06 0.058 0.14 9 0.18 
ENSMUSG00000022012 Enox1 9.50E-05 0.011 0.38 1 9 0.18 
ENSMUSG00000005958 Ephb3 0.022 0.033 0.058 0.6 9 0.17 
ENSMUSG00000089762 Ier5l 0.031 0.024 0.4 0.42 9 0.17 
ENSMUSG00000015605 Srf 0.0058 3.30E-05 0.091 0.33 9 0.17 
ENSMUSG00000030782 Tgfb1i1 0.0079 0.045 0.26 1 9 0.17 
ENSMUSG00000022136 Dnajc3 0.00052 0.0015 0.23 0.22 9 0.16 
ENSMUSG00000006362 Cbfa2t3 0.0013 4.40E-08 0.15 0.075 9 0.16 
ENSMUSG00000031821 Gins2 0.0044 0.00032 0.24 0.81 9 0.16 
ENSMUSG00000035621 Midn 0.024 2.50E-06 0.2 0.31 9 0.16 
ENSMUSG00000045613 Chrm2 0.013 0.04 0.058 0.64 9 0.15 
ENSMUSG00000020387 Jade2 0.0013 0.007 0.3 0.81 9 0.15 
ENSMUSG00000021366 Hivep1 0.0029 3.00E-04 0.097 0.71 9 0.15 
ENSMUSG00000015944 Gatsl2 7.00E-04 0.0032 0.084 0.079 9 0.14 
ENSMUSG00000031608 Galnt7 0.0018 0.01 0.13 0.3 9 0.14 
ENSMUSG00000032012 Pvrl1 0.012 0.0015 0.81 1 9 0.14 
ENSMUSG00000044352 Sowaha 0.0053 0.00011 0.14 0.41 9 0.14 
ENSMUSG00000028402 Mpdz 0.031 0.045 0.19 0.41 9 0.13 
ENSMUSG00000022799 Arhgap31 0.00015 0.029 0.52 1 9 0.13 
ENSMUSG00000022951 Rcan1 0.00027 0.0082 0.13 0.29 9 0.13 
ENSMUSG00000027351 Spred1 0.00035 0.00015 0.084 1 9 0.13 
ENSMUSG00000025582 Nptx1 0.0013 0.033 0.22 0.91 9 0.13 
ENSMUSG00000061143 Maml3 0.00041 0.027 0.84 1 9 0.13 
App.3. Diurnal Genes in Mouse Cortex:  App. 44 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000033618 Map3k13 0.0021 0.0015 0.1 0.33 9 0.13 
ENSMUSG00000071862 Lrrtm2 0.013 0.0052 0.62 0.28 9 0.13 
ENSMUSG00000041130 Zfp598 0.031 0.004 0.29 0.76 9 0.13 
ENSMUSG00000030199 Etv6 0.017 0.007 0.19 0.34 9 0.13 
ENSMUSG00000033730 Egr3 0.0087 4.20E-05 1 1 9 0.12 
ENSMUSG00000016757 Ttll12 0.0048 0.011 0.78 0.97 9 0.12 
ENSMUSG00000039735 Fnbp1l 0.0079 0.0056 0.29 0.29 9 0.12 
ENSMUSG00000035275 Raver2 0.0032 0.0019 0.054 0.36 9 0.11 
ENSMUSG00000061353 Cxcl12 0.0072 0.015 0.2 0.16 9 0.11 
ENSMUSG00000054196 Cthrc1 0.029 0.037 0.57 0.42 9 0.11 
ENSMUSG00000038496 Slc19a3 0.034 0.033 1 1 9 0.11 
ENSMUSG00000091474 2610021A01Rik 0.0026 0.0082 1 0.48 9 0.11 
ENSMUSG00000024789 Jak2 0.0023 0.0032 0.2 0.86 9 0.11 
ENSMUSG00000039449 Prpf18 0.004 0.0065 0.26 0.66 9 0.11 
ENSMUSG00000039372 Mar-04 0.0029 0.0034 0.23 1 9 0.1 
ENSMUSG00000033565 Rbfox2 8.20E-06 0.0076 0.48 1 9 0.1 
ENSMUSG00000029822 Osbpl3 0.0036 0.026 0.16 1 9 0.1 
ENSMUSG00000024817 Uhrf2 0.0014 0.013 0.38 0.14 9 0.1 
ENSMUSG00000020882 Cacnb1 0.02 0.0027 0.27 1 9 0.1 
ENSMUSG00000028385 Snx30 0.0044 0.0065 0.054 0.64 9 0.1 
ENSMUSG00000058881 Zfp516 0.037 0.02 0.44 0.26 9 0.1 
ENSMUSG00000022419 Deptor 0.013 0.00066 1 0.54 9 0.1 
ENSMUSG00000035566 Pcdh17 0.0011 0.018 0.81 0.58 9 0.1 
ENSMUSG00000040896 Kcnd3 0.0016 0.0019 1 1 9 0.1 
ENSMUSG00000034613 Ppm1h 0.0087 0.018 0.4 1 9 0.099 
ENSMUSG00000020074 Ccar1 0.00017 0.027 0.16 0.46 9 0.099 
ENSMUSG00000047466 8030462N17Rik 0.019 0.0034 0.9 0.86 9 0.095 
ENSMUSG00000050600 Zfp831 0.0065 0.0065 0.15 0.34 9 0.095 
ENSMUSG00000063455 D630045J12Rik 0.0018 0.00025 1 0.54 9 0.094 
ENSMUSG00000036698 Ago2 0.0053 0.007 1 1 9 0.093 
ENSMUSG00000006456 Rbm14 0.0048 0.0082 0.33 0.44 9 0.092 
ENSMUSG00000054808 Actn4 0.0032 0.042 0.38 1 9 0.092 
ENSMUSG00000042599 Kdm7a 0.043 8.50E-05 0.27 0.07 9 0.09 
ENSMUSG00000030854 Ptpn5 0.04 0.0088 0.14 1 9 0.089 
ENSMUSG00000037541 Shank2 0.0016 0.0095 0.78 1 9 0.086 
ENSMUSG00000028804 Csmd2 0.02 0.015 1 1 9 0.084 
ENSMUSG00000065954 Tacc1 0.0087 0.00035 0.071 0.17 9 0.081 
ENSMUSG00000015501 Hivep2 0.017 0.0013 1 1 9 0.081 
ENSMUSG00000034832 Tet3 0.011 0.004 0.84 0.62 9 0.08 
ENSMUSG00000097545 Mir124a-1hg 0.04 0.026 0.062 0.52 9 0.079 
ENSMUSG00000036225 Kctd1 0.043 0.027 0.084 1 9 0.078 
ENSMUSG00000037815 Ctnna1 0.00047 0.045 0.058 0.46 9 0.076 
ENSMUSG00000039671 Zmynd8 0.004 0.0095 0.81 1 9 0.074 
ENSMUSG00000072875 Gpr27 0.034 0.017 0.2 0.42 9 0.073 
ENSMUSG00000022494 Shisa9 0.029 0.04 0.57 0.71 9 0.072 
ENSMUSG00000057637 Prdm2 0.0032 0.00043 1 0.26 9 0.072 
ENSMUSG00000021039 Snw1 0.00061 0.027 0.5 1 9 0.071 
App.3. Diurnal Genes in Mouse Cortex:  App. 45 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000042508 Dmtf1 0.0032 0.024 0.23 1 9 0.071 
ENSMUSG00000019188 H13 0.0058 0.004 0.21 0.68 9 0.068 
ENSMUSG00000021779 Thrb 0.011 0.011 0.84 1 9 0.067 
ENSMUSG00000032187 Smarca4 0.043 0.045 0.42 1 9 0.066 
ENSMUSG00000022237 Ankrd33b 0.0058 0.0044 0.52 0.91 9 0.066 
ENSMUSG00000037933 Bicd2 0.031 0.037 1 0.62 9 0.065 
ENSMUSG00000009569 Mkl2 0.0018 0.0088 1 0.64 9 0.063 
ENSMUSG00000004360 9330159F19Rik 0.012 0.00044 1 0.23 9 0.063 
ENSMUSG00000062232 Rapgef2 0.00052 0.0056 0.19 0.95 9 0.06 
ENSMUSG00000044968 Napepld 0.047 0.018 1 0.3 9 0.055 
ENSMUSG00000010797 Wnt2 0.00078 0.021 0.22 1 12 0.37 
ENSMUSG00000024014 Pim1 0.011 6.00E-05 0.13 0.81 12 0.25 
ENSMUSG00000028214 Gem 0.0058 0.035 0.058 1 12 0.23 
ENSMUSG00000060594 Layn 0.0021 0.0019 0.1 0.97 12 0.23 
ENSMUSG00000001774 Chordc1 0.0048 0.0032 0.19 0.18 12 0.22 
ENSMUSG00000037992 Rara 0.004 0.0082 0.16 1 12 0.22 
ENSMUSG00000051590 Map3k19 7.00E-04 0.0019 0.12 1 12 0.21 
ENSMUSG00000030110 Ret 0.0011 0.0023 0.27 1 12 0.21 
ENSMUSG00000032265 Fam46a 0.0018 0.0065 0.062 1 12 0.19 
ENSMUSG00000044224 Dnajc21 0.0048 0.00062 0.084 0.23 12 0.18 
ENSMUSG00000031538 Plat 0.00061 0.0018 0.084 0.62 12 0.18 
ENSMUSG00000021675 F2rl2 0.031 0.048 0.071 1 12 0.17 
ENSMUSG00000020048 Hsp90b1 0.00031 0.012 0.097 1 12 0.17 
ENSMUSG00000000531 Grasp 0.0053 0.0023 0.11 0.25 12 0.15 
ENSMUSG00000021866 Anxa11 0.04 0.005 1 1 12 0.15 
ENSMUSG00000061517 Sox21 0.012 0.00015 0.38 0.13 12 0.14 
ENSMUSG00000021743 Fezf2 0.012 0.016 0.38 1 12 0.13 
ENSMUSG00000051515 Fam181b 0.04 0.00073 0.33 0.28 12 0.13 
ENSMUSG00000027248 Pdia3 7.00E-04 0.045 0.058 0.29 12 0.13 
ENSMUSG00000044017 Adgrd1 0.034 0.0032 0.15 1 12 0.13 
ENSMUSG00000034403 Pja1 0.017 0.0048 0.15 0.91 12 0.13 
ENSMUSG00000040738 Ints8 0.0079 0.018 0.084 0.11 12 0.12 
ENSMUSG00000035126 Wdr78 0.047 0.014 0.16 0.33 12 0.12 
ENSMUSG00000031673 Cdh11 0.019 0.00014 0.19 1 12 0.12 
ENSMUSG00000028243 Ubxn2b 0.0036 0.0021 0.3 0.86 12 0.12 
ENSMUSG00000045034 Ankrd34b 0.0029 0.04 0.4 0.66 12 0.11 
ENSMUSG00000022332 Khdrbs3 7.00E-04 0.0019 0.12 1 12 0.11 
ENSMUSG00000068566 Myadm 0.0018 0.00022 0.38 1 12 0.11 
ENSMUSG00000038074 Fkbp14 0.0087 0.01 0.93 0.97 12 0.11 
ENSMUSG00000039037 St6galnac5 0.047 0.0034 0.12 1 12 0.11 
ENSMUSG00000026482 Rgl1 0.0029 0.031 0.15 1 12 0.11 
ENSMUSG00000064065 Ipcef1 0.0072 0.017 0.19 1 12 0.1 
ENSMUSG00000057060 Slc35f3 0.031 0.035 0.4 1 12 0.1 
ENSMUSG00000020275 Rel 0.0053 0.00035 0.084 0.21 12 0.1 
ENSMUSG00000032846 Zswim6 0.0065 0.037 0.15 0.25 12 0.1 
ENSMUSG00000034145 Tmem63c 0.022 0.018 0.1 1 12 0.099 
ENSMUSG00000023951 Vegfa 0.0095 0.0025 0.14 0.29 12 0.099 
App.3. Diurnal Genes in Mouse Cortex:  App. 46 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000028613 Lrp8 0.037 0.0038 0.87 1 12 0.095 
ENSMUSG00000044949 Ubtd2 0.0044 0.0018 0.058 1 12 0.091 
ENSMUSG00000026077 Npas2 0.0079 0.004 1 1 12 0.087 
ENSMUSG00000021113 Snapc1 0.026 0.004 0.23 1 12 0.085 
ENSMUSG00000042109 Csdc2 0.0044 0.0095 1 1 12 0.082 
ENSMUSG00000028771 Ptpn12 0.0018 0.023 0.15 0.17 12 0.082 
ENSMUSG00000000560 Gabra2 0.0095 0.0056 0.14 1 12 0.079 
ENSMUSG00000025986 Slc39a10 0.019 0.015 0.15 0.64 12 0.076 
ENSMUSG00000025404 R3hdm2 0.013 0.013 0.16 1 12 0.076 
ENSMUSG00000027803 Wwtr1 0.012 0.0048 0.55 0.66 12 0.075 
ENSMUSG00000020580 Rock2 0.034 0.006 0.27 1 12 0.075 
ENSMUSG00000040548 Tex2 0.02 0.0082 0.29 1 12 0.073 
ENSMUSG00000019775 Rgs17 0.029 0.048 1 1 12 0.067 
ENSMUSG00000024096 Ralbp1 0.0044 0.018 1 0.95 12 0.064 
ENSMUSG00000015484 Fam163a 0.015 0.007 0.55 1 12 0.062 
ENSMUSG00000022601 Zbtb11 0.04 0.042 0.75 0.26 12 0.058 
ENSMUSG00000052981 Ube2ql1 0.0014 0.045 0.14 1 12 0.058 
ENSMUSG00000057069 Ero1lb 0.04 0.026 0.64 0.36 12 0.056 
ENSMUSG00000038145 Snrk 0.029 0.0088 0.37 1 12 0.055 
ENSMUSG00000021838 Samd4 0.02 0.00035 0.1 0.56 12 0.047 
ENSMUSG00000036167 Pphln1 0.031 0.00011 0.084 0.6 12 0.047 
ENSMUSG00000015305 Sash1 0.017 0.00022 0.11 0.44 12 0.031 
ENSMUSG00000000957 Mmp14 6.30E-05 0.0015 0.24 0.095 15 0.21 
ENSMUSG00000000317 Bcl6b 0.013 0.0032 0.062 0.29 15 0.18 
ENSMUSG00000056268 Dennd1b 0.047 0.033 0.5 0.81 15 0.17 
ENSMUSG00000047420 Fam180a 0.04 0.042 0.24 1 15 0.17 
ENSMUSG00000026558 Uck2 0.004 0.029 0.23 1 15 0.16 
ENSMUSG00000060373 Hnrnpc 0.015 0.048 0.3 0.95 15 0.1 
ENSMUSG00000076431 Sox4 0.0026 0.006 0.23 0.17 15 0.092 
ENSMUSG00000021991 Cacna2d3 0.0095 0.02 0.75 1 15 0.086 
ENSMUSG00000023175 Bsg 0.0079 0.007 0.17 0.56 15 0.081 
ENSMUSG00000004631 Sgce 0.0044 0.027 0.4 0.3 15 0.08 
ENSMUSG00000030235 Slco1c1 0.0013 0.029 0.078 0.74 15 0.072 
ENSMUSG00000026740 Dnajc1 0.012 0.042 1 0.95 15 0.072 
ENSMUSG00000033009 Ogfod1 0.0058 0.015 0.27 0.3 15 0.072 
ENSMUSG00000037735 2810032G03Rik 0.031 0.013 0.38 1 15 0.067 
ENSMUSG00000024867 Pip5k1b 0.022 0.006 0.67 0.97 15 0.063 
ENSMUSG00000040774 Cept1 0.031 0.017 0.52 1 15 0.062 
ENSMUSG00000020392 Cdkn2aipnl 0.029 0.0048 1 1 15 0.061 
ENSMUSG00000060548 Tnfrsf19 0.011 0.035 0.46 0.91 15 0.055 
ENSMUSG00000027200 Sema6d 0.022 0.024 0.38 1 15 0.044 
ENSMUSG00000076609 Igkc 0.05 0.0064 1 1 18 0.4 
ENSMUSG00000083261 Gm7816 0.0072 0.027 1 1 18 0.32 
ENSMUSG00000020846 Fam101b 1.30E-05 0.0076 0.2 0.46 18 0.18 
ENSMUSG00000041986 Elmod1 0.02 0.018 0.2 0.39 18 0.11 
ENSMUSG00000054277 Arfgap3 0.00035 0.013 0.3 1 18 0.1 
ENSMUSG00000037846 Rtkn2 0.017 0.037 0.071 0.44 18 0.09 
App.3. Diurnal Genes in Mouse Cortex:  App. 47 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000039852 Rere 0.026 0.027 1 1 18 0.08 
ENSMUSG00000037656 Slc20a2 0.0026 6.70E-05 1 0.83 18 0.061 
ENSMUSG00000056091 St3gal5 0.037 0.02 1 1 18 0.05 
ENSMUSG00000007656 Arpp19 0.034 0.035 1 1 18 0.049 
ENSMUSG00000040605 Bace2 0.00052 0.00041 0.14 1 21 0.44 
ENSMUSG00000035142 Nubpl 0.0023 0.024 0.81 1 21 0.36 
ENSMUSG00000023935 Spats1 0.00023 0.0065 0.091 1 21 0.35 
ENSMUSG00000097343 9030407P20Rik 3.00E-05 0.00066 0.12 0.58 21 0.26 
ENSMUSG00000038685 Rtel1 0.02 0.01 0.52 0.62 21 0.25 
ENSMUSG00000092448 Gm20387 0.00011 0.027 0.067 1 21 0.23 
ENSMUSG00000031558 Slit2 0.0058 0.0034 0.42 1 21 0.22 
ENSMUSG00000033590 Myo5c 0.012 0.0088 0.26 0.83 21 0.21 
ENSMUSG00000047155 Cyp4x1 0.00015 0.0088 0.52 1 21 0.21 
ENSMUSG00000031534 Smim19 2.20E-06 0.037 0.15 0.62 21 0.21 
ENSMUSG00000050505 Pcdh20 0.00092 0.0056 0.52 1 21 0.21 
ENSMUSG00000048905 4930539E08Rik 0.00023 0.00018 0.1 0.39 21 0.2 
ENSMUSG00000018417 Myo1b 0.0095 0.0052 0.3 1 21 0.2 
ENSMUSG00000040410 Fbxl4 4.30E-05 0.0065 0.22 0.3 21 0.2 
ENSMUSG00000079434 Neu2 3.50E-05 0.006 0.24 1 21 0.19 
ENSMUSG00000037266 Rsrp1 0.022 0.026 0.24 1 21 0.19 
ENSMUSG00000042807 Hecw2 0.0048 0.017 0.42 1 21 0.19 
ENSMUSG00000025795 Rassf3 0.013 0.006 0.3 1 21 0.19 
ENSMUSG00000086013 Gm15706 0.00019 0.00066 0.13 1 21 0.18 
ENSMUSG00000027188 Pamr1 0.00027 0.00018 1 1 21 0.18 
ENSMUSG00000097842 9330104G04Rik 0.00027 0.027 0.38 1 21 0.18 
ENSMUSG00000041840 Haus1 0.00019 0.042 0.27 0.13 21 0.17 
ENSMUSG00000038982 Bloc1s5 7.80E-06 0.015 0.1 1 21 0.17 
ENSMUSG00000038630 Zkscan16 0.0048 0.0082 0.29 1 21 0.17 
ENSMUSG00000026153 Fam135a 3.50E-05 0.045 0.27 0.36 21 0.16 
ENSMUSG00000032261 Sh3bgrl2 0.0029 0.006 0.084 1 21 0.16 
ENSMUSG00000096687 AA474331 0.0016 0.02 0.091 0.41 21 0.15 
ENSMUSG00000037400 Atp11b 0.0087 4.60E-07 0.32 0.44 21 0.15 
ENSMUSG00000063179 Pstk 0.00017 0.031 0.15 1 21 0.15 
ENSMUSG00000050963 Kcns2 0.001 0.0027 0.11 0.12 21 0.15 
ENSMUSG00000049907 Rasl11b 1.80E-05 6.00E-05 0.42 0.95 21 0.15 
ENSMUSG00000057858 Fam204a 0.00052 0.016 0.13 0.58 21 0.15 
ENSMUSG00000037279 Ovol2 0.026 0.0052 0.67 0.17 21 0.14 
ENSMUSG00000068391 Chrac1 0.0013 0.0065 0.29 1 21 0.14 
ENSMUSG00000039461 Tcta 9.50E-05 0.004 0.24 0.26 21 0.14 
ENSMUSG00000091735 Gpr62 0.0011 0.0015 0.62 1 21 0.14 
ENSMUSG00000055723 Rras2 0.0036 0.0095 0.15 0.52 21 0.13 
ENSMUSG00000060380 C030014I23Rik 0.00078 0.042 0.19 1 21 0.13 
ENSMUSG00000043061 Tmem18 0.00052 0.0015 0.091 0.11 21 0.13 
ENSMUSG00000022685 Parn 0.0048 0.015 0.67 0.48 21 0.13 
ENSMUSG00000091994 E130317F20Rik 0.026 0.033 0.26 1 21 0.13 
ENSMUSG00000050730 Arhgap42 0.00035 0.00066 0.37 1 21 0.13 
ENSMUSG00000045294 Insig1 1.80E-05 0.013 0.17 0.71 21 0.13 
App.3. Diurnal Genes in Mouse Cortex:  App. 48 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000100147 1700047M11Rik 0.04 0.033 1 1 21 0.13 
ENSMUSG00000050967 Creg2 0.0058 0.042 0.3 1 21 0.12 
ENSMUSG00000087143 A830082K12Rik 0.004 0.007 0.59 0.91 21 0.12 
ENSMUSG00000013495 Tmem175 0.02 0.033 0.2 0.46 21 0.12 
ENSMUSG00000022022 Mtrf1 0.00092 0.0018 0.69 1 21 0.12 
ENSMUSG00000020287 Mpg 0.0053 0.0014 0.091 0.48 21 0.12 
ENSMUSG00000024993 Fam45a 0.012 0.04 0.062 0.36 21 0.12 
ENSMUSG00000058298 Mcm9 0.019 0.011 0.2 0.23 21 0.12 
ENSMUSG00000045691 Thtpa 0.029 0.01 0.15 1 21 0.11 
ENSMUSG00000042208 0610010F05Rik 0.0048 0.031 0.37 0.58 21 0.11 
ENSMUSG00000025512 Chid1 0.00035 0.04 0.17 0.97 21 0.11 
ENSMUSG00000024750 Zfand5 0.012 0.0048 0.67 1 21 0.11 
ENSMUSG00000028173 Wls 0.00092 0.035 1 1 21 0.11 
ENSMUSG00000021028 Mbip 0.0011 0.023 0.13 0.66 21 0.11 
ENSMUSG00000035824 Tk2 0.013 0.023 0.24 0.37 21 0.11 
ENSMUSG00000021710 Nln 0.014 0.0082 0.69 1 21 0.11 
ENSMUSG00000039270 Megf9 0.0048 0.017 0.22 0.26 21 0.11 
ENSMUSG00000061455 Stx17 0.0053 0.00082 0.062 0.86 21 0.1 
ENSMUSG00000022338 Eny2 0.00017 0.013 0.38 0.17 21 0.1 
ENSMUSG00000046699 Slitrk4 0.0016 0.0016 0.52 1 21 0.1 
ENSMUSG00000001018 Snapin 0.0032 0.021 0.23 0.6 21 0.1 
ENSMUSG00000073481 Mar-02 0.0016 0.031 1 1 21 0.1 
ENSMUSG00000020642 Rnf144a 0.0026 0.035 0.15 1 21 0.096 
ENSMUSG00000045211 Nudt18 0.0032 0.0056 1 1 21 0.09 
ENSMUSG00000074461 Gm10699 0.0053 0.0082 0.14 0.91 21 0.09 
ENSMUSG00000052298 Cdc42se2 0.0011 0.017 0.2 0.78 21 0.089 
ENSMUSG00000097061 9330151L19Rik 0.0023 0.021 0.33 0.78 21 0.088 
ENSMUSG00000029211 Gabra4 0.014 0.01 0.97 1 21 0.087 
ENSMUSG00000039478 Micu3 0.013 0.011 0.55 1 21 0.087 
ENSMUSG00000085148 Mir22hg 0.0072 0.027 0.5 0.88 21 0.086 
ENSMUSG00000020436 Gabrg2 0.014 0.031 1 1 21 0.085 
ENSMUSG00000028525 Pde4b 0.00027 0.0032 0.46 0.29 21 0.084 
ENSMUSG00000037949 Ano10 0.004 0.014 1 1 21 0.082 
ENSMUSG00000025742 Prps2 0.0029 0.042 0.84 0.91 21 0.081 
ENSMUSG00000014075 Tctex1d2 0.02 0.00044 0.33 1 21 0.08 
ENSMUSG00000025037 Maoa 0.031 0.0034 0.26 1 21 0.075 
ENSMUSG00000044768 D1Ertd622e 0.034 0.021 0.38 1 21 0.073 
ENSMUSG00000037972 Snn 3.00E-05 0.004 0.44 1 21 0.073 
ENSMUSG00000032952 Ap4b1 0.0036 0.029 0.12 0.48 21 0.072 
ENSMUSG00000039770 Ypel5 0.0011 0.016 0.32 1 21 0.067 
ENSMUSG00000022044 Stmn4 0.0014 0.00066 0.26 0.95 21 0.067 
ENSMUSG00000024493 Lars 0.031 0.006 0.32 0.066 21 0.045 
ENSMUSG00000029617 Ccz1 0.037 0.016 0.87 0.055 21 0.04 
ENSMUSG00000079614 Seh1l 0.015 0.037 0.29 0.64 21 0.038 
ENSMUSG00000090300 Gm17119 0.029 0.41 0.57 1 0 0.69 
ENSMUSG00000100274 1700006F04Rik 0.046 0.96 0.22 1 0 0.41 
ENSMUSG00000026736 4930426L09Rik 0.00061 1 0.57 0.97 0 0.39 
App.3. Diurnal Genes in Mouse Cortex:  App. 49 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000083080 Gm11484 0.0032 0.66 1 1 0 0.38 
ENSMUSG00000044254 Pcsk9 0.0053 0.086 0.13 0.74 0 0.38 
ENSMUSG00000099039 Gm27928 0.04 0.93 1 1 0 0.37 
ENSMUSG00000055188 Rbm3os 0.0048 1 0.84 1 0 0.35 
ENSMUSG00000073485 H3f3aos 0.0079 0.58 0.21 0.91 0 0.34 
ENSMUSG00000086121 Gm16068 0.043 0.12 1 0.6 0 0.34 
ENSMUSG00000078906 Gm14444 0.00031 1 0.64 0.62 0 0.33 
ENSMUSG00000022357 Klhl38 0.00019 0.096 0.14 0.64 0 0.33 
ENSMUSG00000082072 Gm15785 0.029 1 1 1 0 0.3 
ENSMUSG00000041809 Efhc1 0.0032 0.45 1 1 0 0.3 
ENSMUSG00000101006 Gm28299 0.014 1 0.97 1 0 0.28 
ENSMUSG00000037313 Tacc3 0.0026 0.51 0.22 1 0 0.27 
ENSMUSG00000041674 BC006965 0.0023 0.17 1 1 0 0.26 
ENSMUSG00000078984 Gm11027 0.024 0.68 0.097 1 0 0.26 
ENSMUSG00000028307 Aldob 0.0087 1 0.14 0.97 0 0.26 
ENSMUSG00000072980 Oip5 0.034 0.83 1 0.66 0 0.25 
ENSMUSG00000081974 Gm11960 0.04 1 1 1 0 0.25 
ENSMUSG00000082765 Gm14411 0.019 0.71 0.93 1 0 0.25 
ENSMUSG00000096943 Gm26721 0.04 0.4 0.23 0.25 0 0.25 
ENSMUSG00000083587 Gm14741 0.0095 1 0.078 0.71 0 0.24 
ENSMUSG00000040350 Trim7 0.022 1 0.12 0.81 0 0.24 
ENSMUSG00000097327 E030030I06Rik 0.037 1 0.97 1 0 0.24 
ENSMUSG00000089901 Gm8113 0.013 0.096 0.14 1 0 0.23 
ENSMUSG00000044534 Ackr2 0.0023 0.43 0.23 0.88 0 0.23 
ENSMUSG00000027635 Dsn1 0.0079 1 0.2 1 0 0.23 
ENSMUSG00000092412 Gm20507 0.004 1 0.44 1 0 0.22 
ENSMUSG00000101587 Gm29036 0.043 1 0.1 1 0 0.22 
ENSMUSG00000097335 Gm26563 0.004 1 0.48 1 0 0.21 
ENSMUSG00000021255 Esrrb 0.015 0.62 1 1 0 0.21 
ENSMUSG00000004105 Angptl2 0.00052 0.14 0.33 1 0 0.21 
ENSMUSG00000074467 Gm10702 7.00E-04 1 1 1 0 0.2 
ENSMUSG00000083877 Gm14740 0.0079 0.3 0.054 1 0 0.2 
ENSMUSG00000001751 Naglu 0.029 0.082 0.24 0.66 0 0.2 
ENSMUSG00000032092 Mpzl2 0.0013 0.23 0.054 0.66 0 0.2 
ENSMUSG00000091577 Gm6211 0.0095 1 0.72 1 0 0.2 
ENSMUSG00000084087 Gm13650 0.029 1 0.67 1 0 0.2 
ENSMUSG00000086291 Gm15513 0.017 0.27 0.17 1 0 0.2 
ENSMUSG00000032374 Plod2 0.037 1 0.62 1 0 0.19 
ENSMUSG00000042895 Abra 0.0065 1 1 1 0 0.19 
ENSMUSG00000094707 A830019P07Rik 0.0079 0.11 0.23 1 0 0.19 
ENSMUSG00000085440 Sorbs2os 0.0072 1 0.29 1 0 0.18 
ENSMUSG00000026779 Mastl 0.0053 0.36 0.084 1 0 0.18 
ENSMUSG00000090353 Gm17555 0.0044 1 1 1 0 0.18 
ENSMUSG00000017550 Atad5 0.031 1 0.17 1 0 0.18 
ENSMUSG00000040174 Alkbh3 0.004 0.23 0.058 0.23 0 0.17 
ENSMUSG00000090284 Gm17613 0.029 1 0.81 1 0 0.17 
ENSMUSG00000025255 Zfhx4 0.029 1 1 1 0 0.17 
App.3. Diurnal Genes in Mouse Cortex:  App. 50 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000027353 Mcm8 0.0032 0.23 0.15 1 0 0.17 
ENSMUSG00000056394 Lig1 0.014 1 1 1 0 0.17 
ENSMUSG00000097876 Gm16892 0.04 1 1 1 0 0.16 
ENSMUSG00000060985 Tdrd5 0.019 0.31 0.22 1 0 0.16 
ENSMUSG00000052384 Nrros 0.04 0.063 0.57 1 0 0.16 
ENSMUSG00000031628 Casp3 0.0018 1 1 0.86 0 0.16 
ENSMUSG00000093490 Gm19932 0.0065 0.26 0.37 1 0 0.16 
ENSMUSG00000097911 Gm26691 0.00015 1 0.071 1 0 0.16 
ENSMUSG00000082593 Gm11331 0.047 1 0.67 1 0 0.16 
ENSMUSG00000092335 Gm7221 0.0095 1 1 1 0 0.16 
ENSMUSG00000097410 Gm26668 0.015 1 0.24 1 0 0.16 
ENSMUSG00000086753 Gm15751 0.022 1 0.5 1 0 0.16 
ENSMUSG00000020014 Cfap54 0.0095 1 0.78 1 0 0.16 
ENSMUSG00000031730 Dhodh 0.0065 0.053 0.1 1 0 0.15 
ENSMUSG00000020709 Adap2 0.037 1 0.5 0.88 0 0.15 
ENSMUSG00000020289 Nprl3 0.019 0.12 0.57 0.76 0 0.15 
ENSMUSG00000031323 Dmrtc1a 0.00078 0.88 0.15 0.64 0 0.15 
ENSMUSG00000094030 Gm21833 0.0026 1 0.72 1 0 0.14 
ENSMUSG00000030528 Blm 0.012 1 0.11 1 0 0.14 
ENSMUSG00000015217 Hmgb3 0.00023 1 0.27 1 0 0.14 
ENSMUSG00000085586 Gm11613 0.019 0.057 0.21 1 0 0.14 
ENSMUSG00000039202 Abhd2 0.017 0.11 0.062 0.36 0 0.14 
ENSMUSG00000032679 Cd59a 0.043 0.66 0.067 0.28 0 0.14 
ENSMUSG00000083849 Gm13477 0.014 0.62 0.16 0.54 0 0.13 
ENSMUSG00000042476 Abcb4 0.04 1 1 1 0 0.13 
ENSMUSG00000039480 Nt5dc1 0.031 0.1 0.48 1 0 0.13 
ENSMUSG00000028637 Ccdc30 0.043 1 0.59 1 0 0.13 
ENSMUSG00000093677 Gm20712 0.037 0.34 1 1 0 0.13 
ENSMUSG00000078624 Olfr613 0.0021 0.26 1 1 0 0.13 
ENSMUSG00000099784 Dalir 0.029 0.11 0.32 1 0 0.13 
ENSMUSG00000084897 Gm14226 0.031 1 0.37 1 0 0.13 
ENSMUSG00000025766 D3Ertd751e 0.0065 1 0.067 1 0 0.13 
ENSMUSG00000017969 Ptgis 0.04 0.078 0.46 1 0 0.13 
ENSMUSG00000042104 Uggt2 0.0044 1 0.15 1 0 0.13 
ENSMUSG00000030316 Tamm41 0.037 0.27 0.84 0.46 0 0.12 
ENSMUSG00000064294 Aox3 0.047 1 0.3 1 0 0.12 
ENSMUSG00000089842 Pitpnm2os2 0.047 1 0.81 1 0 0.12 
ENSMUSG00000015027 Galns 0.043 0.1 0.57 0.5 0 0.12 
ENSMUSG00000093383 Gm20642 0.02 1 1 1 0 0.12 
ENSMUSG00000039795 Zfand1 0.00052 0.05 0.078 0.91 0 0.12 
ENSMUSG00000085125 Gm16070 0.037 0.3 1 1 0 0.12 
ENSMUSG00000059540 Tcea2 0.0053 0.12 0.46 1 0 0.12 
ENSMUSG00000018068 Ints2 0.001 0.063 0.15 0.58 0 0.12 
ENSMUSG00000039929 Urb1 0.047 0.26 1 1 0 0.12 
ENSMUSG00000019846 Lama4 0.014 0.11 0.1 0.76 0 0.11 
ENSMUSG00000020176 Grb10 0.014 0.082 0.067 0.81 0 0.11 
ENSMUSG00000041477 Dcp1b 0.011 0.62 0.5 1 0 0.11 
App.3. Diurnal Genes in Mouse Cortex:  App. 51 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000089940 Gm4117 0.011 0.88 1 1 0 0.11 
ENSMUSG00000038347 Tcte2 0.024 1 0.55 0.78 0 0.11 
ENSMUSG00000097879 Gm26869 0.031 1 0.15 1 0 0.11 
ENSMUSG00000059820 AU019823 0.0095 1 0.071 1 0 0.11 
ENSMUSG00000040557 Wbscr27 0.043 1 1 1 0 0.11 
ENSMUSG00000027509 Rae1 0.04 0.11 0.9 0.14 0 0.11 
ENSMUSG00000090965 Gm17203 0.019 1 0.4 1 0 0.11 
ENSMUSG00000050808 Muc15 0.011 1 0.097 1 0 0.11 
ENSMUSG00000030616 Sytl2 0.0044 0.06 0.29 1 0 0.11 
ENSMUSG00000025762 Larp1b 0.0026 0.23 0.084 1 0 0.11 
ENSMUSG00000078546 2210404O09Rik 0.019 0.33 0.62 0.88 0 0.11 
ENSMUSG00000097431 Gm26782 0.017 0.96 0.24 1 0 0.11 
ENSMUSG00000038776 Ephx1 0.031 1 0.38 1 0 0.11 
ENSMUSG00000029782 Tmem209 0.047 0.56 0.29 1 0 0.11 
ENSMUSG00000028572 Hook1 0.014 1 0.38 1 0 0.11 
ENSMUSG00000041219 Arhgap11a 0.0072 0.23 1 1 0 0.11 
ENSMUSG00000078495 Gm13157 0.034 0.88 0.084 0.31 0 0.1 
ENSMUSG00000052676 Zmat1 0.0087 1 0.29 0.68 0 0.1 
ENSMUSG00000091509 Gm17066 0.024 0.29 0.23 1 0 0.1 
ENSMUSG00000046111 Cep295 0.0058 1 0.2 1 0 0.1 
ENSMUSG00000034206 Polq 0.029 1 0.48 1 0 0.1 
ENSMUSG00000039349 C130074G19Rik 0.011 0.067 0.33 1 0 0.1 
ENSMUSG00000054976 Nyap2 0.00078 1 0.17 0.68 0 0.1 
ENSMUSG00000036863 Syde2 0.029 0.32 0.22 0.26 0 0.1 
ENSMUSG00000086725 A630052C17Rik 0.037 1 0.38 1 0 0.1 
ENSMUSG00000072769 Gm10419 0.04 1 0.27 1 0 0.1 
ENSMUSG00000033446 Lpar6 0.0048 0.71 0.52 1 0 0.1 
ENSMUSG00000021047 Nova1 0.019 1 0.054 0.74 0 0.099 
ENSMUSG00000026672 Optn 0.022 1 0.93 1 0 0.098 
ENSMUSG00000039765 Cc2d2a 0.031 0.85 0.23 0.58 0 0.097 
ENSMUSG00000036894 Rap2b 0.011 1 0.84 0.46 0 0.096 
ENSMUSG00000044791 Setd2 0.024 0.13 0.42 0.64 0 0.093 
ENSMUSG00000032925 Itgbl1 0.015 0.37 0.55 0.62 0 0.092 
ENSMUSG00000032420 Nt5e 0.00061 0.68 0.32 0.81 0 0.092 
ENSMUSG00000039716 Dock3 0.012 1 0.22 1 0 0.091 
ENSMUSG00000041014 Nrg3 0.0087 1 0.55 1 0 0.091 
ENSMUSG00000028331 Trmo 0.02 0.62 0.44 0.64 0 0.091 
ENSMUSG00000097379 Gm26873 0.029 0.55 1 1 0 0.09 
ENSMUSG00000071337 Tia1 0.017 1 0.64 1 0 0.089 
ENSMUSG00000028207 Asph 0.0023 0.17 0.46 1 0 0.088 
ENSMUSG00000039985 Fam60a 0.034 0.66 0.21 1 0 0.088 
ENSMUSG00000028550 Atg4c 0.0018 0.15 0.44 1 0 0.088 
ENSMUSG00000031938 4931406C07Rik 0.0095 1 0.2 1 0 0.088 
ENSMUSG00000039375 Wdr17 0.0048 0.64 1 1 0 0.088 
ENSMUSG00000000948 Gm38393 0.0036 0.66 0.69 1 0 0.087 
ENSMUSG00000045975 C2cd2 0.043 0.45 0.52 1 0 0.087 
ENSMUSG00000039539 Sgcz 0.04 1 1 1 0 0.086 
App.3. Diurnal Genes in Mouse Cortex:  App. 52 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000031337 Mtm1 0.0021 0.34 0.23 0.88 0 0.086 
ENSMUSG00000038446 Cdc40 0.013 1 0.12 0.6 0 0.085 
ENSMUSG00000020453 Patz1 0.029 0.66 0.22 1 0 0.084 
ENSMUSG00000022223 Sdr39u1 0.017 0.12 0.078 1 0 0.084 
ENSMUSG00000040118 Cacna2d1 0.026 0.1 0.75 1 0 0.083 
ENSMUSG00000063558 Aox1 0.047 1 0.26 0.76 0 0.081 
ENSMUSG00000097785 B230217O12Rik 0.0058 0.9 0.11 1 0 0.078 
ENSMUSG00000037572 Wdhd1 0.043 0.078 1 0.97 0 0.078 
ENSMUSG00000032409 Atr 0.015 0.77 0.27 0.33 0 0.076 
ENSMUSG00000027132 Katnbl1 0.037 0.23 0.62 1 0 0.076 
ENSMUSG00000026141 Col19a1 0.012 0.18 0.72 0.48 0 0.075 
ENSMUSG00000000787 Ddx3x 0.0072 1 0.5 1 0 0.075 
ENSMUSG00000055541 Lair1 0.024 0.24 0.058 0.58 0 0.074 
ENSMUSG00000034601 2700049A03Rik 0.043 1 0.23 0.81 0 0.074 
ENSMUSG00000025551 Fgf14 0.043 0.5 0.84 0.64 0 0.072 
ENSMUSG00000025964 Adam23 0.029 1 0.24 1 0 0.071 
ENSMUSG00000029246 Ppat 0.04 1 0.64 0.76 0 0.07 
ENSMUSG00000017561 Crlf3 0.047 0.68 0.55 0.58 0 0.069 
ENSMUSG00000026603 Smyd2 0.0044 0.053 0.87 1 0 0.069 
ENSMUSG00000021712 Trim23 0.037 0.07 0.78 0.62 0 0.069 
ENSMUSG00000030779 Rbbp6 0.047 0.83 1 0.54 0 0.068 
ENSMUSG00000027419 Pcsk2 0.026 0.42 0.38 0.68 0 0.065 
ENSMUSG00000036941 Elac1 0.0036 0.45 0.1 1 0 0.065 
ENSMUSG00000040037 Negr1 0.029 1 1 1 0 0.065 
ENSMUSG00000037369 Kdm6a 0.0048 1 0.62 0.23 0 0.064 
ENSMUSG00000033900 Map9 0.043 1 0.67 0.36 0 0.064 
ENSMUSG00000030094 Xpc 0.024 1 0.15 1 0 0.061 
ENSMUSG00000097605 9430098F02Rik 0.0095 1 0.59 0.28 0 0.061 
ENSMUSG00000056771 Gm10010 0.024 0.25 1 1 0 0.061 
ENSMUSG00000013878 Rnf170 0.02 0.17 0.5 1 0 0.06 
ENSMUSG00000028809 Srrm1 0.031 1 1 1 0 0.057 
ENSMUSG00000028878 Fam76a 0.043 1 1 0.22 0 0.057 
ENSMUSG00000029088 Kcnip4 0.0058 0.45 1 1 0 0.056 
ENSMUSG00000006262 Mob1b 0.024 0.4 0.37 1 0 0.056 
ENSMUSG00000055436 Srsf11 0.034 1 1 1 0 0.055 
ENSMUSG00000025764 Jade1 0.031 0.26 0.091 1 0 0.054 
ENSMUSG00000067942 Zfp160 0.047 0.51 0.78 1 0 0.049 
ENSMUSG00000013663 Pten 0.0087 0.086 0.78 0.37 0 0.045 
ENSMUSG00000026234 Ncl 0.037 1 0.93 1 0 0.043 
ENSMUSG00000100382 Gm28924 0.018 0.33 1 1 3 1.4 
ENSMUSG00000084859 1700080N15Rik 0.017 0.85 0.72 1 3 0.4 
ENSMUSG00000087625 4930419G24Rik 0.014 0.77 1 1 3 0.38 
ENSMUSG00000084994 Gm16352 0.0053 1 1 1 3 0.36 
ENSMUSG00000081608 Gm16210 0.019 0.85 1 1 3 0.35 
ENSMUSG00000082902 Ccl19-ps1 0.033 1 0.11 1 3 0.34 
ENSMUSG00000081656 Gm11246 0.015 0.87 1 1 3 0.3 
ENSMUSG00000086458 Gm2639 0.022 1 1 1 3 0.3 
App.3. Diurnal Genes in Mouse Cortex:  App. 53 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000086027 Gm11250 0.047 1 1 1 3 0.3 
ENSMUSG00000085449 Gm15520 0.031 0.62 0.37 1 3 0.3 
ENSMUSG00000089934 4930473D10Rik 0.031 1 0.9 0.68 3 0.29 
ENSMUSG00000082786 Gm14489 0.024 0.43 0.054 1 3 0.28 
ENSMUSG00000083745 Gm13543 0.019 0.71 1 0.91 3 0.28 
ENSMUSG00000101135 Gm17981 0.029 1 0.93 1 3 0.27 
ENSMUSG00000081551 Gm14654 0.014 1 1 0.76 3 0.27 
ENSMUSG00000001930 Vwf 0.019 0.77 0.062 1 3 0.26 
ENSMUSG00000074529 C330013J21Rik 0.037 1 1 1 3 0.26 
ENSMUSG00000081805 Gm14335 0.043 1 1 1 3 0.26 
ENSMUSG00000042320 Prox2 9.50E-05 1 0.12 1 3 0.26 
ENSMUSG00000082315 Gm16523 0.037 0.24 0.11 1 3 0.24 
ENSMUSG00000078880 Gm14308 0.039 0.88 1 0.81 3 0.24 
ENSMUSG00000097308 Gm6410 0.02 0.83 1 1 3 0.24 
ENSMUSG00000084240 Gm15383 0.015 1 0.97 1 3 0.23 
ENSMUSG00000043541 Casc1 0.026 0.73 1 0.81 3 0.23 
ENSMUSG00000063447 Ube2d2b 0.0048 0.98 0.59 0.68 3 0.22 
ENSMUSG00000093721 Gm3896 0.0053 0.2 0.4 0.29 3 0.22 
ENSMUSG00000096870 Gm21816 0.043 0.43 0.78 1 3 0.22 
ENSMUSG00000053980 Gm9930 0.0016 1 1 1 3 0.21 
ENSMUSG00000091784 Gm17022 0.015 1 1 1 3 0.21 
ENSMUSG00000100833 Gm28988 0.0053 0.27 0.23 1 3 0.21 
ENSMUSG00000087202 Gm15813 0.0026 1 1 1 3 0.21 
ENSMUSG00000093561 Gm20699 0.043 0.53 0.75 0.74 3 0.2 
ENSMUSG00000083050 Gm11242 0.0036 0.39 0.11 0.91 3 0.2 
ENSMUSG00000057342 Sphk2 0.017 0.42 0.084 1 3 0.2 
ENSMUSG00000090263 D730045A05Rik 0.031 0.11 0.4 1 3 0.2 
ENSMUSG00000043366 Olfr78 0.00047 0.34 0.69 1 3 0.2 
ENSMUSG00000085276 Gm15812 0.034 0.85 0.3 0.86 3 0.2 
ENSMUSG00000026228 Htr2b 0.024 1 0.69 1 3 0.2 
ENSMUSG00000001918 Slc1a5 0.017 0.29 0.38 0.66 3 0.2 
ENSMUSG00000063730 Hsd3b2 0.0095 0.66 1 1 3 0.19 
ENSMUSG00000097385 Gm26814 0.011 1 0.32 1 3 0.19 
ENSMUSG00000085247 4930545L23Rik 0.011 1 1 1 3 0.19 
ENSMUSG00000084010 Gm13302 0.04 0.73 1 0.37 3 0.19 
ENSMUSG00000099625 Gm29325 0.0095 1 0.78 1 3 0.19 
ENSMUSG00000074252 Gm10654 0.043 1 0.5 1 3 0.19 
ENSMUSG00000096553 Gm10097 0.026 1 0.1 1 3 0.18 
ENSMUSG00000020633 Dcdc2c 0.02 1 0.62 1 3 0.18 
ENSMUSG00000074569 Gcnt7 0.029 0.62 1 1 3 0.18 
ENSMUSG00000078157 4931440F15Rik 0.043 0.24 0.084 1 3 0.18 
ENSMUSG00000085386 Gm13630 0.004 0.53 0.14 1 3 0.18 
ENSMUSG00000024053 Emilin2 0.0013 0.16 0.64 0.78 3 0.17 
ENSMUSG00000100768 Gm29055 0.013 0.6 0.81 1 3 0.17 
ENSMUSG00000080780 Gm11252 0.0058 1 0.93 1 3 0.17 
ENSMUSG00000028438 Kif24 0.0036 1 0.48 1 3 0.17 
ENSMUSG00000085118 Gm15774 0.02 0.25 0.87 1 3 0.16 
App.3. Diurnal Genes in Mouse Cortex:  App. 54 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000079177 Fam228a 0.034 0.68 1 0.66 3 0.16 
ENSMUSG00000054702 Ap1s3 0.013 0.77 1 1 3 0.16 
ENSMUSG00000024912 Fosl1 0.0079 0.85 0.062 1 3 0.16 
ENSMUSG00000038225 Primpol 0.011 0.48 1 1 3 0.16 
ENSMUSG00000082057 Gm15789 0.0026 1 0.5 0.97 3 0.16 
ENSMUSG00000050503 Fbxl22 0.015 0.34 1 1 3 0.16 
ENSMUSG00000067081 Asb18 0.0048 1 0.93 1 3 0.16 
ENSMUSG00000041147 Brca2 0.0079 0.73 0.12 1 3 0.15 
ENSMUSG00000096991 Gm26789 0.013 0.93 1 1 3 0.15 
ENSMUSG00000097051 Gm26836 0.026 0.27 1 0.5 3 0.15 
ENSMUSG00000090286 Gm17615 0.012 1 0.071 1 3 0.15 
ENSMUSG00000084970 1700060J05Rik 0.029 1 0.78 1 3 0.15 
ENSMUSG00000048612 Myof 0.024 0.17 0.26 1 3 0.15 
ENSMUSG00000029675 Eln 0.037 0.37 0.22 0.42 3 0.15 
ENSMUSG00000089707 Slain1os 0.0065 1 1 1 3 0.15 
ENSMUSG00000044519 Zfp488 0.0058 1 1 1 3 0.14 
ENSMUSG00000089719 Gm15758 0.031 0.43 0.97 0.86 3 0.14 
ENSMUSG00000062488 Ifit3b 0.012 0.26 0.37 0.95 3 0.13 
ENSMUSG00000027536 Chmp4c 0.017 0.17 0.52 1 3 0.13 
ENSMUSG00000101609 Kcnq1ot1 0.043 0.13 1 0.14 3 0.13 
ENSMUSG00000089818 Gm15950 0.0036 1 1 1 3 0.13 
ENSMUSG00000051427 Ccdc157 0.0029 0.11 0.12 1 3 0.12 
ENSMUSG00000024831 Ighmbp2 0.04 0.063 0.52 1 3 0.11 
ENSMUSG00000042606 Hirip3 0.00092 1 0.57 1 3 0.11 
ENSMUSG00000101599 Gm20342 0.034 0.4 0.52 1 3 0.11 
ENSMUSG00000075028 Prdm11 0.047 1 0.81 1 3 0.11 
ENSMUSG00000034371 Tkfc 0.034 0.053 0.38 1 3 0.11 
ENSMUSG00000027185 Nat10 0.034 0.93 1 1 3 0.11 
ENSMUSG00000084241 Gm13416 0.031 0.53 0.72 1 3 0.11 
ENSMUSG00000100826 Snhg14 0.0048 0.36 0.59 1 3 0.1 
ENSMUSG00000032849 Abcc4 0.043 1 0.62 1 3 0.1 
ENSMUSG00000044033 Ccdc141 0.0029 1 0.22 1 3 0.1 
ENSMUSG00000014668 Chfr 0.012 1 0.084 1 3 0.099 
ENSMUSG00000096967 Gm26621 0.04 0.53 1 1 3 0.098 
ENSMUSG00000048721 Fndc9 0.0095 1 1 1 3 0.094 
ENSMUSG00000060924 Csmd1 0.017 0.16 0.27 1 3 0.09 
ENSMUSG00000060798 Intu 0.022 0.8 0.1 1 3 0.09 
ENSMUSG00000056952 Tatdn2 0.0048 0.2 0.64 1 3 0.09 
ENSMUSG00000072847 A530017D24Rik 0.043 1 0.67 1 3 0.088 
ENSMUSG00000063810 Alms1 0.04 1 0.78 1 3 0.087 
ENSMUSG00000097877 Gm26703 0.022 1 0.44 1 3 0.085 
ENSMUSG00000097392 D930016D06Rik 0.047 0.98 1 1 3 0.083 
ENSMUSG00000029823 Luc7l2 0.004 1 0.35 0.71 3 0.082 
ENSMUSG00000097258 Gm26767 0.012 0.51 0.054 1 3 0.08 
ENSMUSG00000035456 Prdm8 0.04 0.83 0.75 0.71 3 0.08 
ENSMUSG00000038495 Otud7b 0.0079 0.43 0.84 1 3 0.079 
ENSMUSG00000039191 Rbpj 0.026 1 0.72 0.81 3 0.079 
App.3. Diurnal Genes in Mouse Cortex:  App. 55 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000028497 Hacd4 0.017 1 0.37 0.97 3 0.073 
ENSMUSG00000021244 Ylpm1 0.0048 0.32 1 0.21 3 0.067 
ENSMUSG00000026361 Cdc73 0.011 0.5 0.59 1 3 0.067 
ENSMUSG00000027425 Csrp2bp 0.037 0.074 0.062 1 3 0.067 
ENSMUSG00000020623 Map2k6 0.034 0.32 0.57 0.56 3 0.065 
ENSMUSG00000026496 Parp1 0.034 0.17 1 1 3 0.062 
ENSMUSG00000097801 Gm26777 0.04 1 0.15 1 3 0.061 
ENSMUSG00000036334 Igsf10 0.012 1 1 1 3 0.061 
ENSMUSG00000034973 Dopey1 0.022 0.1 0.35 1 3 0.06 
ENSMUSG00000057421 Las1l 0.04 1 0.38 1 3 0.06 
ENSMUSG00000036591 Arhgap21 0.024 0.16 0.29 0.68 3 0.057 
ENSMUSG00000028518 Prkaa2 0.0087 0.086 0.69 0.76 3 0.057 
ENSMUSG00000019880 Rspo3 0.0048 0.9 0.44 0.14 3 0.057 
ENSMUSG00000034158 Lrrc58 0.047 0.091 1 1 3 0.054 
ENSMUSG00000027692 Tnik 0.022 1 0.084 0.58 3 0.05 
ENSMUSG00000026618 Iars2 0.04 1 0.37 1 3 0.047 
ENSMUSG00000024104 Fam21 0.013 1 0.24 1 3 0.047 
ENSMUSG00000005506 Celf1 0.037 0.71 0.75 1 3 0.046 
ENSMUSG00000048109 Rbm15 0.04 1 1 1 3 0.043 
ENSMUSG00000028399 Ptprd 0.04 0.11 0.5 1 3 0.04 
ENSMUSG00000032410 Xrn1 0.037 0.88 0.071 1 3 0.039 
ENSMUSG00000064941 Gm23238 0.031 1 0.38 0.86 6 0.54 
ENSMUSG00000077611 Gm23946 0.0029 1 1 1 6 0.5 
ENSMUSG00000085035 Gm12031 0.0087 1 0.67 1 6 0.43 
ENSMUSG00000065251 Gm23971 0.0032 0.93 0.46 1 6 0.39 
ENSMUSG00000077709 Snora64 0.043 1 1 1 6 0.38 
ENSMUSG00000100555 Gm8173 0.037 1 0.22 1 6 0.37 
ENSMUSG00000079076 Gm3086 0.047 1 1 0.66 6 0.36 
ENSMUSG00000023968 Crip3 0.022 0.32 0.42 1 6 0.35 
ENSMUSG00000079173 Zan 0.02 0.56 1 0.76 6 0.33 
ENSMUSG00000100890 1700085C21Rik 0.037 1 0.81 1 6 0.32 
ENSMUSG00000093489 Gm20625 0.001 0.23 1 1 6 0.31 
ENSMUSG00000085008 Dbhos 0.00052 0.34 1 1 6 0.27 
ENSMUSG00000064513 Gm22457 0.022 1 1 1 6 0.26 
ENSMUSG00000092549 Gm20491 0.026 1 1 1 6 0.26 
ENSMUSG00000098143 Gm26937 0.014 1 0.35 1 6 0.26 
ENSMUSG00000089798 1700028K03Rik 0.012 1 0.17 0.76 6 0.25 
ENSMUSG00000080888 Gm14387 0.0095 1 1 1 6 0.25 
ENSMUSG00000019756 Prl8a1 0.02 1 1 0.5 6 0.25 
ENSMUSG00000083606 Gm15916 0.014 1 0.72 1 6 0.25 
ENSMUSG00000020703 5530401A14Rik 0.0021 0.12 0.44 0.95 6 0.24 
ENSMUSG00000070354 Gm21975 0.04 0.4 1 1 6 0.24 
ENSMUSG00000083594 Gm13722 0.0023 1 0.97 1 6 0.23 
ENSMUSG00000049871 Nlrc3 0.047 0.23 1 1 6 0.23 
ENSMUSG00000094891 Olfr55 0.043 1 1 0.6 6 0.23 
ENSMUSG00000097642 Gm26866 0.034 1 0.27 1 6 0.23 
ENSMUSG00000081583 Gm14769 0.024 1 0.84 1 6 0.22 
App.3. Diurnal Genes in Mouse Cortex:  App. 56 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000078898 Gm4723 0.024 1 0.32 1 6 0.22 
ENSMUSG00000099924 Gm28320 0.0044 1 1 1 6 0.22 
ENSMUSG00000031450 Grk1 0.012 1 1 1 6 0.22 
ENSMUSG00000097884 Gm26543 0.0095 1 0.78 1 6 0.22 
ENSMUSG00000089542 Gm25835 0.0053 0.32 1 0.52 6 0.21 
ENSMUSG00000064966 Snord15b 0.015 1 1 0.78 6 0.21 
ENSMUSG00000010751 Tnfrsf22 0.043 1 1 1 6 0.21 
ENSMUSG00000071036 Gm10309 0.034 0.56 1 1 6 0.21 
ENSMUSG00000094856 Gm21962 0.004 1 0.67 1 6 0.21 
ENSMUSG00000086580 Gm15280 0.013 0.46 1 1 6 0.2 
ENSMUSG00000096243 Gm24265 0.047 1 1 1 6 0.2 
ENSMUSG00000045776 Lrtm1 0.019 0.98 1 1 6 0.2 
ENSMUSG00000025747 Tyms 0.0036 1 1 1 6 0.19 
ENSMUSG00000030148 Clec4a2 0.024 0.73 0.97 1 6 0.19 
ENSMUSG00000028655 Mfsd2a 7.00E-04 0.25 0.33 1 6 0.19 
ENSMUSG00000096463 Gm21750 0.0053 0.88 0.75 0.88 6 0.18 
ENSMUSG00000079489 C030013D06Rik 0.012 1 0.69 1 6 0.18 
ENSMUSG00000097695 Gm26905 0.0029 0.85 0.4 0.78 6 0.18 
ENSMUSG00000091318 Gm5415 0.022 0.51 0.44 0.54 6 0.18 
ENSMUSG00000074896 Ifit3 0.0016 0.66 0.15 0.88 6 0.18 
ENSMUSG00000097283 Gm26686 0.0053 0.53 1 1 6 0.18 
ENSMUSG00000090338 Gm17081 0.029 1 0.52 1 6 0.17 
ENSMUSG00000078864 Gm14322 0.0095 0.32 1 0.54 6 0.17 
ENSMUSG00000073609 D2hgdh 0.04 0.37 1 1 6 0.17 
ENSMUSG00000097859 Gm26601 0.047 0.9 0.87 1 6 0.17 
ENSMUSG00000078902 Gm14443 0.0026 0.45 0.48 1 6 0.17 
ENSMUSG00000014813 Stc1 0.00078 0.39 1 0.11 6 0.17 
ENSMUSG00000090691 Gm3667 0.026 0.93 0.87 1 6 0.17 
ENSMUSG00000008348 Ubc 0.029 0.66 0.37 1 6 0.17 
ENSMUSG00000028776 Tinagl1 0.037 0.75 1 1 6 0.16 
ENSMUSG00000079018 Ly6c1 0.0048 1 0.42 1 6 0.16 
ENSMUSG00000090255 4921534H16Rik 0.029 1 1 1 6 0.16 
ENSMUSG00000078190 Dnm3os 0.0053 0.11 1 0.58 6 0.16 
ENSMUSG00000043279 Trim56 0.047 1 1 1 6 0.15 
ENSMUSG00000019232 Etnppl 0.0065 0.62 0.21 0.64 6 0.15 
ENSMUSG00000087458 Gm13999 0.047 1 0.64 1 6 0.15 
ENSMUSG00000072972 Adam4 0.022 1 1 1 6 0.15 
ENSMUSG00000053769 Lysmd1 0.0029 0.6 0.32 1 6 0.15 
ENSMUSG00000084824 Gm16344 0.004 1 1 1 6 0.15 
ENSMUSG00000028840 Zfp593 0.015 1 1 1 6 0.15 
ENSMUSG00000048108 Tmem72 0.029 1 1 1 6 0.15 
ENSMUSG00000087267 4933427J07Rik 0.0072 1 0.12 0.74 6 0.15 
ENSMUSG00000085328 Gm17131 0.004 0.77 0.59 0.68 6 0.15 
ENSMUSG00000016239 Lonrf3 0.0021 0.13 0.87 1 6 0.14 
ENSMUSG00000056947 Mab21l1 0.012 1 1 1 6 0.14 
ENSMUSG00000086040 Wipf3 0.043 0.13 0.24 0.07 6 0.14 
ENSMUSG00000058360 Gm10040 0.0087 0.55 1 1 6 0.14 
App.3. Diurnal Genes in Mouse Cortex:  App. 57 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000086320 Gm12840 0.031 1 1 1 6 0.14 
ENSMUSG00000097551 Gm7976 0.017 0.46 0.97 1 6 0.14 
ENSMUSG00000084904 Gm14827 0.029 0.36 1 1 6 0.13 
ENSMUSG00000044674 Fzd1 0.015 0.93 1 1 6 0.13 
ENSMUSG00000100750 Gm29084 0.0032 0.51 1 1 6 0.13 
ENSMUSG00000054499 Dedd2 0.034 1 0.071 0.97 6 0.13 
ENSMUSG00000094472 Gm21897 0.013 0.43 0.5 0.83 6 0.13 
ENSMUSG00000061331 Gm17132 0.017 0.71 0.5 1 6 0.13 
ENSMUSG00000031445 Proz 0.029 0.31 0.67 1 6 0.13 
ENSMUSG00000083111 Gm14421 0.04 0.93 1 1 6 0.13 
ENSMUSG00000032773 Chrm1 0.026 0.15 0.19 0.64 6 0.13 
ENSMUSG00000042903 Foxo4 0.0048 0.62 0.097 1 6 0.13 
ENSMUSG00000048218 Amigo2 0.034 1 1 0.42 6 0.12 
ENSMUSG00000040447 Spns2 0.004 0.68 0.57 1 6 0.12 
ENSMUSG00000029307 Dmp1 0.029 0.45 1 0.41 6 0.12 
ENSMUSG00000091542 Gm17167 0.04 1 0.97 1 6 0.12 
ENSMUSG00000017009 Sdc4 0.00061 0.2 0.67 0.46 6 0.12 
ENSMUSG00000037653 Kctd8 0.037 1 0.44 0.26 6 0.11 
ENSMUSG00000066640 Fbxl18 0.043 1 0.59 1 6 0.11 
ENSMUSG00000026494 Kif26b 0.0058 1 1 1 6 0.11 
ENSMUSG00000071793 2610005L07Rik 0.017 0.53 0.57 1 6 0.11 
ENSMUSG00000028019 Pdgfc 0.02 1 0.097 1 6 0.11 
ENSMUSG00000090125 Pou3f1 0.047 1 1 1 6 0.11 
ENSMUSG00000027306 Nusap1 0.047 1 1 0.83 6 0.11 
ENSMUSG00000085894 Gm15832 0.022 0.64 1 1 6 0.11 
ENSMUSG00000089756 Gm8898 0.047 1 0.64 1 6 0.1 
ENSMUSG00000051331 Cacna1c 0.04 0.082 1 1 6 0.1 
ENSMUSG00000022508 Bcl6 0.015 0.1 0.93 1 6 0.1 
ENSMUSG00000031028 Tub 0.0048 0.13 0.12 0.95 6 0.1 
ENSMUSG00000034235 Usp54 0.0079 0.17 0.27 0.3 6 0.1 
ENSMUSG00000006641 Slc5a6 0.04 0.58 1 1 6 0.1 
ENSMUSG00000026504 Sdccag8 0.0058 0.16 0.067 0.68 6 0.1 
ENSMUSG00000022462 Slc38a2 0.00052 0.13 0.81 1 6 0.098 
ENSMUSG00000047216 Cdh19 0.0072 0.2 1 0.88 6 0.098 
ENSMUSG00000098243 Gm4258 0.04 1 0.69 1 6 0.097 
ENSMUSG00000040852 Plekhh2 0.0044 0.62 0.75 1 6 0.097 
ENSMUSG00000044712 Slc38a6 0.0026 0.32 1 0.76 6 0.096 
ENSMUSG00000020627 Klhl29 0.0065 0.17 0.75 1 6 0.094 
ENSMUSG00000097583 6430590A07Rik 0.026 0.074 0.81 0.81 6 0.093 
ENSMUSG00000032525 Nktr 0.0058 0.29 1 1 6 0.092 
ENSMUSG00000005580 Adcy9 0.02 0.16 0.24 0.48 6 0.091 
ENSMUSG00000095403 Gm21092 0.037 0.43 1 1 6 0.09 
ENSMUSG00000041229 Phf8 0.0072 0.43 0.84 1 6 0.089 
ENSMUSG00000018500 Adora2b 0.04 0.85 1 0.88 6 0.088 
ENSMUSG00000015522 Arnt 0.0053 0.22 0.87 0.74 6 0.088 
ENSMUSG00000018707 Dync1h1 0.029 0.96 0.33 1 6 0.086 
ENSMUSG00000023959 Clic5 0.043 1 0.69 1 6 0.082 
App.3. Diurnal Genes in Mouse Cortex:  App. 58 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000032602 Slc25a20 0.043 0.26 1 1 6 0.081 
ENSMUSG00000048078 Tenm4 0.026 0.62 1 0.91 6 0.081 
ENSMUSG00000005533 Igf1r 0.031 0.05 0.62 0.71 6 0.081 
ENSMUSG00000054728 Phactr1 0.0048 1 0.38 1 6 0.079 
ENSMUSG00000013275 Slc41a1 0.037 0.11 0.93 1 6 0.079 
ENSMUSG00000096929 A330023F24Rik 0.043 0.32 0.23 1 6 0.078 
ENSMUSG00000042688 Mapk6 0.037 0.13 0.67 1 6 0.078 
ENSMUSG00000034055 Phka1 0.034 0.96 0.87 1 6 0.078 
ENSMUSG00000011257 Pabpc4 0.026 0.39 1 1 6 0.077 
ENSMUSG00000023087 Noct 0.014 0.93 1 0.95 6 0.077 
ENSMUSG00000056763 Cspp1 0.02 0.71 0.69 0.66 6 0.076 
ENSMUSG00000039210 Gpatch2 0.019 1 1 1 6 0.075 
ENSMUSG00000002748 Baz1b 0.037 0.11 0.8 0.86 6 0.075 
ENSMUSG00000074519 Etohi1 0.024 0.27 1 0.56 6 0.074 
ENSMUSG00000016493 Cd46 0.04 0.58 1 1 6 0.074 
ENSMUSG00000034066 Farp2 0.031 1 0.67 1 6 0.074 
ENSMUSG00000073557 Ppp1r12b 0.029 1 0.48 1 6 0.074 
ENSMUSG00000025931 Paqr8 0.0032 0.25 1 0.97 6 0.073 
ENSMUSG00000041341 Atg2b 0.031 1 0.4 1 6 0.073 
ENSMUSG00000038170 Pde4dip 0.001 0.45 0.64 0.86 6 0.073 
ENSMUSG00000022992 Kansl2 0.037 0.13 1 1 6 0.072 
ENSMUSG00000026349 Ccnt2 0.0072 1 0.72 1 6 0.071 
ENSMUSG00000057914 Cacnb2 0.037 0.8 0.33 0.34 6 0.071 
ENSMUSG00000048960 Prex2 0.047 0.074 1 1 6 0.07 
ENSMUSG00000028309 Rnf20 0.037 0.057 0.72 0.91 6 0.069 
ENSMUSG00000003500 Impdh1 0.043 1 0.78 0.52 6 0.067 
ENSMUSG00000054051 Ercc6 0.0095 0.66 0.9 1 6 0.062 
ENSMUSG00000021666 Gfm2 0.04 0.77 1 0.74 6 0.062 
ENSMUSG00000001054 Rmnd5b 0.0087 1 0.29 1 6 0.06 
ENSMUSG00000031691 Tnpo2 0.0087 0.62 1 0.48 6 0.059 
ENSMUSG00000038708 Golga4 0.04 1 0.87 1 6 0.056 
ENSMUSG00000021514 Zfp369 0.013 0.4 0.67 1 6 0.055 
ENSMUSG00000047789 Slc38a9 0.02 0.71 1 1 6 0.055 
ENSMUSG00000029634 Rnf6 0.00019 0.11 1 1 6 0.054 
ENSMUSG00000039473 Ubn1 0.012 0.53 0.38 0.6 6 0.054 
ENSMUSG00000036104 Rab3gap1 0.0013 0.73 0.9 1 6 0.054 
ENSMUSG00000022591 Gm9747 0.047 0.12 1 0.86 6 0.053 
ENSMUSG00000027201 Myef2 0.0048 1 1 1 6 0.052 
ENSMUSG00000031715 Smarca5 0.047 0.51 1 1 6 0.052 
ENSMUSG00000031066 Usp11 0.014 1 0.44 1 6 0.048 
ENSMUSG00000033863 Klf9 0.034 1 0.75 0.39 6 0.048 
ENSMUSG00000038014 Fam120a 0.0036 0.16 0.67 1 6 0.048 
ENSMUSG00000048271 Rbm33 0.02 0.11 0.78 1 6 0.047 
ENSMUSG00000060181 Slc35e3 0.015 0.37 0.52 1 6 0.047 
ENSMUSG00000021311 Mtr 0.014 0.75 1 1 6 0.045 
ENSMUSG00000036323 Srp72 0.0087 1 0.26 0.52 6 0.043 
ENSMUSG00000055204 Ankrd17 0.04 0.27 1 1 6 0.042 
App.3. Diurnal Genes in Mouse Cortex:  App. 59 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000021962 Dcp1a 0.0053 1 0.26 1 6 0.042 
ENSMUSG00000041720 Pi4ka 0.026 1 1 1 6 0.041 
ENSMUSG00000033526 Ppip5k1 0.0095 1 0.058 0.76 6 0.039 
ENSMUSG00000021188 Trip11 0.015 0.3 1 1 6 0.036 
ENSMUSG00000025139 Tollip 0.031 0.057 0.93 0.74 6 0.034 
ENSMUSG00000040242 Fgfr1op2 0.047 0.13 1 0.14 6 0.033 
ENSMUSG00000038506 Dcun1d2 0.022 0.83 0.55 1 6 0.03 
ENSMUSG00000032556 Bfsp2 0.00011 0.98 0.32 1 9 0.42 
ENSMUSG00000036151 Tm6sf2 0.0058 1 0.59 1 9 0.37 
ENSMUSG00000051246 Msantd1 0.019 1 1 1 9 0.36 
ENSMUSG00000047415 Gpr68 9.50E-05 0.12 0.1 0.64 9 0.34 
ENSMUSG00000065145 Vaultrc5 0.015 1 1 1 9 0.32 
ENSMUSG00000008384 Sertad1 0.037 0.14 0.11 0.17 9 0.3 
ENSMUSG00000083386 Gm15426 0.04 0.55 0.57 1 9 0.29 
ENSMUSG00000024136 Dnase1l2 0.0048 1 0.071 0.44 9 0.28 
ENSMUSG00000018166 Erbb3 0.0026 0.31 0.1 0.76 9 0.25 
ENSMUSG00000028909 Ptpru 0.0072 0.17 1 1 9 0.25 
ENSMUSG00000053178 Mterf1b 0.019 0.77 0.64 1 9 0.25 
ENSMUSG00000026796 Fam129b 0.0095 0.39 0.067 0.76 9 0.24 
ENSMUSG00000034209 Rasl10a 0.0087 0.12 0.097 0.41 9 0.24 
ENSMUSG00000063623 C230062I16Rik 0.0018 1 0.59 1 9 0.23 
ENSMUSG00000003541 Ier3 0.022 0.14 0.22 0.62 9 0.23 
ENSMUSG00000022220 Adcy4 0.0036 0.75 0.42 0.23 9 0.22 
ENSMUSG00000024856 Cdk2ap2 0.0036 1 0.17 0.66 9 0.22 
ENSMUSG00000054619 Mettl7a1 0.0058 0.082 0.071 0.18 9 0.22 
ENSMUSG00000034936 Arl4d 0.047 1 0.64 1 9 0.21 
ENSMUSG00000092074 Dynlt1a 0.031 1 0.23 1 9 0.21 
ENSMUSG00000040113 Mettl11b 0.0079 0.34 0.64 1 9 0.2 
ENSMUSG00000042529 Kcnj12 0.00035 0.19 0.37 1 9 0.2 
ENSMUSG00000090210 Itga10 0.019 0.73 0.72 1 9 0.19 
ENSMUSG00000028410 Dnaja1 0.013 0.23 0.058 0.76 9 0.19 
ENSMUSG00000084088 Gm12941 0.043 0.75 0.62 1 9 0.19 
ENSMUSG00000074743 Thbd 0.0044 0.082 0.55 1 9 0.19 
ENSMUSG00000035109 Shc4 0.00023 0.3 0.52 1 9 0.19 
ENSMUSG00000056116 H2-T22 0.0072 1 0.32 0.74 9 0.18 
ENSMUSG00000050248 Evc2 0.0048 0.057 0.15 1 9 0.18 
ENSMUSG00000070372 Capza1 0.047 1 0.11 1 9 0.18 
ENSMUSG00000028978 Nos3 0.0065 0.9 0.72 1 9 0.18 
ENSMUSG00000032323 Cyp11a1 0.0065 1 1 1 9 0.18 
ENSMUSG00000022324 Matn2 0.0087 0.17 0.97 1 9 0.17 
ENSMUSG00000067352 Gm14149 0.037 0.46 1 1 9 0.17 
ENSMUSG00000048897 Zfp710 0.047 0.64 0.97 0.95 9 0.17 
ENSMUSG00000044139 Prss53 0.04 1 1 1 9 0.17 
ENSMUSG00000018906 P4ha2 0.04 0.21 0.44 0.41 9 0.16 
ENSMUSG00000025790 Slco3a1 0.00019 0.17 1 1 9 0.16 
ENSMUSG00000079048 4933413L06Rik 0.04 1 1 1 9 0.16 
ENSMUSG00000045318 Adra2c 0.022 0.2 0.5 0.58 9 0.16 
App.3. Diurnal Genes in Mouse Cortex:  App. 60 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000034006 Pqlc1 0.02 1 0.59 1 9 0.16 
ENSMUSG00000026475 Rgs16 0.02 1 0.48 0.39 9 0.16 
ENSMUSG00000018604 Tbx3 0.0058 0.06 1 0.83 9 0.16 
ENSMUSG00000037703 Lzts3 0.0013 0.074 0.12 1 9 0.16 
ENSMUSG00000032192 Gnb5 0.015 1 0.38 1 9 0.15 
ENSMUSG00000042079 Hnrnpf 0.0058 0.43 0.38 0.81 9 0.15 
ENSMUSG00000051107 Gm15440 0.0058 0.73 0.35 1 9 0.15 
ENSMUSG00000031778 Cx3cl1 0.004 0.086 0.26 1 9 0.15 
ENSMUSG00000079737 3110001I22Rik 0.04 1 0.17 0.37 9 0.15 
ENSMUSG00000026173 Plcd4 0.014 0.053 0.48 1 9 0.15 
ENSMUSG00000039065 Fam173b 0.0032 1 0.5 0.33 9 0.15 
ENSMUSG00000023809 Rps6ka2 0.001 0.85 0.12 1 9 0.15 
ENSMUSG00000031060 Rbm10 0.024 0.36 0.78 1 9 0.15 
ENSMUSG00000097239 Gm27029 0.037 1 0.2 1 9 0.15 
ENSMUSG00000025047 Pdcd11 8.20E-06 0.73 0.12 1 9 0.15 
ENSMUSG00000067653 Ankrd23 0.024 0.68 0.59 1 9 0.15 
ENSMUSG00000022861 Dgkg 0.012 0.14 0.46 1 9 0.15 
ENSMUSG00000032702 Kank1 0.0018 0.25 0.33 1 9 0.15 
ENSMUSG00000030413 Pglyrp1 0.0053 1 1 1 9 0.15 
ENSMUSG00000029436 Mmp17 0.034 1 0.5 1 9 0.15 
ENSMUSG00000003200 Sh3gl1 0.0029 1 0.3 1 9 0.14 
ENSMUSG00000018849 Wwc1 0.004 0.64 0.18 0.56 9 0.14 
ENSMUSG00000014164 Klhl3 0.014 0.15 0.57 1 9 0.14 
ENSMUSG00000039976 Tbc1d16 0.019 0.16 0.42 1 9 0.14 
ENSMUSG00000025020 Slit1 0.026 0.4 1 1 9 0.14 
ENSMUSG00000024451 Arap3 0.019 1 0.097 0.76 9 0.14 
ENSMUSG00000054006 D630008O14Rik 0.014 1 0.72 1 9 0.14 
ENSMUSG00000041592 Sdk2 0.017 0.34 1 1 9 0.14 
ENSMUSG00000040490 Lrfn2 0.0065 0.75 0.24 0.41 9 0.14 
ENSMUSG00000018167 Stard3 0.0023 1 0.11 0.78 9 0.14 
ENSMUSG00000074247 Dda1 0.012 1 0.2 0.42 9 0.14 
ENSMUSG00000054855 Rnd1 0.014 0.51 0.071 1 9 0.14 
ENSMUSG00000078651 Aoc2 0.037 1 0.21 1 9 0.14 
ENSMUSG00000029875 Ccdc184 0.034 1 0.12 0.21 9 0.14 
ENSMUSG00000045349 Sh2d5 0.0072 0.12 1 1 9 0.14 
ENSMUSG00000001552 Jup 0.043 1 0.11 0.26 9 0.14 
ENSMUSG00000020212 Mdm1 0.001 0.078 0.69 1 9 0.14 
ENSMUSG00000031488 Rab11fip1 0.047 0.096 1 0.13 9 0.13 
ENSMUSG00000054675 Tmem119 0.043 0.62 0.52 1 9 0.13 
ENSMUSG00000066406 Akap13 0.013 0.13 0.97 1 9 0.13 
ENSMUSG00000097604 Gm17322 0.0048 1 1 1 9 0.13 
ENSMUSG00000056234 Ncoa4 0.047 1 0.69 1 9 0.13 
ENSMUSG00000052031 Tagap1 0.04 0.62 0.62 1 9 0.13 
ENSMUSG00000000325 Arvcf 0.034 0.36 0.2 0.22 9 0.13 
ENSMUSG00000031833 Mast3 0.04 0.057 0.87 1 9 0.13 
ENSMUSG00000017692 Rhbdl3 0.0058 1 1 1 9 0.13 
ENSMUSG00000040836 Gpr161 0.02 0.067 0.21 1 9 0.13 
App.3. Diurnal Genes in Mouse Cortex:  App. 61 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000030079 Ruvbl1 0.024 1 0.23 1 9 0.13 
ENSMUSG00000022054 Nefm 0.019 0.77 0.38 0.86 9 0.13 
ENSMUSG00000037622 Wdtc1 0.004 1 0.44 1 9 0.12 
ENSMUSG00000003762 Adck4 0.02 0.39 1 1 9 0.12 
ENSMUSG00000001985 Grik3 0.024 0.77 1 1 9 0.12 
ENSMUSG00000096910 Zfp955b 0.022 0.58 0.9 1 9 0.12 
ENSMUSG00000052137 Rbm12b2 0.0036 1 0.4 0.74 9 0.12 
ENSMUSG00000020261 Slc36a1 0.0021 0.96 0.4 1 9 0.12 
ENSMUSG00000036545 Adamts2 0.026 0.58 1 1 9 0.12 
ENSMUSG00000024883 Rin1 0.00015 1 0.97 0.22 9 0.12 
ENSMUSG00000064254 Ethe1 0.026 0.48 0.15 1 9 0.12 
ENSMUSG00000029053 Prkcz 0.04 1 0.22 1 9 0.12 
ENSMUSG00000036206 Sh3bp4 0.017 1 0.071 1 9 0.12 
ENSMUSG00000013646 Sh3bp5l 0.037 0.73 0.27 0.76 9 0.12 
ENSMUSG00000071645 Tut1 0.0087 0.11 0.054 0.31 9 0.12 
ENSMUSG00000020224 Llph 0.02 0.24 0.3 0.68 9 0.12 
ENSMUSG00000020422 Tns3 0.0029 0.13 0.091 1 9 0.12 
ENSMUSG00000040606 Kazn 0.04 0.053 1 1 9 0.12 
ENSMUSG00000024137 E4f1 0.043 0.73 0.22 1 9 0.12 
ENSMUSG00000091512 Lamtor3 0.034 1 0.69 1 9 0.11 
ENSMUSG00000055128 Cgrrf1 0.029 0.074 0.11 1 9 0.11 
ENSMUSG00000052632 Asap2 0.0072 0.063 0.2 1 9 0.11 
ENSMUSG00000002393 Nr2f6 0.031 0.55 1 1 9 0.11 
ENSMUSG00000008855 Hdac5 0.013 1 0.35 1 9 0.11 
ENSMUSG00000067928 Zfp760 0.026 0.096 1 1 9 0.11 
ENSMUSG00000041609 Ccdc64 0.0053 0.29 0.091 0.76 9 0.11 
ENSMUSG00000007682 Dio2 0.0079 0.091 0.64 1 9 0.11 
ENSMUSG00000032840 2410131K14Rik 0.0023 1 1 1 9 0.11 
ENSMUSG00000046997 Spsb4 0.031 1 0.062 1 9 0.11 
ENSMUSG00000030871 Ears2 0.0079 1 1 1 9 0.11 
ENSMUSG00000017314 Mpp2 0.026 0.42 0.24 0.18 9 0.11 
ENSMUSG00000036975 Tmem177 0.0058 1 0.071 0.14 9 0.11 
ENSMUSG00000031511 Arhgef7 8.20E-06 0.31 0.23 1 9 0.11 
ENSMUSG00000031700 Gpt2 0.0013 0.46 0.67 1 9 0.11 
ENSMUSG00000022197 Pdzd2 0.012 0.27 1 1 9 0.11 
ENSMUSG00000035047 Kri1 0.0095 1 0.062 1 9 0.11 
ENSMUSG00000032402 Smad3 0.0079 0.096 0.75 1 9 0.11 
ENSMUSG00000052397 Ezr 0.034 0.42 0.87 0.33 9 0.11 
ENSMUSG00000034617 Mtrr 0.00078 0.15 0.37 1 9 0.11 
ENSMUSG00000043460 Elfn2 0.029 0.057 1 1 9 0.11 
ENSMUSG00000070576 Mn1 0.011 0.091 0.4 0.17 9 0.11 
ENSMUSG00000030811 Fbxl19 0.047 0.4 0.26 0.58 9 0.11 
ENSMUSG00000025980 Hspd1 0.037 0.27 0.48 0.66 9 0.1 
ENSMUSG00000019828 Grm1 0.0021 0.1 0.26 1 9 0.1 
ENSMUSG00000037172 E330009J07Rik 0.0072 1 0.067 1 9 0.1 
ENSMUSG00000062202 Btbd9 2.60E-05 1 0.87 1 9 0.1 
ENSMUSG00000002504 Slc9a3r2 0.037 0.93 1 1 9 0.1 
App.3. Diurnal Genes in Mouse Cortex:  App. 62 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000028933 Xrcc2 0.0036 1 0.67 0.91 9 0.1 
ENSMUSG00000022641 Bbx 0.013 0.05 0.078 0.25 9 0.1 
ENSMUSG00000032232 Cgnl1 0.00078 0.07 0.14 1 9 0.1 
ENSMUSG00000003402 Prkcsh 0.037 0.6 0.17 1 9 0.1 
ENSMUSG00000029130 Rnf32 0.019 0.091 0.17 1 9 0.1 
ENSMUSG00000043439 E130012A19Rik 0.0048 0.14 0.1 0.16 9 0.1 
ENSMUSG00000036061 Smug1 0.02 1 0.59 1 9 0.1 
ENSMUSG00000020086 H2afy2 0.047 1 0.15 1 9 0.1 
ENSMUSG00000046280 She 0.04 0.46 0.4 0.88 9 0.1 
ENSMUSG00000030060 Hmces 0.019 0.75 0.29 0.95 9 0.1 
ENSMUSG00000021448 Shc3 0.014 0.082 1 1 9 0.099 
ENSMUSG00000034674 Tdg 0.037 0.34 0.14 0.68 9 0.099 
ENSMUSG00000037013 Ss18 1.90E-05 0.45 0.078 1 9 0.098 
ENSMUSG00000027221 Chst1 0.029 0.057 1 1 9 0.098 
ENSMUSG00000030805 Stx4a 0.00047 1 0.15 1 9 0.098 
ENSMUSG00000040387 Klhl32 0.0044 1 0.14 0.81 9 0.098 
ENSMUSG00000021692 Dimt1 0.04 0.1 1 1 9 0.097 
ENSMUSG00000038497 Tmco3 0.001 1 0.17 1 9 0.097 
ENSMUSG00000023118 Sympk 0.0032 1 0.37 0.5 9 0.097 
ENSMUSG00000022263 Trio 8.40E-05 0.29 0.17 1 9 0.096 
ENSMUSG00000097596 Gm26673 0.047 1 1 1 9 0.096 
ENSMUSG00000002365 Snx9 0.029 0.057 0.72 0.58 9 0.096 
ENSMUSG00000041688 Amot 0.0011 0.53 0.78 1 9 0.095 
ENSMUSG00000028436 Dcaf12 0.0011 1 0.071 1 9 0.095 
ENSMUSG00000025648 Pfkfb4 0.026 1 0.13 0.46 9 0.095 
ENSMUSG00000031990 Jam3 0.022 0.83 0.75 1 9 0.094 
ENSMUSG00000032536 Trak1 0.011 0.091 0.35 0.56 9 0.094 
ENSMUSG00000089857 Zfp882 0.031 1 1 1 9 0.093 
ENSMUSG00000021750 Fam107a 0.014 0.15 1 0.21 9 0.093 
ENSMUSG00000047959 Kcna3 0.019 1 0.37 1 9 0.093 
ENSMUSG00000054792 Klhl18 0.026 0.58 0.37 1 9 0.092 
ENSMUSG00000029283 Cdc7 0.022 0.85 0.13 1 9 0.092 
ENSMUSG00000025332 Kdm5c 0.00023 0.11 0.062 1 9 0.092 
ENSMUSG00000046312 AI464131 0.0014 0.6 0.38 1 9 0.091 
ENSMUSG00000027580 Helz2 0.026 1 0.69 1 9 0.091 
ENSMUSG00000030098 Grip2 0.016 1 0.17 1 9 0.091 
ENSMUSG00000040009 Gnaz 0.012 0.24 0.64 0.48 9 0.091 
ENSMUSG00000036377 C530008M17Rik 0.0095 1 1 0.88 9 0.091 
ENSMUSG00000018401 Mtmr4 0.0065 1 0.23 0.78 9 0.09 
ENSMUSG00000054715 Zscan22 0.034 0.17 0.27 0.52 9 0.089 
ENSMUSG00000049807 Arhgap23 0.0026 0.17 0.27 0.97 9 0.089 
ENSMUSG00000034271 Jdp2 0.047 0.2 1 1 9 0.088 
ENSMUSG00000063888 Rpl7l1 0.024 1 0.091 1 9 0.088 
ENSMUSG00000030653 Pde2a 0.013 1 0.46 0.76 9 0.088 
ENSMUSG00000016346 Kcnq2 0.0072 1 0.5 1 9 0.088 
ENSMUSG00000031442 Mcf2l 0.001 0.17 0.071 1 9 0.088 
ENSMUSG00000015214 Mtmr1 0.037 0.17 0.4 1 9 0.088 
App.3. Diurnal Genes in Mouse Cortex:  App. 63 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000028974 Dffa 0.013 1 0.14 0.86 9 0.087 
ENSMUSG00000025134 Alyref 0.043 0.26 0.26 1 9 0.087 
ENSMUSG00000042078 Svop 0.022 1 0.21 1 9 0.087 
ENSMUSG00000042406 Atf4 0.0087 0.24 0.24 1 9 0.087 
ENSMUSG00000039879 Heca 0.011 0.75 0.32 0.76 9 0.087 
ENSMUSG00000040723 Rcsd1 0.047 1 1 1 9 0.087 
ENSMUSG00000039533 Mmd2 0.043 0.88 0.17 0.81 9 0.086 
ENSMUSG00000033545 Znrf1 0.043 1 0.22 1 9 0.085 
ENSMUSG00000015829 Tnr 0.037 0.29 1 1 9 0.085 
ENSMUSG00000037022 Mmaa 0.0036 0.83 0.11 1 9 0.085 
ENSMUSG00000039159 Ube2h 0.0044 0.29 0.26 1 9 0.084 
ENSMUSG00000022160 Mettl3 0.043 0.11 0.37 1 9 0.083 
ENSMUSG00000031527 Eri1 0.013 0.1 0.9 0.1 9 0.082 
ENSMUSG00000002279 Lmf1 0.04 0.26 1 1 9 0.082 
ENSMUSG00000067889 Sptbn2 0.037 0.48 1 1 9 0.082 
ENSMUSG00000021068 Nin 0.026 1 0.44 1 9 0.082 
ENSMUSG00000018909 Arrb1 7.00E-04 1 0.21 1 9 0.082 
ENSMUSG00000047719 Ubiad1 0.043 1 1 1 9 0.082 
ENSMUSG00000038208 Pgap3 0.047 1 0.97 1 9 0.081 
ENSMUSG00000033998 Kcnk1 0.014 1 1 1 9 0.079 
ENSMUSG00000040111 Gramd1b 0.0058 0.68 0.4 0.97 9 0.079 
ENSMUSG00000026889 Rbm18 0.047 0.4 0.46 1 9 0.079 
ENSMUSG00000022426 Josd1 0.037 1 1 1 9 0.078 
ENSMUSG00000028330 Ncbp1 0.00052 0.53 0.097 1 9 0.077 
ENSMUSG00000020964 Sel1l 0.0014 0.23 0.17 0.14 9 0.077 
ENSMUSG00000020231 Dip2a 0.0065 0.46 0.78 1 9 0.077 
ENSMUSG00000022604 Cep97 0.037 1 0.84 0.83 9 0.077 
ENSMUSG00000024935 Slc1a1 0.013 0.14 0.5 1 9 0.076 
ENSMUSG00000028039 Efna3 0.047 0.96 1 1 9 0.076 
ENSMUSG00000029420 Rimbp2 0.0023 1 0.64 0.34 9 0.076 
ENSMUSG00000031864 Ints10 0.0023 0.37 0.13 1 9 0.076 
ENSMUSG00000069072 Slc7a14 0.031 0.48 0.69 1 9 0.076 
ENSMUSG00000039662 Icmt 0.0014 0.88 0.22 1 9 0.075 
ENSMUSG00000068394 Cep152 0.029 0.62 0.59 1 9 0.075 
ENSMUSG00000079056 Kcnip3 0.0065 0.77 1 1 9 0.074 
ENSMUSG00000055003 Lrtm2 0.015 1 0.48 1 9 0.074 
ENSMUSG00000024772 Ehd1 0.024 1 0.32 1 9 0.074 
ENSMUSG00000021901 Bap1 0.0053 0.3 0.38 0.91 9 0.073 
ENSMUSG00000024245 Tmem178 0.031 0.77 0.2 1 9 0.073 
ENSMUSG00000037957 Wdr20 0.017 0.48 0.55 0.76 9 0.073 
ENSMUSG00000018474 Chd3 0.0087 1 0.48 1 9 0.073 
ENSMUSG00000006464 Bbs1 0.00035 1 0.14 1 9 0.073 
ENSMUSG00000043079 Synpo 0.031 0.053 0.24 0.26 9 0.073 
ENSMUSG00000021458 2010111I01Rik 0.029 1 0.19 1 9 0.072 
ENSMUSG00000003410 Elavl3 0.0029 0.1 0.17 1 9 0.072 
ENSMUSG00000026442 Nfasc 0.0072 0.53 1 1 9 0.072 
ENSMUSG00000041258 Zfp236 0.0044 0.15 0.22 0.78 9 0.071 
App.3. Diurnal Genes in Mouse Cortex:  App. 64 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000028803 Nipal3 0.047 0.6 0.062 1 9 0.071 
ENSMUSG00000055980 Irs1 0.014 0.21 0.14 0.74 9 0.071 
ENSMUSG00000048486 Fitm2 0.00011 1 0.12 1 9 0.071 
ENSMUSG00000036800 Fam135b 0.019 1 1 1 9 0.071 
ENSMUSG00000020451 Limk2 0.0065 1 0.16 1 9 0.07 
ENSMUSG00000042429 Adora1 0.019 0.96 0.48 1 9 0.07 
ENSMUSG00000035181 Heatr5a 0.04 0.24 0.4 1 9 0.07 
ENSMUSG00000027612 Mmp24 0.0087 1 0.3 1 9 0.07 
ENSMUSG00000020785 Camkk1 0.0032 1 1 1 9 0.07 
ENSMUSG00000023988 Bysl 0.024 0.17 0.64 0.97 9 0.069 
ENSMUSG00000078234 Klhdc7a 0.0053 1 1 0.68 9 0.069 
ENSMUSG00000020607 Fam84a 0.022 0.83 1 0.97 9 0.069 
ENSMUSG00000032897 Nfyc 0.043 1 1 1 9 0.069 
ENSMUSG00000026608 Kctd3 0.00061 1 0.27 1 9 0.069 
ENSMUSG00000001156 Mxd1 0.014 0.2 0.3 0.26 9 0.069 
ENSMUSG00000002346 Slc25a42 0.017 0.83 1 1 9 0.068 
ENSMUSG00000022994 Adcy6 0.04 0.11 0.19 0.41 9 0.068 
ENSMUSG00000021177 Tdp1 0.026 0.98 1 1 9 0.068 
ENSMUSG00000041794 Myrip 0.0087 0.73 0.72 1 9 0.068 
ENSMUSG00000047731 Wbp1l 0.047 0.96 1 1 9 0.068 
ENSMUSG00000025314 Ptprj 0.043 0.057 0.62 1 9 0.068 
ENSMUSG00000022228 Zscan26 0.0072 0.12 1 1 9 0.067 
ENSMUSG00000054252 Fgfr3 0.011 0.9 0.11 1 9 0.066 
ENSMUSG00000096188 Cmtm4 0.0013 1 0.29 1 9 0.066 
ENSMUSG00000034105 Tldc1 0.04 1 1 1 9 0.066 
ENSMUSG00000021451 Sema4d 0.019 0.18 0.3 1 9 0.066 
ENSMUSG00000078515 Ddi2 0.022 0.43 0.23 0.95 9 0.066 
ENSMUSG00000038762 Abcf1 0.0072 0.37 0.11 1 9 0.065 
ENSMUSG00000058297 Spock2 0.015 0.71 0.57 1 9 0.064 
ENSMUSG00000027457 Snph 0.024 1 0.57 0.58 9 0.064 
ENSMUSG00000009681 Bcr 0.011 1 0.48 0.88 9 0.064 
ENSMUSG00000024188 Luc7l 0.0087 0.32 0.38 0.3 9 0.064 
ENSMUSG00000034853 Acot11 0.022 1 1 0.91 9 0.064 
ENSMUSG00000027329 Spef1 0.047 1 0.3 1 9 0.064 
ENSMUSG00000052353 Cemip 0.022 0.07 0.52 1 9 0.064 
ENSMUSG00000089682 Bcl2l2 0.001 0.27 0.062 0.42 9 0.063 
ENSMUSG00000020198 Ap3d1 0.0053 0.64 0.091 1 9 0.062 
ENSMUSG00000001150 Mcm3ap 0.0018 0.55 0.46 1 9 0.062 
ENSMUSG00000020412 Ascc2 0.017 0.44 0.071 0.39 9 0.062 
ENSMUSG00000032009 Sesn3 0.034 0.06 1 1 9 0.061 
ENSMUSG00000031513 Leprotl1 0.0023 1 0.78 1 9 0.061 
ENSMUSG00000048148 Nwd1 0.019 1 1 1 9 0.061 
ENSMUSG00000054920 Klhl5 0.026 1 0.27 1 9 0.061 
ENSMUSG00000039697 Ncoa7 0.037 0.15 1 0.44 9 0.061 
ENSMUSG00000006998 Psmd2 0.0087 0.43 0.058 0.46 9 0.061 
ENSMUSG00000025821 Zfp282 0.02 0.68 0.75 1 9 0.06 
ENSMUSG00000032652 Crebl2 0.022 0.13 0.55 1 9 0.06 
App.3. Diurnal Genes in Mouse Cortex:  App. 65 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000072647 Adam1a 0.0058 0.25 0.72 1 9 0.06 
ENSMUSG00000079469 Pigb 0.013 1 0.32 1 9 0.06 
ENSMUSG00000038366 Lasp1 0.029 0.62 0.097 0.44 9 0.06 
ENSMUSG00000024621 Csf1r 0.037 1 0.062 0.64 9 0.06 
ENSMUSG00000031537 Ikbkb 0.024 1 0.26 1 9 0.06 
ENSMUSG00000001482 Def8 0.017 1 1 1 9 0.06 
ENSMUSG00000002006 Pdzd4 0.034 0.19 0.4 1 9 0.059 
ENSMUSG00000048118 Arid4a 0.012 0.14 0.27 0.56 9 0.059 
ENSMUSG00000029068 Ccnl2 0.047 1 1 1 9 0.059 
ENSMUSG00000021681 Aggf1 0.022 0.24 0.38 0.54 9 0.058 
ENSMUSG00000048458 Fam212b 0.037 0.12 0.33 1 9 0.058 
ENSMUSG00000055319 Sec23ip 0.0023 0.2 0.48 0.78 9 0.058 
ENSMUSG00000039738 Slx4 0.017 0.45 0.32 1 9 0.058 
ENSMUSG00000040383 Aqr 0.043 1 0.59 1 9 0.058 
ENSMUSG00000032788 Pdxk 0.0072 0.98 1 1 9 0.058 
ENSMUSG00000004677 Myo9b 0.015 1 0.062 1 9 0.058 
ENSMUSG00000041037 Irgq 0.011 1 0.32 0.64 9 0.057 
ENSMUSG00000031791 Tmem38a 0.0013 0.73 0.44 1 9 0.056 
ENSMUSG00000004394 Tmed4 0.034 0.71 0.72 1 9 0.056 
ENSMUSG00000029120 Ppp2r2c 0.0079 1 0.15 1 9 0.056 
ENSMUSG00000026305 Lrrfip1 0.014 0.27 0.32 1 9 0.056 
ENSMUSG00000017421 Zfp207 0.0079 0.078 0.16 0.26 9 0.054 
ENSMUSG00000028911 Srsf4 0.029 1 0.84 1 9 0.054 
ENSMUSG00000026848 Tor1b 0.043 0.18 0.062 1 9 0.054 
ENSMUSG00000026918 Brd3 0.011 0.23 0.52 0.76 9 0.054 
ENSMUSG00000040260 Daam2 0.004 0.66 0.84 1 9 0.054 
ENSMUSG00000034931 Dhx8 0.047 1 0.29 0.97 9 0.054 
ENSMUSG00000020661 Dnmt3a 0.024 1 1 0.88 9 0.053 
ENSMUSG00000027236 Eif3j1 0.043 1 0.37 1 9 0.053 
ENSMUSG00000027519 Rab22a 0.013 0.17 1 1 9 0.052 
ENSMUSG00000021573 Tppp 0.0036 0.36 0.75 1 9 0.052 
ENSMUSG00000052698 Tln2 0.029 0.66 1 0.62 9 0.052 
ENSMUSG00000025217 Btrc 0.001 0.58 0.29 1 9 0.051 
ENSMUSG00000030207 Fam234b 0.034 1 0.16 1 9 0.051 
ENSMUSG00000034602 Mon2 0.0079 1 0.15 1 9 0.051 
ENSMUSG00000014763 Fam120b 0.02 0.05 0.084 1 9 0.05 
ENSMUSG00000029502 Golga3 0.00078 1 0.1 1 9 0.05 
ENSMUSG00000028953 Abcf2 0.019 1 0.38 1 9 0.05 
ENSMUSG00000030000 Add2 0.00092 1 0.19 0.66 9 0.05 
ENSMUSG00000027339 Rassf2 0.017 1 0.57 1 9 0.05 
ENSMUSG00000002428 Hltf 0.013 0.96 1 1 9 0.049 
ENSMUSG00000037851 Iars 0.043 0.56 0.81 1 9 0.049 
ENSMUSG00000002455 Prpf6 0.022 1 0.33 1 9 0.049 
ENSMUSG00000045216 Hs6st1 0.043 1 0.37 1 9 0.049 
ENSMUSG00000058979 Cecr5 0.04 0.4 0.097 1 9 0.048 
ENSMUSG00000038013 Wipf2 0.0095 0.074 0.5 0.76 9 0.048 
ENSMUSG00000055013 Agap1 0.019 1 0.87 1 9 0.048 
App.3. Diurnal Genes in Mouse Cortex:  App. 66 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000017639 Rab11fip4 0.004 0.75 0.67 1 9 0.048 
ENSMUSG00000002341 Ncan 0.029 0.62 1 1 9 0.047 
ENSMUSG00000032607 Amt 0.024 1 0.22 1 9 0.046 
ENSMUSG00000049739 Zfp646 0.043 0.58 0.26 1 9 0.046 
ENSMUSG00000049288 Lix1l 0.04 1 0.15 1 9 0.045 
ENSMUSG00000022415 Syngr1 0.043 0.58 0.97 1 9 0.045 
ENSMUSG00000039367 Sec24c 0.0013 1 0.062 1 9 0.045 
ENSMUSG00000034300 Fam53c 0.024 0.77 0.23 1 9 0.045 
ENSMUSG00000057230 Aak1 0.029 1 1 1 9 0.045 
ENSMUSG00000026596 Abl2 0.0095 0.85 1 1 9 0.044 
ENSMUSG00000005615 Pcyt1a 0.013 1 0.23 1 9 0.044 
ENSMUSG00000038615 Nfe2l1 0.022 0.31 0.44 1 9 0.043 
ENSMUSG00000021559 Dapk1 0.012 0.75 0.16 0.54 9 0.043 
ENSMUSG00000022594 Lynx1 0.04 1 1 1 9 0.042 
ENSMUSG00000025417 Pip4k2c 0.02 0.5 1 1 9 0.041 
ENSMUSG00000024743 Syt7 0.012 1 1 1 9 0.041 
ENSMUSG00000037526 Atg14 0.034 0.33 0.2 1 9 0.041 
ENSMUSG00000032086 Bace1 0.02 1 0.21 1 9 0.041 
ENSMUSG00000062519 Zfp398 0.026 0.23 1 0.3 9 0.04 
ENSMUSG00000061911 Myt1l 0.037 0.15 1 1 9 0.039 
ENSMUSG00000031302 Nlgn3 0.034 1 1 1 9 0.039 
ENSMUSG00000064145 Arih2 0.031 0.77 0.93 1 9 0.038 
ENSMUSG00000031153 Gripap1 0.0065 0.68 0.81 1 9 0.038 
ENSMUSG00000071856 Mcc 0.037 0.85 1 0.95 9 0.036 
ENSMUSG00000031824 6430548M08Rik 0.04 1 1 1 9 0.036 
ENSMUSG00000034088 Hdlbp 0.043 0.29 0.78 1 9 0.035 
ENSMUSG00000038486 Sv2a 0.034 1 0.054 1 9 0.034 
ENSMUSG00000022000 Zc3h13 0.022 1 1 1 9 0.031 
ENSMUSG00000040481 Bptf 0.037 0.11 1 0.41 9 0.03 
ENSMUSG00000029106 Add1 0.014 0.51 0.14 1 9 0.029 
ENSMUSG00000002052 Supt6 0.034 1 0.3 1 9 0.019 
ENSMUSG00000099440 Gm29593 0.00061 0.9 0.3 0.86 12 0.66 
ENSMUSG00000037405 Icam1 0.024 0.56 0.46 0.58 12 0.3 
ENSMUSG00000097183 Gm17501 0.004 0.063 0.23 1 12 0.28 
ENSMUSG00000075297 H60b 0.043 0.58 0.23 0.88 12 0.28 
ENSMUSG00000072620 Slfn2 0.0036 1 0.67 1 12 0.27 
ENSMUSG00000097466 D430036J16Rik 0.0087 0.34 0.44 0.6 12 0.26 
ENSMUSG00000055200 Sertad3 0.02 0.26 0.22 0.18 12 0.25 
ENSMUSG00000084899 Gm15344 0.047 1 1 1 12 0.24 
ENSMUSG00000043557 Mdga1 0.015 0.12 0.38 0.81 12 0.23 
ENSMUSG00000046470 Sox18 0.043 0.057 0.084 0.81 12 0.23 
ENSMUSG00000000489 Pdgfb 0.015 0.13 0.11 0.6 12 0.2 
ENSMUSG00000039208 Metrnl 0.017 0.23 0.091 1 12 0.19 
ENSMUSG00000054958 Nt5c1a 0.022 1 0.93 1 12 0.19 
ENSMUSG00000040842 Szrd1 0.024 1 0.67 1 12 0.17 
ENSMUSG00000070583 Fv1 0.001 0.12 0.067 0.26 12 0.17 
ENSMUSG00000020848 Doc2b 0.0032 0.53 0.097 0.88 12 0.16 
App.3. Diurnal Genes in Mouse Cortex:  App. 67 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000097929 Tunar 0.0058 0.3 0.59 1 12 0.16 
ENSMUSG00000027776 Il12a 0.031 0.25 0.33 1 12 0.16 
ENSMUSG00000025425 St8sia5 0.00078 1 0.062 0.95 12 0.16 
ENSMUSG00000024647 Cbln2 0.00015 1 0.16 1 12 0.15 
ENSMUSG00000050002 Idnk 0.00041 1 0.067 0.52 12 0.15 
ENSMUSG00000026563 Tada1 0.0087 1 0.22 1 12 0.15 
ENSMUSG00000026655 Fam107b 0.031 0.21 0.78 1 12 0.14 
ENSMUSG00000005447 Pafah1b3 0.034 0.77 0.93 0.83 12 0.14 
ENSMUSG00000021457 Syk 0.02 0.096 0.15 1 12 0.14 
ENSMUSG00000036968 Cnpy4 0.00047 1 0.054 0.64 12 0.14 
ENSMUSG00000033792 Atp7a 0.0013 0.14 0.091 1 12 0.14 
ENSMUSG00000019853 Hebp2 0.022 0.62 0.078 1 12 0.14 
ENSMUSG00000025817 Nudt5 0.011 0.45 0.69 0.62 12 0.14 
ENSMUSG00000006403 Adamts4 0.015 0.082 0.52 0.97 12 0.14 
ENSMUSG00000021062 Rab15 0.00047 0.12 0.15 1 12 0.14 
ENSMUSG00000017418 Arl5b 0.047 0.074 0.14 1 12 0.14 
ENSMUSG00000073468 Sft2d1 0.011 1 0.9 1 12 0.14 
ENSMUSG00000035237 Lcat 0.014 0.07 0.37 1 12 0.13 
ENSMUSG00000068299 1700019G17Rik 0.04 0.8 0.27 1 12 0.13 
ENSMUSG00000027220 Syt13 7.80E-06 1 0.084 1 12 0.13 
ENSMUSG00000049303 Syt12 0.0036 1 0.27 1 12 0.13 
ENSMUSG00000041308 Sntb2 0.031 0.48 0.27 1 12 0.13 
ENSMUSG00000084159 Gm12696 0.0044 0.091 0.52 1 12 0.13 
ENSMUSG00000038780 Smurf1 0.011 0.32 0.38 1 12 0.13 
ENSMUSG00000030494 Rhpn2 0.043 0.3 0.81 1 12 0.13 
ENSMUSG00000021948 Prkcd 0.02 1 1 0.42 12 0.12 
ENSMUSG00000097102 2310069G16Rik 0.019 0.37 0.4 0.29 12 0.12 
ENSMUSG00000022443 Myh9 8.40E-05 0.64 0.071 0.52 12 0.12 
ENSMUSG00000031799 Tpm4 0.0065 0.14 0.19 1 12 0.12 
ENSMUSG00000032297 Celf6 0.0029 0.33 0.15 0.56 12 0.12 
ENSMUSG00000027649 Ctnnbl1 0.013 0.14 0.21 0.78 12 0.12 
ENSMUSG00000047635 2810006K23Rik 0.0087 0.078 0.11 1 12 0.11 
ENSMUSG00000085069 Gm13111 0.004 0.36 1 1 12 0.11 
ENSMUSG00000004268 Emg1 0.022 1 0.5 0.62 12 0.11 
ENSMUSG00000020577 Tspan13 0.019 1 0.3 1 12 0.11 
ENSMUSG00000036273 Lrrk2 0.0095 0.13 0.14 0.64 12 0.11 
ENSMUSG00000031783 Polr2c 0.024 0.17 0.44 0.6 12 0.11 
ENSMUSG00000057130 Txnl4a 0.034 0.067 0.058 0.36 12 0.11 
ENSMUSG00000024150 Mcfd2 0.0087 1 0.67 1 12 0.11 
ENSMUSG00000066877 Nck2 0.047 0.07 1 1 12 0.11 
ENSMUSG00000025049 Taf5 0.012 0.15 0.062 0.74 12 0.11 
ENSMUSG00000028035 Dnajb4 0.029 0.05 0.72 0.26 12 0.11 
ENSMUSG00000026617 Bpnt1 0.024 0.19 0.27 1 12 0.1 
ENSMUSG00000091002 Tcerg1l 0.031 0.83 0.87 1 12 0.1 
ENSMUSG00000029581 Fscn1 0.014 0.27 0.9 1 12 0.1 
ENSMUSG00000038705 Gmeb2 0.024 0.15 0.1 1 12 0.1 
ENSMUSG00000019039 Dalrd3 0.026 1 0.4 0.68 12 0.1 
App.3. Diurnal Genes in Mouse Cortex:  App. 68 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000029447 Cct6a 0.011 0.6 0.11 1 12 0.099 
ENSMUSG00000051316 Taf7 0.043 0.33 0.87 1 12 0.099 
ENSMUSG00000024999 Noc3l 0.0044 0.17 0.24 1 12 0.098 
ENSMUSG00000020834 Dhrs13 0.015 1 0.48 0.14 12 0.098 
ENSMUSG00000027424 Mgme1 0.0065 0.23 0.3 1 12 0.097 
ENSMUSG00000029334 Prkg2 0.024 0.73 0.23 1 12 0.095 
ENSMUSG00000096979 Gm26880 0.031 0.77 1 1 12 0.095 
ENSMUSG00000032905 Atg12 0.0021 0.11 0.054 1 12 0.094 
ENSMUSG00000020251 Glt8d2 0.019 0.07 0.38 0.97 12 0.093 
ENSMUSG00000075467 Dnlz 0.0044 0.77 0.52 0.86 12 0.092 
ENSMUSG00000030062 Rpn1 0.026 0.29 0.17 1 12 0.091 
ENSMUSG00000042496 Prdm10 0.017 0.25 0.2 1 12 0.091 
ENSMUSG00000031536 Polb 0.017 1 1 0.83 12 0.09 
ENSMUSG00000006289 Osgep 0.022 1 0.67 1 12 0.085 
ENSMUSG00000028698 Pik3r3 0.0044 0.16 0.52 1 12 0.084 
ENSMUSG00000026036 Nif3l1 0.0058 1 0.9 1 12 0.083 
ENSMUSG00000021276 Cinp 0.014 1 0.15 1 12 0.083 
ENSMUSG00000034659 Tmem109 0.022 1 0.15 0.39 12 0.083 
ENSMUSG00000021057 Akap5 0.04 0.2 0.071 1 12 0.082 
ENSMUSG00000032264 Zw10 0.037 0.43 0.058 1 12 0.082 
ENSMUSG00000013076 Amotl1 0.0026 0.55 0.078 0.83 12 0.082 
ENSMUSG00000046691 Chtf8 0.019 0.12 0.11 1 12 0.082 
ENSMUSG00000032867 Fbxw8 0.04 1 0.9 1 12 0.081 
ENSMUSG00000036943 Rab8b 0.0079 0.082 1 0.31 12 0.081 
ENSMUSG00000027642 Rpn2 0.029 1 0.37 0.17 12 0.079 
ENSMUSG00000008398 Elk3 0.047 0.17 0.27 1 12 0.079 
ENSMUSG00000019432 Ddx39b 0.012 0.13 0.4 1 12 0.078 
ENSMUSG00000032118 Fez1 0.0065 1 0.29 1 12 0.078 
ENSMUSG00000042729 Wdr74 0.022 1 0.9 1 12 0.077 
ENSMUSG00000067713 Prkag1 0.012 0.96 0.29 1 12 0.077 
ENSMUSG00000037253 Mex3c 0.043 0.078 1 1 12 0.077 
ENSMUSG00000024668 Sdhaf2 0.037 0.23 0.4 1 12 0.077 
ENSMUSG00000034336 Ina 0.0036 0.053 0.37 0.6 12 0.077 
ENSMUSG00000021810 Ecd 0.026 1 0.9 1 12 0.077 
ENSMUSG00000026202 Tuba4a 0.02 0.11 0.5 1 12 0.077 
ENSMUSG00000027797 Dclk1 0.0058 0.21 0.22 1 12 0.076 
ENSMUSG00000038291 Snx25 0.019 1 0.67 1 12 0.076 
ENSMUSG00000037788 Vopp1 0.0048 0.053 0.058 0.68 12 0.076 
ENSMUSG00000019977 Hbs1l 0.024 1 0.15 0.62 12 0.075 
ENSMUSG00000005823 Gpr108 0.013 1 0.37 1 12 0.074 
ENSMUSG00000034361 Cpne2 0.026 0.15 0.13 1 12 0.074 
ENSMUSG00000031105 Slc25a14 0.04 0.6 0.55 1 12 0.074 
ENSMUSG00000028907 Utp11l 0.029 0.33 1 0.56 12 0.073 
ENSMUSG00000027350 Chgb 0.00047 0.51 0.15 1 12 0.073 
ENSMUSG00000032470 Mras 0.043 0.082 0.17 1 12 0.073 
ENSMUSG00000022964 Tmem50b 0.0058 0.14 0.72 0.74 12 0.073 
ENSMUSG00000029538 Srsf9 0.0095 0.62 0.27 1 12 0.073 
App.3. Diurnal Genes in Mouse Cortex:  App. 69 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000006205 Htra1 0.024 0.1 1 0.76 12 0.072 
ENSMUSG00000037824 Tspan14 0.0095 1 0.27 1 12 0.071 
ENSMUSG00000028883 Sema3a 0.0095 0.54 0.3 1 12 0.071 
ENSMUSG00000018736 Ndel1 0.022 0.45 0.38 1 12 0.071 
ENSMUSG00000044952 Kctd21 0.037 0.11 0.078 1 12 0.069 
ENSMUSG00000085007 Gm11549 0.017 0.43 0.78 1 12 0.068 
ENSMUSG00000022391 Rangap1 0.02 1 0.35 1 12 0.068 
ENSMUSG00000034343 Ube2f 0.04 1 0.37 1 12 0.068 
ENSMUSG00000023932 Cdc5l 0.017 1 0.62 1 12 0.067 
ENSMUSG00000028863 Meaf6 0.037 1 0.35 1 12 0.067 
ENSMUSG00000035776 Cd99l2 0.0013 0.66 0.062 0.66 12 0.067 
ENSMUSG00000030007 Cct7 0.0095 1 0.078 1 12 0.067 
ENSMUSG00000024812 Tjp2 0.0079 1 0.17 1 12 0.066 
ENSMUSG00000058587 Tmod3 0.034 0.22 0.52 0.36 12 0.065 
ENSMUSG00000044117 2900011O08Rik 0.0058 0.43 0.59 1 12 0.065 
ENSMUSG00000013160 Atp6v0d1 0.02 0.71 0.23 1 12 0.065 
ENSMUSG00000001576 Ergic1 0.012 0.25 0.097 1 12 0.064 
ENSMUSG00000013539 Tango2 0.026 1 0.38 1 12 0.063 
ENSMUSG00000039983 Ccdc32 0.047 0.51 1 0.29 12 0.063 
ENSMUSG00000024065 Ehd3 0.04 0.68 0.59 0.68 12 0.062 
ENSMUSG00000015087 Rabl6 0.024 0.2 0.091 1 12 0.062 
ENSMUSG00000020869 Lrrc59 0.0029 0.66 0.15 1 12 0.061 
ENSMUSG00000049792 Bag5 0.043 0.43 0.11 1 12 0.061 
ENSMUSG00000046791 2410016O06Rik 0.0048 0.83 0.16 1 12 0.06 
ENSMUSG00000009575 Cbx5 0.043 0.19 0.72 1 12 0.06 
ENSMUSG00000007815 Rhoa 0.037 1 0.084 0.52 12 0.06 
ENSMUSG00000041438 Cirh1a 0.031 0.51 0.12 1 12 0.06 
ENSMUSG00000029263 Pigg 0.011 0.26 1 1 12 0.059 
ENSMUSG00000041078 Grid1 0.019 0.07 1 1 12 0.058 
ENSMUSG00000028793 Rnf19b 0.00047 0.11 0.29 1 12 0.058 
ENSMUSG00000027680 Fxr1 0.017 0.27 0.78 0.34 12 0.057 
ENSMUSG00000040385 Ppp1ca 0.0087 0.31 0.15 0.36 12 0.057 
ENSMUSG00000017776 Crk 0.047 0.12 0.84 1 12 0.055 
ENSMUSG00000036667 Tcaf1 0.0044 0.31 0.46 0.81 12 0.053 
ENSMUSG00000066900 Suds3 0.026 0.13 0.97 0.5 12 0.052 
ENSMUSG00000021196 Pfkp 0.022 1 0.21 0.56 12 0.051 
ENSMUSG00000026457 Adipor1 0.0095 1 1 1 12 0.049 
ENSMUSG00000004500 Zfp324 0.026 1 0.37 1 12 0.049 
ENSMUSG00000036333 Kidins220 0.019 0.057 0.4 1 12 0.048 
ENSMUSG00000032802 Srxn1 0.04 1 0.5 1 12 0.047 
ENSMUSG00000026104 Stat1 0.024 0.096 0.44 1 12 0.046 
ENSMUSG00000032366 Tpm1 0.04 0.73 0.24 1 12 0.044 
ENSMUSG00000020917 Acly 0.034 0.58 0.23 1 12 0.044 
ENSMUSG00000026851 BC005624 0.043 1 0.46 1 12 0.043 
ENSMUSG00000040446 Rprd1a 0.026 1 0.69 1 12 0.043 
ENSMUSG00000021190 Lgmn 0.0079 1 0.67 0.6 12 0.042 
ENSMUSG00000018398 Sep-08 0.026 0.23 0.3 1 12 0.028 
App.3. Diurnal Genes in Mouse Cortex:  App. 70 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000021476 Habp4 0.013 1 0.84 1 12 0.028 
ENSMUSG00000031410 Nxf7 0.00041 0.15 0.19 0.86 15 0.33 
ENSMUSG00000054488 Gm9946 0.015 1 1 1 15 0.32 
ENSMUSG00000001029 Icam2 0.011 0.33 0.52 0.56 15 0.21 
ENSMUSG00000030865 Chp2 0.043 0.33 1 1 15 0.19 
ENSMUSG00000078773 Rad54b 0.031 1 0.33 1 15 0.18 
ENSMUSG00000032436 Cmtm7 0.024 1 0.23 1 15 0.18 
ENSMUSG00000100801 Gm15459 0.013 0.51 0.062 1 15 0.18 
ENSMUSG00000050666 Vstm4 0.02 0.5 0.21 1 15 0.17 
ENSMUSG00000026946 Nmi 0.047 1 0.93 1 15 0.16 
ENSMUSG00000042439 Zfp532 0.0065 1 0.44 0.66 15 0.16 
ENSMUSG00000024670 Cd6 0.0079 1 1 0.34 15 0.16 
ENSMUSG00000032902 Slc16a1 0.0053 0.33 0.26 0.26 15 0.16 
ENSMUSG00000027954 Efna1 0.047 0.55 0.15 0.5 15 0.16 
ENSMUSG00000049796 Crh 0.0095 0.12 0.4 0.86 15 0.16 
ENSMUSG00000085457 1110046J04Rik 0.026 0.71 0.26 0.86 15 0.15 
ENSMUSG00000022132 Cldn10 0.0053 0.75 0.12 1 15 0.15 
ENSMUSG00000097039 Pvt1 0.013 0.1 1 1 15 0.15 
ENSMUSG00000019577 Pdk4 0.011 1 0.24 1 15 0.14 
ENSMUSG00000051355 Commd1 0.02 1 0.52 1 15 0.14 
ENSMUSG00000083674 Zfp133-ps 0.019 1 1 1 15 0.14 
ENSMUSG00000021696 Elovl7 0.043 0.078 0.81 1 15 0.13 
ENSMUSG00000084946 Dlx1as 0.0044 1 0.067 1 15 0.13 
ENSMUSG00000037286 Stag1 0.034 0.66 1 1 15 0.13 
ENSMUSG00000029649 Pomp 0.019 0.66 0.62 1 15 0.13 
ENSMUSG00000052962 Mrpl35 0.037 1 1 1 15 0.13 
ENSMUSG00000059187 Fam19a1 0.0029 0.93 0.22 1 15 0.13 
ENSMUSG00000023913 Pla2g7 0.014 1 0.42 1 15 0.12 
ENSMUSG00000024889 Rce1 0.013 1 0.52 1 15 0.12 
ENSMUSG00000049489 Fam58b 0.04 0.3 0.67 0.88 15 0.12 
ENSMUSG00000027673 Ndufb5 0.0072 0.85 0.81 1 15 0.12 
ENSMUSG00000074457 S100a16 0.0079 0.13 0.42 1 15 0.12 
ENSMUSG00000032135 Mcam 0.031 0.12 0.27 1 15 0.12 
ENSMUSG00000058318 Phf21a 0.022 0.6 1 1 15 0.12 
ENSMUSG00000002233 Rhoc 0.043 0.086 1 1 15 0.11 
ENSMUSG00000025920 Stau2 0.0036 0.05 0.59 1 15 0.11 
ENSMUSG00000050069 Grem2 0.043 0.15 0.81 1 15 0.11 
ENSMUSG00000037514 Pank2 0.0029 1 1 1 15 0.11 
ENSMUSG00000024844 Banf1 0.037 0.8 0.64 1 15 0.11 
ENSMUSG00000001891 Ugp2 0.0065 1 0.17 1 15 0.11 
ENSMUSG00000054226 Tprkb 0.0087 1 0.11 1 15 0.1 
ENSMUSG00000042487 Leo1 0.013 0.078 0.38 0.44 15 0.1 
ENSMUSG00000028383 Hsdl2 0.0048 0.057 0.3 0.97 15 0.1 
ENSMUSG00000032667 Pon2 0.034 0.39 0.62 0.95 15 0.1 
ENSMUSG00000062797 l7Rn6 0.043 1 1 1 15 0.1 
ENSMUSG00000020717 Pecam1 0.0079 0.26 0.21 0.095 15 0.1 
ENSMUSG00000075486 Commd6 0.019 1 0.81 0.62 15 0.1 
App.3. Diurnal Genes in Mouse Cortex:  App. 71 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000063434 Sorcs3 0.0036 0.21 0.24 0.78 15 0.1 
ENSMUSG00000030638 Sh3gl3 0.022 1 0.62 1 15 0.1 
ENSMUSG00000075701 Vimp 0.024 0.96 0.15 1 15 0.1 
ENSMUSG00000024387 Csnk2b 0.0072 1 0.14 1 15 0.099 
ENSMUSG00000052428 Tmco1 0.037 1 0.16 1 15 0.098 
ENSMUSG00000020415 Pttg1 0.026 0.07 1 1 15 0.098 
ENSMUSG00000047044 D030056L22Rik 0.02 0.14 1 1 15 0.098 
ENSMUSG00000029534 St7 0.0079 1 0.22 0.78 15 0.097 
ENSMUSG00000038402 Foxf2 0.017 0.082 0.15 1 15 0.094 
ENSMUSG00000040128 Pnrc1 0.0079 0.37 0.69 0.62 15 0.092 
ENSMUSG00000022969 Il10rb 0.0058 0.21 0.13 0.64 15 0.092 
ENSMUSG00000025260 Hsd17b10 0.034 0.53 0.2 1 15 0.09 
ENSMUSG00000033216 Eefsec 0.012 1 0.5 1 15 0.09 
ENSMUSG00000041911 Dlx1 0.0048 0.057 0.22 0.71 15 0.09 
ENSMUSG00000096916 Zfp850 0.026 0.23 1 1 15 0.089 
ENSMUSG00000063480 Nhp2l1 0.013 0.62 0.11 1 15 0.088 
ENSMUSG00000027195 Hsd17b12 0.014 0.32 0.16 1 15 0.085 
ENSMUSG00000002015 Bcap31 0.026 1 0.93 0.26 15 0.084 
ENSMUSG00000032507 Fbxl2 0.034 0.55 1 1 15 0.083 
ENSMUSG00000035199 Arl6ip5 0.0053 0.39 1 1 15 0.083 
ENSMUSG00000039682 Lap3 0.037 0.73 0.17 1 15 0.082 
ENSMUSG00000021891 Mettl6 0.022 1 1 0.95 15 0.081 
ENSMUSG00000027384 Ndufaf5 0.037 1 0.46 0.83 15 0.081 
ENSMUSG00000027259 Adal 0.029 0.4 0.14 1 15 0.081 
ENSMUSG00000028822 Tmem50a 0.043 0.55 1 1 15 0.08 
ENSMUSG00000039717 Ralyl 0.015 0.23 0.93 1 15 0.08 
ENSMUSG00000053768 Chchd3 0.02 0.4 0.32 1 15 0.079 
ENSMUSG00000031939 Taf1d 0.024 1 1 1 15 0.077 
ENSMUSG00000028618 Tmem59 0.00078 1 0.26 1 15 0.077 
ENSMUSG00000032199 Polr2m 0.022 1 0.29 1 15 0.076 
ENSMUSG00000051537 Gm5124 0.029 0.85 1 1 15 0.076 
ENSMUSG00000043463 Rab9b 0.04 1 0.59 1 15 0.076 
ENSMUSG00000019810 Fuca2 0.02 1 0.23 1 15 0.075 
ENSMUSG00000022257 Laptm4b 0.0072 1 0.5 1 15 0.074 
ENSMUSG00000019897 Ccdc59 0.029 0.48 0.81 1 15 0.074 
ENSMUSG00000009030 Pdcl 0.037 1 0.84 0.68 15 0.073 
ENSMUSG00000100455 Gm29170 0.037 0.96 0.22 1 15 0.072 
ENSMUSG00000031950 Gabarapl2 0.043 1 0.46 1 15 0.072 
ENSMUSG00000025353 Ormdl2 0.0036 0.29 0.64 1 15 0.071 
ENSMUSG00000017176 Nt5c3b 0.0029 0.17 0.32 1 15 0.07 
ENSMUSG00000028403 Zdhhc21 0.04 1 0.84 1 15 0.07 
ENSMUSG00000032181 Scg3 0.0072 1 0.15 1 15 0.069 
ENSMUSG00000040990 Sh3kbp1 0.02 0.88 0.67 1 15 0.067 
ENSMUSG00000051444 Bbs12 0.04 1 0.81 0.83 15 0.066 
ENSMUSG00000022108 Itm2b 0.015 1 1 1 15 0.066 
ENSMUSG00000055239 Kcmf1 0.011 0.26 0.75 1 15 0.066 
ENSMUSG00000003948 Mmd 0.047 0.17 0.19 1 15 0.064 
App.3. Diurnal Genes in Mouse Cortex:  App. 72 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000058589 Anks1b 0.0013 0.11 0.55 1 15 0.064 
ENSMUSG00000032434 Cmtm6 0.034 1 0.1 1 15 0.062 
ENSMUSG00000008301 Phax 0.047 0.42 0.64 1 15 0.062 
ENSMUSG00000001847 Rac1 0.029 0.71 0.46 1 15 0.059 
ENSMUSG00000028567 Txndc12 0.0095 1 0.097 1 15 0.057 
ENSMUSG00000021219 Rgs6 0.043 1 1 1 15 0.057 
ENSMUSG00000030591 Psmd8 0.047 0.93 0.17 1 15 0.057 
ENSMUSG00000031901 Dus2 0.029 0.68 1 0.54 15 0.055 
ENSMUSG00000021792 Fam213a 0.0087 1 1 0.68 15 0.054 
ENSMUSG00000029405 G3bp2 0.019 0.77 0.38 1 15 0.054 
ENSMUSG00000022403 St13 0.04 1 0.11 1 15 0.054 
ENSMUSG00000074748 Atxn7l3b 0.014 0.14 0.29 1 15 0.053 
ENSMUSG00000026755 Arpc5l 0.014 0.98 0.12 0.5 15 0.053 
ENSMUSG00000032570 Atp2c1 0.037 0.053 0.46 1 15 0.053 
ENSMUSG00000031578 Mak16 0.026 1 0.52 1 15 0.052 
ENSMUSG00000047514 Tspyl1 0.015 1 0.9 1 15 0.051 
ENSMUSG00000013698 Pea15a 0.034 0.14 0.058 1 15 0.049 
ENSMUSG00000020166 Cnot2 0.047 0.29 0.4 0.91 15 0.048 
ENSMUSG00000019843 Fyn 0.011 0.71 0.4 1 15 0.037 
ENSMUSG00000020358 Hnrnpab 0.034 0.51 0.13 0.68 15 0.035 
ENSMUSG00000036275 9530068E07Rik 0.026 1 0.59 1 15 0.026 
ENSMUSG00000055795 Gm5160 0.031 1 1 1 18 0.91 
ENSMUSG00000081043 Gm11512 0.015 0.24 1 1 18 0.76 
ENSMUSG00000080921 Rpl38-ps2 0.046 1 1 1 18 0.74 
ENSMUSG00000065254 Gm23973 0.017 1 1 1 18 0.67 
ENSMUSG00000044751 Gm12231 0.013 0.82 1 1 18 0.63 
ENSMUSG00000098111 Gm4654 0.0058 0.46 1 1 18 0.61 
ENSMUSG00000082908 Gm13736 0.0013 1 1 1 18 0.58 
ENSMUSG00000082120 Gm15720 0.046 1 1 1 18 0.58 
ENSMUSG00000101445 Gm28932 0.0095 1 1 1 18 0.57 
ENSMUSG00000064694 Gm24146 0.047 1 1 1 18 0.57 
ENSMUSG00000067189 Gm7335 0.011 1 1 1 18 0.53 
ENSMUSG00000081603 Gm14681 0.012 1 1 1 18 0.51 
ENSMUSG00000044211 Gm7887 0.019 1 1 1 18 0.5 
ENSMUSG00000083563 Gm13340 0.0036 1 1 1 18 0.48 
ENSMUSG00000081824 BC002163 0.04 1 1 1 18 0.48 
ENSMUSG00000093183 Gm25687 0.036 1 1 1 18 0.48 
ENSMUSG00000084835 Gm12352 0.034 1 0.57 1 18 0.47 
ENSMUSG00000083621 Gm14586 0.0029 1 1 1 18 0.47 
ENSMUSG00000082536 Gm13456 0.0044 0.98 1 1 18 0.46 
ENSMUSG00000089782 Gm3531 0.047 0.32 1 1 18 0.46 
ENSMUSG00000094437 Gm9830 0.026 0.37 1 1 18 0.46 
ENSMUSG00000061684 Rpl21-ps8 0.026 1 1 1 18 0.44 
ENSMUSG00000092072 Gm4540 0.031 1 0.32 1 18 0.43 
ENSMUSG00000064281 Rpl19-ps1 0.015 0.85 1 1 18 0.41 
ENSMUSG00000081400 Gm13680 0.013 0.37 1 1 18 0.41 
ENSMUSG00000066553 Gm6969 0.004 0.5 0.83 1 18 0.41 
App.3. Diurnal Genes in Mouse Cortex:  App. 73 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000050621 Rps27rt 0.022 1 1 0.78 18 0.4 
ENSMUSG00000060787 Olfr464 0.0011 0.13 1 1 18 0.4 
ENSMUSG00000044424 Gm9493 0.00052 0.25 1 1 18 0.4 
ENSMUSG00000092599 1700010K23Rik 0.029 1 1 1 18 0.39 
ENSMUSG00000070343 Gm10288 0.0065 1 1 1 18 0.39 
ENSMUSG00000046721 Rpl14-ps1 0.024 1 1 1 18 0.38 
ENSMUSG00000080848 Gm9385 0.0058 1 0.72 1 18 0.38 
ENSMUSG00000046440 Gm5564 0.013 1 1 1 18 0.37 
ENSMUSG00000083097 Gm14494 0.037 1 1 1 18 0.37 
ENSMUSG00000063902 Gm7964 0.0065 1 1 1 18 0.37 
ENSMUSG00000078193 Gm2000 0.0021 1 1 1 18 0.37 
ENSMUSG00000093006 Gm24157 0.031 1 1 1 18 0.37 
ENSMUSG00000000901 Mmp11 0.02 1 1 1 18 0.35 
ENSMUSG00000055093 Gm8430 0.022 1 1 0.76 18 0.35 
ENSMUSG00000060419 Rps16-ps2 0.0058 1 1 0.76 18 0.35 
ENSMUSG00000083380 Gm3244 0.0087 0.53 1 1 18 0.35 
ENSMUSG00000037096 Gm9762 0.031 1 1 1 18 0.34 
ENSMUSG00000083899 Gm12346 0.0044 1 1 1 18 0.34 
ENSMUSG00000079225 Gm9531 0.013 1 1 1 18 0.34 
ENSMUSG00000095597 Gm6472 0.0018 1 1 1 18 0.33 
ENSMUSG00000040078 Gm9769 0.02 1 1 1 18 0.33 
ENSMUSG00000063656 Gm10135 0.024 1 0.96 1 18 0.32 
ENSMUSG00000046341 Gm11223 0.034 0.93 1 1 18 0.32 
ENSMUSG00000083679 Gm12892 0.019 1 1 1 18 0.32 
ENSMUSG00000071035 Gm5499 0.047 1 1 1 18 0.32 
ENSMUSG00000082896 Gm5844 0.0079 0.56 1 1 18 0.31 
ENSMUSG00000084235 Gm15421 0.0095 1 1 1 18 0.31 
ENSMUSG00000079139 Gm4204 0.004 0.17 1 1 18 0.31 
ENSMUSG00000039617 Gm7488 0.00078 1 1 1 18 0.3 
ENSMUSG00000084319 Tpt1-ps3 0.0044 0.71 1 1 18 0.3 
ENSMUSG00000048949 Gm6206 0.013 1 1 1 18 0.3 
ENSMUSG00000043889 Gm8399 0.043 1 1 1 18 0.29 
ENSMUSG00000081752 Gm14680 0.017 0.17 1 1 18 0.29 
ENSMUSG00000101523 Gm10031 0.0058 0.75 1 1 18 0.29 
ENSMUSG00000085262 Gm11574 0.04 1 1 1 18 0.29 
ENSMUSG00000075053 Vdac3-ps1 0.0048 0.58 1 1 18 0.29 
ENSMUSG00000100153 Gm5601 0.022 0.48 0.69 1 18 0.28 
ENSMUSG00000062611 Rps3a2 0.026 0.53 1 0.81 18 0.27 
ENSMUSG00000091421 Gm4202 0.0058 0.46 1 1 18 0.26 
ENSMUSG00000082762 Gm12366 0.0058 0.46 1 1 18 0.25 
ENSMUSG00000086922 Gm13835 0.0072 0.5 1 1 18 0.25 
ENSMUSG00000100618 Gm29595 0.004 1 0.19 1 18 0.24 
ENSMUSG00000085783 Gm9816 0.043 1 1 1 18 0.24 
ENSMUSG00000081272 Gm13509 0.0087 1 1 1 18 0.23 
ENSMUSG00000063684 Gm13910 0.047 0.98 1 1 18 0.23 
ENSMUSG00000073236 2500004C02Rik 5.20E-05 1 1 1 18 0.22 
ENSMUSG00000040296 Ddx58 0.047 1 1 1 18 0.21 
App.3. Diurnal Genes in Mouse Cortex:  App. 74 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000085881 Gm15912 0.047 1 1 0.37 18 0.21 
ENSMUSG00000043483 Gm6863 0.0058 1 1 1 18 0.2 
ENSMUSG00000101188 Eif4a-ps4 0.022 0.32 1 1 18 0.2 
ENSMUSG00000084416 Rpl10a-ps1 0.04 1 1 1 18 0.2 
ENSMUSG00000032413 Rasa2 0.0079 1 0.48 1 18 0.2 
ENSMUSG00000079297 Gm2223 0.0087 1 1 1 18 0.19 
ENSMUSG00000083327 Vcp-rs 0.0036 1 1 1 18 0.19 
ENSMUSG00000030876 Mettl9 0.004 1 0.57 0.42 18 0.18 
ENSMUSG00000078919 Dpm1 0.0072 1 1 1 18 0.18 
ENSMUSG00000031170 Slc38a5 0.029 0.4 0.64 0.3 18 0.18 
ENSMUSG00000034532 Fbxo16 0.00052 0.21 0.81 1 18 0.18 
ENSMUSG00000073164 2410018L13Rik 0.047 0.29 1 1 18 0.18 
ENSMUSG00000097148 Gm3839 0.04 1 0.64 1 18 0.17 
ENSMUSG00000013584 Aldh1a2 0.012 1 1 0.97 18 0.17 
ENSMUSG00000081051 Gm15427 0.0029 1 1 1 18 0.17 
ENSMUSG00000097745 AI115009 0.012 0.25 0.72 1 18 0.16 
ENSMUSG00000030498 Gas2 0.02 1 1 1 18 0.16 
ENSMUSG00000042793 Lgr6 0.014 1 1 0.33 18 0.16 
ENSMUSG00000089957 A830011K09Rik 0.026 1 1 1 18 0.16 
ENSMUSG00000026384 Ptpn4 0.0053 0.6 1 1 18 0.16 
ENSMUSG00000049539 Hist1h1a 0.031 1 1 1 18 0.15 
ENSMUSG00000032942 Ucp3 0.014 0.58 0.4 0.76 18 0.15 
ENSMUSG00000015568 Lpl 0.022 0.33 0.078 0.42 18 0.15 
ENSMUSG00000006931 P3h4 0.0095 0.12 0.84 0.88 18 0.15 
ENSMUSG00000027746 Ufm1 0.047 1 1 1 18 0.14 
ENSMUSG00000050545 Fam228b 0.024 1 0.5 1 18 0.14 
ENSMUSG00000005886 Ncoa2 0.011 1 1 1 18 0.14 
ENSMUSG00000097649 Gm10561 0.015 0.14 0.46 1 18 0.14 
ENSMUSG00000052544 St6galnac3 0.029 1 0.27 0.36 18 0.14 
ENSMUSG00000022329 Stk3 0.012 0.39 1 1 18 0.13 
ENSMUSG00000044350 Lacc1 0.0065 1 0.062 1 18 0.13 
ENSMUSG00000097515 1700040D17Rik 0.047 1 1 1 18 0.13 
ENSMUSG00000031029 Eif3f 0.043 0.2 0.81 1 18 0.13 
ENSMUSG00000033423 Eri3 0.043 0.73 0.84 1 18 0.13 
ENSMUSG00000022677 Fopnl 0.0021 0.36 0.38 1 18 0.12 
ENSMUSG00000059182 Skap2 0.015 1 0.071 0.58 18 0.12 
ENSMUSG00000031639 Tlr3 0.0065 0.063 0.058 0.83 18 0.12 
ENSMUSG00000048000 Gigyf2 0.0065 0.58 1 0.78 18 0.12 
ENSMUSG00000066233 Tmem42 0.0021 0.091 1 1 18 0.12 
ENSMUSG00000036086 Zranb3 0.034 0.75 1 1 18 0.12 
ENSMUSG00000057406 Whsc1 0.0029 0.98 1 1 18 0.12 
ENSMUSG00000074922 Fam122a 0.013 0.85 1 1 18 0.12 
ENSMUSG00000052712 BC004004 0.00041 0.063 0.59 0.66 18 0.11 
ENSMUSG00000024425 Ndfip1 0.02 0.46 1 1 18 0.11 
ENSMUSG00000037148 Arhgap10 0.0029 1 1 1 18 0.11 
ENSMUSG00000026471 Mr1 0.034 1 0.44 1 18 0.11 
ENSMUSG00000041468 Gpr12 0.04 0.71 1 0.39 18 0.11 
App.3. Diurnal Genes in Mouse Cortex:  App. 75 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000050587 Lrrc4c 0.00047 0.4 1 1 18 0.11 
ENSMUSG00000015289 Lage3 0.0021 0.46 0.2 1 18 0.11 
ENSMUSG00000038240 Pdss2 0.0048 1 1 0.64 18 0.11 
ENSMUSG00000092486 2610524H06Rik 0.00013 1 0.24 0.91 18 0.11 
ENSMUSG00000045160 Bola3 0.043 0.75 1 1 18 0.11 
ENSMUSG00000024099 Ndufv2 0.015 1 0.48 1 18 0.11 
ENSMUSG00000068115 Ninl 0.024 0.42 1 1 18 0.1 
ENSMUSG00000055633 Zfp580 0.0087 0.19 1 1 18 0.1 
ENSMUSG00000007987 Ift22 0.031 0.51 1 0.88 18 0.1 
ENSMUSG00000021767 Kat6b 0.013 1 1 1 18 0.099 
ENSMUSG00000091890 A830073O21Rik 0.014 0.66 0.64 0.48 18 0.099 
ENSMUSG00000024592 C330018D20Rik 0.0021 0.18 0.3 1 18 0.099 
ENSMUSG00000037072 Sep-15 0.024 0.9 0.78 1 18 0.098 
ENSMUSG00000020831 0610010K14Rik 0.047 1 0.9 1 18 0.097 
ENSMUSG00000030525 Chrna7 0.043 1 1 1 18 0.095 
ENSMUSG00000052726 Kcnt2 0.019 1 1 0.52 18 0.095 
ENSMUSG00000099681 1700052K11Rik 0.011 0.66 0.44 1 18 0.093 
ENSMUSG00000041769 Ppp2r2d 0.00078 1 0.52 1 18 0.092 
ENSMUSG00000018446 C1qbp 0.0072 0.24 0.35 1 18 0.091 
ENSMUSG00000019872 Smpdl3a 0.037 0.086 0.2 1 18 0.091 
ENSMUSG00000033102 Cdc14b 0.011 1 1 1 18 0.09 
ENSMUSG00000035239 Neu3 0.043 0.98 0.19 1 18 0.089 
ENSMUSG00000029474 Rnf34 0.024 1 1 0.58 18 0.086 
ENSMUSG00000039831 Arhgap29 0.017 1 1 1 18 0.085 
ENSMUSG00000046688 Tifa 0.029 0.46 1 1 18 0.085 
ENSMUSG00000055313 Pgbd1 0.034 1 0.75 0.97 18 0.085 
ENSMUSG00000027030 Stk39 0.02 1 1 1 18 0.085 
ENSMUSG00000032067 Pts 0.047 1 0.3 0.81 18 0.084 
ENSMUSG00000001289 Pfdn5 0.037 0.73 0.97 1 18 0.083 
ENSMUSG00000007613 Tgfbr1 0.029 0.55 0.078 0.81 18 0.083 
ENSMUSG00000020457 Drg1 0.014 1 1 1 18 0.081 
ENSMUSG00000025198 Erlin1 0.0023 1 1 0.88 18 0.081 
ENSMUSG00000019699 Akt3 0.0058 0.6 1 1 18 0.081 
ENSMUSG00000042705 Commd10 0.024 0.29 0.48 1 18 0.081 
ENSMUSG00000005687 Bcas2 0.0079 1 1 0.48 18 0.08 
ENSMUSG00000022248 Rad1 0.043 1 1 1 18 0.08 
ENSMUSG00000020903 Stx8 0.034 1 1 1 18 0.079 
ENSMUSG00000025040 Fundc1 0.031 1 1 1 18 0.078 
ENSMUSG00000048058 Ldlrad3 0.024 0.55 0.97 1 18 0.077 
ENSMUSG00000026527 Rgs7 0.0044 1 1 1 18 0.077 
ENSMUSG00000021610 Clptm1l 0.0053 0.14 0.067 1 18 0.077 
ENSMUSG00000031997 Trpc6 0.04 0.8 1 1 18 0.076 
ENSMUSG00000021752 Kctd6 0.029 1 0.22 1 18 0.076 
ENSMUSG00000002058 Unc119 0.04 0.58 1 1 18 0.075 
ENSMUSG00000064037 Gpn1 0.043 1 1 1 18 0.075 
ENSMUSG00000058240 Cryzl1 7.00E-04 1 0.67 1 18 0.073 
ENSMUSG00000029518 Rab35 0.022 0.18 0.59 1 18 0.073 
App.3. Diurnal Genes in Mouse Cortex:  App. 76 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000041417 Pik3r1 0.014 1 0.3 0.76 18 0.073 
ENSMUSG00000018042 Cyb5r3 0.031 0.3 1 1 18 0.073 
ENSMUSG00000048249 Crebrf 0.0065 1 1 0.83 18 0.072 
ENSMUSG00000038286 Bphl 0.034 0.9 0.72 1 18 0.072 
ENSMUSG00000068882 Ssb 0.026 0.6 1 1 18 0.072 
ENSMUSG00000026709 Dars2 0.0079 0.5 1 1 18 0.071 
ENSMUSG00000015668 Pdzd11 0.022 1 1 1 18 0.07 
ENSMUSG00000085151 1110018N20Rik 0.019 1 0.4 1 18 0.069 
ENSMUSG00000039530 Tusc3 0.047 0.26 0.93 1 18 0.068 
ENSMUSG00000022635 Zcrb1 0.04 0.93 0.23 1 18 0.068 
ENSMUSG00000051154 Commd3 0.024 0.73 1 1 18 0.068 
ENSMUSG00000027011 Ube2e3 0.0014 1 0.57 1 18 0.065 
ENSMUSG00000029146 Snx17 0.0048 0.14 0.87 1 18 0.065 
ENSMUSG00000022297 Fzd6 0.0095 1 0.42 1 18 0.062 
ENSMUSG00000044600 Smim7 0.031 0.68 0.44 1 18 0.062 
ENSMUSG00000070520 Ndnl2 0.00015 0.096 0.19 0.21 18 0.062 
ENSMUSG00000036766 Dner 0.043 0.32 0.48 1 18 0.062 
ENSMUSG00000021665 Hexb 0.0065 0.6 1 1 18 0.061 
ENSMUSG00000036371 Serbp1 0.022 1 0.59 1 18 0.061 
ENSMUSG00000017286 Glod4 0.013 1 0.55 1 18 0.059 
ENSMUSG00000025451 Paip1 0.034 1 0.69 1 18 0.058 
ENSMUSG00000001127 Araf 0.043 0.39 0.57 1 18 0.055 
ENSMUSG00000021432 Slc35b3 0.017 1 1 1 18 0.054 
ENSMUSG00000048644 Ctxn1 0.0079 0.9 1 1 18 0.051 
ENSMUSG00000054405 Dnajc8 0.015 1 1 1 18 0.047 
ENSMUSG00000040151 Hs2st1 0.0053 1 1 1 18 0.046 
ENSMUSG00000099881 2810013P06Rik 0.037 0.96 0.9 1 18 0.046 
ENSMUSG00000032563 Mrpl3 0.04 0.074 1 0.6 18 0.043 
ENSMUSG00000032046 Abhd12 0.043 1 1 1 18 0.042 
ENSMUSG00000062461 Gm5453 0.013 1 1 1 21 0.78 
ENSMUSG00000082454 Gm12183 0.047 0.5 1 1 21 0.62 
ENSMUSG00000093056 Gm24812 0.026 1 1 1 21 0.59 
ENSMUSG00000093497 Gm20713 0.029 0.9 1 1 21 0.52 
ENSMUSG00000083863 Gm13341 0.029 1 1 1 21 0.48 
ENSMUSG00000097445 Gm26631 0.014 1 1 1 21 0.48 
ENSMUSG00000083992 Gm11478 0.013 1 1 1 21 0.43 
ENSMUSG00000087115 Pcsk2os2 0.043 1 1 1 21 0.38 
ENSMUSG00000097527 1700112J16Rik 0.014 0.25 0.97 1 21 0.34 
ENSMUSG00000055134 9130017K11Rik 0.00035 0.23 0.21 1 21 0.34 
ENSMUSG00000085612 Gm15868 0.04 0.83 1 1 21 0.34 
ENSMUSG00000087114 Gm16099 0.029 1 1 1 21 0.33 
ENSMUSG00000087700 Gm15283 0.031 0.096 1 1 21 0.32 
ENSMUSG00000032400 Zwilch 0.026 1 0.62 1 21 0.32 
ENSMUSG00000085631 9630028H03Rik 0.037 1 1 1 21 0.32 
ENSMUSG00000032565 Nudt16 0.019 1 0.15 0.26 21 0.31 
ENSMUSG00000086728 Man2c1os 0.0087 0.56 0.2 0.66 21 0.31 
ENSMUSG00000086022 Rad51ap2 5.20E-05 0.12 0.13 1 21 0.3 
App.3. Diurnal Genes in Mouse Cortex:  App. 77 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000062588 Gm6104 0.012 1 0.33 1 21 0.29 
ENSMUSG00000085517 Gm12963 0.047 1 1 1 21 0.28 
ENSMUSG00000002007 Srpk3 0.0072 0.14 0.16 1 21 0.28 
ENSMUSG00000085738 Gm12335 0.028 0.9 1 1 21 0.28 
ENSMUSG00000051224 Tceanc 0.004 0.11 0.091 1 21 0.28 
ENSMUSG00000024352 Spata24 0.0026 0.36 0.15 1 21 0.28 
ENSMUSG00000047773 Ankfn1 0.0095 0.43 1 1 21 0.28 
ENSMUSG00000097574 C920006O11Rik 0.00061 1 0.27 1 21 0.27 
ENSMUSG00000025038 Efhc2 0.0065 0.17 0.3 1 21 0.27 
ENSMUSG00000039496 Cdnf 0.034 1 0.19 1 21 0.27 
ENSMUSG00000097123 Gm6297 0.0018 0.34 0.33 0.74 21 0.26 
ENSMUSG00000079330 Lemd1 0.00017 0.26 0.062 1 21 0.25 
ENSMUSG00000038403 Hfe2 0.004 0.77 0.67 1 21 0.25 
ENSMUSG00000044364 Tmem74b 0.0044 0.05 0.26 1 21 0.24 
ENSMUSG00000030757 Zkscan2 0.012 0.13 0.15 0.71 21 0.24 
ENSMUSG00000002059 Rab34 0.0058 0.13 0.071 0.6 21 0.23 
ENSMUSG00000009566 Fpgs 0.0095 1 0.87 1 21 0.23 
ENSMUSG00000032558 Nphp3 0.0013 1 0.32 0.33 21 0.23 
ENSMUSG00000097350 4732491K20Rik 0.0053 1 0.14 0.26 21 0.23 
ENSMUSG00000085436 Zfp335os 0.0095 1 0.72 0.68 21 0.23 
ENSMUSG00000020330 Hmmr 0.0053 1 0.52 1 21 0.23 
ENSMUSG00000074771 Ankef1 0.0021 1 0.48 1 21 0.22 
ENSMUSG00000084898 Gm12371 0.00092 0.14 0.44 0.95 21 0.22 
ENSMUSG00000097166 9330179D12Rik 0.00011 0.17 0.33 1 21 0.22 
ENSMUSG00000027550 Lrrcc1 0.00035 0.31 0.16 1 21 0.22 
ENSMUSG00000084985 Gm16135 0.043 1 1 1 21 0.22 
ENSMUSG00000034842 Art3 0.0018 0.26 0.69 0.64 21 0.22 
ENSMUSG00000086487 Gm11638 0.017 1 0.2 0.3 21 0.21 
ENSMUSG00000097838 C530050E15Rik 0.017 0.6 0.13 0.29 21 0.21 
ENSMUSG00000085408 C530005A16Rik 0.014 1 0.24 1 21 0.21 
ENSMUSG00000027955 Fam198b 0.0044 0.62 0.55 1 21 0.2 
ENSMUSG00000032062 2310030G06Rik 0.011 1 0.26 1 21 0.2 
ENSMUSG00000029513 Prkab1 0.014 0.23 0.12 1 21 0.2 
ENSMUSG00000028654 Mycl 0.043 0.05 0.23 1 21 0.2 
ENSMUSG00000035125 Gcfc2 0.0053 0.93 0.097 1 21 0.2 
ENSMUSG00000034035 Ccdc17 0.004 1 1 0.5 21 0.2 
ENSMUSG00000036196 Slc26a8 0.0029 0.13 0.11 1 21 0.19 
ENSMUSG00000020912 Krt12 0.00023 0.078 0.33 1 21 0.19 
ENSMUSG00000042616 Oscp1 0.029 0.83 0.22 1 21 0.19 
ENSMUSG00000069835 Sat2 0.00015 0.19 0.48 0.39 21 0.18 
ENSMUSG00000079450 Cldn34c1 0.0087 0.13 0.091 1 21 0.18 
ENSMUSG00000045322 Tlr9 0.031 0.13 0.16 0.26 21 0.18 
ENSMUSG00000042834 Nrep 0.0018 0.21 0.87 1 21 0.18 
ENSMUSG00000084803 5830444B04Rik 0.011 0.3 1 1 21 0.17 
ENSMUSG00000070632 Btbd8 0.0026 0.12 0.21 0.71 21 0.17 
ENSMUSG00000032498 Mlh1 0.037 0.46 0.26 1 21 0.17 
ENSMUSG00000052331 Ankrd44 0.017 0.25 0.59 1 21 0.17 
App.3. Diurnal Genes in Mouse Cortex:  App. 78 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000097787 2700046G09Rik 0.0036 0.34 0.3 0.66 21 0.17 
ENSMUSG00000021319 Sfrp4 0.02 1 0.17 0.46 21 0.17 
ENSMUSG00000094595 Fsbp 0.0079 0.64 0.17 1 21 0.17 
ENSMUSG00000032298 Neil1 0.043 0.063 0.29 1 21 0.17 
ENSMUSG00000022375 Lrrc6 0.0023 0.37 0.52 0.41 21 0.17 
ENSMUSG00000026383 Epb41l5 0.0044 0.51 0.16 1 21 0.17 
ENSMUSG00000074863 Platr25 0.012 1 1 1 21 0.17 
ENSMUSG00000073427 Gm4924 0.013 1 0.24 1 21 0.16 
ENSMUSG00000020374 Rasgef1c 0.011 0.23 0.44 1 21 0.16 
ENSMUSG00000072969 Armcx5 2.60E-05 0.96 0.11 0.71 21 0.16 
ENSMUSG00000097456 Gm16958 0.031 0.27 0.38 1 21 0.16 
ENSMUSG00000035032 Nek11 0.047 0.5 0.15 1 21 0.16 
ENSMUSG00000090667 Gm765 0.043 1 0.23 0.91 21 0.16 
ENSMUSG00000055612 Cdca7 0.043 1 0.59 1 21 0.16 
ENSMUSG00000093553 Gm20633 0.001 0.11 1 1 21 0.16 
ENSMUSG00000046287 Pnma3 0.011 0.11 0.72 0.2 21 0.16 
ENSMUSG00000085829 Gm4285 0.0058 1 0.11 1 21 0.15 
ENSMUSG00000028007 Snx7 0.00019 0.19 1 0.86 21 0.15 
ENSMUSG00000099473 Gm18775 0.04 1 1 1 21 0.15 
ENSMUSG00000068117 Mei1 0.013 0.5 0.091 1 21 0.15 
ENSMUSG00000029333 Rasgef1b 0.0029 0.074 0.22 1 21 0.15 
ENSMUSG00000097571 Jpx 0.0065 0.43 0.97 1 21 0.15 
ENSMUSG00000039913 Pak7 0.00052 0.27 0.81 1 21 0.15 
ENSMUSG00000045930 Clec14a 0.0072 1 1 0.88 21 0.15 
ENSMUSG00000099966 2810402E24Rik 0.022 1 0.17 1 21 0.15 
ENSMUSG00000063446 Plppr1 0.013 0.2 0.67 1 21 0.14 
ENSMUSG00000072809 9330160F10Rik 0.0023 0.19 0.87 1 21 0.14 
ENSMUSG00000093606 B130034C11Rik 0.0016 0.074 1 1 21 0.14 
ENSMUSG00000029798 Herc6 0.00013 1 0.37 1 21 0.14 
ENSMUSG00000063626 Unc5d 0.029 0.23 0.44 1 21 0.14 
ENSMUSG00000038147 Cd84 0.034 0.71 1 1 21 0.14 
ENSMUSG00000097062 Gm17586 0.0087 0.11 0.19 1 21 0.14 
ENSMUSG00000042155 Klhl23 0.0023 0.6 0.16 1 21 0.14 
ENSMUSG00000032718 Mansc1 7.00E-04 0.27 0.054 1 21 0.14 
ENSMUSG00000037808 Fam76b 0.015 0.43 0.11 0.91 21 0.14 
ENSMUSG00000004446 Bid 0.029 0.62 0.3 0.58 21 0.14 
ENSMUSG00000032122 Slc37a2 0.0087 0.75 1 1 21 0.13 
ENSMUSG00000047989 Ino80c 0.00019 0.51 0.23 1 21 0.13 
ENSMUSG00000073073 Gm8098 0.012 0.29 0.2 0.56 21 0.13 
ENSMUSG00000079109 Pms2 0.024 0.93 0.12 1 21 0.13 
ENSMUSG00000078307 AI593442 0.02 0.091 0.054 0.68 21 0.13 
ENSMUSG00000056459 Zbtb25 0.0058 1 0.058 1 21 0.13 
ENSMUSG00000099760 Gm28800 0.031 1 0.22 1 21 0.13 
ENSMUSG00000013622 Atraid 0.00035 0.12 0.16 1 21 0.13 
ENSMUSG00000036916 Zfp280c 0.0095 1 0.59 1 21 0.13 
ENSMUSG00000031548 Sfrp1 0.0087 1 0.24 1 21 0.13 
ENSMUSG00000040016 Ptger3 0.019 1 0.15 1 21 0.13 
App.3. Diurnal Genes in Mouse Cortex:  App. 79 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000040370 Lyrm5 0.0011 1 0.12 1 21 0.12 
ENSMUSG00000026189 Pecr 0.022 0.06 0.071 1 21 0.12 
ENSMUSG00000074227 Spint2 0.0053 1 1 0.29 21 0.12 
ENSMUSG00000032415 Ube2cbp 0.015 1 1 1 21 0.12 
ENSMUSG00000030802 Bckdk 0.00047 0.39 0.78 1 21 0.12 
ENSMUSG00000026896 Ifih1 0.014 1 0.72 1 21 0.12 
ENSMUSG00000000392 Fap 0.024 0.19 0.27 0.78 21 0.12 
ENSMUSG00000096995 2810029C07Rik 0.037 1 0.071 0.91 21 0.12 
ENSMUSG00000025888 Casp1 0.017 1 1 1 21 0.12 
ENSMUSG00000022360 Atad2 0.0032 1 1 0.83 21 0.12 
ENSMUSG00000049612 Omg 8.40E-05 1 0.59 1 21 0.12 
ENSMUSG00000039630 Hnrnpu 0.024 0.25 0.5 1 21 0.12 
ENSMUSG00000042097 Zfp239 0.034 0.32 1 1 21 0.12 
ENSMUSG00000019917 Sep-10 0.013 1 0.35 1 21 0.12 
ENSMUSG00000074211 Sdhaf1 0.022 0.26 1 1 21 0.12 
ENSMUSG00000040586 Ofd1 0.017 0.14 0.058 1 21 0.12 
ENSMUSG00000037376 Trmt6 0.0023 1 0.1 1 21 0.12 
ENSMUSG00000042225 Ammecr1 0.013 1 0.97 0.48 21 0.12 
ENSMUSG00000026017 Carf 0.043 0.48 1 1 21 0.12 
ENSMUSG00000034401 Spata6 0.0013 1 0.55 1 21 0.12 
ENSMUSG00000031129 Slc9a9 0.00052 0.13 0.19 1 21 0.12 
ENSMUSG00000029186 Pi4k2b 0.031 0.77 0.26 1 21 0.12 
ENSMUSG00000015405 Ace2 0.047 1 0.46 0.76 21 0.11 
ENSMUSG00000030031 Kbtbd8 0.0029 0.31 0.054 1 21 0.11 
ENSMUSG00000027167 Elp4 0.015 0.77 0.59 1 21 0.11 
ENSMUSG00000025321 Itgb8 0.0065 0.12 0.33 0.97 21 0.11 
ENSMUSG00000029469 Ift81 0.00035 1 0.37 1 21 0.11 
ENSMUSG00000022656 Pvrl3 0.014 1 1 1 21 0.11 
ENSMUSG00000027615 Hps3 0.029 0.68 0.46 0.83 21 0.11 
ENSMUSG00000024818 Slc25a45 0.0052 1 1 1 21 0.11 
ENSMUSG00000037089 Slc35b2 0.031 0.078 0.81 0.34 21 0.11 
ENSMUSG00000002109 Ddb2 0.011 0.063 0.062 1 21 0.11 
ENSMUSG00000026977 Mar-07 0.0023 0.96 0.78 1 21 0.11 
ENSMUSG00000071252 2210408I21Rik 0.001 0.091 0.48 0.95 21 0.11 
ENSMUSG00000068615 Gjd2 0.013 0.15 0.15 1 21 0.11 
ENSMUSG00000026037 Orc2 0.0014 1 0.62 1 21 0.11 
ENSMUSG00000021326 Trim27 0.047 0.51 1 0.95 21 0.11 
ENSMUSG00000022358 Fbxo32 0.047 0.18 0.21 1 21 0.11 
ENSMUSG00000047022 Mipol1 0.0032 0.37 0.11 1 21 0.11 
ENSMUSG00000028329 Xpa 0.0095 1 0.37 1 21 0.1 
ENSMUSG00000020263 Appl2 0.043 1 1 1 21 0.1 
ENSMUSG00000043154 Ppp2r3a 0.02 0.53 0.64 0.71 21 0.1 
ENSMUSG00000057895 Zfp105 0.047 1 0.1 0.079 21 0.1 
ENSMUSG00000035509 Fbxl21 0.04 0.98 0.19 1 21 0.1 
ENSMUSG00000047669 Msl3l2 0.013 1 0.2 1 21 0.1 
ENSMUSG00000021209 Ppp4r4 0.034 0.13 0.5 0.2 21 0.1 
ENSMUSG00000007589 Tinf2 0.029 0.18 0.48 1 21 0.1 
App.3. Diurnal Genes in Mouse Cortex:  App. 80 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000017677 Wsb1 0.012 0.23 0.33 1 21 0.1 
ENSMUSG00000022827 Rabl3 0.043 0.11 0.078 1 21 0.1 
ENSMUSG00000018796 Acsl1 0.012 1 0.62 1 21 0.1 
ENSMUSG00000044566 Cage1 0.022 1 1 1 21 0.1 
ENSMUSG00000055401 Fbxo6 0.013 0.063 0.69 1 21 0.1 
ENSMUSG00000033632 AW554918 0.00078 1 1 1 21 0.1 
ENSMUSG00000021282 Eif5 0.0011 1 0.19 1 21 0.1 
ENSMUSG00000055963 Triqk 0.0095 0.17 0.59 1 21 0.099 
ENSMUSG00000028295 Smim8 0.02 1 0.35 1 21 0.099 
ENSMUSG00000049122 Frmd3 0.015 0.62 0.12 1 21 0.099 
ENSMUSG00000028683 Eif2b3 0.0095 0.45 0.4 0.64 21 0.099 
ENSMUSG00000022881 Rfc4 0.014 1 0.5 1 21 0.099 
ENSMUSG00000026495 Efcab2 0.022 1 1 1 21 0.099 
ENSMUSG00000040771 Oard1 0.0032 1 0.29 1 21 0.099 
ENSMUSG00000030254 Rad18 0.031 0.6 0.19 1 21 0.098 
ENSMUSG00000002068 Ccne1 0.013 1 1 1 21 0.098 
ENSMUSG00000031198 Fundc2 0.014 1 0.59 1 21 0.098 
ENSMUSG00000051022 Hs3st1 0.02 0.71 0.067 0.56 21 0.097 
ENSMUSG00000022255 Mtdh 0.029 1 0.75 0.68 21 0.097 
ENSMUSG00000044229 Nxpe4 0.047 0.11 0.24 0.1 21 0.097 
ENSMUSG00000021115 Vrk1 0.037 0.11 0.46 0.37 21 0.097 
ENSMUSG00000028222 Calb1 0.024 0.14 1 1 21 0.096 
ENSMUSG00000024498 Tcerg1 0.014 1 0.11 1 21 0.096 
ENSMUSG00000029328 Hnrnpdl 0.0014 1 0.5 1 21 0.095 
ENSMUSG00000030042 Pole4 0.0058 0.53 0.78 1 21 0.095 
ENSMUSG00000038507 Parp12 0.0095 1 0.48 1 21 0.095 
ENSMUSG00000032355 Mlip 7.80E-06 0.8 0.48 1 21 0.094 
ENSMUSG00000021619 Atg10 0.022 0.77 0.59 1 21 0.094 
ENSMUSG00000036890 Gtdc1 0.0048 1 0.81 0.48 21 0.094 
ENSMUSG00000033931 Rbm34 0.0072 1 0.13 1 21 0.093 
ENSMUSG00000079659 Tmem243 0.0013 1 1 1 21 0.093 
ENSMUSG00000087674 4930447M23Rik 0.034 0.77 1 1 21 0.092 
ENSMUSG00000039043 Arpin 0.014 1 1 1 21 0.092 
ENSMUSG00000036632 Alg5 0.0095 0.078 0.27 1 21 0.09 
ENSMUSG00000022707 Gbe1 0.0032 0.091 0.48 1 21 0.09 
ENSMUSG00000026035 Ppil3 0.017 1 0.3 0.29 21 0.09 
ENSMUSG00000024253 Dync2li1 0.011 0.32 0.57 0.97 21 0.09 
ENSMUSG00000034321 Exosc1 0.00017 0.77 0.78 1 21 0.089 
ENSMUSG00000028568 Btf3l4 0.037 1 0.93 1 21 0.089 
ENSMUSG00000029270 Fam69a 0.0018 0.12 0.67 0.97 21 0.089 
ENSMUSG00000019792 Trmt11 0.0058 1 0.13 1 21 0.089 
ENSMUSG00000022864 D16Ertd472e 0.034 1 1 1 21 0.089 
ENSMUSG00000038047 Haus6 0.0048 0.64 0.48 0.62 21 0.089 
ENSMUSG00000037492 Zmat4 0.04 0.3 0.062 1 21 0.089 
ENSMUSG00000008136 Fhl2 0.013 0.12 0.058 0.13 21 0.088 
ENSMUSG00000008333 Snrpb2 0.013 0.17 0.4 0.68 21 0.088 
ENSMUSG00000021537 Cetn3 0.017 1 0.37 1 21 0.088 
App.3. Diurnal Genes in Mouse Cortex:  App. 81 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000022837 Iqcb1 0.0053 1 0.37 1 21 0.088 
ENSMUSG00000025658 Cnksr2 0.022 1 0.37 0.3 21 0.088 
ENSMUSG00000031671 Setd6 0.0058 1 1 1 21 0.087 
ENSMUSG00000021033 Gstz1 0.0079 0.1 1 1 21 0.086 
ENSMUSG00000053012 Krcc1 0.019 1 0.3 0.97 21 0.086 
ENSMUSG00000029234 Tmem165 0.00041 0.96 1 1 21 0.085 
ENSMUSG00000055835 Zfp1 0.034 1 0.29 1 21 0.085 
ENSMUSG00000055900 Tmem69 0.022 1 0.38 1 21 0.085 
ENSMUSG00000039242 B3galnt2 0.047 0.46 0.48 1 21 0.085 
ENSMUSG00000041488 Stx3 0.013 0.33 0.16 0.97 21 0.085 
ENSMUSG00000035958 Tdp2 0.0023 1 0.33 1 21 0.085 
ENSMUSG00000039529 Atp8b1 0.04 1 1 1 21 0.084 
ENSMUSG00000022092 Ppp3cc 0.0026 0.68 0.27 1 21 0.084 
ENSMUSG00000020956 Dtd2 0.0072 0.45 0.27 1 21 0.084 
ENSMUSG00000027499 Pkia 0.011 1 1 1 21 0.084 
ENSMUSG00000055409 Nell1 0.015 0.17 0.17 1 21 0.083 
ENSMUSG00000069844 Sco1 0.0065 1 0.64 0.66 21 0.083 
ENSMUSG00000059142 Zfp945 0.029 0.73 1 1 21 0.082 
ENSMUSG00000020840 Blmh 0.043 1 0.33 1 21 0.082 
ENSMUSG00000056004 9330182L06Rik 0.037 0.31 0.35 1 21 0.081 
ENSMUSG00000025170 Rab40b 0.04 0.074 1 1 21 0.081 
ENSMUSG00000027667 Zfp639 0.031 1 0.3 1 21 0.08 
ENSMUSG00000028292 Rars2 0.02 0.85 1 0.83 21 0.079 
ENSMUSG00000016494 Cd34 0.0053 0.68 0.3 1 21 0.079 
ENSMUSG00000031591 Asah1 0.026 0.71 0.48 1 21 0.079 
ENSMUSG00000050697 Prkaa1 0.022 1 0.24 0.42 21 0.078 
ENSMUSG00000036598 Ccdc113 0.04 1 1 1 21 0.078 
ENSMUSG00000021852 Slc35f4 0.02 0.24 0.75 1 21 0.077 
ENSMUSG00000030967 Zranb1 0.024 1 0.75 1 21 0.077 
ENSMUSG00000016487 Ppfibp1 0.037 0.5 0.84 1 21 0.077 
ENSMUSG00000033319 Fem1c 0.014 0.48 0.24 1 21 0.077 
ENSMUSG00000038622 Med30 0.0018 0.98 0.42 1 21 0.077 
ENSMUSG00000057497 Fam136a 0.00031 0.091 0.097 0.11 21 0.077 
ENSMUSG00000032468 Armc8 0.0087 0.13 0.33 0.62 21 0.077 
ENSMUSG00000061665 Cd2ap 0.015 1 0.29 1 21 0.076 
ENSMUSG00000034480 Diaph2 0.0072 1 0.67 0.58 21 0.076 
ENSMUSG00000061119 Prcp 0.0087 0.85 0.17 0.6 21 0.076 
ENSMUSG00000033910 Gucy1a3 0.037 1 0.44 1 21 0.076 
ENSMUSG00000022523 Fgf12 0.0072 1 0.52 1 21 0.075 
ENSMUSG00000038844 Kif16b 0.0095 0.091 0.23 1 21 0.075 
ENSMUSG00000078771 Evi2a 0.037 0.58 1 1 21 0.075 
ENSMUSG00000034442 Trmt5 0.031 0.85 0.44 1 21 0.074 
ENSMUSG00000097823 Gm16701 0.0095 1 0.5 1 21 0.074 
ENSMUSG00000031583 Wrn 0.024 0.14 0.52 0.76 21 0.074 
ENSMUSG00000065979 Cpped1 0.0079 0.34 1 0.83 21 0.074 
ENSMUSG00000071253 Slc25a16 0.029 0.56 0.35 0.29 21 0.074 
ENSMUSG00000030423 Pop4 0.014 1 0.78 1 21 0.073 
App.3. Diurnal Genes in Mouse Cortex:  App. 82 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000046603 Tcaim 0.043 0.43 0.78 1 21 0.073 
ENSMUSG00000045259 Klhdc9 0.034 1 1 1 21 0.073 
ENSMUSG00000039166 Akap7 0.0036 1 1 1 21 0.073 
ENSMUSG00000038991 Txndc5 0.015 0.06 1 1 21 0.072 
ENSMUSG00000021368 Tbc1d7 0.031 0.64 0.81 1 21 0.071 
ENSMUSG00000004642 Slbp 0.026 0.85 0.57 1 21 0.07 
ENSMUSG00000075478 Slitrk1 0.024 0.05 0.17 1 21 0.07 
ENSMUSG00000028646 Rragc 0.012 1 0.69 1 21 0.07 
ENSMUSG00000072623 Zfp9 0.0087 1 0.21 1 21 0.07 
ENSMUSG00000051730 Mettl5 0.02 0.23 0.15 1 21 0.07 
ENSMUSG00000031292 Cdkl5 0.034 1 0.42 0.81 21 0.07 
ENSMUSG00000027080 Med19 0.04 1 1 1 21 0.069 
ENSMUSG00000019818 Cd164 0.0072 1 0.19 0.28 21 0.069 
ENSMUSG00000074165 Zfp788 0.017 0.16 0.2 1 21 0.069 
ENSMUSG00000023106 Denr 0.012 1 0.24 1 21 0.069 
ENSMUSG00000028869 Gnl2 0.0044 1 0.93 1 21 0.068 
ENSMUSG00000026187 Xrcc5 0.04 0.25 0.93 0.95 21 0.068 
ENSMUSG00000033781 Asb13 0.015 0.091 0.21 1 21 0.068 
ENSMUSG00000046314 Stxbp6 0.0065 0.68 0.81 1 21 0.067 
ENSMUSG00000020691 Mettl2 0.024 0.6 0.87 1 21 0.067 
ENSMUSG00000097718 Gm26896 0.017 0.93 0.42 0.5 21 0.067 
ENSMUSG00000021377 Dek 0.014 0.71 1 1 21 0.067 
ENSMUSG00000042447 Mios 0.011 0.53 0.46 1 21 0.066 
ENSMUSG00000020459 Mtif2 0.037 1 0.72 0.86 21 0.066 
ENSMUSG00000027810 Eif2a 0.015 1 1 1 21 0.066 
ENSMUSG00000032417 Rwdd2a 0.037 0.77 0.59 1 21 0.065 
ENSMUSG00000021357 Exoc2 0.0087 0.078 0.13 0.64 21 0.064 
ENSMUSG00000026634 Angel2 0.047 0.23 0.42 1 21 0.064 
ENSMUSG00000042579 4632404H12Rik 0.02 1 0.19 1 21 0.064 
ENSMUSG00000070705 Eid2b 0.047 0.25 0.57 0.64 21 0.064 
ENSMUSG00000018651 Tada2a 0.031 1 0.52 1 21 0.063 
ENSMUSG00000042446 Zmym4 0.031 0.34 0.15 1 21 0.063 
ENSMUSG00000033918 Parl 0.0044 0.42 0.57 0.97 21 0.063 
ENSMUSG00000063296 Tmem117 0.047 1 0.59 1 21 0.063 
ENSMUSG00000033166 Dis3 0.043 0.98 0.35 0.95 21 0.062 
ENSMUSG00000022339 Ebag9 0.034 1 0.35 1 21 0.061 
ENSMUSG00000039047 Pigk 0.0013 0.83 0.55 0.68 21 0.06 
ENSMUSG00000021684 Pde8b 0.022 0.34 0.72 1 21 0.059 
ENSMUSG00000028223 Decr1 0.043 1 0.16 1 21 0.059 
ENSMUSG00000049092 Gpr137c 0.047 0.64 0.11 1 21 0.059 
ENSMUSG00000005583 Mef2c 0.024 1 0.93 1 21 0.059 
ENSMUSG00000062995 Ica1 0.014 1 1 1 21 0.058 
ENSMUSG00000046311 Zfp62 0.02 0.1 1 1 21 0.058 
ENSMUSG00000038070 Cntln 0.029 1 0.62 1 21 0.058 
ENSMUSG00000027834 Serpini1 0.04 0.71 1 1 21 0.058 
ENSMUSG00000015733 Capza2 0.011 1 0.5 1 21 0.057 
ENSMUSG00000044442 N6amt1 0.031 0.8 0.078 1 21 0.056 
App.3. Diurnal Genes in Mouse Cortex:  App. 83 
Ensembl_ ID Gene q_C q_SD3 q_SD6 q_SD12 C_Phase Amp 
ENSMUSG00000043190 Rfesd 0.012 0.19 0.097 1 21 0.055 
ENSMUSG00000020952 Scfd1 0.029 0.057 0.26 0.25 21 0.054 
ENSMUSG00000028015 Ctso 0.015 0.39 0.11 0.76 21 0.053 
ENSMUSG00000041935 AW549877 0.019 1 0.35 1 21 0.053 
ENSMUSG00000059669 Taf1b 0.024 0.21 0.57 0.88 21 0.052 
ENSMUSG00000025898 Cwf19l2 0.031 1 0.57 1 21 0.052 
ENSMUSG00000037573 Tob1 0.015 0.057 0.13 1 21 0.052 
ENSMUSG00000066456 Hmgn3 0.015 1 0.9 1 21 0.052 
ENSMUSG00000096210 H1f0 0.0018 0.93 0.33 1 21 0.052 
ENSMUSG00000050379 Sep-06 0.034 1 1 1 21 0.052 
ENSMUSG00000020794 Ube2g1 0.034 1 0.69 1 21 0.051 
ENSMUSG00000028790 Khdrbs1 0.026 1 0.38 1 21 0.05 
ENSMUSG00000022808 Snx4 0.029 1 0.87 1 21 0.05 
ENSMUSG00000035762 Tmem161b 0.047 1 0.62 0.62 21 0.049 
ENSMUSG00000020677 Ddx52 0.015 0.53 1 0.54 21 0.048 
ENSMUSG00000064138 Fam172a 0.019 0.75 0.62 1 21 0.048 
ENSMUSG00000021929 Kpna3 0.022 1 0.3 1 21 0.047 
ENSMUSG00000032252 Glce 0.024 1 1 1 21 0.047 
ENSMUSG00000030978 Rrm1 0.047 0.46 1 1 21 0.047 
ENSMUSG00000033111 3830406C13Rik 0.026 0.8 0.75 1 21 0.047 
ENSMUSG00000029833 Trim24 0.031 1 0.32 1 21 0.047 
ENSMUSG00000014353 Tmem87b 0.047 0.45 0.81 1 21 0.047 
ENSMUSG00000026771 Spopl 0.034 1 1 0.56 21 0.046 
ENSMUSG00000006599 Gtf2h1 0.022 1 1 1 21 0.046 
ENSMUSG00000034007 Scaper 0.04 1 0.87 1 21 0.046 
ENSMUSG00000033653 Vps8 0.029 1 1 1 21 0.045 
ENSMUSG00000037787 Apopt1 0.014 0.66 0.97 1 21 0.045 
ENSMUSG00000023307 Mar-05 0.034 0.22 1 1 21 0.045 
ENSMUSG00000023460 Rab12 0.0079 0.31 0.55 1 21 0.044 
ENSMUSG00000049536 Tceal1 0.024 0.27 0.52 0.78 21 0.042 
ENSMUSG00000026098 Pms1 0.034 0.8 1 1 21 0.042 
ENSMUSG00000028397 Kdm4c 0.0072 1 0.37 1 21 0.042 
ENSMUSG00000025609 Mkln1 0.047 1 1 1 21 0.04 
ENSMUSG00000046671 Mtfr1l 0.019 0.27 0.57 1 21 0.038 
ENSMUSG00000024234 Mtpap 0.024 1 0.75 1 21 0.038 
ENSMUSG00000028086 Fbxw7 0.029 1 1 1 21 0.035 
ENSMUSG00000019795 Pcmt1 0.022 0.36 1 1 21 0.034 
ENSMUSG00000025264 Tsr2 0.037 0.88 1 1 21 0.034 
ENSMUSG00000027108 Ola1 0.043 1 0.93 1 21 0.033 
 
 
App.4. Diurnal Genes Affected by Sleep Deprivation:  App. 84 
App.4. Diurnal Genes Affected by Sleep Deprivation 
  
App.5. Non-Diurnal Genes Affected by Sleep Deprivation:  App. 85 
 
App.5. Non-Diurnal Genes Affected by Sleep Deprivation 
 
App.6. Genes showing a Homeostatic Expression Profile:  App. 86 
App.6. Genes showing a Homeostatic Expression Profile 
 
  
App.7. Genes showing a Stress Expression Profile:  App. 87 
App.7. Genes showing a Stress Expression Profile 
 
 
App.8. Diurnal Transcripts Affected by Sleep Deprivation:  App. 88 
App.8. Diurnal Transcripts Affected by Sleep Deprivation  
App.9. Non-Diurnal Transcripts Affected by Sleep Deprivation:  App. 89 
 
App.9. Non-Diurnal Transcripts Affected by Sleep Deprivation 
 
  
App.10. Isoforms showing a Homeostatic Expression Profile:  App. 90 
App.10. Isoforms showing a Homeostatic Expression Profile 
 
App.10. Isoforms showing a Homeostatic Expression Profile:  App. 91 
  
App.11. Isoform showing a Stress Expression Profiles:  App. 92 
App.11. Isoform showing a Stress Expression Profiles 
 
App.11. Isoform showing a Stress Expression Profiles:  App. 93 
  
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 94 
 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation 
Abundance of proteins modulated by sleep deprivation, normalised to the abundance in non-sleep 
deprived animals. T_00 represents 6pm Day0 (0hrs SD), T_24 represents 6pm Day1 (12hrs SD, 
0hrs Recovery), whilst T_48 represents 6pm Day2 (12hrs SD, 24hrs Recovery). Also included are the 
fdr adjusted p-values for the pairwise comparisons. 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
GNAL 1 1.73 1.5 0.00364 0.0272 0.0269 
PC4L1 1 1.47 1.79 0.00474 0.0226 0.0165 
F6RT34 1 1.28 1.2 0.0112 0.0272 0.0268 
CRYM 1 1.27 1.18 0.00273 0.0449 0.0198 
AINX 1 1.19 1.11 0.00474 0.0309 0.0362 
LRC57 1 0.962 0.891 0.0288 0.0172 0.0131 
RP3A 1 0.955 0.905 0.0414 0.0172 0.0197 
S4A10 1 0.92 0.96 0.00494 0.0272 0.0172 
GFAP 1 0.894 1.14 0.0135 0.0307 0.00329 
RL21 1 0.894 0.812 0.0309 0.0493 0.0472 
HMGN2 1 0.892 1.41 0.0195 0.0272 0.00568 
CO3 1 0.888 1.14 0.00378 0.0172 7.00E-04 
RL8 1 0.877 0.755 0.0175 0.0272 0.0204 
VIME 1 0.87 1.3 0.039 0.0299 0.0149 
H4 1 0.808 0.671 0.0112 0.0172 0.0169 
H31 1 0.793 0.652 0.0101 0.0469 0.0299 
HPT 1 0.654 1.73 0.0297 0.0172 0.00787 
TY3H 1 2.02 1.49 0.0122 0.0493 0.0554 
MOG 1 1.33 1.32 0.00788 0.0272 0.85 
CN37 1 1.31 1.23 0.00294 0.0449 0.149 
GLTP 1 1.31 1.31 0.00273 0.0172 0.928 
SIR2 1 1.21 1.16 0.0122 0.0429 0.103 
NFM 1 1.2 1.13 0.00761 0.0469 0.0716 
ENPP6 1 1.2 1.24 0.0487 0.0335 0.525 
CRYAB 1 1.18 1.13 0.0165 0.0352 0.185 
ACY2 1 1.16 1.17 0.00762 0.0299 0.499 
NDRG4 1 1.06 1.07 0.0406 0.0469 0.562 
SYGP1 1 0.972 0.945 0.0484 0.0384 0.0717 
PLEC 1 0.96 0.932 0.0309 0.0299 0.0531 
NED4L 1 0.956 0.952 0.00378 0.0272 0.48 
CTRO 1 0.944 0.924 0.0297 0.0272 0.188 
NMDZ1 1 0.938 0.914 0.0404 0.0272 0.188 
KPCD 1 0.937 0.905 0.0372 0.0335 0.143 
ARPC3 1 0.931 0.894 0.0402 0.0316 0.108 
KALRN 1 0.928 0.925 0.039 0.0469 0.871 
SRBS2 1 0.92 0.903 0.0163 0.0335 0.223 
S12A5 1 0.913 0.877 0.0131 0.0272 0.107 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 95 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
GRM2 1 0.903 0.925 0.00273 0.0243 0.0824 
KCD16 1 0.891 0.9 0.0135 0.0401 0.649 
AT1A1 1 0.88 0.852 0.0406 0.0386 0.329 
RL36A 1 0.877 0.836 0.0165 0.0352 0.203 
RL13A 1 0.876 0.826 0.0195 0.0385 0.185 
H12 1 0.808 0.688 0.0323 0.0425 0.0821 
RL34 1 0.794 0.711 0.00396 0.0385 0.135 
H13 1 0.776 0.695 0.0297 0.0272 0.169 
H14 1 0.694 0.615 0.0178 0.0402 0.0888 
S10A9 1 2.91 0.955 0.0135 0.736 0.00329 
CO1A1 1 1.96 0.863 0.00378 0.0884 0.00371 
PDE10 1 1.94 1.09 0.00364 0.317 0.00371 
CO1A2 1 1.79 0.84 0.00273 0.0561 7.00E-04 
GBG7 1 1.69 1.1 0.00273 0.137 0.00329 
NTCP4 1 1.65 1.08 0.00474 0.353 0.0117 
ADCY5 1 1.62 1.04 0.00364 0.452 0.00329 
PDE1B 1 1.54 1.04 0.00827 0.193 0.00329 
PPR1B 1 1.53 1 0.00474 0.999 0.00781 
ANR63 1 1.48 1.07 0.0112 0.327 0.0165 
ARP21 1 1.26 0.99 0.0234 0.843 0.0117 
PCP4 1 1.25 1.1 0.0178 0.0975 0.00329 
SCN4B 1 1.21 0.924 0.0414 0.235 0.0197 
AL1A1 1 1.2 0.942 0.00783 0.234 0.0246 
IPP2 1 1.19 1.05 0.0112 0.107 0.0251 
ACTN2 1 1.18 1 0.0178 0.854 0.00493 
RCN1 1 1.16 1.02 0.0122 0.546 0.0208 
MAOM 1 1.15 1.03 0.0452 0.21 0.0301 
PHAR1 1 1.11 0.958 0.0406 0.133 0.023 
CATA 1 0.941 1.03 0.00273 0.0974 0.0169 
E9Q8N8 1 0.924 1.01 0.0476 0.538 0.0459 
IDE 1 0.918 0.994 0.0122 0.572 0.0147 
STXB6 1 0.91 1.03 0.0417 0.249 0.0169 
PVRL1 1 0.908 0.962 0.00396 0.112 0.0357 
FIBG 1 0.899 0.969 0.0178 0.193 0.024 
FIBA 1 0.892 0.977 0.0309 0.383 0.0371 
S6A11 1 0.89 1.21 0.0452 0.104 0.0268 
BLVRB 1 0.888 1.02 0.00898 0.297 0.00371 
A2AP 1 0.878 1.02 0.0127 0.394 0.0362 
E9Q035 1 0.869 1.01 0.00635 0.659 0.00329 
H2AW 1 0.867 1.1 0.0215 0.0836 0.00732 
SPTA1 1 0.866 1.04 0.0262 0.188 0.0147 
HEMO 1 0.859 1.08 0.0373 0.112 0.00761 
PLMN 1 0.842 1 0.0177 0.94 0.0343 
ALBU 1 0.836 0.979 0.0293 0.468 0.00782 
PZP 1 0.822 1.02 0.00762 0.308 0.00271 
A1AT4 1 0.82 0.994 0.00474 0.74 0.0049 
SPA3K 1 0.815 0.966 0.00776 0.214 0.00755 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 96 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
APOC1 1 0.787 0.92 0.00378 0.104 0.0251 
A8DUK4 1 0.742 0.973 0.00399 0.476 0.0197 
HBA 1 0.709 0.974 0.0177 0.513 0.00568 
SEGN 1 0.588 3.62 0.0405 0.0833 0.0468 
PENK 1 1.41 1.11 0.037 0.386 0.126 
MOBP 1 1.32 1.27 0.00474 0.0678 0.283 
GRP2 1 1.28 1.03 0.00913 0.638 0.0631 
ANLN 1 1.27 1.26 0.0297 0.113 0.948 
MYPR 1 1.27 1.29 0.00474 0.0795 0.799 
F7A0B0 1 1.24 1.1 0.0269 0.323 0.239 
CD82 1 1.22 1.21 0.00679 0.107 0.84 
MAG 1 1.21 1.16 0.014 0.0545 0.135 
MYO1D 1 1.21 1.22 0.0234 0.0706 0.785 
BCAS1 1 1.2 1.14 0.0309 0.0985 0.326 
NFL 1 1.2 1.12 0.00679 0.0572 0.0752 
ILEUA 1 1.2 1.22 0.00378 0.12 0.769 
MK03 1 1.15 1.14 0.0484 0.0671 0.89 
CDK18 1 1.14 1.1 0.0309 0.0935 0.337 
NECA2 1 1.13 1.11 0.0052 0.139 0.504 
HPCL1 1 1.13 1.14 0.0372 0.134 0.926 
AGAP2 1 1.13 1.05 0.0464 0.223 0.122 
NUCB2 1 1.11 1.14 0.0482 0.222 0.75 
DC1I1 1 1.1 1.12 0.0404 0.115 0.533 
BCR 1 1.1 1.02 0.0135 0.633 0.182 
CAD13 1 1.09 1.05 0.0406 0.123 0.157 
AMPL 1 1.09 1.07 0.0406 0.107 0.164 
CTL1 1 1.09 1.11 0.0241 0.0649 0.318 
FMN2 1 1.08 0.992 0.035 0.865 0.184 
CRAC1 1 1.07 1.1 0.0409 0.201 0.624 
LACE1 1 1.06 1.05 0.0178 0.363 0.834 
DDAH1 1 1.06 1.05 0.0452 0.179 0.745 
PLCL1 1 1.06 1.06 0.0417 0.13 0.789 
AOFB 1 1.05 1.01 0.0405 0.638 0.358 
PDE1A 1 1.05 1.02 0.0297 0.325 0.176 
PLXA2 1 1.04 0.998 0.035 0.896 0.116 
NSF1C 1 1.04 1.05 0.0059 0.189 0.709 
PEX19 1 1.03 1.07 0.0297 0.267 0.431 
IQEC1 1 0.973 0.953 0.0135 0.147 0.328 
ERC2 1 0.968 0.939 0.0309 0.0857 0.177 
KIF5C 1 0.965 0.994 0.015 0.625 0.162 
VP13A 1 0.965 0.96 0.0297 0.0698 0.602 
RB33A 1 0.963 0.97 0.0297 0.157 0.696 
WDR7 1 0.963 0.951 0.0297 0.132 0.475 
CAD10 1 0.961 0.942 0.0452 0.128 0.419 
AL1L1 1 0.961 0.96 0.0181 0.155 0.977 
NRX1A 1 0.958 0.952 0.033 0.117 0.72 
SHRM2 1 0.958 0.972 0.0297 0.12 0.253 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 97 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
ANK1 1 0.956 0.983 0.0309 0.411 0.259 
Q68FG2 1 0.955 0.915 0.0317 0.0705 0.129 
BIEA 1 0.954 0.948 0.045 0.0852 0.693 
AP2S1 1 0.954 0.928 0.0297 0.105 0.314 
PPCEL 1 0.951 0.969 0.00273 0.277 0.481 
CADM2 1 0.948 0.901 0.0405 0.0795 0.119 
TCPR1 1 0.947 0.946 0.0406 0.0625 0.978 
AUP1 1 0.945 0.956 0.0405 0.413 0.855 
LRRT4 1 0.944 0.956 0.0321 0.0747 0.381 
CTNB1 1 0.944 0.953 0.0161 0.0501 0.453 
GRIA2 1 0.943 0.934 0.0309 0.0997 0.68 
KLC2 1 0.942 0.98 0.0317 0.223 0.0741 
RAB14 1 0.939 0.938 0.0405 0.203 0.987 
A2APX7 1 0.938 0.906 0.0298 0.0977 0.322 
DHE3 1 0.934 0.936 0.0321 0.132 0.964 
DLGP2 1 0.934 0.906 0.0135 0.0975 0.332 
CTND1 1 0.931 0.936 0.039 0.0706 0.828 
MPP2 1 0.931 0.932 0.037 0.0675 0.957 
PYGM 1 0.931 0.932 0.0297 0.0692 0.956 
ANXA2 1 0.928 0.966 0.00273 0.324 0.319 
SHAN1 1 0.922 0.941 0.0444 0.0871 0.201 
CBR2 1 0.92 1.05 0.0441 0.227 0.0821 
ANXA5 1 0.919 1 0.0135 0.836 0.0565 
TMM65 1 0.914 0.918 0.0415 0.227 0.949 
B2RUS7 1 0.911 0.904 0.0405 0.49 0.972 
GBG2 1 0.902 0.88 0.0452 0.206 0.765 
EPHB3 1 0.902 0.91 0.0309 0.0673 0.748 
RL36 1 0.9 0.823 0.0452 0.0779 0.147 
PMGE 1 0.9 1.02 0.0223 0.621 0.14 
KCC2G 1 0.899 0.881 0.0475 0.0977 0.501 
COCA1 1 0.895 1 0.0297 0.967 0.366 
VGLU2 1 0.895 0.926 0.0117 0.151 0.404 
UBTD1 1 0.892 0.931 0.0297 0.209 0.392 
SC6A1 1 0.891 0.939 0.0174 0.248 0.342 
TAGL 1 0.883 0.943 0.0444 0.143 0.135 
CAH1 1 0.88 1.02 0.0244 0.712 0.0737 
SYT2 1 0.872 0.852 0.0101 0.0706 0.559 
MUG1 1 0.865 0.993 0.00827 0.873 0.152 
RL13 1 0.86 0.77 0.00364 0.0881 0.17 
MOT1 1 0.858 0.93 0.0444 0.354 0.378 
RL28 1 0.835 0.754 0.0452 0.078 0.0564 
RL4 1 0.802 0.759 0.00152 0.0643 0.25 
B3AT 1 0.8 1.02 0.0309 0.65 0.0678 
H15 1 0.775 0.738 0.0291 0.0647 0.392 
CEP95 1 0.775 0.81 0.0415 0.0706 0.49 
WDR33 1 0.765 0.971 0.0372 0.662 0.0739 
J3QMC5 1 1.1 1.92 0.244 0.0272 0.00325 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 98 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
PSIP1 1 1.08 1.18 0.126 0.0402 0.0498 
CMGA 1 1.07 1.43 0.368 0.0272 0.0493 
AKAP5 1 1.04 0.893 0.0863 0.0272 0.00329 
ITIH4 1 1.04 1.34 0.478 0.0172 0.023 
D19L1 1 1.02 1.52 0.827 0.0485 0.0298 
KCC4 1 1.01 0.883 0.556 0.0386 0.0396 
SAE1 1 1.01 1.1 0.71 0.0352 0.0177 
TMEDA 1 1 1.14 0.932 0.0272 0.0266 
XRP2 1 1 1.3 0.964 0.0243 0.0321 
ROCK2 1 1 0.96 0.841 0.0383 0.0139 
MYO6 1 1 1.26 0.981 0.0478 0.0169 
NOE1 1 0.997 0.931 0.841 0.0352 0.0115 
GPSM1 1 0.995 1.26 0.841 0.0243 0.00454 
KINH 1 0.988 1.13 0.349 0.0172 0.00329 
PCBP3 1 0.988 1.32 0.797 0.0272 0.00329 
CDC42 1 0.986 1.18 0.573 0.0405 0.0478 
LMNB1 1 0.982 1.15 0.145 0.0157 0.000976 
Sep-05 1 0.979 0.922 0.139 0.0272 0.0157 
NDUA2 1 0.976 0.866 0.444 0.0352 0.0242 
RL14 1 0.975 0.767 0.465 0.0499 0.0169 
E9QK48 1 0.963 1.19 0.411 0.0272 0.0169 
AMPD2 1 0.963 1.16 0.288 0.0493 0.00271 
RL7A 1 0.96 0.768 0.279 0.0307 0.00493 
RL18 1 0.952 0.797 0.359 0.0488 0.048 
FABP7 1 0.944 1.44 0.383 0.0384 0.00761 
RS8 1 0.936 0.806 0.161 0.0386 0.0222 
RL18A 1 0.935 0.829 0.0884 0.0243 0.024 
CALB2 1 0.93 1.9 0.125 0.0384 0.00477 
ACBD7 1 0.908 1.83 0.247 0.0352 0.0153 
S10A5 1 0.83 11.3 0.302 0.0469 0.0266 
A1AG1 1 0.817 1.11 0.0835 0.0299 0.0299 
OMP 1 0.773 7.08 0.0945 0.0307 0.00731 
RL6 1 0.745 0.645 0.0687 0.0469 0.0251 
MVP 1 1.15 1.29 0.147 0.0272 0.153 
NMRL1 1 1.13 1.16 0.0687 0.0172 0.358 
RGS14 1 1.09 1.07 0.097 0.0402 0.488 
E9Q7G0 1 1.08 1.14 0.224 0.0352 0.374 
CALU 1 1.07 1.1 0.144 0.0352 0.394 
NOP56 1 1.07 1.12 0.0784 0.0449 0.107 
SAFB1 1 1.05 1.1 0.184 0.0272 0.172 
E9Q5C9 1 1.05 1.09 0.142 0.0272 0.101 
Q3UJB0 1 1.04 1.08 0.244 0.0469 0.225 
EIF3A 1 1.04 1.05 0.0915 0.0469 0.297 
SYAC 1 1.01 1.03 0.234 0.0386 0.154 
RMD3 1 1.01 1.04 0.696 0.0402 0.108 
GLRX3 1 1 0.963 0.846 0.0335 0.0716 
ADHX 1 0.999 1.07 0.935 0.0385 0.0659 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 99 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
SHAN3 1 0.997 0.918 0.848 0.0469 0.0591 
SNX3 1 0.988 0.942 0.601 0.0405 0.12 
ACSL1 1 0.986 0.951 0.449 0.0449 0.103 
OGT1 1 0.985 0.96 0.303 0.0385 0.12 
KAD1 1 0.98 1.06 0.499 0.0402 0.0785 
TBAL3 1 0.98 1.14 0.76 0.0172 0.101 
RS9 1 0.979 0.854 0.381 0.0384 0.0752 
F8VPN4 1 0.976 0.966 0.0808 0.0383 0.233 
AGRL1 1 0.974 0.951 0.134 0.0494 0.112 
AGAP3 1 0.973 0.951 0.307 0.0452 0.328 
PITM2 1 0.971 0.958 0.114 0.0469 0.268 
ACYP1 1 0.969 0.93 0.166 0.0413 0.0821 
PREP 1 0.968 0.939 0.0749 0.0493 0.0583 
ARP2 1 0.966 0.922 0.0687 0.0494 0.0748 
BSN 1 0.964 0.928 0.179 0.0494 0.0785 
UN13A 1 0.962 0.931 0.0808 0.0384 0.0695 
AT2B1 1 0.962 0.911 0.142 0.0352 0.0693 
DGLA 1 0.96 0.93 0.118 0.0498 0.177 
COR1A 1 0.954 0.895 0.193 0.0469 0.101 
NPTN 1 0.947 0.905 0.0946 0.0384 0.116 
SYN1 1 0.946 0.93 0.0649 0.0386 0.369 
MAGI2 1 0.943 0.933 0.0808 0.0243 0.602 
BRNP1 1 0.941 0.933 0.0865 0.0499 0.737 
SYT1 1 0.937 0.913 0.132 0.0412 0.426 
TAGL3 1 0.937 0.889 0.0515 0.0449 0.113 
HOME1 1 0.925 0.876 0.0693 0.0469 0.139 
GBB5 1 0.919 0.907 0.1 0.0493 0.493 
AKA7G 1 0.912 0.854 0.0613 0.0385 0.107 
NU5M 1 0.911 0.846 0.166 0.0469 0.224 
RL31 1 0.91 0.88 0.0835 0.0352 0.333 
RL3 1 0.902 0.867 0.0687 0.0487 0.203 
RGS6 1 0.892 0.864 0.103 0.0494 0.328 
RL19 1 0.862 0.77 0.0808 0.0402 0.138 
RL24 1 0.835 0.693 0.114 0.0386 0.105 
RL29 1 0.818 0.68 0.108 0.0352 0.0571 
CPNE9 1 0.803 0.723 0.0535 0.0307 0.224 
DDC 1 1.35 1.06 0.101 0.518 0.023 
PTN5 1 1.19 0.935 0.0915 0.23 7.00E-04 
NETO2 1 1.16 0.971 0.242 0.735 0.0445 
E9Q6Y8 1 1.15 1 0.123 0.939 0.0205 
GRIN3 1 1.14 0.879 0.31 0.276 0.0169 
CYB5B 1 1.13 0.956 0.0808 0.327 0.0169 
TOP1 1 1.11 0.955 0.271 0.52 0.0269 
SYT10 1 1.11 1.59 0.433 0.0583 0.00787 
CBR3 1 1.1 0.996 0.192 0.937 0.0162 
PTMA 1 1.09 1.22 0.179 0.0663 0.0468 
HTRA1 1 1.08 0.962 0.335 0.525 0.00577 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 100 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
CPNE5 1 1.08 0.974 0.114 0.316 0.0179 
NSMA2 1 1.08 0.937 0.13 0.128 0.0315 
ISLR2 1 1.08 0.957 0.296 0.434 0.0493 
MAP1B 1 1.07 1.13 0.0687 0.0728 0.0365 
RT07 1 1.07 0.902 0.257 0.137 0.0407 
RIFK 1 1.07 1.14 0.537 0.252 0.0459 
STRN 1 1.07 0.992 0.0535 0.585 0.00454 
DGKB 1 1.06 0.944 0.149 0.165 0.0049 
HP1B3 1 1.06 0.853 0.394 0.12 0.0418 
SLK 1 1.06 1.01 0.0781 0.387 0.00493 
RPGP1 1 1.06 0.976 0.11 0.327 0.0407 
MAON 1 1.05 0.984 0.157 0.43 0.00329 
KPCB 1 1.05 0.962 0.194 0.213 0.0285 
NIPS1 1 1.05 0.88 0.104 0.0721 0.0169 
LMNB2 1 1.05 1.13 0.342 0.106 0.0363 
TOM70 1 1.05 0.935 0.128 0.132 0.0334 
GABT 1 1.04 0.984 0.361 0.66 0.0445 
F136A 1 1.04 0.977 0.274 0.45 0.0459 
GRP78 1 1.04 0.963 0.247 0.251 0.0407 
SRSF2 1 1.04 1.11 0.197 0.0556 0.0407 
ITPR1 1 1.04 0.945 0.0535 0.0648 0.00727 
UCHL1 1 1.04 0.981 0.48 0.645 0.0246 
BAIP2 1 1.04 0.938 0.244 0.106 0.0197 
HNRPC 1 1.03 1.1 0.131 0.0678 0.0495 
PP2AA 1 1.03 0.982 0.468 0.625 0.0396 
DAPK3 1 1.03 0.95 0.468 0.26 0.0301 
FUS 1 1.03 1.11 0.303 0.0586 0.0413 
CYLD 1 1.03 0.938 0.0835 0.051 0.00974 
ENSA 1 1.03 0.955 0.506 0.321 0.0169 
HPCA 1 1.03 0.945 0.34 0.166 0.00544 
NDUA5 1 1.03 0.925 0.155 0.0728 0.0268 
RLBP1 1 1.03 1.14 0.553 0.121 0.00329 
MLF2 1 1.03 0.961 0.61 0.417 0.0127 
CPLX2 1 1.03 0.957 0.265 0.135 0.00371 
ATP5H 1 1.02 0.952 0.547 0.259 0.0169 
NDUA6 1 1.02 0.835 0.658 0.084 0.0169 
AKA12 1 1.02 1.15 0.639 0.102 0.0169 
IST1 1 1.02 1.11 0.535 0.105 0.0072 
RHG44 1 1.02 0.973 0.507 0.291 0.0334 
TCPA 1 1.02 0.998 0.476 0.93 0.0139 
E9PV14 1 1.02 1.13 0.617 0.0649 0.0495 
SCN2B 1 1.02 0.957 0.594 0.198 0.0246 
MLP3B 1 1.01 1.08 0.573 0.0836 0.0488 
ATPO 1 1.01 0.929 0.729 0.149 0.046 
CD166 1 1.01 0.95 0.677 0.197 0.0365 
NGEF 1 1.01 0.905 0.726 0.104 0.0169 
OSCP1 1 1.01 1.12 0.354 0.0706 0.0429 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 101 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
RGS20 1 1.01 0.914 0.815 0.159 0.0198 
CX6B1 1 1.01 0.936 0.793 0.183 0.0459 
RS2 1 1.01 0.94 0.759 0.165 0.0268 
SKP1 1 1.01 0.943 0.787 0.182 0.0415 
PUF60 1 1.01 1.07 0.766 0.113 0.027 
CAH2 1 1.01 1.08 0.762 0.108 0.0472 
NDUB4 1 1.01 0.941 0.818 0.164 0.0486 
AK1A1 1 1.01 0.969 0.468 0.0833 0.0325 
GDN 1 1.01 1.14 0.854 0.0556 0.0407 
BCS1 1 1.01 0.965 0.868 0.378 0.0478 
SFPQ 1 1.01 1.07 0.776 0.0779 0.0347 
F177A 1 1.01 1.09 0.793 0.0795 0.00454 
RS10 1 1.01 0.954 0.854 0.184 0.0486 
HPLN1 1 1.01 0.97 0.822 0.256 0.0165 
PDE2A 1 1.01 0.953 0.586 0.0975 0.0232 
PFKAP 1 1 0.967 0.89 0.343 0.012 
NBEA 1 1 0.974 0.926 0.341 0.0407 
CA2D3 1 1 0.897 0.952 0.1 0.0358 
COQ9 1 1 0.954 0.943 0.189 0.0346 
KAP3 1 1 0.896 0.961 0.0795 0.041 
PPM1E 1 1 1.09 0.975 0.121 0.0197 
SUCB1 1 1 0.948 0.976 0.145 0.048 
LRRC7 1 1 0.95 0.985 0.227 0.041 
BPHL 1 0.999 0.912 0.995 0.171 0.0174 
A3KGU7 1 0.999 0.961 0.94 0.0583 0.0484 
CCZ1 1 0.999 1.09 0.986 0.171 0.0407 
PHB 1 0.999 0.938 0.967 0.124 0.0408 
CPNE6 1 0.997 1.15 0.866 0.0643 0.00605 
TCOF 1 0.996 1.09 0.934 0.157 0.0408 
INPP 1 0.995 1.02 0.874 0.36 0.0493 
UBP4 1 0.995 1.05 0.814 0.137 0.00493 
LDHA 1 0.995 0.94 0.898 0.177 0.0478 
MYH10 1 0.995 0.952 0.746 0.084 0.0246 
QCR7 1 0.994 0.914 0.806 0.084 0.0049 
GLSK 1 0.994 0.935 0.874 0.182 0.0266 
ABLM2 1 0.994 0.917 0.908 0.175 0.0495 
MIF 1 0.993 0.85 0.945 0.196 0.045 
CNRP1 1 0.993 0.893 0.882 0.165 0.0169 
U5S1 1 0.993 1.04 0.567 0.0779 0.0315 
PLCB1 1 0.992 0.952 0.807 0.213 0.0396 
GMFB 1 0.991 0.932 0.818 0.157 0.0454 
RUFY1 1 0.991 1.1 0.894 0.208 0.0169 
F169A 1 0.991 1.04 0.793 0.237 0.0459 
RAB6A 1 0.99 0.937 0.621 0.071 0.0277 
KAP1 1 0.99 0.934 0.606 0.0747 0.0266 
PGM2L 1 0.99 0.941 0.714 0.135 0.0424 
LAP2A 1 0.99 1.13 0.743 0.0742 0.0328 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 102 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
S4R1W8 1 0.99 1.43 0.955 0.0955 0.0268 
ATPB 1 0.989 0.934 0.617 0.128 0.0486 
NANP 1 0.987 0.897 0.667 0.0974 0.00974 
HECD1 1 0.987 1.03 0.726 0.375 0.0199 
MYO1B 1 0.985 0.927 0.444 0.0664 0.0169 
KIF2A 1 0.985 1.04 0.553 0.192 0.0334 
Q14BI1 1 0.983 0.933 0.782 0.288 0.0472 
NDUAA 1 0.983 0.903 0.653 0.114 0.0424 
XPO1 1 0.982 1.04 0.42 0.137 0.0208 
VAC14 1 0.981 1.04 0.687 0.376 0.0494 
PHB2 1 0.979 0.918 0.476 0.107 0.0195 
HCFC1 1 0.979 1.03 0.518 0.308 0.0347 
KBTBB 1 0.979 1.04 0.309 0.115 0.0325 
E9Q616 1 0.978 1.03 0.531 0.358 0.0268 
GTR3 1 0.978 0.921 0.411 0.0795 0.0493 
HS12A 1 0.978 0.921 0.384 0.084 0.0493 
ROA3 1 0.975 1.07 0.657 0.255 0.0298 
E9PZ43 1 0.974 1.07 0.496 0.19 0.00341 
LMNA 1 0.973 1.03 0.0946 0.168 0.048 
41 1 0.972 1.05 0.57 0.286 0.023 
KKCC2 1 0.972 0.918 0.343 0.0837 0.048 
PCLO 1 0.972 0.942 0.29 0.12 0.0266 
APC 1 0.972 1.03 0.163 0.137 0.0208 
PRDX6 1 0.969 0.909 0.328 0.108 0.0358 
LSAMP 1 0.968 0.937 0.696 0.414 0.0293 
LYSM1 1 0.967 1.01 0.586 0.916 0.0268 
A1BN54 1 0.967 0.905 0.297 0.102 0.0101 
PREX1 1 0.966 0.995 0.13 0.701 0.0445 
GRIN1 1 0.965 1.02 0.255 0.315 0.0334 
NDUB7 1 0.965 0.899 0.355 0.112 0.0276 
GPM6B 1 0.96 0.858 0.475 0.112 0.0486 
NRX3A 1 0.958 1.02 0.0852 0.291 0.0208 
RS25 1 0.957 0.863 0.149 0.061 0.0459 
ACSF2 1 0.957 0.996 0.159 0.822 0.041 
HCDH 1 0.956 1.02 0.142 0.383 0.0387 
B1AQX9 1 0.954 0.919 0.0852 0.0676 0.0341 
RL27 1 0.951 0.835 0.359 0.0833 0.0226 
SO1C1 1 0.944 2 0.761 0.0705 0.0169 
RB27B 1 0.941 0.891 0.246 0.12 0.0385 
CXA1 1 0.939 1.01 0.351 0.853 0.024 
DLGP3 1 0.937 0.883 0.44 0.209 0.0424 
CIRBP 1 0.935 1.06 0.136 0.176 0.0197 
CLGN 1 0.933 1.51 0.695 0.086 0.023 
EST1C 1 0.924 1.04 0.0586 0.259 0.0409 
KCJ10 1 0.921 1.15 0.224 0.0934 0.015 
ADCY8 1 0.91 1.05 0.103 0.207 0.0268 
CO4A2 1 0.908 0.764 0.199 0.0721 0.0408 
App.12. Protein whose Abundance is Modulated by Sleep Deprivation:  App. 103 
Protein T_00 T_24 T_48 q_00_24 q_00_48 q_24_48 
RL7 1 0.904 0.722 0.293 0.105 0.00329 
TBA8 1 0.903 1.06 0.144 0.216 0.00371 
NQO1 1 0.901 1.48 0.114 0.084 0.0454 
RL35 1 0.885 0.738 0.224 0.084 0.0177 
UT1 1 0.879 1.02 0.151 0.734 0.0161 
KNG1 1 0.871 1.03 0.114 0.446 0.0169 
CFAH 1 0.868 0.996 0.108 0.925 0.041 
VTDB 1 0.86 1.02 0.0884 0.583 0.00325 
CENPE 1 0.859 0.954 0.11 0.366 0.0472 
ENOB 1 0.852 1.09 0.142 0.207 0.0478 
SYNPR 1 0.848 0.99 0.108 0.849 0.0179 
A1AT2 1 0.832 1.04 0.116 0.353 0.0169 
A1AT3 1 0.831 0.978 0.0649 0.654 0.0402 
APOA1 1 0.823 0.913 0.103 0.195 0.0115 
E9QAF8 1 0.799 0.956 0.108 0.514 0.0459 
  
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells:  App. 104 
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells 
Data are presented that show the genes identified by Cuffdiff as being modulated by at least twofold 
immediately following 6 hour illumination in opsin expressing SH-SY5Y cells, compared to dark 
maintained cells collected at the same timepoint. The FPKM value of expression is listed for Control 
and Illuminated cells, and the log2 fold change, together with the Benjamini adjusted p-value for this 
comparison.  
Ensembl_Gene_ID Gene_name Control Illuminated Log2_FC q_val 
ENSG00000279047 CTC-270D5.1 6.66E-07 0.504 19.5 0.0329 
ENSG00000232656 IDI2-AS1 0.0104 0.481 5.53 0.0477 
ENSG00000231728 LL0XNC01-116E7.2 0.224 6.26 4.8 0.00255 
ENSG00000230615 RP5-1198O20.4 0.733 12.7 4.11 0.00138 
ENSG00000183715 OPCML 0.0367 0.618 4.07 0.00138 
ENSG00000119508 NR4A3 0.182 2.71 3.89 0.00138 
ENSG00000232176 RP11-146N23.1 0.345 4.26 3.63 0.0123 
ENSG00000170345 FOS 0.841 9.78 3.54 0.00138 
ENSG00000123700 KCNJ2 0.382 4.08 3.42 0.00138 
ENSG00000225358 MIPEPP1 0.102 0.79 2.95 0.0181 
ENSG00000223414 LINC00473 9.72 70.4 2.86 0.00138 
ENSG00000130513 GDF15 13 91.4 2.81 0.00138 
ENSG00000130164 LDLR 7.13 50 2.81 0.00138 
ENSG00000176058 TPRN 6.06 40.7 2.75 0.00138 
ENSG00000230333 AC004538.3 0.216 1.36 2.66 0.0276 
ENSG00000186480 INSIG1 12.9 80.6 2.64 0.00138 
ENSG00000277813 ACEA_U3 8070 49300 2.61 0.00138 
ENSG00000213626 LBH 15.3 91 2.57 0.00138 
ENSG00000123358 NR4A1 12.8 72.1 2.49 0.00138 
ENSG00000266289 RP11-1C8.6 0.38 2.06 2.44 0.00739 
ENSG00000204584 RP11-304F15.3 0.181 0.972 2.43 0.00138 
ENSG00000160870 CYP3A7 0.138 0.728 2.4 0.0323 
ENSG00000052802 MSMO1 15.8 81.5 2.37 0.00138 
ENSG00000172339 ALG14 2.05 10.3 2.33 0.00462 
ENSG00000131471 AOC3 0.167 0.83 2.32 0.00138 
ENSG00000273674 CTD-2378E12.1 0.177 0.867 2.29 0.0264 
ENSG00000015520 NPC1L1 0.116 0.565 2.28 0.00138 
ENSG00000112773 FAM46A 5.2 25 2.27 0.00138 
ENSG00000144655 CSRNP1 1.15 5.43 2.24 0.00138 
ENSG00000134007 ADAM20 0.166 0.765 2.21 0.00138 
ENSG00000128564 VGF 247 1070 2.11 0.00138 
ENSG00000095794 CREM 19.2 82.5 2.11 0.00138 
ENSG00000274588 DGKK 0.404 1.72 2.09 0.00138 
ENSG00000163406 SLC15A2 0.274 1.16 2.08 0.00255 
ENSG00000164442 CITED2 0.284 1.19 2.06 0.00255 
ENSG00000215458 AP001053.11 0.4 1.67 2.06 0.00138 
ENSG00000103257 SLC7A5 19.8 82.2 2.05 0.00138 
ENSG00000259326 RP11-102L12.2 0.151 0.615 2.02 0.0427 
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells:  App. 105 
Ensembl_Gene_ID Gene_name Control Illuminated Log2_FC q_val 
ENSG00000166762 CATSPER2 0.65 2.6 2 0.0091 
ENSG00000112972 HMGCS1 23.2 92.7 2 0.00138 
ENSG00000116741 RGS2 15.8 62.3 1.98 0.00138 
ENSG00000179094 PER1 14.2 55.8 1.97 0.00138 
ENSG00000181773 GPR3 2.15 8.29 1.95 0.00138 
ENSG00000166401 SERPINB8 0.133 0.511 1.95 0.0091 
ENSG00000112541 PDE10A 12.2 46.7 1.94 0.00138 
ENSG00000162616 DNAJB4 10.8 41.3 1.94 0.00138 
ENSG00000166546 BEAN1 0.286 1.09 1.93 0.0239 
ENSG00000272468 RP1-86C11.7 0.674 2.55 1.92 0.0145 
ENSG00000124762 CDKN1A 142 537 1.92 0.00138 
ENSG00000277352 KB-68A7.2 2.99 11.2 1.91 0.00138 
ENSG00000149201 CCDC81 0.385 1.44 1.91 0.00138 
ENSG00000067064 IDI1 11.7 44 1.91 0.00138 
ENSG00000105825 TFPI2 64.3 236 1.87 0.00138 
ENSG00000132002 DNAJB1 35.8 128 1.84 0.00138 
ENSG00000106366 SERPINE1 0.269 0.953 1.83 0.00138 
ENSG00000075426 FOSL2 6.77 23.4 1.79 0.00138 
ENSG00000179528 LBX2 2.46 8.44 1.78 0.0447 
ENSG00000278959 RP11-455I9.1 1.03 3.43 1.74 0.00138 
ENSG00000236453 AC003092.1 0.601 2.01 1.74 0.0264 
ENSG00000085465 OVGP1 0.57 1.9 1.74 0.00138 
ENSG00000257017 HP 0.207 0.691 1.74 0.04 
ENSG00000253490 AC145110.1 0.204 0.664 1.71 0.00138 
ENSG00000237854 LINC00674 3.18 10.3 1.7 0.00138 
ENSG00000242265 PEG10 40.3 131 1.7 0.00138 
ENSG00000162772 ATF3 8.07 25.8 1.67 0.00138 
ENSG00000100625 SIX4 1.18 3.7 1.66 0.00138 
ENSG00000167210 LOXHD1 0.186 0.582 1.65 0.0318 
ENSG00000013375 PGM3 17.7 55.3 1.64 0.00138 
ENSG00000280287 RP13-554M15.7 1.08 3.33 1.63 0.00462 
ENSG00000272512 RP11-54O7.17 0.565 1.73 1.62 0.00138 
ENSG00000177508 IRX3 0.413 1.27 1.62 0.00558 
ENSG00000177710 SLC35G5 0.225 0.688 1.61 0.0357 
ENSG00000044574 HSPA5 55.6 170 1.61 0.00138 
ENSG00000010818 HIVEP2 8.85 27.1 1.61 0.0406 
ENSG00000145632 PLK2 6.31 19.1 1.6 0.00138 
ENSG00000232872 CTAGE3P 0.233 0.698 1.58 0.00138 
ENSG00000229091 HSPA8P8 0.361 1.07 1.56 0.00992 
ENSG00000197019 SERTAD1 1.21 3.59 1.56 0.00138 
ENSG00000182308 DCAF4L1 0.226 0.666 1.56 0.00138 
ENSG00000140743 CDR2 5.59 16.3 1.54 0.00138 
ENSG00000120694 HSPH1 40.1 116 1.53 0.00138 
ENSG00000165181 C9orf84 0.167 0.484 1.53 0.00138 
ENSG00000197978 GOLGA6L9 0.615 1.78 1.53 0.0208 
ENSG00000099251 HSD17B7P2 0.878 2.53 1.53 0.00138 
ENSG00000164326 CARTPT 5.34 15.4 1.53 0.00138 
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells:  App. 106 
Ensembl_Gene_ID Gene_name Control Illuminated Log2_FC q_val 
ENSG00000167508 MVD 10.5 29.7 1.5 0.00138 
ENSG00000154734 ADAMTS1 42.4 119 1.49 0.00138 
ENSG00000120875 DUSP4 66.7 186 1.48 0.00138 
ENSG00000224411 RP11-1033A18.1 19.4 53.9 1.47 0.00138 
ENSG00000152457 DCLRE1C 4.72 13.1 1.47 0.00138 
ENSG00000196689 TRPV1 0.543 1.49 1.46 0.0447 
ENSG00000104549 SQLE 16.1 44.4 1.46 0.00138 
ENSG00000233974 RP11-823P9.3 0.603 1.66 1.46 0.0167 
ENSG00000198857 HSD3BP5 0.549 1.48 1.43 0.0065 
ENSG00000276445 LLNLR-268E12.1 1.04 2.8 1.42 0.0208 
ENSG00000116717 GADD45A 14 37.4 1.42 0.00138 
ENSG00000218208 RP11-367G18.2 1.54 4.1 1.41 0.0335 
ENSG00000268654 MIMT1 0.29 0.769 1.41 0.0341 
ENSG00000105321 CCDC9 4.72 12.3 1.39 0.00138 
ENSG00000280219 RP11-752L20.3 0.465 1.21 1.38 0.00739 
ENSG00000021826 CPS1 0.779 2.03 1.38 0.00558 
ENSG00000070495 JMJD6 17.3 44.8 1.37 0.00138 
ENSG00000100219 XBP1 42.4 109 1.36 0.00138 
ENSG00000113161 HMGCR 26.2 67.3 1.36 0.00138 
ENSG00000176142 TMEM39A 11.2 28.6 1.36 0.00138 
ENSG00000080824 HSP90AA1 280 715 1.35 0.00138 
ENSG00000006327 TNFRSF12A 10.5 26.6 1.34 0.00138 
ENSG00000273356 RP11-804H8.6 0.749 1.9 1.34 0.0239 
ENSG00000203589 RP5-886K2.1 0.474 1.19 1.33 0.0201 
ENSG00000268234 FKBP4P6 0.334 0.835 1.32 0.0323 
ENSG00000126368 NR1D1 2.6 6.49 1.32 0.0437 
ENSG00000261087 KB-1460A1.5 1.3 3.23 1.31 0.00138 
ENSG00000139278 GLIPR1 1.16 2.87 1.31 0.00462 
ENSG00000131480 AOC2 1.48 3.66 1.3 0.00138 
ENSG00000171790 SLFNL1 0.63 1.55 1.3 0.00138 
ENSG00000280385 AP000648.5 2.98 7.29 1.29 0.00138 
ENSG00000130522 JUND 81.9 200 1.29 0.00138 
ENSG00000111664 GNB3 2.36 5.71 1.28 0.016 
ENSG00000147059 SPIN2A 0.279 0.668 1.26 0.0201 
ENSG00000115232 ITGA4 1.99 4.73 1.25 0.00138 
ENSG00000184205 TSPYL2 6.34 15 1.24 0.00138 
ENSG00000083857 FAT1 50.7 120 1.24 0.00138 
ENSG00000170458 CD14 0.515 1.22 1.24 0.00138 
ENSG00000172071 EIF2AK3 8.48 20 1.24 0.00138 
ENSG00000164675 IQUB 0.386 0.905 1.23 0.0214 
ENSG00000204950 LRRC10B 1.3 3.03 1.23 0.00138 
ENSG00000228709 AP001065.15 28.1 65.7 1.22 0.00138 
ENSG00000128590 DNAJB9 9.92 23.1 1.22 0.0264 
ENSG00000105327 BBC3 16 37.1 1.22 0.00138 
ENSG00000110172 CHORDC1 14.1 32.6 1.21 0.00138 
ENSG00000160570 DEDD2 12.6 29.1 1.2 0.00138 
ENSG00000273319 RP11-138A9.2 0.515 1.18 1.2 0.00138 
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells:  App. 107 
Ensembl_Gene_ID Gene_name Control Illuminated Log2_FC q_val 
ENSG00000104856 RELB 1.67 3.85 1.2 0.00138 
ENSG00000276112 AL353644.6 7760 17700 1.19 0.00138 
ENSG00000261646 RP11-489G11.3 0.34 0.776 1.19 0.0091 
ENSG00000173846 PLK3 7.03 16 1.19 0.00138 
ENSG00000213599 SLX1A-SULT1A3 0.487 1.1 1.17 0.00826 
ENSG00000279602 CTD-3014M21.1 3.08 6.94 1.17 0.00138 
ENSG00000248905 FMN1 5.43 12.2 1.17 0.00138 
ENSG00000106211 HSPB1 249 560 1.17 0.00138 
ENSG00000260518 BMS1P8 3.54 7.87 1.15 0.00138 
ENSG00000280347 AC000123.2 3.91 8.7 1.15 0.00138 
ENSG00000059728 MXD1 3.93 8.65 1.14 0.00138 
ENSG00000215007 DNAJA1P3 0.482 1.06 1.13 0.0341 
ENSG00000109089 CDR2L 23.5 51.5 1.13 0.00138 
ENSG00000129993 CBFA2T3 2.78 6.08 1.13 0.00138 
ENSG00000275993 CH507-42P11.8 0.588 1.29 1.13 0.00138 
ENSG00000260645 RP11-250B2.5 0.926 2.03 1.13 0.00138 
ENSG00000268089 GABRQ 0.331 0.722 1.13 0.00138 
ENSG00000203685 C1orf95 0.369 0.802 1.12 0.0467 
ENSG00000233967 RP11-250B2.3 1.36 2.96 1.12 0.0341 
ENSG00000236255 AC009404.2 3.32 7.2 1.12 0.00138 
ENSG00000130766 SESN2 6.44 13.9 1.11 0.00138 
ENSG00000280111 CTA-292E10.9 0.493 1.06 1.1 0.0346 
ENSG00000228502 EEF1A1P11 0.62 1.33 1.1 0.00826 
ENSG00000260034 LCMT1-AS2 1.49 3.18 1.1 0.00255 
ENSG00000135362 PRR5L 0.551 1.18 1.1 0.0487 
ENSG00000141441 GAREM 23.7 50.7 1.1 0.00138 
ENSG00000181450 ZNF678 8.03 16.9 1.08 0.00138 
ENSG00000233588 CYP51A1P2 3.34 7.04 1.08 0.00138 
ENSG00000131016 AKAP12 12.2 25.4 1.06 0.00138 
ENSG00000137094 DNAJB5 15.1 31.5 1.06 0.00138 
ENSG00000241095 CYP51A1P1 2.74 5.73 1.06 0.00138 
ENSG00000272106 RP11-345P4.9 7.64 15.9 1.06 0.00138 
ENSG00000117479 SLC19A2 5.91 12.3 1.06 0.00138 
ENSG00000223345 HIST2H2BA 7.23 15 1.05 0.00255 
ENSG00000215156 RP11-1023L17.2 1.57 3.24 1.05 0.00255 
ENSG00000181026 AEN 25.6 52.9 1.05 0.00138 
ENSG00000269955 LUC7L2 7.17 14.8 1.05 0.0421 
ENSG00000130254 SAFB2 19.9 41.1 1.04 0.0138 
ENSG00000128683 GAD1 5.91 12.2 1.04 0.00138 
ENSG00000185304 RGPD2 0.259 0.533 1.04 0.00739 
ENSG00000149257 SERPINH1 40.7 83.6 1.04 0.00138 
ENSG00000272947 RP11-71H17.9 1.57 3.21 1.03 0.016 
ENSG00000234857 HNRNPUL2-BSCL2 7.13 14.6 1.03 0.0167 
ENSG00000133874 RNF122 13.9 28.3 1.03 0.00138 
ENSG00000277007 RP11-392O17.2 0.329 0.673 1.03 0.0174 
ENSG00000279792 RP11-893F2.18 2.15 4.4 1.03 0.0432 
ENSG00000120306 CYSTM1 4.85 9.82 1.02 0.00255 
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells:  App. 108 
Ensembl_Gene_ID Gene_name Control Illuminated Log2_FC q_val 
ENSG00000176641 RNF152 27.3 55.4 1.02 0.00362 
ENSG00000271204 RP11-138A9.1 0.549 1.11 1.02 0.0091 
ENSG00000173276 ZBTB21 8.73 17.6 1.01 0.00138 
ENSG00000168003 SLC3A2 32.3 65.1 1.01 0.00138 
ENSG00000166455 C16orf46 2.36 4.74 1.01 0.00138 
ENSG00000280047 CTC-463A16.1 0.407 0.817 1.01 0.00362 
ENSG00000263931 RP11-180P8.1 11.8 23.7 1 0.00138 
ENSG00000112599 GUCA1B 0.8 1.6 1 0.0239 
ENSG00000134070 IRAK2 0.369 0.739 1 0.00255 
ENSG00000265763 ZNF488 0.61 0.304 -1 0.00255 
ENSG00000151657 KIN 9.35 4.65 -1.01 0.0312 
ENSG00000187952 HS6ST1P1 3.66 1.81 -1.02 0.00138 
ENSG00000204947 ZNF425 2.4 1.19 -1.02 0.0346 
ENSG00000214654 RP11-27I1.4 8.85 4.34 -1.03 0.00138 
ENSG00000160111 CPAMD8 1.01 0.493 -1.03 0.0442 
ENSG00000224383 PRR29 1.42 0.698 -1.03 0.0379 
ENSG00000128394 APOBEC3F 2.38 1.16 -1.03 0.0145 
ENSG00000136531 SCN2A 5.54 2.7 -1.04 0.00138 
ENSG00000113196 HAND1 28.2 13.7 -1.04 0.00138 
ENSG00000277287 RP4-794I6.4 0.912 0.442 -1.04 0.00138 
ENSG00000148225 WDR31 1.71 0.829 -1.04 0.00462 
ENSG00000008283 CYB561 181 87.5 -1.05 0.00138 
ENSG00000182747 SLC35D3 6.65 3.22 -1.05 0.00138 
ENSG00000205634 LINC00898 0.628 0.304 -1.05 0.0174 
ENSG00000179921 GPBAR1 2.07 1 -1.05 0.00138 
ENSG00000066926 FECH 19.1 9.22 -1.05 0.00138 
ENSG00000255690 TRIL 12.6 6.06 -1.06 0.00138 
ENSG00000198246 SLC29A3 6.1 2.93 -1.06 0.00138 
ENSG00000214248 CTD-3193O13.12 0.744 0.355 -1.07 0.00138 
ENSG00000007237 GAS7 2.81 1.34 -1.07 0.0487 
ENSG00000143365 RORC 0.735 0.349 -1.08 0.0065 
ENSG00000176381 PRR18 1.4 0.659 -1.08 0.0294 
ENSG00000277831 RP11-269C23.5 3.72 1.75 -1.09 0.0447 
ENSG00000270964 RP11-502I4.3 1.48 0.699 -1.09 0.00138 
ENSG00000228903 RASA4CP 1.3 0.611 -1.09 0.0346 
ENSG00000230658 KLHL7-AS1 1.04 0.487 -1.09 0.00138 
ENSG00000119938 PPP1R3C 4.7 2.21 -1.09 0.00138 
ENSG00000130518 KIAA1683 4.43 2.08 -1.09 0.00138 
ENSG00000071794 HLTF 26 12.2 -1.09 0.00138 
ENSG00000181004 BBS12 3.26 1.53 -1.09 0.00138 
ENSG00000130700 GATA5 2 0.934 -1.1 0.00138 
ENSG00000171729 TMEM51 25.1 11.7 -1.1 0.00138 
ENSG00000172086 KRCC1 13.7 6.36 -1.11 0.00138 
ENSG00000225391 NHEG1 0.848 0.391 -1.12 0.0427 
ENSG00000272425 RP11-363E6.4 0.693 0.319 -1.12 0.0227 
ENSG00000141540 TTYH2 5.62 2.59 -1.12 0.00138 
ENSG00000187624 C17orf97 2.67 1.23 -1.12 0.00138 
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells:  App. 109 
Ensembl_Gene_ID Gene_name Control Illuminated Log2_FC q_val 
ENSG00000265692 RP13-516M14.4 0.826 0.379 -1.12 0.0188 
ENSG00000258701 LINC00638 1.5 0.689 -1.13 0.00138 
ENSG00000176909 MAMSTR 2.07 0.942 -1.13 0.00138 
ENSG00000205683 DPF3 2.65 1.2 -1.14 0.0195 
ENSG00000178403 NEUROG2 23.2 10.5 -1.15 0.00138 
ENSG00000165555 NOXRED1 0.641 0.288 -1.15 0.0312 
ENSG00000158008 EXTL1 2.35 1.05 -1.15 0.0427 
ENSG00000008196 TFAP2B 103 46.2 -1.16 0.00138 
ENSG00000175279 APITD1 10.9 4.87 -1.16 0.00138 
ENSG00000156253 RWDD2B 15.9 7.06 -1.17 0.00138 
ENSG00000184828 ZBTB7C 3.63 1.61 -1.18 0.00138 
ENSG00000152582 SPEF2 1.8 0.796 -1.18 0.0452 
ENSG00000172159 FRMD3 11.3 4.99 -1.18 0.00138 
ENSG00000157978 LDLRAP1 1.44 0.637 -1.18 0.0221 
ENSG00000261251 RP3-388M5.9 1.16 0.509 -1.18 0.0312 
ENSG00000251408 RP11-586D19.2 0.691 0.304 -1.19 0.0294 
ENSG00000180938 ZNF572 2.96 1.3 -1.19 0.00138 
ENSG00000168874 ATOH8 0.894 0.392 -1.19 0.0167 
ENSG00000262075 DKFZP434A062 0.539 0.236 -1.19 0.00138 
ENSG00000260400 RP11-119F7.5 4.32 1.89 -1.19 0.00138 
ENSG00000180787 ZFP3 5.39 2.35 -1.2 0.00138 
ENSG00000197557 TTC30A 6.37 2.76 -1.21 0.00138 
ENSG00000179240 RP11-111M22.2 5.45 2.36 -1.21 0.00138 
ENSG00000121075 TBX4 2.96 1.28 -1.21 0.00138 
ENSG00000163827 LRRC2 0.697 0.301 -1.21 0.0174 
ENSG00000106018 VIPR2 1.33 0.573 -1.21 0.00138 
ENSG00000159674 SPON2 3.25 1.4 -1.22 0.0065 
ENSG00000231345 RP11-564C4.6 2.64 1.13 -1.22 0.00138 
ENSG00000213171 LINGO4 0.901 0.386 -1.22 0.00138 
ENSG00000266405 CBX3P2 2.23 0.956 -1.22 0.0252 
ENSG00000004799 PDK4 1.28 0.547 -1.23 0.0091 
ENSG00000260589 STAM-AS1 1.05 0.443 -1.24 0.00362 
ENSG00000160172 FAM86C2P 3.78 1.6 -1.24 0.00138 
ENSG00000227087 RBMX2P5 1.36 0.574 -1.25 0.0395 
ENSG00000170915 PAQR8 27.1 11.2 -1.27 0.00138 
ENSG00000164743 C8orf48 0.863 0.355 -1.28 0.0174 
ENSG00000141668 CBLN2 6.76 2.78 -1.28 0.00138 
ENSG00000171388 APLN 1.79 0.732 -1.29 0.00138 
ENSG00000278002 RP11-596C23.2 1.45 0.592 -1.29 0.00138 
ENSG00000135835 KIAA1614 6.02 2.46 -1.29 0.0138 
ENSG00000047621 C12orf4 6.15 2.5 -1.3 0.00138 
ENSG00000197180 CH17-340M24.3 3.26 1.32 -1.3 0.0123 
ENSG00000127903 ZNF835 2.38 0.959 -1.31 0.00138 
ENSG00000157429 ZNF19 3.64 1.46 -1.32 0.0174 
ENSG00000161544 CYGB 83.4 33.3 -1.32 0.00138 
ENSG00000163449 TMEM169 14 5.59 -1.32 0.00138 
ENSG00000074416 MGLL 3.56 1.41 -1.34 0.00739 
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells:  App. 110 
Ensembl_Gene_ID Gene_name Control Illuminated Log2_FC q_val 
ENSG00000119714 GPR68 2.33 0.915 -1.35 0.00138 
ENSG00000117971 CHRNB4 18.5 7.18 -1.36 0.00138 
ENSG00000186280 KDM4D 1.58 0.608 -1.38 0.00138 
ENSG00000139832 RAB20 2.28 0.873 -1.38 0.00138 
ENSG00000171631 P2RY6 1.54 0.588 -1.39 0.00138 
ENSG00000026508 CD44 0.544 0.207 -1.39 0.00255 
ENSG00000280187 CTC-351M12.1 2.52 0.958 -1.4 0.00138 
ENSG00000251484 RP5-1065J22.4 2.04 0.771 -1.4 0.0452 
ENSG00000269051 CTD-2245F17.3 3.04 1.15 -1.4 0.0341 
ENSG00000275329 RP11-83N9.6 0.69 0.259 -1.41 0.0131 
ENSG00000204815 TTC25 3.13 1.17 -1.42 0.00362 
ENSG00000140986 RPL3L 0.672 0.247 -1.44 0.0138 
ENSG00000108622 ICAM2 2.74 0.995 -1.46 0.00138 
ENSG00000267355 RPL9P29 2.58 0.924 -1.48 0.0233 
ENSG00000110324 IL10RA 1.33 0.473 -1.49 0.0437 
ENSG00000212916 MAP10 2.3 0.816 -1.49 0.00138 
ENSG00000111859 NEDD9 9.05 3.18 -1.51 0.00138 
ENSG00000260063 RP5-968P14.2 0.789 0.277 -1.51 0.00462 
ENSG00000247317 RP11-273G15.2 2.46 0.86 -1.51 0.00138 
ENSG00000236437 AP001891.1 1.94 0.675 -1.52 0.00138 
ENSG00000234996 RP11-480I12.7 0.845 0.294 -1.52 0.00138 
ENSG00000198416 ZNF658B 0.49 0.17 -1.53 0.00255 
ENSG00000229921 KIF25-AS1 0.66 0.228 -1.53 0.00138 
ENSG00000251661 RP11-326C3.11 2.03 0.702 -1.53 0.0195 
ENSG00000166510 CCDC68 0.583 0.2 -1.55 0.00462 
ENSG00000224090 AC097468.4 1.45 0.483 -1.58 0.00462 
ENSG00000121966 CXCR4 219 71.7 -1.61 0.00138 
ENSG00000247796 CTD-2366F13.1 1.75 0.57 -1.62 0.027 
ENSG00000162407 PPAP2B 6.09 1.98 -1.62 0.00138 
ENSG00000249669 MIR143HG 4.6 1.48 -1.63 0.00138 
ENSG00000234944 RP11-124O11.1 1.32 0.422 -1.65 0.00138 
ENSG00000100336 APOL4 0.98 0.311 -1.66 0.0115 
ENSG00000269936 MIR145 3.55 1.11 -1.68 0.00138 
ENSG00000100060 MFNG 1.23 0.384 -1.68 0.0174 
ENSG00000279137 RP11-205K6.3 1.03 0.322 -1.68 0.00138 
ENSG00000273218 LLNLR-246C6.1 0.562 0.175 -1.69 0.0195 
ENSG00000272960 RP11-339B21.15 6.03 1.87 -1.69 0.0406 
ENSG00000113578 FGF1 2.21 0.679 -1.7 0.0123 
ENSG00000237954 RP11-14O19.2 3.34 1.01 -1.73 0.00138 
ENSG00000253669 KB-1732A1.1 0.524 0.154 -1.77 0.0497 
ENSG00000041353 RAB27B 4.86 1.42 -1.78 0.00138 
ENSG00000130222 GADD45G 1.18 0.332 -1.83 0.00826 
ENSG00000168404 MLKL 0.621 0.174 -1.83 0.0312 
ENSG00000172554 SNTG2 0.606 0.167 -1.86 0.03 
ENSG00000169302 STK32A 0.96 0.258 -1.89 0.0188 
ENSG00000139352 ASCL1 6.61 1.73 -1.93 0.00138 
ENSG00000224008 LINC01441 0.93 0.241 -1.95 0.00138 
App.13. Genes Affected by 6 hour Illumination in SH-SY5Y Cells:  App. 111 
Ensembl_Gene_ID Gene_name Control Illuminated Log2_FC q_val 
ENSG00000187800 PEAR1 6.15 1.5 -2.04 0.00138 
ENSG00000134323 MYCN 5.82 1.39 -2.07 0.00138 
ENSG00000165695 AK8 1.18 0.279 -2.08 0.0447 
ENSG00000112769 LAMA4 1.11 0.258 -2.1 0.00138 
ENSG00000100302 RASD2 0.855 0.194 -2.14 0.00138 
ENSG00000101292 PROKR2 1.78 0.393 -2.18 0.00138 
ENSG00000108771 DHX58 0.92 0.198 -2.22 0.0167 
ENSG00000231473 LINC00441 1.39 0.285 -2.29 0.0115 
ENSG00000170983 LINC00208 0.594 0.116 -2.35 0.00558 
ENSG00000148950 IMMP1L 247 47.1 -2.39 0.00138 
ENSG00000189350 FAM179A 0.543 0.0978 -2.47 0.0258 
ENSG00000266524 GDF10 3.13 0.555 -2.49 0.00138 
ENSG00000145975 FAM217A 1.09 0.0348 -4.97 0.0201 
 
  
App.14. Genes affected by 12 hour illumination:  App. 112 
App.14. Genes affected by 12 hour illumination 
Data are presented that show the genes identified by Cuffdiff as being modulated by at least twofold 
immediately following 12 hour illumination in opsin expressing SH-SY5Y cells, compared to dark 
maintained cells collected at the same timepoint. The FPKM value of expression is listed for Control 
and Illuminated cells, and the log2 fold change, together with the Benjamini adjusted p-value for this 
comparison.  
 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000225358 MIPEPP1 0.0265 0.467 4.14 0.0368 
ENSG00000167210 LOXHD1 0.0964 1.28 3.73 0.00138 
ENSG00000172901 AQPEP 0.0552 0.726 3.72 0.0115 
ENSG00000170345 FOS 1.13 14.8 3.71 0.00138 
ENSG00000152818 UTRN 0.204 2.22 3.45 0.00138 
ENSG00000130513 GDF15 22.6 210 3.22 0.00138 
ENSG00000204584 RP11-304F15.3 0.143 1.16 3.02 0.00138 
ENSG00000179950 PUF60 0.181 1.44 2.99 0.00138 
ENSG00000186480 INSIG1 12.7 96.1 2.92 0.00138 
ENSG00000123700 KCNJ2 0.29 2.16 2.9 0.00138 
ENSG00000130675 MNX1 0.117 0.864 2.88 0.0416 
ENSG00000044574 HSPA5 50.8 365 2.84 0.00138 
ENSG00000052802 MSMO1 16.6 118 2.83 0.00138 
ENSG00000130164 LDLR 7.39 51.5 2.8 0.00138 
ENSG00000172927 MYEOV 0.158 1.1 2.8 0.00558 
ENSG00000176358 TAC4 0.198 1.33 2.74 0.00826 
ENSG00000204055 RP11-247A12.2 0.246 1.61 2.71 0.0107 
ENSG00000176761 ZNF285B 0.139 0.897 2.69 0.00138 
ENSG00000119508 NR4A3 0.265 1.69 2.67 0.00138 
ENSG00000227676 LINC01068 0.431 2.48 2.52 0.0153 
ENSG00000105219 CNTD2 0.164 0.907 2.47 0.00462 
ENSG00000112972 HMGCS1 22.6 124 2.46 0.00138 
ENSG00000004776 HSPB6 0.124 0.681 2.46 0.0294 
ENSG00000270011 ZNF559-ZNF177 0.836 4.44 2.41 0.00138 
ENSG00000124762 CDKN1A 192 1010 2.4 0.00138 
ENSG00000237854 LINC00674 3.35 17.6 2.39 0.00138 
ENSG00000105825 TFPI2 67.2 349 2.38 0.00138 
ENSG00000253955 CTB-33O18.3 0.167 0.853 2.36 0.00362 
ENSG00000270948 RP11-460N20.7 0.259 1.3 2.33 0.0288 
ENSG00000131471 AOC3 0.31 1.52 2.3 0.00138 
ENSG00000015520 NPC1L1 0.196 0.937 2.26 0.00138 
ENSG00000226803 RP11-203B9.4 1.59 7.43 2.22 0.0131 
ENSG00000274588 DGKK 0.394 1.83 2.22 0.00138 
ENSG00000160471 COX6B2 2.09 9.64 2.21 0.00138 
ENSG00000278959 RP11-455I9.1 0.9 4.13 2.2 0.00138 
ENSG00000165457 FOLR2 0.147 0.672 2.19 0.0123 
App.14. Genes affected by 12 hour illumination:  App. 113 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000162772 ATF3 8.01 36.5 2.19 0.00138 
ENSG00000171786 NHLH1 0.109 0.494 2.18 0.00739 
ENSG00000179094 PER1 12.7 57.6 2.18 0.00138 
ENSG00000132002 DNAJB1 35.8 160 2.17 0.00138 
ENSG00000175197 DDIT3 10.9 48.7 2.16 0.0167 
ENSG00000181773 GPR3 2.06 8.91 2.11 0.00138 
ENSG00000128564 VGF 247 1060 2.1 0.00138 
ENSG00000242265 PEG10 39.6 165 2.06 0.00138 
ENSG00000182308 DCAF4L1 0.25 1.04 2.05 0.00138 
ENSG00000072858 SIDT1 0.169 0.696 2.04 0.0131 
ENSG00000140961 OSGIN1 1.55 6.34 2.03 0.00138 
ENSG00000144655 CSRNP1 1.22 4.94 2.02 0.00138 
ENSG00000102524 TNFSF13B 0.427 1.74 2.02 0.0174 
ENSG00000145050 MANF 26.9 109 2.02 0.00138 
ENSG00000131480 AOC2 1.55 6.24 2.01 0.00138 
ENSG00000165181 C9orf84 0.119 0.476 2 0.00138 
ENSG00000279602 CTD-3014M21.1 2.8 11.2 2 0.00138 
ENSG00000164442 CITED2 0.145 0.582 2 0.0065 
ENSG00000175592 FOSL1 0.22 0.873 1.99 0.00138 
ENSG00000067064 IDI1 11.4 44.6 1.97 0.00138 
ENSG00000213626 LBH 18.3 71.6 1.97 0.00138 
ENSG00000147724 FAM135B 0.179 0.695 1.95 0.00138 
ENSG00000118985 ELL2 0.567 2.18 1.95 0.0201 
ENSG00000250337 LINC01021 1.87 7.16 1.94 0.00138 
ENSG00000145632 PLK2 7.32 27.7 1.92 0.00138 
ENSG00000162631 NTNG1 0.278 1.05 1.92 0.00138 
ENSG00000260640 KB-1000E4.2 0.812 3.06 1.91 0.0442 
ENSG00000189292 FAM150B 0.371 1.39 1.9 0.0138 
ENSG00000186395 KRT10 1.06 3.94 1.9 0.00138 
ENSG00000111711 GOLT1B 14.5 53.7 1.89 0.00138 
ENSG00000123358 NR4A1 14 51.4 1.88 0.00138 
ENSG00000210082 MT-RNR2 161 576 1.84 0.00138 
ENSG00000197019 SERTAD1 1.26 4.44 1.82 0.00138 
ENSG00000275066 SYNRG 0.363 1.27 1.81 0.0406 
ENSG00000100625 SIX4 1.19 4.12 1.79 0.00138 
ENSG00000211459 MT-RNR1 155 534 1.78 0.00138 
ENSG00000115232 ITGA4 1.96 6.72 1.78 0.00138 
ENSG00000112773 FAM46A 5.12 17.4 1.77 0.00138 
ENSG00000104549 SQLE 16.3 55.3 1.76 0.00138 
ENSG00000105321 CCDC9 4.9 16.6 1.76 0.00138 
ENSG00000162616 DNAJB4 13.4 45.4 1.76 0.00138 
ENSG00000126368 NR1D1 3.09 10.4 1.75 0.00138 
ENSG00000276445 LLNLR-268E12.1 1.59 5.27 1.73 0.00138 
ENSG00000236453 AC003092.1 1.07 3.55 1.73 0.00138 
ENSG00000125657 TNFSF9 0.199 0.656 1.72 0.0131 
ENSG00000175356 SCUBE2 0.333 1.1 1.72 0.0335 
ENSG00000116717 GADD45A 16.2 53.3 1.72 0.00138 
App.14. Genes affected by 12 hour illumination:  App. 114 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000259326 RP11-102L12.2 0.226 0.742 1.72 0.0346 
ENSG00000116741 RGS2 15.3 50.1 1.71 0.00138 
ENSG00000163141 BNIPL 0.989 3.21 1.7 0.00362 
ENSG00000266289 RP11-1C8.6 0.649 2.09 1.69 0.00826 
ENSG00000182223 ZAR1 0.25 0.802 1.68 0.0123 
ENSG00000074416 MGLL 2.54 8.13 1.68 0.0201 
ENSG00000114315 HES1 1.23 3.9 1.67 0.00138 
ENSG00000106211 HSPB1 241 762 1.66 0.00138 
ENSG00000128590 DNAJB9 9.99 31.1 1.64 0.00138 
ENSG00000102580 DNAJC3 4.34 13.5 1.64 0.00138 
ENSG00000272716 RP11-563N4.1 0.929 2.89 1.64 0.00362 
ENSG00000104856 RELB 1.59 4.92 1.64 0.00138 
ENSG00000100219 XBP1 39.8 122 1.62 0.00138 
ENSG00000070495 JMJD6 16.8 51.4 1.62 0.00138 
ENSG00000177337 DLGAP1-AS1 2 6.12 1.61 0.0323 
ENSG00000167508 MVD 13.8 41.5 1.59 0.00138 
ENSG00000271840 RP1-224A6.9 0.864 2.59 1.59 0.00255 
ENSG00000166592 RRAD 0.26 0.779 1.58 0.0201 
ENSG00000262772 RP11-353N14.2 0.198 0.592 1.58 0.0239 
ENSG00000240132 ETF1P2 1.13 3.38 1.58 0.0306 
ENSG00000168003 SLC3A2 31.9 95.1 1.58 0.00138 
ENSG00000149257 SERPINH1 44 131 1.58 0.00138 
ENSG00000248905 FMN1 6.18 18.3 1.57 0.00138 
ENSG00000234857 HNRNPUL2-BSCL2 4.12 12.2 1.56 0.0357 
ENSG00000103257 SLC7A5 21.8 64.2 1.56 0.00138 
ENSG00000106366 SERPINE1 0.373 1.1 1.56 0.00138 
ENSG00000087074 PPP1R15A 6.97 20.5 1.56 0.00138 
ENSG00000108551 RASD1 0.584 1.72 1.56 0.00138 
ENSG00000095794 CREM 18.4 53.7 1.55 0.00138 
ENSG00000260708 CTA-29F11.1 13.8 40.2 1.54 0.00138 
ENSG00000130066 SAT1 7.18 20.9 1.54 0.00138 
ENSG00000140743 CDR2 6.33 18.3 1.53 0.00138 
ENSG00000095303 PTGS1 2.62 7.59 1.53 0.00138 
ENSG00000177710 SLC35G5 0.242 0.699 1.53 0.0432 
ENSG00000280184 AL023806.1 0.52 1.49 1.52 0.00138 
ENSG00000143217 PVRL4 0.678 1.94 1.52 0.00138 
ENSG00000149201 CCDC81 0.548 1.56 1.51 0.00138 
ENSG00000273356 RP11-804H8.6 1.05 2.96 1.5 0.00138 
ENSG00000170458 CD14 0.658 1.86 1.5 0.00138 
ENSG00000139278 GLIPR1 1.26 3.55 1.49 0.00138 
ENSG00000120875 DUSP4 80.3 226 1.49 0.00138 
ENSG00000166455 C16orf46 2.7 7.52 1.48 0.0201 
ENSG00000120437 ACAT2 7.54 21 1.48 0.0201 
ENSG00000166598 HSP90B1 174 483 1.47 0.00138 
ENSG00000181450 ZNF678 8.04 22.2 1.46 0.00138 
ENSG00000121068 TBX2 69.1 190 1.46 0.00138 
ENSG00000080824 HSP90AA1 266 730 1.46 0.00138 
App.14. Genes affected by 12 hour illumination:  App. 115 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000118194 TNNT2 0.313 0.859 1.46 0.0174 
ENSG00000136235 GPNMB 0.413 1.13 1.45 0.0091 
ENSG00000196139 AKR1C3 0.704 1.92 1.45 0.00462 
ENSG00000125844 RRBP1 6 16.4 1.45 0.00138 
ENSG00000223414 LINC00473 10.7 29.1 1.45 0.00138 
ENSG00000106258 CYP3A5 0.947 2.57 1.44 0.00138 
ENSG00000085465 OVGP1 0.865 2.34 1.44 0.00362 
ENSG00000148600 CDHR1 0.295 0.797 1.43 0.0115 
ENSG00000267280 TBX2-AS1 92.6 250 1.43 0.0416 
ENSG00000113161 HMGCR 25.8 69.6 1.43 0.00138 
ENSG00000182827 ACBD3 25.1 67.6 1.43 0.00138 
ENSG00000108256 NUFIP2 15.6 41.9 1.43 0.00138 
ENSG00000173846 PLK3 7.63 20.5 1.43 0.00138 
ENSG00000135842 FAM129A 0.276 0.742 1.43 0.00362 
ENSG00000147604 RPL7 8.59 23 1.42 0.00362 
ENSG00000167074 TEF 2.2 5.91 1.42 0.00138 
ENSG00000119777 TMEM214 26.8 71.8 1.42 0.00138 
ENSG00000059728 MXD1 4.07 10.8 1.42 0.00138 
ENSG00000198300 PEG3 6.13 16.4 1.42 0.00138 
ENSG00000185684 EP400NL 3.36 8.94 1.41 0.00138 
ENSG00000234704 BRD2 1.83 4.85 1.41 0.00138 
ENSG00000262074 SNORD3B-2 1.27 3.36 1.41 0.00362 
ENSG00000111664 GNB3 2.91 7.71 1.4 0.00255 
ENSG00000079459 FDFT1 49.8 131 1.4 0.00138 
ENSG00000155660 PDIA4 74.6 196 1.4 0.00138 
ENSG00000144674 GOLGA4 17.1 45 1.4 0.00138 
ENSG00000168389 MFSD2A 1.82 4.76 1.39 0.00138 
ENSG00000241095 CYP51A1P1 2.94 7.68 1.38 0.00138 
ENSG00000176142 TMEM39A 11.9 31 1.38 0.00138 
ENSG00000229670 PKP4P1 0.269 0.698 1.38 0.00462 
ENSG00000176641 RNF152 27.4 70.7 1.36 0.00138 
ENSG00000151135 TMEM263 17.5 44.8 1.36 0.00138 
ENSG00000130766 SESN2 6.15 15.7 1.36 0.00138 
ENSG00000099251 HSD17B7P2 1.08 2.77 1.35 0.00138 
ENSG00000224411 RP11-1033A18.1 19 48.3 1.35 0.00138 
ENSG00000189129 PLAC9 0.588 1.5 1.35 0.0323 
ENSG00000008405 CRY1 8.72 22.2 1.35 0.00138 
ENSG00000164211 STARD4 32.2 81.8 1.35 0.00138 
ENSG00000146676 PURB 12.9 32.8 1.35 0.00138 
ENSG00000279541 CTC-444N24.7 10.2 26 1.35 0.00138 
ENSG00000276112 AL353644.6 7190 18300 1.34 0.00138 
ENSG00000267731 RP11-147L13.8 0.449 1.14 1.34 0.00138 
ENSG00000279254 RP11-536C12.1 0.725 1.84 1.34 0.00138 
ENSG00000128342 LIF 0.697 1.77 1.34 0.0115 
ENSG00000130254 SAFB2 17.9 45.2 1.34 0.00138 
ENSG00000047634 SCML1 1.83 4.64 1.34 0.00138 
ENSG00000163644 PPM1K 7.06 17.8 1.34 0.00138 
App.14. Genes affected by 12 hour illumination:  App. 116 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000105327 BBC3 18.9 47.7 1.34 0.00138 
ENSG00000205213 LGR4 3.52 8.88 1.34 0.00138 
ENSG00000051108 HERPUD1 22.2 55.9 1.33 0.00138 
ENSG00000170385 SLC30A1 7.97 20.1 1.33 0.00138 
ENSG00000225536 STIP1P3 0.748 1.88 1.33 0.00138 
ENSG00000280385 AP000648.5 3.07 7.7 1.33 0.00138 
ENSG00000117318 ID3 42.5 106 1.32 0.00138 
ENSG00000113811 SELK 33.2 82.6 1.32 0.00138 
ENSG00000231340 ACTG1P10 1.46 3.64 1.31 0.0258 
ENSG00000236255 AC009404.2 3.85 9.55 1.31 0.00138 
ENSG00000152932 RAB3C 32.6 80.8 1.31 0.00138 
ENSG00000001630 CYP51A1 23.2 57.5 1.31 0.00138 
ENSG00000041982 TNC 0.419 1.03 1.3 0.0107 
ENSG00000137558 PI15 1.29 3.18 1.3 0.00138 
ENSG00000167797 CDK2AP2 16 39.5 1.3 0.00138 
ENSG00000133816 MICAL2 0.389 0.959 1.3 0.0195 
ENSG00000272106 RP11-345P4.9 8.01 19.7 1.3 0.00138 
ENSG00000102100 SLC35A2 12.4 30.5 1.3 0.00138 
ENSG00000188816 HMX2 0.279 0.684 1.29 0.039 
ENSG00000006327 TNFRSF12A 11.2 27.4 1.29 0.00138 
ENSG00000172733 PURG 7.47 18.2 1.29 0.00138 
ENSG00000280328 RP11-972P1.7 1.06 2.58 1.29 0.00462 
ENSG00000184205 TSPYL2 6.47 15.8 1.28 0.00138 
ENSG00000166856 GPR182 0.363 0.884 1.28 0.0208 
ENSG00000179119 SPTY2D1 9.29 22.6 1.28 0.00138 
ENSG00000083857 FAT1 59 144 1.28 0.00138 
ENSG00000260034 LCMT1-AS2 1.37 3.32 1.28 0.00138 
ENSG00000154734 ADAMTS1 39.8 96.5 1.28 0.00138 
ENSG00000158887 MPZ 4.19 10.1 1.27 0.00138 
ENSG00000232872 CTAGE3P 0.272 0.656 1.27 0.0091 
ENSG00000233588 CYP51A1P2 3.44 8.3 1.27 0.00138 
ENSG00000271755 RP1-153G14.4 0.361 0.872 1.27 0.00138 
ENSG00000133424 LARGE 1.83 4.41 1.27 0.00255 
ENSG00000105499 PLA2G4C 1.37 3.31 1.27 0.0174 
ENSG00000180667 YOD1 6.38 15.3 1.26 0.00138 
ENSG00000113615 SEC24A 7.73 18.5 1.26 0.00138 
ENSG00000280047 CTC-463A16.1 0.399 0.956 1.26 0.00138 
ENSG00000225880 LINC00115 2.04 4.86 1.26 0.0195 
ENSG00000197063 MAFG 13 31.1 1.25 0.00138 
ENSG00000134070 IRAK2 0.343 0.816 1.25 0.00138 
ENSG00000147526 TACC1 2.43 5.78 1.25 0.00138 
ENSG00000117360 PRPF3 33.9 80.5 1.25 0.00138 
ENSG00000120694 HSPH1 40 95 1.25 0.00138 
ENSG00000004478 FKBP4 65.2 154 1.25 0.00138 
ENSG00000172893 DHCR7 18.2 43.2 1.25 0.00138 
ENSG00000217801 RP11-465B22.3 1.95 4.61 1.24 0.00138 
ENSG00000173334 TRIB1 4.14 9.8 1.24 0.00138 
App.14. Genes affected by 12 hour illumination:  App. 117 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000166012 TAF1D 112 266 1.24 0.00138 
ENSG00000164326 CARTPT 4.24 10 1.24 0.00138 
ENSG00000163697 APBB2 13.2 31.2 1.24 0.00138 
ENSG00000213599 SLX1A-SULT1A3 0.395 0.932 1.24 0.00462 
ENSG00000160570 DEDD2 12.5 29.5 1.24 0.00138 
ENSG00000261087 KB-1460A1.5 1.64 3.87 1.24 0.00138 
ENSG00000156232 WHAMM 1.44 3.4 1.24 0.00138 
ENSG00000163660 CCNL1 59.9 141 1.23 0.00138 
ENSG00000184226 PCDH9 4.37 10.3 1.23 0.00138 
ENSG00000132326 PER2 1.06 2.49 1.23 0.00739 
ENSG00000128228 SDF2L1 7.52 17.5 1.22 0.00138 
ENSG00000232442 CTD-3184A7.4 7.05 16.4 1.22 0.00138 
ENSG00000154359 LONRF1 5.56 12.9 1.21 0.00138 
ENSG00000203930 LINC00632 6.98 16.2 1.21 0.00138 
ENSG00000164236 ANKRD33B 0.332 0.768 1.21 0.0312 
ENSG00000197780 TAF13 20.6 47.8 1.21 0.00138 
ENSG00000108829 LRRC59 44.3 102 1.21 0.00138 
ENSG00000224945 RP11-82L18.2 5.32 12.3 1.2 0.0091 
ENSG00000165997 ARL5B 12.4 28.5 1.2 0.00138 
ENSG00000099194 SCD 57.2 132 1.2 0.00138 
ENSG00000162783 IER5 13.6 31.3 1.2 0.00138 
ENSG00000011007 TCEB3 19 43.7 1.2 0.00138 
ENSG00000229091 HSPA8P8 0.421 0.964 1.2 0.0318 
ENSG00000179218 CALR 200 457 1.2 0.00138 
ENSG00000181026 AEN 27.9 63.8 1.19 0.00138 
ENSG00000279145 RP11-547D13.1 0.244 0.557 1.19 0.00138 
ENSG00000204524 ZNF805 7.36 16.8 1.19 0.00138 
ENSG00000168374 ARF4 65.5 149 1.18 0.00138 
ENSG00000126947 ARMCX1 8.35 18.9 1.18 0.00138 
ENSG00000115289 PCGF1 31.7 71.5 1.17 0.00138 
ENSG00000164136 IL15 0.64 1.44 1.17 0.027 
ENSG00000119547 ONECUT2 3.6 8.1 1.17 0.00138 
ENSG00000138434 SSFA2 16.6 37.3 1.17 0.00138 
ENSG00000261505 LA16c-358B7.3 2.15 4.83 1.17 0.0432 
ENSG00000256223 ZNF10 4.06 9.11 1.17 0.00558 
ENSG00000280347 AC000123.2 3.98 8.92 1.16 0.00138 
ENSG00000260941 LINC00622 0.741 1.66 1.16 0.00255 
ENSG00000119973 PRLHR 0.472 1.06 1.16 0.00138 
ENSG00000112541 PDE10A 13.3 29.6 1.16 0.00255 
ENSG00000158615 PPP1R15B 30 66.6 1.15 0.00138 
ENSG00000144749 LRIG1 0.649 1.44 1.15 0.0363 
ENSG00000117479 SLC19A2 6.33 14 1.15 0.00138 
ENSG00000130522 JUND 86.5 192 1.15 0.00138 
ENSG00000078804 TP53INP2 16.6 36.9 1.15 0.00138 
ENSG00000268089 GABRQ 0.346 0.763 1.14 0.00138 
ENSG00000104722 NEFM 20.2 44.5 1.14 0.00138 
ENSG00000120149 MSX2 3.74 8.24 1.14 0.00138 
App.14. Genes affected by 12 hour illumination:  App. 118 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000121671 CRY2 6.16 13.6 1.14 0.00138 
ENSG00000120742 SERP1 36.2 79.8 1.14 0.00138 
ENSG00000242125 SNHG3 47.5 104 1.14 0.0264 
ENSG00000168439 STIP1 74.4 164 1.14 0.00138 
ENSG00000095574 IKZF5 4.97 10.9 1.14 0.0107 
ENSG00000232531 AC027612.1 0.822 1.81 1.14 0.0123 
ENSG00000250910 AC097467.2 0.455 1 1.14 0.04 
ENSG00000136240 KDELR2 53.4 117 1.13 0.00138 
ENSG00000164244 PRRC1 19.3 42.3 1.13 0.00138 
ENSG00000078237 C12orf5 5.39 11.7 1.12 0.00138 
ENSG00000187601 MAGEH1 9.65 21 1.12 0.00138 
ENSG00000102401 ARMCX3 12 26 1.12 0.00138 
ENSG00000112218 GPR63 1.41 3.06 1.12 0.00138 
ENSG00000111641 NOP2 33.1 71.8 1.12 0.00138 
ENSG00000206082 LINC01002 1.16 2.5 1.11 0.00138 
ENSG00000162775 RBM15 10.7 23 1.11 0.00138 
ENSG00000273448 RP11-166O4.6 0.969 2.09 1.11 0.00138 
ENSG00000268713 CTC-444N24.8 3.92 8.46 1.11 0.00138 
ENSG00000181472 ZBTB2 4.74 10.2 1.11 0.00138 
ENSG00000228653 HNRNPCP7 1.02 2.2 1.11 0.0107 
ENSG00000026508 CD44 0.449 0.966 1.11 0.0123 
ENSG00000116604 MEF2D 15.7 33.8 1.1 0.00138 
ENSG00000127920 GNG11 16.3 35.1 1.1 0.00138 
ENSG00000133398 MED10 26.9 57.8 1.1 0.00138 
ENSG00000131016 AKAP12 11.4 24.5 1.1 0.00138 
ENSG00000160888 IER2 17 36.4 1.1 0.00138 
ENSG00000219665 CTD-2006C1.2 7.4 15.8 1.1 0.00739 
ENSG00000152332 UHMK1 13.9 29.8 1.1 0.00138 
ENSG00000111860 CEP85L 4.91 10.5 1.09 0.00255 
ENSG00000129493 HEATR5A 14.2 30.3 1.09 0.0233 
ENSG00000157020 SEC13 45.5 96.9 1.09 0.00138 
ENSG00000272512 RP11-54O7.17 0.568 1.21 1.09 0.00138 
ENSG00000115520 COQ10B 8.5 18.1 1.09 0.00138 
ENSG00000226549 SCDP1 1.15 2.44 1.09 0.00362 
ENSG00000168994 PXDC1 0.839 1.78 1.09 0.00138 
ENSG00000110172 CHORDC1 13.7 29 1.09 0.00138 
ENSG00000150991 UBC 375 797 1.08 0.00138 
ENSG00000280111 CTA-292E10.9 0.513 1.09 1.08 0.0264 
ENSG00000139112 GABARAPL1 13.7 29 1.08 0.00138 
ENSG00000128016 ZFP36 5.18 11 1.08 0.00138 
ENSG00000182700 IGIP 1.96 4.15 1.08 0.00138 
ENSG00000059769 DNAJC25 3.61 7.63 1.08 0.00138 
ENSG00000136159 NUDT15 8.09 17.1 1.08 0.00138 
ENSG00000167110 GOLGA2 23.4 49.2 1.08 0.00138 
ENSG00000066405 CLDN18 0.495 1.04 1.07 0.00138 
ENSG00000123485 HJURP 6.61 13.9 1.07 0.00138 
ENSG00000280138 RP11-463O12.5 0.484 1.02 1.07 0.00138 
App.14. Genes affected by 12 hour illumination:  App. 119 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000112893 MAN2A1 5.42 11.3 1.07 0.00138 
ENSG00000122420 PTGFR 0.909 1.9 1.06 0.00138 
ENSG00000198912 C1orf174 8.89 18.5 1.06 0.00138 
ENSG00000198380 GFPT1 8 16.6 1.06 0.00255 
ENSG00000257315 ZBED6 34.9 72.4 1.05 0.00138 
ENSG00000170889 RPS9 1.09 2.25 1.05 0.00138 
ENSG00000229222 KRT18P4 0.466 0.965 1.05 0.0437 
ENSG00000143384 MCL1 60.6 126 1.05 0.00138 
ENSG00000006194 ZNF263 24.7 51.2 1.05 0.00138 
ENSG00000184432 COPB2 50.6 105 1.05 0.00138 
ENSG00000110048 OSBP 14.1 29.2 1.04 0.00138 
ENSG00000172667 ZMAT3 29.5 60.7 1.04 0.00138 
ENSG00000224773 HSPA8P7 1.09 2.24 1.04 0.00138 
ENSG00000261609 GAN 3.21 6.6 1.04 0.00138 
ENSG00000135045 C9orf40 3.23 6.62 1.04 0.00138 
ENSG00000251201 TMED7-TICAM2 5.72 11.7 1.03 0.00558 
ENSG00000185222 WBP5 18 36.9 1.03 0.00138 
ENSG00000070444 MNT 11 22.4 1.03 0.00138 
ENSG00000255387 RP11-23F23.3 0.367 0.751 1.03 0.0406 
ENSG00000109089 CDR2L 24.4 49.9 1.03 0.00138 
ENSG00000118298 CA14 2.74 5.58 1.02 0.00138 
ENSG00000075702 WDR62 2.21 4.49 1.02 0.00138 
ENSG00000112599 GUCA1B 1.1 2.22 1.02 0.00826 
ENSG00000169057 MECP2 12.6 25.6 1.02 0.00138 
ENSG00000124370 MCEE 5.18 10.5 1.02 0.00138 
ENSG00000162734 PEA15 55.7 112 1.01 0.00138 
ENSG00000120129 DUSP1 9.96 20.1 1.01 0.00138 
ENSG00000105472 CLEC11A 4.15 8.38 1.01 0.00138 
ENSG00000169239 CA5B 4.32 8.72 1.01 0.0318 
ENSG00000234176 HSPA8P1 0.895 1.8 1.01 0.00138 
ENSG00000279821 RP11-1334A24.5 0.334 0.672 1.01 0.00826 
ENSG00000049130 KITLG 2.49 5.01 1.01 0.00138 
ENSG00000174010 KLHL15 3.96 7.98 1.01 0.00138 
ENSG00000151247 EIF4E 5.04 10.1 1.01 0.0472 
ENSG00000153487 ING1 7.06 14.2 1.01 0.00138 
ENSG00000151929 BAG3 4.39 8.82 1 0.00138 
ENSG00000185245 GP1BA 0.486 0.974 1 0.00255 
ENSG00000139354 GAS2L3 6.55 13.1 1 0.0065 
ENSG00000196850 PPTC7 6.8 13.6 1 0.00138 
ENSG00000104228 TRIM35 7.63 15.3 1 0.0115 
ENSG00000139163 ETNK1 19.1 38.2 1 0.0201 
ENSG00000101445 PPP1R16B 7.12 3.55 -1 0.0335 
ENSG00000241345 RP4-630C24.3 1.35 0.672 -1 0.00558 
ENSG00000132334 PTPRE 2.72 1.36 -1 0.0131 
ENSG00000135472 FAIM2 4.86 2.42 -1.01 0.00138 
ENSG00000248540 RP11-247C2.2 7.29 3.63 -1.01 0.00138 
ENSG00000006756 ARSD 2.46 1.23 -1.01 0.0442 
App.14. Genes affected by 12 hour illumination:  App. 120 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000172159 FRMD3 9.38 4.66 -1.01 0.00138 
ENSG00000080166 DCT 0.646 0.321 -1.01 0.0174 
ENSG00000255248 RP11-166D19.1 1.44 0.715 -1.01 0.0395 
ENSG00000197191 CYSRT1 1.48 0.734 -1.01 0.0188 
ENSG00000087008 ACOX3 7.83 3.88 -1.01 0.0091 
ENSG00000183160 TMEM119 32.3 16 -1.02 0.00138 
ENSG00000226384 GTF2H4 4.19 2.07 -1.02 0.0437 
ENSG00000108175 ZMIZ1 75.1 37.1 -1.02 0.00138 
ENSG00000166145 SPINT1 1.64 0.806 -1.02 0.0214 
ENSG00000137124 ALDH1B1 17.4 8.52 -1.03 0.00138 
ENSG00000159399 HK2 29.3 14.4 -1.03 0.00138 
ENSG00000178802 MPI 47 23.1 -1.03 0.00138 
ENSG00000136213 CHST12 15 7.38 -1.03 0.00138 
ENSG00000280057 RP1-168L15.6 0.846 0.415 -1.03 0.0323 
ENSG00000163364 LINC01116 31.6 15.5 -1.03 0.00138 
ENSG00000276855 CTD-3157E16.2 3.38 1.66 -1.03 0.0123 
ENSG00000266411 RP11-180P8.3 6.35 3.11 -1.03 0.00138 
ENSG00000204934 ATP6V0E2-AS1 3.97 1.94 -1.03 0.0138 
ENSG00000137860 SLC28A2 0.96 0.47 -1.03 0.0195 
ENSG00000136870 ZNF189 11.6 5.66 -1.03 0.00138 
ENSG00000114520 SNX4 20.6 10.1 -1.03 0.00138 
ENSG00000141736 ERBB2 7.7 3.76 -1.03 0.0276 
ENSG00000134333 LDHA 350 171 -1.04 0.00138 
ENSG00000119888 EPCAM 2.46 1.2 -1.04 0.0091 
ENSG00000162407 PPAP2B 4.7 2.29 -1.04 0.00138 
ENSG00000173281 PPP1R3B 20.8 10.1 -1.04 0.00138 
ENSG00000177674 AGTRAP 12.8 6.2 -1.04 0.00138 
ENSG00000115866 DARS 81 39.3 -1.04 0.00138 
ENSG00000064225 ST3GAL6 40.8 19.8 -1.04 0.00138 
ENSG00000204947 ZNF425 3.14 1.52 -1.04 0.027 
ENSG00000005882 PDK2 22.3 10.8 -1.04 0.00362 
ENSG00000102466 FGF14 21.2 10.3 -1.05 0.0138 
ENSG00000162552 WNT4 1.11 0.536 -1.05 0.0432 
ENSG00000112320 SOBP 24.7 12 -1.05 0.00138 
ENSG00000130475 FCHO1 3.4 1.64 -1.05 0.00255 
ENSG00000203943 SAMD13 2.12 1.02 -1.05 0.00739 
ENSG00000137872 SEMA6D 3.28 1.58 -1.05 0.027 
ENSG00000128581 IFT22 25.6 12.4 -1.05 0.00138 
ENSG00000164967 RPP25L 10.6 5.1 -1.05 0.00138 
ENSG00000185669 SNAI3 1.17 0.566 -1.05 0.0411 
ENSG00000124279 FASTKD3 6.25 3.02 -1.05 0.0123 
ENSG00000206559 ZCWPW2 1.28 0.62 -1.05 0.0416 
ENSG00000120860 CCDC53 23 11.1 -1.05 0.00138 
ENSG00000158483 FAM86C1 4.31 2.08 -1.05 0.00739 
ENSG00000198185 ZNF334 6.52 3.14 -1.05 0.00138 
ENSG00000152969 JAKMIP1 10.1 4.83 -1.06 0.00138 
ENSG00000141540 TTYH2 5.26 2.53 -1.06 0.00138 
App.14. Genes affected by 12 hour illumination:  App. 121 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000272365 RP11-389C8.3 2.48 1.19 -1.06 0.0208 
ENSG00000124688 MAD2L1BP 41.6 19.9 -1.06 0.00138 
ENSG00000268996 MAN1B1-AS1 1.54 0.74 -1.06 0.0065 
ENSG00000069712 KIAA1107 4.38 2.09 -1.06 0.00138 
ENSG00000229186 ADAM1A 1.99 0.954 -1.06 0.00138 
ENSG00000103852 TTC23 4.16 1.99 -1.06 0.0115 
ENSG00000260400 RP11-119F7.5 4.21 2.01 -1.06 0.00138 
ENSG00000228889 UBAC2-AS1 2.06 0.986 -1.06 0.00138 
ENSG00000151552 QDPR 31 14.8 -1.06 0.00138 
ENSG00000165698 C9orf9 4.51 2.15 -1.07 0.00138 
ENSG00000110400 PVRL1 40 19 -1.07 0.00138 
ENSG00000157927 RADIL 8.03 3.82 -1.07 0.00138 
ENSG00000159307 SCUBE1 3.52 1.67 -1.07 0.0306 
ENSG00000179627 ZBTB42 1.25 0.594 -1.08 0.0115 
ENSG00000134954 ETS1 6.08 2.89 -1.08 0.00138 
ENSG00000074370 ATP2A3 5.29 2.51 -1.08 0.00138 
ENSG00000273081 RP4-813F11.4 0.968 0.458 -1.08 0.00362 
ENSG00000080644 CHRNA3 141 66.6 -1.08 0.00138 
ENSG00000162849 KIF26B 1.8 0.849 -1.08 0.00138 
ENSG00000223658 AC011242.6 8.64 4.08 -1.08 0.00138 
ENSG00000136279 DBNL 32.9 15.5 -1.09 0.00138 
ENSG00000181754 AMIGO1 1.86 0.874 -1.09 0.00138 
ENSG00000276550 HERC2P2 2.73 1.28 -1.09 0.00138 
ENSG00000133256 PDE6B 12.6 5.89 -1.09 0.00138 
ENSG00000274225 KB-68A7.1 2.5 1.17 -1.09 0.0487 
ENSG00000204624 PTCHD2 3.45 1.62 -1.09 0.00138 
ENSG00000027001 MIPEP 3.39 1.59 -1.09 0.00739 
ENSG00000168398 BDKRB2 2.53 1.19 -1.09 0.00138 
ENSG00000278266 RP11-575F12.3 2.18 1.02 -1.1 0.00138 
ENSG00000225791 TRAM2-AS1 8.99 4.2 -1.1 0.00138 
ENSG00000060642 PIGV 11.1 5.2 -1.1 0.00138 
ENSG00000224596 ZMIZ1-AS1 0.555 0.258 -1.1 0.0335 
ENSG00000122971 ACADS 4.78 2.22 -1.1 0.00138 
ENSG00000008196 TFAP2B 92.7 43.1 -1.1 0.00138 
ENSG00000100234 TIMP3 3.81 1.77 -1.1 0.00138 
ENSG00000184828 ZBTB7C 3.25 1.51 -1.11 0.00138 
ENSG00000158445 KCNB1 2.83 1.32 -1.11 0.00138 
ENSG00000138642 HERC6 1.82 0.842 -1.11 0.0312 
ENSG00000259877 RP11-46C24.7 4.59 2.12 -1.11 0.00138 
ENSG00000264247 LINC00909 8.98 4.16 -1.11 0.0091 
ENSG00000271151 RP11-394I13.2 2.02 0.936 -1.11 0.00826 
ENSG00000280187 CTC-351M12.1 2.6 1.2 -1.11 0.00138 
ENSG00000155085 AK9 4.94 2.28 -1.11 0.00138 
ENSG00000226137 BAIAP2-AS1 6.44 2.97 -1.12 0.00138 
ENSG00000165912 PACSIN3 16.7 7.69 -1.12 0.00138 
ENSG00000273456 RP11-686O6.2 2.17 1 -1.12 0.0482 
ENSG00000146966 DENND2A 8.94 4.11 -1.12 0.027 
App.14. Genes affected by 12 hour illumination:  App. 122 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000234882 EIF3EP1 2.24 1.03 -1.12 0.0472 
ENSG00000197748 CFAP43 1.91 0.876 -1.12 0.00362 
ENSG00000231584 FAHD2CP 8.18 3.75 -1.12 0.00138 
ENSG00000143355 LHX9 1.74 0.795 -1.12 0.00558 
ENSG00000186280 KDM4D 1.67 0.765 -1.13 0.00138 
ENSG00000170629 DPY19L2P2 1.3 0.594 -1.13 0.00138 
ENSG00000170382 LRRN2 3.51 1.61 -1.13 0.00138 
ENSG00000145911 N4BP3 2.17 0.992 -1.13 0.00138 
ENSG00000188859 FAM78B 5.67 2.59 -1.13 0.00138 
ENSG00000188549 C15orf52 5.55 2.53 -1.13 0.00138 
ENSG00000136859 ANGPTL2 4.05 1.85 -1.14 0.0487 
ENSG00000162755 KLHDC9 5.09 2.32 -1.14 0.0264 
ENSG00000186301 MST1P2 0.472 0.214 -1.14 0.0406 
ENSG00000118965 WDR35 20.6 9.38 -1.14 0.00138 
ENSG00000089041 P2RX7 2.57 1.17 -1.14 0.00138 
ENSG00000178171 AMER3 5.43 2.46 -1.14 0.00138 
ENSG00000174807 CD248 8.84 3.99 -1.15 0.00138 
ENSG00000262075 DKFZP434A062 0.497 0.224 -1.15 0.00138 
ENSG00000165171 WBSCR27 4.4 1.98 -1.15 0.00138 
ENSG00000264548 RP13-516M14.2 0.507 0.228 -1.15 0.0346 
ENSG00000157557 ETS2 17.2 7.71 -1.15 0.00138 
ENSG00000261754 CTC-523E23.1 1.53 0.685 -1.16 0.00255 
ENSG00000124212 PTGIS 1.03 0.46 -1.16 0.00138 
ENSG00000258701 LINC00638 1.22 0.547 -1.16 0.00138 
ENSG00000205502 C2CD4B 2.72 1.22 -1.16 0.00138 
ENSG00000135245 HILPDA 24.4 10.9 -1.16 0.00362 
ENSG00000128805 ARHGAP22 5.5 2.45 -1.17 0.00138 
ENSG00000168806 LCMT2 8.89 3.95 -1.17 0.00138 
ENSG00000168899 VAMP5 2.29 1.02 -1.17 0.027 
ENSG00000084453 SLCO1A2 3.84 1.7 -1.17 0.0352 
ENSG00000107807 TLX1 6.55 2.91 -1.17 0.00362 
ENSG00000082684 SEMA5B 12 5.31 -1.17 0.00138 
ENSG00000066926 FECH 19 8.41 -1.17 0.00138 
ENSG00000198890 PRMT6 14.7 6.5 -1.17 0.00138 
ENSG00000235169 SMIM1 12.6 5.59 -1.18 0.00138 
ENSG00000185614 FAM212A 2.38 1.05 -1.18 0.00558 
ENSG00000041353 RAB27B 2.7 1.19 -1.18 0.00138 
ENSG00000138496 PARP9 2 0.881 -1.18 0.00255 
ENSG00000120899 PTK2B 6.64 2.93 -1.18 0.00558 
ENSG00000241852 C8orf58 16.7 7.35 -1.18 0.00138 
ENSG00000138316 ADAMTS14 1.73 0.762 -1.18 0.00138 
ENSG00000279205 RP11-632P5.1 0.726 0.319 -1.19 0.0282 
ENSG00000186918 ZNF395 41.9 18.4 -1.19 0.00138 
ENSG00000171729 TMEM51 23.3 10.2 -1.19 0.00138 
ENSG00000174460 ZCCHC12 2.09 0.918 -1.19 0.00138 
ENSG00000181652 ATG9B 3.88 1.7 -1.19 0.0282 
ENSG00000150672 DLG2 11.5 5.05 -1.19 0.00138 
App.14. Genes affected by 12 hour illumination:  App. 123 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000233198 RNF224 1.12 0.492 -1.19 0.00462 
ENSG00000176349 AC110781.3 1.51 0.659 -1.2 0.0195 
ENSG00000072163 LIMS2 0.8 0.349 -1.2 0.0146 
ENSG00000111490 TBC1D30 1.63 0.711 -1.2 0.016 
ENSG00000136720 HS6ST1 19.9 8.68 -1.2 0.00138 
ENSG00000230444 TFAMP1 2.84 1.24 -1.2 0.0107 
ENSG00000150977 RILPL2 0.788 0.343 -1.2 0.00138 
ENSG00000141668 CBLN2 5.8 2.52 -1.2 0.00138 
ENSG00000140876 NUDT7 8.41 3.66 -1.2 0.00138 
ENSG00000144452 ABCA12 1.49 0.646 -1.2 0.00138 
ENSG00000110031 LPXN 1.72 0.747 -1.21 0.0138 
ENSG00000249307 LINC01088 2.26 0.978 -1.21 0.0195 
ENSG00000248927 CTD-2334D19.1 1.22 0.527 -1.21 0.0188 
ENSG00000176381 PRR18 1.47 0.636 -1.21 0.0195 
ENSG00000267194 RP1-193H18.2 3.19 1.38 -1.21 0.00138 
ENSG00000214273 AGGF1P1 0.864 0.373 -1.21 0.00362 
ENSG00000228612 HK2P1 1.42 0.611 -1.21 0.00138 
ENSG00000214654 RP11-27I1.4 8.91 3.84 -1.21 0.00138 
ENSG00000156384 SFR1 6.49 2.8 -1.21 0.00138 
ENSG00000197872 FAM49A 5.76 2.48 -1.21 0.0123 
ENSG00000173826 KCNH6 5.54 2.38 -1.22 0.00138 
ENSG00000196155 PLEKHG4 15.1 6.5 -1.22 0.00138 
ENSG00000243819 RN7SL832P 1.23 0.527 -1.22 0.00255 
ENSG00000149292 TTC12 12.7 5.42 -1.22 0.00138 
ENSG00000063761 ADCK1 2.6 1.11 -1.22 0.00138 
ENSG00000273297 RP11-38M8.1 6.96 2.98 -1.23 0.00138 
ENSG00000184986 TMEM121 3.16 1.35 -1.23 0.00138 
ENSG00000186231 KLHL32 0.56 0.239 -1.23 0.0174 
ENSG00000130518 KIAA1683 5.08 2.16 -1.23 0.00138 
ENSG00000154102 C16orf74 4.22 1.8 -1.23 0.00138 
ENSG00000158008 EXTL1 1.95 0.83 -1.24 0.0426 
ENSG00000255036 RP11-23J9.4 0.723 0.307 -1.24 0.0091 
ENSG00000132437 DDC 146 62 -1.24 0.00138 
ENSG00000205037 RP11-863P13.4 6.33 2.68 -1.24 0.0368 
ENSG00000119714 GPR68 2.29 0.968 -1.24 0.00138 
ENSG00000185112 FAM43A 6.46 2.73 -1.25 0.00138 
ENSG00000187952 HS6ST1P1 2.4 1.01 -1.25 0.00138 
ENSG00000160172 FAM86C2P 3.98 1.67 -1.26 0.00138 
ENSG00000166073 GPR176 20.1 8.42 -1.26 0.00138 
ENSG00000278928 RP11-481F24.3 33.4 14 -1.26 0.00138 
ENSG00000082458 DLG3 1.75 0.734 -1.26 0.0323 
ENSG00000233922 AL133493.2 4.02 1.68 -1.26 0.00138 
ENSG00000218510 LINC00339 16.2 6.75 -1.26 0.00138 
ENSG00000182957 SPATA13 2.01 0.835 -1.27 0.027 
ENSG00000165061 ZMAT4 11.7 4.83 -1.27 0.00138 
ENSG00000116574 RHOU 16.4 6.77 -1.28 0.00138 
ENSG00000141665 FBXO15 1.31 0.538 -1.28 0.00739 
App.14. Genes affected by 12 hour illumination:  App. 124 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000181004 BBS12 3.32 1.36 -1.28 0.00138 
ENSG00000219891 ZSCAN12P1 14.7 6.06 -1.28 0.00138 
ENSG00000175279 APITD1 9.66 3.96 -1.29 0.00138 
ENSG00000279518 AC083843.4 0.479 0.196 -1.29 0.0282 
ENSG00000101347 SAMHD1 2.96 1.21 -1.29 0.00138 
ENSG00000157429 ZNF19 3.26 1.33 -1.29 0.0487 
ENSG00000164649 CDCA7L 19.3 7.87 -1.29 0.00138 
ENSG00000138380 CARF 7.83 3.19 -1.3 0.0091 
ENSG00000174327 SLC16A13 3.87 1.57 -1.3 0.00138 
ENSG00000172086 KRCC1 14.5 5.88 -1.3 0.00138 
ENSG00000176720 BOK 4.66 1.89 -1.3 0.00138 
ENSG00000163082 SGPP2 0.756 0.306 -1.3 0.00138 
ENSG00000095203 EPB41L4B 10 4.06 -1.31 0.00138 
ENSG00000213171 LINGO4 0.76 0.307 -1.31 0.00138 
ENSG00000171811 CFAP46 1.39 0.559 -1.32 0.0167 
ENSG00000139187 KLRG1 7.15 2.86 -1.32 0.00138 
ENSG00000008283 CYB561 160 64 -1.32 0.00138 
ENSG00000253426 RP11-10A14.4 4.59 1.84 -1.32 0.00826 
ENSG00000186496 ZNF396 2.33 0.933 -1.32 0.00138 
ENSG00000071794 HLTF 26.6 10.6 -1.33 0.00138 
ENSG00000251216 RP11-161D15.3 31.4 12.4 -1.34 0.00138 
ENSG00000229481 CTD-2554C21.3 0.718 0.284 -1.34 0.0065 
ENSG00000224090 AC097468.4 1.43 0.564 -1.34 0.0065 
ENSG00000196660 SLC30A10 0.862 0.34 -1.34 0.00138 
ENSG00000260000 RP3-467N11.1 2.1 0.827 -1.34 0.00138 
ENSG00000156253 RWDD2B 14.8 5.83 -1.35 0.00138 
ENSG00000140368 PSTPIP1 0.925 0.363 -1.35 0.0306 
ENSG00000006071 ABCC8 4.99 1.95 -1.36 0.0487 
ENSG00000004799 PDK4 1.05 0.41 -1.36 0.00138 
ENSG00000071282 LMCD1 11.6 4.54 -1.36 0.00138 
ENSG00000107014 RLN2 1.72 0.671 -1.36 0.0346 
ENSG00000104953 TLE6 9.04 3.52 -1.36 0.00138 
ENSG00000235750 KIAA0040 1.46 0.569 -1.36 0.0115 
ENSG00000168491 CCDC110 2.12 0.824 -1.36 0.00255 
ENSG00000240204 SMKR1 5.29 2.06 -1.36 0.00138 
ENSG00000183798 EMILIN3 3.35 1.3 -1.37 0.00138 
ENSG00000170743 SYT9 7.42 2.87 -1.37 0.00138 
ENSG00000100336 APOL4 1 0.387 -1.37 0.0174 
ENSG00000164086 DUSP7 32 12.3 -1.37 0.00138 
ENSG00000126562 WNK4 3.19 1.23 -1.37 0.0221 
ENSG00000230438 SERPINB9P1 0.993 0.383 -1.38 0.0437 
ENSG00000154319 FAM167A 49.7 19.2 -1.38 0.00138 
ENSG00000026559 KCNG1 3.13 1.2 -1.38 0.00138 
ENSG00000135835 KIAA1614 4.63 1.78 -1.38 0.0065 
ENSG00000204815 TTC25 3.38 1.3 -1.38 0.00138 
ENSG00000271270 TMCC1-AS1 8.25 3.17 -1.38 0.00138 
ENSG00000198865 CCDC152 0.836 0.32 -1.38 0.00362 
App.14. Genes affected by 12 hour illumination:  App. 125 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000166407 LMO1 52.2 20 -1.39 0.00138 
ENSG00000213888 LINC01521 7.58 2.9 -1.39 0.00138 
ENSG00000092850 TEKT2 1.13 0.432 -1.39 0.00558 
ENSG00000007402 CACNA2D2 94.5 36 -1.39 0.00138 
ENSG00000223486 AC092198.1 0.738 0.281 -1.39 0.00138 
ENSG00000030419 IKZF2 1.56 0.592 -1.4 0.0306 
ENSG00000271122 RP11-379H18.1 12.9 4.89 -1.4 0.00138 
ENSG00000171992 SYNPO 38.2 14.4 -1.41 0.00138 
ENSG00000255121 RP11-110I1.12 1.73 0.652 -1.41 0.00255 
ENSG00000168874 ATOH8 0.928 0.349 -1.41 0.0107 
ENSG00000198075 SULT1C4 28.9 10.8 -1.41 0.00138 
ENSG00000170915 PAQR8 26.7 10 -1.42 0.00138 
ENSG00000156564 LRFN2 4.66 1.74 -1.42 0.00138 
ENSG00000129910 CDH15 0.604 0.225 -1.43 0.00362 
ENSG00000272097 RP11-421M1.8 3.55 1.32 -1.43 0.00138 
ENSG00000254656 RTL1 402 149 -1.43 0.00138 
ENSG00000275917 CHRFAM7A 0.792 0.294 -1.43 0.00362 
ENSG00000226508 AC104655.3 3.83 1.42 -1.43 0.0131 
ENSG00000134107 BHLHE40 8 2.96 -1.43 0.00138 
ENSG00000183831 ANKRD45 1.85 0.682 -1.44 0.00138 
ENSG00000198520 C1orf228 4.54 1.67 -1.44 0.00362 
ENSG00000269837 IPO5P1 3.64 1.34 -1.44 0.0065 
ENSG00000114541 FRMD4B 2.96 1.09 -1.44 0.00138 
ENSG00000196659 TTC30B 5.25 1.93 -1.44 0.00138 
ENSG00000198570 RD3 16.4 5.98 -1.45 0.00138 
ENSG00000179431 FJX1 1.02 0.371 -1.45 0.00138 
ENSG00000179921 GPBAR1 1.66 0.603 -1.46 0.00138 
ENSG00000265096 C1QTNF1-AS1 5.04 1.84 -1.46 0.0195 
ENSG00000177106 EPS8L2 4.45 1.62 -1.46 0.0065 
ENSG00000181072 CHRM2 5.66 2.05 -1.46 0.00138 
ENSG00000108984 MAP2K6 8.06 2.92 -1.46 0.00138 
ENSG00000180787 ZFP3 5.17 1.87 -1.47 0.00138 
ENSG00000089127 OAS1 1.3 0.47 -1.47 0.0276 
ENSG00000133805 AMPD3 4.14 1.5 -1.47 0.00138 
ENSG00000112357 PEX7 10.1 3.65 -1.47 0.00138 
ENSG00000078114 NEBL 2.52 0.903 -1.48 0.00138 
ENSG00000106003 LFNG 5.19 1.85 -1.49 0.00138 
ENSG00000149050 ZNF214 1.68 0.599 -1.49 0.00362 
ENSG00000091409 ITGA6 0.905 0.322 -1.49 0.0368 
ENSG00000245848 CEBPA 3.25 1.16 -1.49 0.00138 
ENSG00000108771 DHX58 0.98 0.349 -1.49 0.0318 
ENSG00000232859 LYRM9 3.19 1.13 -1.49 0.00826 
ENSG00000117394 SLC2A1 51.7 18.2 -1.51 0.00138 
ENSG00000228903 RASA4CP 1.15 0.404 -1.51 0.0107 
ENSG00000138061 CYP1B1 0.712 0.249 -1.52 0.0437 
ENSG00000267355 RPL9P29 2.4 0.841 -1.52 0.0282 
ENSG00000214376 VSTM5 0.526 0.184 -1.52 0.0492 
App.14. Genes affected by 12 hour illumination:  App. 126 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000169218 RSPO1 0.663 0.232 -1.52 0.00255 
ENSG00000136205 TNS3 8.37 2.92 -1.52 0.00138 
ENSG00000114268 PFKFB4 15.2 5.28 -1.52 0.00138 
ENSG00000183111 ARHGEF37 3.63 1.26 -1.52 0.00138 
ENSG00000279368 RP1-80N2.4 1.12 0.39 -1.52 0.00138 
ENSG00000251408 RP11-586D19.2 0.829 0.288 -1.53 0.0288 
ENSG00000169129 AFAP1L2 0.959 0.332 -1.53 0.00255 
ENSG00000112715 VEGFA 246 85.1 -1.53 0.00138 
ENSG00000072952 MRVI1 3.49 1.21 -1.53 0.00138 
ENSG00000224008 LINC01441 0.751 0.26 -1.53 0.00138 
ENSG00000168481 LGI3 1.38 0.473 -1.54 0.00138 
ENSG00000171608 PIK3CD 5.48 1.87 -1.55 0.0384 
ENSG00000197557 TTC30A 6.14 2.1 -1.55 0.00138 
ENSG00000251011 TMEM108-AS1 4.6 1.56 -1.56 0.0437 
ENSG00000272667 RP11-395A13.2 3.44 1.16 -1.57 0.00462 
ENSG00000166432 ZMAT1 2.5 0.838 -1.58 0.00138 
ENSG00000171084 FAM86JP 3.74 1.25 -1.58 0.00138 
ENSG00000117152 RGS4 1130 379 -1.58 0.00138 
ENSG00000227028 SLC8A1-AS1 1.45 0.483 -1.58 0.0432 
ENSG00000198939 ZFP2 0.969 0.323 -1.59 0.00362 
ENSG00000007062 PROM1 0.629 0.209 -1.59 0.0146 
ENSG00000158050 DUSP2 0.878 0.291 -1.59 0.00138 
ENSG00000152256 PDK1 25.6 8.43 -1.6 0.00138 
ENSG00000254202 RP11-120I21.2 3.15 1.04 -1.61 0.00255 
ENSG00000131095 GFAP 0.824 0.269 -1.61 0.039 
ENSG00000204175 GPRIN2 0.958 0.313 -1.61 0.00138 
ENSG00000173727 CMB9-22P13.1 1.95 0.635 -1.62 0.00739 
ENSG00000161544 CYGB 79.4 25.8 -1.62 0.00138 
ENSG00000142621 FHAD1 1.7 0.551 -1.62 0.00462 
ENSG00000235597 LINC01102 5.57 1.8 -1.63 0.00138 
ENSG00000224383 PRR29 1.63 0.527 -1.63 0.00462 
ENSG00000214140 PRCD 99.8 32.2 -1.63 0.00138 
ENSG00000182389 CACNB4 3.52 1.13 -1.64 0.00138 
ENSG00000175147 TMEM51-AS1 4.82 1.55 -1.64 0.00138 
ENSG00000137198 GMPR 1.01 0.323 -1.64 0.0146 
ENSG00000196668 LINC00173 2.6 0.826 -1.65 0.0123 
ENSG00000182489 XKRX 1.56 0.496 -1.65 0.00138 
ENSG00000273145 CITF22-92A6.1 0.885 0.278 -1.67 0.0227 
ENSG00000105880 DLX5 0.76 0.238 -1.68 0.0239 
ENSG00000196542 SPTSSB 1.14 0.357 -1.68 0.00826 
ENSG00000254162 RP11-48B3.3 0.702 0.219 -1.68 0.00138 
ENSG00000278291 RP11-172H24.4 1.06 0.33 -1.68 0.00138 
ENSG00000258555 SPECC1L-ADORA2A 16.6 5.17 -1.68 0.00138 
ENSG00000133121 STARD13 5.06 1.57 -1.68 0.00138 
ENSG00000275763 FLJ44313 0.546 0.17 -1.68 0.0146 
ENSG00000224843 LINC00240 1.12 0.349 -1.69 0.039 
ENSG00000247796 CTD-2366F13.1 2.57 0.798 -1.69 0.0146 
App.14. Genes affected by 12 hour illumination:  App. 127 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000110665 C11orf21 1.16 0.361 -1.69 0.0258 
ENSG00000101292 PROKR2 1.25 0.387 -1.69 0.00138 
ENSG00000117971 CHRNB4 18.2 5.59 -1.7 0.00138 
ENSG00000212916 MAP10 2.33 0.715 -1.71 0.00138 
ENSG00000075290 WNT8B 0.496 0.152 -1.71 0.016 
ENSG00000165633 VSTM4 2.78 0.85 -1.71 0.00138 
ENSG00000148225 WDR31 1.61 0.492 -1.71 0.00138 
ENSG00000279673 RP11-185E8.2 0.726 0.222 -1.71 0.0138 
ENSG00000117148 ACTL8 2.76 0.84 -1.72 0.00138 
ENSG00000132669 RIN2 3.13 0.95 -1.72 0.00462 
ENSG00000165025 SYK 1.13 0.341 -1.72 0.00138 
ENSG00000178403 NEUROG2 25.7 7.76 -1.73 0.00138 
ENSG00000255690 TRIL 13.2 3.98 -1.73 0.00138 
ENSG00000196990 FAM163B 38.3 11.5 -1.73 0.00138 
ENSG00000180938 ZNF572 3.09 0.923 -1.74 0.00138 
ENSG00000259583 RP11-66B24.4 2.95 0.881 -1.74 0.00138 
ENSG00000016402 IL20RA 0.575 0.171 -1.75 0.0264 
ENSG00000184254 ALDH1A3 7.13 2.09 -1.77 0.00138 
ENSG00000156097 GPR61 1.91 0.559 -1.77 0.00138 
ENSG00000113578 FGF1 2.11 0.618 -1.77 0.0346 
ENSG00000278709 RP5-1059L7.1 2.59 0.744 -1.8 0.016 
ENSG00000251669 FAM86EP 4.04 1.16 -1.8 0.00138 
ENSG00000183873 SCN5A 5.98 1.69 -1.82 0.00138 
ENSG00000129757 CDKN1C 3.67 1.02 -1.84 0.00138 
ENSG00000269998 RP11-272L13.3 1.58 0.44 -1.84 0.0115 
ENSG00000163827 LRRC2 0.731 0.203 -1.85 0.00362 
ENSG00000183208 GDPGP1 3.4 0.944 -1.85 0.027 
ENSG00000260317 RP11-48B3.4 0.832 0.229 -1.86 0.00138 
ENSG00000139352 ASCL1 5.55 1.49 -1.9 0.00138 
ENSG00000248866 USP46-AS1 2.28 0.607 -1.91 0.00138 
ENSG00000183250 C21orf67 2.76 0.734 -1.91 0.00138 
ENSG00000182747 SLC35D3 6.85 1.79 -1.94 0.00138 
ENSG00000123146 CD97 0.81 0.212 -1.94 0.00138 
ENSG00000205634 LINC00898 0.574 0.149 -1.94 0.00138 
ENSG00000073464 CLCN4 3.62 0.942 -1.94 0.00138 
ENSG00000139832 RAB20 2.56 0.657 -1.96 0.00138 
ENSG00000136944 LMX1B 0.67 0.171 -1.97 0.00138 
ENSG00000167191 GPRC5B 14.4 3.6 -2 0.00138 
ENSG00000185567 AHNAK2 2.37 0.595 -2 0.00138 
ENSG00000168830 HTR1E 14.1 3.52 -2.01 0.00138 
ENSG00000267296 CEBPA-AS1 0.575 0.14 -2.04 0.00826 
ENSG00000224699 LAMTOR5-AS1 7.55 1.83 -2.04 0.00362 
ENSG00000232160 RAP2C-AS1 2.4 0.57 -2.07 0.0091 
ENSG00000147082 CCNB3 2.15 0.512 -2.07 0.0288 
ENSG00000186523 FAM86B1 1.99 0.469 -2.09 0.00138 
ENSG00000167767 KRT80 1.61 0.373 -2.11 0.00138 
ENSG00000229921 KIF25-AS1 0.772 0.176 -2.13 0.00558 
App.14. Genes affected by 12 hour illumination:  App. 128 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000147571 CRH 0.97 0.22 -2.14 0.00362 
ENSG00000272425 RP11-363E6.4 0.775 0.176 -2.14 0.00138 
ENSG00000237989 AP001046.5 0.603 0.131 -2.2 0.00138 
ENSG00000279137 RP11-205K6.3 0.803 0.173 -2.22 0.00138 
ENSG00000111186 WNT5B 2.07 0.437 -2.24 0.00255 
ENSG00000172339 ALG14 9.34 1.94 -2.26 0.0174 
ENSG00000196581 AJAP1 2.84 0.581 -2.29 0.00138 
ENSG00000223749 MIR503HG 1.28 0.262 -2.29 0.0329 
ENSG00000129521 EGLN3 63.1 12.8 -2.3 0.00138 
ENSG00000198416 ZNF658B 0.606 0.122 -2.32 0.00138 
ENSG00000133665 DYDC2 0.659 0.132 -2.32 0.0233 
ENSG00000137699 TRIM29 37.5 7.49 -2.32 0.00138 
ENSG00000100302 RASD2 1.34 0.267 -2.33 0.00138 
ENSG00000247095 MIR210HG 3.73 0.728 -2.36 0.016 
ENSG00000100060 MFNG 1.66 0.311 -2.42 0.00138 
ENSG00000119938 PPP1R3C 4.33 0.786 -2.46 0.00138 
ENSG00000077713 SLC25A43 0.676 0.116 -2.55 0.00992 
ENSG00000166394 CYB5R2 0.776 0.133 -2.55 0.00138 
ENSG00000231881 RP5-1120P11.3 1.23 0.204 -2.59 0.0329 
ENSG00000214548 MEG3 0.646 0.107 -2.59 0.00138 
ENSG00000121966 CXCR4 228 37.7 -2.6 0.00138 
ENSG00000234944 RP11-124O11.1 1.5 0.242 -2.63 0.00138 
ENSG00000134323 MYCN 5.98 0.954 -2.65 0.00138 
ENSG00000160013 PTGIR 2.98 0.466 -2.68 0.00138 
ENSG00000225968 ELFN1 10.3 1.6 -2.69 0.00138 
ENSG00000187800 PEAR1 5.76 0.894 -2.69 0.00138 
ENSG00000152582 SPEF2 1.29 0.198 -2.7 0.00138 
ENSG00000170983 LINC00208 0.589 0.0894 -2.72 0.0258 
ENSG00000253669 KB-1732A1.1 0.692 0.104 -2.73 0.0115 
ENSG00000170161 GLIDR 6.81 1 -2.76 0.00462 
ENSG00000183690 EFHC2 0.731 0.106 -2.79 0.00138 
ENSG00000056998 GYG2 0.604 0.0867 -2.8 0.00138 
ENSG00000163485 ADORA1 0.761 0.108 -2.81 0.00138 
ENSG00000251661 RP11-326C3.11 2.42 0.325 -2.9 0.00138 
ENSG00000164303 ENPP6 1.42 0.188 -2.92 0.00138 
ENSG00000236437 AP001891.1 2.22 0.286 -2.95 0.00362 
ENSG00000171388 APLN 1.83 0.236 -2.95 0.00138 
ENSG00000034239 EFCAB1 0.819 0.1 -3.03 0.00362 
ENSG00000108622 ICAM2 3.4 0.415 -3.03 0.00138 
ENSG00000249669 MIR143HG 5.48 0.669 -3.04 0.00138 
ENSG00000231890 DARS-AS1 2.68 0.325 -3.04 0.00138 
ENSG00000236204 LINC01376 0.547 0.0614 -3.15 0.0123 
ENSG00000269936 MIR145 4.79 0.426 -3.49 0.00138 
ENSG00000237954 RP11-14O19.2 3.49 0.304 -3.52 0.00138 
ENSG00000172995 ARPP21 1.24 0.1 -3.62 0.00138 
ENSG00000225373 WASH5P 1.23 0.089 -3.78 0.00138 
ENSG00000111859 NEDD9 8.48 0.506 -4.07 0.00138 
App.14. Genes affected by 12 hour illumination:  App. 129 
Ensembl_Gene_ID Gene_name Control Illuminated Log2 FC q_val 
ENSG00000261143 ADAMTS7P3 2.24 0.132 -4.08 0.0384 
ENSG00000155974 GRIP1 0.907 0.0351 -4.69 0.00138