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Towards smart molecular biosensors: Modulation of DNA strand displacement reactions with transcription factors for robust and tunable arsenic quantification

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cam.restrictionthesis_access_embargoed
cam.supervisorAjioka, Jim
cam.supervisorDavenport, Peter
cam.supervisorYordanov, Boyan
dc.contributor.authorCoxon, James
dc.date.accessioned2021-11-09T21:31:26Z
dc.date.available2021-11-09T21:31:26Z
dc.date.submitted2021-06
dc.description.abstractBiosensors are increasingly important for gathering information in modern life. Advances in synthetic biology tools, such as isothermal DNA amplification, CRISPR-Cas and DNA strand displacement (DSD), have accelerated this transition and the ongoing COVID-19 pandemic has made biological testing ubiquitous. Nucleic acid diagnostics have been at the forefront of this trend, particularly for viral genomes, but increasingly other analytes are being addressed. Arsenic is a pervasive contaminant of drinking water in many countries, especially Bangladesh where the WHO has described the situation as the ‘largest poisoning of a population in history’. Reliable, sensitive, affordable and practical sensors are urgently required to reduce prevalence of chronic exposure. Here, a novel in vitro biosensory system is presented, using allosteric transcription factors (aTF) to modulate reversible DSD reactions. By designing fluorescent probes containing an appropriate aTF operator sequence, repressor concentration can influence the underlying DSD reaction equilibrium. Once a stable state between these components is established, addition of the allosteric ligand, in this case arsenite, perturbs the system, and the resulting equilibrium shift and intermediate dynamics can be correlated with ligand concentration, enabling quantification. In this project, the Corynebacterium glutamicum arsenic repressor (CgArsR) was expressed, purified and characterised, and its suitability for DSD integration established. A mutant operator sequence was designed to provide a structureless, high-affinity DNA probe with an architecture enabling comparison of alternative sequences while maintaining a consistent fluorophore environment. This was combined with a bespoke normalisation methodology for meaningful, reliable signal conversion to concentration or reaction balance metrics, allowing optimisation of both speed and sensitivity of arsenic quantification. Finally, integration of a parallel arsenite-insensitive mutant repressor and a differential rate analysis enabled not only robust selectivity but detection below the 10ppb WHO threshold in under an hour. The resulting system has great potential for further enhancement. Addition of chelating and cryopreservative agents are likely to improve ability to withstand environmental variation and lyophilisation, while an improved mechanistic model could permit optimisation of component concentrations and properties. Combined with suitable hardware, a practical arsenic biosensor may be possible. Perhaps more importantly, however, this project provides clear demonstration that allosteric transcription factors can modulate DSD reactions, creating potential for many other applications, whether directly as biosensors for alternative analytes or as bespoke signal control elements for DSD computation.
dc.description.sponsorshipMicrosoft Research PhD Scholarship; Department of Pathology 4th year PhD funding
dc.identifier.doi10.17863/CAM.77948
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330505
dc.language.isoeng
dc.publisher.collegeJesus
dc.publisher.institutionUniversity of Cambridge
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectsynthetic biology
dc.subjectDNA strand displacement
dc.subjectbiosensor
dc.subjectarsenic
dc.titleTowards smart molecular biosensors: Modulation of DNA strand displacement reactions with transcription factors for robust and tunable arsenic quantification
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.type.qualificationtitlePhD in Pathology
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved/
rioxxterms.typeThesis

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