Common Pathophysiological Features of Charcot-Marie-Tooth Disease
University of Cambridge
Doctor of Philosophy (PhD)
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Jennings, M. (2022). Common Pathophysiological Features of Charcot-Marie-Tooth Disease (Doctoral thesis). https://doi.org/10.17863/CAM.83321
Hereditary peripheral neuropathy, also known as Charcot-Marie-Tooth disease (CMT) and related disorders, are a group of genetic disorders causing length-dependant neuropathy, resulting in motor and/or sensory loss progressing from the lower extremities toward the spine. With a population prevalence of 1 in 2,500, CMT is the most common inherited neuromuscular disorder and among the most common of all Mendelian diseases. Despite advances in the understanding of CMT genomics, a significant number of CMT patients remain genetically undiagnosed, and disease-modifying therapeutics are not available for the vast majority of CMT patients. In this thesis I present four topics addressing related areas of unmet need within the CMT field. My aim is to identify biological features relevant to a broad range of subtypes and experimental models, bridging the division between clinical and basic science. Firstly, I perform a systemic review of therapeutic studies of inherited peripheral neuropathies, with a focus specifically on gene and variant-level, rather than disease-level, treatment outcomes. Therapeutics for neuromuscular diseases are often gene- or mutation-specific, and I identify several variant-specific therapies and therapeutic approaches for inherited neuropathies, as well as variant-specific outcomes for gene-specific therapies, to be integrated into an online genetic diagnostic platform. Within the clinical trials I identify lack of responsive CMT biomarkers as a major limiting factor for therapeutic trials. Therefore, I next seek to identify novel biomarkers, by applying proteomics in a cohort of CMT patients and mouse models to identify neural cell adhesion molecular 1 (NCAM1) and growth differentiation factor 15 (GDF15) as serum biomarkers of CMT. NCAM1 may potentially function as a disease-progression biomarker, whereas GDF15 is indicated to be a very accurate diagnostic biomarker. Additionally, I find that broad elevation of serum complement proteins across CMT subtypes, which, while less promising as biomarkers, may indicate that dysregulation of this system. Thirdly, whilst the pathophysiology of some individual CMT genotypes is well understood, in many cases there is not currently a clear shared mechanism linking basic CMT-associated gene families to neuropathy pathogenesis. In particular, it is unclear why dominant missense mutations in several tRNA synthetases lead to axonal neuropathy, whilst other variants in these genes cause very variable human disease. Therefore, I develop an induced neural progenitor cell (iNPC) model derived from skin fibroblasts of patients with these genetic defects and perform transcriptomic and proteomic analyses of dysregulated pathways. Finally, to address the particularly large proportion of undiagnosed patients with distal hereditary motor neuropathy, I test the possibility of using functional transcriptomic and proteomic datasets to identify causative variants of axonal neuropathies, and develop a machine learning model to identify neuropathy-causative variants within large datasets of whole exome or genome sequencing. Although CMT is highly heterogenous at a genetic level, I show across these topics that there are several molecular signatures common across many subtypes. The identification of two non-subtype-specific biomarkers will aid the development of CMT therapeutics. Dysregulation of cell adhesion molecules is identified as a major feature in both patient sera and patient-derived iNPC models, while the machine learning model I develop demonstrates that protein and RNA enrichment in the axon is a key feature of CMT.
Charcot-Marie-Tooth, peripheral nerve, neuropathy, neurogenetic, neuromuscular, neuroscience, genetics, neurodegeneration, bioinformatics, biomarkers
Personal funding from MRC DiMeN and Cambridge Doctoral Training Partnerships (DTPs).
MRC (via University of Sheffield) (Unknown)
Evelyn Trust (19/14)
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This record's DOI: https://doi.org/10.17863/CAM.83321
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