Abstract
Utilising a multi-disciplinary approach, this work combines bioinformatics, genomics, and electrophysiology to explore pain signalling by nociceptors. I aimed to develop new ways to classify nociceptors and identify their mechanism of transduction in mice to test whether electrophysiological measures can be used to infer their functional roles and allow translation from rodent to human. This has been complemented by an investigation into whether a gene that is relatively selectively expressed in nociceptors and that is causally linked to rare inherited pain conditions is associated with an increased risk of pain at a population level. Collectively, this research will help understand how a translational science and a personalised medicine approach can benefit difficult-to-treat patients.Recordings from single nociceptors can provide detailed mechanistic insight into pain generation in health and disease. In mice, the ex vivo skin-nerve preparation can be used to achieve this objective, whereas the equivalent technique in humans is microneurography. Electrical stimulation protocols with varying frequencies can identify C-fibre classes by examining activity-dependent slowing patterns with remarkable accuracy in humans, and analogous findings have been reported in rats, pigs, and macaques. I aimed to quantify the accuracy of this approach across species. However, when applied to mice with carefully classified fibre types, I found that these stimulation paradigms do not accurately distinguish between important classes of nociceptors. The extent of this inability to classify murine C-fibres based on activity-dependent slowing, unlike other species, is explored through comparative statistical modelling.
To develop novel analgesics, reliable translational biomarkers are needed to ensure target engagement and confirm mechanistic efficacy. I aimed to co-develop, test, and validate a tool for nociceptor electrical threshold tracking, APTrack. This tool enabled the reliable quantification of changes in nociceptor electrical thresholds and showed dose-dependent effects of the local anaesthetic, lidocaine, on C- and Aδ-fibres, providing proof of principle for the approach.
Mutations in the NaV1.7 protein are reported as the cause of severe inherited pain conditions, including erythromelalgia, paroxysmal extreme pain disorder, and small-fibre neuropathy. These variants all have electrophysiological evidence of causing a gain of function. I aimed to understand the prevalence and effect of these putative pathogenic mutations in the general population. By leveraging the exome sequencing data of 470k participants in UK BioBank, I show that many of these reported pathogenic NaV1.7 mutations are present at unexpectedly high frequencies. The carriers of these NaV1.7 mutations have no evidence of an increased risk of chronic pain, neuropathic pain, or requirement for analgesic prescription.
One possible explanation for the lack of pain in these mutation carriers is that other unknown mutations are required to cause the phenotype or are present that protect the carriers from pain. I aimed to develop a new gene-level aggregation and burden testing tool in R for identifying modifier genes, called rarevaR. Analyses using this tool successfully identified known and novel modifier genes for the red hair genetic phenotype but did not identify the presence of protective modifier genes for the NaV1.7 mutations. This evidence argues against the role of many of the reported NaV1.7 mutations in causing inherited pain conditions.
| Date of Award | 18 Jun 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Anthony Edward Pickering (Supervisor), Jim Dunham (Supervisor) & Achim Kless (Supervisor) |