An exploration of genetic paradigms to investigate neuroregulatory genetic pathways in cardiovascular health

Abstract

Physiological homeostasis is constantly monitored and mediated by neuroregulatory mechanisms in the central nervous system (CNS). Deviation from optimal homeostatic regulation in response to the environment is a major driver of the prevalence of chronic non-communicable diseases such as hypertension, obesity and type-2 diabetes in the population. The CNS is the most complex biological system. It comprises interconnected neural networks operating a high degree a functional specificity which are regulated by diverse gene expression profiles. The particular characteristics of the CNS present unique challenges for scientific evaluation of the distinct biological processes important in health and disease. Progress made in the field of genetics, for example the identification of functionally distinct gene expression profiles or the identification of robust genetic associations with disease enriched in the CNS represent major milestones in how molecular genetic data can improve our fundamental understanding of the brain.
The goal of this thesis is to explore how genetic analyses in different contexts contribute to our understanding of neural processes in health and disease. First, molecular genetic and Next-generation sequencing technologies are implemented to characterize a transcriptional network underlying the homeostatic regulation of fluid balance in the brain using a rat animal model. Secondly, the use of statistical genetics to harness human genetic datasets is explored in order to investigate relationships between the KRAB-zinc finger family of transcriptional regulators (which are enriched in the brain) and complex health outcomes. Lastly, this thesis demonstrates how published population level genetic datasets may be harnessed to isolate neural mediated effects contributing to complex traits. A demonstration is provided of how such tissue-specific effects may be integrated into genetic epidemiological analyses to estimate their effect on disease risk. Overall, this thesis aims to provide a perspective on how multidisciplinary genetic analyses may contribute to complementary research paradigms.

Date of Award	9 May 2023
Original language	English
Awarding Institution	University of Bristol
Sponsors	British Heart Foundation
Supervisor	David Murphy (Supervisor), Tom G Richardson (Supervisor) & Michael P Greenwood (Supervisor)