Lead Supervisor Dr Nick Owens, College of Medicine and Health, University of Exeter
Dr Sarah Flanagan, College of Medicine and Health, University of Exeter Dr Elisa De Franco, College of Medicine and Health, University of Exeter
Due to a major recent award, applications are invited from students wishing to further their scientific careers by undertaking a PhD in a diabetes related area of research. Up to four studentships will be fully funded from autumn 2020 with enhanced stipends funded from a new £6 million award. This award reflects Exeter as a world renowned centre of excellence for diabetes research.
Students can select from any of the advertised four projects. These projects have been carefully selected to provide students with an excellent scientific training in an important area of diabetes research, the latest laboratory and computing skills, outstanding resources, and with world leading scientists as supervisors. They cover various aspects of diabetes research, including autoimmunity in the pancreas; neuro-endocrinology to understand the relationship between the brain, mental health and the endocrine system; gene regulation in the placenta and fetal development of the pancreas; rare genetic forms of diabetes; muscle physiology; and the use of electronic medical records to understand disease causes, treatments and progression. Students will learn a wide range of state-of-the-art techniques, which could include CRISPR-Cas9 gene editing, DNA methylation, DNA sequence analysis, muscle insulin sensitivity physiology, brain electrophysiology, medical statistics, R for statistics and data visualisation and programming in python, data science including machine learning, in vivo metabolic phenotype skills and cell biology including 3D stem cell culture. Students will have access to outstanding resources, including cohorts of >5000 patients with rare defects in insulin secretion, a world leading collection of samples for study of pancreas pathology, resources of electronic medical records and biobanks from millions of people and unique resources for studying human development of the pancreas and brain.
This studentship will utilise genetic and genomic datasets to gain understanding of non-coding sequence mutations in disease. The student will develop their knowledge of chromatin biology and receive training in data science and machine learning techniques with the aim of developing prediction models to classify the impact of non-coding mutations.
The majority of disease associated mutations identified affect coding regions of the genome. However, this likely reflects an observational bias as genetic testing is commonly directed at the easier to interpret coding exomes. Rare pathogenic mutations falling outside the coding regions have been reported to cause Mendelian disease. These include but are not limited to, non-coding RNAs, enhancers, promoters and structural elements controlling three-dimensional genome architecture.
Mutations which disrupt transcription factor binding sites of FOXA2 and PDX1 in a distal enhancer regulating the developmental transcription factor PTF1A result in a failure of pancreatic development leading to neonatal diabetes. Further, mutations in the promoter of the PMM2 gene cause hyperinsulinemic hypoglycemia and congenital polycystic kidney disease by impaired binding of ZNF143 which alters chromatin looping. Understanding the impact of non-coding mutations in monogenic disease is important to highlight regulatory networks and provide insights into disease mechanism.
The student will investigate a catalogue of previously reported non-coding sequence variants, intersecting these with known chromatin features and epigenetic states utilising genomic datasets such as transcription factor binding, DNA accessibility and methylation, nucleosome positioning and histone modifications. The student will explore how the mutations affecting enhancers, promoters and regions controlling genomic architecture impact disease. The student will then build predictive models to understand how variants from genome sequencing data of individuals with monogenic forms of beta cell dysfunction impact transcription factor binding. This work will aim to discover novel variants that have a causative role in disease.
The project will allow the student to develop expertise in chromatin biology and predictive data analysis techniques. The student should be interested in genomics and gene regulation, they should be comfortable with data analysis and statistical methods. They will develop their bioinformatic skills and receive training in the analysis of genomic data and the application of machine learning techniques. The student will join a nascent interdisciplinary research team.
This is a 3 year fully-funded PhD studentship. Stipends are at an enhanced rate of £17,059 (2020-21) and all Home/EU tuition fees are covered. Funds will also be available for travel and research costs.