Uncovering the cis-regulatory code in glucose regulation disorders

A fundamental challenge in study of cell function and disease is understanding how cis-regulatory regions control gene expression. These regions are often denoted non-coding as they do not encode protein, yet a sequence code read by transcription factors underlies these regions. Our understanding of the code is insufficient to predict how changes in sequence impact function. This is a significant limitation in our understanding of disease as it impairs our ability to interpret genetic variants within cis-regulatory regions. This interdisciplinary research programme will significantly advance our understanding of the sequence basis of cis-regulatory control of gene expression. It will use rare and common glucose regulatory disorders studied in pancreas development and function as a model to understand the cis-regulatory code. I will use the novel technology of single-molecule footprinting to measure base-pair level activity of cis-regulatory regions. Pairing this with my expertise in data science and machine learning, I will build predictive algorithms to understand how the cis-regulatory code integrates information allowing the interpretation of cis-regulatory variants in disease. This proposal will make fundamental insights into the study of gene regulation and the genetic causes of disease. It will act as a model to understand cis-regulatory variation in human disease.