Active Genetics & Molecular Biology Cells, Biochemistry & Physiology

Uncovering the cis-regulatory code in glucose regulation disorders

Funder Wellcome Charity-funded
Recipient organisation University of Exeter
Funding £2.1M
Period Oct 2023 — Oct 2031

In plain English

AI plain-English summary

Every cell in the human body carries the same DNA, yet a pancreas cell and a brain cell do completely different jobs—because stretches of “non-coding” DNA act as switches that turn genes on or off, and scientists still cannot reliably predict what happens when those switches break. This research tackles a fundamental gap in biology: the inability to read the regulatory code that controls gene expression. Without that code, thousands of genetic variants found in non-coding DNA remain uninterpretable, even when they are linked to diseases like diabetes. The researcher will use a new technique called single-molecule footprinting to measure, base by base, how regulatory regions in pancreatic cells actually behave. By feeding that data into machine-learning algorithms, they aim to build predictive models that can explain how sequence changes alter gene activity. This is fundamental science. If it succeeds, it will not immediately change a patient’s treatment. But it will provide a general framework for interpreting non-coding variants in any disease—from metabolic disorders to cancer. Past breakthroughs in understanding the genetic code itself led to gene therapies and personalised medicine; a similar advance in the regulatory code could eventually allow clinicians to diagnose, predict, or treat conditions whose genetic causes are currently invisible.

View original technical description
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.

View the original record at the funder ↗

Researchers

Nick Owens (EPMC Awardee)

Related Research

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Harnessing genome characterization to uncover disease mechanisms
Context-specific mapping of genetic variants to function
Characterizing gene regulation in single cells through integration of scRNA-seq and scATAC-seq data with generic multi-modal prior information
Human-specific gene regulation in pancreatic beta-cell development
Understanding gene regulation utilising high-resolution gene interaction data generated with Micro Capture-C

Original classification

Career Development Award

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.