# Deep Mutational Scanning of Monogenic Diabetes Genes to Facilitate Precision Diagnostics for Diabetes

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $644,473

## Abstract

PROJECT SUMMARY/ABSTRACT
Diabetes is a global health challenge and affects around 463 million people worldwide. For up to 5% of these
individuals their diabetes is due to a defect in a single gene. These monogenic varieties of diabetes provide
important opportunities for precision medicine altering the first line treatment for patients, providing information
on prognosis and risk for family members. Our inability to interpret DNA sequence variation results in uncertainty
regarding the impact of a DNA change on protein function leading to variants of unknown significance (VUS)
which are a barrier to precision medicine. A proposed solution is the deployment of multi-variant assays of
effects (MAVES) which generate a comprehensive catalogue of the effect of DNA sequence on protein function
which can then aid clinical variant interpretation and provide important information on protein structure and
function. The choice of assay and its alignment with gold-standard low throughput approaches is paramount for
accurate variant interpretation.
The overarching aim of our research program is to generate comprehensive maps of variant effects for
genes involved in monogenic diabetes to deliver precision diagnostics to enable precision medicine.
We will capitalize on our expertise in gold-standard assays for variant characterization in monogenic diabetes
genes and alignment with the Clin Gen Monogenic Diabetes Expert Panel. We will deploy state-of-the-art
microfluidic platforms and use authentic human cell models coupled to gene and disease relevant assays to
determine the effects of all missense variants which can be generated by a single nucleotide substitution for the
two most common causes of monogenic diabetes, defects in the key glycolytic enzyme glucokinase (GCK) and
the transcription factor hepatocyte nuclear factor 1 alpha (HNF1A). We will achieve this by [1] Quantitatively
characterizing the `functional enzymatic signatures' across 1000s of GCK variants using High-
Throughput Microfluidic Enzyme Kinetics (HI-MEK); [2] Determining HNF1A transcription factor function at
scale using Simultaneous Transcription factor Affinity Measurements via Microfluidic Protein Arrays (STAMMP)
to determine effects on DNA binding and coupling this with a high-throughput pooled screen for insulin secretion
in human beta cells and; [3] working with international leaders in monogenic diabetes to Integrate our variant
maps into clinical diagnostics through variant curation & the developing improved prediction models.
Outcome: We will deliver a roadmap for design, validation, and implementation of MAVES to assess the impact
of protein coding variants in any medically actionable gene relevant to diabetes risk.

## Key facts

- **NIH application ID:** 10943838
- **Project number:** 1R01DK140555-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Anna Louise Gloyn
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $644,473
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10943838

## Citation

> US National Institutes of Health, RePORTER application 10943838, Deep Mutational Scanning of Monogenic Diabetes Genes to Facilitate Precision Diagnostics for Diabetes (1R01DK140555-01). Retrieved via AI Analytics 2026-06-14 from https://api.ai-analytics.org/grant/nih/10943838. Licensed CC0.

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