# Identifying gene expression networks that improve beta-cell function in a model of naturally resolving hyperglycemia

> **NIH NIH F31** · WASHINGTON UNIVERSITY · 2020 · $30,638

## Abstract

Project Summary/Abstract
 Obese patients need therapies that improve glycemic control to prevent the deadly consequences of
diabetes. Obesity places metabolic stress on pancreatic β-cells, leading to β-cell dysfunction, loss of insulin
production, and hyperglycemia. Efforts to improve β-cell function are hindered by β-cell heterogeneity, where
cells differ in proliferative potential, insulin secretion, and stress resistance. Understanding the genetic networks
underlying these β-cell subpopulations will lead to the identification of pathways that can be exploited to increase
insulin secretion in diabetes. Single cell RNA sequencing allows for identification of gene expression signatures
of subpopulations, but has limited capacity to reveal genetic networks due to low read coverage. Bulk islet RNA
sequencing is highly powered to detected lowly expressed genes, but is confounded by cell population structure
within the islet. Both strategies need to be integrated to gain in-depth understanding of how β-cell subpopulations
behave in health and diabetic obesity. The Lawson utilizes a novel mouse model to understand how of glycemic
control can be improved in obesity. Fed a high fat diet until 20 weeks of age, SM/J mice display characteristics
of diabetic-obesity, including elevated adiposity, hyperglycemia, glucose intolerance, and deficient insulin
production. By 30 weeks of age, their hyperglycemia naturally resolves, characterized by normoglycemia,
improved glucose tolerance, and increased insulin levels all while remaining obese. Over this 10-week span,
islets from SM/J mice grow in size and dramatically improve insulin secreting capacity. I hypothesize the
improvement in insulin secretion seen in SM/J mice is driven by proliferation and maturation of immature
β-cells. To test this hypothesis, I will employ a novel RNA sequencing analysis strategy that incorporates single
cell and bulk RNA sequencing technology on islets isolated before and after the resolution of hyperglycemia in
SM/J mice. I will identify subpopulations of β-cells based on expression signatures, assess how they change
during the resolution of hyperglycemia using pseudotime analysis, and identify differentially expressed genes
within each subpopulation. I will then de-convolute the bulk RNA sequencing data using the single cell analysis
to control for gene expression changes caused by differences in cell population structure, identify gene
expression networks associated with the unique subpopulation expression signatures, assess how the networks
change during the resolution of hyperglycemia, and correlate the networks with diabetic traits using RNA
sequencing analysis pipelines developed by the Lawson lab. Through my proposed training activities, I will
generate a comprehensive assessment of β-cell subpopulations in obese SM/J mice, how they change during
the resolution of hyperglycemia, and how gene expression networks within each subtype relate to diabetic
phenotypes. Results wil...

## Key facts

- **NIH application ID:** 9991441
- **Project number:** 1F31DK125023-01
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Mario Miranda
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $30,638
- **Award type:** 1
- **Project period:** 2020-09-01 → 2021-08-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991441

## Citation

> US National Institutes of Health, RePORTER application 9991441, Identifying gene expression networks that improve beta-cell function in a model of naturally resolving hyperglycemia (1F31DK125023-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9991441. Licensed CC0.

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