RFX6-mediated dysregulation defines human β cell dysfunction in early type 2 diabetes
By
John T Walker,
Diane C Saunders,
Vivek Rai,
Chunhua Dai,
Peter Orchard,
Alexander L Hopkirk,
Conrad V Reihsmann,
Yicheng Tao,
Simin Fan,
Shristi Shrestha,
Arushi Varshney,
Jordan J Wright,
Yasminye D Pettway,
Christa Ventresca,
Samir Agarwala,
Radhika Aramandla,
Greg Poffenberger,
Regina Jenkins,
Nathaniel J Hart,
Dale L Greiner,
Leonard D. Shultz,
Rita Bottino,
Human Pancreas Analysis Program,
Jie Liu,
Stephen C. J Parker,
Alvin Powers,
Marcela Brissova
Posted 17 Dec 2021
bioRxiv DOI: 10.1101/2021.12.16.466282
A hallmark of type 2 diabetes (T2D), a major cause of world-wide morbidity and mortality, is dysfunction of insulin-producing pancreatic islet {beta} cells. T2D genome-wide association studies (GWAS) have identified hundreds of signals, mostly in the non-coding genome and overlapping {beta} cell regulatory elements, but translating these into biological mechanisms has been challenging. To identify early disease-driving events, we performed single cell spatial proteomics, sorted cell transcriptomics, and assessed islet physiology on pancreatic tissue from short-duration T2D and control donors. Here, through integrative analyses of these diverse modalities, we show that multiple gene regulatory modules are associated with early-stage T2D {beta} cell-intrinsic defects. One notable example is the transcription factor RFX6, which we show is a highly connected {beta} cell hub gene that is reduced in T2D and governs a gene regulatory network associated with insulin secretion defects and T2D GWAS variants. We validated the critical role of RFX6 in {beta} cells through direct perturbation in primary human islets followed by physiological and single nucleus multiome profiling, which showed reduced dynamic insulin secretion and large-scale changes in the {beta} cell transcriptome and chromatin accessibility landscape. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs, and individuals and thus we anticipate this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits with GWAS data.
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