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Genetic risk converges on regulatory networks mediating early type 2 diabetes.

Authors :
Walker JT
Saunders DC
Rai V
Chen HH
Orchard P
Dai C
Pettway YD
Hopkirk AL
Reihsmann CV
Tao Y
Fan S
Shrestha S
Varshney A
Petty LE
Wright JJ
Ventresca C
Agarwala S
Aramandla R
Poffenberger G
Jenkins R
Mei S
Hart NJ
Phillips S
Kang H
Greiner DL
Shultz LD
Bottino R
Liu J
Below JE
Parker SCJ
Powers AC
Brissova M
Source :
Nature [Nature] 2023 Dec; Vol. 624 (7992), pp. 621-629. Date of Electronic Publication: 2023 Dec 04.
Publication Year :
2023

Abstract

Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet β cells <superscript>1,2</superscript> . T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and β cell regulatory genomic regions, but deciphering their biological mechanisms remains challenging <superscript>3-5</superscript> . Here, to identify early disease-driving events, we performed traditional and multiplexed pancreatic tissue imaging, sorted-islet cell transcriptomics and islet functional analysis of early-stage T2D and control donors. By integrating diverse modalities, we show that early-stage T2D is characterized by β cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the β cell hub gene and transcription factor RFX6 within one such module, we demonstrated multiple layers of genetic risk that converge on an RFX6-mediated network to reduce insulin secretion by β cells. RFX6 perturbation in primary human islet cells alters β cell chromatin architecture at regions enriched for T2D GWAS signals, and population-scale genetic analyses causally link genetically predicted reduced RFX6 expression with increased T2D risk. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs and individuals, and thus we anticipate that this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits using GWAS data.<br /> (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Details

Language :
English
ISSN :
1476-4687
Volume :
624
Issue :
7992
Database :
MEDLINE
Journal :
Nature
Publication Type :
Academic Journal
Accession number :
38049589
Full Text :
https://doi.org/10.1038/s41586-023-06693-2