Genetic risk converges on regulatory networks mediating early type 2 diabetes.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Walker John T, Saunders Diane C, Rai Vivek, Chen Hung-Hsin, Orchard Peter, Dai Chunhua, Pettway Yasminye D, Hopkirk Alexander L, Reihsmann Conrad V, Tao Yicheng, Fan Simin, Shrestha Shristi, Varshney Arushi, Petty Lauren E, Wright Jordan J, Ventresca Christa, Agarwala Samir, Aramandla Radhika, Poffenberger Greg, Jenkins Regina, Mei Shaojun, Hart Nathaniel J, Phillips Sharon, Kang Hakmook, Greiner Dale L, Shultz Leonard D, Bottino Rita, Liu Jie, Below Jennifer E, HPAP Consortium, Parker Stephen C J, Powers Alvin C, Brissova Marcela
Аннотация
Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet β cells1,2. 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 challenging3-5. 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.