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Genetic risk converges on regulatory networks mediating early type 2 diabetes.
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; Parker, Stephen C J; Powers, Alvin C; Brissova, Marcela.
Afiliação
  • Walker JT; Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA.
  • Saunders DC; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Rai V; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Chen HH; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Orchard P; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Dai C; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Pettway YD; Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA.
  • Hopkirk AL; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Reihsmann CV; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Tao Y; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Fan S; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Shrestha S; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Varshney A; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Petty LE; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Wright JJ; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Ventresca C; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
  • Agarwala S; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Aramandla R; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Poffenberger G; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Jenkins R; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Mei S; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Hart NJ; Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Phillips S; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Kang H; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Greiner DL; Department of Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA.
  • Shultz LD; The Jackson Laboratory, Bar Harbor, ME, USA.
  • Bottino R; Imagine Pharma, Devon, PA, USA.
  • Liu J; Institute of Cellular Therapeutics, Allegheny-Singer Research Institute, Allegheny Health Network, Pittsburgh, PA, USA.
  • Below JE; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Powers AC; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. scjp@umich.edu.
  • Brissova M; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA. scjp@umich.edu.
Nature ; 624(7992): 621-629, 2023 Dec.
Article em En | MEDLINE | ID: mdl-38049589
Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet ß cells1,2. T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and ß 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 ß cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the ß 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 ß cells. RFX6 perturbation in primary human islet cells alters ß 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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ilhotas Pancreáticas / Predisposição Genética para Doença / Perfilação da Expressão Gênica / Diabetes Mellitus Tipo 2 / Redes Reguladoras de Genes Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ilhotas Pancreáticas / Predisposição Genética para Doença / Perfilação da Expressão Gênica / Diabetes Mellitus Tipo 2 / Redes Reguladoras de Genes Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article