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人类胰腺中的细胞谱系和疾病风险的染色质基础。

A Chromatin Basis for Cell Lineage and Disease Risk in the Human Pancreas.

机构信息

Department of Developmental Biology, Stanford University School of Medicine, 279 Campus Drive, Stanford, CA 94305, USA.

Center for Personal Dynamic Regulomes, 269 Campus Drive CCSR 2145, Stanford, CA 94305, USA.

出版信息

Cell Syst. 2018 Sep 26;7(3):310-322.e4. doi: 10.1016/j.cels.2018.07.007. Epub 2018 Aug 22.

DOI:10.1016/j.cels.2018.07.007
PMID:30145115
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6347013/
Abstract

Understanding the genomic logic that underlies cellular diversity and developmental potential in the human pancreas will accelerate the growth of cell replacement therapies and reveal genetic risk mechanisms in diabetes. Here, we identified and characterized thousands of chromatin regions governing cell-specific gene regulation in human pancreatic endocrine and exocrine lineages, including islet β cells, α cells, duct, and acinar cells. Our findings have captured cellular ontogenies at the chromatin level, identified lineage-specific regulators potentially acting on these sites, and uncovered hallmarks of regulatory plasticity between cell types that suggest mechanisms to regenerate β cells from pancreatic endocrine or exocrine cells. Our work shows that disease risk variants related to pancreas are significantly enriched in these regulatory regions and reveals previously unrecognized links between endocrine and exocrine pancreas in diabetes risk.

摘要

理解人类胰腺中细胞多样性和发育潜力的基因组逻辑将加速细胞替代疗法的发展,并揭示糖尿病的遗传风险机制。在这里,我们鉴定和描述了数千个染色质区域,这些区域控制着人类胰腺内分泌和外分泌谱系中细胞特异性基因调控,包括胰岛β细胞、α 细胞、导管和腺泡细胞。我们的研究结果在染色质水平上捕获了细胞发生,鉴定了潜在作用于这些位点的谱系特异性调节剂,并揭示了细胞类型之间的调节可塑性特征,这表明了从胰腺内分泌或外分泌细胞中再生β细胞的机制。我们的工作表明,与胰腺相关的疾病风险变异在这些调节区域中显著富集,并揭示了糖尿病风险中内分泌和外分泌胰腺之间以前未被认识到的联系。

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本文引用的文献

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Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci.整合人类胰腺胰岛基因组数据可改善 2 型糖尿病易感基因座的调控机制。
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Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns.
使用PACS对scATAC-seq数据进行染色质可及性的深度校正多因素剖析。
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