bioRxivGenomicsPreprint
EndoC-βH1 multi-genomic profiling defines gene regulatory programs governing human pancreatic β cell identity and function
Nathan T LawloriD, Eladio J. MárqueziD, Peter OrchardiD, Narisu NarisuiD, Muhammad S. ShamimiD, Asa ThibodeauiD, Arushi VarshneyiD, Romy KursaweiD, Michael R. ErdosiD, Matt KankeiD, Huiya Gu, Evgenia D. Pak, Amalia Dutra, Sheikh RusselliD, Xingwang LiiD, Emaly Piecuch, Oscar Junhong LuoiD, Peter S. ChinesiD, Christian Fuchbserger, Praveen SethupathyiD, Aviva Presser AideniD, Yijun RuaniD, Erez Lieberman AideniD, Francis S. CollinsiD, Duygu UcariD, , Michael L. StitzeliD
Abstract
EndoC-{beta}H1 is emerging as a critical human beta cell model to study the genetic and environmental etiologies of beta cell function, especially in the context of diabetes. Comprehensive knowledge of its molecular landscape is lacking yet required to fully take advantage of this model. Here, we report extensive chromosomal (spectral karyotyping), genetic (genotyping), epigenetic (ChIP-seq, ATAC-seq), chromatin interaction (Hi-C, Pol2 ChIA-PET), and transcriptomic (RNA-seq, miRNA-seq) maps of this cell model. Integrated analyses of these maps define known (e.g., PDX1, ISL1) and putative (e.g., PCSK1, mir-375) beta cell-specific chromatin interactions and transcriptional cis-regulatory networks, and identify allelic effects on cis-regulatory element use and expression.\n\nImportantly, comparative analyses with maps generated in primary human islets/beta cells indicate substantial preservation of chromatin looping, but also highlight chromosomal heterogeneity and fetal genomic signatures in EndoC-{beta}H1. Together, these maps, and an interactive web application we have created for their exploration, provide important tools for the broad community in the design and success of experiments to probe and manipulate the genetic programs governing beta cell identity and (dys)function in diabetes.
- organism: human
- assay: ChIP-seq
- assay: ATAC-seq
- assay: Hi-C
- assay: DNA methylation
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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