EndoC-βH1 multi-genomic profiling defines gene regulatory programs governing human pancreatic β cell identity and function

Lawlor, Nathan; Ej, Marquez; Orchard, Peter; Narisu, N; Ms, Shamim; Thibodeau, Asa; Varshney, Arushi; Kursawe, Romy; Mr, Erdos; Kanke, Matt; Gu, Heng; E, Pak; Dutra, Amalia; Russell, Sheikh; X, Li; Piecuch, Emaly; O, Luo; Ps, Chines; Fuchbserger, Christian; Sethupathy, Praveen; Ap, Aiden; Ruan, Yijun; Fs, Collins; Ucar, Duygu; Sc, Parker; Ml, Stitzel

doi:10.1101/399139

Cited by 3 publications

(2 citation statements)

References 99 publications

(4 reference statements)

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“…Genomic interactions can reveal the spatial proximity and regulatory events of genomic sites [23,28,29]. Interaction mapping of cancer-related genes may increase our understanding of the regulatory elements of these genes.…”

Section: High-resolution Loops Map Whole-genome Regulatory Relationsh...mentioning

confidence: 99%

Mapping Multiple Factors Mediated Chromatin Interactions to Assess Regulatory Network and Dysregulation of Lung Cancer-Related Genes

Zhang

zhang

et al. 2022

Preprint

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The study of lung cancer genome is an indispensable basis for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, the dysregulation of long-range chromatin interactions in lung cancer has been poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we generated high-resolution data revealing chromatin interactions associated with RNAPII (RNA Polymerase II), CTCF (CCCTC-binding factor), EZH2 (Enhancer Of Zeste 2 Polycomb Repressive Complex 2 Subunit), and H3K27me3 (Histone 3 lysine 27 trimethylation) by using specific antibodies with the long-read ChIA-PET (Chromatin interaction analysis by paired-end tag sequencing) method. EZH2/H3K27me3-mediated interactions are found to further silence target genes, either through loops or domains, and have distributions along the genome that are distinct from and complementary to those associated with RNAPII. We found that cancer-related genes are highly enriched in chromatin interactions. We identified abnormal interactions on oncogenes and tumor suppressors, such as additional repressive interactions on FOXO4 and promoter-promoter interactions between NF1 and RNF135. The knock-out of the abnormal interaction can reverse the dysregulation of cancer-related genes, which suggest that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings provide a 3D landscape and gene regulatory relationship of lung cancer genome.

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Section: High-resolution Loops Map Whole-genome Regulatory Relationsh...mentioning

confidence: 99%

Mapping Multiple Factors Mediated Chromatin Interactions to Assess Regulatory Network and Dysregulation of Lung Cancer-Related Genes

Zhang

zhang

et al. 2022

Preprint

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“…functional experiments such as glucose-stimulated insulin secretion (14)(15)(16) and marker expression profiling by qPCR (9,16). Recently, EndoC-βH1 open chromatin, transcriptomics and miRNA landscapes were reported to be most similar to adult human β cells or islets compared to α cells, adipocytes, muscle and peripheral blood cells (17). Two proteomics studies investigated the response to interferons in EndoC-βH1 cells (18,19), but did not validate EndoC-βH1 as a β cell model.…”

Section: Validity Of Endoc-βh1 Cells As a Human β Cell Model Has Been Previously Established Based Onmentioning

confidence: 99%

Characterization of the secretome, transcriptome and proteome of human β cell line EndoC-βH1

Ryaboshapkina

Saitoski

Hamza

et al. 2021

Preprint

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Early diabetes research is hampered by limited availability, variable quality and instability of human pancreatic islets in culture. Little is known about the human β cell secretome, and recent studies question translatability of rodent β cell secretory profiles. Here, we verify representativeness of EndoC-βH1, one of the most widely used human β cell lines, as a translational human β cell model based on omics and characterize the EndoC-βH1 secretome. We profiled EndoC-βH1 cells using RNA-seq, Data Independent Acquisition (DIA) and Tandem Mass Tag proteomics of cell lysate. Omics profiles of EndoC-βH1 cells were compared to human β cells and insulinomas. Secretome composition was assessed by DIA proteomics. Agreement between EndoC-βH1 cells and primary adult human β cells was ~90% for global omics profiles as well as for β cell markers, transcription factors and enzymes. Discrepancies in expression were due to elevated proliferation rate of EndoC-βH1 cells compared to adult β cells. Consistently, similarity was slightly higher with benign non-metastatic insulinomas. EndoC-βH1 secreted 671 proteins in untreated baseline state and 3,278 proteins when stressed with non-targeting control siRNA, including known β cell hormones INS, IAPP, and IGF2. Further, EndoC-βH1 secreted proteins known to generate bioactive peptides such as granins and enzymes required for production of bioactive peptides. Unexpectedly, exosomes appeared to be a major mode of secretion in EndoC-βH1 cells. We believe that secretion of exosomes and bioactive peptides warrant further investigation with specialized proteomics workflows in future studies.

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A neural network based model effectively predicts enhancers from clinical ATAC-seq samples

Thibodeau

Uyar

Khetan

et al. 2018

Sci Rep

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Enhancers are cis-acting sequences that regulate transcription rates of their target genes in a cell-specific manner and harbor disease-associated sequence variants in cognate cell types. Many complex diseases are associated with enhancer malfunction, necessitating the discovery and study of enhancers from clinical samples. Assay for Transposase Accessible Chromatin (ATAC-seq) technology can interrogate chromatin accessibility from small cell numbers and facilitate studying enhancers in pathologies. However, on average, ~35% of open chromatin regions (OCRs) from ATAC-seq samples map to enhancers. We developed a neural network-based model, Predicting Enhancers from ATAC-Seq data (PEAS), to effectively infer enhancers from clinical ATAC-seq samples by extracting ATAC-seq data features and integrating these with sequence-related features (e.g., GC ratio). PEAS recapitulated ChromHMM-defined enhancers in CD14+ monocytes, CD4+ T cells, GM12878, peripheral blood mononuclear cells, and pancreatic islets. PEAS models trained on these 5 cell types effectively predicted enhancers in four cell types that are not used in model training (EndoC-βH1, naïve CD8+ T, MCF7, and K562 cells). Finally, PEAS inferred individual-specific enhancers from 19 islet ATAC-seq samples and revealed variability in enhancer activity across individuals, including those driven by genetic differences. PEAS is an easy-to-use tool developed to study enhancers in pathologies by taking advantage of the increasing number of clinical epigenomes.

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EndoC-βH1 multi-genomic profiling defines gene regulatory programs governing human pancreatic β cell identity and function

Cited by 3 publications

References 99 publications

Mapping Multiple Factors Mediated Chromatin Interactions to Assess Regulatory Network and Dysregulation of Lung Cancer-Related Genes

Mapping Multiple Factors Mediated Chromatin Interactions to Assess Regulatory Network and Dysregulation of Lung Cancer-Related Genes

Characterization of the secretome, transcriptome and proteome of human β cell line EndoC-βH1

A neural network based model effectively predicts enhancers from clinical ATAC-seq samples

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