SCAN database: facilitating integrative analyses of cytosine modification and expression QTL

Abstract Somatic mutations of the chromatin remodeling gene ARID2 are observed in about 7% of human lung adenocarcinoma (LUAD). However, the role of ARID2 in the pathogenesis of LUAD remains largely unknown. Here we find that ARID2 expression is decreased during the malignant progression of both human and mice LUAD. Using two KrasG12D-based genetically engineered murine models (GEMM), we demonstrate that ARID2 knockout significantly promotes lung cancer malignant progression and shortens the overall survival. Consistently, ARID2 knockdown significantly promotes cell proliferation in human and mice lung cancer cells. Through integrative analyses of Chip-Seq and RNA-Seq data, we find that Hspa1a is up-regulated by Arid2 loss. Knockdown of Hspa1a specifically inhibits malignant progression of Arid2-deficient but not Arid2-wt lung cancers in both cell lines as well as animal models. Treatment with Hspa1a inhibitor could significantly inhibit the malignant progression of lung cancer with Arid2 deficiency. Together, our findings establish ARID2 as an important tumor suppressor in LUAD with novel mechanistic insights, and further identify HSPA1A as a potential therapeutic target in ARID2-deficient LUAD.

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Targeting DNA Methylation Depletes Uterine Leiomyoma Stem Cell–enriched Population by Stimulating Their Differentiation

Endocrinology ◽

10.1210/endocr/bqaa143 ◽

2020 ◽

Vol 161 (10) ◽

Cited By ~ 2

Author(s):

Shimeng Liu ◽

Ping Yin ◽

Jingting Xu ◽

Ariel J Dotts ◽

Stacy A Kujawa ◽

...

Keyword(s):

Dna Methylation ◽

Stem Cell ◽

Cell Population ◽

Uterine Leiomyoma ◽

Stem Cell Population ◽

Epigenetic Landscape ◽

Stem Cell Regulation ◽

Integrative Analyses ◽

Associated Functions ◽

Intermediate Cells

Abstract Uterine leiomyoma (LM) is the most common tumor in women and can cause severe morbidity. Leiomyoma growth requires the maintenance and proliferation of a stem cell population. Dysregulated deoxyribonucleic acid (DNA) methylation has been reported in LM, but its role in LM stem cell regulation remains unclear. Here, we fluorescence-activated cell sorting (FACS)-sorted cells from human LM tissues into 3 populations: LM stem cell–like cells (LSC, 5%), LM intermediate cells (LIC, 7%), and differentiated LM cells (LDC, 88%), and we analyzed the transcriptome and epigenetic landscape of LM cells at different differentiation stages. Leiomyoma stem cell–like cells harbored a unique methylome, with 8862 differentially methylated regions compared to LIC and 9444 compared to LDC, most of which were hypermethylated. Consistent with global hypermethylation, transcript levels of TET1 and TET3 methylcytosine dioxygenases were lower in LSC. Integrative analyses revealed an inverse relationship between methylation and gene expression changes during LSC differentiation. In LSC, hypermethylation suppressed the genes important for myometrium- and LM-associated functions, including muscle contraction and hormone action, to maintain stemness. The hypomethylating drug, 5′-Aza, stimulated LSC differentiation, depleting the stem cell population and inhibiting tumor initiation. Our data suggest that DNA methylation maintains the pool of LSC, which is critical for the regeneration of LM tumors.

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A critical and Integrated View of the Yeast Interactome

Comparative and Functional Genomics ◽

10.1002/cfg.412 ◽

2004 ◽

Vol 5 (5) ◽

pp. 382-402 ◽

Cited By ~ 12

Author(s):

Michael Cornell ◽

Norman W. Paton ◽

Stephen G. Oliver

Keyword(s):

High Throughput ◽

Protein Interactions ◽

False Positive ◽

Affinity Purification ◽

Information Management System ◽

Positive Interactions ◽

Global Studies ◽

Protein Protein Interactions ◽

Integrative Analyses ◽

Genome Information

Global studies of protein–protein interactions are crucial to both elucidating gene function and producing an integrated view of the workings of living cells. High-throughput studies of the yeast interactome have been performed using both genetic and biochemical screens. Despite their size, the overlap between these experimental datasets is very limited. This could be due to each approach sampling only a small fraction of the total interactome. Alternatively, a large proportion of the data from these screens may represent false-positive interactions. We have used the Genome Information Management System (GIMS) to integrate interactome datasets with transcriptome and protein annotation data and have found significant evidence that the proportion of false-positive results is high. Not all high-throughput datasets are similarly contaminated, and the tandem affinity purification (TAP) approach appears to yield a high proportion of reliable interactions for which corroborating evidence is available. From our integrative analyses, we have generated a set of verified interactome data for yeast.

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TFutils: Data structures for transcription factor bioinformatics

F1000Research ◽

10.12688/f1000research.17976.1 ◽

2019 ◽

Vol 8 ◽

pp. 152

Author(s):

Benjamin J. Stubbs ◽

Shweta Gopaulakrishnan ◽

Kimberly Glass ◽

Nathalie Pochet ◽

Celine Everaert ◽

...

Keyword(s):

Transcription Factor ◽

Transcription Factors ◽

Data Structures ◽

Binding Sites ◽

Genome Wide Association Study ◽

Genome Wide Association ◽

Bioconductor Package ◽

Genome Wide ◽

Study Results ◽

Integrative Analyses

DNA transcription is intrinsically complex. Bioinformatic work with transcription factors (TFs) is complicated by a multiplicity of data resources and annotations. The Bioconductor package TFutils includes data structures and functions to enhance the precision and utility of integrative analyses that have components involving TFs. TFutils provides catalogs of human TFs from three reference sources (CISBP, HOCOMOCO, and GO), a catalog of TF targets derived from MSigDb, and multiple approaches to enumerating TF binding sites. Aspects of integration of TF binding patterns and genome-wide association study results are explored in examples.

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Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

Genome Research ◽

10.1101/gr.268482.120 ◽

2021 ◽

Author(s):

Peter Orchard ◽

Nandini Manickam ◽

Christa Ventresca ◽

Swarooparani Vadlamudi ◽

Arushi Varshney ◽

...

Keyword(s):

Skeletal Muscle ◽

Muscle Cell ◽

Muscle Fiber ◽

Cell Types ◽

Chromatin Accessibility ◽

Skeletal Muscle Cell ◽

Rna Seq ◽

Rat Skeletal Muscle ◽

Integrative Analyses ◽

Single Nucleus

Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell–specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site–distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.

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