Similar Datasets
Project description:To understand the combinatorial binding of different PcG proteins and their binding dynamics across cell types, we performed multi-omics profiling in three Drosophila cell lines, including S2, Kc167 and/or D17-c3 cells. The profiled data included: 1) RNA-seq; 2) ATAC-seq; 3) ChIP-seq of multiple PcG proteins and other related proteins, including E(z), GAGA, Pc, Pcl, Ph, Psc, Scm, Sfmbt, Pho, Spps; 4) Multiple active and repressive histone modifications, including H3K27ac, H3K27me3, H3Krme2, H3K4me3, H3K36me2, H3K36me3; 5) Micro-C data.
2023-11-14 | GSE246517 | GEO
Project description:To understand the combinatorial binding of different PcG proteins and their binding dynamics across cell types, we performed multi-omics profiling in three Drosophila cell lines, including S2, Kc167 and/or D17-c3 cells. The profiled data included: 1) RNA-seq; 2) ATAC-seq; 3) ChIP-seq of multiple PcG proteins and other related proteins, including E(z), GAGA, Pc, Pcl, Ph, Psc, Scm, Sfmbt, Pho, Spps; 4) Multiple active and repressive histone modifications, including H3K27ac, H3K27me3, H3Krme2, H3K4me3, H3K36me2, H3K36me3.
2023-11-14 | GSE245078 | GEO
Project description:To understand the combinatorial binding of different PcG proteins and their binding dynamics across cell types, we performed multi-omics profiling in three Drosophila cell lines, including S2, Kc167 and/or D17-c3 cells. The profiled data included: 1) RNA-seq; 2) ATAC-seq; 3) ChIP-seq of multiple PcG proteins and other related proteins, including E(z), GAGA, Pc, Pcl, Ph, Psc, Scm, Sfmbt, Pho, Spps; 4) Multiple active and repressive histone modifications, including H3K27ac, H3K27me3, H3Krme2, H3K4me3, H3K36me2, H3K36me3.
2023-11-14 | GSE245077 | GEO
Project description:To understand the combinatorial binding of different PcG proteins and their binding dynamics across cell types, we performed multi-omics profiling in three Drosophila cell lines, including S2, Kc167 and/or D17-c3 cells. The profiled data included: 1) RNA-seq; 2) ATAC-seq; 3) ChIP-seq of multiple PcG proteins and other related proteins, including E(z), GAGA, Pc, Pcl, Ph, Psc, Scm, Sfmbt, Pho, Spps; 4) Multiple active and repressive histone modifications, including H3K27ac, H3K27me3, H3Krme2, H3K4me3, H3K36me2, H3K36me3.
2023-11-14 | GSE245076 | GEO
Project description:Polycomb group (PcG) proteins are able to maintain the memory of silent transcriptional states of homeotic genes throughout development. In Drosophila, they form multimeric complexes that bind to specific DNA regulatory elements named PcG response elements (PREs). To date, few PREs have been identified and the chromosomal distribution of PcG proteins during development is unknown. We used chromatin immunoprecipitation (ChIP) with genomic tiling path microarrays to analyze the binding profile of the PcG proteins Polycomb (PC) and Polyhomeotic (PH) across 10 Mb of euchromatin. We also analyzed the distribution of GAGA factor (GAF), a sequence-specific DNA binding protein that is found at most previously identified PREs. Our data show that PC and PH often bind to clustered regions within large loci that encode transcription factors which play multiple roles in developmental patterning and in the regulation of cell proliferation. GAF co-localizes with PC and PH to a limited extent, suggesting that GAF is not a necessary component of chromatin at PREs. Finally, the chromosome-association profile of PC and PH changes during development, suggesting that the function of these proteins in the regulation of some of their target genes might be more dynamic than previously anticipated.
| S-EPMC1440717 | biostudies-literature
Project description:BackgroundOvarian cancer (OC) has the highest mortality rate among gynecologic malignancy. Hypoxia is a driver of the malignant progression in OC, which results in poor prognosis. We herein aimed to develop a validated model that was based on the hypoxia genes to systematically evaluate its prognosis in tumor immune microenvironment (TIM).ResultsWe identified 395 hypoxia-immune genes using weighted gene co-expression network analysis (WGCNA). We then established a nine hypoxia-related genes risk model using least absolute shrinkage and selection operator (LASSO) Cox regression, which efficiently distinguished high-risk patients from low-risk ones. We found that high-risk patients were significantly related to poor prognosis. The high-risk group showed unique immunosuppressive microenvironment, lower antigen presentation, and higher levels of inhibitory cytokines. There were also significant differences in somatic copy number alterations (SCNAs) and mutations between the high- and low-risk groups, indicating immune escape in the high-risk group. Tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms showed that low-risk patients are significantly responsive to programmed cell death protein-1 (PD-1) inhibitors.ConclusionsIn this study, we highlighted the clinical significance of hypoxia in OC and established a hypoxia-related model for predicting prognosis and providing potential immunotherapy strategies.
| S-EPMC8327177 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2020-01-31 | GSE108667 | GEO
Project description:BACKGROUND:MicroRNAs (miRNAs) play a key role in mediating the action of insulin on cell growth and the development of diabetes. However, few studies have been conducted to provide a comprehensive overview of the miRNA-mediated signaling network in response to glucose in pancreatic beta cells. In our study, we established a computational framework integrating multi-omics profiles analyses, including RNA sequencing (RNA-seq) and small RNA sequencing (sRNA-seq) data analysis, inverse expression pattern analysis, public data integration, and miRNA targets prediction to illustrate the miRNA-mediated regulatory network at different glucose concentrations in INS-1 pancreatic beta cells (INS-1), which display important characteristics of the pancreatic beta cells. RESULTS:We applied our computational framework to the expression profiles of miRNA/mRNA of INS-1, at different glucose concentrations. A total of 1437 differentially expressed genes (DEGs) and 153 differentially expressed miRNAs (DEmiRs) were identified from multi-omics profiles. In particular, 121 DEmiRs putatively regulated a total of 237 DEGs involved in glucose metabolism, fatty acid oxidation, ion channels, exocytosis, homeostasis, and insulin gene regulation. Moreover, Argonaute 2 immunoprecipitation sequencing, qRT-PCR, and luciferase assay identified Crem, Fn1, and Stc1 are direct targets of miR-146b and elucidated that miR-146b acted as a potential regulator and promising target to understand the insulin signaling network. CONCLUSIONS:In this study, the integration of experimentally verified data with system biology framework extracts the miRNA network for exploring potential insulin-associated miRNA and their target genes. The findings offer a potentially significant effect on the understanding of miRNA-mediated insulin signaling network in the development and progression of pancreatic diabetes.
| S-EPMC7496206 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2024-09-20 | GSE249247 | GEO
Project description:BackgroundInference of biological pathway activity via gene set enrichment analysis is frequently used in the interpretation of clinical and other omics data. With the proliferation of new omics profiling approaches and ever-growing size of data sets generated, there is a lack of tools available to perform and visualise gene set enrichments in analyses involving multiple contrasts.ResultsTo address this, we developed mitch, an R package for multi-contrast gene set enrichment analysis. It uses a rank-MANOVA statistical approach to identify sets of genes that exhibit joint enrichment across multiple contrasts. Its unique visualisation features enable the exploration of enrichments in up to 20 contrasts. We demonstrate the utility of mitch with case studies spanning multi-contrast RNA expression profiling, integrative multi-omics, tool benchmarking and single-cell RNA sequencing. Using simulated data we show that mitch has similar accuracy to state of the art tools for single-contrast enrichment analysis, and superior accuracy in identifying multi-contrast enrichments.Conclusionmitch is a versatile tool for rapidly and accurately identifying and visualising gene set enrichments in multi-contrast omics data. Mitch is available from Bioconductor ( https://bioconductor.org/packages/mitch ).
| S-EPMC7325150 | biostudies-literature