2a55ff79-e333-4e5c-b9cb-854fbf65deb3 - samples
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ABSTRACT:
PROVIDER: EGAD00001005205 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:14 ChIP-Seq datasets of H3K27ac in human pancreatic islets from 14 donors, where islets were treated in high (11mM) glucose conditions. Samples IDs HI-129, HI-130, HI-131, HI-132, HI-135, HI-137 and HI-152 were also cultured in low glucose conditions.
| EGAD00001005204 | EGA
Project description:7 RNA-Seq datasets in human pancreatic islets from 7 donors, where islets were treated in high (11mM) glucose conditions
| EGAD00001005207 | EGA
Project description:7 RNA-Seq datasets in human pancreatic islets from 7 donors, where islets were using low (4mM) glucose conditions.
| EGAD00001005208 | EGA
Project description:6 ChIP-Seq datasets of Mediator in human pancreatic islets from 6 donors
| EGAD00001005202 | EGA
Project description:3 ChIP-Seq datasets of Cohesin in human pancreatic islets from 3 donors
| EGAD00001005203 | EGA
Project description:<p>In this study we profile the epigenomic enhancer landscapes of CLL B cells (CD19+/CD5+) harvested from peripheral blood of patients from our Center. Included are results of ChIPseq profiling using chromatin immunoprecipitation of the enhancer histone mark H3K27ac (acetylated lysine 27 on histone H3), and open chromatin profiles using ATAC-seq (assay for transposase accessible chromatin). These profiles are used to define the global enhancer and super enhancer landscape of CLL B cells, and to derive active transcription factor networks associated with this disease. Also included are H3K27ac ChIP-seq and ATAC-seq datasets for non-CLL B cells obtained from the peripheral blood of normal adult donors.</p>
| phs001704 | dbGaP
Project description:13 ATAC-Seq datasets of human pancreatic islets from 13 donors
| EGAD00001005201 | EGA
Project description:Enhancers are fundamental to gene regulation. Post-translational modifications by the small ubiquitin-like modifiers (SUMO) modify chromatin regulation enzymes, including histone acetylases and deacetylases. However, it remains unclear whether SUMOylation regulates enhancer marks, acetylation at the 27th lysine residue of the histone H3 protein (H3K27Ac). We hypothesize that SUMOylation regulates H3K27Ac. To test this hypothesis, we performed genome-wide ChIP-seq analyses. We discovered that knockdown (KD) of the SUMO activating enzyme catalytic subunit UBA2 reduced H3K27Ac at most enhancers. Bioinformatic analysis revealed that TFAP2C-binding sites are enriched in enhancers whose H3K27Ac was reduced by UBA2 KD. ChIP-seq analysis in combination with molecular biological methods showed that TFAP2C binding to enhancers increased upon UBA2 KD or inhibition of SUMOylation by a small molecule SUMOylation inhibitor. However, this is not due to the SUMOylation of TFAP2C itself. Proteomics analysis of TFAP2C interactome on the chromatin identified histone deacetylation (HDAC) machinery. TFAP2C KD reduced HDAC binding to chromatin and increased H3K27Ac marks at enhancer regions, suggesting that TFAP2C is involved in recruiting HDAC. Taken together, our findings provide important insights into regulation of enhancer marks by SUMOylation.
2024-04-13 | PXD045644 | Pride
Project description:Background: The multiome is an integrated assembly of distinct classes of molecules and molecular properties, or “omes,” measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies. Methods: Tissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LCMS/ MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression. Results: MultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LCMS/ MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually. Conclusions: The MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies – for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.
2025-02-14 | PXD041462 | Pride
Project description:C57BLKS/J mice are susceptible to diabetes, because of islet dysfunction, whereas C57BL6/J mice are not. Differences in gene expression between the two strains may account for this sensitivity. Furthermore these differences may only be evident in the hyperstimulated (diabetic or hyperglycemic) state. To this end profiling islets from these two strains cultured in both low and high glucose may reveal the underlying mechanism. Keywords: Mouse strain comparison under different culture conditions In the study presented here, pancreatic islets from 20 mice grown in low and high glucose conditions were assayed for differences in gene expression. (five C57BLKS/J low glucose, four C57BLKS/J high glucose, six C57BL6/J low glucose, five C57BL6/J high glucose). Technical replicates are achieved by labeling each sample with both Cy3 and Cy5, and combining the values for each hybridization.
2008-12-30 | E-GEOD-11257 | biostudies-arrayexpress