Ontology highlight
ABSTRACT:
PROVIDER: PRJNA766745 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR16214657_1.fastq.gz | Fastqsanger.gz | |||
| SRR16214657_2.fastq.gz | Fastqsanger.gz | |||
| SRR16214658_1.fastq.gz | Fastqsanger.gz | |||
| SRR16214658_2.fastq.gz | Fastqsanger.gz | |||
| SRR16214659_1.fastq.gz | Fastqsanger.gz |
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Project description:SOX9 ChIP-seq data of E7 chicken developing limb buds and male gonads
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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.
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Project description:ChIP-seq was used to generate chromatin state maps, profile binding patterns of key architectural proteins and locate putative enhancer regions in the early development (TB stage) and developing gonads of the marine chrodate Oikopleura dioica.
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Project description:ChIP-seq analysis of CIC and H3K27Ac in the developing mouse brain
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Project description:<p>Gene expression is a biological process regulated at different molecular levels, including chromatin accessibility, transcription, and RNA maturation and transport. In addition, these regulatory mechanisms have strong links with cellular metabolism. Here we present a multi-omics dataset that captures different aspects of this multi-layered process in yeast. We obtained RNA-seq, metabolomics, and H4K12Ac ChIP-seq data for wild-type and mip6delta strains during a heat-shock time course. Mip6 is an RNA-binding protein that contributes to RNA export during environmental stress and is informative of the contribution of post-transcriptional regulation to control cellular adaptations to environmental changes. The experiment was performed in quadruplicate, and the different omics measurements were obtained from the same biological samples, which facilitates the integration and analysis of data using covariance-based methods. We validate our dataset by showing that ChIP-seq, RNA-seq and metabolomics signals recapitulate existing knowledge about the response of ribosomal genes and the contribution of trehalose metabolism to heat stress.</p>
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Project description:Abstract. Very little is known regarding how hormonal exposures impact the epigenetic landscape of developing tissues in the context of a whole organism, in contrast to the impact on cultured cells. Here we took a global approach to understanding how neonatal exposure to the xenoestrogen, diethylstilbestrol (DES), alters the uterine epigenome. RNA-seq and ChIP-seq (H3K4me3, H3K27me3, H3K27ac and H3K4me1) were performed on DES-treated and control uteri. The most striking finding was differential association of H3K27ac and H3K4me1 at typical and super-enhancer regions of 79% of altered genes. These peaks overlapped with previously reported estrogen receptor a (ERα) ChIP-seq peaks. Conditional uterine deletion of ERα (Esr1cKO) conferred protection of 88% of altered genes. H3K27ac ChIP-seq on Esr1cKO samples showed that 72% of protected genes had a differential H3K27ac enhancer. These data suggest that DES regulates gene expression in the neonatal mouse uterus by H3K27ac association at ERα binding sites near estrogen-regulated genes.
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Project description:We report the distribution of histone H3K4me1 and H3K27ac within the genome of mouse embryonic midbrain. We prepared the chromatin from 11 dpc embryonic midbrain and made chromatin precipitation with antibody against H3K4me1 and H3K27ac (rabbit polyclonal antibody). High-throughput sequencing applied for the ChIP analysis revealed the differential distribution of modified histone within developing midbrain. ChIP analysis of mouse 11 dpc embryonic midbrain and forelimb buds against anti-H3K4me1 and H3K27ac antibody.
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Project description:RUVBL2 is most important AAA+ ATPase for RNA polymerase II assembly and transcription regulation, through DNA remodeling or by directly interaction with PIC，this study will comprehensively to study the promiscuous functions of this proteins through the ChIP-MS, ChIP-seq, RNA-seq and nascent RNA seq and biochemistry analysis. Our study would provide more systematic and novel responsibility of this molecule, especially for the development and carcinomas.
2022-09-29 | PXD036528 | Pride
Project description:<p>The NIH-funded BrainSpan (<a href="http://www.brainspan.org/" target="_blank">www.brainspan.org</a>) and PsychENCODE (<a href="http://development.psychencode.org" target="_blank">www.psychencode.org</a>). Consortia sought to generate and analyze multi-dimensional genomics data from the developing and adult human brain in healthy and disease states. One of the main goals has been to perform large-scale and integrated analysis of the genome, transcriptome, and epigenome of the human brain to broaden our understanding of human neurodevelopment. This dataset consists of sixteen regions, including eleven neocortical areas, of human donors of both sexes and various ethnic groups. In the first stage of this project, we provide the genome-wide exon-level transcriptome data generated using the Affymetrix GeneChip Human Exon 1.0 ST Arrays, and the genome-wide genotyping data for 2.5 million markers using the Illumina Human Omni 2.5-Quad Bead Chips. In the second stage of this project, we provide whole-genome sequencing data, transcriptome data by mRNA-Seq, small RNA data by smRNA-seq, DNA cytosine methylation by Infinium HumanMethylation450 BeadChip, and epigenomic/epigenetic data by ChIP-Seq for H3K4me3, H3K27me3, H3K27ac and CTCF.</p>
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