RNA-SEQ of mutants B cell for IgH 3''RR and Emu Overall design: CD43- splenic B-cells from wt, Eµ-deficient or 3''RR deficient mice, non stimulated (NS) or stimulated (S) with 5mg/ml LPS.
E<sub>μ</sub> and 3'RR IgH enhancers show hierarchic unilateral dependence in mature B-cells.
No sample metadata fields
View SamplesWe evaluated by RNA-seq obveral transcripts in B cells (resting and activated for 2 days with LPS) sorted from several KO mice models devoid of portion or all the IgH 3'' Regulatory Region Overall design: One RNA-seq point was realized per condition (resting or stimulated) and per genotype. Each point corresponds to a pool of equivalent number of B cells sorted from 4 animals
Sequential activation and distinct functions for distal and proximal modules within the IgH 3' regulatory region.
Specimen part, Cell line, Subject
View SamplesWe used microarrays to detail the global gene expression signature of PDAC and to identify distinct up- and down-regulated transcripts in these tumors compared to control pancreas. We also established from this dataset the metabolic signature of PDAC in order to define new metabolic therapeutic target for pancreatic cancer.
Cholesterol uptake disruption, in association with chemotherapy, is a promising combined metabolic therapy for pancreatic adenocarcinoma.
Sex, Age, Specimen part
View SamplesT-cell clones were obtained by limiting dilution culture of PBMC of HTLV-1 carriers. Exon expression profiling was performed using Affymetrix exon array (Affymetrix Human Exon 1.0 ST Array) according to the manufacturer's instructions. Gene version of CEL files 01 to 12 are presented in GSE46518.
HTLV-1-infected CD4+ T-cells display alternative exon usages that culminate in adult T-cell leukemia.
Specimen part
View SamplesT-cell clones were obtained by limiting dilution culture of PBMC of HTLV-1 carriers. Exon expression profiling was performed using Affymetrix exon array (Affymetrix Human Exon 1.0 ST Array) according to the manufacturer's instructions.
HTLV-1 bZIP factor HBZ promotes cell proliferation and genetic instability by activating OncomiRs.
Specimen part
View SamplesPurpose: mRNA translation into protein is highly regulated, but the role of mRNA isoforms, noncoding RNAs (ncRNAs), and genetic variants has yet to be systematically studied. Using high-throughput sequencing (RNA-seq), we have measured cellular levels of mRNAs and ncRNAs, and their isoforms, in lymphoblast cell lines (LCL) and in polysomal fractions, the latter shown to yield strong correlations of mRNAs with expressed protein levels. Analysis of allelic RNA ratios at heterozygous SNPs served to reveal genetic factors in ribosomal loading. Methods: RNA-seq was performed on cytosolic extracts and polysomal fractions (3 ribosomes or more) from three lymphoblastoid cell lines. As each RNA fraction was amplified (NuGen kit), and relative contributions from various RNA classes differed between cytosol and polysomes, the fraction of any given RNA species loaded onto polysomes was difficult to quantitate. Therefore, we focused on relative recovery of the various RNA classes and rank order of single RNAs compared to total RNA. Results: RNA-seq of coding and non-coding RNAs (including microRNAs) in three LCLs revealed significant differences in polysomal loading of individual RNAs and isoforms, and between RNA classes. Moreover, correlated distribution between protein-coding and non-coding RNAs suggests possible interactions between them. Allele-selective RNA recruitment revealed strong genetic influence on polysomal loading for multiple RNAs. Allelic effects can be attributed to generation of different RNA isoforms before polysomal loading or to differential loading onto polysomes, the latter defining a direct genetic effect on translation. Several variants and genes identified by this approach are also associated with RNA expression and clinical phenotypes in various databases. Conclusions: These results provide a novel approach using complete transcriptome RNA-seq to study polysomal RNA recruitment and regulatory variants affecting protein translation. Overall design: cells from 3 samples were grown to 5x105 cells/mL density in T75 tissue culture flask and harvested, total RNA and polysome bound RNA was sequenced by Ion Proton
Allele-Selective Transcriptome Recruitment to Polysomes Primed for Translation: Protein-Coding and Noncoding RNAs, and RNA Isoforms.
No sample metadata fields
View SamplesGene expression analysis of three sets of patient-derived T-ALL xenografted murine lines treated or not treated with Givinostat, to investigate the immediate anti-leukemic effects after 6 hours of in vivo treatment with this histone deacetylase inhibitor.
An immediate transcriptional signature associated with response to the histone deacetylase inhibitor Givinostat in T acute lymphoblastic leukemia xenografts.
Specimen part, Treatment
View SamplesThis SuperSeries is composed of the SubSeries listed below.
KAP1 regulates gene networks controlling T-cell development and responsiveness.
Specimen part
View SamplesThe modulation of chromatin status at specific genomic loci controls lymphoid differentiation. Here, we investigated the role played in this process by KAP1, the universal cofactor of KRAB-containing Zinc Finger Proteins (KRAB-ZFPs), a tetrapod-restricted family of transcriptional repressors. T cell-specific Kap1 knockout mice displayed a significant expansion of immature thymocytes and imbalances in the ratios of mature T cells in the thymus and the spleen, with impaired responses to TCR stimulation. Transcriptome and chromatin studies revealed that KAP1 directly controls the expression of a number of genes involved in TCR and cytokine signalling, among which Traf1 and FoxO1, and is strongly associated with cis-acting regulatory elements marked by the H3K9me3 repressive mark on the genome of thymic T cells. Likely responsible for tethering KAP1 to at least part of its genomic targets, a small number of KRAB/ZFPs are selectively expressed in T lymphoid cells. These results reveal the so far unsuspected yet important role of KRAB/KAP1-mediated epigenetic regulation in T lymphocyte differentiation and activation.
KAP1 regulates gene networks controlling T-cell development and responsiveness.
No sample metadata fields
View SamplesExperiment to understand relationships between sheep rumen wall transcriptome and microbial methane emissions Overall design: RNA seq of ventral rumen wall of Australian sheep
Across-Experiment Transcriptomics of Sheep Rumen Identifies Expression of Lipid/Oxo-Acid Metabolism and Muscle Cell Junction Genes Associated With Variation in Methane-Related Phenotypes.
Subject
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