Differential programming of B cells in Aicda-/- mice
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
SUBMITTER: Marc Hogenbirk
PROVIDER: E-GEOD-47705 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:We sequenced mRNA from naive, in vitro activated, and GC B cells obtained from both Aicda-/- and Aicda+/+ mice. Examination of mRNA levels in naive, in vitro activated, and GC B cells.
2013-06-07 | E-GEOD-47703 | biostudies-arrayexpress
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Project description:There is little insight into or agreement about the signals that control differentiation of memory B cells (MBC) and long-lived plasma cells (LLPC). By performing BrdU pulse-labeling studies, we found that MBC formation preceded the formation of LLPC in an adoptive transfer immunization system, which allowed for a synchronized Ag-specific response with homogeneous Ag-receptor, yet at natural precursor frequencies. We confirmed observations in wild type (WT) mice and extended them with germinal center (GC) disruption experiments and variable region gene sequencing. We thus show that the GC response undergo a temporal switch in its output as it matures, revealing that the reaction engenders both MBC subsets with different immune effector function and, ultimately, LLPC at largely separate points in time. These data demonstrate the kinetics of the formation of the cells that provide stable humoral immunity and therefore have implications for autoimmunity, vaccine development, and for understanding long-term pathogen resistance. Adoptive transfer of B1-8i+/- genetically targeted BALB/cJ mice B cells into AM14 Transgenic (Tg) x Vκ8R genetically targeted BALB/cJ mice. Naive, memory, early and late GC B cells.
2016-01-05 | E-GEOD-76502 | biostudies-arrayexpress
Project description:Purpose: the goal of this study is to investigate the consequences of USP3 deletion on gene expression in mouse LSK hematopoietic progenitors and in splenic B cells Methods: mRNA profiles of 8 weeks-old wild-type (WT) and ubiquitin specific protease 3 knockout (Usp3−/−) mice were generated by deep sequencing, in duplicate, using Illumina Hiseq2000. The sequence reads that passed quality filters were mapped with TopHat and the gene expressions were calculated using HTSeq-count. qRT–PCR validation was performed using SYBR Green assays Results: We assigned about 8-16 million reads per sample uniquely to a gene of the mouse reference genome (mm9). We identified 23,429 genes in the LSKs, naive B cells and activate B cells of WT and USP3−/− mice using TopHat in combination with HTSeq-count. Comparison of the RNAseq data from LSK with naive or activated B cells show that both the wt and the Usp3-/- LSKs largely exibited a gene expression profile that is specific for wt LSK and distinct from B cells (as supported by statistical significant difference between the transciptional profile of LSK versus naive or activated B cells, p value<0.0001 by Student t test). Comparison of normalized gene expression data for Wt LSKs versus naive B cells of one representative experiment shows Pearson coefficient of r=0.874, and R2=0.763. Distinct LSK-specific expressed genes (such as the MlI receptor and the Kit receptor) and B cells specific genes (such as the MS4A1/CD20 and Spi-B transcription factors) are identified. Expression of a set of 19 genes was assessed by RT-qPCR in three independent LSK mRNA per each genotype. qRT-PCR and the RNA-seq normalized expression data for these genes had a good linear relationship, validating the RNAseq analysis. Comparison of normalized gene expression data for Usp3-/- versus Wt LSK show Pearson coefficient r=0.986; R2=0.9738), naive B cells (Pearson coefficient r=0.987, R2=0.974) and LPS activated B cells (Pearson coefficient r=0.991, R2=0.983). RT-qPCR of a subset of hematopoietic stem cell genes, including Mlp2, ENg, Tek and Fdzl3, show no significant difference beteewn wt and Usp3-/- LSK cells. Less than 100 genes showed differential expression (up or down regulated) between the Wt and Usp3-/- LSK, with a fold change ≥1.5 and p value <0.05. Conclusions: Our results represent the first detailed analyis of the consequences of USP3 deletion on gene expression in hematopoietic populations such as LSKs progenitors and B cells by genome wide expression profiling in wt and Usp3-/- mice. RNAseq of two freshly isolated biological replicas of sorted LSKs from 8 weeks old Usp3-/- animals showed a very limited number of genes either slighly up or down regulated (<100 out of about 25.000) in Usp3-/- LSKs, none of which are reported to be directly involved in hematopoietic stem cell maintenance or to be linked to premature differentiation. We confirmed that Usp3-/- and wt LSKs express hematopoietic stem cell-specific genes to a similar extent. We conclude that young adult hematopoietic stem and progenitor cells (LSKs) perpetuated a stable gene expression program regardless of the homozygous deletion of USP3. mRNA profiles of 8 weeks-old wild type (WT) and Usp3-/- mice were generated by deep sequencing, in duplicate, using Illumina Hiseq2000. For each experiment wt n=4, Usp3-/- n=4 mice were analized. FACS sorted cells from from individual animals were pooled and subjected to deep sequencing. Cells were: LSK (Lin- Sca1+ cKit+) from bone marrow, sorted naive B cells from spleens (CD19+) and activated B cells harvested and FACS sorted after 4 days stimulation with lipopolysaccharide (LPS) in culture.
2014-07-24 | E-GEOD-58495 | biostudies-arrayexpress
Project description:4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes. 4C-Seq experiments from Igh and Cd83 bait in activated B cells and Tcrb (Eb) bait in double negative T cells and immature B cells. RNA-Seq and ATAC-Seq experiments in DN and Immature B cells.
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