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SRP160510
Homo sapiens
66 Downloadable Samples
Description
Purpose: Here we describe the modulation of a gene expression program involved in cell fate. Methods: We depleted U2AF1 in human induced pluripotent stem cells (hiPSCs) to the level found in differentiated cells using an inducible shRNA system, followed by high-throughput RNAseq, revealing a gene expression program involved in cell fate determination. Results: Approximately 85% of the total raw reads were mapped to the human genome sequence (GRCh37), giving an average of 200 million human reads per sample for total RNA and 15 million human reads per sample for small RNA libraries. Conclusions: Our results show that transcriptional control of gene expression in hiPSCs can be set by the CSF U2AF1, establishing a direct link between transcription and AS during cell fate determination. Overall design: hiPSCs were differentiated into the three germ layers following the described protocol in the study (Gifford et al., 2013).
Publication Title
The core spliceosomal factor U2AF1 controls cell-fate determination via the modulation of transcriptional networks.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 11 Downloadable Samples
- Illumina Genome Analyzer II, Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Transcriptional network analysis in muscle reveals AP-1 as a partner of PGC-1α in the regulation of the hypoxic gene program.
Sample Metadata Fields
Specimen part, Treatment
View Samples- Mus musculus
- 7 Downloadable Samples
Affymetrix Mouse Gene 1.0 ST Array (mogene10st), Illumina Genome Analyzer II
Description
Skeletal muscle tissue shows an extraordinary cellular plasticity, but the underlying molecular mechanisms are still poorly understood. Here we use a combination of experimental and computational approaches to unravel the complex transcriptional network of muscle cell plasticity centered on the peroxisome proliferator-activated receptor coactivator 1 (PGC-1), a regulatory nexus in endurance training adaptation. By integrating data on genome-wide binding of PGC-1 and gene expression upon PGC-1 over-expression with comprehensive computational prediction of transcription factor binding sites (TFBSs), we uncover a hitherto underestimated number of transcription factor partners involved in mediating PGC-1 action. In particular, principal component analysis of TFBSs at PGC-1 binding regions predicts that, besides the well-known role of the estrogen-related receptor (ERR), the activator protein-1 complex (AP-1) plays a major role in regulating the PGC-1-controlled gene program of hypoxia response. Our findings thus reveal the complex transcriptional network of muscle cell plasticity controlled by PGC-1.
Publication Title
Transcriptional network analysis in muscle reveals AP-1 as a partner of PGC-1α in the regulation of the hypoxic gene program.
Sample Metadata Fields
Treatment
View Samples- Mus musculus
- 9 Downloadable Samples
Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
The peroxisome proliferator-activated receptor co-activator 1 (PGC-1) coordinates the transcriptional network response to promote an improved endurance capacity in skeletal muscle, e.g. by co-activating the estrogen-related receptor (ERR) in the regulation of oxidative substrate metabolism. Despite a close functional relationship, the interaction between these two proteins has not been studied on a genomic level. We now mapped the genome-wide binding of ERR to DNA in skeletal muscle cell line with elevated PGC-1 and linked the DNA recruitment to global PGC-1 target gene regulation. We found that, surprisingly, ERR co-activation by PGC-1 is only observed in the minority of all PGC-1 recruitment sites. Nevertheless, a majority of PGC-1 target gene expression is dependent on ERR. Intriguingly, the interaction between these two proteins is controlled by the genomic context of response elements, in particular the relative GC and CpG content, monomeric and dimeric repeat binding site configuration for ERR, and adjacent recruitment of the transcription factor SP1. These findings thus not only reveal an unprecedented insight into the regulatory network underlying muscle cell plasticity, but also strongly link the genomic context of DNA response elements to control transcription factor - co-regulator interactions.
Publication Title
The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 4 Downloadable Samples
- Illumina Genome Analyzer II, Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
Skeletal muscle tissue shows an extraordinary cellular plasticity, but the underlying molecular mechanisms are still poorly understood. Here we use a combination of experimental and computational approaches to unravel the complex transcriptional network of muscle cell plasticity centered on the peroxisome proliferator-activated receptor coactivator 1 (PGC-1), a regulatory nexus in endurance training adaptation. By integrating data on genome-wide binding of PGC-1 and gene expression upon PGC-1 over-expression with comprehensive computational prediction of transcription factor binding sites (TFBSs), we uncover a hitherto underestimated number of transcription factor partners involved in mediating PGC-1 action. In particular, principal component analysis of TFBSs at PGC-1 binding regions predicts that, besides the well-known role of the estrogen-related receptor (ERR), the activator protein-1 complex (AP-1) plays a major role in regulating the PGC-1-controlled gene program of hypoxia response. Our findings thus reveal the complex transcriptional network of muscle cell plasticity controlled by PGC-1.
Publication Title
Transcriptional network analysis in muscle reveals AP-1 as a partner of PGC-1α in the regulation of the hypoxic gene program.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 9 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 7 Downloadable Samples
- IlluminaHiSeq2500
Description
During activation, T cells integrate multiple signals from APCs and cytokine milieu. The blockade of these signals can have clinical benefits as exemplified by CTLA4-Ig, which blocks interaction of B7 co-stimulatory molecules on APCs with CD28 on T cells. Variants of CTLA4-Ig, abatacept and belatacept are FDA approved as immunosuppressive agents in arthritis and transplantation whereas murine studies suggested that CTLA4-Ig can be beneficial in a number of other diseases. However, detailed analysis of human CD4 cell hyporesponsivness induced by CTLA4-Ig has not been performed. Herein, we established a model to study effect of CTLA4-Ig on the activation of human naïve T cells in a human mixed lymphocytes system. Comparison of human CD4 cells activated in the presence or absence of CTLA4-Ig, showed that co-stimulation blockade during TCR activation does not affect NFAT signaling but results in decreased activation of NF-kB and AP-1 transcription factors followed by profound decrease in proliferation and cytokine production. The resulting T cells become hyporesponsive to secondary activation and, although capable of receiving TCR signals, fail to proliferate or produce cytokines, demonstrating properties of anergic cells. However, unlike some models of T cell anergy, these cells did not possess increased levels of TCR signaling inhibitor CBLB. Rather, the CTLA4-Ig induced hyporesponsiveness was associated with an elevated level of p27kip1 cyclin-dependent kinase inhibitor. Overall design: Time series. Human resting and activated T cell dUTP mRNA-Seq profiles were generated on Illumina HiSeq2500
Publication Title
Functional characterization of human T cell hyporesponsiveness induced by CTLA4-Ig.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 9 Downloadable Samples
- Illumina HiSeq 4000
Description
The development of CRISPR-Cas systems for targeting DNA and RNA in diverse organisms has transformed biotechnology and biological research. Moreover, the CRISPR revolution has highlighted bacterial adaptive immune systems as a rich and largely unexplored frontier for discovery of new genome engineering technologies. In particular, the class 2 CRISPR-Cas systems, which use single RNA-guided DNA-targeting nucleases such as Cas9, have been widely applied for targeting DNA sequences in eukaryotic genomes. Here, we report DNA-targeting and transcriptional control with class I CRISPR-Cas systems. Specifically, we repurpose the effector complex from type I variants of class 1 CRISPR-Cas systems, the most prevalent CRISPR loci in nature, that target DNA via a multi-component RNA-guided complex termed Cascade. We validate Cascade expression, complex formation, and nuclear localization in human cells and demonstrate programmable CRISPR RNA (crRNA)-mediated targeting of specific loci in the human genome. By tethering transactivation domains to Cascade, we modulate the expression of targeted chromosomal genes in both human cells and plants. This study expands the toolbox for engineering eukaryotic genomes and establishes Cascade as a novel CRISPR-based technology for targeted eukaryotic gene regulation. Overall design: Examination of transcriptome-wide changes in gene expression with Cascade-mediated activation of endogenous genes.
Publication Title
Targeted transcriptional modulation with type I CRISPR-Cas systems in human cells.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Mus musculus
- 24 Downloadable Samples
- Illumina HiSeq 2500
Description
In this study we used Genome Wide Transcriptional Modelling (GWTM) to investigate the temporal transcriptional changes during CD4 Th0, Th1 and Th2 differentiation in the first 24 hours after T cell activation. We measured the transcriptional response by RNA seq every four hours for a 24 hour time course. Overall design: WT CD4 T cells were isolated and purified from adult murine spleen. The purified CD4 cells were then set up in culture under three different conditions: Th0, Th1 and Th2. Cells were extracted at 4 hour timepoints during a 24hour timecourse and RNA was extracted for each timepoint under each condition. This RNA was further sequenced to analyse the genome wide transcriptional changes through time under each of the three conditions.
Publication Title
IFITM proteins drive type 2 T helper cell differentiation and exacerbate allergic airway inflammation.
Sample Metadata Fields
Cell line, Subject
View Samples- Mus musculus
- 4 Downloadable Samples
Description
Gametogenesis is dependent on the expression of germline-specific genes. However, it remains unknown how the germline epigenome is distinctly established from that of somatic lineages. Here we show that genes commonly expressed in somatic lineages and spermatogenesis-progenitor cells undergo repression in a genome-wide manner in late stages of the male germline and identify underlying mechanisms. SCML2, a germline-specific subunit of a Polycomb repressive complex 1 (PRC1), establishes the unique epigenome of the male germline through two distinct antithetical mechanisms. SCML2 works with PRC1 and promotes RNF2-dependent ubiquitination of H2A, thereby marking somatic/progenitor genes on autosomes for repression. Paradoxically, SCML2 also prevents RNF2-dependent ubiquitination of H2A on sex chromosomes during meiosis, thereby enabling unique epigenetic programming of sex chromosomes for male reproduction. Our results reveal divergent mechanisms involving a shared regulator by which the male germline epigenome is distinguished from that of the soma and progenitor cells. Overall design: RNA-seq and ChIP-seq analyses using wild-type and Scml2-KO spermatogenic cells
Publication Title
Poised chromatin and bivalent domains facilitate the mitosis-to-meiosis transition in the male germline.
Sample Metadata Fields
No sample metadata fields
View Samples