This SuperSeries is composed of the SubSeries listed below.
Genome-wide nucleosome specificity and function of chromatin remodellers in ES cells.
No sample metadata fields
View SamplesHow various ATP-dependent chromatin remodellers bind to nucleosomes to regulate transcription is not well defined in mammalian cells. Here, we present genome-wide remodeller-interacting nucleosome profiles for Chd1, Chd2, Chd4, Chd6, Chd8, Chd9, Brg1 and Ep400 in mouse embryonic stem (ES) cells. These remodellers bind to nucleosomes at specific positions, either at one or both nucleosomes that flank each side of nucleosome-free promoter regions (NFRs), at enhancer elements, or within gene bodies. At promoters, bidirectional transcription commonly initiates on either side of remodeller-bound nucleosomes. Transcriptome analysis upon remodeller depletion reveals reciprocal mechanisms of transcriptional regulation by remodellers. At active genes, certain remodellers are positive regulators of transcription, whereas others act as repressors. At bivalent genes, which are bound by repressive Polycomb complexes, the same remodellers act in the opposite way. Together, these findings reveal how remodellers integrate promoter nucleosomal architecture to regulate ES cell transcription programs.
Genome-wide nucleosome specificity and function of chromatin remodellers in ES cells.
No sample metadata fields
View SamplesHow various ATP-dependent chromatin remodellers bind to nucleosomes to regulate transcription is not well defined in mammalian cells. Here, we present genome-wide remodeller-interacting nucleosome profiles for Chd1, Chd2, Chd4, Chd6, Chd8, Chd9, Brg1 and Ep400 in mouse embryonic stem (ES) cells. These remodellers bind to nucleosomes at specific positions, either at one or both nucleosomes that flank each side of nucleosome-free promoter regions (NFRs), at enhancer elements, or within gene bodies. At promoters, bidirectional transcription commonly initiates on either side of remodeller-bound nucleosomes. Transcriptome analysis upon remodeller depletion reveals reciprocal mechanisms of transcriptional regulation by remodellers. At active genes, certain remodellers are positive regulators of transcription, whereas others act as repressors. At bivalent genes, which are bound by repressive Polycomb complexes, the same remodellers act in the opposite way. Together, these findings reveal how remodellers integrate promoter nucleosomal architecture to regulate ES cell transcription programs.
Genome-wide nucleosome specificity and function of chromatin remodellers in ES cells.
No sample metadata fields
View SamplesMasitinib is a tyrosine kinase inhibitor of c-Kit, PDGFR and , and to some extent Lyn of the Src kinase family. We evaluated the therapeutic potential of masitinib in vitro on human pancreatic tumour cell lines and in vivo in a mouse model of human pancreatic cancer.
Masitinib combined with standard gemcitabine chemotherapy: in vitro and in vivo studies in human pancreatic tumour cell lines and ectopic mouse model.
Specimen part, Cell line, Treatment
View SamplesThe aim of this study was to characterize the stroma displayed by different models of breast cancer tumors in mice. For this purpose, transcriptomic and flow cytometry analyses on murine populations were performed in a series of 25 PDXs and 2 most commonly used GEMs (MMTV-PyMT and MMTV-erBb2). Specifically, macrophages from 5 models were sorted and profiled by gene expression and phenotypically characterized by flow cytometry.
Characterization of Breast Cancer Preclinical Models Reveals a Specific Pattern of Macrophage Polarization.
Specimen part, Subject
View SamplesThe neurobiological functions of a number of kinases expressed in the brain are unknown. Here, we report new findings on DCLK3 (Doublecortin-like kinase 3) which is preferentially expressed in neurons in the striatum and dentate gyrus. Its function has never been investigated. DCLK3 expression is markedly reduced in Huntington''s disease. Recent data obtained in studies related to cancer suggest DCLK3 could have anti-apoptotic effect. Thus, we hypothesized that early loss of DCLK3 in Huntington''s disease may render striatal neurons more susceptible to mutant huntingtin (mHtt). We discovered that DCLK3 silencing in the striatum of mice exacerbated the toxicity of an N-terminal fragment of mHtt. Conversely, overexpression of DCLK3 reduced neurodegeneration produced by mHtt. DCLK3 also produced beneficial effects on motor symptoms in a knock-in mouse model of Huntington''s disease. Using different mutants of DCLK3, we found that the kinase activity of the protein plays a key role in neuroprotection. To investigate the potential mechanisms underlying DCLK3 effects, we studied the transcriptional changes produced by the kinase domain in human striatal neurons in culture. Results show that DCLK3 regulates in a kinase-dependent manner the expression of many genes involved in transcription regulation and nucleosome/chromatin remodeling. Consistent with this, histological evaluation showed DCLK3 is present in the nucleus of striatal neurons and, protein-protein interaction experiments suggested that the kinase domain interacts with zinc finger proteins, including TADA3, a core component of SAGA complex. Our novel findings suggest that the presence of DCLK3 in striatal neurons may play a key role in transcription regulation and chromatin remodeling in these brain cells, and show that reduced expression of the kinase in Huntington's disease could render the striatum highly vulnerable to neurodegeneration. Examination of DCLK3 as neuroprotector against mutant huntingtin in vivo and in vitro models. Overall design: Examination of DCLK3 as neuroprotector against mutant huntingtin in vitro experiments.
The striatal kinase DCLK3 produces neuroprotection against mutant huntingtin.
Specimen part, Cell line, Subject
View SamplesThe wider transcriptional effects of MYD88L265P were explored by analysing the microarray datasets using the limma package. We focussed on evidence for differential expression between Myd88L265P and Card11L232LI transduced B cells because both cell populations were actively proliferating at the time of RNA isolation.
Synergistic cooperation and crosstalk between <i>MYD88<sup>L265P</sup></i> and mutations that dysregulate CD79B and surface IgM.
Specimen part
View SamplesGene expression profiles in synovial biopsies from patients with rheumatoid arthritis (RA) display a high level of plasticity related to disease activity and response to therapy.
Higher expression of TNFα-induced genes in the synovium of patients with early rheumatoid arthritis correlates with disease activity, and predicts absence of response to first line therapy.
Sex, Age, Disease
View SamplesThese samples are part of the ENCODE consortiums proposed time-limited Pilot Study for confirmation of the utility of RNA abundance measurements as a standard reference phenotyping tool.
The accessible chromatin landscape of the human genome.
Cell line
View SamplesBackground Published multi-gene classifiers suggested outcome prediction for patients with stage UICC II colon cancer based on different gene expression signatures. However, there is currently no translation of these classifiers for application in routine diagnostic. Therefore, we aimed at validating own and published gene expression signatures employing methods which enable RNA and protein detection in routine diagnostic specimens. Results Immunohistochemistry was applied to 68 stage UICC II colon cancers to determine the protein expression of five selected previously published classifier genes (CDH17, LAT, CA2, EMR3, and TNFRSF11A). Correlation of protein expression data with clinical outcome within a 5-year post-surgery course failed to separate patients with a disease-free follow-up [Group DF] and relapse [Group R]). In addition, RNA from macrodissected tumor samples from 53 of these 68 patients was profiled on Affymetrix GeneChips (HG-U133 Plus 2.0). Prognostic signatures were generated by Nearest Shrunken Centroids with cross-validation. Although gene expression profiling allowed the identification of differentially expressed genes between the groups DF and R, a stable classification and prognosis signature was not discernable in our data. Furthermore, the application of previously published gene signatures consisting of 22 and 19 genes, respectively, to our gene expression data set using global tests and leave-one-out cross-validation was unable to predict clinical outcome (prediction rate 75.5% and 64.2%; n.s.). T-stage was the only independent prognostic factor for relapse in multivariate analysis with established clinical and pathological parameters including microsatellite status. Conclusions Our protein and gene expression analyses currently do not support application of molecular classifiers for prediction of clinical outcome in routine diagnostic as a basis for patient-orientated therapy in stage UICC II colon cancer. Further studies are needed to develop prognosis signatures applicable in patient care.
Molecular profiles and clinical outcome of stage UICC II colon cancer patients.
Sex
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