Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA377298 | ENA |
REPOSITORIES: ENA
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Project description:We applied quantitative mass spectrometry (MS)-based proteomics to study the roles of Cbl and Cbl-b in long-term signaling responses related to neurite outgrowth and differentiation of SH-SY5Y neuroblastoma cells. Using stable isotope labeling by amino acids in cell culture (SILAC) and tandem mass tag (TMT)-labeling in combination with off-line high-pH reversed-phase fractionation and LC-MS/MS we analyzed how Cbl and Cbl-b depletion by siRNA affected the proteome, phosphoproteome and ubiquitylome of the neuroblastoma cells. SILAC proteome SILAC (Light Arg0/Lys0, medium Arg6/Lys4, heavy Arg10/Lys8) SH-SY5Y cells were treated with Cbl and Cbl-b or control (GFP) siRNA for 72 hours. For combined stimulation with ligand cocktail (FGF-2, IGF-1 PDGF-BB, TGFα) cells were treated with ligands for 48 h. Samples were analyzed in triplicates with set-up as described below: Set-up 1, 3 replicates (R1-3): Light: siGFP, Heavy: siCbl/siCbl-b Set-up 2, 3 replicates (E1-3): Light: siGFP + ligand cocktail, Medium: siCbl/siCbl-b, Heavy: siCbl/siCbl-b + ligand cocktail TMT phosphoproteome and proteome SH-SY5Y cells were treated with Cbl and Cbl-b siRNA, control (GFP) siRNA or Retinoic acid (RA) for 24 hours. Samples were prepared in triplicates and labelled with TMT10-plex reagents according to the set-up below: TMT10-126: siGFP E1 TMT10-127N: siCbl/siCbl-b E1 TMT10-127C: Retinoic acid E1 TMT10-128N: siGFP E2 TMT10-128C: siCbl/siCbl-b E2 TMT10-129N: Retinoic acid E2 TMT10-129C: siGFP E3 TMT10-130N: siCbl/siCbl-b E3 TMT10-130C: Retinoic acid E3 TMT10-131: Mix of the 9 samples SILAC Ubiquitin pulldown SILAC (Light Arg0/Lys0, heavy Arg10/Lys8) SH-SY5Y cells were treated with Cbl and Cbl-b or control (GFP) siRNA for 24 hours. Samples were analyzed in duplicates with set-up as described below: Light: siGFP, Heavy: siCbl/siCbl-b
2021-03-30 | PXD020389 | Pride
Project description:We report the development of a new computational method to assess differences in cell-cell interactions between conditions through utilizing single-cell RNA sequencing data. The pipeline, known as Cell Interaction Network Inference from Single-cell Expression data (CINS), combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie these interactions.
2022-09-28 | GSE165638 | GEO
Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.
2017-06-12 | GSE60683 | GEO
Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.
2017-06-12 | GSE65880 | GEO
Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.
2017-06-12 | GSE95508 | GEO
Project description:We examine the role of Klf6 in oligodendrocyte progenitor cells and determine that Klf6 acts as a gp130-sensitive transactivator of the nuclear import factor importin-α5 (Impα5), a key controller of nuclear trafficking in oligodendrocytes. Examination of expression profiles of 2 different cell stages exposed to siRNA vs. control
2016-04-01 | E-GEOD-79244 | biostudies-arrayexpress
Project description:Gene-expression profiles of liver and hepatocellular carcinoma induced by diethylnitrosamine (DEN) in KLF6 +/- and wild type KLF6 mice. Inactivation of the KLF6 tumor suppressor is common in HCC due to hepatitis C virus (HCV), consistent with its anti-proliferative activity in HCC-derived cell lines and in hepatocytes of transgenic mice. We have evaluated the impact of KLF6 depletion on human HCC and experimental hepatocarcinogenesis. In patients with surgically resected HCC, those with significantly reduced tumor expression of KLF6 had a significantly decreased survival. We modeled this event in KLF6 +/- mice, which displayed significantly more tumorigenicity than KLF6 +/+ animals in response to the hepatic carcinogen DEN, associated with recapitulation of gene signatures in both surrounding tissue and tumors that are associated with aggressive human HCCs. In DNA microarrays, mdm2 mRNA expression was increased in tumors from KLF6 +/- compared to KLF6 +/+ mice, which was validated by realtime qPCR and Western blot in both human HCC and DEN-induced murine tumors. Moreover, chromosomal immunoprecipitation and co-transfection assays established the P2 intronic promoter of mdm2 as a bona fide transcriptional target repressed by KLF6. Whereas KLF6 over-expression in HCC cell lines led to reduced MDM2 levels and increased p53 protein and transcriptional activity, reduction in KLF6 by siRNA led to increased MDM2 and reduced p53. Our findings indicate that KLF6 deficiency contributes significantly to the carcinogenic milieu in human and murine HCC, and uncover a novel tumor suppressor activity of KLF6 in HCC, by linking its transcriptional repression of MDM2 to stabilization of p53. Keywords: Liver, Hepatocellular carcinoma, Expression array, Exon array, Affymetrix
2011-08-01 | GSE23375 | GEO
Project description:Gene-expression profiles of liver and hepatocellular carcinoma induced by diethylnitrosamine (DEN) in KLF6 +/- and wild type KLF6 mice. Inactivation of the KLF6 tumor suppressor is common in HCC due to hepatitis C virus (HCV), consistent with its anti-proliferative activity in HCC-derived cell lines and in hepatocytes of transgenic mice. We have evaluated the impact of KLF6 depletion on human HCC and experimental hepatocarcinogenesis. In patients with surgically resected HCC, those with significantly reduced tumor expression of KLF6 had a significantly decreased survival. We modeled this event in KLF6 +/- mice, which displayed significantly more tumorigenicity than KLF6 +/+ animals in response to the hepatic carcinogen DEN, associated with recapitulation of gene signatures in both surrounding tissue and tumors that are associated with aggressive human HCCs. In DNA microarrays, mdm2 mRNA expression was increased in tumors from KLF6 +/- compared to KLF6 +/+ mice, which was validated by realtime qPCR and Western blot in both human HCC and DEN-induced murine tumors. Moreover, chromosomal immunoprecipitation and co-transfection assays established the P2 intronic promoter of mdm2 as a bona fide transcriptional target repressed by KLF6. Whereas KLF6 over-expression in HCC cell lines led to reduced MDM2 levels and increased p53 protein and transcriptional activity, reduction in KLF6 by siRNA led to increased MDM2 and reduced p53. Our findings indicate that KLF6 deficiency contributes significantly to the carcinogenic milieu in human and murine HCC, and uncover a novel tumor suppressor activity of KLF6 in HCC, by linking its transcriptional repression of MDM2 to stabilization of p53. Keywords: Liver, Hepatocellular carcinoma, Expression array, Exon array, Affymetrix KLF6 +/- mice were previously generated by homologous recombination in which exon 2 was targeted using an 11-kb targeting construct, and replaced with neomycin/lacZ cassette. After selection with neomycin, the ES clones were injected into C57BL/6 mouse blastocysts and implanted into pseudo pregnant females; two lines of KLF6 +/- mice were generated from the resulting chimeric animals (Blood 107;1357, Oncogene 26;4428). Whereas KLF6 -/- mice are embryonic lethal, KLF6 +/- animals had no demonstrable abnormalities in the absence of any stressor. Male KLF6 +/- mice were bred with wild type C57BL/6 to generate mixed litters of KLF6 +/- and KLF6 +/+ animals. Progeny were genotyped using PCR-amplified tail DNA, using primers as previously described (Oncogene 26;4428). Amplified fragments were separated on a 2.5% agarose gel, revealing bands of ~200 bp (wild type KLF6) and ~100 bp (Neo), as expected. At 2 weeks of age, KLF6 +/+ and KLF6 +/- mice were injected intraperitoneally with either a single dose of diethyl nitrosamine (DEN), 5 µg/g body weight in 100 µl of saline, or vehicle alone. Vehicle and DEN-treated mice were maintained on standard chow, and then sacrificed 3, 6 or 9 months later. At the time of sacrifice the animals were weighed, and blood and liver samples were harvested for analysis and tumor quantification.
2011-07-31 | E-GEOD-23375 | biostudies-arrayexpress
Project description:Regulation of gene expression in biological systems is a complex, nonlinear process composed of context specific interactions, from signaling and transcription to genome modification. Modeling gene regulatory networks (GRNs) can be limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are consequently forced to model covariance between regulatory genes and their targets as a proxy for causal interactions. This in turn complicates validation and reuse of predictive modeling frameworks. To disentangle covariance and casual influence require aggregation of independent and complementary sets of evidences, such as transcription factor (TF) binding and target gene expression. Common approaches include the overlap of evidence to infer causal relations. However the complete state of the system, e.g. TF activity (TFA) is unknown. Other methods tries to estimate these latent features. These models often use linear frameworks that are unable to account for non-linearities, TF-TF interactions, and other higher order features. Deep learning frameworks can be used to model complex interactions between features and capture latent features of higher order. However deep learning methods often discard central concepts in biological systems modeling such as sparsity and latent feature interpretability in favour of increased complexity of the model. In this work we demonstrate that gene regulatory network inference using latent features such as transcription factor activity can be built into a single framework. We present a novel deep learning approach (the Supirfactor framework) that incorporates multiple data-type orthogonal evidence of regulation and maintains interpretable parameter estimates.
2023-01-31 | GSE218089 | GEO
Project description:EGFR degradation is delayed in Cbl, Cbl-b double-deficient MCF10A but EGF stimulation does not enhance their growth. We performed a transcriptome analysis to gain insights into biological consequences of Cbl, Cbl-b loss in mammary epithelial cells. We compared the transcriptome of MCF10A cells expressing shRNA against Cbl and Cbl-b with control shRNA-expressing cells using Affymetrix U133 Plus 2.0. Each sample was prepared in duplicates. Data were analyzed by GSEA.
2016-03-25 | E-GEOD-67301 | biostudies-arrayexpress