Differential expression between Sh2b3 knockout and wild type mice
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE65348 | GEO | 2015/06/17
SECONDARY ACCESSION(S): PRJNA273746
REPOSITORIES: GEO
Similar Datasets
Project description:To validate the predicted Sh2b3 derived gene regulatory subnetwork using integrative network approach in human population study, we examined the gene expression levels of whole blood in WT (wild-type) and Sh2b3-/- mice by RNA sequencing, and identified the differentially expressed genes. RNA sequencing whole blood samples from 4 WT and 4 Sh2b3-/- mice.
2015-06-17 | E-GEOD-65348 | biostudies-arrayexpress
Project description: Not available
| S-EPMC4912085 | biostudies-literature
Project description:Purpose: Epigenetic regulation contributes to pathogenesis of neurondegenerative disease. We found that dis-regulation of a 242-gene subnetwork related to DNA methylation regulated neuronal differentiation are common in AD and HD. There are two DNA methyltransferases, DNMT1 and DNMT3A, in the subnetwork. DNMT1 is one of top hub genes in the network and likely to play a key regulatory role in the subnetwork. To validate that DNMT1 is a key regulator for the subnetwork, and DNMT3A not as one, we constructed two brain-specific conditional knockout mice. Methods: Cortices were dissected from Dnmt1 conditional knockout (CKO), Dnmt3a CKO, and respective littermate control mice at 18 weeks. RNA was isolated from three biological replicates for each genotype using TRIzol (Invitrogen) extraction and isopropanol precipitation. RNA samples were resuspended in water and further purified with RNeasy columns with on-column DNase treatment (Qiagen). RNA purity was assessed by measuring the A260/A280 ratio using a NanoDrop and RNA quality checked using an Agilent 2100 Bioanalyzer (Agilent Technologies). Approximately 250 ng of total RNA per sample were used for library construction by the TruSeq RNA Sample Prep Kit (Illumina) and sequenced using the Illumina HiSeq 2500 instrument according to the manufacturer's instructions. Sequence reads were aligned to mouse genome assembly mm10 and quantified using Cufflinks. Results: Genes differentially expressed in CKO mice compared to littermate control mice, or the Dnmt1 CKO signature, including GSN (the genes with the largest number of connection in the subnetwork) and SOX10 (a key transcription factor of myelination), significantly overlaps with the subnetwork. Broadly, the Dnmt1 CKO signature is enriched for genes involved in GO biological processes immune response, lipid metabolism, endocytosis, glial cell differentiation, and nerve ensheatment, consistent with biological function of the subnetwork. Moreover, the Dnmt1 CKO mice show increased predilection to seizures, an incidence also increased in patients with AD (Amatniek et al, 2006) as well as juvenile form of HD (Cloud et al, 2012). In contrast, the Dnmt3a CKO signature does not overlap with the subnetwork (p=0.07) and is not enriched for any GO biological process. Conclusions: These results suggest the Dnmt1 regulates common genes related with AD and HD while Dnmt3a has little effect. All of the mice used in this study were handled in accordance with IACUC-approved protocols. Dnmt1flox/flox (Fan et al, 2001; Jackson-Grusby et al, 2001) and Dnmt3aflox/flox (Nguyen et al, 2007) mice were backcrossed onto a C57BL/6 background and crossed with Olig1-cre mice to generate Dnmt1 conditional knockout (Olig1cre/+;Dnmt1flox/flox) and littermate control (Olig1+/+;Dnmt1flox/flox) mice, and Dnmt3a conditional knockout (Olig1cre/+;Dnmt3aflox/flox) and littermate control (Olig1+/+;Dnmt3aflox/flox) mice.
2014-07-31 | E-GEOD-58261 | biostudies-arrayexpress
Project description:Purpose: Epigenetic regulation contributes to pathogenesis of neurondegenerative disease. We found that dis-regulation of a 242-gene subnetwork related to DNA methylation regulated neuronal differentiation are common in AD and HD. There are two DNA methyltransferases, DNMT1 and DNMT3A, in the subnetwork. DNMT1 is one of top hub genes in the network and likely to play a key regulatory role in the subnetwork. To validate that DNMT1 is a key regulator for the subnetwork, and DNMT3A not as one, we constructed two brain-specific conditional knockout mice. Methods: Cortices were dissected from Dnmt1 conditional knockout (CKO), Dnmt3a CKO, and respective littermate control mice at 18 weeks. RNA was isolated from three biological replicates for each genotype using TRIzol (Invitrogen) extraction and isopropanol precipitation. RNA samples were resuspended in water and further purified with RNeasy columns with on-column DNase treatment (Qiagen). RNA purity was assessed by measuring the A260/A280 ratio using a NanoDrop and RNA quality checked using an Agilent 2100 Bioanalyzer (Agilent Technologies). Approximately 250 ng of total RNA per sample were used for library construction by the TruSeq RNA Sample Prep Kit (Illumina) and sequenced using the Illumina HiSeq 2500 instrument according to the manufacturer's instructions. Sequence reads were aligned to mouse genome assembly mm10 and quantified using Cufflinks. Results: Genes differentially expressed in CKO mice compared to littermate control mice, or the Dnmt1 CKO signature, including GSN (the genes with the largest number of connection in the subnetwork) and SOX10 (a key transcription factor of myelination), significantly overlaps with the subnetwork. Broadly, the Dnmt1 CKO signature is enriched for genes involved in GO biological processes immune response, lipid metabolism, endocytosis, glial cell differentiation, and nerve ensheatment, consistent with biological function of the subnetwork. Moreover, the Dnmt1 CKO mice show increased predilection to seizures, an incidence also increased in patients with AD (Amatniek et al, 2006) as well as juvenile form of HD (Cloud et al, 2012). In contrast, the Dnmt3a CKO signature does not overlap with the subnetwork (p=0.07) and is not enriched for any GO biological process. Conclusions: These results suggest the Dnmt1 regulates common genes related with AD and HD while Dnmt3a has little effect.
2014-07-31 | GSE58261 | GEO
Project description:Despite the enormous amounts of molecular, cellular, and clinical data that are increasingly available for many different types of cancer, it remains a challenge to integrate different dimensions of data to construct mechanistic models that can robustly distinguish key driver genes from passenger genes, predict tumor progression, and tailor therapies optimally for individual patients. We present an integrative biology approach to constructing and analyzing multiscale regulatory networks of breast cancer. We systematically uncover not only known and novel gene subnetworks (modules) linked to breast cancer, but also their key drivers, the majority of which are not transcription factors or signaling molecules. A number of independent lines of evidence support that the predicted key drivers play central roles in breast cancer biology. We predict and experimentally validate ARF1 as a key driver of breast tumor phenotypes, and then demonstrate the ARF1-controlled subnetwork is a novel regulator of intra- and inter- cellular vesicle dynamics involved in epithelial-mesenchymal transition (EMT). We aimed to study the underlying mechanism by which ARF1 has been predicted playing crucial role during carcinogenesis and metastasis. To attest the key function of ARF1 during epithelial mesenchymal transition (EMT), loss of function in vitro study by means of siRNA knockdown of ARF1 in MDA-MB-231 breast cancer cell line was designed. Genome-wide gene expression of nine ARF1 knockdown samples and three control samples were profiled by using the Illumina HumanHT-12 V4.0 expression beadchip platform.
2013-07-18 | E-GEOD-49005 | biostudies-arrayexpress
Project description:Despite the enormous amounts of molecular, cellular, and clinical data that are increasingly available for many different types of cancer, it remains a challenge to integrate different dimensions of data to construct mechanistic models that can robustly distinguish key driver genes from passenger genes, predict tumor progression, and tailor therapies optimally for individual patients. We present an integrative biology approach to constructing and analyzing multiscale regulatory networks of breast cancer. We systematically uncover not only known and novel gene subnetworks (modules) linked to breast cancer, but also their key drivers, the majority of which are not transcription factors or signaling molecules. A number of independent lines of evidence support that the predicted key drivers play central roles in breast cancer biology. We predict and experimentally validate ARF1 as a key driver of breast tumor phenotypes, and then demonstrate the ARF1-controlled subnetwork is a novel regulator of intra- and inter- cellular vesicle dynamics involved in epithelial-mesenchymal transition (EMT).
2013-07-18 | GSE49005 | GEO
Project description:Spinal cord injury (SCI) is a devastating neurological condition for which there are currently no effective treatment options to restore function. A major obstacle to the development of new therapies is our fragmentary understanding of the coordinated pathophysiological processeses triggered by damage to the human spinal cord. An additional challenge to translation of preclinical therapies is the reliance of clinical trials on standardized neurological assessments to enrol and stratify patients, rather than objective injury biomarkers. Here, we describe a systems biology approach to integrate decades of small-scale experiments with unbiased, genome-wide gene expression from the human spinal cord, revealing a gene regulatory network signature of the pathophysiological response to SCI. Our integrative analyses converge on an evolutionarily conserved gene subnetwork enriched for genes associated with the response to SCI by small-scale experiments, and whose expression is upregulated in a severity-dependent manner following injury and downregulated in functional recovery. We validate the severity-dependent upregulation of this subnetwork in prospective transcriptomic and proteomic studies. Our analysis provides a systems-level view of the coordinated molecular processes activated in response to SCI. Further, our results nominate quantitative biomarkers of injury severity and functional recovery, with the potential to facilitate development and translation of novel therapies.
2018-09-25 | PXD010192 | Pride
Project description:To formally address the tumor suppressor activity of Sh2b3 in vivo, we tested the interaction between oncogenic NOTCH1 and Sh2b3 loss in a retroviral- transduction bone marrow transplantation model of NOTCH-induced T-ALL Forced expression of activated NOTCH1 in this model typically results in full leukemia transformation 5-10 weeks later. We performed microarray gene expression analysis of Sh2b3 wild type and Sh2b3–/– NOTCH1 induced leukemias
2013-11-06 | E-GEOD-44025 | biostudies-arrayexpress
Project description: Not available
| S-EPMC4022672 | biostudies-literature
Project description:Spinal cord injury (SCI) is a devastating neurological condition for which there are currently no effective treatment options to restore function. A major obstacle to the development of new therapies is our fragmentary understanding of the coordinated pathophysiological processeses triggered by damage to the human spinal cord. An additional challenge to translation of preclinical therapies is the reliance of clinical trials on standardized neurological assessments to enrol and stratify patients, rather than objective injury biomarkers. Here, we describe a systems biology approach to integrate decades of small-scale experiments with unbiased, genome-wide gene expression from the human spinal cord, revealing a gene regulatory network signature of the pathophysiological response to SCI. Our integrative analyses converge on an evolutionarily conserved gene subnetwork enriched for genes associated with the response to SCI by small-scale experiments, and whose expression is upregulated in a severity-dependent manner following injury and downregulated in functional recovery. We validate the severity-dependent upregulation of this subnetwork in prospective transcriptomic and proteomic studies. Our analysis provides a systems-level view of the coordinated molecular processes activated in response to SCI. Further, our results nominate quantitative biomarkers of injury severity and functional recovery, with the potential to facilitate development and translation of novel therapies.
2018-10-02 | GSE115067 | GEO