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accession-icon GSE12000
Obesity study in transgenic and knockout animals
  • organism-icon Mus musculus
  • sample-icon 48 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID), Rosetta/Merck Mouse TOE 75k Array 1 microarray

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11999
Lactb male transgenic liver expression vs FVB male wildtype control
  • organism-icon Mus musculus
  • sample-icon 16 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID)

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11996
Gas7 male transgenic liver expression vs FVB male wildtype control
  • organism-icon Mus musculus
  • sample-icon 14 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID)

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11998
Gyk female heterozygous liver expression vs C57Bl/6J female wildtype control
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID)

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11997
Gpx3 male transgenic liver expression vs B6/DBA male wildtype control
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconSentrix MouseRef-8 Expression BeadChip (Target ID)

Description

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes, and identification of involved pathways and networks.

Publication Title

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE42294
Identification of BORIS-bound transcripts in neural progenitor cells and young neurons
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

This study compares the transcripts bound to BORIS in neural progenitor cells and cells differentiated for 6 days into young neurons

Publication Title

BORIS/CTCFL is an RNA-binding protein that associates with polysomes.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE29955
Expression data from cells with siRNA-mediated knockdown of OPG and from HVSMCs incubated with RANKL or TRAIL
  • organism-icon Homo sapiens
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

We used microarrays to assess gene expression changes in cells with siRNA-mediated knockdown of OPG compared to normal cells. Furthermore, we used microarrays to assess gene expression in cells treated with either RANKL or TRAIL compared to vehicle-treated cells.

Publication Title

No influence of OPG and its ligands, RANKL and TRAIL, on proliferation and regulation of the calcification process in primary human vascular smooth muscle cells.

Sample Metadata Fields

Specimen part, Treatment

View Samples
accession-icon E-TOXM-30
Transcription profiling of rat liver and kidney (F344 strain) following exposure to benzene, trichloroethylene, methyl mercury and their mixtures
  • organism-icon Rattus norvegicus
  • sample-icon 90 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Expression 230A Array (rae230a)

Description

The present research aimed to study the interaction of three chemicals, methyl mercury, benzene and trichloroethylene, on mRNA expression alterations in rat liver and kidney measured by microarray analysis. These compounds were selected on presumed different modes of action. The chemicals were administered daily for 14 days at the Lowest-Observed-Adverse-Effect-Level (LOAEL) or at a two- or three-fold lower concentration individually or in binary or ternary mixtures. The compounds had strong antagonistic effects on each others gene expression changes, which included several genes encoding Phase I and II metabolizing enzymes. On the other hand, the mixtures affected the expression of “novel” genes that were not or little affected by the individual compounds. Based on gene expression changes, the three compounds exhibited a synergistic interaction at the LOAEL in the liver and both at the sub-LOAEL and LOAEL in the kidney. Many of the genes induced by mixtures but not by single compounds, such as Id2, Nr2f6, Tnfrsf1a, Ccng1, Mdm2 and Nfkb1 in the liver, are known to affect cellular proliferation, apoptosis and function. This indicates a shift from compound specific response on exposure to individual compounds to a more generic stress response to mixtures. Most of the effects on cell viability as concluded from transcriptomics were not detected by classical toxicological research illustrating the difference in sensitivity of these techniques. These results emphasize the benefit of applying toxicogenomics in mixture interaction studies, which yields biomarkers for joint toxicity and eventually can result in an interaction model for most known toxins.

Publication Title

Transcriptomics analysis of interactive effects of benzene, trichloroethylene and methyl mercury within binary and ternary mixtures on the liver and kidney following subchronic exposure in the rat.

Sample Metadata Fields

Sex, Age, Specimen part, Treatment, Compound

View Samples
accession-icon GSE95286
Expression data of undifferentiated cells and xenografts of human pluripotent stem cells and embryonal carcinoma cells
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Differentiation-Defective Human Induced Pluripotent Stem Cells Reveal Strengths and Limitations of the Teratoma Assay and In Vitro Pluripotency Assays.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE95285
Xenografts of human pluripotent stem cells and embryonal carcinoma cells
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Here we perfomed the Teratoma assay for a normal human embryonic stem cell line (H9(+Dox)), a human embryonic stem cell line with a mesendodermal differentiation bias (H9Hyb), a normal human induced pluripotent stem cell line (LU07), a human induced pluripotent stem cell line with reactivated transgenes (LU07+Dox) and a human embryonal carcinoma cell line (EC) and anayzed their gene expression.

Publication Title

Differentiation-Defective Human Induced Pluripotent Stem Cells Reveal Strengths and Limitations of the Teratoma Assay and In Vitro Pluripotency Assays.

Sample Metadata Fields

No sample metadata fields

View Samples

refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

fund-icon Fund the CCDL

Developed by the Childhood Cancer Data Lab

Powered by Alex's Lemonade Stand Foundation

Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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