Hepatic stellate cells (HSCs) are the primary cell type responsible for liver fibrosis, the final common pathway leading to cirrhosis and liver failure for nearly every cause of chronic liver disease. Activation of HSCs in response to injury represents the key step in hepatic fibrogenesis, and is characterized by a phenotypic change from a non-fibrogenic, quiescent HSC to a fibrogenic HSC myofibroblast that secretes extracellular matrix proteins responsible for the fibrotic scar. We developed a small molecule screen to identify compounds that revert fibrotic human HSC myofibroblasts to an inactive phenotype through the quantification of lipid droplets with fluorescent microscopy. Conditions were optimized in a 384-well format using culture in Matrigel as a positive control. We screened 1600 compounds and identified 30 small molecules that induce reversion to an inactive phenotype. Among the hits, we identified five tricyclic antidepressants (TCAs) and showed that this class of drugs also repressed ACTA2 and COL1A1 while promoting PPAR-gamma expression. RNA sequencing analysis implicated extracellular matrix proteins and the sphingolipid pathway as a target of the TCAs. Overall design: HSCs and HSCs stimulated with TGF-beta were treated with the TCA, nortriptyline or ethanol vehicle for 48 hours. RNA-seq was performed in duplicate for each condition
Tricyclic Antidepressants Promote Ceramide Accumulation to Regulate Collagen Production in Human Hepatic Stellate Cells.
No sample metadata fields
View SamplesThese data are from the brains (amygdala and hippocampus) of mice originally derived from a cross between C57BL/6J and A/J inbred strains. We used short-term selection to produce outbred mouse lines with differences in contextual fear conditioning, which is a measure of fear learning. We selected for a total of 4 generations. Fear learning differed in the selected lines and this difference was stronger with each successive generation of selection. We identified several QTLs for the selection response, including a highly significant QTL at the tyr locus (p < 9.6(-10)). We used Affymetrix microarrays to identify many differentially expressed genes in the amygdala and hippocampus of mice from the final generation of selection. Amygdala and hippocampus samples were rapidly dissected out of experimentally nave mice from each selected line. Three samples were pooled and hybridized to each array. Experimentally nave mice were used because the behavior of the mice can be reliably a nticipated due to their lineage. Thus these gene expression differences are not due to the response to human handling, foot shock or fear-inducing conditioned stimuli. We have a second similar study that focuses on a different selected population that was based on C57BL/6J and DBA/2J mice (see GES4035).
Rapid selection response for contextual fear conditioning in a cross between C57BL/6J and A/J: behavioral, QTL and gene expression analysis.
No sample metadata fields
View SamplesThese data are from the brains (amygdala and hippocampus) of mice originally derived from a cross between C57BL/6J and DBA/2J inbred strains. We used short-term selection to produce outbred mouse lines with differences in contextual fear conditioning, which is a measure of fear learning. We selected for a total of 4 generations. Fear learning differed in the selected lines and this difference was stronger with each successive generation of selection. These mice also showed differences for measures of anxiety-like behavior, but were not different for tests of non-fear motivated learning, suggesting that selection altered alleles that are specifically involved in emotional behaviors. We identified several QTLs for the selection response. We used Affymetrix microarrays to identify differentially expressed genes in the amygdala and hippocampus of mice from the final generation of selection. Amygdala and hippocampus samples were rapidly dissected out of experimentally nave mice f rom each selected line. Three samples were pooled and hybridized to each array. Experimentally nave mice were used because the behavior of the mice can be reliably anticipated due to their lineage. Thus, these gene expression differences are not due to the response to human handling, foot shock or fear-inducing conditioned stimuli. We have a second similar study that focuses on a different selected population that was based on C57BL/6J and A/J mice (see GES4034).
Selection for contextual fear conditioning affects anxiety-like behaviors and gene expression.
No sample metadata fields
View SamplesGenome-wide association studies have identified a locus within the second intron of the FGFR2 gene that is consistently the most strongly associated with estrogen receptor-poisive breast cancer risk. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Previously, a systems biology approach was adopted to elucidate the regulatory networks operating in MCF-7 breast cancer cells in order to examine the role of FGFR2 in mediating risk. Here, the same approach has been employed using a number of different estrogen receptor-positive breast cancer cell lines in order to see if the previous findings are reproducible and consistent in estrogen receptor-positive disease.
Regulators of genetic risk of breast cancer identified by integrative network analysis.
Specimen part, Cell line, Treatment
View SamplesThis SuperSeries is composed of the SubSeries listed below.
Master regulators of FGFR2 signalling and breast cancer risk.
Specimen part, Cell line
View SamplesGenome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways
Master regulators of FGFR2 signalling and breast cancer risk.
Cell line
View SamplesGenome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways
Master regulators of FGFR2 signalling and breast cancer risk.
Cell line
View SamplesThis SuperSeries is composed of the SubSeries listed below.
FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness.
Specimen part, Cell line, Treatment
View SamplesGenome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways
Master regulators of FGFR2 signalling and breast cancer risk.
Cell line
View SamplesGenome-wide association studies have identified a locus within the second intron of the FGFR2 gene that is consistently the most strongly associated with estrogen receptor-poisive breast cancer risk. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Previously, a systems biology approach was adopted to elucidate the regulatory networks operating in MCF-7 breast cancer cells in order to examine the role of FGFR2 in mediating risk. Here, the same approach has been employed using MCF-7 cells that have been treated with siRNA directed against FGFR2, in order to knock-down FGFR2 expression, to confirm that the differential gene expression that we see when FGF10 signalling is perturbed, on a background of estrogen signalling, is mediated via FGFR2 stimulation.
FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness.
Specimen part, Cell line
View Samples