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GSE45652
Mus musculus
30 Downloadable Samples
Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
Previously we had shown in a mouse model of bronchial asthma that thrombomodulin (TM; CD141; BDCA3) can convert immunogenic conventional dendritic cells into tolerogenic dendritic cells while inducing its own expression on the cell surface. Thrombomodulin+ dendritic cells are tolerogenic while thrombomodulin- dendritic cells are pro-inflammatory and immunogenic. Here we hypothesized that thrombomodulin treatment of dendritic cells would modulate inflammatory gene expression. Murine bone marrow derived dendritic cells were treated with soluble thrombomodulin and expression of surface markers was determined. Treatment with thrombomodulin reduces the expression of maturation markers and increases the expression of TM on the DC surface. Thrombomodulin treated and control dendritic cells were sorted into thrombomodulin+ and thrombomodulin- dendritic cells before their mRNA was analyzed by microarray. mRNAs encoding pro-inflammatory genes and dendritic cells maturation markers were reduced while cell cycle genes were increased in thrombomodulin-treated and thrombomodulin+ dendritic cells compared to control dendritic cells and thrombomodulin- dendritic cells.
Publication Title
Differential gene expression in thrombomodulin (TM; CD141)(+) and TM(-) dendritic cell subsets.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 8 Downloadable Samples
Illumina HiSeq 2500
Description
Lamina propria and muscularis macrophages, were sorted at steady steate and 2h after oral exposure to an attenuated form of Salmonella, comparison among these populations showed that the muscularis macrophages quckly respond to the presence of intestinal bacteria, upregulating some important tissue protective genes. Overall design: intestinal macrophages from 3 mice were pooled into one RNA sample, the experiment was done control X infected and was repeated twice
Publication Title
Neuro-immune Interactions Drive Tissue Programming in Intestinal Macrophages.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Escherichia coli
- 12 Downloadable Samples
- Affymetrix E. coli Genome 2.0 Array (ecoli2)
Description
Human Peptidoglycan Recognition Proteins (PGRPs) kill bacteria, likely by over-activating stress responses in bacteria. To gain insight into the mechanism of PGRP killing of Escherichia coli and bacterial defense against PGRP killing, gene expression in E. coli treated with a control protein (bovine serum albumin, BSA), human recombinant PGRP (PGLYRP4), gentamicin (aminoglycoside antibiotic), and CCCP (membrane potential decoupler) were compared. Each treatment induced unique and somewhat overlapping pattern of gene expression. PGRP highly increased expression of genes for oxidative and disulfide stress, detoxification and efflux of Cu, As, and Zn, repair of damaged proteins and DNA, methionine and histidine synthesis, energy generation, and Fe-S clusters repair. PGRP also caused marked decrease in the expression of genes for Fe uptake and motility.
Publication Title
Peptidoglycan recognition proteins kill bacteria by inducing oxidative, thiol, and metal stress.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 6 Downloadable Samples
- Affymetrix Mouse Gene 2.0 ST Array (mogene20st)
Description
JG-98 reduces migration of macrophages. We assessed how this compound affects expression of genes associated with motility and migration. A number of motility/migration genes were significantly downregulated.
Publication Title
Anticancer Effects of Targeting Hsp70 in Tumor Stromal Cells.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 16 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
The mechanisms regulating breast cancer differentiation state are poorly understood. Of particular interest are molecular regulators controlling the highly aggressive and poorly differentiated traits of basal-like breast carcinomas. Here we show that the Polycomb factor EZH2 maintains the differentiation state of basal-like breast cancer cells, and promotes the expression of progenitor-associated and basal-lineage genes. Specifically, EZH2 regulates the composition of basal-like breast cancer cell populations by promoting a bi-lineage differentiation state, in which cells co-express basal- and luminal-lineage markers. We show that human basal-like breast cancers contain a subpopulation of bi-lineage cells, and that EZH2-deficient cells give rise to tumors with a decreased proportion of such cells. Bi-lineage cells express genes that are active in normal luminal progenitors, and possess increased colony formation capacity, consistent with a primitive differentiation state. We found that GATA3, a driver of luminal differentiation, performs a function opposite to EZH2, acting to suppress bi-lineage identity and luminal progenitor gene expression. GATA3 levels increase upon EZH2 silencing, leading to the observed decrease in bi-lineage cell numbers. Our findings reveal a novel role for EZH2 in controlling basal-like breast cancer differentiation state and intra-tumoral cell composition.
Publication Title
EZH2 promotes a bi-lineage identity in basal-like breast cancer cells.
Sample Metadata Fields
Specimen part, Cell line, Treatment
View Samples- Homo sapiens
- 15 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
IFN alpha mediated gene expression pattern. The effect of IFN alpha on human CD8 T cells responding to antigen (signal 1) and costimulatory signals (signal 2) provided by beads coated with anti-CD3 and anti-CD28 mAbs.
Publication Title
Effects of IFN-α as a signal-3 cytokine on human naïve and antigen-experienced CD8(+) T cells.
Sample Metadata Fields
Specimen part, Subject, Time
View Samples- Homo sapiens
- 32 Downloadable Samples
- Illumina HiSeq 2500
Description
Differentiation events contribute to cellular heterogeneity within tumors and influence disease progression and response to therapy. Here we dissect the mechanisms controlling intratumoral heterogeneity within basal-like breast cancers. We show that cancer cells can transition between a differentiation state related to that of normal luminal progenitors and a state closer to that of mature luminal cells, and that this occurs through asymmetric cell divisions. The Polycomb factor EZH2 and the Notch pathway act to increase the rates of symmetric divisions that produce progenitor-like cells, while the FOXA1 transcription factor promotes asymmetric divisions that reduce the numbers of such cells. Through functional screening, we identified a group of regulators that control cancer cell differentiation state and the relative proportions of tumor cell subpopulations. Our findings highlight the regulation of asymmetric cell divisions as a mechanism controlling intratumoral heterogeneity, and identify molecular pathways that control breast cancer cellular composition. Overall design: Expression profiles of HCC70 cells expressing shRNAs targeting regulatory factors that influence basal-like cancer cell population composition
Publication Title
Regulation of Cellular Heterogeneity and Rates of Symmetric and Asymmetric Divisions in Triple-Negative Breast Cancer.
Sample Metadata Fields
Cell line, Subject
View Samples- Homo sapiens
- 6 Downloadable Samples
- Illumina HiSeq 2500
Description
Differentiation events contribute to cellular heterogeneity within tumors and influence disease progression and response to therapy. Here we dissect the mechanisms controlling intratumoral heterogeneity within basal-like breast cancers. We show that cancer cells can transition between a differentiation state related to that of normal luminal progenitors and a state closer to that of mature luminal cells, and that this occurs through asymmetric cell divisions. The Polycomb factor EZH2 and the Notch pathway act to increase the rates of symmetric divisions that produce progenitor-like cells, while the FOXA1 transcription factor promotes asymmetric divisions that reduce the numbers of such cells. Through functional screening, we identified a group of regulators that control cancer cell differentiation state and the relative proportions of tumor cell subpopulations. Our findings highlight the regulation of asymmetric cell divisions as a mechanism controlling intratumoral heterogeneity, and identify molecular pathways that control breast cancer cellular composition. Overall design: Expression profiles of three cell subpopulations – K18+, K18+K14+ and K18+Vim+ – sorted from the breast cancer cell lines HCC70 and MDA-MB-468
Publication Title
Regulation of Cellular Heterogeneity and Rates of Symmetric and Asymmetric Divisions in Triple-Negative Breast Cancer.
Sample Metadata Fields
Cell line, Subject
View Samples- Mus musculus
- 5 Downloadable Samples
- Illumina HiSeq 2000
Description
Transcriptome of beta-cells isolated from mice expressing p16ink4a and GFP transgenes and of control ß-cells isolated from mice expressing only the GFP transgene Overall design: RNAseq of murine beta-cells sorted based on GFP expression from three Ins-rtTA/tet-GFP/tet-p16ink4a mice and two control Ins-rtTA/tet-GFP mice following 10 days tet-mediated induction.
Publication Title
p16(Ink4a)-induced senescence of pancreatic beta cells enhances insulin secretion.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 41 Downloadable Samples
- Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st)
Description
Filtered selection coupled with support vector machines generate functionally relevant prediction model for colorectal cancer. In this study, we built a model that uses Support Vector Machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300 and 500 genes most relevant to CRC using the Minimum-RedundancyMaximum-Relevance (mRMR) technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function and sigmoid).
Publication Title
Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer.
Sample Metadata Fields
Sex, Age, Specimen part, Disease stage
View Samples