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GSE55457
Homo sapiens
32 Downloadable Samples
Affymetrix Human Genome U133A Array (hgu133a)
Description
Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendls statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.
Publication Title
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 29 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendls statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.
Publication Title
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.
Sample Metadata Fields
Specimen part, Disease, Disease stage
View Samples- Homo sapiens
- 15 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendls statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for RA), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.
Publication Title
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 8 Downloadable Samples
- Affymetrix Human Transcriptome Array 2.0 (hta20)
Description
We investigated the influence of SCFAs on human, monocyte derived DCs that represent a reliable in vitro model to study circulating DCs, one of the key regulators of our immune system. We studied the individual effect exerted by SCFA, the main metabolic end-products of fermentation by anaerobic bacteria in the gut, on the gene expression of immature and mature DC, exploring the potential of circulating bacterial metabolites to directly influence immune system cells. We found that SCFAs have little effect on the transcriptome of immature DC, whereas the transcriptome of mature DC was highly perturbed especially by butyrate and propionate. Our findings show an overall down-regulation of LPS-induced inflammatory responses and provide new insights into host-microbiome interactions.
Publication Title
The effect of short-chain fatty acids on human monocyte-derived dendritic cells.
Sample Metadata Fields
Specimen part, Treatment
View Samples- Homo sapiens
- 5 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
In this study we compared the effects of ALK inhibitor on the gene expression, activation of cell signaling pathways, and functional properties of cells derived from a patient with Anaplastic Large Cell Lymphoma. we used microarrays to map the genome-wide gene expression patterns in ALK+TCL cells in response to ALK inhibition.
Publication Title
Malignant transformation of CD4+ T lymphocytes mediated by oncogenic kinase NPM/ALK recapitulates IL-2-induced cell signaling and gene expression reprogramming.
Sample Metadata Fields
Cell line
View Samples- Homo sapiens
- 7 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Patients with the genetic skin blistering disease recessive dystrophic epidermolysis bullosa (RDEB) develop aggressive and metastatic cutaneous squamous cell carcinoma which is the principal cause of premature mortality in this patient group. We performed gene expression profiling of RDEB-SCC cells compared to RDEB keratinocytes in order to identify tumor-specific molecules that could potentially be exploited for detection, diagnosis, and therapy of this devastating disease.
Publication Title
Extracellular Vesicles as Biomarkers for the Detection of a Tumor Marker Gene in Epidermolysis Bullosa-Associated Squamous Cell Carcinoma.
Sample Metadata Fields
Specimen part, Disease
View Samples- Homo sapiens
- 30 Downloadable Samples
- Illumina HumanHT-12 V3.0 expression beadchip
Description
Design: Persistent latently infected CD4+ T cells represent a major obstacle to HIV eradication. Histone deacetylase inhibitors (HDACis) are a promising activation therapy in a shock and kill strategy. However, off-target effects of HDACis on host gene expression are poorly understood in primary cells of the immune system. We hypothesized that HDACi-modulated genes would be best identified with a dose response analysis. Methods: Resting primary CD4+ T cells were treated with increasing concentrations (0.34, 1, 3, or 10 M) of the HDACi, suberoylanilide hydroxamic acid (SAHA), for 24 hours and then subjected to microarray gene expression analysis. Genes with dose-correlated expression were identified with a likelihood ratio test using Isogene GX and a subset of these genes with a consistent trend of up or downregulation at each dose of SAHA were identified as dose-responsive. Histone modifications were characterized in promoter regions of the top 6 SAHA dose-responsive genes by RT-qPCR analysis of immunopreciptated chromatin (ChIP). Results: A large number of genes were shown to be up (N=657) or down (N=725) regulated by SAHA in a dose-responsive manner (FDR p-value < 0.05 and fold change |2|). Several of these genes (CTNNAL1, DPEP2, H1F0, IRGM, PHF15, and SELL) are potential in vivo biomarkers of SAHA activity. SAHA dose-responsive gene categories included transcription factors, HIV restriction factors, histone methyltransferases, and host proteins that interact with HIV proteins or the HIV LTR. Pathway analysis suggested net downregulation of T cell activation with increasing SAHA dose. Histone acetylation was not correlated with host expression, but plausible alternative mechanisms for SAHA-modulated expression were identified. Conclusions: Numerous host genes in CD4+ T cells are modulated by SAHA in a dose-responsive manner, including genes that may negatively influence HIV activation from latency. Our study suggests that SAHA influences gene expression through a confluence of several mechanisms, including histone acetylation, histone methylation, and altered expression and activity of transcription factors.
Publication Title
Dose-responsive gene expression in suberoylanilide hydroxamic acid-treated resting CD4+ T cells.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 17 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Temporal changes of the expression levels of the complete human transcriptome during the first 24 hours following infection of IFN-pre-treated macrophages. This approach has allowed us to identify genes involved in the IFN signaling that have an impact on HIV-1 infection of macrophages
Publication Title
TRAF6 and IRF7 control HIV replication in macrophages.
Sample Metadata Fields
Specimen part, Time
View Samples- Mus musculus
- 8 Downloadable Samples
- Affymetrix Murine Genome U74A Version 2 Array (mgu74av2)
Description
Mouse strains have been identified that are resistant (i.e. DBA/2) or susceptible (i.e. C57BL/6) to infection from pathogenic fungus Coccidioides immitis. However, the genetic and immunological basis for this difference has not been fully characterized.
Publication Title
Factors regulated by interferon gamma and hypoxia-inducible factor 1A contribute to responses that protect mice from Coccidioides immitis infection.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 8 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
This in-vitro study suggests the inflammatory environment of naive epithelial cells can induce epigenetic modulation of innate immune responses at the level of histone methylation and potentially lead to long-term impacts on anti-viral immunity.
Publication Title
IFN-γ Influences Epithelial Antiviral Responses via Histone Methylation of the RIG-I Promoter.
Sample Metadata Fields
Cell line, Treatment
View Samples