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GSE7669
Homo sapiens
12 Downloadable Samples
Affymetrix Human Genome U95 Version 2 Array (hgu95av2)
Description
mRNA expression levels in synovial fibroblasts in 6 rheumatoid arthritis patients versus 6 osteoarthritis patients.
Publication Title
Constitutive upregulation of the transforming growth factor-beta pathway in rheumatoid arthritis synovial fibroblasts.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 20 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
The aim of this study was to identify differential gene expression resulting from the inhibition of class IIa HDACs in human PBMC.
Publication Title
Selective class IIa histone deacetylase inhibition via a nonchelating zinc-binding group.
Sample Metadata Fields
Specimen part, Treatment
View Samples- Homo sapiens
- 57 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Background
Publication Title
Adapted Boolean network models for extracellular matrix formation.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 57 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Background. Rheumatoid arthritis (RA) is a chronic inflammatory and destructive joint disease, characterized by overexpression of pro-inflammatory/-destructive genes and other activating genes (e.g., proto-oncogenes) in the synovial membrane (SM). The gene expression in disease is often characterized by significant inter-individual variances via specific synchronization/ desynchronization of gene expression. To elucidate the contribution of the variance to the pathogenesis of disease, expression variances were tested in SM samples of RA patients, osteoarthritis (OA) patients, and normal controls (NC).
Publication Title
Identification of intra-group, inter-individual, and gene-specific variances in mRNA expression profiles in the rheumatoid arthritis synovial membrane.
Sample Metadata Fields
Sex, Age, Disease
View Samples- 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- Caenorhabditis elegans
- 5 Downloadable Samples
Illumina HiSeq 2000
Description
Transciptomic analysis of germline tumor cells to understand the role of autophagy and neuronal differentiation in lifespan extension. Overall design: Methods: Worms were grown on control L444 seeded plates or gld-1 RNAi seeded plates and subjected to RNA isolation and sequencing using standard Illumina protocols. Conclusions: Fasting of animals expressing tumors increases their lifespan two-fold through autophagy and modular changes in transcription as well as metabolism.
Publication Title
Autophagy and modular restructuring of metabolism control germline tumor differentiation and proliferation in C. elegans.
Sample Metadata Fields
Subject
View Samples- Homo sapiens
- 4 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
We established a novel model to assess the function of proteins under in vivo conditions. The model relies on the expansion of HEK293 cells in immunodeficient NOD.Scid mice. To validate the novel model, we performed microarray gene expression profiling of NOD.Scid-expanded HEK293 cells relative to conventionally cultivated cells. Microarray analysis revealed that cell expansion in NOD.Scid mice restored an imbalanced chaperone system without inducing a major upregulation of the entire protein folding machinery.
Publication Title
Establishment of an in vivo model facilitates B2 receptor protein maturation and heterodimerization.
Sample Metadata Fields
Specimen part, Cell line
View Samples- Mus musculus
- 10 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
We have recently shown a remarkable regenerative capacity of the prenatal heart using a genetic model of mosaic mitochondrial dysfunction in mice. This model is based on inactivation of the X-linked gene encoding holocytochrome c synthase (Hccs) specifically in the developing heart. Loss of HCCS activity results in respiratory chain dysfunction, disturbed cardiomyocyte differentiation and reduced cell cycle activity. The Hccs gene is subjected to X chromosome inactivation, such that in females heterozygous for the heart conditional Hccs knockout approximately 50% of cardiac cells keep the defective X chromosome active and develop mitochondrial dysfunction while the other 50% remain healthy. During heart development, however, the contribution of HCCS deficient cells to the cardiac tissue decreases from 50% at midgestation to 10% at birth. This regeneration of the prenatal heart is mediated by increased proliferation of the healthy cardiac cell population, which compensate for the defective cells and allow the formation of a fully functional heart at birth. Here we performed microarray expression ananlyses on 13.5 dpc control and heterozygous Hccs knockout hearts to identify molecular mechanisms that drive embryonic heart regeneration.
Publication Title
Embryonic cardiomyocytes can orchestrate various cell protective mechanisms to survive mitochondrial stress.
Sample Metadata Fields
Sex, Specimen part
View Samples