MicroRNA expression profile in macrophages from rheumatoid arthritis and osteoarthritis patients
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ABSTRACT:
ORGANISM(S): Mus musculus Rattus norvegicus Human gammaherpesvirus 8 JC polyomavirus Betapolyomavirus macacae Human immunodeficiency virus 1 Homo sapiens Murid gammaherpesvirus 4 Human betaherpesvirus 5 Human alphaherpesvirus 1 Betapolyomavirus hominis human gammaherpesvirus 4 Murid betaherpesvirus 1
PROVIDER: GSE63745 | GEO | 2016/02/05
SECONDARY ACCESSION(S): PRJNA268962
REPOSITORIES: GEO
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Project description:In osteoarthritis (OA) and rheumatoid arthritis (RA), many pathological alterations result from aberrant macrophage hyperactivation in the inflamed synovial membrane, and inhibition of macrophage effector functions is a therapeutic strategy in both diseases. Little is known, about the specific genetic circuits that are differentially deregulated in RA and OA macrophages. microRNA (miR) are short single stranded non-coding RNAs involved in the post-transcriptional regulation of gene expression. Altered expression of miRs has been described under various pathological conditions, including rheumatic and other autoimmune diseases. Here we compared the miR expression profile in macrophages isolated from OA and RA patients miR expression in OA and RA macrophages was analyzed using Exiqon miRCURY microarrays. Three biological replicates (patients) were analyzed. Two samples (RA vs OA) were hybridized per microarray.
2016-02-05 | E-GEOD-63745 | biostudies-arrayexpress
Project description:Rheumatoid synoviocytes, which consist of fibroblast-like synoviocytes (FLS) and synovial macrophages (SM), are crucial for the progression of rheumatoid arthritis (RA). Particularly, FLS of RA patients (RA-FLS) exhibit invasive characteristics reminiscent of cancer cells, destroying cartilage and bone, although it remains unresolved how RA-FLS exhibit invasive phenotype. RA-FLS and SM originate differently from mesenchymal and myeloid cells, respectively, but share many pathologic functions. However, the molecular signatures and biological networks representing the distinct and shared features of the two cell types are unknown. Presently, we performed global transcriptome profiling of FLS and SM obtained from RA and osteoarthritis patients. By comparing the transcriptomes, we identified distinct molecular signatures and cellular processes defining invasiveness of RA-FLS and pro-inflammatory properties of RA synovial macrophages (RA-SM), respectively. Interestingly, under interleukin1β-stimulated condition, RA-FLS newly acquired pro-inflammatory signature mimicking RA-SM without losing invasive properties. We next reconstructed a network model that delineates the shared, RA-FLS-dominant (invasive), and RA-SM-dominant (inflammatory) processes. From the network model, we selected 13 genes, including POSTN and TWIST1, as novel regulator candidates responsible for FLS invasiveness. Of note, POSTN and TWIST1 expressions were elevated in independent RA-FLS and were further instigated by interleukin1β. In vitro functional assays demonstrated the requirement of POSTN and TWIST1 for migration and invasion of RA-FLS stimulated with interleukin1β. Taken together, our systems approach to rheumatoid synovitis provides a basis for identifying novel regulators responsible for pathological features of RA-FLS and RA-SM, demonstrating how a certain type of cells acquires functional redundancy under chronic inflammatory conditions. To identify molecular signatures of FLS and MLS in RA joints, we isolated FLS from synovial tissues of RA and osteoarthritis (OA) patients, obtained synovial macrophages from synovial fluid of RA patients, and differentiated control macrophages from peripheral blood of healthy subjects. Also, we stimulated FLS with IL1β, and then analyzed gene expression profiles of both IL1β-stimulated RA-FLS and OA-FLS
2014-01-09 | E-GEOD-49604 | biostudies-arrayexpress
Project description:Homo sapiens microRNAs (miRNAs) are invovolved in the regulation of multiple cellular processes and have been linked to many conditions in humans, including rheumatic diseases such as rheumatoid arthritis (RA). Here, we used high throughput expression analysis to assess the overall miRNA expression level in FLS isolated from RA patients in comparison with miRNA expressed in FLS from osteoarthritic (OA) patients.
2015-09-01 | GSE72564 | GEO
Project description:Homo sapiens microRNAs (miRNAs) are invovolved in the regulation of multiple cellular processes and have been linked to many conditions in humans, including rheumatic diseases such as rheumatoid arthritis (RA). Here, we used high throughput expression analysis to assess the overall miRNA expression level in FLS isolated from RA patients in comparison with miRNA expressed in FLS from osteoarthritic (OA) patients. FLS isolated from synovial biopsies from 4 RA and 4 OA patients were cultivated for 4 passages. Cells were lysed and RNA purified (Trizol). Then miRNA expression was performed using the Qiagen Human miRNome miScript miRNA PCR Array (V16.0, 384-well) profiling the expression of the 1066 most abundantly expressed and best characterized miRNA sequences in the human miRNA genome (miRNome) as annotated in miRBase Release 16 (www.mirbase.org).
2015-09-01 | E-GEOD-72564 | biostudies-arrayexpress
Project description:Gene expression profiling of BMMC from patients with rheumatoid arthritis (RA) vs. osteoarthritis (OA). Bone marrow was obtained from patients with RA (n=9) or OA (n=10). The bone marrow samples from the 10 OA patients are used as controls.
2011-05-31 | E-GEOD-27390 | biostudies-arrayexpress
Project 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 Kiendl’s 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. Synovial tisssue from healthy joints, OA joints, and RA joints.
2014-02-21 | E-GEOD-55235 | biostudies-arrayexpress
Project description:The severity of osteoarthritis (OA) and cartilage degeneration are highly correlated with the development of synovitis, which is mediated by the activity of inflammatory macrophages. A better understanding of intercellular communication between inflammatory macrophages and chondrocytes should aid in the discovery of novel therapeutic targets. Here, we explored the pathological role of inflammatory macrophage-extracellular vesicles (EVs) in cartilage degeneration. Macrophages were stimulated by treatment with bacterial lipopolysaccharides to mimic the state of inflammatory macrophages and the resulting EVs (M-LPS EVs) were harvested for chondrocyte stimulation and intraarticular injection in a mouse model. This stimulation resulted in increased catabolism of chondrocytes and cartilage degeneration. Consistently, RNA-seq analyses of stimulated chondrocytes indicated that upregulated genes are mainly categorized into apoptotic process and TNF-signaling pathway which suggests the induction of apoptotic process. These chondrocytes exhibited a significant elevation in the expression of pyroptosis-related molecules that were correlated with the expression of chondrocyte catabolic factors. The disruption of caspase-11 significantly alleviated pyroptotic and catabolic processes in stimulated chondrocytes and the pathological changes in collagenase-induced OA model. Our results provide a new insight into the pathological mechanisms of OA and suggest that non-canonical pyroptosis signaling in chondrocytes represents an attractive therapeutic target for future treatment.
2023-09-20 | GSE199193 | GEO
Project description:We compared the circulating microRNA expression profiles of KBD, osteoarthritis (OA), rheumatoid arthritis (RA) and healthy controls. Blood specimens were collected from 3 KBD patients, 3 OA patients, 3 RA patients and 3 healthy controls. miRNAs expression profiling was performed using Exiqon miRCURY LNATM miRNAs Array.
2016-06-01 | E-MTAB-3514 | biostudies-arrayexpress
Project 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 Kiendl’s 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. 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 Kiendl’s 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.
2014-03-05 | E-GEOD-55584 | biostudies-arrayexpress
Project 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 Kiendl’s 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. 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 Kiendl’s 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.
2014-03-05 | E-GEOD-55457 | biostudies-arrayexpress