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Whole blood transcriptional profiling in ankylosing spondylitis identifies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects.

ABSTRACT: INTRODUCTION: A number of genetic-association studies have identified genes contributing to ankylosing spondylitis (AS) susceptibility but such approaches provide little information as to the gene activity changes occurring during the disease process. Transcriptional profiling generates a 'snapshot' of the sampled cells' activity and thus can provide insights into the molecular processes driving the disease process. We undertook a whole-genome microarray approach to identify candidate genes associated with AS and validated these gene-expression changes in a larger sample cohort. METHODS: A total of 18 active AS patients, classified according to the New York criteria, and 18 gender- and age-matched controls were profiled using Illumina HT-12 whole-genome expression BeadChips which carry cDNAs for 48,000 genes and transcripts. Class comparison analysis identified a number of differentially expressed candidate genes. These candidate genes were then validated in a larger cohort using qPCR-based TaqMan low density arrays (TLDAs). RESULTS: A total of 239 probes corresponding to 221 genes were identified as being significantly different between patients and controls with a P-value <0.0005 (80% confidence level of false discovery rate). Forty-seven genes were then selected for validation studies, using the TLDAs. Thirteen of these genes were validated in the second patient cohort with 12 downregulated 1.3- to 2-fold and only 1 upregulated (1.6-fold). Among a number of identified genes with well-documented inflammatory roles we also validated genes that might be of great interest to the understanding of AS progression such as SPOCK2 (osteonectin) and EP300, which modulate cartilage and bone metabolism. CONCLUSIONS: We have validated a gene expression signature for AS from whole blood and identified strong candidate genes that may play roles in both the inflammatory and joint destruction aspects of the disease.

SUBMITTER: Pimentel-Santos FM

PROVIDER: S-EPMC3132052 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Whole blood transcriptional profiling in ankylosing spondylitis identifies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects.

Pimentel-Santos Fernando M FM Ligeiro Dário D Matos Mafalda M Mourão Ana F AF Costa José J Santos Helena H Barcelos Anabela A Godinho Fátima F Pinto Patricia P Cruz Margarida M Fonseca João E JE Guedes-Pinto Henrique H Branco Jaime C JC Brown Matthew A MA Thomas Gethin P GP

Arthritis research & therapy 20110407 2

<h4>Introduction</h4>A number of genetic-association studies have identified genes contributing to ankylosing spondylitis (AS) susceptibility but such approaches provide little information as to the gene activity changes occurring during the disease process. Transcriptional profiling generates a 'snapshot' of the sampled cells' activity and thus can provide insights into the molecular processes driving the disease process. We undertook a whole-genome microarray approach to identify candidate gen ...[more]

PMID: 21470430

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Project description:Introduction: A number of genetic-association studies have identified genes contributing to AS susceptibility but such approaches provide little information as to the gene activity changes occurring during the disease process. Transcriptional profiling generates a “snapshot” of the sampled cells activity and thus can provide insights into the molecular processes driving the disease process. We undertook a whole-genome microarray approach to identify candidate genes associated with AS and validated these gene-expression changes in a larger sample cohort. Methods: 18 active AS patients, classified according to the New York criteria. and 18 gender-and age-matched controls were profiled using Illumina HT-12 Whole-Genome Expression BeadChips which carry cDNAs for 48000 genes and transcripts. Class comparison analysis identified a number of differentially expressed candidate genes. These candidate genes were then validated in a larger cohort using qPCR-based TaqMan Low Density Arrays (TLDAs). Results: 239 probes corresponding to 221 genes were identified as being significantly different between patients and controls with a p-value <0.0005 (80% confidence level of false discovery rate). Forty seven genes were then selected for validation studies, using the TLDAs. Thirteen of these genes were validated in the second patient cohort with 12 down-regulated 1.3-2-fold and only 1 upregulated (1.6-fold). Among a number of identified genes with well-documented inflammatory roles we also validated genes that might be of great interest to the understanding of AS progression such as SPOCK2 (osteonectin) and EP300 which modulate cartilage and bone metabolism. Conclusion: We have validated a gene expression signature for AS from whole blood and identified strong candidate genes that may play roles in both the inflammatory and joint destruction aspects of the disease. RNA was extracted from whole blood using PAXGene tubes. 16 AS patients with active disease and 16 gender- and age-matched controls were analysed.

Project description:BackgroundWe sought to discover serum biomarkers of ankylosing spondylitis (AS) for diagnosis and monitoring disease activity.MethodsWe studied biologic-treatment-naïve AS and healthy control (HC) patients' sera. Eighty samples matched by age, gender, and race (1:1:1 ratio) for AS patients with active disease, inactive disease, and HC were analyzed with SOMAscan™, an aptamer-based discovery platform. T-tests tests were performed for high/low-disease activity AS patients versus HCs (diagnosis) and high versus low disease activity (Monitoring) in a 2:1 and 1:1 ratio, respectively, to identify differentially expressed proteins (DEPs). We used the Cytoscape Molecular Complex Detection (MCODE) plugin to find clusters in protein-protein interaction networks and Ingenuity Pathway Analysis (IPA) for upstream regulators. Lasso regression analysis was performed for diagnosis.ResultsOf the 1317 proteins detected in our diagnosis and monitoring analyses, 367 and 167 (317 and 59, FDR-corrected q < .05) DEPs, respectively, were detected. MCODE identified complement, IL-10 signaling, and immune/interleukin signaling as the top 3 diagnosis PPI clusters. Complement, extracellular matrix organization/proteoglycans, and MAPK/RAS signaling were the top 3 monitoring PPI clusters. IPA showed interleukin 23/17 (interleukin 22, interleukin 23A), TNF (TNF receptor-associated factor 3), cGAS-STING (cyclic GMP-AMP synthase, Stimulator of Interferon Gene 1), and Jak/Stat (Signal transducer and activator of transcription 1), signaling in predicted upstream regulators. Lasso regression identified a Diagnostic 13-protein model predictive of AS. This model had a sensitivity of 0.75, specificity of 0.90, a kappa of 0.59, and overall accuracy of 0.80 (95% CI: 0.61-0.92). The AS vs HC ROC curve was 0.79 (95% CI: 0.61-0.96).ConclusionWe identified multiple candidate AS diagnostic and disease activity monitoring serum biomarkers using a comprehensive proteomic screen. Enrichment analysis identified key pathways in AS diagnosis and monitoring. Lasso regression identified a multi-protein panel with modest predictive ability.

Project description:We have compared synovial biopsies from ankylosing spondylitis and undifferentiated spondylitis patients with healthy controls and osteoarthritis patients Objective: In spondylarthropies, whole-genome gene expression profiling studies have been limited to peripheral blood to date. By undertaking a study in knee synovial biopsies from spondylarthropy (SpA) and ankylosing spondylitis (AS) patients we aimed to identified joint-specific candidate genes and pathways. These pathways may mediate systemic inflammation driven joint damaging processes and more specifically, the osteoproliferation that is characteristic of these conditions. Methods: RNA was extracted from six seronegative SpA, two AS, three osteoarthritis (OA) and four normal control knee synovial biopsies. Whole genome expression profiling was undertaken using the Illumina DASL system, which assays 24000 cDNA probes. Differentially expressed candidate genes were then validated using quantitative PCR and immunohistochemistry. Results: 416 differentially expressed genes were identified that clearly delineated between AS/SpA and control groups. Pathway analysis showed altered gene-expression in oxidoreductase activity, osteoblast activity, B-cell associated, matrix catabolic, and metabolic pathways. The inflammatory mediator, MMP3, was strongly upregulated in AS/SpA samples and the Wnt pathway inhibitors DKK3 and Kremen1 were downregulated. Conclusion: Pathways mediating both systemic inflammation as well as local tissue changes were identified. This suggests initial systemic inflammation in spondylarthropies transfers to and persists in the local joint environment, subsequently mediating changes in genes directly involved in the destructive tissue remodelling. Fifteen knee synovial biopsy tissue samples consisting of six seronegative spondyloarthropy (SpA), two ankylosing spondylitis (AS), three osteoarthritis (OA) and four normal control biopsies were obtained from the Synovial Tissue Bank at the Repatriation General Hospital in Adelaide, South Australia with the appropriate ethical approval (Supplementary Table 1). All patients provided informed written consent.

Dataset Information

Whole blood transcriptional profiling in ankylosing spondylitis identifies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects.

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Whole blood transcriptional profiling in ankylosing spondylitis identifies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects.

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