Dataset Information

Expression profiling in whole blood in ankylosing spondylitis patients and controls

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE25101 | GEO | 2010/11/04

SECONDARY ACCESSION(S): PRJNA134725

REPOSITORIES: GEO

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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.

Dataset Information

Expression profiling in whole blood in ankylosing spondylitis patients and controls

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