FRI0309 The genome-wide expression of human osteoarthritic cartilage shows different GENE-expression profiles OA-related. (23rd January 2014)

• Record Type: Journal Article
• Authors: Fernandez-Tajes, J.; Soto-Hermida, A.; Fernandez-Moreno, M.; Vazquez-Mosquera, M.E.; Oreiro, N.; Fernandez-Lopez, C.; Cortes-Pereira, E.; Fernandez-Relaño, S.; Rego-Perez, I.; Blanco, F.J.
• Is Part Of: Annals of the rheumatic diseases. Volume 71(2012)Supplement 3
• Page Start: 418
• Page End: 418
• Publication Date: 2014-01-23
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2012-eular.2766
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Many genes, many of them still unknown, are involved in the etiology and development of OA. Today, different tools are available to try to identify some of the key genes related to the OA process. Objectives: To perform a genome-wide expression assay in order to identify different expression profiles in the OA disease Methods: Total RNA from OA cartilage samples was isolated with RNeasy Kit (Qiagen, Madrin, Spain) following manufacturer's instructions. RNA was checked for integrity and purity with the Agilent Bioanalyzer (Agilent Technologies) and NanoDrop spectrophotometer (Thermo Scientific). 150 nanograms of total RNA were used for cDNA synthesis using the Ambion WT Expression kit (Ambion). The fragmented cDNA was hybridized against the Human Gene 1.1 ST array strip (Affymetrix) and scanned using the GeneTitan system (Affymetrix). Quality controls, normalization, pre-processing, differential gene expression and functional analyses were carried out with Bioconductor packages using R software. Results: Human Gene 1.1 ST Array, which interrogates more than 28, 000 well-annotated genes of 33, 297 probes, was used for studying the genome wide expression profile of 23 OA-patients tissue samples. A non-specific filtering was previously applied for removing those probes with non-annotation information and with low intra-array variation. An unsupervised machine learning approach revealed a group of samples highly different (Figure 1). The differential expression analysis between this cluster, comprising a total of five OA-patients, and the rest of the samples allowed for the identification of 176 differentially expressed probes with an adjusted p value below 0.0001. The 20 differentially expressed probes with higher log Fold Change are presented in Table 1. The analysis of the biological processes related to these differentially expressed genes showed that inflammation and immune processes were the main pathways found to be altered when a gene set enrichment analysis was applied. Conclusions: The genome-wide expression analysis shows a clearly distinct profile for a group of OA patients. Both inflammation and immune response processes appeared to be altered and therefore are revealed as key factors in the development of the OA disease. Disclosure of Interest: None Declared