Bioinformatic and data mining analysis of proteomic and genechip microarray data from an explant model of articular cartilage inflammation

点击翻译按钮获取中文摘要

Articular cartilage degradation is the major cause of joint dysfunction and disability in osteoarthritis (OA) in humans and companion animals and leads to chronic morbidity, pain and impaired quality of life. OA is a complex, multifactorial disease and we are investigating the early stages of disease and the possible role of diet/functional actives to support joint health. As part of this work, we have recently developed and exploited an explant culture model of equine articular cartilage to identify secreted biomarkers using proteomics and cross-species peptide matching techniques. We have also used human Affymetrix GeneChip Microarrays to study gene expression in cartilage by cross-hybridizing messenger RNA from equine articular chondrocytes onto this human microarray platform. This work is the result of an ongoing BBSRC CASE studentship in collaboration with WALTHAM and the School of Veterinary Medicine and Science at the University of Nottingham which started in October 2006 and has just entered its third year (BBSRC grant BS/S/M/2006/13141). This approach has been fruitful and is generating large amounts of proteomic and gene expression datasets that require detailed and comparative analysis by bioinformatics and data-mining techniques. This CASE project will apply data mining and network biology approaches to identify potential biomarkers of early OA development, with an expectation that this work will also provide insight into the response of chondrocyte to inflammatory perturbation. We aim to achieve this by comparing differences between control cartilage samples and experimental samples exposed to a variety of disease relevant catabolic stimuli. The catabolic stimuli we propose to use include pro-inflammatory cytokines (i.e. interleukin 1 beta (IL-1beta), interleukin 6 (IL-6) and tumour necrosis factor alpha (TNF-alpha)), reactive oxygen species, static mechanical load and reduced extracellular pH. We will also study the effects of anabolic growth factors such as insulin-like growth factor I (IGF-I), transforming growth factor beta (TGF-beta), connective tissue growth factor (CTGF) and a panel of carefully selected nutrient derived actives known for their putative anti-inflammatory properties in the cartilage explant system. The proteomic and gene expression data extracted using this approach will help identify biomarkers with the capacity to discriminate between control and stimulated explants. We will develop the most appropriate model to discriminate between samples and determine at which stage of the protocol the data mining process is better able to identify individual biomarkers for the domain at hand, using data from proteomic and mass spectroscopy experiments and differentially expressed genes from microarray studies. Additionally a bioinformatics-intensive stage will focus on the characterization of sequence fragments to identify which proteins the peptide fragments originate from, identify potential role(s) for the secreted proteins present in the samples and, finally, infer the biological functions and interaction networks of the proteins.