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

对关节软骨炎症外植体模型的蛋白质组学和基因芯片微阵列数据进行生物信息学和数据挖掘分析

• 批准号: BB/G018030/1
• 金额: $ 9.48万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FG018030%2F1
• 关键词: Bioinformatic; data; mining; analysis; proteomic

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.

关节软骨降解是人类和伴侣动物中骨关节炎（OA）关节功能障碍和残疾的主要原因，并导致长期发病，疼痛和生活质量受损。 OA是一种复杂的多因素疾病，我们正在研究疾病的早期阶段，以及饮食/功能性活性的可能作用，以支持联合健康。作为这项工作的一部分，我们最近开发并利用了马关节软骨的外植体培养模型，以使用蛋白质组学和跨物种肽匹配技术鉴定分泌的生物标志物。我们还使用人类Affymetrix Genechip微阵列来研究软骨中的基因表达，这是通过从马关节软骨细胞到这个人类微阵列平台的跨杂交信使RNA研究。这项工作是与沃尔瑟姆（Waltham）合作的BBSRC案例学生和诺丁汉大学兽医医学与科学学院合作的结果，该学院于2006年10月开始，刚刚进入第三年（BBSRC Grant BS/S/M/M/2006/13141）。这种方法是富有成果的，正在生成大量的蛋白质组学和基因表达数据集，这些数据集需要通过生物信息学和数据挖掘技术进行详细和比较分析。该案例项目将采用数据挖掘和网络生物学方法来识别早期OA开发的潜在生物标志物，并期望这项工作还可以洞悉软骨细胞对炎症性扰动的反应。我们的目的是通过比较控制软骨样品与暴露于各种疾病相关的分解代谢刺激之间的差异来实现这一目标。我们建议使用的分解代谢刺激包括促炎细胞因子（即白介素1β（IL-1BETA），白介素6（IL-6）和肿瘤坏死因子α（TNF-alpha），反应性氧气，反应性氧气，静态的机械载荷和减少外胞外pH。我们还将研究合成代谢生长因子的影响，例如胰岛素样生长因子I（IGF-I），转化生长因子β（TGF-β），结缔组织生长因子（CTGF）和一批精心选择的营养衍生活的活性剂，以其推定的抗抑制性特性而闻名。使用这种方法提取的蛋白质组学和基因表达数据将有助于鉴定具有区分控制和刺激外植体能力的生物标志物。我们将开发最合适的模型，以区分样品并确定在协议的哪个阶段数据挖掘过程可以更好地识别手头域的单个生物标志物，并使用来自蛋白质组学和质谱实验的数据以及来自微阵列研究的差异表达的基因。另外，生物信息学密集型阶段将集中在序列片段的表征上，以识别哪种蛋白质起源于样品中存在的分泌蛋白的潜在作用，最后推断蛋白质的生物学功能和相互作用网络。