Integration of gene expression data with network-based analysis to identify signaling and metabolic pathways regulated during the development of osteoarthritis.
Integration of gene expression data with network-based analysis to identify signaling and metabolic pathways regulated during the development of osteoarthritis.
复制标题
DOI：
10.1016/j.gene.2014.03.022
复制
发表时间：
2014-05-25
期刊：
影响因子：
3.5
通讯作者：
Loeser RF
中科院分区：
文献类型：
--
作者：
Olex AL;Turkett WH;Fetrow JS;Loeser RF
骨关节炎（OA）的特征是关节组织的重塑和降解。微阵列研究已经使人们更好地了解了受OA等疾病影响的组织中发生的分子变化;然而，这种分析仅限于鉴定一系列转录表达改变的基因，通常是在疾病进展期间的单个时间点。虽然这些列表已经确定了许多在疾病过程中发生改变的新基因，但它们无法确定基因和基因产物之间的干扰关系。在这项工作中，我们已经整合了一个时间过程的基因表达数据集与网络分析，以获得更好的系统水平的理解，在小鼠模型的OA的发展过程中发生的早期事件。在检查的一个或多个时间点（诱导OA后2、4、8和16周）富集的子网络包含来自几种途径的基因，这些途径被认为对OA过程很重要，包括细胞外基质-受体相互作用和粘着斑途径以及Wnt、Hedgehog和TGF-β信号传导途径。子网络内的基因在第2周和第4周的时间点最活跃，并且包括先前在OA过程中未研究的基因。一个独特的途径，核黄素代谢，在4周的时间点活跃。这些结果表明，将网络型分析沿着时间序列微阵列数据将导致我们在系统水平上对复杂疾病（如OA）的理解取得进展，并可能为疾病发病机制中涉及的途径和过程提供新的见解。
Osteoarthritis (OA) is characterized by remodeling and degradation of joint tissues. Microarray studies have led to a better understanding of the molecular changes that occur in tissues affected by conditions such as OA; however, such analyses are limited to the identification of a list of genes with altered transcript expression, usually at a single time point during disease progression. While these lists have identified many novel genes that are altered during the disease process, they are unable to identify perturbed relationships between genes and gene products. In this work, we have integrated a time course gene expression data set with network analysis to gain a better systems level understanding of the early events that occur during the development of OA in a mouse model. The subnetworks that were enriched at one or more of the time points examined (2, 4, 8, and 16 weeks after induction of OA) contained genes from several pathways proposed to be important to the OA process, including the extracellular matrix-receptor interaction and the focal adhesion pathways and the Wnt, Hedgehog and TGF-β signaling pathways. The genes within the subnetworks were most active at the 2 and 4 week time points and included genes not previously studied in the OA process. A unique pathway, riboflavin metabolism, was active at the 4 week time point. These results suggest that the incorporation of network-type analyses along with time series microarray data will lead to advancements in our understanding of complex diseases such as OA at a systems level, and may provide novel insights into the pathways and processes involved in disease pathogenesis.
登录
查看更多内容
影响因子：
--
作者：
Lawrence, Reva C.;Felson, David T.;Wolfe, Frederick
通讯作者：
Wolfe, Frederick
影响因子：
--
作者：
Loeser, Richard F.;Olex, Amy L.;McNulty, Margaret A.;Carlson, Cathy S.;Callahan, Michael F.;Ferguson, Cristin M.;Chou, Jeff;Leng, Xiaoyan;Fetrow, Jacquelyn S.
通讯作者：
Fetrow, Jacquelyn S.
影响因子：
7
作者：
Loeser, R. F.
通讯作者：
Loeser, R. F.
DOI：
10.1093/bioinformatics/btq675
发表时间：
2011-02-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Smoot ME;Ono K;Ruscheinski J;Wang PL;Ideker T
通讯作者：
Ideker T
影响因子：
9.9
作者：
通讯作者：
--