Pathway analysis of coronary atherosclerosis
Pathway analysis of coronary atherosclerosis
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DOI：
10.1152/physiolgenomics.00101.2005
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发表时间：
2005-09-21
影响因子：
4.6
通讯作者：
Ashley, EA
中科院分区：
文献类型：
--
作者：
King, JY;Ferrara, R;Ashley, EA
大规模基因表达研究提供了对癌症等疾病过程中差异调节基因的重要见解。然而，这些研究对动脉粥样硬化等多系统、多细胞疾病的了解有限。解释基因相互作用、整合非转录调控基因并整合先验知识的系统生物学方法具有许多优势。我们使用从 22 名心脏移植患者的移植心脏中分离出的 51 个冠状动脉段，对冠状动脉粥样硬化进行了全面的基因水平评估。根据美国心脏协会指南对血管段进行组织学分级后，将分离的 RNA 杂交到定制的 22-K 寡核苷酸微阵列上，并进行微阵列的显着性分析和基因本体分析，以鉴定显着的基因表达谱。我们的研究表明，分化平滑肌细胞基因表达的丧失是动脉粥样硬化疾病进展的主要表达特征。此外，我们深入了解与糖尿病相关的严重冠状动脉疾病，报告糖尿病患者存在过多的免疫和炎症信号。我们提出了一种基于连接性的通路开发新方法，连接性由已发表文献的语言解析确定，排名由网络中差异调节基因的重要性确定。在此过程中，我们确定了高度关联的“关系”基因，这些基因是治疗靶向和后续研究的有吸引力的候选基因。我们使用途径技术将动脉粥样硬化作为基因相互作用的集成网络来研究，它扩展了传统的微阵列分析方法，并强调了基于系统的方法分析复杂疾病的显着优势。
Large-scale gene expression studies provide significant insight into genes differentially regulated in disease processes such as cancer. However, these investigations offer limited understanding of multisystem, multicellular diseases such as atherosclerosis. A systems biology approach that accounts for gene interactions, incorporates nontranscriptionally regulated genes, and integrates prior knowledge offers many advantages. We performed a comprehensive gene level assessment of coronary atherosclerosis using 51 coronary artery segments isolated from the explanted hearts of 22 cardiac transplant patients. After histological grading of vascular segments according to American Heart Association guidelines, isolated RNA was hybridized onto a customized 22-K oligonucleotide microarray, and significance analysis of microarrays and gene ontology analyses were performed to identify significant gene expression profiles. Our studies revealed that loss of differentiated smooth muscle cell gene expression is the primary expression signature of disease progression in atherosclerosis. Furthermore, we provide insight into the severe form of coronary artery disease associated with diabetes, reporting an overabundance of immune and inflammatory signals in diabetics. We present a novel approach to pathway development based on connectivity, determined by language parsing of the published literature, and ranking, determined by the significance of differentially regulated genes in the network. In doing this, we identify highly connected "nexus" genes that are attractive candidates for therapeutic targeting and followup studies. Our use of pathway techniques to study atherosclerosis as an integrated network of gene interactions expands on traditional microarray analysis methods and emphasizes the significant advantages of a systems-based approach to analyzing complex disease.