Understanding gene expression in coronary artery disease through global profiling, network analysis and independent validation of key candidate genes

Journal of genetics

Jiny Nair¹  Prathima Arvind¹  Srikarthika Jambunathan¹  Shanker Jayashree1¹  Vijay V. Kakkar¹ 

[1] Mary and Garry Weston Functional Genomics Unit, Thrombosis Research Institute, Bengaluru 560 099, India$$

关键词: coronary artery disease;    gene expression;    microarray;    candidate genes;    biological network;    Asian Indians.;   

DOI  :  

学科分类：生物科学（综合）

来源: Indian Academy of Sciences

【 摘 要 】

Molecular mechanism underlying the patho-physiology of coronary artery disease (CAD) is complex. We used global expression profiling combined with analysis of biological network to dissect out potential genes and pathways associated with CAD in a representative case–control Asian Indian cohort. We initially performed blood transcriptomics profiling in 20 subjects, including 10 CAD patients and 10 healthy controls on the Agilent microarray platform. Data was analysed with Gene Spring Gx12.5, followed by network analysis using David v 6.7 and Reactome databases. The most significant differentially expressed genes from microarray were independently validated by real time PCR in 97 cases and 97 controls. A total of 190 gene transcripts showed significant differential expression (fold change > 2, P < 0.05) between the cases and the controls of which 142 genes were upregulated and 48 genes were downregulated. Genes associated with inflammation, immune response, cell regula- tion, proliferation and apoptotic pathways were enriched, while inflammatory and immune response genes were displayed as hubs in the network, having greater number of interactions with the neighbouring genes. Expression of 𝐸𝐺𝑅1/2/3, 𝐼𝐿8, 𝐶𝑋𝐶𝐿1, 𝑃𝑇𝐺𝑆2, 𝐶𝐷69, 𝐼𝐹𝑁𝐺, 𝐹𝐴𝑆𝐿𝐺, 𝐶𝐶𝐿4, 𝐶𝐷𝐶42, 𝐷𝐷𝑋58, 𝑁𝐹𝐾𝐵𝐼𝐷 and 𝑁𝑅4𝐴2 genes were independently validated; 𝐸𝐺𝑅1/2/3 and 𝐼𝐿8 showed >8-fold higher expression in cases relative to the controls implying their important role in CAD. In conclusion, global gene expression profiling combined with network analysis can help in identifying key genes and pathways for CAD.

【 授权许可】

Unknown