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1 Division of Cardiovascular Medicine, Stanford University, Stanford, California, USA
2 Agilent Laboratories, Palo Alto, California, USA
* To whom correspondence should be addressed. E-mail: tomq1{at}stanford.edu.
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 multi-system, multi-cellular diseases such as atherosclerosis. A systems biology approach that accounts for gene interactions, incorporates non-transcriptionally 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 histology grading according to American Heart Association guidelines, isolated RNA was hybridized onto a customized 22K oligonucleotide microarray and Significance Analysis of Microarrays and Gene Ontology analyses performed to identify significant gene expression profiles. Our studies reveal that loss of differentiated smooth muscle cell gene expression is the primary expression signature of disease progression in atherosclerosis. Further, we provide insight into the severe form of coronary artery disease associated with diabetes, reporting overabundance of immune and inflammatory signals in diabetics. We present a novel approach to pathway development based upon 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, which are attractive candidates for therapeutic targeting and follow-up 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.
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