Pathway analysis of coronary atherosclerosis

Abstract

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.

Transcriptional Profiling of Foam Cells Reveals Induction of Guanylate‐Binding Proteins Following Western Diet Acceleration of Atherosclerosis in the Absence of Global Changes in Inflammation

“…Changes in genes in the antigen processing and presentation pathway could be related to the increased uptake of modified lipoproteins by foam cells of mice under WD. The reasons for the over‐representation of genes in the ubiquitin‐mediated proteolysis pathway are not clear, though it was reported that protein ubiquitination was increased in atherosclerotic plaques, and ubiquitin‐mediated proteolysis was the most over‐represented pathway in plaques of diabetic patients 27, 28. Whereas these two pathways were the only ones affected in the 2‐week WD group, the higher number of genes differently expressed in the 14‐week WD group was reflected in 11 additional pathways significantly affected in this group (Figure 4C).…”

“…However, the absence of immune and inflammatory mediators was still evident, and, accordingly, pathways related to the inflammatory and immune responses were not significantly affected (data not shown). Next, we focused the analysis on expression of transcripts coding for major cytokines and chemokines that had been related to progression of atherosclerosis or development of more‐severe plaque phenotypes 16, 28, 29, 30, 31. As seen in Figure 5A, in both WD groups, the levels of most transcripts coding for interleukins or for CC or CXC cytokines were very close or within ≈1‐fold change of the levels observed in the CHOW group.…”

Genome-Wide Expression Studies of Atherosclerosis

“…To address this, comparison statistics (eg, t test, 19,21,13,15 ANOVA, 17 Wilcoxon ANOVA 19 ) need to be used to assign a confidence level to the differential expression. These statistics require replicates and use the variability within the replicates to assign a probability value that indicates the probability of incorrectly classifying a gene as differentially regulated.…”

“…At present, however, there is no consensus about the best method to correct for multiple testing in microarray experiments. To this end, 5 recent studies have used permutation-based methods: significance analysis of microarrays 37 (SAM), 13,21 custom-made analysis algorithms including permutations to study multiple sets of time-course microarray data, 9,13 or by comparison with randomized data sets to quantify false-positive frequency in the data sets. 26 The statistical techniques discussed above aim to identify genes that are differentially expressed on a gene-by-gene basis.…”

“…In 7 studies, 12,13,18,20 -22,28 differentially expressed genes were categorized based on GO terms. However, to identify genes that warrant further study, GO-based enrichment procedures are more appropriate to identify GO terms that are overrepresented in a set of differentially expressed genes (eg, in King et al 21 ). In these enrichment procedures several statistical methods 41 can be used to calculate whether more genes from a particular pathway or classification are differentially expressed as would be expected by chance (expressed as probability value, z-score, or odds ratio 41 ).…”