Signature patterns of gene expression in mouse atherosclerosis and their correlation to human coronary disease

Abstract

The propensity for developing atherosclerosis is dependent on underlying genetic risk and varies as a function of age and exposure to environmental risk factors. Employing three mouse models with different disease susceptibility, two diets, and a longitudinal experimental design, it was possible to manipulate each of these factors to focus analysis on genes most likely to have a specific disease-related function. To identify differences in longitudinal gene expression patterns of atherosclerosis, we have developed and employed a statistical algorithm that relies on generalized regression and permutation analysis. Comprehensive annotation of the array with ontology and pathway terms has allowed rigorous identification of molecular and biological processes that underlie disease pathophysiology. The repertoire of atherosclerosis-related immunomodulatory genes has been extended, and additional fundamental pathways have been identified. This highly disease-specific group of mouse genes was combined with an extensive human coronary artery data set to identify a shared group of genes differentially regulated among atherosclerotic tissues from different species and different vascular beds. A small core subset of these differentially regulated genes was sufficient to accurately classify various stages of the disease in mouse. The same gene subset was also found to accurately classify human coronary lesion severity. In addition, this classifier gene set was able to distinguish with high accuracy atherectomy specimens from native coronary artery disease vs. those collected from in-stent restenosis lesions, thus identifying molecular differences between these two processes. These studies significantly focus efforts aimed at identifying central gene regulatory pathways that mediate atherosclerotic disease, and the identification of classification gene sets offers unique insights into potential diagnostic and therapeutic strategies in atherosclerotic 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

“…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.…”

“…From a molecular point of view, gene expression patterns in mouse aortas that defined different stages of atherosclerosis development also correlated with severity of human coronary lesions 16. Like in humans, atherosclerosis development in apoE −/− mice is driven by hypercholesterolemia, and plasma cholesterol levels and the extent of lesion development are directly related to the cholesterol content in their diet 17.…”

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.…”

Genomic Analysis of Circulating Cells: A Window Into Atherosclerosis

“…Carefully designed studies have revealed genes capable of identifying different stages of atherosclerotic disease in human aortas, the SNPs for which may be useful for genomic classification of patients with clinical implications (Seo et al 2004). Extending this type of approach, combined results from microarray experiments using mouse atherosclerosis models and human coronary gene expression datasets have also identified a shared group of genes for classifying disease stage (Tabibiazar et al 2005). These classifier genes were able to distinguish between native coronary artery disease and in-stent restenosis.…”