Application of an integrative computational framework in trancriptomic data of atherosclerotic mice suggests numerous molecular players

Abstract

Atherosclerosis is a multifactorial disease involving a lot of genes and proteins recruited throughout its manifestation. The present study aims to exploit bioinformatic tools in order to analyze microarray data of atherosclerotic aortic lesions of ApoE knockout mice, a model widely used in atherosclerosis research. In particular, a dynamic analysis was performed among young and aged animals, resulting in a list of 852 significantly altered genes. Pathway analysis indicated alterations in critical cellular processes related to cell communication and signal transduction, immune response, lipid transport, and metabolism. Cluster analysis partitioned the significantly differentiated genes in three major clusters of similar expression profile. Promoter analysis applied to functional related groups of the same cluster revealed shared putative cis-elements potentially contributing to a common regulatory mechanism. Finally, by reverse engineering the functional relevance of differentially expressed genes with specific cellular pathways, putative genes acting as hubs, were identified, linking functionally disparate cellular processes in the context of traditional molecular description.

Figure 1

Schematic representation of the proposed workflow. Arrows depict the various analysis steps. The bioinformatic tools and databases used for the implementation of each step are also shown.

Figure 2

Volcano plots of the gene list as yielded by ANOVA. Each panel represents filtered and normalized data from each experimental condition (3, 6, and 78 weeks old mice). The horizontal axes depict the fold change ratio between ApoE deficient and wild type mice, for each age in log2 scale, while the vertical axes represent statistical significance by depicting the −log10 (FDR).

Figure 3

Hierarchical clustering of the 1033 statistically significant differentiated probesets. Fold changes between the gene expressions in ApoE knockout as compared to age-matched wild type mice are grouped in three major clusters.