Research Article: Application of an Integrative Computational Framework in Trancriptomic Data of Atherosclerotic Mice Suggests Numerous Molecular Players

Date Published: November 6, 2012

Publisher: Hindawi Publishing Corporation

Author(s): Olga Papadodima, Allan Sirsjö, Fragiskos N. Kolisis, Aristotelis Chatziioannou.


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.

Partial Text

Atherosclerosis is the leading pathological contributor to cardiovascular morbidity and mortality worldwide, characterized by the progressive accumulation of lipid and fibrous depositions in the vessel wall of medium-sized and large arteries. Although it has traditionally been viewed as simple deposition of lipids within the vessel wall, it is now assumed that atherosclerosis is a multifactorial disease that involves several genes and proteins, activated during its genesis, progress, and phenotypic manifestation. During atherogenesis, a complex endothelial activation and dysfunction induced by elevated and modified low-density lipoproteins and many other factors leads to a compensatory inflammatory response [1]. Current evidence supports a central role for inflammation, in all phases of the atherosclerotic process. Substantial biological data implicate inflammatory pathways in early atherogenesis, in the progression of lesions, and finally in the thrombotic complications of this disease [2].

In this study, we presented a detailed, multi-stage, translational bioinformatic analysis of ApoE knockout mice, exploiting different methods in order to identify critical altered molecular mechanisms and important central players. Our approach was to apply a generic computational framework, which exploits rigorous statistical or computational measures at every analytical step, for the efficient systems level interpretation of the results of ApoE dataset. The workflow proposed here integrates various software products, in a unified translational pipeline, able to cope with versatile, high-volume investigation tasks, and at the same time provide a reliable systemic interpretation for the biological mechanism studied. In this way, a powerful translational backbone is set, which connects the wet-lab part with the theoretical knowledge for the biological problem interrogated, as rescued in molecular databases, controlled ontological vocabularies or the literature. The workflow presented in this study, currently in the phase of implementation as regards to its software components integration, represents an efficient and highly innovative effort, either in terms of speed of analytical performance, as well as real biological value of the results. This is so because it provides results which are qualified from a composite framework that combines ideally both individual and group quality measures, together with an insightful comprehension of the underlying topological networks, actively involved in the mechanism studied. The correlation of the results of the molecular analysis with literature-derived associations manages to highlight and propose promising, novel candidates that have not been studied in the context of the given pathology. They could thus represent ideal targets for further biological experimentation. Maximizing the total information gain encompassing all analytical steps of the proposed workflow represents a critical parameter regarding the implementation of the web application. However as the derivation of automated statistical thresholds for such high-volume data processing in an unsupervised manner both in terms of performance and computational speed is a very challenging task, this still remains an open issue for extensive research work and testing, representing an important point for future work.




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