P72Inferring systems-level cardiac aging biomarkers through integromics network analysis

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Abstract

From the perspective of Systems Medicine, cardiac aging is re-addressed through large scale diverse omics investigations and more importantly through their integration. Nowadays, the micronome revolutionized our comprehension of the underlying molecular mechanisms and established its role as a player of utmost importance in cardiac development, hypertrophy and longevity. A recent study [1] elucidated that the altered expression of miR-34a during aging is highly correlated with the cardiac function decline. Also the study of [2] identified a set of 65 age-dependent miRs and miRs* in the mouse model. Despite the significance of these discoveries, heart aging is a highly complex process that cannot be featured through changes on individual molecular components but rather through the changes on integromics sub-networks. In addition, despite the boom experienced in recent years in the study of gene regulation by the action of miRNAs, the analysis of genome-wide interaction networks among miRNAs and their targets has lagged behind.

Motivated by the challenge to set a more realistic cardiac aging model, we examined the viewpoint that whole micronome-transcriptome-proteome interaction analysis is required to define age-related biomarkers and explore the potential consequences of miRNA (de)regulation as well as the cooperative/combinatorial targeting. To accomplish this, initially, we compiled a cohort of mRNA/miRNA cardiac tissue expression data from various mouse inbred strains, protein-protein and signaling pathway interactions, and miRNA-mRNA interactions. A multilevel network was constructed with two types of nodes (mRNA and miRNA) and three types of interactions (mRNA-mRNA, miRNA-mRNA and miRNA-miRNA), while the expression data served as means for weighting the final network so as to alleviate the identification of significantly altered modules (i.e. dense sub-networks with distinct functional role), via a module-detecting algorithm, due to aging factor.

Our analysis revises recent discoveries and provides a signature set of integromics modules that will be valuable for future biomarker studies in humans. An indicative example module that offers novel hypotheses is the module constructed around miR-34a including HRAS1, EOMES, PIWIL2 and ZDHHC18 as interactors. Finally, our biomarkers pinpoint the involvement of miRs* in heart longevity as well as reveal many aspects of miRNA synergism.

[1] Boon RA, et al. Nature. 2013;495:107-10.

[2] Zhang X, et al. PLoS One. 2012;7:e34688.

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