In-silico cardiac aging regulatory model including microRNA post-transcriptional regulation.

In most developed countries, cardiovascular diseases are among the top causes of death and their development has been shown closely related to aging. In this context, because of their ability to pervasively influence gene networks, miRs have been found as possible key players in the development of cardiac pathologies, suggesting their potential role as therapeutic targets or diagnostic markers. Based on these assumptions, we hereby present a computational study that applies data fusion techniques coupled with network analysis theory to identify a regulatory model able to represent the relationship between key genes and miRs involved in cardiac senescence processes. The proposed model has been validated through an extensive literature analysis, which confirmed that 94% of the identified genes and miRs are related with cardiac senescence. Furthermore, two relevant genes of the model have been also validated by Western blot experiments on heart samples from young and old mice, confirming in vitro their ectopic expression in aged hearts. The pure computationally inferred model presented in the paper is therefore a good candidate to represent the relationship between key genes and miRs involved in cardiac senescence processes, and represents a reliable selection of genes and miRs for further studies, in order to elucidate and better detail their involvement in cardiac aging.