Exosomal MicroRNA Clusters Are Important for the Therapeutic Effect of Cardiac Progenitor Cells

Abstract

Although current evidence indicates that cardiac progenitor cells (CPCs) may improve cardiac repair after ischemic injury, the exact mechanism for this cardioprotective effect to date remains undefined. In this issue of Circulation Research , Gray et al1 report on the use of systems biology to link the change in exosomal microRNAs (miRNA) to the beneficial effects of CPCs during ischemic injury in mice. Expression analysis showed that exosomes from hypoxic CPCs contain a higher level of a subset of miRNAs than exosomes coming from normoxic CPCs. To connect the relevance of the change in miRNA levels to the biological effect of exosomes, the authors performed a principle component analysis of the miRNA expression profiles in exosomes, which identified 4 unique miRNA clusters, which after further mathematical modeling seemed to correlate to a biological effect related to the therapeutic benefit. Instead of studying the biological function of a single miRNA, computational modeling can be applied to integrate multiple variants to gain more insight into the overall relationship and interaction between the different datasets. Gray et al1 show that especially for miRNA biology, where a miRNA can target up to hundreds of genes in a parallel, systems biology could turn out to be a suitable approach for predicting a biological outcome or therapeutic benefit. Article, see p 255 Cells communicate with each other via direct cell–cell contact and secretion of soluble factors. Soluble factors that are gaining an increasing amount of attention are extracellular membrane vesicles. The ability of vesicles to transport different molecules, such as proteins, peptides, mRNA, and microRNAs, from one cell to the other and their general body-wide distribution make them attractive candidates for horizontal information transfer between cells.2 Although the release of vesicles to influence other cells seems to be straightforward, multiple mechanisms, size distributions, …