Predicting Functional Responses of Progenitor Cell Exosome Potential with Computational Modeling

Abstract

Abstract Congenital heart disease can lead to severe right ventricular heart failure (RVHF). We have shown that aggregated c‐kit+ progenitor cells (CPCs) can improve RVHF repair, likely due to exosome‐mediated effects. Here, we demonstrate that miRNA content from monolayer (2D) and aggregated (3D) CPC exosomes can be related to in vitro angiogenesis and antifibrosis responses using partial least squares regression (PLSR). PLSR reduced the dimensionality of the data set to the top 40 miRNAs with the highest weighted coefficients for the in vitro biological responses. Target pathway analysis of these top 40 miRNAs demonstrated significant fit to cardiac angiogenesis and fibrosis pathways. Although the model was trained on in vitro data, we demonstrate that the model can predict angiogenesis and fibrosis responses to exosome treatment in vivo with a strong correlation with published in vivo responses. These studies demonstrate that PLSR modeling of exosome miRNA content has the potential to inform preclinical trials and predict new promising CPC therapies. Stem Cells Translational Medicine 2019;8:1212–1221

Keywords

• Stem cells
• Computational biology
• Heart failure
• MicroRNAs
• Statistical regression
• Least‐squares analysis