Predicting Functional Responses of Progenitor Cell Exosome Potential with Computational Modeling

• Type of Material: Article
• Authors: David Trac, Georgia Institute of Technology; Jessica R. Hoffman, Emory University; Sruti Bheri, Georgia Institute of Technology; Joshua Maxwell, Emory University; Manu Platt, Emory University; Michael Davis, Emory University
• Language: English
• Date: 2019-11-01
• Publisher: Wiley Open Access: Creative Commons Attribution Non-Commercial No Derivatives
• Publication Version: Final Published Version
• Copyright Statement: © 2019 The Authors.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/sctm.19-0059
• Title of Journal or Parent Work: Stem Cells Translational Medicine
• ISSN: 2157-6564
• Volume: 8
• Issue: 11
• Start Page: 1212
• End Page: 1221
• Grant/Funding Information: This work was supported by funds from Alliance Data, the Betkowski Family Fund, and HL145644 awarded to M.E.D., and by the American Heart Association Predoctoral Fellowship (17PRE33460129) awarded to D.T.; The Yerkes NHP Genomics Core is supported in part by ORIP/OD P51OD011132.
• Keywords: MicroRNAs; FATE; Life Sciences & Biomedicine; SYSTEMS; HEART-DISEASE; Heart failure; Cell & Tissue Engineering; STEM-CELLS; Cell Biology; Least-squares analysis; Statistical regression; ACTIVATION; Stem cells; Science & Technology; Computational biology
• Research Categories: Biology, Cell

Abstract

Stem Cells Translational Medicine published by Wiley Periodicals, Inc. on behalf of AlphaMed Press 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.