Predicting Functional Responses of Progenitor Cell Exosome Potential with Computational Modeling. (6th August 2019)

• Record Type: Journal Article
• Authors: Trac, David; Hoffman, Jessica R.; Bheri, Sruti; Maxwell, Joshua T.; Platt, Manu O.; Davis, Michael E.
• Is Part Of: Stem cells translational medicine. Volume 8:Number 11(2019)
• Page Start: 1212
• Page End: 1221
• Publication Date: 2019-08-06
• Subjects: Stem cells -- Computational biology -- Heart failure -- MicroRNAs -- Statistical regression -- Least-squares analysis; Stem cells -- Periodicals; Regenerative medicine -- Periodicals; Periodicals; 616.0277405
• DOI: 10.1002/sctm.19-0059
• Languages: English
• ISSNs: 2157-6564

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 : Abstract : Partial least squares regression was used to relate miRNA content (signal) from monolayer (2D) and aggregated (3D) c-kit + progenitor cell exosomes (cues) to exosome-mediated in vitro angiogenesis and antifibrosis responses. The model predicts responses to exosome treatment in vivo with strong correlation to observed values. Partial least squares regression modeling may inform preclinical trials and predict new promising c-kit + progenitor cell therapies.