Machine Learning Identifies Distinct Treg-Mediated Remodeling in HFpEF Hearts Treated with Neonatal Mesenchymal Stem Cells and Their Secretome

Ge, Z.-D.; Han, J.; Takaesu, F.; Mishra, R.; Sharma, S.; Chen, L.; Fu, X.; Filipp, M.; Wai, C. M.; Shao, N.-Y.; Sinha, A.; Saha, P.; Puvvala, A.; Ventura, L.; Bileweska, A.; Stefenwicz, A.; Guru, S.; Gunasekaran, M.; Yang, E.; Dawn, B.; Yang, P.; Shah, S.; Thorp, E. B.; Davis, M.; Kaushal, S.

doi:10.64898/2026.01.29.702616

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bioRxiv · 10.64898/2026.01.29.702616

Machine Learning Identifies Distinct Treg-Mediated Remodeling in HFpEF Hearts Treated with Neonatal Mesenchymal Stem Cells and Their Secretome

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Abstract

BackgroundHeart failure with preserved ejection fraction (HFpEF) remains a major therapeutic challenge due to its complex pathophysiology and pronounced heterogeneity. Regenerative approaches using neonatal mesenchymal stromal cells (nMSCs) and their secretome (SEC) have shown promise in other heart failure contexts. ObjectivesHowever, the effect of these therapies in HFpEF, and the underlying molecular mechanisms and causal pathways remain poorly understood. MethodsHFpEF was established in two distinct murine models, followed by treatment with either nMSCs or SEC. Functional and histological endpoints were assessed. We developed a novel machine learning framework, VIPcell, which integrates data augmentation, Partial Least Squares (PLS) regression, and causal structure inference to identify genes causally linked to cardiac function using single-nucleus RNA sequencing (snRNA-seq) data. VIPcell was applied to heart tissues from treated HFpEF animals to uncover key regulators of cardiac remodeling. ResultsBoth nMSC and SEC therapies significantly improved diastolic function in two independent rodent HFpEF models. These improvements were associated with reduced inflammation, attenuated myocardial fibrosis, and improved exercise capacity. Intercellular communication analysis revealed widespread, system-level signaling in nMSC-treated hearts, compared to more localized endothelial-cardiomyocyte crosstalk in SEC-treated hearts. Causal inference via VIPcell suggested overlapping upstream regulators in both treatment groups, particularly genes involved in regulatory T cell (Treg) biology and immunomodulatory signaling pathways, including FOXO signaling, NLRP3 inflammasome inhibition, and Tie2 activation. In vivo validation confirmed selective expansion of Tregs following nMSC and SEC therapy. In vitro, nMSCs induced significantly greater Treg expansion compared to multiple adult stem cell types. Critically, chemical depletion of Tregs abrogated the therapeutic effects of both treatments, establishing Tregs as central mediators of diastolic function recovery in the HFpEF preclinical model. ConclusionsnMSC and SEC therapies improve diastolic function in HFpEF through distinct remodeling mechanisms converging on Treg-mediated immune modulation. VIPcell supported identification of causal regulators, highlighting Treg-related signaling as a key driver of myocardial recovery in HFpEF. These findings offer mechanistic insight into cellular therapies for HFpEF and support the development of targeted, Treg-focused interventions.

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Ge, Z.-D., Han, J., Takaesu, F., Mishra, R., Sharma, S., Chen, L., Fu, X., Filipp, M., Wai, C. M., Shao, N.-Y., Sinha, A., Saha, P., Puvvala, A., Ventura, L., Bileweska, A., Stefenwicz, A., Guru, S., Gunasekaran, M., Yang, E., Dawn, B., Yang, P., Shah, S., Thorp, E. B., Davis, M., Kaushal, S.. 2026-01-30. Machine Learning Identifies Distinct Treg-Mediated Remodeling in HFpEF Hearts Treated with Neonatal Mesenchymal Stem Cells and Their Secretome. https://doi.org/10.64898/2026.01.29.702616

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