Machine Learning Identifies Distinct Treg-Mediated Remodeling in HFpEF Hearts Treated with Neonatal Mesenchymal Stem Cells and Their Secretome
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Abstract Background Heart 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. Objectives However, the effect of these therapies in HFpEF, and the underlying molecular mechanisms and causal pathways remain poorly understood. Methods HFpEF 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. Results Both 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 (T reg ) biology and immunomodulatory signaling pathways, including FOXO signaling, NLRP3 inflammasome inhibition, and Tie2 activation. In vivo validation confirmed selective expansion of T regs following nMSC and SEC therapy. In vitro, nMSCs induced significantly greater T reg expansion compared to multiple adult stem cell types. Critically, chemical depletion of T regs abrogated the therapeutic effects of both treatments, establishing T regs as central mediators of diastolic function recovery in the HFpEF preclinical model. Conclusions nMSC and SEC therapies improve diastolic function in HFpEF through distinct remodeling mechanisms converging on T reg -mediated immune modulation. VIPcell supported identification of causal regulators, highlighting T reg -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, T reg -focused interventions.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Machine Learning Identifies Distinct Treg-Mediated Remodeling in HFpEF Hearts Treated with Neonatal Mesenchymal Stem Cells and Their Secretome
- Date Crossref
- 30/01/2026
- Éditeur
- openRxiv
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
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Lurie Children's Hospital pays non établi dans la noticeÉtablissement de santé
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The Wallace H. Coulter Department of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Emory University Wallace H. Coulter Department of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Lurie Children's Hospital, The Wallace H. Coulter Department of Biomedical Engineering et Wallace H. Coulter Department of Biomedical Engineering — Emory University, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.