AI-based Predictive Signaling Pathway Profiling in Cardiac Fibrosis Suggests a Novel Combinatorial Treatment Strategy

Abstract

BackgroundCardiovascular disease (CVD) remains the leading cause of global mortality, with myocardial fibrosis characterized by excessive extracellular matrix (ECM) deposition representing a common endpoint associated with progressive cardiac dysfunction. While studies in animal models of heart disease suggest that preventing or reducing fibrosis can antagonize negative ventricular remodeling, current therapeutic strategies remain clinically limited and ineffective, in part due to an incomplete understanding of the multifactorial nature of the fibrotic process. Methods and ResultsWe employed artificial intelligence (AI) trained from 6,528 cardiac fibrosis publications over the past decade, which suggested 10 nodal signaling pathways underlying the fibrotic process. Single-cell RNA sequencing was used to quantify pathway activities across mouse models and clinical samples encompassing acute and chronic cardiac injury. Dynamic enrichment analysis revealed 2 critical pathways involved in acute myocardial infarction (MI) injury driven temporally-regulated fibrosis whereby Janus kinase - Signal transducer and activator of transcription (JAK-STAT) signaling peaked early (day 7) while transforming growth factor-{beta} (TGF-{beta}) pathway activity peaked mid-phase (day 14). With more chronically-driven fibrosis, JAK-STAT signaling again emerged, which this time was persistently active during chronic transverse aortic constriction (TAC) injury. Experimentally, single-cell analysis identified a pathogenic myofibroblast subpopulation characterized by high JAK-STAT signaling and ECM secretion capacity. Fibroblast-specific Jak1/2 gene-deleted mice significantly reduced TAC-induced fibrosis by blocking myofibroblast formation from this subpopulation, although these same mice failed to show attenuated fibrosis following MI injury, suggesting a pathway that would be ideal to therapeutically target for chronic fibrosis without affecting necessary acute scar formation. Indeed, based on the predictive AI driven algorithm, a temporal inhibitory strategy was generated for JAK-STAT and TGF-{beta} that permitted compensatory scar formation while more effectively preventing progressive fibrosis and worsened cardiac function versus either singular pathway or chronic antagonism. ConclusionHere we employed the wealth of past signaling pathway data underlying cardiac fibrosis to train an AI model, which suggested an optimized approach of inhibiting 2 nodal signaling pathways but with differential timing as a more effective therapeutic strategy that reduces pathologic cardiac fibrosis without negatively impacting compensatory fibrotic activity with acute MI injury. The therapeutic approach involves altering the timing of JAK-STAT signaling blockade with ruxolitinib (RUX) in combination with delayed and temporary TGF-{beta} signaling inhibition post MI injury with pirfenidone (PFD).