Abstract
A computational platform, Boolean network explorer (BoNE), has recently been developed to infuse AI-enhanced precision into drug discovery; it enables invariant Boolean Implication Networks of disease maps for prioritizing high-value targets. Here we used BoNE to query an Inflammatory Bowel Disease (IBD)-map and prioritize a therapeutic strategy that involves dual agonism of two nuclear receptors, PPARα/γ. Balanced agonism of PPARα/γ was predicted to modulate macrophage processes, ameliorate colitis, ‘reset’ the gene expression network from disease to health. Predictions were validated using a balanced and potent PPARα/γ-dual-agonist (PAR5359) in Citrobacter rodentium- and DSS-induced murine colitis models. Using inhibitors and agonists, we show that balanced-dual agonism promotes bacterial clearance efficiently than individual agonists, both in vivo and in vitro. PPARα is required and sufficient to induce the pro-inflammatory cytokines and cellular ROS, which are essential for bacterial clearance and immunity, whereas PPARγ-agonism blunts these responses, delays microbial clearance; balanced dual agonism achieved controlled inflammation while protecting the gut barrier and ‘reversal’ of the transcriptomic network. Furthermore, dual agonism reversed the defective bacterial clearance observed in PBMCs derived from IBD patients. These findings not only deliver a macrophage modulator for use as barrier-protective therapy in IBD, but also highlight the potential of BoNE to rationalize combination therapy.
Highlights
A computational platform, Boolean network explorer (BoNE), has recently been developed to infuse AI-enhanced precision into drug discovery; it enables invariant Boolean Implication Networks of disease maps for prioritizing high-value targets
Defects in the resolution of intestinal inflammation have been attributed to altered monocyte–macrophage processes in Inflammatory Bowel Disease (IBD), macrophage modulators are yet to emerge as treatment modalities in IBD8
We recently developed and validated an AI-guided drug discovery pipeline that uses large transcriptomic datasets to build a Boolean network of gene clusters[9] (Fig. 1; Step 0); this network differs from other computational methods (e.g., Bayesian and Differential Expression Analyses) because gene clusters here are interconnected by directed edges that represent Boolean implication relationships (BIRs) that invariably hold true in every dataset within the cohort
Summary
A computational platform, Boolean network explorer (BoNE), has recently been developed to infuse AI-enhanced precision into drug discovery; it enables invariant Boolean Implication Networks of disease maps for prioritizing high-value targets. Balanced agonism of PPARα/γ was predicted to modulate macrophage processes, ameliorate colitis, ‘reset’ the gene expression network from disease to health. Dual agonism reversed the defective bacterial clearance observed in PBMCs derived from IBD patients These findings deliver a macrophage modulator for use as barrierprotective therapy in IBD, and highlight the potential of BoNE to rationalize combination therapy. Gene-clusters that maintain the integrity of the mucosal barrier emerged as the genes that are invariably downregulated in IBD, whose pharmacologic augmentation/induction was predicted to ‘reset’ the network These insights were exploited to prioritize one target, choose appropriate pre-clinical murine models for target validation, and design patient-derived organoid models (Fig. 1; Step 0)[9]. We demonstrate the accuracy and predictive power of this networkrationalized approach and reveal the efficacy of balanced-dual agonists of PPARα/γ in two pre-clinical murine models (Fig. 1; Step 3) and in patient-derived PBMCs (Fig. 1; Step 4)
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