Decoding microbiome-dependent metabolite signaling and immune modulation in the kidney - The kidney plays a central role in clearing chemically diverse small molecules from the body, making it a key site of exposure to both endogenous and microbiome-derived metabolites. These exposures can shape disease progression, particularly in chronic kidney disease, but the molecular logic by which kidney cells sense and respond to this chemical diversity remains largely unknown. In this talk, I will present our lab’s integrative approach to decoding metabolite-driven signaling in the kidney using tools from metabolomics, single-cell biology, and machine learning. We apply advanced models—including transformers, graph neural networks, and autoencoders—to predict ligand-protein interactions and infer cell-type specific responses to metabolite exposure. By integrating these predictions with spatial transcriptomics and network-based modeling, we are building a systems-level view of how metabolic signals influence kidney function and pathology. This research opens new paths for identifying therapeutic targets and designing precision interventions in kidney disease.