Caldwell, J. Z. K. | Search bioRxiv

bioRxiv2025

Cell type-specific master metabolic regulators of Alzheimer's disease

Alzheimers disease (AD) exhibits metabolic heterogeneity; yet, the consequences on metabolic dynamics in a cell-type-specific manner and the underlying metabolite-sensor network basis remain unclear. Here, we show that neurons exhibit a striking decrease in energy and lipid-related metabolic activity, contrasted by an increase in microglial metabolism associated with neuroinflammation. To identify cell-type specific master metabolic regulators of AD underlying the metabolic alterations in AD, we introduce scFUMES (single cell FUnctional MEtabolite-Sensor), an algorithm integrating single-cell RNA sequencing, interactomics, genomics, transcriptomics, and metabolomics from human brain biobanks. Applied to two AD-vulnerable regions (middle temporal gyrus and dorsolateral prefrontal cortex), scFUMES uncovers hundreds of AD-associated regulators, with neurons and microglia showing the most interactions. Particularly, scFUMES pinpoints genetics-informed master metabolic regulators across AD severity, sex and APOE genotype (e.g., PPARD-glycerol in microglia). Experimental testing reveals that two interaction pairs predicted by scFUMES, neuronal palmitic acid bound fatty acid binding protein 3 and gut metabolite indole-3-propionic acid binding to kynurenine aminotransferase 1, both lower pathological tau species in AD. Collectively, scFUMES systematically maps AD master metabolic regulators, offering insights into cellular metabolic heterogeneity and therapeutic strategies for AD and other AD-related dementia if broadly applied.

systems biology↗

bioRxiv2022

Comprehensive characterization of multi-omic landscapes between gut-microbiota metabolites and the G-protein-coupled receptors in Alzheimer's disease

Accumulating evidence suggests that gut-microbiota metabolites contribute to human disease pathophysiology, yet the host receptors that sense these metabolites are largely unknown. Here, we developed a systems pharmacogenomics framework that integrates machine learning (ML), AlphaFold2-derived structural pharmacology, and multi-omics to identify disease-relevant metabolites derived from gut-microbiota with non-olfactory G-protein-coupled receptors (GPCRome). Specifically, we evaluated 1.68 million metabolite-protein pairs connecting 408 human GPCRs and 516 gut metabolites using an Extra Trees algorithm-improved structural pharmacology strategy. Using genetics-derived Mendelian randomization and multi-omics (including transcriptomic and proteomic) analyses, we identified likely causal GPCR targets (C3AR, FPR1, GALR1 and TAS2R60) in Alzheimers disease (AD). Using three-dimensional structural fingerprint analysis of the metabolite-GPCR complexome, we identified over 60% of the allosteric pockets of orphan GPCR models for gut metabolites in the GPCRome, including AD-related orphan GPCRs (GPR27, GPR34, and GPR84). We additionally identified the potential targets (e.g., C3AR) of two AD-related metabolites (3-hydroxybutyric acid and Indole-3-pyruvic acid) and four metabolites from AD-related bacterium Eubacterium rectale, and also showed that tridecylic acid is a candidate ligand for orphan GPR84 in AD. In summary, this study presents a systems pharmacogenomics approach that serves to uncover the GPCR molecular targets of gut microbiota in AD and likely many other human diseases if broadly applied.

systems biology↗