The prevalence of liver disease is steadily increasing, coupled with the limited availability of therapeutic treatments. Recent literature points to metabolic reprogramming as a key feature of liver failure. Hence, we sought to uncover the metabolic pathways and mechanisms associated with liver disease and acute liver failure. We generated patient-specific genome scale metabolic models by integrating RNA-seq data from patient liver samples with a generalized human metabolic model. Flux balance analysis simulations showed a distinct separation of non-alcohol associated and alcohol-associated disease states. Our analysis suggests that the alcohol associated liver has an increased flux through nucleotide and glycerophospholipid metabolic pathways. By contrast, non-alcohol associated liver has an increased flux through fatty acid oxidation, the carnitine shuttle, and bile acid recycling pathways. Importantly, there was significant variation of metabolic fluxes between patients within the same clinical category of disease stage, pointing to the necessity and opportunity for personalized medicine in treating liver disease. We conclude that the metabolic reprogramming occurring in alcohol-associated liver disease is likely distinct from the adaptations in non-alcohol associated liver disease, potentially requiring alternative therapeutic approaches.
Manchel, Alexandra; Hoek, Jan B.; Bataller, Ramon; Mahadevan, Radhakrishnan; and Vadigepalli, Rajanikanth, "Patient-Specific Genome-Scale Metabolic Models for Individualized Predictions of Liver Disease" (2022). Department of Pathology, Anatomy, and Cell Biology Faculty Papers. Paper 400.
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