Combined stem cell and predictive models reveal flavin cofactors as targets in metabolic liver dysfunction

作者：Julian Weihs, Fatima Baldo, Alessandra Cardinali, Gehad Youssef, Katarzyna A. Ludwik, Nils Haep, Peter Tang, Pavitra Kumar, Cornelius Engelmann, Susanna Quach, Mijuna Meindl, Martin Kucklick, Susanne Engelmann, Bruno Chillian, Michael Rothe, David Meierhofer, Isabella Lurje, Linda Hammerich, Prakash Ramachandran, Timothy J. Kendall, Jonathan A. Fallowfield, Harald Stachelscheid, Igor M. Sauer, Frank Tacke, Philip Bufler, Christian Hudert, Namshik Han, Milad Rezvani · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2024 · DOI：10.1101/2024.10.10.617610 · 被引用次数：2 · 研究领域：Metabolism and Genetic Disorders

Drug discovery for multifactorial diseases like metabolic dysfunction-associated steatotic liver disease (MASLD) remains challenging due to inadequate models and untargeted drug screenings. We combined stem-cell-based modeling with computational drug predictions identifying flavin pathways as therapeutic targets in MASLD. For disease stage-specific discovery, we established a MASLD testing model, compounding metabolic triggers to intensify mitochondrial crisis. In vitro injuries included adipo- and myokines, immune cell co-culture, and genomic risk factors. Benchmarking experiments revealed similarities with advanced human MASLD. To query therapeutic compounds, protein-protein-interaction networks, weighted gene co-expression, and knowledge graph-based analyses independently predicted flavin adenine dinucleotide (FAD) as an anti-MASLD factor. Dysregulated flavoproteomes in vitro and in vivo –in pediatric and adult MASLD patients– supported our flavin network-focused strategy. We established therapeutic FAD concentrations to mitigate metabolic injury and fibro-inflammation in human multicellular liver organoids and other assays. We enhanced therapeutic FAD effects through genetic mitochondrial biogenic augmentation and identified orally available flavo-active compounds—including Aspirin—restoring mitochondrial respiration. Our study demonstrates how integrating stem cell-derived disease modeling with computed drug predictions can expedite therapeutic discovery.