Integrating text mining with network models for successful target identification: in vitro validation in MASH-induced liver fibrosis - Venhorst, Jennifer; Hanemaaijer, Roeland; Dulos, Remon; Caspers, Martien P. M.; Toet, Karin; Attema, Joline; de Ruiter, Christa; Kalkman, Gino; Rouhani Rankouhi, Tanja; de Jong, Jelle C. B. C.; Verschuren, Lars (Frontiers in Pharmacology, 2024)
An in silico target discovery pipeline was developed that includes a directional and weighted molecular disease network for metabolic dysfunction-associated steatohepatitis (MASH)-induced liver fibrosis. The applied methodology integrates text-mining, network biology, and artificial intelligence/machine learning approaches with clinical transcriptome data for optimal translational power. On the mechanistic level, critical players in the progression of the disease were identified from the constructed disease network using in silico knockouts. Top-ranked genes were subjected to a target efficacy analysis; the top 5 candidate targets were validated in vitro. For three targets, including EP300, their role in liver fibrosis was confirmed. EP300 gene-silencing reduced collagen by 37%; compound intervention studies performed in human primary hepatic stellate cells and the hepatic stellate cell line LX-2 resulted in significant inhibition of collagen of 81% compared to the TGF-stimulated control (1M inobrodib in LX-2 cells). The validated in silico pipeline presents a unique approach for the identification of human disease mechanism-relevant drug targets. The directionality of the network ensures adherence to physiologically relevant signaling cascades, whereas inclusion of clinical data boosts translational power and ensures that the most relevant disease pathways are identified. In silico knockouts provide molecular insights crucial for successful target identification.