Unlocking liver physiology: comprehensive pathway maps for mechanistic understanding

In silico methods provide a resourceful toolbox for new approach methodologies (NAMs). They can revolutionize chemical safety assessment by offering more efficient and human-relevant alternatives to traditional animal testing. In this study, we introduce two Liver Physiological Maps (PMs); comprehensive and machine-readable graphical representations of the intricate mechanisms governing two major liver functions.

Two PMs were developed through manual literature curation, integrating data from established pathway resources and domain expert knowledge. Cell-type specificity was validated using Human Protein Atlas datasets. An interactive version is available online for exploration. Cross-comparison analysis with existing Adverse Outcome Pathway (AOP) networks was performed to benchmark physiological coverage and identify knowledge gaps.

The LiverLipidPM focuses on liver lipid metabolism, detailing pathways involved in fatty acid synthesis, triglycerides, cholesterol metabolism, and lipid catabolism in hepatocytes. And the LiverBilePM represents bile acid biosynthesis and secretion processes, detailing biosynthesis, transport, and secretion processes between hepatocytes and cholangiocytes. Both maps integrate metabolism with signaling pathways and regulatory networks. The interactive maps enable visualization of molecular pathways, linkage to external ontologies, and overlay of experimental data. Comparative analysis revealed unique mechanisms to each map and overlaps with existing AOP networks. Chemical-target queries identified new potential targets in both PMs, which might represent new molecular initiating events for AOP network extension.

The developed liver PMs serve as valuable resources for hepatology research, with a special focus on hepatotoxicity, supporting the refinement of AOP networks and the development of human-oriented in vitro test batteries for chemical toxicity assessment. These maps provide a foundation for creating computational models and mode-of-action ontologies while potentially extending their utility to systems biology and drug discovery applications.