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Modelling the Liver’s Regenerative Capacity across Different Clinical Conditions
Journal article   Open access   Peer reviewed

Modelling the Liver’s Regenerative Capacity across Different Clinical Conditions

Anh Thu Nguyen Lefebvre, Soumita Ghosh, Cristina Baciu, Bima J Hasjim, Sara Naimimohasses, Graziano Oldani, Elisa Pasini, Michael Brudno, Nazia Selzner, Jeffrey Wrana, …
JHEP reports, Vol.7(8), 101465
08/2025
DOI: 10.1016/j.jhepr.2025.101465
PMCID: PMC12284365
PMID: 40704068
url
https://doi.org/10.1016/j.jhepr.2025.101465View
Published (Version of record) Open Access

Abstract

AbstractBackground & AimsLiver regeneration is essential for recovery following injury, but this process can be impaired by factors such as sex, age, metabolic disorders, fibrosis, and immunosuppressive therapies. We aimed to identify key transcriptomic, proteomic, and serum biomarkers of regeneration in mouse models under these diverse conditions using systems biology and machine learning approaches. MethodsSix mouse models, each undergoing 75% hepatectomy, were used to study regeneration across distinct clinical contexts: young males and females, aged mice, stage 2 fibrosis, steatosis, and tacrolimus exposure. A novel contrastive deep learning framework with triplet loss was developed to map regenerative trajectories and identify genes associated with regenerative efficiency. ResultsDespite achieving ≥75% liver mass restoration by day 7, regeneration was significantly delayed in aged, steatotic, and fibrotic models, as indicated by reduced Ki-67 staining on day 2 (p < 0.0001 for all). Interestingly, fibrotic livers exhibited reduced collagen deposition and partial regression to stage 1 fibrosis post-hepatectomy. Transcriptomic and proteomic analyses revealed consistent downregulation of cell cycle genes in impaired regeneration. The deep learning model integrating clinical and transcriptomic data predicted regenerative outcomes with 87.9% accuracy. SHapley Additive exPlanations (SHAP) highlighted six key predictive genes: Wee1, Rbl1, Gnl3, Mdm2, Cdk2, and Ccne2. Proteomic validation and human SPLiT-seq (split-pool ligation-based transcriptome sequencing) data further supported their relevance across species. ConclusionsThis study identifies conserved cell cycle regulators underlying efficient liver regeneration and provides a predictive framework for evaluating regenerative capacity. The integration of deep learning and multi-omics profiling provides a promising approach to better understand liver regeneration and may help guide therapeutic strategies, especially in complex clinical settings.
 Gastroenterology and Hepatology

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