Modelling the liver’s regenerative capacity across different clinical conditions
Background & Aims: Liver 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 regenera...
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Elsevier
2025-08-01
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| Series: | JHEP Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589555925001430 |
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| author | Anh Thu Nguyen-Lefebvre Soumita Ghosh Cristina Baciu Bima J. Hasjim Sara Naimimohasses Graziano Oldani Elisa Pasini Michael Brudno Nazia Selzner Jeffrey Wrana Mamatha Bhat |
| author_facet | Anh Thu Nguyen-Lefebvre Soumita Ghosh Cristina Baciu Bima J. Hasjim Sara Naimimohasses Graziano Oldani Elisa Pasini Michael Brudno Nazia Selzner Jeffrey Wrana Mamatha Bhat |
| author_sort | Anh Thu Nguyen-Lefebvre |
| collection | DOAJ |
| description | Background & Aims: Liver 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. Methods: Six 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. Results: Despite 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. SHAP (SHapley Additive exPlanations) 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. Conclusions: This 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. Impact and implications: The aim of this study was to identify key transcriptomic, proteomic, and serum biomarkers of regeneration in mouse models under diverse conditions, using systems biology and machine learning approaches. Key molecular drivers of liver regeneration across diverse clinical conditions were identified using innovative deep learning and multi-omics approaches. By identifying conserved cell cycle genes predictive of regenerative outcomes, this study offers a powerful framework to assess and potentially enhance liver recovery in older patients, those with fibrosis or steatosis, and/or those under immunosuppression. |
| format | Article |
| id | doaj-art-04fb6868774c49dd9a66d0efff7b5dfa |
| institution | Kabale University |
| issn | 2589-5559 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JHEP Reports |
| spelling | doaj-art-04fb6868774c49dd9a66d0efff7b5dfa2025-08-20T03:51:13ZengElsevierJHEP Reports2589-55592025-08-017810146510.1016/j.jhepr.2025.101465Modelling the liver’s regenerative capacity across different clinical conditionsAnh Thu Nguyen-Lefebvre0Soumita Ghosh1Cristina Baciu2Bima J. Hasjim3Sara Naimimohasses4Graziano Oldani5Elisa Pasini6Michael Brudno7Nazia Selzner8Jeffrey Wrana9Mamatha Bhat10Ajmera Transplant Program, University Health Network, Toronto, Ontario, Canada; Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, CanadaAjmera Transplant Program, University Health Network, Toronto, Ontario, Canada; Department of Medicine, University of Toronto, Ontario, CanadaAjmera Transplant Program, University Health Network, Toronto, Ontario, CanadaDepartment of Surgery, University of California - Irvine, Orange, California, USA; Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, Ontario, CanadaAjmera Transplant Program, University Health Network, Toronto, Ontario, Canada; Division of Gastroenterology and Hepatology, Faculty of Medicine, University of Toronto, Toronto, Ontario, CanadaDepartment of Surgery, University of British Columbia, Canada; Division of Abdominal Surgery, Department of Surgery, Faculty of Medicine, University of Geneva, SwitzerlandAjmera Transplant Program, University Health Network, Toronto, Ontario, CanadaDepartment of Computer Science, University of Toronto, Ontario, CanadaAjmera Transplant Program, University Health Network, Toronto, Ontario, Canada; Division of Gastroenterology and Hepatology, Faculty of Medicine, University of Toronto, Toronto, Ontario, CanadaLunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Ontario, CanadaAjmera Transplant Program, University Health Network, Toronto, Ontario, Canada; Division of Gastroenterology and Hepatology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Corresponding author. Address: MaRS 9-9955, 585 University Avenue, Toronto, Ontario, M5G 2N2, Canada; Tel.: +1 416-581-7512.Background & Aims: Liver 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. Methods: Six 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. Results: Despite 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. SHAP (SHapley Additive exPlanations) 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. Conclusions: This 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. Impact and implications: The aim of this study was to identify key transcriptomic, proteomic, and serum biomarkers of regeneration in mouse models under diverse conditions, using systems biology and machine learning approaches. Key molecular drivers of liver regeneration across diverse clinical conditions were identified using innovative deep learning and multi-omics approaches. By identifying conserved cell cycle genes predictive of regenerative outcomes, this study offers a powerful framework to assess and potentially enhance liver recovery in older patients, those with fibrosis or steatosis, and/or those under immunosuppression.http://www.sciencedirect.com/science/article/pii/S2589555925001430Liver regenerationpartial hepatectomydeep learningtranscriptome analysisproteome analysis |
| spellingShingle | Anh Thu Nguyen-Lefebvre Soumita Ghosh Cristina Baciu Bima J. Hasjim Sara Naimimohasses Graziano Oldani Elisa Pasini Michael Brudno Nazia Selzner Jeffrey Wrana Mamatha Bhat Modelling the liver’s regenerative capacity across different clinical conditions JHEP Reports Liver regeneration partial hepatectomy deep learning transcriptome analysis proteome analysis |
| title | Modelling the liver’s regenerative capacity across different clinical conditions |
| title_full | Modelling the liver’s regenerative capacity across different clinical conditions |
| title_fullStr | Modelling the liver’s regenerative capacity across different clinical conditions |
| title_full_unstemmed | Modelling the liver’s regenerative capacity across different clinical conditions |
| title_short | Modelling the liver’s regenerative capacity across different clinical conditions |
| title_sort | modelling the liver s regenerative capacity across different clinical conditions |
| topic | Liver regeneration partial hepatectomy deep learning transcriptome analysis proteome analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2589555925001430 |
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