Liver regeneration after partial hepatectomy

National Institute Of Diabetes And Digestive And Kidney Diseases

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Abstract

The network of interactions underlying liver regeneration is robust and precise with liver resections resulting in controlled hyperplasia (cell proliferation) that terminates when the liver regains its lost mass. The interplay of cytokines and growth factors responsible for the inception and termination of this hyperplasia is not well understood. We developed a model for this network of interactions based on the known data of liver resections. This model reproduces the relevant published data on liver regeneration and provides geometric insights into the experimental observations. Live donor liver transplants (LDLT) are increasingly used to treat end-stage liver diseases such as hepatocellular carcinomas, non-alcoholic fatty liver disease, primary sclerosing cholangitis, and others, due to shortages of cadaveric organs. LDLT have the advantage of proactive treatment before the recipients condition deteriorates, but rare complications have occurred in donors. The Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL) was undertaken to investigate the risks and benefits to LDLT donors and recipients. A subset of donors in the A2ALL study was recruited for a detailed 6-month study of hepatic function and regeneration known as the DQLFT (Donor Quantitative Liver Function Tests) study (1). Liver volume measurements and blood measurements were taken at 4 time points (0 days, 4 days, 3 months, 6 months post-surgery) from these donors. The DQLFT study was distinct in that it quantified liver regeneration during the initial 2-week period when human livers regenerate most quickly (2-6). This detailed data led us to make improvements in a mathematical model of rat liver regeneration developed by Furchtgott et al. (7) and adapted with limited data for human liver regeneration by Periwal et al. (8) With new expression data from our collaboration with Dr. Testa, we developed a machine learning–driven Personalized Progressive Mechanistic Digital Twin (PePMDT) for living donor liver transplant (LDLT) recovery. This framework integrates longitudinal blood-derived gene expression data with a mechanistic mathematical model of hepatocyte transitions, enabling the prediction of individual donor recovery trajectories. We analyzed whole transcriptome RNA sequencing data from 12 healthy LDLT donors, collected at 14 time points over one year. Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified distinct gene expression patterns associated with liver regeneration. These gene expression patterns were mapped onto variables in a differential equation model describing hepatocyte transitions among quiescent, primed, and proliferating states. A deep learning-based framework was employed to establish a bidirectional mapping between gene expression data and model dynamics. The resulting PePMDT predicts individual recovery trajectories by leveraging blood-derived gene expression data to simulate regenerative responses. This approach bridges clinical genomics and mathematical modeling, providing a robust platform for personalized medicine. The PePMDT framework offers a data-driven approach to monitor and predict donor-specific recovery, facilitating safer transplant procedures and improved donor care.