Liver regeneration after partial hepatectomy

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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 are working towards a virtual patient model for liver regeneration. The aim is to correlate accessible blood measurements of gene expression with variables in our model in a two-way map. The aim is to find specific genes whose expression in the blood of liver donors can be used to define specific values of our mathematical model's variables. The mathematical model may then be simulated with some patient characteristics and the resulting future values of the model variables can be used to predict expected values of specific genes. Any measured deviation from these predicted values could then be investigated. The difficulty is that there are only a few subjects and about 40,000 genes that were measured, so we are developing techniques to find the optimal set of genes for this two-way map to be predictive.