There is a drastic shortage of transplantable livers, because many donor grafts are discarded due to either high warm ischemia times (WIT) during procurement or excessive macrovesicular steatosis. As a way to improve the quality of these suboptimal livers prior to transplantation, machine perfusion has recently emerged as a novel platform to recondition organs ex vivo. In our group, we have established a protocol for subnormotherimc machine perfusion (SNMP), where the liver is subject to a flow of nutrient-rich media for three hours at room temperature. This metabolic reconditioning of the liver prior to transplantation offers the opportunity to administer therapeutic cocktails with the goal of alleviating organ-specific deficiencies that may cause reperfusion injury and graft failure in the recipient. However, discovering novel therapeutics for liver perfusion is challenging, because hepatic metabolism is so complex and inter-connected that drug-induced metabolic perturbations meant to influence specific pathways may also cause undesirable off-target effects. In this regard, a theoretical modeling framework to characterize hepatic metabolism using systems-biology approaches would enable an investigator to better understand the metabolic dynamics during perfusion, and also propose novel targets for intervention to address either WIT-induced injury or steatosis, while minimizing perturbations to other essential physiological functions of the liver. Our long-term goal is to expand the number of donor livers that are suitable for transplantation using SNMP as a modality for pre-transplant organ conditioning. The objective of the proposed study is to characterize the metabolic dynamics of human livers from several liver groups, including control, high WIT, and high degree of macrosteatosis. We will achieve this using time course metabolomics analysis of tissue biopsies during perfusion to train a graph-based network model of hepatocyte metabolism. We will apply modularity analysis on the networks to determine groups of reactions that mutually influence each other. The central hypothesis of the research is that the metabolic state of the liver will impact the engagement of reaction fluxes across different metabolic pathways and consequently determine the state-dependent modularity, or functional organization, of the hepatocyte metabolic network. The work described here is expected to provide a theoretical systems-oriented framework to characterize dynamic metabolic effects of targeted interventions. To achieve this, we are proposing novel methodologies involving the of use time-course metabolomics to deduce system-wide changes in reaction flux trajectories during perfusion. These methodologies will also be generally applicable to study the dynamics of other organ/tissue or cell culture system. The successful completion of the work proposed here will enable us to propose novel therapeutics to treat donor human livers during perfusion, tailored to the specific demands of the organ, and will lay a strong framework for several future studies. 1
Chronic liver disease and cirrhosis accounts for approximately 30,000 victims per year and is the 12th leading cause of death in the US. However, there is a drastic shortage of transplantable livers, as only approximately one third of the ~17,000 on the waiting list receive a transplantation. This project aims to expand the pool of transplantable livers by developing computational tools to predict effective therapeutics for organ treatment.
| Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar et al. (2017) Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics. Metabolites 7: |
| Bruinsma, Bote G; Uygun, Korkut (2017) Subzero organ preservation: the dawn of a new ice age? Curr Opin Organ Transplant 22:281-286 |
| Bruinsma, Bote G; Avruch, James H; Sridharan, Gautham V et al. (2017) Peritransplant Energy Changes and Their Correlation to Outcome After Human Liver Transplantation. Transplantation 101:1637-1644 |
| Bruinsma, Bote G; Sridharan, Gautham V; Weeder, Pepijn D et al. (2016) Metabolic profiling during ex vivo machine perfusion of the human liver. Sci Rep 6:22415 |
