Expanding the pool of transplantable human livers by modeling perfusion dynamics

描述(申请人提供)：在美国，可移植肝脏严重短缺，每年大约有17,000人在等待名单上，但每年只有6,000人接受移植。如果具有次优特性的边缘肝，如因心脏死亡供体(DCD)、供体年龄较大或中度脂肪变性而延长热缺血时间(WIT)的边缘肝也可以被包括在可移植肝池中，则这种短缺可以显著减少。在这个项目中，我们建议将亚常温机器灌流(SNMP)作为一个平台，在移植前对边缘肝进行代谢预适应和恢复，从而扩大供体池的规模。在这项提案的目标1中，我们的目标是 首次在废弃的人体肝脏上测试SNMP作为概念验证，这些肝脏大约每周捐赠给我们的实验室一次。我们将收集灌流液、胆汁和组织活检样本，以分析肝脏在灌流过程中的动态代谢情况。组织活检样本将被送往加州大学戴维斯分校代谢组学核心，以确定主要代谢物中~150种细胞内代谢物和~300种脂类化合物的相对水平。然后，我们将使用多向主成分分析和多元回归分析将动态代谢曲线与供体特征相关联。这一目标的预期结果将是根据供体特征(年龄、智商和脂肪变性)开发一套新的截断值，以确定肝是否可在SNMP后移植。在目标2中，我们的目标是更好地了解灌流过程中的代谢动力学，并获得更多关于SNMP如何逆转WIT诱导的损伤的机械性见解。我们将借用系统生物学的计算建模方法来解释用时间进程细胞内代谢组学数据观察到的趋势，并确定哪些代谢途径在灌流过程中上调或下调。我们将构建一个化学计量学/调节反应网络来描述肝细胞的代谢，并应用结构动力学模型(SKM)来确定最可能解释途径流量变化的原因来解释代谢物数据。通过比较健康的肝脏和智慧丰富的肝脏的动力学，我们期望这一目标的结果是发现哪些代谢途径负责逆转灌流中的缺血损伤。我们的长期目标是取代目前的供体肝脏评估系统，包括一个SNMP步骤，这将提供两个关键的好处：1)改善次优移植物的代谢状况，特别是来自DCDs的移植物。为手术室的移植外科医生提供一套更可靠的基于血流灌注测量的定量生物标志物，以评估肝脏是否可以移植。这项工作的更广泛影响是，它有可能挽救数千名肝功能衰竭患者的生命。此外，据我们所知，这项研究是第一次整合系统生物学和代谢组学数据来模拟活体人体器官的动力学，应该为其他试图使用时间进程活组织活检来模拟活体组织代谢的人提供一个例子。

DESCRIPTION (provided by applicant): There is a drastic shortage of transplantable livers in the US with approximately 17,000 individuals each year on the waiting list, but only 6,000 per year receiving transplants. This shortage can be significantly reduced if marginal livers with suboptimal characteristics, such as those with prolonged warm ischemia time (WIT) due to cardiac death donor (DCD), older donor age, or moderate steatosis, can also be included in the pool of transplantable livers. In this project, we propose subnormothermic machine perfusion (SNMP) as a platform to metabolically precondition and recover marginal livers prior to transplantation, thus expanding the donor pool size. In Aim 1 of this proposal, our objective is to test SNMP for the first time as a proof of concept on discarded human livers, which get donated to our lab approximately once per week. We will collect perfusate, bile, and tissue biopsy samples to analyze the liver's dynamic metabolic profile in perfusion. The tissue biopsy samples will be sent to the UC Davis Metabolomics Core to determine relative levels of ~150 intracellular metabolites from primary metabolites and ~300 lipid compounds. We will then correlate the dynamic metabolic profiles to donor characteristics using multi-way principal component analysis and multiple regression analysis. The expected outcome of this aim will be to develop a new set of cutoff metrics based on donor characteristics (age, WIT, and steatosis) to determine if a liver is transplantable after SNMP. In Aim 2, our objective is to better understand metabolic dynamics during perfusion and gain more mechanistic insight into how SNMP reverses WIT-induced injury. We will borrow computational modeling approaches from systems biology to explain trends observed with the time-course intracellular metabolomics data and determine which metabolic pathways are up or down regulated during perfusion. We will construct a stoichiometric/regulatory reaction network to describe hepatocyte metabolism, and apply structural kinetic modeling (SKM) to determine the most likely explanation of pathway flux changes to explain the metabolite data. By comparing the dynamics of a healthy liver, and one with long WIT, our expected outcome of this aim is to discover which metabolic pathways are responsible for reversing ischemic injury in perfusion. Our long term goal is to replace the current system of donor liver assessment to include an SNMP step, which will provide two key benefits: 1.) Improve the metabolic condition of suboptimal grafts, particularly those from DCDs with longer WITs and 2.) Provide the transplant surgeon in the operating room with a more robust set of quantitative biomarkers based on perfusion measurements to assess if a liver can be transplanted. The broader impact of this work is that it has the potential to save thousands of lives of those with liver failure. In addition, this study is, to our knowledge, the first to integate systems biology and metabolomics data to model the dynamics of a live human organ and should serve as an example for others trying to use time-course biopsies to model tissue metabolism in vivo.