Quality Assessment by Bile Composition in Normothermic Machine Perfusion of Rat Livers.

Background: The persistent challenge of organ scarcity in liver transplantation leads to an escalating dependence on organs obtained from extended criteria donors (ECD). Normothermic machine perfusion (NMP) is used for improved preservation. Due to the mimicked in vivo conditions during normothermic machine perfusion, the liver is metabolically active, which allows quality assessment during perfusion. Bile seems to be of rising interest in clinical studies, as it is easily collectible for analysis. As there are currently no data on biliary bile acids during NMP, the primary objective of this study was to use our experimental rodent NMP model to assess changes in bile composition through organ damage during perfusion to inform clinical evaluation of donor organs during NMP. Methods: Thirty livers from male Sprague-Dawley rats in five groups underwent 6 h of NMP using either erythrocyte-supplemented DMEM or Steen solution, with or without 30 min of warm ischemia time (WIT). We conducted regular measurements of AST, ALT, LDH, and urea levels in the perfusate at 3-hour intervals. Bile samples were analyzed for biliary pH, LDH, and gamma glutamyltransferase, as well as biliary bile acids via mass spectrometry and UHPLC. Results: Compared with regular livers, liver injury parameters were significantly higher in our donation after circulatory death (DCD) model. Bile production was significantly reduced in livers exposed to WIT, and the bile showed a significantly more alkaline pH. This correlated with the concentration of total bile acids, which was significantly higher in livers experiencing WIT. However, regular livers produced a higher total amount of biliary bile acids during perfusion. Taurocholic acid and its metabolites were most prominent. Secondary bile acids were significantly reduced during perfusion due to the missing enterohepatic circulation. Conclusions: WIT-induced liver injury affects bile composition within our small-animal NMP model. We hypothesize this phenomenon to be attributed to the energy-driven nature of bile secretion, potentially explaining why DCD livers produce less, yet more concentrated, bile. Our results may inform clinical studies, in which biliary bile acids might have a potential as a quantifiable viability marker in human NMP liver transplantation studies.

Serum Starvation Accelerates Intracellular Metabolism in Endothelial Cells.

Periods of low energy supply are challenging conditions for organisms and cells during fasting or famine. Although changes in nutrient levels in the blood are first sensed by endothelial cells, studies on their metabolic adaptations to diminished energy supply are lacking. We analyzed the dynamic metabolic activity of human umbilical vein endothelial cells (HUVECs) in basal conditions and after serum starvation. Metabolites of glycolysis, the tricarboxylic acid (TCA) cycle, and the glycerol pathway showed lower levels after serum starvation, whereas amino acids had increased levels. A metabolic flux analysis with 13C-glucose or 13C-glutamine labeling for different time points reached a plateau phase of incorporation after 30 h for 13C-glucose and after 8 h for 13C-glutamine under both experimental conditions. Notably, we observed a faster label incorporation for both 13C-glucose and 13C-glutamine after serum starvation. In the linear range of label incorporation after 3 h, we found a significantly faster incorporation of central carbon metabolites after serum starvation compared to the basal state. These findings may indicate that endothelial cells develop increased metabolic activity to cope with energy deficiency. Physiologically, it can be a prerequisite for endothelial cells to form new blood vessels under unfavorable conditions during the process of angiogenesis in vivo.

Analysis of adherent cell culture lysates with low metabolite concentrations using the Biocrates AbsoluteIDQ p400 HR kit.

The AbsoluteIDQ p400 HR kit is a commercial product for targeted metabolomics. While the kit has been validated for human plasma and serum, adherent cell lysates have not yet been evaluated. We have optimized the detection of polar and lipid metabolites in cell lysates using the kit to enable robust and repeatable analysis of the detected metabolites. Parameters optimized include total cell mass, loading volume and extraction solvent. We present a cell preparation and analytical method and report on the performance of the kit with regard to detectability of the targeted metabolites and their repeatability. The kit can be successfully used for a relative quantification analysis of cell lysates from adherent cells although validated only for human plasma and serum. Most metabolites are below the limit of the Biocrates' set quantification limits and we confirmed that this relative quantification can be used for further statistical analysis. Using this approach, up to 45% of the total metabolites in the kit can be detected with a reasonable analytical performance (lowest median RSD 9% and 13% for LC and FIA, respectively) dependent on the method used. We recommend using ethanol as the extraction solvent for cell lysates of osteosarcoma cell lines for the broadest metabolite coverage and 25 mg of cell mass with a loading volume of 20 µL per sample.

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies: MeTaQuaC.

Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data are essential for confidence in analytical results and are particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory's routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire data set alongside standard analyses of quality control samples can be used to identify outliers and quantify systematic trends to improve downstream analysis. Finally, we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and other target data sets and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.