Selective organ ischaemia/reperfusion identifies liver as the key driver of the post-injury plasma metabolome derangements

COVID-19 infection alters kynurenine and fatty acid metabolism, correlating with IL-6 levels and renal status

BACKGROUNDReprogramming of host metabolism supports viral pathogenesis by fueling viral proliferation, by providing, for example, free amino acids and fatty acids as building blocks.METHODSTo investigate metabolic effects of SARS-CoV-2 infection, we evaluated serum metabolites of patients with COVID-19 (n = 33; diagnosed by nucleic acid testing), as compared with COVID-19-negative controls (n = 16).RESULTSTargeted and untargeted metabolomics analyses identified altered tryptophan metabolism into the kynurenine pathway, which regulates inflammation and immunity. Indeed, these changes in tryptophan metabolism correlated with interleukin-6 (IL-6) levels. Widespread dysregulation of nitrogen metabolism was also seen in infected patients, with altered levels of most amino acids, along with increased markers of oxidant stress (e.g., methionine sulfoxide, cystine), proteolysis, and renal dysfunction (e.g., creatine, creatinine, polyamines). Increased circulating levels of glucose and free fatty acids were also observed, consistent with altered carbon homeostasis. Interestingly, metabolite levels in these pathways correlated with clinical laboratory markers of inflammation (i.e., IL-6 and C-reactive protein) and renal function (i.e., blood urea nitrogen).CONCLUSIONIn conclusion, this initial observational study identified amino acid and fatty acid metabolism as correlates of COVID-19, providing mechanistic insights, potential markers of clinical severity, and potential therapeutic targets.FUNDINGBoettcher Foundation Webb-Waring Biomedical Research Award; National Institute of General and Medical Sciences, NIH; and National Heart, Lung, and Blood Institute, NIH.

Application of Multiblock Analysis on Small Metabolomic Multi-Tissue Dataset

Data integration has been proven to provide valuable information. The information extracted using data integration in the form of multiblock analysis can pinpoint both common and unique trends in the different blocks. When working with small multiblock datasets the number of possible integration methods is drastically reduced. To investigate the application of multiblock analysis in cases where one has a few number of samples and a lack of statistical power, we studied a small metabolomic multiblock dataset containing six blocks (i.e., tissue types), only including common metabolites. We used a single model multiblock analysis method called the joint and unique multiblock analysis (JUMBA) and compared it to a commonly used method, concatenated principal component analysis (PCA). These methods were used to detect trends in the dataset and identify underlying factors responsible for metabolic variations. Using JUMBA, we were able to interpret the extracted components and link them to relevant biological properties. JUMBA shows how the observations are related to one another, the stability of these relationships, and to what extent each of the blocks contribute to the components. These results indicate that multiblock methods can be useful even with a small number of samples.