One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSn spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSn spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC–MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC–MS features selected as the precursor ion for MS2, the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.

Drug-induced liver injury (DILI) is an adverse toxic hepatic clinical reaction associated to the administration of a drug that can occur both at early clinical stages of drug development, as well after normal clinical usage of approved drugs. Because of its unpredictability and clinical relevance, it is of medical concern. Three DILI phenotypes (hepatocellular, cholestatic, and mixed) are currently recognized, based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values. However, this classification lacks accuracy to distinguish among the many intermediate mixed types, or even to estimate the magnitude and progression of the injury. It was found desirable to have additional elements for better evaluation criteria of DILI. With this aim, we have examined the serum metabolomic changes occurring in 79 DILI patients recruited and monitored using established clinical criteria, along the course of the disease and until recovery. Results revealed that free and conjugated bile acids, and glycerophospholipids were among the most relevant metabolite classes for DILI phenotype characterization. Using an ensemble of PLS–DA models, metabolomic information was integrated into a ternary diagram to display the disease phenotype, the severity of the liver damage, and its progression. The modeling implemented and the use of such compiled information in an easily understandable and visual manner facilitates a straightforward DILI phenotyping and allow to monitor its progression and recovery prediction, usefully complementing the concise information drawn out by the ALT and ALP classification.

Ultra-performance liquid chromatography – mass spectrometry (UPLC-MS) is widely used for untargeted metabolomics in biomedical research. To optimize the quality and precision of UPLC-MS metabolomic analysis, evaluation of blank samples for the elimination of background features is required. Although blanks are usually run either at the beginning or at the end of a sequence of samples, a systematic analysis of their effect of the instrument performance has not been properly documented. Using the analysis of two common bio-fluids (plasma and urine), we describe how the injection of blank samples within a sequence of samples may affect both the chromatographic and MS detection performance depending on several factors, including the sample matrix and the physicochemical properties of the metabolites of interest. The analysis of blanks and post-blank conditioning samples using t-tests, PCA and guided-PCA provides useful information for the elimination of background UPLC-MS features, the identification of column carry over and the selection of the number of samples required to achieve a stable performance.

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The vitamin D receptor (VDR) must be relevant to liver lipid metabolism because VDR deficient mice are protected from hepatosteatosis. Therefore, our objective was to define the role of VDR on the overall lipid metabolism in human hepatocytes. We developed an adenoviral vector for human VDR and performed transcriptomic and metabolomic analyses of cultured human hepatocytes upon VDR activation by vitamin D (VitD). Twenty percent of the VDR responsive genes were related to lipid metabolism, including MOGAT1, LPGAT1, AGPAT2, and DGAT1 (glycerolipid metabolism); CDS1, PCTP, and MAT1A (phospholipid metabolism); and FATP2, SLC6A12, and AQP3 (uptake of fatty acids, betaine, and glycerol, respectively). They were rapidly induced (4–6 h) upon VDR activation by 10 nM VitD or 100 µM lithocholic acid (LCA). Most of these genes were also upregulated by VDR/VitD in mouse livers in vivo. Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) metabolomics demonstrated intracellular accumulation of triglycerides, with concomitant decreases in diglycerides and phosphatidates, at 8 and 24 h upon VDR activation. Significant alterations in phosphatidylcholines, increases in lyso-phosphatidylcholines and decreases in phosphatidylethanolamines and phosphatidylethanolamine plasmalogens were also observed. In conclusion, active VitD/VDR signaling in hepatocytes triggers an unanticipated coordinated gene response leading to triglyceride synthesis and to important perturbations in glycerolipids and phospholipids.