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Direct observation of metabolites in living cells by mass spectrometry offers a bright future for biological studies but also suffers a severe challenge to untargeted peak assignment to tentative metabolite candidates. In this study, we developed a method combining stable isotope tracing and induced electrospray mass spectrometry for living-cells metabolite measurement and identification. By using 13 C 6 -glucose and ammonium chloride- 15 N as the sole carbon and nitrogen sources for cell culture, Escherichia coli synthesized metabolites with 15 N and 13 C elements. Tracing the number of carbon and nitrogen atoms could offer a complementary dimension for candidate peak searching. As a result, the identification confidence of metabolites achieved a universal improvement based on carbon/nitrogen labelling and filtration., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Septic shock is the most severe phase of sepsis and is associated with high rates of mortality. However, early stage prediction of septic shock outcomes remains difficult. Metabolomic techniques have emerged as a promising tool for improving prognosis., Methods: Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models separating the serum metabolomes of survivors from those of non-survivors were established with samples obtained at the intensive care unit (ICU) admission (H0) and 24 h later (H24). For 51 patients with available H0 and H24 samples, multi-level modeling was performed to provide insight into different metabolic evolutions that occurred between H0 and H24 in the surviving and non-surviving patients. Relative quantification and receiver operational characteristic curves (ROC) were applied to estimate the predictability of key discriminatory metabolites for septic shock mortality., Results: Metabolites that were involved in energy supply and protein breakdown were primarily responsible for differentiating survivors from non-survivors. This was not only seen in the H0 and H24 discriminatory models, but also in the H0-H24 paired models. Reanalysis of extra H0-H24 paired samples in the established multi-level model demonstrated good performance of the model for the classification of samplings. According to the ROC results, nine discriminatory metabolites defined consistently from the unpaired model and the H0-H24 time-trend change (Δ H24-H0 ) show good prediction of mortality. These results suggest that NMR-based metabolomic analysis is useful for a better overall assessment of septic shock patients., Conclusions: Dysregulation of the metabolites identified by this study is associated with poor outcomes for septic shock. Evaluation of these compounds during the first 24 h after ICU admission in the septic shock patient may be helpful for estimating the severity of cases and for predicting outcomes., Trial Registration: All human serum samples were collected and stored, provided by the "center of biologic resources for liver disease", in Jean Verdier Hospital, Bondy, France (BB-0033-00027).

Septic shock is the most severe form of sepsis, which is still one of the leading causes of death in the intensive care unit (ICU). Even though early prognosis and diagnosis are known to be indispensable for reaching an optimistic outcome, pathogenic complexities and the lack of specific treatment make it difficult to predict the outcome individually. In the present study, serum samples from surviving and non-surviving septic shock patients were drawn before clinical intervention at admission. Metabolic profiles of all the samples were analyzed by liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. One thousand four hundred nineteen peaks in positive mode and 1878 peaks in negative mode were retained with their relative standard deviation (RSD) below 30 %, in which 187 metabolites were initially identified by retention time and database in the light of the exact molecular mass. Differences between samples from the survivors and the non-survivors were investigated using multivariate and univariate analysis. Finally, 43 significantly varied metabolites were found in the comparison between survivors and non-survivors. Concretely, metabolites in the tricarboxylic acid (TCA) cycle, amino acids, and several energy metabolism-related metabolites were up-regulated in the non-survivors, whereas those in the urea cycle and fatty acids were generally down-regulated. Metabolites such as lysine, alanine, and methionine did not present significant changes in the comparison. Six metabolites were further defined as primary discriminators differentiating the survivors from the non-survivors at the early stage of septic shock. Our findings reveal that LC-MS-based metabolomics is a useful tool for studying septic shock. Graphical abstract ᅟ.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disorder that is implicated in dysregulations in multiple biological pathways, orchestrated by interactions between genetic predisposition, metabolic syndromes and environmental factors. The limited knowledge of its pathogenesis is one of the bottlenecks in the development of prognostic and therapeutic options for MAFLD. Moreover, the extent to which metabolic pathways are altered due to ongoing hepatic steatosis, inflammation and fibrosis and subsequent liver damage remains unclear. To uncover potential MAFLD pathogenesis in humans, we employed an untargeted nuclear magnetic resonance (NMR) spectroscopy- and high-resolution mass spectrometry (HRMS)-based multiplatform approach combined with a computational multiblock omics framework to characterize the plasma metabolomes and lipidomes of obese patients without (n = 19) or with liver biopsy confirmed MAFLD (n = 63). Metabolite features associated with MAFLD were identified using a metabolome-wide association study pipeline that tested for the relationships between feature responses and MAFLD. A metabolic pathway enrichment analysis revealed 16 pathways associated with MAFLD and highlighted pathway changes, including amino acid metabolism, bile acid metabolism, carnitine shuttle, fatty acid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and steroid metabolism. These results suggested that there were alterations in energy metabolism, specifically amino acid and lipid metabolism, and pointed to the pathways being implicated in alerted liver function, mitochondrial dysfunctions and immune system disorders, which have previously been linked to MAFLD in human and animal studies. Together, this study revealed specific metabolic alterations associated with MAFLD and supported the idea that MAFLD is fundamentally a metabolism-related disorder, thereby providing new perspectives for diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

BACKGROUND Insect tolerance to low oxygen (hypoxia) and high carbon dioxide (hypercapnia) is critical for insect control. On the basis of bioassay, metabolism profiles were built to investigate adaptive mechanisms in bean weevil under hypoxia (2% O2), hypoxia/hypercapnia (2% O2 + 18% CO2) and normoxia (control, 20% O2 + 80% N2) using gas chromatography/time-of-flight mass spectrometry ( GC/ TOF-MS). RESULTS The growth and development of bean weevils were significantly suppressed by the two hypoxia conditions; hypercapnia enhanced the mortality, but after 24 days of exposure, the surviving insects emerged as adults earlier than those under hypoxia only. Metabolism profiles also showed striking differences in metabolites among the treatment and control groups, both quantitatively and qualitatively. Pairwise comparisons of the three groups showed that 61 metabolites changed significantly, 40 in the hypoxia group and 37 in the hypoxia/hypercapnia group relative to the control group, while only 16 were shared equally by the hypoxia and hypoxia/hypercapnia groups. Increased metabolites were mainly carbohydrates, amino acids and organic acids, while free fatty acids were decreased. Furthermore, the changes were strengthened by the addition of hypercapnia, but excluding free fatty acids. CONCLUSION The findings show that bean weevil has high tolerance to hypoxia or even hypoxia/hypercapnia at biologically achievable levels and provide more direct evidence for stored product insect mechanism regulation under hypoxia stress, especially free fatty acid regulation by hypercapnia but not by hypoxia. © 2016 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Simple Summary: Correct identification of subjects at high risk is critical in the prevention and early screening of prostate cancer (PCa). Analysis of metabolites in biofluids has shown to be a promising method to identify novel PCa biomarkers. To identify potential biomarkers of PCa, we conducted metabolic profiling of pre-diagnosis plasma metabolite profiles from a large prospective male cohort (n = 418), which included 146 males who developed PCa during a 13-year follow-up and 272 matched controls to investigate the relationship with long-term PCa risk. We show metabolite profiles discriminate males who subsequently developed PCa during the follow-up from matched controls with a high degree of accuracy (AU-ROC 0.92) and highlight 10 metabolites associated with a high risk of PCa. These results suggest that the dysregulation of amino acids and sphingolipid metabolism is associated with future risk of PCa. Background: The prevention and early screening of PCa is highly dependent on the identification of new biomarkers. In this study, we investigated whether plasma metabolic profiles from healthy males provide novel early biomarkers associated with future risk of PCa. Methods: Using the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, we identified plasma samples collected from 146 PCa cases up to 13 years prior to diagnosis and 272 matched controls. Plasma metabolic profiles were characterized using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Results: Orthogonal partial least squares discriminant analysis (OPLS-DA) discriminated PCa cases from controls, with a median area under the receiver operating characteristic curve (AU-ROC) of 0.92 using a 1000-time repeated random sub-sampling validation. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) identified the top 10 most important metabolites (p < 0.001) discriminating PCa cases from controls. Among them, phosphate, ethyl oleate, eicosadienoic acid were higher in individuals that developed PCa than in the controls during the follow-up. In contrast, 2-hydroxyadenine, sphinganine, L-glutamic acid, serotonin, 7-keto cholesterol, tiglyl carnitine, and sphingosine were lower. Conclusion: Our results support the dysregulation of amino acids and sphingolipid metabolism during the development of PCa. After validation in an independent cohort, these signatures may promote the development of new prevention and screening strategies to identify males at future risk of PCa. [ABSTRACT FROM AUTHOR]