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Background & Objective: The progression of dengue fever to severe dengue (SD) is a major public health concern that impairs the capacity of the medical system to predict and treat dengue patients. Hence, the present study used a metabolomic approach integrated with machine models to identify differentially expressed metabolites in patients with SD compared to nonsevere patients and healthy controls., Methods: Comprehensively, the plasma was collected at different clinical phases during dengue without warning signs (DWOW, N = 10), dengue with warning signs (DWW, N = 10), and SD (N = 10) at different stages [i.e., day of admission (DOA), day of defervescence (DOD), and day of convalescent (DOC)] in comparison to healthy control (HC). The samples were subjected to LC‒ESI‒MS/MS to identify metabolites. Statistical and machine learning analyses were performed using R and Python language. Further, biomarker, pathway and correlation analysis was performed to identify potential predictors of dengue., Results & Conclusion: A total of 423 metabolites were identified in all the study groups. Paired and unpaired t-tests revealed 14 highly differentially expressed metabolites between and across the dengue groups, with four metabolites (shikimic acid, ureidosuccinic acid, propionyl carnitine, and alpha-tocopherol) showing significant differences compared to HC. Furthermore, biomarker (ROC) analysis revealed 11 potential molecules with a significant AUC value of 1 that could serve as potential biomarkers for identifying different dengue clinical stages that are beneficial for predicting dengue disease outcomes. The logistic regression model revealed that S-adenosylhomocysteine, hypotaurine, and shikimic acid metabolites could be beneficial indicators for predicting severe dengue, with an accuracy and AUC of 0.75. The data showed that dengue infection is related to lipid metabolism, oxidative stress, inflammation, metabolomic adaptation, and virus manipulation. Moreover, the biomarkers had a significant correlation with biochemical parameters like platelet count, and hematocrit. These results shed some light on host-derived small-molecule biomarkers that are associated with dengue severity and novel insights into metabolomics mechanisms interlinked with disease severity., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Diethylnitrosamine (DEN) was applied to create the primary liver cancer (PLC) animal model. In the study, the normal group, model group, cyclophosphamide (CTX) group, Cortex Juglandis Mandshuricae (CJM) extract group, myricetin group and myricitrin group were divided. LC-MS/MS technology was applied to determine the metabolites of liver tissue samples from different locations (nodular and non-nodular parts of liver tissue) in each group of rats. Through metabolomics research, the connection and difference of anti-PLC induced by the CJM extract, myricetin and myricitrin was analyzed. The surface of the liver tissues of rats in the model group was rough, dimly colored, inelastic, on which there were scattered gray white cancer nodules and blood stasis points. The number of cancer nodules was significantly reduced, and the degree of cell malignancy was low, but there were some inflammatory cell infiltrations, necrosis area and karyokinesis in the CJM extract group, myricetin group, myricitrin group and CTX group. The result of metabolic research indicated that 45 potential biomarkers of the PLC were found, as gamma-aminoisobutyrate, taurochenodeoxycholate, xanthurenic acid, etc. There were 22 differential metabolites in the CTX group, 16 differential metabolites in the CJM extract group, 14 differential metabolites in the myricetin group, 14 differential metabolites in the myricitrin group., 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 © 2024 Elsevier B.V. All rights reserved.)

To explore the influence of tea trichomes on the quality of white tea, liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), and headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) were used to identify non-volatile and volatile compounds white tea without trichomes (WTwt) and pure trichomes (PT). It was found that the bitter and astringent compounds, caffeine (CAF), epigallocatechin gallate (EGCG), epicatechin gallate (ECG) and flavonol glycosides, were mainly enriched in the WTwt, with 16.3-fold, 47.1-fold and 28.7-fold decrease in CAF and EGCG and ECG, respectively, and the content of these compounds in PT were lower than the taste thresholds. In PT, kaempferol-3-O-(p-coumaroyl)-glucoside and kaempferol-3-O-(di-p-coumaroyl)-glucoside were non-volatile marker compounds, and decanal was significant aroma contributor with rOAV = 250.86. Moreover, the compounds in trichomes mainly contributed to the fruity and floral aroma of white tea, among which benzyl alcohol, (E)-geranylacetone, decanal, dodecanal and 6-methyl-5-hepten-2-one were the crucial aroma components, which were 2.1, 1.7, 1.8, 1.4 and 2.2 times as much as the WTwt in the PT, respectively. In conclusion, trichomes can improve the quality of white tea by reducing the bitterness and astringency, increasing the umami, as well as enhancing the fruity and floral aromas., 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 © 2024 Elsevier Ltd. All rights reserved.)

Background: State-of-the-art quantitative metabolomics relies on isotope dilution using internal standards (IS) derived from fully 13 C labeled biomass. By spiking samples and external standards with known amounts of IS, the spike characterization demands are kept to a minimum. In fact, it is sufficient to experimentally assess the isotopic enrichment of the IS. This study develops the yeast derived IS toolbox further, (1) by characterizing the concentration levels of hydrophilic metabolites in a yeast fermentation batch and (2) by exploring the analytical figures of merit of one-point IS versus multipoint external calibration using IS, the established gold-standard for quantitative metabolomics., Results: Independent reverse isotope dilution experiments using different chromatographic methods over a period of several months, delivered a list of 83 13 C-labeled metabolites with fully characterized concentration and their uncertainty, covering 5 orders of magnitude, from the nanomolar to the low millimolar range. The 13 C-labeled yeast-derived IS showed excellent intermediate stability with 92 % of molecules showing inter-method RSDs ≤30 % (75 % of molecules showed RSDs ≤15 %) over a timeframe of five months. One-point internal standardization with the characterized labeled biomass achieved figures of merit equivalent to multipoint calibrations for the majority of metabolites., Significance: The proposed calibration workflow rationalizes time and standard expenditure and is particularly beneficial for laboratories dealing with wide-target assays and small analysis batches. The present assessment serves as a seminal study for further developments of the concept towards absolute quantification from archive high-resolution MS data of U 13 C-biomass-spiked samples and the implementation of quick biomass recalibration with each experiment, promising seamless transition between internal standards derived from different fermentation batches., Competing Interests: Declaration of competing interest The authors declare no competing financial interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: To analyze the blood differential metabolites of patients with intrahepatic cholestasis (IHC) by liquid chromatography-mass spectrometry metabolomics technology so as to find potential metabolic target. Method: Serum samples were collected from thirty patients with intrahepatic cholestasis and thirty healthy individuals after metabolomics analysis. The differential metabolites were initially screened based on the multiple differences and significance. KEGG enrichment analysis was performed on the differential metabolites to determine the candidate targets. The potential clinical application value of these characteristic metabolites was analyzed using the receiver operating characteristic curve. Result: A total of thirty patients with intrahepatic cholestasis and thirty healthy adults were included. The age difference between the two groups was not statistically significant ( P >0.05). The clinical condition was consistent with the statistically significant differences in liver biochemical indicators, blood routine, coagulation, and inflammatory indicators between the two groups ( P <0.05). Furthermore, a blood metabolomics screening analysis revealed 99 differentially expressed metabolites associated with intrahepatic cholestasis. Of these, 15 showed statistically significant differences. Glucose, lipid, and energy metabolisms were the various primary types of differential metabolites involved. The receiver operating characteristic curve>0.9 included the following twelve kinds of metabolites: 1H-indole-3-carboxaldehyde, 6-hydroxy-1H-indole-3-acetamide, phenylalanyl tryptophan, 1-methylguanosine, 2-ethoxy-5-methylpyrazine, p-hydroxybenzaldehyde, 5-(2-chlorophenyl)-3,4-dihydro-2H-pyrrole, methylthioadenosine, alanylisoleucine, anabsinthin, N-acetyl-DL-histidine monohydrate, N-methylnicotinamide, and others. The fifteen metabolites that were previously identified and calculated according to the differential quantitative value of the metabolite corresponding ratio exhibited fold-changes in the upregulated and downregulated potential biomarkers (phenylalanine tryptophan, phenylalanine, 5'-methylthioadenosine, anabsinthin, and N-methylnicotinamide) in combination with the area under the receiver operating characteristic curve>0.9. Conclusion: Phenylalanyl tryptophan, phenylalanylalanine, 5'-methylthioadenosine, anabsinthin, and N-methylnicotinamide may serve as potential metabolic markers to distinguish patients with cholestasis from healthy controls. N-methylnicotinamide, among them, is of great importance as a potential marker.

Background: Coronary atherosclerosis (CAS) is a prevalent and chronic life-threatening disease. However, the detection of CAS at an early stage is difficult because of the lack of effective noninvasive diagnostic methods. The present study aimed to characterize the plasma metabolome of early-stage CAS patients to discover metabolomic biomarkers, develop a novel metabolite-based model for accurate noninvasive diagnosis of early-stage CAS, and explore the underlying metabolic mechanisms involved., Methods: A total of 100 patients with early-stage CAS and 120 age- and sex-matched control subjects were recruited from the Chinese Han population and further randomly divided into training (n = 120) and test sets (n = 100). The metabolomic profiles of the plasma samples were analyzed by an integrated untargeted liquid chromatography-mass spectrometry approach, including two separation modes and two ionization modes. Univariate and multivariate statistical analyses were employed to identify potential biomarkers and construct an early-stage CAS diagnostic model., Results: The integrated analytical method established herein improved metabolite coverage compared with single chromatographic separation and MS ionization mode. A total of 80 metabolites were identified as potential biomarkers of early-stage CAS, and these metabolites were mainly involved in glycerophospholipid, fatty acid, sphingolipid, and amino acid metabolism. An effective diagnostic model for early-stage CAS was established, incorporating 11 metabolites and achieving areas under the receiver operating characteristic curve (AUCs) of 0.984 and 0.908 in the training and test sets, respectively., Conclusions: Our study not only successfully developed an effective noninvasive diagnostic model for identifying early-stage CAS but also provided novel insights into the pathogenesis of CAS., 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 © 2024 Elsevier B.V. All rights reserved.)

Background: Dioscorea opposita Thunb. cv. Tiegun maturity (DM) is an important factor influencing its quality. However, there are few studies on the impact of harvest time on its maturation. In the present study, a NMR-based metabolomics approach was applied to investigate the dynamic metabolic changes of D. opposita Thunb. cv. Tiegun at six different harvest stages: stage 1 (S1), stage 2 (S2), Stage 3 (S3), stage 4 (S4), stage 5 (S5) and stage 6 (S6)., Results: Principal component analysis showed distinct segregation of samples obtained from S1, S2 and S3 compared to those derived from S4, S5 and S6. Interestingly, these samples from the two periods were obtained before and after frost, indicating that frost descent might be important for DM. Eight differential metabolites responsible for good separation of different groups were identified by the principal component analysis loading plot and partial least squares-discriminant analysis. In addition, quantitative analysis of these metabolites using liquid chromatography-tandem mass spectrometry determined the effects of harvest time on these metabolite contents, two of which, sucrose and allantoin, were considered as potential biomarkers to determine DM., Conclusion: The present study demonstrated that NMR-based metabolomics approach could serve as a powerful tool to identify differential metabolites during harvesting processes, also offering a fresh insight into understanding the DM and the potential mechanism of quality formation. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Tartary buckwheat, celebrated as the "king of grains" for its flavonoid and phenolic acid richness, has health-promoting properties. Despite significant morphological and metabolic variations in mature achenes, research on their developmental process is limited. Utilizing Liquid chromatography-mass spectrometry and atmospheric pressure matrix-assisted laser desorption/ionization mass spectrometry imaging, we conducted spatial-temporal metabolomics on two cultivars during achene development. Metabolic profiles including 17 phenolic acids and 83 flavonoids are influenced by both varietal distinctions and developmental intricacies. Notably, flavonols, as major flavonoids, accumulated with achene ripening and showed a tissue-specific distribution. Specifically, flavonol glycosides and aglycones concentrated in the embryo, while methylated flavonols and procyanidins in the hull. Black achenes at the green achene stage have higher bioactive compounds and enhanced antioxidant capacity. These findings provide insights into spatial and temporal characteristics of metabolites in Tartary buckwheat achenes and serve as a theoretical guide for selecting optimal resources for food production., Competing Interests: Declaration of competing interest The authors declare no known financial interests or personal relationships that could have influenced the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Metabolomics commonly relies on using one-dimensional (1D) 1 H NMR spectroscopy or liquid chromatography-mass spectrometry (LC-MS) to derive scientific insights from large collections of biological samples. NMR and MS approaches to metabolomics require, among other issues, a data processing pipeline. Quantitative assessment of the performance of these software platforms is challenged by a lack of standardized data sets with "known" outcomes. To resolve this issue, we created a novel simulated LC-MS data set with known peak locations and intensities, defined metabolite differences between groups (i.e., fold change > 2, coefficient of variation ≤ 25%), and different amounts of added Gaussian noise (0, 5, or 10%) and missing features (0, 10, or 20%). This data set was developed to improve benchmarking of existing LC-MS metabolomics software and to validate the updated version of our MVAPACK software, which added gas chromatography-MS and LC-MS functionality to its existing 1D and two-dimensional NMR data processing capabilities. We also included two experimental LC-MS data sets acquired from a standard mixture and Mycobacterium smegmatis cell lysates since a simulated data set alone may not capture all the unique characteristics and variability of real spectra needed to assess software performance properly. Our simulated and experimental LC-MS data sets were processed with the MS-DIAL and XCMSOnline software packages and our MVAPACK toolkit to showcase the utility of our data sets to benchmark MVAPACK against community standards. Our results demonstrate the enhanced objectivity and clarity of software assessment that can be achieved when both simulated and experimental data are employed since distinctly different software performances were observed with the simulated and experimental LC-MS data sets. We also demonstrate that the performance of MVAPACK is equivalent to or exceeds existing LC-MS software programs while providing a single platform for processing and analyzing both NMR and MS data sets.

Immature citruses are an important resource for the pharmaceutical industry due to their high levels of metabolites with health benefits. In this study, we used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to investigate the changes associated with fruit size in immature citrus fruits in the first weeks of growth. Three orange cultivars ( Citrus sinensis 'Navel', Citrus sinensis 'Valencia', and Citrus sinensis 'Valencia Late') and a mandarin ( Citrus reticulata Blanco 'Fremont') were separated into eight fruit sizes, extracted, and analyzed. Statistical analyses revealed a distinct separation between the mandarin and the oranges based on 56 metabolites, with an additional separation between the 'Navel' orange and the 'Valencia' and 'Valencia Late' oranges based on 21 metabolites. Then, metabolites that evolved significantly with fruit size growth were identified, including 40 up-regulated and 31 down-regulated metabolites. This study provides new insights into the metabolite modifications of immature Citrus sinensis and Citrus reticulata in the first weeks of growth and emphasizes the significance of including early sampled fruits in citrus maturation studies.

Ischemic stroke is the most important cause of disability and death worldwide, but current treatments remain limited. Traditional Chinese medicine (TCM) including the herb pair of Zhiqiao-Danggui (ZD) offers a multifaceted treatment approach through promoting blood circulation, yet its specific anti-ischemic mechanism remains unclear. This study used the photochemically induced thrombosis (PIT) mouse model and the oxygen glucose deprivation/reoxygenation (OGD/R) cell model to explore the therapeutic effect of ZD on ischemic stroke. Mice were treated with high and low doses of ZD extract or positive control. Behavior was assessed using the grid test. The brain tissue was then subjected to infarct volume assessment, histopathology, oxidative stress marker detection, LC/MS metabolomic analysis and qRT-PCR validation. The therapeutic effect of ZD-medicated serum on OGD/R model was tested on cells. Experimental results show that ZD can improve motor function, reduce infarct size, neuronal damage and apoptosis as well as alleviate oxidative stress in mice. ZD-medicated serum promotes endothelial cell proliferation, improves cell survival against OGD/R-induced injury, reduces oxidative damage and protects mitochondrial function. Metabolomics reveals ZD regulation of metabolites in energy metabolism, amino acid metabolism, TCA cycle, and angiogenesis signaling pathways. qRT-PCR results also showed that ZD could attenuate abnormal conduction of angiogenic signals and enhance vessel stability. This study confirmed the neuroprotective and vasoprotective effects of ZD, highlighted its potential in treating ischemic stroke, and provided a scientific basis for the traditional use of ZD., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

mTORC1/2 dual inhibitors may be more effective than mTORC1 inhibitor rapamycin. However, their metabolic impacts on colon cancer cells remain unexplored. We conducted a comparative analysis of the anti-proliferative effects of rapamycin and the novel OSI-027 in colon cancer cells HCT-116, evaluating their metabolic influences through ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS/MS). Our results demonstrate that OSI-027 more effectively inhibits colon cancer cell proliferation than rapamycin. Additionally, we identified nearly 600 metabolites from the spectra, revealing significant differences in metabolic patterns between cells treated with OSI-027 and rapamycin. Through VIP value screening, we pinpointed crucial metabolites contributing to these distinctions. For inhibiting glycolysis and reducing glucose consumption, OSI-027 was likely to be more potent than rapamycin. For amino acids metabolism, although OSI-027 has a broad effect as rapamycin, their effects in degrees were not exactly the same. These findings address the knowledge gap regarding mTORC1/2 dual inhibitors and lay a foundation for their further development and research., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Growing clinical evidence reveals that systematic molecular alterations in the brain occur 20 years before the onset of AD pathological features. Apolipoprotein E4 (ApoE4) is one of the most significant genetic risk factors for Alzheimer's disease (AD), which is not only associated with the AD pathological features such as amyloid-β deposition, phosphorylation of tau proteins, and neuroinflammation but is also involved in metabolism, neuron growth, and synaptic plasticity. Multiomics, such as metabolomics and proteomics, are applied widely in identifying key disease-related molecular alterations and disease-progression-related changes. Despite recent advances in the development of analytical technologies, screening the entire profile of metabolites remains challenging due to the numerous classes of compounds with diverse chemical properties that require different extraction processes for mass spectrometry. In this study, we utilized Orbitrap Secondary Ion Mass Spectrometry (OrbiSIMS) as a chemical filtering screening tool to examine molecular alterations in ApoE4-carried neuroglioma cells compared to wild-type H4 cells. The findings were compared using liquid chromatography (LC)-MS/MS targeted metabolomics analysis for the confirmation of specific metabolite classes. Detected alterations in peptide fragments by OrbiSIMS provided preliminary indications of protein changes. These were extensively analyzed through proteomics to explore ApoE4's impact on proteins. Our metabolomics approach, combining OrbiSIMS and LC-MS/MS, revealed disruptions in lipid metabolism, including glycerophospholipids and sphingolipids, as well as amino acid metabolism, encompassing alanine, aspartate, and glutamate metabolism; aminoacyl-tRNA biosynthesis; glutamine metabolism; and taurine and hypotaurine metabolism. Further LC-MS/MS proteomics studies confirmed the dysfunction in amino acid and tRNA aminoacylation metabolic processes, and highlighted RNA splicing alterations influenced by ApoE4.

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: During the Metabolomics 2023 conference, the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) presented a QA/QC workshop for LC-MS-based untargeted metabolomics., Objectives: The Best Practices Working Group disseminated recent findings from community forums and discussed aspects to include in a living guidance document., Methods: Presentations focused on reference materials, data quality review, metabolite identification/annotation and quality assurance., Results: Live polling results and follow-up discussions offered a broad international perspective on QA/QC practices., Conclusions: Community input gathered from this workshop series is being used to shape the living guidance document, a continually evolving QA/QC best practices resource for metabolomics researchers., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

In recent years, saline-alkaline aquaculture development has become an important measure for China to expand its fishery development space to ensure food safety. Previous studies have verified that salinity and alkalinity positively influence the quality of Chinese mitten crabs (Eriocheir sinensis). However, the regulatory mechanism of E. sinensis endures saline-alkaline stress which remains obscure. This study investigated the metabolic changes in puberty-molting E. sinensis gills exposed to freshwater (FW), sodium chloride salinity of 5 ppt (SW), and carbonate alkalinity 10.00 mmol/L (AW) for 50 days using untargeted liquid chromatography-mass spectrometry metabolomics (LC-MS). A total of 5802 (positive-ion mode) and 6520 (negative-ion mode) peaks were extracted by LC-MS, respectively. A total of 188 (50 upregulated and 138 downregulated), 141 (94 upregulated and 47 downregulated), and 130 (87 upregulated and 43 downregulated) significantly regulated metabolites (SRMs) were observed in the FW-SW, FW-AW, and SW-AW treatments, respectively, wherein 42 generic SRMs were also found by Venn diagram analysis. Seven of the top 10 SRMs with the highest (variable importance in projection) VIP values were similarly identified in FW-SW and SW-AW. Integrated analysis of key metabolic pathways revealed glycerophospholipid, choline in cancer, phenylalanine, and butanoate metabolism. Overall, significant differences were observed in the metabolites and key metabolic pathways of E. sinensis gill exposed to salinity and alkalinity stress. These results will be helpful in understanding the environmental adaptability of aquatic crustaceans to saline-alkaline water., 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 © 2024 Elsevier Inc. All rights reserved.)

In recent years, there is a great concern about the potential adverse effects of carbon nanotubes (CNTs) on the aquatic systems due to their increasingly extensive application. In this study, juvenile Cyprinus carpio were exposed to multi-walled CNTs (MWCNTs) at concentrations of 0, 0.25, and 2.5 mg L -1 for 28 days. Then, oxidative stress indicators and metabolite profile of the livers were assessed. Results showed the significant increase of malondialdehyde (MDA) content and decrease of glutathione (GSH) activities in fish treated with 2.5 mg L -1 MWCNTs. LC-MS untargeted metabolomics demonstrated that 406 and 274 metabolites in fish treated with 2.5 mg L -1 MWCNTs were significantly up- and down-regulated, respectively. KEGG functional annotation analysis showed the disturbance of amino acid metabolism, lipid metabolism, and nucleotide metabolism. In addition, ferroptosis signaling pathway was detected. Therefore, iron content analysis and quantitative real-time RT-PCR assay were performed furtherly to validate the contribution of ferroptosis to MWCNTs-induced hepatotoxicity. The iron content increased significantly and the mRNA levels of ferroptosis-related genes including STEAP3, ACSL4, NCOA4, TFR1, NRF2, SLC3A2, SLC7A11, GPX4, and FPN1 were also obviously changed. Taken together, our study suggested that MWCNTs exposure-induced ferroptosis were associated with iron overload and lipid peroxidation via NRF2/SLC7A11/GSH/GPX4 axis. Our findings provide essential information to understand the mechanism of CNTs-induced hepatotoxicity in fish and explore potential biomarkers., 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 © 2024 Elsevier B.V. All rights reserved.)

Background: Untargeted metabonomics has provided new insight into the pathogenesis of sarcopenia. In this study, we explored plasma metabolic signatures linked to a heightened risk of sarcopenia in a cohort study by LC-MS-based untargeted metabonomics., Methods: In this nested case-control study from the Adult Physical Fitness and Health Cohort Study (APFHCS), we collected blood plasma samples from 30 new-onset sarcopenia subjects (mean age 73.2 ± 5.6 years) and 30 healthy controls (mean age 74.2 ± 4.6 years) matched by age, sex, BMI, lifestyle, and comorbidities. An untargeted metabolomics methodology was employed to discern the metabolomic profile alterations present in individuals exhibiting newly diagnosed sarcopenia., Results: In comparing individuals with new-onset sarcopenia to normal controls, a comprehensive analysis using liquid chromatography-mass spectrometry (LC-MS) identified a total of 62 metabolites, predominantly comprising lipids, lipid-like molecules, organic acids, and derivatives. Receiver operating characteristic (ROC) curve analysis indicated that the three metabolites hypoxanthine (AUC=0.819, 95% CI=0.711-0.927), L-2-amino-3-oxobutanoic acid (AUC=0.733, 95% CI=0.598-0.868) and PC(14:0/20:2(11Z,14Z)) (AUC= 0.717, 95% CI=0.587-0.846) had the highest areas under the curve. Then, these significant metabolites were observed to be notably enriched in four distinct metabolic pathways, namely, "purine metabolism"; "parathyroid hormone synthesis, secretion and action"; "choline metabolism in cancer"; and "tuberculosis"., Conclusion: The current investigation elucidates the metabolic perturbations observed in individuals diagnosed with sarcopenia. The identified metabolites hold promise as potential biomarkers, offering avenues for exploring the underlying pathological mechanisms associated with sarcopenia., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Han, Chen, Liang, Liu, Yu, Song, Zhao, Zhang, Zhu, Shi and Guo.)

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

Metabolite profiling has the potential to comprehensively bridge phenotypes and complex heterogeneous physiological and pathological states. We performed a metabolomics study using parallel liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis to screen for biomarkers of primary aldosteronism (PA) from a cohort of 111 PA patients and 218 primary hypertension (PH) patients. Hydrophilic interaction chromatography and reversed-phase liquid chromatography separations were employed to obtain a global plasma metabolome of endogenous metabolites. The satisfactory classification between PA and PH patients was obtained using the MVDA model. A total of 35 differential metabolites were screened out and identified. A diagnostic biomarker panel was established using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model and receiver operating characteristic analysis. Joint analysis with clinical indicators, including plasma supine aldosterone level, plasma orthostatic aldosterone level, body mass index, and blood potassium, revealed that the combination of metabolite biomarker panel and plasma supine aldosterone has the best clinical diagnostic efficacy., (© 2024 John Wiley & Sons Ltd.)

Tris(1-chloro-2-propyl) phosphate (TCPP) and tris(2-butoxyethyl) phosphate (TBEP) as pollutants of emerging concern have aroused the rising attention due to their potential risks on aquatic ecosystem and public health. Nevertheless, there is a lack of toxicological mechanisms exploration of TCPP and TBEP at molecular levels. Herein, the toxicity effects and molecular mechanism of them were fully researched and summarized on Escherichia coli (E.coli). Acute exposure to them significantly activated antioxidant defense system and caused lipid peroxidation, as proved by the changes of antioxidant enzymes and MDA. The ROS overload resulted in the drop of membrane potential as well as the downregulated synthesis of ATPase, endorsing that E. coli cytotoxicity was ascribed to oxidative stress damage induced by TCPP and TBEP. The combination of GC-MS and LC-MS based metabolomics validated that TCPP and TBEP induced metabolic reprogramming in E.coli. More specifically, the responsive metabolites in carbohydrate metabolism, lipids metabolism, nucleotide metabolism, amino acid metabolism, and organic acids metabolism were significantly disturbed by TCPP and TBEP, confirming the negative effects on metabolic functions and key bioprocesses. Additionally, several biomarkers including PE(16:1(5Z)/15:0), PA(17:1(9Z)/18:2(9Z,12Z)), PE(19:1(9Z)/0:0), and LysoPE(0:0/18:1(11Z)) were remarkably upregulated, verifying that the protection of cellular membrane was conducted by regulating the expression of lipids-associated metabolites. Collectively, this work sheds new light on the potential molecular toxicity mechanism of TCPP and TBEP on aquatic organisms, and these findings using GC-MS and LC-MS metabolomics generate a fresh insight into assessing the effects of OPFRs on target and non-target aquatic organisms., 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 © 2024 Elsevier Ltd. All rights reserved.)

Background: Ershiwuwei Zhenzhu pills was originally recorded in the Tibetan medical book Si Bu Yi Dian in the 8th century AD and is now included in the Pharmacopoeia of the People's Republic of China (2020). The pills can calm the nerves and open the mind as well as treat cerebral ischemia reperfusion injury, stroke, hemiplegia. However, its quality standards have not yet been established, and the therapeutic effect on cerebral ischemia by regulating the mitochondrial apoptosis pathway has not been elucidated., Study Design and Methods: LC-MS was used to establish quality standards for Ershiwuwei Zhenzhu pills. Metabonomics, molecular docking, neuroethology, cerebral infarction ratio, pathological detection of diencephalon, cortex, and hippocampus, and molecular biology techniques were used to reveal the mechanism of the pills in regulating the mitochondrial apoptosis pathway to treat cerebral ischemia., Results: The contents of 20 chemical components in Ershiwuwei Zhenzhu pills from 12 batches and 8 manufacturers was determined for the first time. Eleven differential metabolites and three metabolic pathways, namely, fructose and mannose metabolism, glycerophospholipid metabolism, and purine metabolism, were identified by metabonomics. The pills improved the neuroethology abnormalities of MCAO rats and the pathological damage in the diencephalon and decreased the ratio of cerebral infarction. It also significantly reduced the mRNA expression of AIF, Apaf-1, cleared caspase8, CytC, and P53 mRNA in the brain tissue and the protein expression of Apaf-1 and CYTC and increased the protein expression of NDRG4., Conclusion: In vitro quantitative analysis of the in vitro chemical components of Ershiwuwei Zhenzhu pills has laid the foundation for improving its quality control. The potential mechanism of the pills in treating cerebral ischemia may be related to the Apaf-1/CYTC/NDRG4 apoptosis pathway. This work provides guidance for clinical drug use for patients., 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 © 2024 Elsevier GmbH. All rights reserved.)

Accurate and reliable quantification of organic acids with carboxylic acid functional groups in complex biological samples remains a major analytical challenge in clinical chemistry. Issues such as spontaneous decarboxylation during ionization, poor chromatographic resolution, and retention on a reverse-phase column hinder sensitivity, specificity, and reproducibility in multiple-reaction monitoring (MRM)-based LC-MS assays. We report a targeted metabolomics method using phenylenediamine derivatization for quantifying carboxylic acid-containing metabolites (CCMs). This method achieves accurate and sensitive quantification in various biological matrices, with recovery rates from 90% to 105% and CVs ≤ 10%. It shows linearity from 0.1 ng/mL to 10 µg/mL with linear regression coefficients of 0.99 and LODs as low as 0.01 ng/mL. The library included a wide variety of structurally variant CCMs such as amino acids/conjugates, short- to medium-chain organic acids, di/tri-carboxylic acids/conjugates, fatty acids, and some ring-containing CCMs. Comparing CCM profiles of pancreatic cancer cells to normal pancreatic cells identified potential biomarkers and their correlation with key metabolic pathways. This method enables sensitive, specific, and high-throughput quantification of CCMs from small samples, supporting a wide range of applications in basic, clinical, and translational research.

Aspergillus cristatus is a crucial edible fungus used in tea fermentation. In the industrial fermentation process, the fungus experiences a low to high osmotic pressure environment. To explore the law of material metabolism changes during osmotic pressure changes, NaCl was used here to construct different osmotic pressure environments. Liquid chromatography-mass spectrometry (LC-MS) combined with multivariate analysis was performed to analyze the distribution and composition of A. cristatus under different salt concentrations. At the same time, the in vitro antioxidant activity was evaluated. The LC-MS metabolomics analysis revealed significant differences between three A. cristatus mycelium samples grown on media with and without NaCl concentrations of 8% and 18%. The contents of gibberellin A3, A124, and prostaglandin A2 related to mycelial growth and those of arabitol and fructose-1,6-diphosphate related to osmotic pressure regulation were significantly reduced at high NaCl concentrations. The biosynthesis of energy-related pantothenol and pantothenic acid and antagonism-related fluvastatin, aflatoxin, and alternariol significantly increased at high NaCl concentrations. Several antioxidant capacities of A. cristatus mycelia were directly related to osmotic pressure and exhibited a significant downward trend with an increase in environmental osmotic pressure. The aforementioned results indicate that A. cristatus adapts to changes in salt concentration by adjusting their metabolite synthesis. At the same time, a unique set of strategies was developed to cope with high salt stress, including growth restriction, osmotic pressure balance, oxidative stress response, antioxidant defense, and survival competition.

Introduction: Bio stimulants are substances and/or microorganisms that are used to improve plant growth and crop yields by modulating physiological processes and metabolism of plants. While research has primarily focused on the broad effects of bio stimulants in crops, understanding their cellular and molecular influences in plants, using metabolomic analysis, could elucidate their effectiveness and offer possibilities for fine-tuning their application. One such bio stimulant containing galacturonic acid as elicitor is used in agriculture to improve wheat vigor and strengthen resistance to lodging., Objective: However, whether a metabolic response is evolved by plants treated with this bio stimulant and the manner in which the latter might regulate plant metabolism have not been studied., Method: Therefore, the present study used 1 H-NMR and LC-MS to assess changes in primary and secondary metabolites in the roots, stems, and leaves of wheat (Triticum aestivum) treated with the bio stimulant. Orthogonal partial least squares discriminant analysis effectively distinguished between treated and control samples, confirming a metabolic response to treatment in the roots, stems, and leaves of wheat., Results: Fold-change analysis indicated that treatment with the bio stimulation solution appeared to increase the levels of hydroxycinnamic acid amides, lignin, and flavonoid metabolism in different plant parts, potentially promoting root growth, implantation, and developmental cell wall maturation and lignification., Conclusion: These results demonstrate how non-targeted metabolomic approaches can be utilized to investigate and monitor the effects of new agroecological solutions based on systemic responses., (© 2024. The Author(s).)

An early diagnosis of cancer is fundamental not only in regard to reducing its mortality rate but also in terms of counteracting the progression of the tumor in the initial stages. Breast cancer (BC) is the most common tumor pathology in women and the second deathliest cancer worldwide, although its survival rate is increasing thanks to improvements in screening programs. However, the most common techniques to detect a breast tumor tend to be time-consuming, unspecific or invasive. Herein, the use of untargeted hydrophilic interaction liquid chromatography-mass spectrometry analysis appears as an analytical technique with potential use for the early detection of biomarkers in liquid biopsies from BC patients. In this research, plasma samples from 134 BC patients were compared with 136 from healthy controls (HC), and multivariate statistical analyses showed a clear separation between four BC phenotypes (LA, LB, HER2, and TN) and the HC group. As a result, we identified two candidate biomarkers that discriminated between the groups under study with a VIP > 1 and an AUC of 0.958. Thus, targeting the specific aberrant metabolic pathways in future studies may allow for better molecular stratification or early detection of the disease.

Erythrodermic psoriasis (EP) is a rare and life-threatening disease, the pathogenesis of which remains to be largely unknown. Metabolomics analysis can provide global information on disease pathophysiology, candidate biomarkers, and potential intervention strategies. To gain a better understanding of the mechanisms of EP and explore the serum metabolic signature of EP, we conducted an untargeted metabolomics analysis from 20 EP patients and 20 healthy controls. Furthermore, targeted metabolomics for focused metabolites were identified in the serum samples of 30 EP patients and 30 psoriasis vulgaris (PsV) patients. In the untargeted analysis, a total of 2992 molecular features were extracted from each sample, and the peak intensity of each feature was obtained. Principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed significant difference between groups. After screening, 98 metabolites were found to be significantly dysregulated in EP, including 67 down-regulated and 31 up-regulated. EP patients had lower levels of L-tryptophan, L-isoleucine, retinol, lysophosphatidylcholine (LPC), and higher levels of betaine and uric acid. KEGG analysis showed differential metabolites were enriched in amino acid metabolism and glycerophospholipid metabolism. The targeted metabolomics showed lower L-tryptophan in EP than PsV with significant difference and L-tryptophan levels were negatively correlated with the PASI scores. The serum metabolic signature of EP was discovered. Amino acid and glycerophospholipid metabolism were dysregulated in EP. The metabolite differences provide clues for pathogenesis of EP and they may provide insights for therapeutic interventions., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Purpose: This study was aimed to analyze serum amino acid metabolite profiles in frailty patients, gain a better understanding of the metabolic mechanisms in frailty, and assess the diagnostic value of metabolomics-based biomarkers of frailty., Experimental Design: This study utilized the ultra-performance liquid chromatography tandem mass spectrometry to examine amino acids associated with frailty. Additionally, we employed multivariate statistical methods, metabolomic data analysis, receiver operating characteristic (ROC) curve analysis, and pathway enrichment analysis., Results: Among the assayed amino acid metabolites, we identified biomarkers for frailty. ROC curve analysis for frailty diagnosis based on the modified Fried's frailty index showed that the areas under ROC curve of tryptophan, phenylalanine, aspartic acid, and combination were 0.775, 0.679, 0.667, and 0.807, respectively. ROC curve analysis for frailty diagnosis based on Frail Scale showed that the areas under ROC curve of cystine, phenylalanine, and combination of amino acids (cystine, L-Glutamine, citrulline, tyrosine, kynurenine, phenylalanine, glutamin acid) were 0.834, 0.708, and 0.854 respectively., Conclusion and Clinical Relevance: In this study, we explored the serum amino acid metabolite profiles in frailty patients. These present metabolic analyses may provide valuable information on the potential biomarkers and the possible pathogenic mechanisms of frailty., Clinical Significance: Frailty is a clinical syndrome, as a consequence it is challenging to identify at early course of the disease, even based on the existing frailty scales. Early diagnosis and appropriate patient management are the key to improve the survival and limit disabilities in frailty patients. Proven by the extensive laboratory and clinical studies on frailty, comprehensive analysis of metabolic levels in frail patients, identification of biomarkers and study of pathogenic pathways of metabolites contribute to the prediction and early diagnosis of frailty. In this study, we explored the serum amino acid metabolite profiles in frailty patients. These present metabolic analyses may provide valuable information on the potential biomarkers and the possible pathogenic mechanisms of frailty., (© 2024 Wiley‐VCH GmbH.)

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment., (© 2024. The Author(s).)

Prunella vulgaris, aptly named for its withering at the summer solstice, displays significant variation in quality arising from differing harvest time. However, research on the chemical composition changes of its spikes at various stages is limited, and the specific metabolites remain unclear. In order to elucidate the metabolites and metabolic pathways of the spikes of P. vulgaris, the current study deployed ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS) and targeted metabolomics to characterize the compound variability in the spikes of P. vulgaris across different periods. Multivariate statistical techniques such as principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were used to identify the differences in metabolites, and relevant metabolic pathways were analyzed. A total of 602 metabolites were identified by metabolomics, of which organic acids and their derivatives were the most abundant, followed by flavonoids. Multiple differential metabolites, including p-hydroxybenzoic acids and gallic acids were identified based on variable importance in projection(VIP)&gt;1 and P&lt;0.05. The results of enrichment analysis suggested that isoflavonoids biosynthesis, aminobenzoate degradation, benzoate degradation, anthocyanins biosynthesis, metabolic pathways, microbial metabolism in different environments, secondary plant metabolite biosynthesis, tryptophan metabolism, and phenylpropanoid synthesis were the main metabolic pathways. These results intend to elucidate the dynamic changes of differential metabolites of P. vulgaris and provide a theoretical basis for further study of the harvesting mechanism of spikes of P. vulgaris.

To explore the underlying mechanisms by which superchilling (SC, -3 °C within 5 h of slaughter) improves beef tenderness, an untargeted metabolomics strategy was employed. M. Longissimus lumborum (LL) muscles from twelve beef carcasses were assigned to either SC or very fast chilling (VFC, 0 °C within 5 h of slaughter) treatments, with conventional chilling (CC, 0 ∼ 4 °C until 24 h post-mortem) serving as the control (6 per group). Biochemical properties and metabolites were investigated during the early post-mortem period. The results showed that the degradation of μ-calpain and caspase 3 occurred earlier in SC treated sample, which might be attributed to the accelerated accumulation of free Ca 2+ . The metabolomic profiles of samples from the SC and CC treatments were clearly distinguished based on partial least squares-discriminant analysis (PLS-DA) at each time point. It is noteworthy that more IMP and 4-hydroxyproline were found in the comparison between SC and CC treatments. According to the results of metabolic pathways analysis and the correlation analysis between traits related to tenderness and metabolites with significant differences (SC vs. CC), it can be suggested that the tenderization effect of the SC treatment may be related to the alteration of arginine and proline metabolism, and purine metabolism in the early post-mortem phase., 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 © 2024. Published by Elsevier Ltd.)

Background: Geographical factors affect the nutritional, therapeutic and commercial values of fruits. Dragon fruit (Hylocereus spp) is a popular fruit in Asia and a potential functional food with diverse pharmacological attributes. Although it is produced in various localities, the information related to the altitudinal variation of dragon fruit nutrients and active compounds is scarce. Hence, this study aimed to investigate the variations in metabolite profiles of H. polyrhizus (variety Jindu1) fruit pulps from three different altitudes of China, including Wangmo (WM, 650 m), Luodian (LD, 420 m), and Zhenning (ZN, 356 m). Jindu1 is the main cultivated pitaya variety in Guizhou province, China., Results: The LC-MS (liquid chromatography-mass spectroscopy)-based widely targeted metabolic profiling identified 645 metabolites, of which flavonoids (22.64%), lipids (13.80%), phenolic acids (12.40%), amino acids and derivatives (10.39%), alkaloids (8.84%), and organic acids (8.37%) were dominant. Multivariate analyses unveiled that the metabolite profiles of the fruit differed regarding the altitude. Fruits from WM (highest altitude) were prime in quality, with higher levels of flavonoids, alkaloids, nucleotides and derivatives, amino acids and derivatives, and vitamins. Fruits from LD and ZN had the highest relative content of phenolic acids and terpenoids, respectively. We identified 69 significantly differentially accumulated metabolites across the pulps of the fruits from the three locations. KEGG analysis revealed that flavone and flavonol biosynthesis and isoflavonoid biosynthesis were the most differentially regulated. It was noteworthy that most active flavonoid compounds exhibited an increasing accumulation pattern along with the increase in altitude. Vitexin and isovitexin were the major differentially accumulated flavonoids. Furthermore, we identified two potential metabolic biomarkers (vitexin and kaempferol 3-O-[2-O-β-D-galactose-6-O-a-L-rhamnose]-β-D-glucoside) to discriminate between dragon fruits from different geographical origins., Conclusion: Our findings provide insights into metabolic changes in dragon fruits grown at different altitudes. Furthermore, they show that growing pitaya at high altitudes can produce fruit with higher levels of bioactive compounds, particularly flavonoids., (© 2024. The Author(s).)

Natural products exhibit interesting structural features and significant biological activities. The discovery of new bioactive molecules is a complex process that requires high-quality metabolite profiling data to properly target the isolation of compounds of interest and enable their complete structural characterization. The same metabolite profiling data can also be used to better understand chemotaxonomic links between species. This Data Descriptor details a dataset resulting from the untargeted liquid chromatography-mass spectrometry metabolite profiling of 76 natural extracts of the Celastraceae family. The spectral annotation results and related chemical and taxonomic metadata are shared, along with proposed examples of data reuse. This data can be further studied by researchers exploring the chemical diversity of natural products. This can serve as a reference sample set for deep metabolome investigation of this chemically rich plant family., (© 2024. The Author(s).)

While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography-mass spectrometry (LC-MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice., (© 2024. The Author(s).)

Here, we present a targeted polar metabolomics protocol for the analysis of biofluids and frozen tissue biopsies using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We describe steps for sample pretreatment, liquid-liquid extraction, and isolation of polar metabolites. We then detail procedures for target LC-MS/MS analysis. In this protocol, we focus on the analysis of plasma and serum samples. We also provide brief instructions on how to process other biological matrices as supplemental information. For complete details on the use and execution of this protocol, please refer to Coskun et al. (2022). 1 ., Competing Interests: Declaration of interests J.A.W., Y.L., and V.P. are employees and stockholders of Eli Lilly and Company., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.com/HuanLab/AVIR). The rationale is that metabolic features with computational variation have a poor correlation between chromatographic peak area and peak height-based quantifications across the samples in a study. AVIR was trained on a set of 696 manually curated metabolic features and achieved an accuracy of 94% in a 10-fold cross-validation. When tested on various external data sets from public metabolomics repositories, AVIR demonstrated an accuracy range of 84%-97%. Finally, tested on a large-scale metabolomics study, AVIR clearly indicated features with computational variation and thus guided us to manually correct them. Our results show that 75.3% of the samples with computational variation had a relative intensity difference of over 20% after correction. This demonstrates the critical role of AVIR in reducing computational variation to improve quantitative certainty in untargeted metabolomics analysis.

Peak alignment is a crucial data-processing step in untargeted metabolomics analysis that aims to integrate metabolite data from multiple liquid chromatography-mass spectrometry (LC-MS) batches for enhanced comparability and reliability. However, slight variations in the chromatographic separation conditions can result in retention time (RT) shifts between consecutive analyses, adversely affecting peak alignment accuracy. In this study, we present a retention index (RI)-based chromatographic peak-shift correction (CPSC) strategy to address RT shifts and align chromatographic peaks for metabolomics studies. A series of N -acyl glycine homologues (C2-C23) was synthesized as calibrants, and an LC RI system was established. This system effectively corrected RT shifts arising from variations in flow rate, gradient elution, instrument systems, and chromatographic columns. Leveraging the RI system, we successfully adjusted the RT of raw data to mitigate RT shifts and then implemented the Joint Aligner algorithm for peak alignment. We assessed the accuracy of the RI-based CPSC strategy using pooled human fecal samples as a test model. Notably, the application of the RI-based CPSC strategy to a long-term dataset spanning 157 d as an illustration revealed a significant enhancement in peak alignment accuracy from 15.5% to 80.9%, indicating its ability to substantially improve peak-alignment precision in multibatch LC-MS analyses.

Objective: This study aimed to reveal the urinary and serum metabolic pattern of endometrial cancer (EC) and establish diagnostic models to identify EC from controls, high-risk from low-risk EC, and type II from type I EC., Method: This study included 146 EC patients (comprising 79 low-risk and 67 high-risk patients, including 124 type I and 22 type II) and 59 controls. The serum and urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry. Analysis was used to elucidate the distinct metabolites and altered metabolic pathways. Receiver operating characteristic (ROC) analyses were employed to discover and validate the potential biomarker models., Results: Serum and urine metabolomes displayed significant differences between EC and controls, with metabolites related to amino acid and nicotinamide metabolisms. The serum and urine panels distinguished these two groups with Area Under the Curve (AUC) of 0.821 and 0.902, respectively. The panel consisting of serum and urine metabolites demonstrated the best predictive ability (AUC = 0.953 and 0.976 in discovering and validation group). In comparing high-risk and low risk EC, differential metabolites were enriched in purine and glutamine metabolism. The AUC values for serum and urine panels were 0.818, and 0.843, respectively. The combined panel exhibited better predictive accuracy (0.881 in discovering group and 0.936 in external validation). In the comparison between type I and type II group, altered folic acid metabolism was identified. The serum, urine and combined panels discriminated these two groups with the AUC of 0.829, 0.913 and 0.922, respectively., Conclusion: The combined urine and serum metabolome effectively revealed the metabolic patterns in EC patients, offering valuable diagnostic models for EC diagnosis and classification., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets., Results: As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols., Conclusions: Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies., (© 2024. The Author(s).)

Red seabream (Pagrus major) is widely consumed in East Asia. As nuclear wastewater is discharged into Japanese waterbodies, the country of origin of marine products must be accurately labeled. Here, we aimed to discover candidate metabolite biomarkers to discriminate between Korean and Japanese red seabream using LC-Orbitrap mass spectrometry. In total, 95 and 138 putative metabolites were detected via chromatographic separation of fish sampled in the warm and cold seasons, respectively. The spectrometric and chromatographic data were analyzed using principal component analysis and orthogonal partial least squares discriminant analysis. We identified 12 and 19 influential metabolites to discriminate between each origin fish in the warm and cold seasons, respectively, using variable importance in projection scores and p values. Anserine was further selected as a candidate biomarker based on receiver operating characteristic curve analysis. This study provides a basis for using anserine to determine the geographic origin of red seabream., 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 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Stress can affect different groups of plant metabolites and multiple signaling pathways. Untargeted metabolomics enables the collection of whole-spectrum data for the entire metabolite content present in plant tissues at that point in time. We present a thorough approach for large-scale, untargeted metabolomics of plant tissues using reverse-phase liquid chromatography connected to high-resolution mass spectrometry (LC-MS) of dilute methanolic extract. MZmine is a specialized computer software that automates the alignment and baseline modification of all derived mass peaks across all samples, resulting in precise information on the relative abundance of hundreds of metabolites reflected by thousands of mass signals. Further processing with statistic and bioinformatic techniques will provide a comprehensive perspective of the variations and connections among groups of samples., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

WenDanTang (WDT) is a Chinese herbal formula used to treat various diseases, including neurodegenerative diseases. However, the neuroprotective metabolic pathways and the components involved in this process are not fully understood. In this study, we examined the neuroprotective metabolic pathways of WDT in rat brains using cerebrospinal fluid metabolomics and ultra-high-performance liquid chromatography-high-resolution mass spectrometry. Twelve rats were randomly divided into a WDT (administrated with WDT solution) and a control group. The ultra-high-performance liquid chromatography technique was used to explore the components of the WDT solution and cerebrospinal fluid, and secondary mass spectra of cerebrospinal fluid were used to identify possible brain-incorporating components after WDT. The results of the differential metabolism analysis showed that eight metabolites were typically altered (all p < 0.05). By comparing the secondary mass spectra of the cerebrospinal fluid of rats and WDT solution, two possible brain-incorporating components of WDT, stachydrine and α-methoxyphenylacetic acid, were identified. The data also suggested that WDT affects nucleotide metabolism, glutathione metabolism, and B-vitamin metabolic pathways, the central differential metabolic pathways. These data suggest that WDT protects neurons through its active components, such as stachydrine, and regulates biochemical metabolism to affect the brain's energy metabolism and antioxidant capacity., (© 2024 Wiley-VCH GmbH.)

Introduction: Cervical cancer is one of the malignant cancers with high mortality among women worldwide. Although vaccines and early detection have reduced cervical cancer mortality, it remains a malignancy with a high mortality rate in women., Objectives: We aimed to develop a novel integrated strategy that combines metabolomics with network pharmacology to explore the therapeutic mechanisms of naringin in cervical cancer. The mechanism of naringin intervention in cervical cancer was initially clarified by metabolomics and network pharmacology., Methods: The method of LC-MS and network pharmacology for the detection and identification of potential biomarkers and the mechanisms of action of naringin was used. The metabolites were detected and identified based on ultra-high-performance liquid chromatography coupled with Quadrupole- Exactive Orbitrap MS (UHPLC-Q-Exactive Orbitrap MS) and followed by the network pharmacology analysis., Results: In network pharmacology, naringin played a synergetic role through regulatory shared pathways, such as steroid hormone biosynthesis, sphingolipid signaling pathway and arachidonic acid metabolism, etc. Besides, the metabolomics analysis showed that 20 differential metabolites and 10 metabolic pathways were mainly involved in the therapeutic effect of naringin on cervical cancer. The result showed that naringin treatment for cervical cancer mainly occurs through the following metabolic pathways: amino acid metabolism and arachidonic acid metabolism., Conclusion: This work provided valuable information and a scientific basis for further studies of naringin in the treatment of cervical cancer., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

The analysis of the brain extracellular metabolome is of interest for numerous subdomains within neuroscience. Not only does it provide information about normal physiological functions, it is even more of interest for biomarker discovery and target discovery in disease. The extracellular analysis of the brain is particularly interesting as it provides information about the release of mediators in the brain extracellular fluid to look at cellular signaling and metabolic pathways through the release, diffusion and re-uptake of neurochemicals. In vivo samples are obtained through microdialysis, cerebral open-flow microperfusion or solid-phase microextraction. The analytes of potential interest are typically low in concentration and can have a wide range of physicochemical properties. Liquid chromatography coupled to mass spectrometry has proven its usefulness in brain metabolomics. It allows sensitive and specific analysis of low sample volumes, obtained through different approaches. Several strategies for the analysis of the extracellular fluid have been proposed. The most widely used approaches apply sample derivatization, specific stationary phases and/or hydrophilic interaction liquid chromatography. Miniaturization of these methods allows an even higher sensitivity. The development of chiral metabolomics is indispensable, as it allows to compare the enantiomeric ratio of compounds and provides even more challenges. Some limitations continue to exist for the previously developed methods and the development of new, more sensitive methods remains needed. This review provides an overview of the methods developed for sampling and liquid chromatography-mass spectrometry analysis of the extracellular metabolome., 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.)

Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC-MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12 C-reagent labeled individual samples spiked with a 13 C-reagent labeled reference or pooled sample, LC-MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC-RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC-RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC-RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyzes, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. 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.)

Wood-decaying fungi are the major decomposers of plant litter. Heavy sequencing efforts on genomes of wood-decaying fungi have recently been made due to the interest in their lignocellulolytic enzymes; however, most parts of their proteomes remain uncharted. We hypothesized that wood-decaying fungi would possess promiscuous enzymes for detoxifying antifungal phytochemicals remaining in the dead plant bodies, which can be useful biocatalysts. We designed a computational mass spectrometry-based untargeted metabolomics pipeline for the phenotyping of biotransformation and applied it to 264 fungal cultures supplemented with antifungal plant phenolics. The analysis identified the occurrence of diverse reactivities by the tested fungal species. Among those, we focused on O -xylosylation of multiple phenolics by one of the species tested, Lentinus brumalis . By integrating the metabolic phenotyping results with publicly available genome sequences and transcriptome analysis, a UDP-glycosyltransferase designated UGT66A1 was identified and validated as an enzyme catalyzing O -xylosylation with broad substrate specificity. We anticipate that our analytical workflow will accelerate the further characterization of fungal enzymes as promising biocatalysts.

Background: Existing research has suggested that depression results in disorders of glucose metabolism in the organism which causing insufficient energy supply. However, the overall changes in glucose metabolism that arise from depression have not been clarified., Methods: In this study, the depression-like behavior in chronically unpredictable mild stressed rats was investigated, and the fate of glucose was tracked through isotope tracing and mass spectrometry, with a focus on metabolite changes in cecal contents., Results: As indicated by the results, the isotopic results of cecal contents can indicate the metabolic end of the organism. Moreover, the TCA cycle activity was notably reduced, and the gluconeogenesis pathway was abnormally up-regulated in the CUMS-induced rats. The organism expedited other glucose metabolism pathways to make up for the insufficiency of energy. As a result, the activity of the inefficient glycolysis pathway was increased., Limitations: Existing research has only investigated the metabolism of 13 C-glucose, and lipids and proteins have been rarely explored., Conclusions: The chronic unpredictable mild stress can inhibit the entry of pyruvate into mitochondria in SD rats, such that the activity of TCA is reduced, and insufficient energy supply is caused. The organism is capable of expediting other glucose metabolism rate pathways to make up for the insufficiency of energy, whereas it still cannot compensate for the loss of energy. As a result, CUMS-induced rats exhibited high-rate and low-efficiency glucose metabolism., Competing Interests: Conflict of interest The authors declare no competing financial interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Hypertensive cerebral microbleeds (HCMB) may be the early stage of hypertensive intracerebral hemorrhage (HICH), which is a serious threat to health due to its high mortality and disability rates. The early clinical symptoms of HCMB may not be significant. Moreover, it is difficult to achieve early diagnosis and intervention for targeted prevention of HICH. Although hypertension (HTN) is a predisposition for HCMB, it remains unclear whether there is any difference between hypertensive patients with or without HCMB. Therefore, we carried out liquid chromatography-mass spectrometry (LC-MS) to analyze early biomarkers for HCMB in mice with hypertension and to lay the foundation for early prevention of HICH in hypertensive patients. In total, 18 C57 male mice were randomly divided into the HCMB (n = 6), HTN (n = 6), and control groups (CON, n = 6). Hematoxylin-eosin and diaminobenzidine staining were used to assess the reliability of the model. The metabolite expression level and sample category stability were tested using the displacement test of orthogonal partial least squares discriminant analysis (OPLS-DA). Significant differences in metabolites were screened out using variable importance in the projection (VIP > 1), which were determined using the OPLS-DA model and the P-value of the t-test ( P < 0.05) combined with the nonparametric rank-sum test. With an area under the curve (AUC) > 0.85 and a P- value of 0.05, the receiver operating characteristic curve (ROC) was used to further screen the distinct metabolites of HCMB. Compared with the HTN and CON groups, the HCMB group had significantly higher blood pressure and lower average body weight ( P < 0.05). Through untargeted LC-MS analysis, 93 distinct metabolites were identified in the HCMB ( P < 0.05, VIP > 1) group. Among these potential biomarkers, six significantly decreased and eight significantly increased differential metabolites were found. Meanwhile, we found that the HCMB group had statistically distinct arginine and purine metabolism pathways ( P < 0.05), and citrulline may be the most significant possible biomarker of HCMB (AUC > 0.85, P < 0.05). All of these potential biomarkers may serve as early biomarkers for HICH in hypertension., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Zou, Wang, Zhou, Wu, Zhang, Yao, Chen, Li, Zeng and Zhang.)

The benefits of combining drift time ion mobility (DTIMS) with liquid chromatography-high-resolution mass spectrometry (HRMS) have been reported for metabolomics but the use of differential time mobility spectrometry (DMS) is less obvious due to the need for rapid scanning of the DMS cell. Drift DTIMS provides additional precursor ion selectivity and collisional cross-section information but the separation resolution between analytes remains cell- and component-dependent. With DMS, the addition of 2-propanol modifier can improve the selectivity but on cost of analyte MS response. In the present work, we investigate the liquid chromatography-mass spectrometry (LC-MS) analysis of a mix of 50 analytes, representative for urine and plasma metabolites, using scanning DMS with the single modifiers cyclohexane (Ch), toluene (Tol), acetonitrile (ACN), ethanol (EtOH), and 2-propanol (IPA), and a binary modifier mixture (cyclohexane/2-propanol) with emphasis on selectivity and signal sensitivity. 1.5% IPA in the N 2 stream was found to suppress the signal of 50% of the analytes which could be partially recovered with the use of IPA to 0.05% as a Ch/IPA mixture. The potential to use the separation voltage/compensation voltage/modifier (SV/CoV/Mod) feature as an additional analyte identifier for qualitative analysis is also presented and applied to a data-independent LCxDMS-SWATH-MS workflow for the analysis of endogenous metabolites and drugs of abuse in human urine samples from traffic control., (© 2023. The Author(s).)

Background: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS)., Aim of Review: This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software., Key Scientific Concepts of Review: We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)