MS-based Untargeted Metabolomics Research Articles

Depicting the Chemical Diversity of Bioactive Meroterpenoids Produced by the Largest Organism on Earth.

In this investigation, we explored the diversity of melleolide-type meroterpenoids produced by Armillaria ostoyae, one of the largest and oldest organisms on Earth, using extracts from liquid and solid fermentation media. The study unveiled three unprecedented dimeric bismelleolides and three novel fatty-acid-substituted congeners, along with 11 new and 21 known derivatives. The structures were elucidated by 1D and 2D NMR spectroscopy and HRESI-MS, and ROESY spectral analysis for relative configurations. Absolute configurations were determined from crystal structures and through ECD spectra comparison. A compound library of melleolide-type meroterpenoids facilitated metabolomics-wide associations, revealing production patterns under different culture conditions. The library enabled assessments of antimicrobial and cytotoxic activities, revealing that the Δ2,4 double bond is not crucial for antifungal activity. Cytotoxicity was linked to the presence of an aldehyde at C1, but lost with hydroxylation at C13. Chemoinformatic analyses demonstrated the intricate interplay of chemical modifications on biological properties. This study marks the first systematic exploration of Armillaria spp. meroterpenoid diversity by MS-based untargeted metabolomics, offering insight into structure-activity relationships through innovative chemoinformatics.

Identification of sedative-hypnotic compounds shared by five medicinal Polyporales mushrooms using UPLC-Q-TOF-MS/MS-based untargeted metabolomics

BackgroundFive Polyporales mushrooms, namely Amauroderma rugosum, Ganoderma lucidum, G. resinaceum, G. sinense and Trametes versicolor, are commonly used in China for managing insomnia. However, their active components for this application are not fully understood, restricting their universal recognition. PurposeIn this study, we aimed to identify sedative-hypnotic compounds shared by these five Polyporales mushrooms. Study design and MethodsA UPLC-Q-TOF-MS/MS-based untargeted metabolomics, including OPLS-DA (orthogonal projection of potential structure discriminant analysis) and OPLS (orthogonal projections to latent structures) analysis together with mouse assays, were used to identify the main sedative-hypnotic compounds shared by the five Polyporales mushrooms. A pentobarbital sodium-induced sleeping model was used to investigate the sedative-hypnotic effects of the five mushrooms and their sedative-hypnotic compounds. ResultsNinety-two shared compounds in the five mushrooms were identified. Mouse assays showed that these mushrooms exerted sedative-hypnotic effects, with different potencies. Six triterpenes [four ganoderic acids (B, C1, F and H) and two ganoderenic acids (A and D)] were found to be the main sedative-hypnotic compounds shared by the five mushrooms. ConclusionWe for the first time found that these six triterpenes contribute to the sedative-hypnotic ability of the five mushrooms. Our novel findings provide pharmacological and chemical justifications for the use of the five medicinal mushrooms in managing insomnia.

Computational Expansion of High-Resolution-MSn Spectral Libraries.

Commonly, in MS-based untargeted metabolomics, some metabolites cannot be confidently identified due to ambiguities in resolving isobars and structurally similar species. To address this, analytical techniques beyond traditional MS2 analysis, such as MSn fragmentation, can be applied to probe metabolites for additional structural information. In MSn fragmentation, recursive cycles of activation are applied to fragment ions originating from the same precursor ion detected on an MS1 spectrum. This resonant-type collision-activated dissociation (CAD) can yield information that cannot be ascertained from MS2 spectra alone, which helps improve the performance of metabolite identification workflows. However, most approaches for metabolite identification require mass-to-charge (m/z) values measured with high resolution, as this enables the determination of accurate mass values. Unfortunately, high-resolution-MSn spectra are relatively rare in spectral libraries. Here, we describe a computational approach to generate a database of high-resolution-MSn spectra by converting existing low-resolution-MSn spectra using complementary high-resolution-MS2 spectra generated by beam-type CAD. Using this method, we have generated a database, derived from the NIST20 MS/MS database, of MSn spectral trees representing 9637 compounds and 19386 precursor ions where at least 90% of signal intensity was converted from low-to-high resolution.

Fuzi decoction treats chronic heart failure by regulating the gut microbiota, increasing the short-chain fatty acid levels and improving metabolic disorders

Fuzi decoction (FZD) is clinically used to treat chronic heart failure (CHF) in China, but the mechanism underlying FZD treatment in CHF remains unclear. Here, we investigated the potential mechanism underlying FZD treatment of CHF in rats. First, the compounds in FZD-containing serum of rats were identified, and 16 S rRNA sequencing and GC–MS-based untargeted metabolomics analysis were then performed. The levels of fecal short-chain fatty acids (SCFAs) were determined and compared, and fecal microbiota transplantation (FMT) was used to verify the role of the gut microbiota. Our results identified 27 in FD-containing serum. FZD increased the Firmicutes-to-Bacteroidetes ratio and the Lactobacillus abundance and affected the β diversity of the gut microbiota in rats with CHF. Differential species analysis showed that Lactobacillus and Prevotella were biomarkers of FZD treatment of CHF. Untargeted metabolomics analysis revealed that FZD affected valine, leucine and isoleucine biosynthesis; galactose metabolism; and aminoacyl-tRNA biosynthesis in rats with CHF. Furthermore, FZD significantly increased the acetic acid, propionic acid, butyric acid and isopentanoic acid levels in the feces of rats with CHF. Correlation analysis showed that the butyric acid and Lactobacillus levels had the strongest correlation in the control, sham and high-dose FZD (HFZD) groups, and many microbiota components were closely related to differentially abundant metabolites. FMT revealed that the fecal microbiota obtained from the HFZD group changed the heart rate; the brain natriuretic peptide (BNP), acetic acid, propionic acid, butyric acid, and metabolite levels; and the gut microbiota in rats with CHF. In summary, our study revealed that the mechanism of action of FZD in CHF treatment may be related to improvements in the gut microbiota, elevations in the SCFA content and the regulation of valine, leucine, and isoleucine biosynthesis; galactose metabolism; and other metabolic pathways.

Influence of drought stress on increasing bioactive compounds of pomegranate (Punica granatum L.) juice. Exploratory study using LC–MS-based untargeted metabolomics approach

Water deficit is one of the most important abiotic factors that reduce the agricultural productivity. The impact of irrigation levels on bioactive compounds of one commercial pomegranate cultivar (‘Mollar de Elche’) was studied through LC–MS-based untargeted metabolomics approach. Two treatments were applied to explore crop responses to the timing and the regime of the irrigation applications. The main goal of the present study was to validate the untargeted metabolomics approach as analytical tool to find new markers related with the water deficit. Robust classification and prediction model was built with the use of supervised technique, partial least squares discriminant analysis (PLS-DA). The results revealed that pomegranate juices obtained from plants grown under DIr conditions experimented physiological stress, which significantly increased their bioactive compounds (polyphenols, phenylpropanoids, peptides, benzoic acid, tannins, and phospholipids) in comparison with the Control treatments. The results indicate that controlled irrigation stress would increase the production of bioactive compounds by increasing the phenylpropanoids metabolism. New metabolites were tentatively identified in pomegranate juice by the untargeted metabolomics techniques. This study showed, for the first time, the satisfactory application of the untargeted metabolomics approach using an UHPLC-QTOF system to identify bioactive secondary metabolites from pomegranate juices to discriminate among different irrigation conditions.

GC–MS based untargeted metabolomics reveals the metabolic response of earthworm (Eudriluseugeniae) after chronic combinatorial exposure to three different pesticides

In this study GC–MS-based untargeted metabolomics was used to identify the metabolic response of earthworm; Eudriluseugeniae exposed to sub-lethal concentrations of chlorpyrifos-CHL, cypermethrin-CYP, Glyphosate-GLY, and Combined-C (all three pesticides) at the concentrations of 3, 6, and 12 mg/kg. Principal component analysis of the obtained datasets revealed a clear distinction between the control and treatment groups. The mean weight of the worms in the treated groups decreased significantly (p < 0.05). Among the identified metabolites, oleic acid (~ 93.47%), lysine (~ 92.20%), glutamic acid (~ 91.81%), leucine (~ 90.20%), asparagine (~ 94.20%), methionine (~ 92.27%), malic acid (~ 93.37%), turanose (~ 95.04%), maltose (~ 92.36%), cholesta-3,5-diene (~ 86.11%), galactose (~ 93.20%), cholesterol (~ 91.56%), tocopherol (~ 85.09%), decreased significantly (p < 0.05), whereas myoinositol (~ 83%) and isoleucine (78.09%) increased significantly (p < 0.05) upon exposure to the CHL, CYP, GLY, and C. Overall, the findings suggest that earthworms might be a new entry point for the pesticides into the food chain. The present study highlights that metabolomics can be a reliable approach to understand the effect of different xenobiotics including pesticides on the metabolic response of earthworms.

Encoding LC-MS-Based Untargeted Metabolomics Data into Images toward AI-Based Clinical Diagnosis.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides comprehensive and quantitative profiling of metabolites in clinical investigations. The use of whole metabolome profiles is a promising strategy for disease diagnosis but technically challenging. Here, we developed an approach, namely MetImage, to encode LC-MS-based untargeted metabolomics data into multi-channel digital images. Then, the images that represent the comprehensive metabolome profiles can be employed for developing deep learning-based AI models toward clinical diagnosis. In this work, we demonstrated the application of MetImage for clinical screening of esophageal squamous cell carcinoma (ESCC) in a clinical cohort with 1104 participants. A convolutional neuronal network-based AI model was trained to distinguish ESCC screening positive and negative subjects using their serum metabolomics data. Superior performances such as sensitivity (85%), specificity (92%), and area under curve (0.95) were validated in an independent testing cohort (N = 442). Importantly, we demonstrated that our AI-based ESCC screening model is not a "black box". The encoded images reserved the characteristics of mass spectra from the raw LC-MS data; therefore, metabolite identifications in key image features were readily achieved. Altogether, MetImage is a unique approach that encodes raw LC-MS-based untargeted metabolomics data into images and facilitates the utilization of whole metabolome profiles for AI-based clinical applications with improved interpretability.

Model-driven data curation pipeline for LC–MS-based untargeted metabolomics

There is still no community consensus regarding strategies for data quality review in liquid chromatography mass spectrometry (LC-MS)-based untargeted metabolomics. Assessing the analytical robustness of data, which is relevant for inter-laboratory comparisons and reproducibility, remains a challenge despite the wide variety of tools available for data processing. The aim of this study was to provide a model to describe the sources of variation in LC-MS-based untargeted metabolomics measurements, to use it to build a comprehensive curation pipeline, and to provide quality assessment tools for data quality review. Human serum samples (n=392) were analyzed by ultraperformance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) using an untargeted metabolomics approach. The pipeline and tools used to process this dataset were implemented as part of the open source, publicly available TidyMS Python-based package. The model was applied to understand data curation practices used by the metabolomics community. Sources of variation, which are often overlooked in untargeted metabolomic studies, were identified in the analysis. New tools were used to characterize certain types of variations. The developed pipeline allowed confirming data robustness by comparing the experimental results with expected values predicted by the model. New quality control practices were introduced to assess the analytical quality of data.

Integrated analysis of plasma and urine reveals unique metabolomic profiles in idiopathic inflammatory myopathies subtypes.

ObjectivesIdiopathic inflammatory myopathies (IIM) are a class of autoimmune diseases with high heterogeneity that can be divided into different subtypes based on clinical manifestations and myositis‐specific autoantibodies (MSAs). However, even in each IIM subtype, the clinical symptoms and prognoses of patients are very different. Thus, the identification of more potential biomarkers associated with IIM classification, clinical symptoms, and prognosis is urgently needed.MethodsPlasma and urine samples from 79 newly diagnosed IIM patients (mean disease duration 4 months) and 52 normal control (NC) samples were analysed by high‐performance liquid chromatography of quadrupole time‐of‐flight mass spectrometry (HPLC‐Q‐TOF‐MS)/MS‐based untargeted metabolomics. Orthogonal partial least‐squares discriminate analysis (OPLS‐DA) were performed to measure the significance of metabolites. Pathway enrichment analysis was conducted based on the KEGG human metabolic pathways. Ten machine learning (ML) algorithms [linear support vector machine (SVM), radial basis function SVM, random forest, nearest neighbour, Gaussian processes, decision trees, neural networks, adaptive boosting (AdaBoost), Gaussian naive Bayes and quadratic discriminant analysis] were used to classify each IIM subtype and select the most important metabolites as potential biomarkers.ResultsOPLS‐DA showed a clear separation between NC and IIM subtypes in plasma and urine metabolic profiles. KEGG pathway enrichment analysis revealed multiple unique and shared disturbed metabolic pathways in IIM main [dermatomyositis (DM), anti‐synthetase syndrome (ASS), and immune‐mediated necrotizing myopathy (IMNM)] and MSA‐defined subtypes (anti‐Mi2+, anti‐MDA5+, anti‐TIF1γ+, anti‐Jo1+, anti‐PL7+, anti‐PL12+, anti‐EJ+, and anti‐SRP+), such that fatty acid biosynthesis was significantly altered in both plasma and urine in all main IIM subtypes (enrichment ratio > 1). Random forest and AdaBoost performed best in classifying each IIM subtype among the 10 ML models. Using the feature selection methods in ML models, we identified 9 plasma and 10 urine metabolites that contributed most to separate IIM main subtypes and MSA‐defined subtypes, such as plasma creatine (fold change = 3.344, P = 0.024) in IMNM subtype and urine tiglylcarnitine (fold change = 0.351, P = 0.037) in anti‐EJ+ ASS subtype. Sixteen common metabolites were found in both the plasma and urine samples of IIM subtypes. Among them, some were correlated with clinical features, such as plasma hypogeic acid (r = −0.416, P = 0.005) and urine malonyl carnitine (r = −0.374, P = 0.042), which were negatively correlated with the prevalence of interstitial lung disease.ConclusionsIn both plasma and urine samples, IIM main and MSA‐defined subtypes have specific metabolic signatures and pathways. This study provides useful clues for understanding the molecular mechanisms, searching potential diagnosis biomarkers and therapeutic targets for IIM.

A GC–MS-based untargeted metabolomics approach for comprehensive metabolic profiling of vancomycin-induced toxicity in mice

BackgroundVancomycin is a glycopeptide antibiotic that is commonly used for severe drug-resistant infections treatment. Application of vancomycin frequently leads to severe ototoxicity, hepatotoxicity, and nephrotoxicity; however, the comprehensive metabolic analysis of vancomycin-induced toxicity is lacking. PurposeThis study attempted to investigate the metabolic changes after vancomycin administration in mice. MethodsExperimental mice (n = 9) received continuous intraperitoneal injection of vancomycin (400 mg/kg) every day for 7 days, and mice in control group (n = 9) were treated with the same amount of normal saline. Pathological changes of the kidney were examined using haematoxylin and eosin (HE) staining. A gas chromatography-mass spectrometry (GC-MS) approach was used to identify discriminant metabolites in serum and various organs including the heart, liver, kidney, spleen, cerebral cortex, hippocampus, inner ear, lung, and intestine. The potential metabolites were identified using orthogonal partial least squares discrimination analysis (OPLS-DA). Subsequently, the MetaboAnalyst 5.0 (http://www.metaboanalyst.ca) and Kyoto Encyclopedia of Genes and Genomes database (KEGG, http://www.kegg.jp) were employed to depict the metabolic pathways. ResultsCompared with the control group, the vancomycin induced 13, 17, 27, 22, 16, 10, 17, 11, 10, and 7 differential metabolites in the serum, liver, kidney, heart, cerebral cortex, lung, spleen, intestine, hippocampus, and inner ear, respectively. Further pathway analyses identified that amino acids metabolism, fatty acids biosynthesis, energy metabolism, and lipid metabolism were disrupted after VCM exposure. ConclusionVancomycin affects the metabolism in various organs in mice, which provides new insights for identification of vancomycin-induced toxicity, and facilitate to better understanding of the metabolic pathogenesis of vancomycin.

Protective Effects of Naringenin from Citrus sinensis (var. Valencia) Peels against CCl4-Induced Hepatic and Renal Injuries in Rats Assessed by Metabolomics, Histological and Biochemical Analyses.

Citrus fruits are grown worldwide for their special nutritive and several health benefits. Among citrus bioactives, naringenin, a major flavanone, exhibits a potential hepatoprotective effect that is not fully elucidated. Herein, serum biochemical parameters and histopathological assays were used to estimate the hepatoprotective activity of naringenin, isolated from Citrus sinensis (var. Valencia) peels, in CCl4-induced injury in a rat model. Further, GC–MS-based untargeted metabolomics was used to characterize the potential metabolite biomarkers associated with its activity. Present results revealed that naringenin could ameliorate the increases in liver enzymes (ALT and AST) induced by CCl4 and attenuate the pathological changes in liver tissue. Naringenin decreased urea, creatinine and uric acid levels and improved the kidney tissue architecture, suggesting its role in treating renal disorders. In addition, naringenin increased the expression of the antiapoptoic cell marker, Bcl-2. Significant changes in serum metabolic profiling were noticed in the naringenin-treated group compared to the CCl4 group, exemplified by increases in palmitic acid, stearic acid, myristic acid and lauric acids and decrease levels of alanine, tryptophan, lactic acid, glucosamine and glucose in CCl4 model rats. The results suggested that naringenin’s potential hepato- and renoprotective effects could be related to its ability to regulate fatty acids (FAs), amino acids and energy metabolism, which may become effective targets for liver and kidney toxicity management. In conclusion, the current study presents new insights into the hepato- and renoprotective mechanisms of naringenin against CCl4-induced toxicity.

Community metabolomics provides insights into mechanisms of pollution-induced community tolerance of periphyton

Chemical pollution is a major concern for freshwater ecosystems, but the impact and mechanisms of chemical stressors on communities are barely understood. Pollution stress beyond natural homeostatic capacities can trigger succession of tolerant species within a community, enhancing the overall community tolerance. This process was operationalized in the Pollution-Induced Community Tolerance (PICT) concept and applied in many case studies, however, the molecular mechanisms of community tolerance and implications for ecological functions remain largely unexplored. Our study aimed to demonstrate that 1) community metabolomics can unravel potential mechanisms of PICT in periphyton and 2) induced tolerance helps to maintain primary production under re-occuring pollution. To this end, we grew periphyton for 5 weeks with and without the model herbicide diuron in microcosms, quantified PICT, and determined the related metabolic fingerprint of periphyton by GC–MS-based untargeted metabolomics. Further, we explored the autotrophic community based on pigment composition and functional parameters including photosynthesis and gross primary production. Chronic diuron exposure resulted in a shift in pigment composition, higher community tolerance and an individual metabolic fingerprint in the contaminated communities. Opposing responses of selected metabolites during a short-term exposure indicated differences in diuron pre-adaptation in the different communities. Metabolites (threonic acid and two sugar acid lactones) were found to be related to tolerance development, suggesting that ascorbate metabolism was induced in contaminated communities. Despite these compensating mechanism, contaminated communities were compromised in production-to-respiration ratio and biomass. A ranking of sensitivity thresholds of different biological endpoints revealed that metabolites were less sensitive than photosynthetic parameters, which reflects the mode-of-action of the herbicide. In conclusion, we could demonstrate that community metabolomics is able to unravel complex biochemical changes and allows mechanistic insights into community tolerance. Moreover, we were able to show that induced community tolerance was insufficient to safeguard functions like primary production.

Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis

LC–MS-based untargeted metabolomics is heavily dependent on algorithms for automated peak detection and data preprocessing due to the complexity and size of the raw data generated. These algorithms are generally designed to be as inclusive as possible in order to minimize the number of missed peaks. This is known to result in an abundance of false positive peaks that further complicate downstream data processing and analysis. As a consequence, considerable effort is spent identifying features of interest that might represent peak detection artifacts. Here, we present the CPC algorithm, which allows automated characterization of detected peaks with subsequent filtering of low quality peaks using quality criteria familiar to analytical chemists. We provide a thorough description of the methods in addition to applying the algorithms to authentic metabolomics data. In the example presented, the algorithm removed about 35% of the peaks detected by XCMS, a majority of which exhibited a low signal-to-noise ratio. The algorithm is made available as an R-package and can be fully integrated into a standard XCMS workflow.

Saliva metabolomic profile of COVID-19 patients associates with disease severity.

IntroductionCoronavirus disease 2019 (COVID-19) is strongly linked to dysregulation of various molecular, cellular, and physiological processes that change abundance of different biomolecules including metabolites that may be ultimately used as biomarkers for disease progression and severity. It is important at early stage to readily distinguish those patients that are likely to progress to moderate and severe stages.ObjectivesThis study aimed to investigate the utility of saliva and plasma metabolomic profiles as a potential parameter for risk stratifying COVID-19 patients.MethodLC–MS/MS-based untargeted metabolomics were used to profile the changes in saliva and plasma metabolomic profiles of COVID-19 patients with different severities.ResultsSaliva and plasma metabolites were screened in 62 COVID-19 patients and 18 non-infected controls. The COVID-19 group included 16 severe, 15 moderate, 16 mild, and 15 asymptomatic cases. Thirty-six differential metabolites were detected in COVID-19 versus control comparisons. SARS-CoV-2 induced metabolic derangement differed with infection severity. The metabolic changes were identified in saliva and plasma, however, saliva showed higher intensity of metabolic changes. Levels of saliva metabolites such as sphingosine and kynurenine were significantly different between COVID-19 infected and non-infected individuals; while linoleic acid and Alpha-ketoisovaleric acid were specifically increased in severe compared to non-severe patients. As expected, the two prognostic biomarkers of C-reactive protein and D-dimer were negatively correlated with sphingosine and 5-Aminolevulinic acid, and positively correlated with l-Tryptophan and l-Kynurenine.ConclusionSaliva disease-specific and severity-specific metabolite could be employed as potential COVID-19 diagnostic and prognostic biomarkers.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s11306-022-01936-1.

A GC–MS-Based Untargeted Metabolomics Approach for Systemic Metabolic Profiles of Vancomycin-Induced Toxicity in Mice

A GC–MS-Based Untargeted Metabolomics Approach for Systemic Metabolic Profiles of Vancomycin-Induced Toxicity in Mice

Living with a giant, flowering parasite: metabolic differences between Tetrastigma loheri Gagnep. (Vitaceae) shoots uninfected and infected with Rafflesia (Rafflesiaceae) and potential applications for propagation

Main conclusionMetabolites in Rafflesia-infected and non-infected Tetrastigma were compared which may have applications in Rafflesia propagation. Benzylisoquinoline alkaloids, here reported for the first time in Vitaceae, were abundant in non-infected shoots and may be a form of defense. In Rafflesia-infected shoots, oxylipins, which mediate immune response, were elevated.Endemic to the forests of Southeast Asia, Rafflesia (Rafflesiaceae) is a genus of holoparasitic plants producing the largest flowers in the world, yet completely dependent on its host, the tropical grape vine, Tetrastigma. Rafflesia species are threatened with extinction, making them an iconic symbol of plant conservation. Thus far, propagation has proved challenging, greatly decreasing efficacy of conservation efforts. This study compared the metabolites in the shoots of Rafflesia-infected and non-infected Tetrastigma loheri to examine how Rafflesia infection affects host metabolomics and elucidate the Rafflesia infection process. Results from LC–MS-based untargeted metabolomics analysis showed benzylisoquinoline alkaloids were naturally more abundant in non-infected shoots and are here reported for the first time in the genus Tetrastigma, and in the grape family, Vitaceae. These metabolites have been implicated in plant defense mechanisms and may prevent a Rafflesia infection. In Rafflesia-infected shoots, oxygenated fatty acids, or oxylipins, and a flavonoid, previously shown involved in plant immune response, were significantly elevated. This study provides a preliminary assessment of metabolites that differ between Rafflesia-infected and non-infected Tetrastigma hosts and may have applications in Rafflesia propagation to meet conservation goals.

Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers.

ObjectiveTo characterize the serum metabolomic profile and its role in the prediction of poor ovarian response (POR).Patient(s)Twenty-five women with normal ovarian reserve (24-33 years, antral follicle count [AFC] ≥5, anti-Müllerian hormone [AMH] ≥1.2 ng/ml) as the control group and another twenty-five women with POR (19-35 years, AFC <5, AMH < 1.2 ng/ml) as the study group were collected in our study. The serum levels of the women in both groups were determined from their whole blood by untargeted liquid chromatography–mass spectrometry (LC-MS). Multivariate statistical analysis and cell signal pathways analysis were used to reveal the results.ResultsA total of 538 different metabolites were finally identified in the two groups. Tetracosanoic acid, 2-arachidonoylglycerol, lidocaine, cortexolone, prostaglandin H2,1-naphthylamine, 5-hydroxymethyl-2-furancarboxaldehyde, 2,4-dinitrophenol, and D-erythrulose1-phosphate in POR were significantly different from control as were most important metabolites in support vector machines (p <0.05). Metabolomic profiling, together with support vector machines and pathway analysis found that the nicotinate and nicotinamide metabolism pathway, including L-aspartic acid, 6-hydroxynicotinate, maleic acid, and succinic acid semialdehyde, was identified to have significant differences in POR women compared to control women, which may be associated with ovarian reserve.ConclusionThis study indicated that LC–MS-based untargeted metabolomics analysis of serum provided biological markers for women with POR. The nicotinate and nicotinamide metabolism pathway may offer new insight into the complementary prediction and therapeutic potential of POR. The functional associations of these metabolites need further investigation.

The Mycorrhiza-and Trichoderma-Mediated Elicitation of Secondary Metabolism and Modulation of Phytohormone Profile in Tomato Plants

Arbuscular mycorrhiza and Trichoderma are well-known beneficial fungi whose plant growth promotion and defense elicitation effects are known. However, the molecular and biochemical processes underlying the beneficial effects of these priming microorganisms have not been fully elucidated yet. On this basis, the present work aimed to use metabolomics to dissect comprehensively the modulation of secondary metabolism induced by mycorrhiza and Trichoderma, using tomato as a model plant. To this aim, either mycorrhiza or Trichoderma were applied to tomato roots at transplanting using a commercial formulation and then harvested once the mutualistic relationship was well established. Shoots were analyzed using an MS-based untargeted metabolomics approach, and differential metabolites identified by multivariate statistics were subjected to pathway analysis. Together with promoting plant growth, the treatments induced a broad molecular reprogramming with the phenylpropanoid biosynthetic pathway (including defense phenolics like coumarins and glycosylated anthocyanins) being strongly elicited. An accumulation of auxins, cytokinins, and jasmonate (especially after treatment with Trichoderma) could be observed concerning phytohormone profiles. Overall, the broad and distinctive effects triggered by mycorrhiza and Trichoderma in tomato secondary metabolism supported both plant growth promotion and immunity.

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics.

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.

Automated Sequential Analysis of Hydrophilic and Lipophilic Fractions of Biological Samples: Increasing Single-Injection Chemical Coverage in Untargeted Metabolomics.

In order to increase metabolite coverage in LC–MS-based untargeted metabolomics, HILIC- and RPLC-mode separations are often combined. Unfortunately, these two techniques pose opposite requirements on sample composition, necessitating either dual sample preparations, increasing needed sample volume, or manipulation of the samples after the first analysis, potentially leading to loss of analytes. When sample material is precious, the number of analyses that can be performed is limited. To that end, an automated single-injection LC–MS method for sequential analysis of both the hydrophilic and lipophilic fractions of biological samples is described. Early eluting compounds in a HILIC separation are collected on a trap column and subsequently analyzed in the RPLC mode. The instrument configuration, composed of commercially available components, allows easy modulation of the dilution ratio of the collected effluent, with sufficient dilution to obtain peak compression in the RPLC column. Furthermore, the method is validated and shown to be fit for purpose for application in untargeted metabolomics. Repeatability in both retention times and peak areas was excellent across over 140 injections of protein-precipitated blood plasma. Finally, the method has been applied to the analysis of real perilymph samples collected in a guinea pig model. The QC sample injections clustered tightly in the PCA scores plot and showed a high repeatability in both retention times and peak areas for selected compounds.