How close are we to complete annotation of metabolomes?

Environmental Markers of Plastics and Microplastics

The slow reaction rates to chemical and photochemical degradation are well-known properties of plastics. However, large plastic surfaces exposed to environmental conditions release particles and compounds that affect ecosystems and human health. The aim of this work was to identify compounds associated with the degradation of polyethylene (PE), polystyrene (PS), and polyvinyl chloride (PVC) microplastics (markers) on silica and sand and evaluate their use to screen microplastics on natural sand. Products were identified by using targeted and untargeted LC-HRMS analysis. All polymers underwent chemical oxidation on silica. PE released dicarboxylic acids (HO2C-(CH2)n-CO2H (n = 4-30), while PS released cis/trans-chalcone, trans-dypnone, 3-phenylpropiophenone, and dibenzoylmethane. PVC released dicarboxylic acids and aromatic compounds. Upon irradiation, PE was stable while PS released the same compounds as under chemical oxidation but at lower yields. Under the above condition, PVC generated HO2C-[CH2-CHCl]n-CH2-CO2H and HO2C-[CH2-CHCl]n-CO2H (n = 2-19) dicarboxylic acids. The same products were detected on sand but at a lower concentration than on silica due to better retention within the pores. Detection of markers of PE and PS on natural sand allowed us to screen microplastics by following a targeted analysis. Markers of PVC were not detected before or after thermal/photo-oxidation due to the low release of compounds and limitations associated with surface exposure/penetration of radiation.

Development of Nontargeted Workflow of Occupational Exposure by Infrared Ion Spectroscopy and Silicone Wristbands' Passive Sampling

A new approach using orthogonal analytical techniques is developed for chemical identification. High resolution mass spectrometry and infrared ion spectroscopy are applied through a 5-level confidence paradigm to demonstrate the effectiveness of nontargeted workflow for the identification of hazardous organophosphates. Triphenyl phosphate is used as a surrogate organophosphate for occupational exposure, and silicone wristbands are used to represent personal samplers. Spectral data of a target compound is combined with spectral data of the sodium adduct and quantum chemical calculations to achieve a confirmed identification. Here, we demonstrate a nontargeted workflow that identifies organophosphate exposure and provides a mechanism for selecting validated methods for quantitative analyses.

Optimization of volatile organic compounds sampling from dairy cow exhaled breath using polymer-based solid-phase extraction cartridges for gas chromatographic analysis

We explored appropriate technical setups for the detection of volatile organic compounds (VOCs) from exhaled cow breath by comparing six different polymer-based solid-phase extraction (SPE) cartridges currently on the market for gas chromatography/mass spectrometry (GC-MS) screening. Exhaled breath was sampled at a single timepoint from five lactating dairy cows using six different SPE cartridges (Bond Elut ENV (ENV); Chromabond HRX (HRX); Chromabond HRP (HRP); Chromabond HLB (HLB); Chromabond HR-XCW (XCW) and Chromabond HR-XAW (XAW)). The trapped VOCs were analyzed by dynamic headspace vacuum in-tube extraction GC-MS (DHS-V-ITEX-GC-MS). Depending on the SPE cartridge, we detected 1174-1312 VOCs per cartridge. Most VOCs were alkenes, alkanes, esters, ketones, alcohols, aldehydes, amines, nitriles, ethers, amides, carboxylic acids, alkynes, azoles, terpenes, pyridines, or sulfur-containing compounds. The six SPE cartridges differed in their specificity for the chemical compounds, with the XAW cartridge showing the best specificity for ketones. The greatest differences between the tested SPE cartridges appeared in the detection of specific VOCs. In total, 176 different VOCs were detected with a match factor >80%. The greatest number of specific VOCs was captured by XAW (149), followed by ENV (118), HLB (117), HRP (115), HRX (114), and XCW (114). We conclude that the tested SPE cartridges are suitable for VOC sampling from exhaled cow breath, but the SPE cartridge choice enormously affects the detected chemical groups and the number of detected VOCs. Therefore, an appropriate SPE adsorbent cartridge should be selected according to our proposed inclusion criteria. For targeted metabolomics approaches, the SPE cartridge choice depends on the VOCs or chemical compound groups of interest based on our provided VOC list. For untargeted approaches without information on the animals' metabolic condition, we suggest using multi-sorbent SPE cartridges or multiple cartridges per animal.

Identification of key neuronal mechanisms triggered by dimethyl fumarate in SH-SY5Y human neuroblastoma cells through a metabolomic approach

Dimethyl fumarate (DMF) is an old drug used for psoriasis treatment that has recently been repurposed to treat relapse-remitting multiple sclerosis, mostly due to its neuro- and immunomodulatory actions. However, mining of a pharmacovigilance database recently ranked DMF as the second pharmaceutical most associated with cognitive adverse events. To our best knowledge, the signaling mechanisms underlying its therapeutic and neurotoxic outcomes remain mostly undisclosed. This work thus represents the first-hand assessment of DMF-induced metabolic changes in undifferentiated SH-SY5Y human neuroblastoma cells, through an untargeted metabolomic approach using gas chromatography-mass spectrometry (GC-MS). The endometabolome was analyzed following 24 h and 96 h of exposure to two pharmacologically relevant DMF concentrations (0.1 and 10 μM). None of these conditions significantly reduced metabolic activity (MTT reduction assay). Our data showed that 24 h-exposure to DMF at both concentrations tested mainly affected metabolic pathways involved in mitochondrial activity (e.g., citric acid cycle, de novo triacylglycerol biosynthesis), and the synthesis of catecholamines and serotonin by changing the levels of their respective precursors, namely phenylalanine (0.68-fold decrease for 10 μM DMF vs vehicle), and tryptophan (1.36-fold increase for 0.1 μM DMF vs vehicle). Interestingly, taurine, whose levels can be modulated via Nrf2 signaling (DMF's primary target), emerged as a key mediator of DMF's neuronal action, displaying a 3.86-fold increase and 0.27-fold decrease for 10 μM DMF at 24 h and 96 h, respectively. A 96 h-exposure to DMF seemed to mainly trigger pathways associated with glucose production (e.g., gluconeogenesis, glucose-alanine cycle, malate-aspartate shuttle), possibly related to the metabolism of DMF into monomethyl fumarate and its further conversion into glucose via activation of the citric acid cycle. Overall, our data contribute to improving the understanding of the events associated with neuronal exposure to DMF.

Integrated annotation prioritizes metabolites with bioactivity in inflammatory bowel disease

Microbial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates.

Systematic review of the association between short chain fatty acids and allergic diseases

We performed a systematic review to investigate the current evidence on the association between allergic diseases and short chain fatty acids (SCFAs), which are microbially produced and suggested as one mechanism on how gut microbiome affects the risk of allergic diseases. Medline, Embase and Web of Science were searched from data inception until September 2022. We identified 37 papers, of which 17 investigated prenatal or early childhood SCFAs and the development of allergic diseases in childhood, and 20 assessed SCFAs in patients with pre-existing allergic diseases. Study design, study populations, outcome definition, analysis method and reporting of the results varied between papers. Overall, there was some evidence showing that the three main SCFAs (acetate, propionate and butyrate) in the first few years of life had a protective effect against allergic diseases, especially for atopic dermatitis, wheeze or asthma and IgE-mediated food allergy in childhood. The association between each SCFA and allergic disease appeared to be different by disease and the age of assessment. Further research that can determine the potentially timing specific effect of each SCFA will be useful to investigate how SCFAs can be used in treatment or in prevention against allergic diseases.

An exercise physiologist's guide to metabolomics

The field of exercise physiology has undergone significant technological advancements since the pioneering works of exercise physiologists in the early to mid-20th century. Historically, the ability to detect metabolites in biofluids from exercising participants was limited to single-metabolite analyses. However, the rise of metabolomics, a discipline focused on the comprehensive analysis of metabolites within a biological system, has facilitated a more intricate understanding of metabolic pathways and networks in exercise. This review explores some of the pivotal technological and bioinformatic advancements that have propelled metabolomics to the forefront of exercise physiology research. Metabolomics offers a unique 'fingerprint' of cellular activity, offering a broader spectrum than traditional single-metabolite assays. Techniques, including mass spectrometry and nuclear magnetic resonance spectroscopy, have significantly improved the speed and sensitivity of metabolite analysis. Nonetheless, challenges persist, including study design and data interpretation issues. This review aims to serve as a guide for exercise physiologists to facilitate better research design, data analysis and interpretation within metabolomics. The potential of metabolomics in bridging the gap between genotype and phenotype is emphasised, underscoring the critical importance of careful study design and the selection of appropriate metabolomics techniques. Furthermore, the paper highlights the need to deeply understand the broader scientific context to discern meaningful metabolic changes. The emerging field of fluxomics, which seeks to quantify metabolic reaction rates, is also introduced as a promising avenue for future research.

Global metabolomics revealed deviations from the metabolic aging clock in colorectal cancer patients

Background: Markers of aging hold promise in the context of colorectal cancer (CRC) care. Utilizing high-resolution metabolomic profiling, we can unveil distinctive age-related patterns that have the potential to predict early CRC development. Our study aims to unearth a panel of aging markers and delve into the metabolomic alterations associated with aging and CRC. Methods: We assembled a serum cohort comprising 5,649 individuals, consisting of 3,002 healthy volunteers, 715 patients diagnosed with colorectal advanced precancerous lesions (APL), and 1,932 CRC patients, to perform a comprehensive metabolomic analysis. Results: We successfully identified unique age-associated patterns across 42 metabolic pathways. Moreover, we established a metabolic aging clock, comprising 9 key metabolites, using an elastic net regularized regression model that accurately estimates chronological age. Notably, we observed significant chronological disparities among the healthy population, APL patients, and CRC patients. By combining the analysis of circulative carcinoembryonic antigen levels with the categorization of individuals into the "hypo" metabolic aging subgroup, our blood test demonstrates the ability to detect APL and CRC with positive predictive values of 68.4% (64.3%, 72.2%) and 21.4% (17.8%, 25.9%), respectively. Conclusions: This innovative approach utilizing our metabolic aging clock holds significant promise for accurately assessing biological age and enhancing our capacity to detect APL and CRC.

Reverse metabolomics for the discovery of chemical structures from humans

Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

MSdeCIpher: A Tool to Link Data from Complementary Ionization Techniques in High-Resolution GC-MS to Identify Molecular Ions

Electron ionization (EI) and molecular ion-generating techniques like chemical ionization (CI) are complementary ionization methods in gas chromatography (GC)-mass spectrometry (MS). However, manual curation effort and expert knowledge are required to correctly assign molecular ions to fragment spectra. MSdeCIpher is a software tool that enables the combination of two separate datasets from fragment-rich spectra, like EI-spectra, and soft ionization spectra containing molecular ion candidates. Using high-resolution GC-MS data, it identifies and assigns molecular ions based on retention time matching, user-defined adduct/neutral loss criteria, and sum formula matching. To our knowledge, no other freely available or vendor tool is currently capable of combining fragment-rich and soft ionization datasets in this manner. The tool's performance was evaluated on three test datasets. When molecular ions are present, MSdeCIpher consistently ranks the correct molecular ion for each fragment spectrum in one of the top positions, with average ranks of 1.5, 1, and 1.2 in the three datasets, respectively. MSdeCIpher effectively reduces candidate molecular ions for each fragment spectrum and thus enables the usage of compound identification tools that require molecular masses as input. It paves the way towards rapid annotations in untargeted analysis with high-resolution GC-MS.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Metabolomic Signatures of Treatment Response in Bladder Cancer

Bladder cancer (BC) stands as one of the most prevalent urological malignancies, with over 500 thousand newly diagnosed cases annually. Treatment decisions in BC depend on factors like the risk of recurrence, the type of tumor, and the stage of the disease. While standard therapeutic approaches encompass transurethral resection of the bladder tumor, radical cystectomy, and chemo- or immunotherapy, these methods exhibit limited efficacy in mitigating the aggressive and recurrent nature of bladder tumors. To overcome this challenge, it is crucial to develop innovative methods for monitoring and predicting treatment responses among patients with BC. Metabolomics is gaining recognition as a promising approach for discovering biomarkers. It has the potential to reveal metabolic disruptions that precisely reflect how BC patients respond to particular treatments, providing a revolutionary method to improve accuracy in monitoring and predicting outcomes. In this article, we present a comprehensive review of studies employing metabolomics approaches to investigate the metabolic responses associated with different treatment modalities for BC. The review encompasses an exploration of various models, samples, and analytical techniques applied in this context. Special emphasis is placed on the reported changes in metabolite levels derived from these studies, highlighting their potential as biomarkers for personalized medicine in BC.

Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese

INTRODUCTION

Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction.

METHODS

A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years.

RESULTS

Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D.

CONCLUSIONS

Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.

Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection

Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of Swertia chirayita (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.

Tracing the identity of Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" cheese using NMR spectroscopy and multivariate data analysis

BACKGROUND

Nuclear magnetic resonance (NMR) spectroscopy is one of the well-established tools for food metabolomic analysis, as it proved to be very effective in authenticity and quality control of dairy products, as well as to follow product evolution during processing and storage. The analytical assessment of the EU mountain denomination label, specifically for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, has received limited attention. Although it was established in 2012 the EU mountain denomination label has not been much studied from an analytical point of view. Nonetheless, tracing a specific profile for the mountain products is essential to support the value chain of this specialty.

RESULTS

The aim of the study was to produce an identity profile for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, and to differentiate it from Parmigiano Reggiano PDO samples (conventional-PDO) using 1H NMR spectroscopy coupled with multivariate data analysis. Three different approaches were applied and compared. First, the spectra-as-such were analysed after proper preprocessing. For the other two approaches, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used for signals resolution and features extraction, either individually on manually-defined spectral intervals or by reapplying MCR-ALS on the whole spectra with selectivity constraints using the reconstructed "pure profiles" as initial estimates and targets. All approaches provided comparable information regarding the samples' distribution, as in all three cases the separation between the two product categories conventional-PDO and Mountain-CQ could be highlighted. Moreover, a novel MATLAB toolbox for features extraction via MCR-ALS was developed and used in synergy with the Chenomx library, allowing for a putative identification of the selected features.

SIGNIFICANCE

A first identity profile for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" obtained by interpreting the metabolites signals in NMR spectroscopy was obtained. Our workflow and toolbox for generating the features dataset allows a more straightforward interpretation of the results, to overcome the limitations due to dimensionality and to peaks overlapping, but also to include the signals assignment and matching since the early stages of the data processing and analysis.

Polarity-extended liquid chromatography-triple quadrupole mass spectrometry for simultaneous hydrophilic and hydrophobic metabolite analysis

Although various metabolomic methods have been reported in recent years, simultaneous detection of hydrophilic and hydrophobic metabolites in a single analysis remains a technical challenge. In this study, based on the combination of hydrophilic interaction liquid chromatography (HILIC) and reversed phase liquid chromatography (RPLC), an online two-dimensional liquid chromatography/triple quadrupole mass spectrometry method (2D-LC/TQMS) was developed for the simultaneous analysis of hydrophilic and hydrophobic metabolites of various biological samples. The method can measure 417 biologically important metabolites (e.g., amino acids and peptides, pyrimidines, purines, monosaccharides, fatty acids and conjugates, organic dicarboxylic acids, and others) with logP values ranging from -10.3 to 21.9. The metabolites are involved in a variety of metabolic pathways (e.g., purine metabolism, pyrimidine metabolism, tyrosine metabolism, galactose metabolism, gluconeogenesis, and TCA cycle). The developed method has good intra- and inter-day reproducibility (RSD of retention time <2%, RSD of peak area <30%), good linearity (R2 > 0.9) and wide linear range (from 0.0025 μg/mL to 5 μg/mL). The applicability of the method was tested using different biological samples (i.e., plasma, serum, urine, fecal, seminal plasma and liver) and it was found that 208 (out of 417) identical metabolites were detected in all biological samples. Furthermore, the metabolomic method was applied to a case/control study of urinary of bladder cancer. Thirty differential metabolites were identified that were involved in carbohydrate and amino acid metabolism.

Urinary Paraben Concentrations and Associations with the Periconceptional Urinary Metabolome: Untargeted and Targeted Metabolomics Analyses of Participants from the Early Pregnancy Study

BACKGROUND

Parabens, found in everyday items from personal care products to foods, are chemicals with endocrine-disrupting activity, which has been shown to influence reproductive function.

OBJECTIVES

This study investigated whether urinary concentrations of methylparaben, propylparaben, or butylparaben were associated with the urinary metabolome during the periconceptional period, a critical window for female reproductive function. Changes to the periconceptional urinary metabolome could provide insights into the mechanisms by which parabens could impact fertility.

METHODS

Urinary paraben concentrations were measured in paired pre- and postconception urine samples from 42 participants in the Early Pregnancy Study, a prospective cohort of 221 women attempting to conceive. We performed untargeted and targeted metabolomics analyses using ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry. We used principal component analysis, orthogonal partial least-squares discriminant analysis, and permutation testing, coupled with univariate statistical analyses, to find metabolites associated with paraben concentration at the two time points. Potential confounders were identified with a directed acyclic graph and used to adjust results with multivariable linear regression. Metabolites were identified using fragmentation data.

RESULTS

Seven metabolites were associated with paraben concentration (variable importance to projection score > 1 , false discovery rate-corrected q -value < 0.1 ). We identified four diet-related metabolites to the Metabolomics Standards Initiative (MSI) certainty of identification level 2, including metabolites from smoke flavoring, grapes, and olive oil. One metabolite was identified to the class level only (MSI level 3). Two metabolites were unidentified (MSI level 4). After adjustment, three metabolites remained associated with methylparaben and propylparaben, two of which were diet-related. No metabolomic markers of endocrine disruption were associated with paraben concentrations.

DISCUSSION

This study identified novel relationships between urinary paraben concentrations and diet-related metabolites but not with metabolites on endocrine-disrupting pathways, as hypothesized. It demonstrates the feasibility of integrating untargeted metabolomics data with environmental exposure information and epidemiological adjustment for confounders. The findings underscore a potentially important connection between diet and paraben exposure, with applications to nutritional epidemiology and dietary exposure assessment. https://doi.org/10.1289/EHP12125.

Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites

Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the internal standards generally used in the quality control of the LC-MS experiments. Fitted Gaussian Process models (GPs) were used to perform a high level correction of the RT drift between the experiments, which was followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Quick Decline and Stem Pitting Citrus tristeza virus Isolates Induce a Distinct Metabolomic Profile and Antioxidant Enzyme Activity in the Phloem Sap of Two Citrus Species

Susceptibility to the severe Citrus tristeza virus (CTV), T36, is higher for Citrus macrophylla (CM) than for C. aurantium (CA). How host-virus interactions are reflected in host physiology is largely unknown. In this study, the profile of metabolites and the antioxidant activity in the phloem sap of healthy and infected CA and CM plants were evaluated. The phloem sap of quick decline (T36) and stem pitting (T318A) infected citrus, and control plants was collected by centrifugation, and the enzymes and metabolites analyzed. The activity of the antioxidant enzymes, superoxide dismutase (SOD) and catalase (CAT), in infected plants increased significantly in CM and decreased in CA, compared to the healthy controls. Using LC-HRMS² a metabolic profile rich in secondary metabolites was assigned to healthy CA, compared to healthy CM. CTV infection of CA caused a drastic reduction in secondary metabolites, but not in CM. In conclusion, CA and CM have a different response to severe CTV isolates and we propose that the low susceptibility of CA to T36 may be related to the interaction of the virus with the host's metabolism, which reduces significantly the synthesis of flavonoids and antioxidant enzyme activity.

Metabolic Profiling of Primary and Secondary Metabolites in Kohlrabi (Brassica oleracea var. gongylodes) Sprouts Exposed to Different Light-Emitting Diodes

Light-emitting diode (LED) technology is one of the most important light sources in the plant industry for enhancing growth and specific metabolites in plants. In this study, we analyzed the growth, primary and secondary metabolites of 10 days old kohlrabi (Brassica oleracea var. gongylodes) sprouts exposed to different LED light conditions. The results showed that the highest fresh weight was achieved under red LED light, whereas the highest shoot and root lengths were recorded below the blue LED light. Furthermore, high-performance liquid chromatography (HPLC) analysis revealed the presence of 13 phenylpropanoid compounds, 8 glucosinolates (GSLs), and 5 different carotenoids. The phenylpropanoid and GSL contents were highest under blue LED light. In contrast, the carotenoid content was found to be maximum beneath white LED light. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) of the 71 identified metabolites using HPLC and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) showed a clear separation, indicating that different LEDs exhibited variation in the accumulation of primary and secondary metabolites. A heat map and hierarchical clustering analysis revealed that blue LED light accumulated the highest amount of primary and secondary metabolites. Overall, our results demonstrate that exposure of kohlrabi sprouts to blue LED light is the most suitable condition for the highest growth and is effective in increasing the phenylpropanoid and GSL content, whereas white light might be used to enhance carotenoid compounds in kohlrabi sprouts.

Metabolomics Reveals Novel Serum Metabolic Signatures in Gastric Cancer by a Mass Spectrometry Platform

Gastric cancer (GAS) is one of the malignant tumors of the gastrointestinal system. Alterations in metabolite composition can reflect pathological processes of GAS and constitute a basis for diagnosis and treatment improvements. In this study, a total of 301 serum samples from 150 GAS patients at different tumor-node-metastasis (TNM) stages and 151 healthy controls were collected. Mass spectrometry platforms were performed to investigate the changes in GAS-related metabolites and explore the new potential serum biomarkers and the metabolic dysregulation associated with GAS progression. Twelve differential metabolites (ethyl 2,4-dimethyl-1,3-dioxolane-2-acetate, D-urobilinogen, 14-HDoHE, 13-hydroxy-9-methoxy-10-oxo-11-octadecenoic acid, 5,6-dihydroxyprostaglandin F1a, 9'-carboxy-gamma-tocotrienol, glutaric acid, alanine, tyrosine, C18:2(FFA), adipic acid, and suberic acid) were identified to establish the diagnosis model for GAS. The defined biomarker panel was also statistically significant for GAS progression with different TNM stages. KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment revealed the metabolic dysregulation associated with GAS progression. In conclusion, a diagnostic panel was established and validated, which could be used to further stage the early and advanced GAS patients from healthy controls. These findings may provide useful information for explaining the GAS metabolic alterations and try to facilitate the characterization of GAS patients in the early stage.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data

BACKGROUND

Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices.

RESULTS

We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Comparative metabolomic analysis of exudates of microcystin-producing and microcystin-free Microcystis aeruginosa strains

Cyanobacterial harmful algal blooms (cHABs) dominated by Microcystis aeruginosa threaten the ecological integrity and beneficial uses of lakes globally. In addition to producing hepatotoxic microcystins (MC), M. aeruginosa exudates (MaE) contain various compounds with demonstrated toxicity to aquatic biota. Previously, we found that the ecotoxicity of MaE differed between MC-producing and MC-free strains at exponential (E-phase) and stationary (S-phase) growth phases. However, the components in these exudates and their specific harmful effects were unclear. In this study, we performed untargeted metabolomics based on liquid chromatography-mass spectrometry to reveal the constituents in MaE of a MC-producing and a MC-free strain at both E-phase and S-phase. A total of 409 metabolites were identified and quantified based on their relative abundance. These compounds included lipids, organoheterocyclic compounds, organic acid, benzenoids and organic oxygen compounds. Multivariate analysis revealed that strains and growth phases significantly influenced the metabolite profile. The MC-producing strain had greater total metabolites abundance than the MC-free strain at S-phase, whereas the MC-free strain released higher concentrations of benzenoids, lipids, organic oxygen, organic nitrogen and organoheterocyclic compounds than the MC-producing strain at E-phase. Total metabolites had higher abundance in S-phase than in E- phase in both strains. Analysis of differential metabolites (DMs) and pathways suggest that lipids metabolism and biosynthesis of secondary metabolites were more tightly coupled to growth phases than to strains. Abundance of some toxic lipids and benzenoids DMs were significantly higher in the MC-free strain than the MC-producing one. This study builds on the understanding of MaE chemicals and their biotoxicity, and adds to evidence that non-MC-producing strains of cyanobacteria may also pose a threat to ecosystem health.

Prepregnancy Body Mass Index and Lipoprotein Fractions are Associated with Changes in Women's Serum Metabolome from Late Pregnancy to the First Months of Postpartum

BACKGROUND

Pregnancy and postpartum are periods of intense changes in women's metabolism. The knowledge of the metabolites and maternal factors underlying these changes is limited.

OBJECTIVES

We aimed to investigate the maternal factors that could influence serum metabolome changes from late pregnancy to the first months of postpartum.

METHODS

Sixty-eight healthy women from a Brazilian prospective cohort were included. Maternal blood and general characteristics were collected during pregnancy (28-35 wk) and postpartum (27-45 d). A targeted metabolomics approach was applied to quantify 132 serum metabolites, including amino acids, biogenic amines, acylcarnitines, lysophosphatidylcholines (LPC), diacyl phosphatidylcholines (PC), alkyl:acyl phosphatidylcholines (PC-O), sphingomyelins with (SM) and without hydroxylation [SM(OH)], and hexoses. Metabolome changes from pregnancy to postpartum were measured as log₂ fold change (log₂FC), and simple linear regressions were employed to evaluate associations between maternal variables and metabolite log₂FC. Multiple comparison-adjusted P values of < 0.05 were considered significant.

RESULTS

Of 132 metabolites quantified in serum, 90 changed from pregnancy to postpartum. Most metabolites belonging to PC and PC-O classes decreased, whereas most LPC, acylcarnitines, biogenic amines, and a few amino acids increased in postpartum. Maternal prepregnancy body mass index (ppBMI) showed positive associations with leucine and proline. A clear opposite change pattern was observed for most metabolites across ppBMI categories. Few phosphatidylcholines were decreased in women with normal ppBMI, while an increase was observed in women with obesity. Similarly, women with high postpartum levels of total cholesterol, LDL cholesterol, and non-HDL cholesterol showed increased sphingomyelins, whereas a decrease was observed for women with lower levels of those lipoproteins.

CONCLUSIONS

The results revealed several maternal serum metabolomic changes from pregnancy to postpartum, and the maternal ppBMI and plasma lipoproteins were associated with these changes. We highlight the importance of the nutritional care of women prepregnancy to improve their metabolic risk profile.

Untargeted Fecal Metabolomic Analyses across an Industrialization Gradient Reveal Shared Metabolites and Impact of Industrialization on Fecal Microbiome-Metabolome Interactions

The metabolome is a central determinant of human phenotypes and includes the plethora of small molecules produced by host and microbiome or taken up from exogenous sources. However, studies of the metabolome have so far focused predominantly on urban, industrialized populations. Through an untargeted metabolomic analysis of 90 fecal samples from human individuals from Africa and the Americas-the birthplace and the last continental expansion of our species, respectively-we characterized a shared human fecal metabolome. The majority of detected metabolite features were ubiquitous across populations, despite any geographic, dietary, or behavioral differences. Such shared metabolite features included hyocholic acid and cholesterol. However, any characterization of the shared human fecal metabolome is insufficient without exploring the influence of industrialization. Here, we show chemical differences along an industrialization gradient, where the degree of industrialization correlates with metabolomic changes. We identified differential metabolite features such as amino acid-conjugated bile acids and urobilin as major metabolic correlates of these behavioral shifts. Additionally, coanalyses with over 5,000 publicly available human fecal samples and cooccurrence probability analyses with the gut microbiome highlight connections between the human fecal metabolome and gut microbiome. Our results indicate that industrialization significantly influences the human fecal metabolome, but diverse human lifestyles and behavior still maintain a shared human fecal metabolome. This study represents the first characterization of the shared human fecal metabolome through untargeted analyses of populations along an industrialization gradient. IMPORTANCE As the world becomes increasingly industrialized, understanding the biological consequences of these lifestyle shifts and what it means for past, present, and future human health is critical. Indeed, industrialization is associated with rises in allergic and autoimmune health conditions and reduced microbial diversity. Exploring these health effects on a chemical level requires consideration of human lifestyle diversity, but understanding the significance of any differences also requires knowledge of what molecular components are shared between human groups. Our study reveals the key chemistry of the human gut as defined by varied industrialization-based differences and ubiquitous shared features. Ultimately, these novel findings extend our knowledge of human molecular biology, especially as it is influenced by lifestyle and behavior, and provide steps toward understanding how human biology has changed over our species' history.

Maternal obesity alters offspring liver and skeletal muscle metabolism in early post-puberty despite maintaining a normal post-weaning dietary lifestyle

Maternal obesity (MO) during pregnancy is linked to increased and premature risk of age-related metabolic diseases in the offspring. However, the underlying molecular mechanisms still remain not fully understood. Using a well-established nonhuman primate model of MO, we analyzed tissue biopsies and plasma samples obtained from post-pubertal offspring (3-6.5 y) of MO mothers (n = 19) and from control animals born to mothers fed a standard diet (CON, n = 13). All offspring ate a healthy chow diet after weaning. Using untargeted gas chromatography-mass spectrometry metabolomics analysis, we quantified a total of 351 liver, 316 skeletal muscle, and 423 plasma metabolites. We identified 58 metabolites significantly altered in the liver and 46 in the skeletal muscle of MO offspring, with 8 metabolites shared between both tissues. Several metabolites were changed in opposite directions in males and females in both liver and skeletal muscle. Several tissue-specific and 4 shared metabolic pathways were identified from these dysregulated metabolites. Interestingly, none of the tissue-specific metabolic changes were reflected in plasma. Overall, our study describes characteristic metabolic perturbations in the liver and skeletal muscle in MO offspring, indicating that metabolic programming in utero persists postnatally, and revealing potential novel mechanisms that may contribute to age-related metabolic diseases later in life.

Progress and opportunities in microbial community metabolomics

The metabolome lies at the interface of host-microbiome crosstalk. Previous work has established links between chemically diverse microbial metabolites and a myriad of host physiological processes and diseases. Coupled with scalable and cost-effective technologies, metabolomics is thus gaining popularity as a tool for characterization of microbial communities, particularly when combined with metagenomics as a window into microbiome function. A systematic interrogation of microbial community metabolomes can uncover key microbial compounds, metabolic capabilities of the microbiome, and also provide critical mechanistic insights into microbiome-linked host phenotypes. In this review, we discuss methods and accompanying resources that have been developed for these purposes. The accomplishments of these methods demonstrate that metabolomes can be used to functionally characterize microbial communities, and that microbial properties can be used to identify and investigate chemical compounds.

Study on potential markers for diagnosis of renal cell carcinoma by serum untargeted metabolomics based on UPLC-MS/MS

Objective: Renal cell carcinoma (RCC) is the most common malignancy of the kidney. However, there is no reliable biomarker with high sensitivity and specificity for diagnosis and differential diagnosis. This study aims to analyze serum metabolite profile of patients with RCC and screen for potential diagnostic biomarkers. Methods: Forty-five healthy controls (HC), 40 patients with benign kidney tumor (BKT) and 46 patients with RCC were enrolled in this study. Serum metabolites were detected by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and then subjected to multivariate statistical analysis, metabolic pathway analysis and diagnostic performance evaluation. Results: The changes of glycerophospholipid metabolism, phosphatidylinositol signaling system, glycerolipid metabolism, d-glutamine and d-glutamate metabolism, galactose metabolism, and folate biosynthesis were observed in RCC group. Two hundred and forty differential metabolites were screened between RCC and HC groups, and 64 differential metabolites were screened between RCC and BKT groups. Among them, 4 differential metabolites, including 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, lysophosphatidylcholine (LPC) 19:2, and γ-Aminobutyryl-lysine (an amino acid metabolite), were of high clinical value not only in the diagnosis of RCC (RCC group vs. HC group; AUC = 0.990, 0.916, 0.909, and 0.962; Sensitivity = 97.73%, 97.73%, 93.18%, and 86.36%; Specificity = 100.00%, 73.33%, 80.00%, and 95.56%), but also in the differential diagnosis of benign and malignant kidney tumors (RCC group vs. BKT group; AUC = 0.989, 0.941, 0.845 and 0.981; Sensitivity = 93.33%, 93.33%, 77.27% and 93.33%; Specificity = 100.00%, 84.21%, 78.38% and 92.11%). Conclusion: The occurrence of RCC may involve changes in multiple metabolic pathways. The 3-β-D-Galactosyl-sn-glycerol, 7,8-Dihydroneopterin, LPC 19:2 and γ-Aminobutyryl-lysine may be potential biomarkers for the diagnosis or differential diagnosis of RCC.

Integrated metabolomic and transcriptomic analysis of the anthocyanin and proanthocyanidin regulatory networks in red walnut natural hybrid progeny leaves

Background

Walnuts are among the most important dry fruit crops worldwide, typically exhibiting green leaves and yellow-brown or gray-yellow seed coats. A specific walnut accession with red leaves and seed coats, 'RW-1', was selected for study because of its high anthocyanin and proanthocyanidin (PA) contents. Anthocyanins and PAs are important secondary metabolites and play key roles in plant responses to biotic and abiotic stresses. However, few studies have focused on the molecular mechanism of anthocyanin biosynthesis in walnuts.

Methods

In this study, we determined the anthocyanin and PA components and their contents in different color leaves of 'RW-1' natural hybrid progenies at various developmental stages. Integrated transcriptome and metabolome analyses were used to identify the differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs). We also performed conjoint analyses on DEGs and DAMs to ascertain the degree pathways, and explore the regulation of anthocyanin and PA biosynthesis.

Results

The results of widely targeted metabolome profiling and anthocyanin detection revealed 395 substances, including four PAs and 26 anthocyanins, in red (SR) and green leaves (SG) of 'RW-1' natural hybrid progenies. From the research, the contents of all anthocyanin components in SR were higher than that in SG. Among them, the contents of delphinidin 3-O-galactoside, cyanidin 3-O-galactoside, delphinidin 3-O-arabinoside and cyanidin 3-O-glucoside were significantly higher than others, and they were considered as the main types of anthocyanins. However, nine anthocyanins were detected only in SR. For PAs, the content of procyanidin C1 was higher in SR compared with SG, while procyanidin B1 and procyanidin B3 were higher in SR-1 and SR-3 but downregulated in SR-2 compared with the controls. Furthermore, transcriptome analysis revealed that the expressions of structural genes (C4H, F3H, F3'5'H, UFGT, LAR and ANR), three MYBs predicted as the activators of anthocyanin and PA biosynthesis, two MYBs predicted as the repressors of anthocyanin biosynthesis, and five WD40s in the anthocyanin and PA biosynthetic pathways were significantly higher in the SR walnuts. Gene-metabolite correlation analyses revealed a core set of 31 genes that were strongly correlated with four anthocyanins and one PA metabolites. The alteration of gene coding sequence altered the binding or regulation of regulatory factors to structural genes in different color leaves, resulting in the effective increase of anthocyanins and PAs accumulation in red walnut.

Conclusions

This study provides valuable information on anthocyanin and PA metabolites and candidate genes for anthocyanin and PA biosynthesis, yielding new insights into anthocyanin and PA biosynthesis in walnuts.

Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics

Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.

Plasma Lipidomics Approach in Early and Specific Alzheimer’s Disease Diagnosis

Background: The brain is rich in lipid content, so a physiopathological pathway in Alzheimer’s disease (AD) could be related to lipid metabolism impairment. The study of lipid profiles in plasma samples could help in the identification of early AD changes and new potential biomarkers. Methods: An untargeted lipidomic analysis was carried out in plasma samples from preclinical AD (n = 11), mild cognitive impairment-AD (MCI-AD) (n = 31), and healthy (n = 20) participants. Variables were identified by means of two complementary methods, and lipid families’ profiles were studied. Then, a targeted analysis was carried out for some identified lipids. Results: Statistically significant differences were obtained for the diglycerol (DG), lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), monoglyceride (MG), and sphingomyelin (SM) families as well as for monounsaturated (MUFAs) lipids, among the participant groups. In addition, statistically significant differences in the levels of lipid families (ceramides (Cer), LPE, LPC, MG, and SM) were observed between the preclinical AD and healthy groups, while statistically significant differences in the levels of DG, MG, and PE were observed between the MCI-AD and healthy groups. In addition, 18:1 LPE showed statistically significant differences in the targeted analysis between early AD (preclinical and MCI) and healthy participants. Conclusion: The different plasma lipid profiles could be useful in the early and minimally invasive detection of AD. Among the lipid families, relevant results were obtained from DGs, LPEs, LPCs, MGs, and SMs. Specifically, MGs could be potentially useful in AD detection; while LPEs, LPCs, and SM seem to be more related to the preclinical stage, while DGs are more related to the MCI stage. Specifically, 18:1 LPE showed a potential utility as an AD biomarker.

HPLC–(Q)-TOF-MS-Based Study of Plasma Metabolic Profile Differences Associated with Age in Pediatric Population Using an Animal Model

A deep knowledge about the biological development of children is essential for appropriate drug administration and dosage in pediatrics. In this sense, the best approximation to study organ maturation is the analysis of tissue samples, but it requires invasive methods. For this reason, surrogate matrices should be explored. Among them, plasma emerges as a potential alternative since it represents a snapshot of global organ metabolism. In this work, plasma metabolic profiles from piglets of different ages (newborns, infants, and children) obtained by HPLC–(Q)-TOF-MS at positive and negative ionization modes were studied. Improved clustering within groups was achieved using multiblock principal component analysis compared to classical principal component analysis. Furthermore, the separation observed among groups was better resolved by using partial least squares-discriminant analysis, which was validated by bootstrapping and permutation testing. Thanks to univariate analysis, 13 metabolites in positive and 21 in negative ionization modes were found to be significant to discriminate the three groups of piglets. From these features, an acylcarnitine and eight glycerophospholipids were annotated and identified as metabolites of interest. The findings indicate that there is a relevant change with age in lipid metabolism in which lysophosphatidylcholines and lysophoshatidylethanolamines play an important role.

Anti-Inflammatory Effects and Molecular Mechanisms of Shenmai Injection in Treating Acute Pancreatitis: Network Pharmacology Analysis and Experimental Verification

Background

Acute pancreatitis (AP) is an inflammatory disorder of the exocrine pancreas without specific treatment. Shenmai injection (SMI) was reported to eliminate the severity of experimental AP. This study aimed to explore the mechanisms underlying the synergistic protective effects of SMI on AP based on network pharmacology and experimental validation.

Methods

Network pharmacology analysis and molecular docking based on identified components were performed to construct the potential therapeutic targets and pathways. The principal components of SMI were detected via ultra-high-performance liquid chromatography-coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). Effect of SMI and the identified components on cellular injury and IL6/STAT3 signaling was assessed on mouse pancreatic acinar cell line 266–6 cells. Finally, 4% sodium taurocholate (NaT) was used to induce AP model to assess the effects of SMI in treating AP and validate the potential molecular mechanisms.

Results

By searching the TCMSP and ETCM databases, 119 candidate components of SMI were obtained. UHPLC-QTOF/MS analysis successfully determined the representative components of SMI: ginsenoside Rb1, ginsenoside Rg1, ginsenoside Re, and ophiopogonin D. Fifteen hub targets and eight related pathways were obtained to establish the main pharmacology network. Subnetwork analysis and molecular docking indicated that the effects of these four main SMI components were mostly related to the interleukin (IL) 6/STAT3 pathway. In vitro, SMI, ginsenoside Rb1, ginsenoside Rg1, ginsenoside Re, and ophiopogonin D increased the cell viability of NaT-stimulated mouse pancreatic acinar 266–6 cells and decreased IL6 and STAT3 expression. In vivo, 10 mL/kg SMI significantly alleviated the pancreatic histopathological changes and the expression of IL6 and STAT3 in the AP mice.

Conclusion

This study demonstrated SMI may exert anti-inflammatory effects against AP by suppressing IL6/STAT3 activation, thus providing a basis for its potential use in clinical practice and further study in treating AP.

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

Background

Vancomycin 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.

Purpose

This study attempted to investigate the metabolic changes after vancomycin administration in mice.

Methods

Experimental 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.

Results

Compared 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.

Conclusion

Vancomycin 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.

Metabolic and metabolomics insights into dilated cardiomyopathy (DCM)

BACKGROUND

Dilated cardiomyopathy (DCM) is the most common form of heart muscle disease characterized by progressive dilatation and ventricular dysfunction. Metabolomics is an emerging and powerful discipline that provides a global information on the phenotype of mammalian systems via the study of endogenous and exogenous metabolites in cells, tissues and biofluids. These studies aid in the identification of biomarkers to prevent diseases in later life or help to early detect onset of diseases as well as aiding in the elucidation of disease mechanisms.

SUMMARY

Metabolomics provides a unique opportunity to discover biomarkers for DCM. This review demonstrates evidence of metabolite-based biomarkers useful for predicting, diagnosing and monitoring therapeutic interventions of DCM. Key metabolites identified as potential biomarkers for diagnosing DCM include acyl-carnitines, succinic acid, malate, methylhistidine, aspartate, methionine, phenylalanine. In terms of differentiating DCM from ICM, potential biomarkers including 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol, fatty acid esters of hydroxy fatty acid, and phosphatidylcholine were identified. Acyl-carnitines, isoleucine and linoleic acid and tryptophan were the main biomarkers to monitor treatment response to DCM. Mapping metabolites to metabolic pathways revealed dysregulation of BCAA, glycolysis, tricarboxylic acid cycle and triacylglycerol and pentose phosphate metabolism which have therapeutic potential for DCM. This review shows several limitations including the use of small sample sizes, lack of interpretation of age and sex differences in most studies and the fact that studies have so far been limited to case-control study designs.

KEY MESSAGES

Metabolites have close proximity to disease phenotype. With recent advancements in metabolomics field, potential biomarkers for DCM have been identified based on studies using different biological and metabolomics technologies. However, multi-center studies with larger populations that will lead to validation of these identified biomarkers to enable their clinical translation and utilization are still needed.

Comprehending Cardiac Dysfunction by Oxidative Stress: Untargeted Metabolomics of In Vitro Samples

Cardiovascular diseases (CVDs) are noncommunicable diseases known for their complex etiology and high mortality rate. Oxidative stress (OS), a condition in which the release of free radical exceeds endogenous antioxidant capacity, is pivotal in CVC, such as myocardial infarction, ischemia/reperfusion, and heart failure. Due to the lack of information about the implications of OS on cardiovascular conditions, several methodologies have been applied to investigate the causes and consequences, and to find new ways of diagnosis and treatment as well. In the present study, cardiac dysfunction was evaluated by analyzing cells’ alterations with untargeted metabolomics, after simulation of an oxidative stress condition using hydrogen peroxide (H₂O₂) in H9c2 myocytes. Optimizations of H₂O₂ concentration, cell exposure, and cell recovery times were performed through MTT assays. Intracellular metabolites were analyzed right after the oxidative stress (oxidative stress group) and after 48 h of cell recovery (recovery group) by ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) in positive and negative ESI ionization mode. Significant alterations were found in pathways such as “alanine, aspartate and glutamate metabolism”, “glycolysis”, and “glutathione metabolism”, mostly with increased metabolites (upregulated). Furthermore, our results indicated that the LC-MS method is effective for studying metabolism in cardiomyocytes and generated excellent fit (R²Y > 0.987) and predictability (Q² > 0.84) values.

Trypanosoma brucei: Metabolomics for analysis of cellular metabolism and drug discovery

BACKGROUND

Trypanosoma brucei is the causative agent of Human African Trypanosomiasis (also known as sleeping sickness), a disease causing serious neurological disorders and fatal if left untreated. Due to its lethal pathogenicity, a variety of treatments have been developed over the years, but which have some important limitations such as acute toxicity and parasite resistance. Metabolomics is an innovative tool used to better understand the parasite's cellular metabolism, and identify new potential targets, modes of action and resistance mechanisms. The metabolomic approach is mainly associated with robust analytical techniques, such as NMR and Mass Spectrometry. Applying these tools to the trypanosome parasite is, thus, useful for providing new insights into the sleeping sickness pathology and guidance towards innovative treatments.

AIM OF REVIEW

The present review aims to comprehensively describe the T. brucei biology and identify targets for new or commercialized antitrypanosomal drugs. Recent metabolomic applications to provide a deeper knowledge about the mechanisms of action of drugs or potential drugs against T. brucei are highlighted. Additionally, the advantages of metabolomics, alone or combined with other methods, are discussed.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Compared to other parasites, only few studies employing metabolomics have to date been reported on Trypanosoma brucei. Published metabolic studies, treatments and modes of action are discussed. The main interest is to evaluate the metabolomics contribution to the understanding of T. brucei's metabolism.

Mineral Content and Volatile Profiling of Prunus avium L. (Sweet Cherry) By-Products from Fundão Region (Portugal)

Large amounts of Prunus avium L. by-products result from sweet cherry production and processing. This work aimed to evaluate the mineral content and volatile profiling of the cherry stems, leaves, and flowers of the Saco cultivar collected from the Fundão region (Portugal). A total of 18 minerals were determined by ICP-MS, namely 8 essential and 10 non-essential elements. Phosphorus (P) was the most abundant mineral, while lithium (Li) was detected in trace amounts. Three different preparations were used in this work to determine volatiles: hydroethanolic extracts, crude extracts, and aqueous infusions. A total of 117 volatile compounds were identified using HS-SPME/GC-MS, distributed among different chemical classes: 31 aldehydes, 14 alcohols, 16 ketones, 30 esters, 4 acids, 4 monoterpenes, 3 norisoprenoids, 4 hydrocarbons, 7 heterocyclics, 1 lactone, 1 phenol, and 2 phenylpropenes. Benzaldehyde, 4-methyl-benzaldehyde, hexanal, lilac aldehyde, and 6-methyl-5-hepten-2-one were the major volatile compounds. Differences in the types of volatiles and their respective amounts in the different extracts were found. This is the first study that describes the mineral and volatile composition of Portuguese sweet cherry by-products, demonstrating that they could have great potential as nutraceutical ingredients and natural flavoring agents to be used in the pharmaceutical, cosmetic, and food industries.

Recent applications of metabolomics in plant breeding

Metabolites play a central role in maintaining organismal life and in defining crop phenotypes, such as nutritional value, fragrance, color, and stress resistance. Among the 'omes' in biology, the metabolome is the closest to the phenotype. Consequently, metabolomics has been applied to crop improvement as method for monitoring changes in chemical compositions, clarifying the mechanisms underlying cellular functions, discovering markers and diagnostics, and phenotyping for mQTL, mGWAS, and metabolite-genome predictions. In this review, 359 reports of the most recent applications of metabolomics to plant breeding-related studies were examined. In addition to the major crops, more than 160 other crops including rare medicinal plants were considered. One bottleneck associated with using metabolomics is the wide array of instruments that are used to obtain data and the ambiguity associated with metabolite identification and quantification. To further the application of metabolomics to plant breeding, the features and perspectives of the technology are discussed.

Comparative metabolite fingerprinting of chia, flax and sesame seeds using LC-MS untargeted metabolomics

Chia, flax, and sesame seeds are well known for their nutritional quality and are commonly included in bakery products. So far, the development of methods to verify their presence and authenticity in foods is a requisite and a raised need. In this work we applied untargeted metabolomics to propose authenticity markers. Seeds were analyzed by HPLC-MS/MS and 9938 features in negative mode and 9044 in positive mode were obtained by Mzmine. After isotopes grouping, alignment, gap-filling, filtering adducts, and normalization, PCA was applied to explore the dataset and recognize pre-existent classification patterns. OPLS-DA analysis and S-Plots were used as supervised methods. Twenty-five molecules (12 in negative mode and 13 in positive mode) were selected as discriminant for the three seeds, polyphenols and lignans were identified among them. To the best of our knowledge, this is the first approach using non-target HPLC-MS/MS for the authentication of chia, flax and sesame seeds.

Systems approaches to understand oxygen sensing: how multi-omics has driven advances in understanding oxygen-based signalling

Hypoxia is a common denominator in the pathophysiology of a variety of human disease states. Insight into how cells detect, and respond to low oxygen is crucial to understanding the role of hypoxia in disease. Central to the hypoxic response is rapid changes in the expression of genes essential to carry out a wide range of functions to adapt the cell/tissue to decreased oxygen availability. These changes in gene expression are co-ordinated by specialised transcription factors, changes to chromatin architecture and intricate balances between protein synthesis and destruction that together establish changes to the cellular proteome. In this article, we will discuss the advances of our understanding of the cellular oxygen sensing machinery achieved through the application of 'omics-based experimental approaches.

Comparative metabolomics with Metaboseek reveals functions of a conserved fat metabolism pathway in C. elegans

Untargeted metabolomics via high-resolution mass spectrometry can reveal more than 100,000 molecular features in a single sample, many of which may represent unidentified metabolites, posing significant challenges to data analysis. We here introduce Metaboseek, an open-source analysis platform designed for untargeted comparative metabolomics and demonstrate its utility by uncovering biosynthetic functions of a conserved fat metabolism pathway, α-oxidation, using C. elegans as a model. Metaboseek integrates modules for molecular feature detection, statistics, molecular formula prediction, and fragmentation analysis, which uncovers more than 200 previously uncharacterized α-oxidation-dependent metabolites in an untargeted comparison of wildtype and α-oxidation-defective hacl-1 mutants. The identified metabolites support the predicted enzymatic function of HACL-1 and reveal that α-oxidation participates in metabolism of endogenous β-methyl-branched fatty acids and food-derived cyclopropane lipids. Our results showcase compound discovery and feature annotation at scale via untargeted comparative metabolomics applied to a conserved primary metabolic pathway and suggest a model for the metabolism of cyclopropane lipids.

Untargeted mass spectrometry-based metabolomics can reveal new biochemistry, but data analysis is challenging. Here, the authors develop Metaboseek, an open-source software that facilitates metabolite discovery, and apply it to characterize fatty acid alpha-oxidation in C. elegans.

Chemical Cartography Approaches to Study Trypanosomatid Infection

Pathogen tropism and disease tropism refer to the tissue locations selectively colonized or damaged by pathogens, leading to localized disease symptoms. Human-infective trypanosomatid parasites include Trypanosoma cruzi, the causative agent of Chagas disease; Trypanosoma brucei, the causative agent of sleeping sickness; and Leishmania species, causative agents of leishmaniasis. Jointly, they affect 20 million people across the globe. These parasites show specific tropism: heart, esophagus, colon for T. cruzi, adipose tissue, pancreas, skin, circulatory system and central nervous system for T. brucei, skin for dermotropic Leishmania strains, and liver, spleen, and bone marrow for viscerotropic Leishmania strains. A spatial perspective is therefore essential to understand trypanosomatid disease pathogenesis. Chemical cartography generates 3D visualizations of small molecule abundance generated via liquid chromatography-mass spectrometry, in comparison to microbiological and immunological parameters. This protocol demonstrates how chemical cartography can be applied to study pathogenic processes during trypanosomatid infection, beginning from systematic tissue sampling and metabolite extraction, followed by liquid chromatography-tandem mass spectrometry data acquisition, and concluding with the generation of 3D maps of metabolite distribution. This method can be used for multiple research questions, such as nutrient requirements for tissue colonization by T. cruzi, T. brucei, or Leishmania, immunometabolism at sites of infection, and the relationship between local tissue metabolic perturbation and clinical disease symptoms, leading to comprehensive insight into trypanosomatid disease pathogenesis.

Spatially Mapping the Baseline and Bisphenol-A Exposed Daphnia magna Lipidome Using Desorption Electrospray Ionization-Mass Spectrometry

Untargeted lipidomics has previously been applied to the study of daphnids and the discovery of biomarkers that are indicative of toxicity. Typically, liquid chromatography—mass spectrometry is used to measure the changes in lipid abundance in whole-body homogenates of daphnids, each only ca. 3 mm in length which limits any biochemical interpretation of site-specific toxicity. Here, we applied mass spectrometry imaging of Daphnia magna to combine untargeted lipidomics with spatial resolution to map the molecular perturbations to defined anatomical regions. A desorption electrospray ionization—mass spectrometry (DESI-MS) method was optimized and applied to tissue sections of daphnids exposed to bisphenol-A (BPA) compared to unexposed controls, generating an untargeted mass spectrum at each pixel (35 µm²/pixel) within each section. First, unique lipid profiles from distinct tissue types were identified in whole-body daphnids using principal component analysis, specifically distinguishing appendages, eggs, eye, and gut. Second, changes in the lipidome were mapped over four stages of normal egg development and then the effect of BPA exposure on the egg lipidome was characterized. The primary perturbations to the lipidome were annotated as triacylglycerides and phosphatidylcholine, and the distributions of the individual lipid species within these classes were visualized in whole-body D. magna sections as ion images. Using an optimized DESI-MS workflow, the first ion images of D. magna tissue sections were generated, mapping both their baseline and BPA-perturbed lipidomes.

The Impact of the hAPP695SW Transgene and Associated Amyloid-β Accumulation on Murine Hippocampal Biochemical Pathways

Background: Alzheimer’s disease (AD) is a neurodegenerative disease characterized by the accumulation of amyloid-β (Aβ) peptide in the brain. Objective: Gain a better insight into alterations in major biochemical pathways underlying AD. Methods: We compared metabolomic profiles of hippocampal tissue of 20-month-old female Tg2576 mice expressing the familial AD-associated hAPP695SW transgene with their 20-month-old wild type female littermates. Results: The hAPP695SW transgene causes overproduction and accumulation of Aβ in the brain. Out of 180 annotated metabolites, 54 metabolites differed (30 higher and 24 lower in Tg2576 versus wild-type hippocampal tissue) and were linked to the amino acid, nucleic acid, glycerophospholipid, ceramide, and fatty acid metabolism. Our results point to 1) heightened metabolic activity as indicated by higher levels of urea, enhanced fatty acid β-oxidation, and lower fatty acid levels; 2) enhanced redox regulation; and 3) an imbalance of neuro-excitatory and neuro-inhibitory metabolites in hippocampal tissue of aged hAPP695SW transgenic mice. Conclusion: Taken together, our results suggest that dysregulation of multiple metabolic pathways associated with a concomitant shift to an excitatory-inhibitory imbalance are contributing mechanisms of AD-related pathology in the Tg2576 mouse.

Hydrogen/Deuterium Exchange Aiding Metabolite Identification in Single-Cell Nanospray High-Resolution Mass Spectrometry Analysis

The identification of metabolites in single-cell or small-volume tissue samples using single-cell mass spectrometry (MS) is challenging. In this study, hydrogen/deuterium (H/D) exchange was combined with microsampling nanospray high-resolution mass spectrometry (HRMS) to improve the efficiency and confidence level of metabolite identification in a single cell using commercial software. A nanospray ion source showed an improved reaction depth of 8% for H/D exchange compared with an electrospray ion source. In total, 273 metabolites were identified in Allium cepa L. single cells by searching commercial databases. Generally, more than one candidate is given for a precursor ion by MS or tandem MS (MS²) databases such as ChemSpider, MetDNA, MassBank, and mzCloud. With the help of the H/D exchange technique, the number of candidates decreased and reduction of the search space by a factor of 8 was achieved. In addition, two enzymolysis products of isoalliin, the transient intermediate and its isomer, were tracked at the single-cell level using the proposed method.

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

Xanthohumol Pyrazole Derivative Improves Diet-Induced Obesity and Induces Energy Expenditure in High-Fat Diet-Fed Mice

The energy intake exceeding energy expenditure (EE) results in a positive energy balance, leading to storage of excess energy and weight gain. Here, we investigate the potential of a newly synthesized compound as an inducer of EE for the management of diet-induced obesity and insulin resistance. Xanthohumol (XN), a prenylated flavonoid from hops, was used as a precursor for the synthesis of a pyrazole derivative tested for its properties on high-fat diet (HFD)-induced metabolic impairments. In a comparative study with XN, we report that 4-(5-(4-hydroxyphenyl)-1-methyl-1H-pyrazol-3-yl)-5-methoxy-2-(3-methylbut-2-en-1-yl)benzene-1,3-diol (XP) uncouples oxidative phosphorylation in C2C12 cells. In HFD-fed mice, XP improved glucose tolerance and decreased weight gain by increasing EE and locomotor activity. Using an untargeted metabolomics approach, we assessed the effects of treatment on metabolites and their corresponding biochemical pathways. We found that XP and XN reduced purine metabolites and other energy metabolites in the plasma of HFD-fed mice. The induction of locomotor activity was associated with an increase in inosine monophosphate in the cortex of XP-treated mice. Together, these results suggest that XP, better than XN, affects mitochondrial respiration and cellular energy metabolism to prevent obesity in HFD-fed mice.

A Validated HPLC-PDA-HRMS Method to Investigate the Biological Stability and Metabolism of Antiparasitic Triterpenic Esters

Pentacyclic triterpenes (PTs) are commonly found in medicinal plants with well-known antiparasitic effects. Previous research on C-3 and C-27 triterpenic esters showed effective and selective in vitro antiparasitic activities and in vivo effectiveness by parenteral routes. The aim of this study was to determine triterpenic esters' stability in different biological-like media and the main microsomal degradation products. An HPLC-PDA method was developed and validated to simultaneously analyze and quantify bioactive triterpenic esters in methanol (LOQ: 2.5 and 1.25-100 µg/mL) and plasma (LOQ: 5-125 µg/mL). Overall, both triterpenic esters showed a stable profile in aqueous and buffered solutions as well as in entire plasma, suggesting gaining access to the ester function is difficult for plasma enzymes. Conversely, after 1 h, 30% esters degradation in acidic media was observed with potential different hydrolysis mechanisms. C-3 (15 and 150 µM) and C-27 esters (150 µM) showed a relatively low hepatic microsomal metabolism (<23%) after 1 h, which was significantly higher in the lowest concentration of C-27 esters (15 µM) (>40% degradation). Metabolic HPLC-PDA-HRMS studies suggested hydrolysis, hydroxylation, dehydration, O-methylation, hydroxylation and/or the reduction of hydrolyzed derivatives, depending on the concentration and the position of the ester link. Further permeability and absorption studies are required to better define triterpenic esters pharmacokinetic and specific formulations designed to increase their oral bioavailability.