METLIN: A Tandem Mass Spectral Library of Standards

Quality evaluation of metabolite annotation based on comprehensive simulation of MS/MS data from high-resolution mass spectrometry (HRMS) and similarity scoring

Metabolite annotation is a critical step in discovery metabolomics, but remains a significant challenge. In this study, the accuracy of metabolite annotation was systematically evaluated by leveraging the proposed strategies for simulation of tandem mass spectrometry (MS/MS) data from high-resolution mass spectrometry (HRMS) and then construction of a large-scale virtual database. Furthermore, various similarity scoring methods were comprehensively compared to assess the performance for annotation. First, three key characteristics that are essential for simulating MS/MS spectra to closely resemble experimental data were identified: (i) the number of mass-to-charge ratio (m/z) features, (ii) the differences between neighboring m/z values, and (iii) the intensity distribution of MS/MS features. These factors were employed to generate representative MS/MS spectra for subsequent study. A meticulously designed virtual MS/MS database was constructed to facilitate accurate annotation assessment, which covered over 100,000 metabolites with diverse structural similarities and differences. To evaluate annotation quality, two simulation strategies on the basis of strong and weak data inference were respectively proposed to replicate MS/MS spectra for unknown metabolites. These simulated spectra were then compared with the virtual database, which provided insights into the expected variations in experimental MS/MS data. Furthermore, eight similarity evaluation methods, including entropy similarity (ES) and weighted dot product (W/DP) algorithms, were rigorously evaluated for their effectiveness in metabolite annotation. The results revealed that some methods, such as ES, exhibited strong resistance to interference and broad adaptability across different MS/MS patterns, whereas others selectively yielded reliable outcomes under specific conditions. This study provided a systematic framework for quality evaluation in metabolite annotation and offered strategies to mitigate false-positive identifications. The findings held great significance for advancing metabolomics research and further improving annotation reliability in complex biological samples.

Mass-spectrometry based metabolomics: an overview of workflows, strategies, data analysis and applications

BACKGROUND

Metabolomics, a burgeoning field within systems biology, focuses on the comprehensive study of small molecules present in biological systems. Mass spectrometry (MS) has emerged as a powerful tool for metabolomic analysis due to its high sensitivity, resolution, and ability to characterize a wide range of metabolites thus offering deep insights into the metabolic profiles of living systems.

AIM OF REVIEW

This review provides an overview of the methodologies, workflows, strategies, data analysis techniques, and applications associated with mass spectrometry-based metabolomics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We discuss workflows, key strategies, experimental procedures, data analysis techniques, and diverse applications of metabolomics in various research domains. Nuances of sample preparation, metabolite extraction, separation using chromatographic techniques, mass spectrometry analysis, and data processing are elaborated. Moreover, standards, quality controls, metabolite annotation, software for statistical and pathway analysis are also covered. In conclusion, this review aims to facilitate the understanding and adoption of mass spectrometry-based metabolomics by newcomers and researchers alike by providing a foundational understanding and insights into the current state and future directions of this dynamic field.

Serum untargeted metabolomics combined with mouse models reveals potential mechanisms of ChengShu QingChu decoction for the treatment of vitiligo

To illustrate the potential immunological mechanisms of ChengShu QingChu Decoction (CSQC) in vitiligo treatment. An untargeted metabolomic approach was used to detect serum metabolites in 30 patients with progressive vitiligo. Annotation of the differential metabolites were performed using MetaboAnalyst 5.0 and the KEGG database. In addition, 58 Hub genes associated with metabolic pathways were obtained from the GSEA and KEGG databases, and functional enrichment for Hub genes. Finally, it was validated by a mouse model. 102 down-regulated and 86 up-regulated metabolites were detected in serum. The main metabolic pathways enriched for differential metabolites were sphingolipid metabolism and glycerophospholipid metabolism, and the signaling pathways included PI3K-Akt and JAK-STAT signaling pathways, and immune cells included CD56 bright natural killer cell and Central memry CD8 T cell. In the mouse model, a significant decrease in the number of CD8+T cells as well as a decrease in the mRNA expression of JAK1, JAK2, and STAT1 was observed in addition to a trend toward increased melanocytes after drug treatment. This study utilizes metabolomics and bioinformatics analyses, combined with in vivo experimental validation, to elucidate the potential mechanism of CSQC in the treatment of vitiligo.

Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics

The rediscovery of known drug classes represents a major challenge in natural products drug discovery. Compound rediscovery inhibits the ability of researchers to explore novel natural products and wastes significant amounts of time and resources. This study introduces a novel machine learning framework that can effectively characterize the bioactivity of natural products by leveraging liquid chromatography tandem mass spectrometry and untargeted metabolomics analysis. This accelerates natural product drug discovery by addressing the challenge of dereplicating previously discovered bioactive compounds. Utilizing the SIRIUS 5 metabolomics software suite and in-silico-generated fragmentation spectra, we have trained a ML model capable of predicting a compound's drug class. This approach enables the rapid identification of bioactive scaffolds from LC-MS/MS data, even without reference experimental spectra. The model was trained on a diverse set of molecular fingerprints generated by SIRIUS 5 to effectively classify compounds based on their core pharmacophores. Our model robustly classified 21 diverse bioactive drug classes, achieving accuracies greater than 93% on experimental spectra. This study underscores the potential of ML combined with MFPs to dereplicate bioactive natural products based on pharmacophore, streamlining the discovery process and expediting improved methods of isolating novel antibacterial and antifungal agents.

Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Multimodal chemical speciation techniques based on simultaneous high resolution molecular/atomic mass spectrometry applied to online target/non-target analysis: A tutorial review

BACKGROUND

If identifying target species is challenging regarding chemical speciation, non-target species present even more significant difficulties. Thus, to improve the performance of the methods, multimodal online coupling involving atomic and molecular mass spectrometry (LC-ICP-MS-ESI-HRMS) is an advance in this direction. Then, this kind of coupling is highlighted in this Tutorial Review, as well as some references emphasizing its potentialities and possible limitations. Some crucial definitions of speciomics, chemical speciation, and others are also included.

RESULTS

The main parameters that influence the coupling of an inductively coupled plasma mass spectrometer with a high-resolution mass spectrometer through a chromatographic system are critically commented on, and a diversity of results is demonstrated by using a turtle liver (Caretta caretta) as a model sample. The parameters were discussed in detail in a step-by-step manner: ICP-MS/MS acquisition modes and instrumental parameters, HRMS acquisition modes and instrumental parameters, and data processing strategies (Full MS - Top N, All Ion Fragmentation - AIF, Parallel Reaction Monitoring - PRM). Additionally, this Tutorial Review also demonstrates a diversity of results through target and non-target analysis.

SIGNIFICANCE

Constituting a guide for those who are interested in a non-targeted analysis of molecular non-volatile/semi-volatile compounds, this Tutorial Review presents trans and multidisciplinary proposals for those communities involving chemistry, biochemistry, medicine, biology, environmental, pharmaceutical, food safety, and omics, among others, where metal (also metalloids or semi-metals and non-metals or heteroatoms) and molecular species are necessary for a good understanding of the studied system. This kind of coupling also allows the discovery of novel biological active elemental species in diverse matrices.

Coal-straw co-digestion-induced biogenic methane production: perspectives on microbial communities and associated metabolic pathways

This study assessed the impacts of wheat straw as a cosubstrate on coal biocoverion into methane and the associated mechanism within methane metabolic pathways. Co-digestion of coal with varying wheat straw concentrations resulted in a remarkable (1246.05%) increase in methane yield compared to that of the control (CK). Moreover, microbial analysis revealed a uniform distribution of Methanosarcinaceae (51.14%) and Methanobacteriaceae (39.90%) in the co-digestion of coal and wheat straw (CWS1) at a ratio of 3:1 (w/w) compared to other treatments such as coal and wheat straw (CWS2) at a ratio of 3:0.5. In addition, Hungatieclostridiaceae and Rhodobacteriaceae were abundant in both co-digesters, whereas the bacterial communities in the CK group were significantly different and more abundant than those in the Peptostreptococcaceae and Enterobacteriaceae groups. The key enzymes related to methanogenic metabolic pathways, including EC: 1.2.99.5 and EC: 2.1.1.86 (facilitating the conversion of CO2 into methane), and EC:1.12.98.1 exhibited significant abundance within CWS1. Aromatic compounds such as 4-(2-chloroanilino)-4-oxobutanoic acid and phthalic acid were substantially more abundant in CWS1 and CWS2 than in CK, indicating the increased bioavailability of coal to microbial activities. This novel approach demonstrates that wheat straw co-digestion with coal during anaerobic digestion modulates microbial communities and their metabolic pathways to enhance methane production from complex substrates such as coal.

Heterologous biosynthesis of betanin triggers metabolic reprogramming in tobacco

Engineering of a specialized metabolic pathway in plants is a promising approach to produce high-value bioactive compounds to address the challenges of climate change and population growth. Understanding the interaction between the heterologous pathway and the native metabolic network of the host plant is crucial for optimizing the engineered system and maximizing the yield of the target compound. In this study, we performed transcriptomic, metabolomic and metagenomic analysis of tobacco (Nicotiana tabacum) plants engineered to produce betanin, an alkaloid pigment that is found in Caryophyllaceae plants. Our data reveals that, in a dose-dependent manor, the biosynthesis of betanin promotes carbohydrate metabolism and represses nitrogen metabolism in the leaf, but enhances nitrogen assimilation and metabolism in the root. By supplying nitrate or ammonium, the accumulation of betanin increased by 1.5-3.8-fold in leaves and roots of the transgenic plants, confirming the pivotal role of nitrogen in betanin production. In addition, the rhizosphere microbial community is reshaped to reduce denitrification and increase respiration and oxidation, assistant to suppress nitrogen loss. Our analysis not only provides a framework for evaluating the pleiotropic effects of an engineered metabolic pathway on the host plant, but also facilitates the development of novel strategies to balance the heterologous process and the native metabolic network for the high-yield and nutrient-efficient production of bioactive compounds in plants.

Integration analysis of transcriptome and metabolome revealed the potential mechanism of spermatogenesis in Tibetan sheep (Ovis aries) at extreme high altitude

Testis has an indispensable function in male reproduction of domestic animals. Numerous genes and metabolites were related to testicular development and spermatogenesis. However, little is known about the biological regulation pathways associated with fecundity in male Tibetan sheep. In this study, Testes were collected from Huoba Tibetan sheep (HB, 4614 m) and Gangba Tibetan sheep (GB, 4401 m) at extreme high altitude, and Alpine Merino sheep (AM, 2500 m, control group) at medium-high altitude, investigating the genes and metabolites levels of them. The histological analysis of testicular tissue using hematoxylin-eosin (HE) staining was performed for Tibetan sheep and Alpine Merino sheep, and the testes of them were analyzed by transcriptomics and metabolomics to explore the potential mechanism of testicular development and spermatogenesis. The statistical results showed that the cross-sectional area of testicular seminiferous tubules, diameter of seminiferous tubules, and spermatogenic epithelium thickness were significantly smaller in HB and GB than in AM (P < 0.05). Overall, 5648 differentially expressed genes (DEGs) and 336 differential metabolites (DMs) were identified in three sheep breeds, which were significantly enriched in spermatogenesis and other related pathways. According to integrated metabolomic and transcriptomic analysis, glycolysis/gluconeogenesis, AMPK signaling pathway, and TCA cycle, were predicted to have dramatic effects on the spermatogenesis of Tibetan sheep. Several genes (including Wnt2, Rab3a, Sox9, Hspa8, and Slc38a2) and metabolites (including L-histidinol, Glucose, Fumaric acid, Malic acid, and Galactose) were significantly enriched in pathways related to testicular development and spermatogenesis, and might affect the reproduction of Tibetan sheep by regulating the acrosome reaction, meiotic gene expression, and the production of sex hormones. Our results provide further understanding of the key genes and metabolites involved in testicular development and spermatogenesis in Tibetan sheep.

Bioactive (Poly)phenol Concentrations in Plant-Based Milk Alternatives in the US Market

Plant-based milk alternatives (PBMAs) are increasingly consumed as a dairy alternative [Olson, S. Milk and Non-Dairy Milk - US - 2021, 2021.]. Plant foods are rich sources of (poly)phenols, but concentrations of these bioactive phytochemicals in processed PBMAs are not well documented. We procured twenty-seven PBMA products of 6 types (almond, coconut, oat, pea, rice, and soy) for (poly)phenol analysis. Samples were analyzed via ultra high-performance liquid chromatography-diode array with mass spectrometry. The (poly)phenol content of PBMAs varies and is dependent on plant source, brand, and added flavorings. Soy milk had the highest concentration and rice milk had the lowest (91.9 ± 2.7 and 0.9 ± 0.2 mean mg ± SD/cup serving, respectively). Almond milk, the most widely consumed PBMA, averaged 12.1 ± 8.2 mg/cup serving, but the majority of (poly)phenols are derived from added flavorings. PBMAs contain a wide range of potentially bioactive (poly)phenols and may contribute significantly to overall dietary (poly)phenol intake with the potential to impact health outcomes.

Phosphate availability conditions caspofungin tolerance, capsule attachment and titan cell formation in Cryptococcus neoformans

There is an urgent need for new antifungal drugs to treat invasive fungal diseases. Unfortunately, the echinocandin drugs that are fungicidal against other important fungal pathogens are ineffective against Cryptococcus neoformans, the causative agent of life-threatening meningoencephalitis in immunocompromised people. Contributing mechanisms for echinocandin tolerance are emerging with connections to calcineurin signaling, the cell wall, and membrane composition. In this context, we discovered that a defect in phosphate uptake impairs the tolerance of C. neoformans to the echinocandin caspofungin. Our previous analysis of mutants lacking three high affinity phosphate transporters revealed reduced elaboration of the polysaccharide capsule and attenuated virulence in mice. We investigated the underlying mechanisms and found that loss of the transporters and altered phosphate availability influences the cell wall and membrane composition. These changes contribute to the shedding of capsule polysaccharide thus explaining the reduced size of capsules on mutants lacking the phosphate transporters. We also found an influence of the calcineurin pathway including calcium sensitivity and an involvement of the endoplasmic reticulum in the response to phosphate limitation. Furthermore, we identified membrane and lipid composition changes consistent with the role of phosphate in phospholipid biosynthesis and with previous studies implicating membrane integrity in caspofungin tolerance. Finally, we discovered a contribution of phosphate to titan cell formation, a cell type that displays modified cell wall and capsule composition. Overall, our analysis reinforces the importance of phosphate as a regulator of cell wall and membrane composition with implications for capsule attachment and antifungal drug susceptibility.

Towards nutrition with precision: unlocking biomarkers as dietary assessment tools

Precision nutrition requires precise tools to monitor dietary habits. Yet current dietary assessment instruments are subjective, limiting our understanding of the causal relationships between diet and health. Biomarkers of food intake (BFIs) hold promise to increase the objectivity and accuracy of dietary assessment, enabling adjustment for compliance and misreporting. Here, we update current concepts and provide a comprehensive overview of BFIs measured in urine and blood. We rank BFIs based on a four-level utility scale to guide selection and identify combinations of BFIs that specifically reflect complex food intakes, making them applicable as dietary instruments. We discuss the main challenges in biomarker development and illustrate key solutions for the application of BFIs in human studies, highlighting different strategies for selecting and combining BFIs to support specific study designs. Finally, we present a roadmap for BFI development and implementation to leverage current knowledge and enable precision in nutrition research.

Acod1 expression in cancer cells promotes immune evasion through the generation of inhibitory peptides

Targeting programmed cell death protein 1 (PD-1) is an important component of many immune checkpoint blockade (ICB) therapeutic approaches. However, ICB is not an efficacious strategy in a variety of cancer types, in part due to immunosuppressive metabolites in the tumor microenvironment. Here, we find that αPD-1-resistant cancer cells produce abundant itaconate (ITA) due to enhanced levels of aconitate decarboxylase (Acod1). Acod1 has an important role in the resistance to αPD-1, as decreasing Acod1 levels in αPD-1-resistant cancer cells can sensitize tumors to αPD-1 therapy. Mechanistically, cancer cells with high Acod1 inhibit the proliferation of naive CD8+ T cells through the secretion of inhibitory factors. Surprisingly, inhibition of CD8+ T cell proliferation is not dependent on the secretion of ITA but is instead a consequence of the release of small inhibitory peptides. Our study suggests that strategies to counter the activity of Acod1 in cancer cells may sensitize tumors to ICB therapy.

Ion Mobility Mass Spectrometry for Large Synthetic Molecules: Expanding the Analytical Toolbox

Understanding the composition, structure and stability of larger synthetic molecules is crucial for their design, yet currently the analytical tools commonly used do not always provide this information. In this perspective, we show how ion mobility mass spectrometry (IM-MS), in combination with tandem mass spectrometry, complementary techniques and computational methods, can be used to structurally characterize synthetic molecules, make and predict new complexes, monitor disassembly processes and determine stability. Using IM-MS, we present an experimental and computational framework for the analysis and design of complex molecular architectures such as (metallo)supramolecular cages, nanoclusters, interlocked molecules, rotaxanes, dendrimers, polymers and host-guest complexes.

Integrated network pharmacology and metabolomics reveal the action mechanisms of vincristine combined with celastrol against colon cancer

Colon cancer is associated with a high mortality rate. Vincristine (VCR) is a commonly used chemotherapeutic drug. Celastrol (CEL) is an effective component which exerts inhibitory effects on colon cancer. Combination treatment improves resistance to chemotherapeutic drugs and enhances their efficacy. Therefore, we aimed to explore the molecular mechanisms of VCR combined with CEL in colon cancer treatment. We verified the effects of VCR combined with CEL on the proliferation, cell cycle, and apoptosis of HCT-8 cells. Non-targeted metabolomic techniques were used to analyse the changes in cellular metabolites after administration. Finally, network pharmacology technology was used to screen the potential targets and pathways. VCR combined with CEL had synergistic inhibitory effects on HCT-8 colon cancer cells. Cell metabolomics identified 12 metabolites enriched in metabolic pathways, such as the phenylalanine, tyrosine and tryptophan biosynthesis pathways. Network pharmacology revealed that MAPK1, AKT1, PIK3CB, EGFR, and VEGFA were the key targets. Western blotting revealed that VCR combined with CEL activated the P53 pathway by suppressing the PI3K/AKT signalling pathway activation and Bcl-2 expression, promoting the Bax expression. Therefore, VCR combined with CEL potentially treats colon cancer by increasing the apoptosis, improving energy metabolism, and inhibiting PI3K/AKT pathway in colon cancer cells.

Candidate urinary biomarkers show promise for distinguishing between calcium oxalate versus struvite urolithiasis in dogs

OBJECTIVE

To identify metabolites and metabolic pathways affected in dogs with struvite and calcium oxalate urolithiasis compared to healthy dogs. To explore the candidate urinary biomarkers to distinguish dogs with struvite and calcium oxalate urolithiasis.

ANIMALS

13 dogs with calcium oxalate urolithiasis, 7 dogs with struvite urolithiasis, and 13 healthy dogs were recruited between September 2021 and January 2023.

METHODS

Metabolomic profiles were analyzed from urine samples using UPLC-MS MS. According to the variable importance in the projection (> 1) and correlation coefficient (P < .05) obtained by orthogonal partial least squares discriminant analysis, the differential metabolites were screened. The Kyoto Encyclopedia of Genes and Genomes database was used to identify the metabolic pathways involved.

RESULTS

Compared to healthy dogs, those with calcium oxalate urolithiasis exhibited distinct metabolites primarily associated with phenylalanine metabolism, nicotinic acid, and nicotinamide metabolic pathways. Conversely, dogs with struvite urolithiasis demonstrated variations in metabolites mainly linked to tryptophan metabolism and glycerophospholipid metabolic pathways. Between calcium oxalate and struvite groups, pyocyanin, glycylprolylarginine, traumatin, cysteinyl-leucine, and 8-hydroxydodecylcarnitine are candidate urinary biomarkers.

CLINICAL RELEVANCE

Our findings provide an in-depth analysis of metabolic perturbations associated with calcium oxalate and struvite urolithiasis in dogs. We also identified candidate urinary biomarkers distinguishing between dogs with calcium oxalate and struvite urolithiasis, which can be subsequently validated to assist in stone diagnosis and guide treatment choices.

Data-Driven Compound Identification in Atmospheric Mass Spectrometry

Aerosol particles found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol particle formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Large amounts of data are collected in each measurement of current time-of-flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data-driven compound identification approaches could alleviate the problem, yet remain rare to non-existent in atmospheric science. In this perspective, the authors review the current state of data-driven compound identification with mass spectrometry in atmospheric science and discuss current challenges and possible future steps toward a digital era for atmospheric mass spectrometry.

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

From heat to flavor: Unlocking new chemical signatures to discriminate Wuyi rock tea under light and moderate roasting

Roasting is crucial for the distinct flavor of Wuyi rock tea (WRT). This study applied untargeted metabolomics to investigate the effects of roasting on 139 WRT samples roasted at light fire (LF) or moderate fire (MF) intensities. Compared to LF, MF roasting led to a decrease in the cis/trans flavanol ratio by 56% and theanine by 85%, while increasing the levels of N-ethyl-2-pyrrolidione-substituted flavanols (EPSFs), flavonol aglycones and flavone C-glycosides. Two new roast markers, 3-p-coumaroyl 1,5-lactone and 4-p-coumaroyl 1,5-lactone, were identified in WRT and their formation increased with roasting temperature. MF roasting facilitated the formation of diverse heterocycles (e.g., pyrazines) and aldehydes (e.g., (Z)-4-heptenal and (E,E)-2.4-decadienal) that contributed to the augmented roasted and fatty odors in WRT. Additionally, the Maillard product furfuryl methyl ether was solely detected in MF samples. These findings provide novel insights into roast markers in WRT with implications for improving quality control measures during tea roasting.

MATLAB language assisted data acquisition and processing in liquid chromatography Orbitrap mass spectrometry: Application to the identification and differentiation of Radix Bupleuri from its adulterants

Comprehensive and rapid analysis of secondary metabolites like saponins remains challenging. This study aimed to establish a semi-automated workflow for filtration, identification, and characterization of saikosaponins in six Bupleurum species. Radix Bupleuri, a high-sales herbal medicine, is often adulterated, restricting its quality control and applications. Two authentic Radix Bupleuri species and four major adulterants were analyzed through UHPLC-LTQ-Orbitrap-MS for targeted saikosaponin analysis. To reveal trace saikosaponins and obtain quality fragment data, a MATLAB-based process automatically enumerating "sugar chain + aglycone + side chain" combinations and deduplicating generated a predicted saikosaponin database covering all possible saikosaponins as a precursor ion list for comprehensive targeted acquisition. To focus on informative ions and reduce MS analysis workload, we utilized MATLAB to automatically filtrate the false positive ions by MS1 and MS2 spectrometry. The newly established MATLAB-assisted data acquisition approach exhibited 50 % improvement in characterization of targeted saikosaponins. Furthermore, positive and negative ionization workflows were designed for accurate saikosaponins characterization based on fragmentation rules. In total, 707 saikosaponins were characterized, including over 500 potential new compounds and previously unreported C29 aglycones. We identified 25 saikosaponins present in both authentic species but absent in adulterants as potential markers. This unprecedented comprehensive multi-origin species differentiation demonstrates the promise of MATLAB-assisted acquisition and processing to advance saponin identification and standardize the Radix Bupleuri market.

Salmon Food-Specific Compounds and Their Metabolites Increase in Human Plasma and Are Associated with Cardiometabolic Health Indicators Following a Mediterranean-Style Diet Intervention

BACKGROUND

Nutrimetabolomics allows for the comprehensive analysis of foods and human biospecimens to identify biomarkers of intake and begin to probe their associations with health. Salmon contains hundreds of compounds that may provide cardiometabolic benefits.

OBJECTIVES

We used untargeted metabolomics to identify salmon food-specific compounds (FSCs) and their predicted metabolites that were found in plasma after a salmon-containing Mediterranean-style (MED) diet intervention. Associations between changes in salmon FSCs and changes in cardiometabolic health indicators (CHIs) were also explored.

METHODS

For this secondary analysis of a randomized, crossover, controlled feeding trial, 41 participants consumed MED diets with 2 servings of salmon per week for 2 5-wk periods. CHIs were assessed, and fasting plasma was collected pre- and postintervention. Plasma, salmon, and 99 MED foods were analyzed using liquid chromatography-mass spectrometry-based metabolomics. Compounds were characterized as salmon FSCs if detected in all salmon replicates but none of the other foods. Metabolites of salmon FSCs were predicted using machine learning. For salmon FSCs and metabolites found in plasma, linear mixed-effect models were used to assess change from pre- to postintervention and associations with changes in CHIs.

RESULTS

Relative to the other 99 MED foods, there were 508 salmon FSCs with 237 unique metabolites. A total of 143 salmon FSCs and 106 metabolites were detected in plasma. Forty-eight salmon FSCs and 30 metabolites increased after the intervention (false discovery rate <0.05). Increases in 2 annotated salmon FSCs and 2 metabolites were associated with improvements in CHIs, including total cholesterol, low-density lipoprotein cholesterol, triglycerides, and apolipoprotein B.

CONCLUSIONS

A data-driven nutrimetabolomics strategy identified salmon FSCs and their predicted metabolites that were detectable in plasma and changed after consumption of a salmon-containing MED diet. Findings support this approach for the discovery of compounds in foods that may serve, upon further validation, as biomarkers or act as bioactive components influential to health. The trials supporting this work were registered at NCT02573129 (Mediterranean-style diet intervention) and NCT05500976 (ongoing clinical trial).

Database-Driven Spatially Resolved Lipidomics Highlights Heterogeneous Metabolic Alterations in Type 2 Diabetic Mice

Spatially resolved lipidomics is pivotal for detecting and interpreting lipidomes within spatial contexts using the mass spectrometry imaging (MSI) technique. However, comprehensive and efficient lipid identification in MSI remains challenging. Herein, we introduce a high-coverage, database-driven approach combined with air-flow-assisted desorption electrospray ionization (AFADESI)-MSI to generate spatial lipid profiles across whole-body mice. Using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), we identified 2868 unique lipids in the serum and various organs of mice. Subsequently, we systematically evaluated the distinct ionization properties of the lipids between LC-MS and MSI and created a detailed MSI database containing 14 123 ions. This method enabled the visualization of aberrant fatty acid and phospholipid metabolism across organs in a diabetic mouse model. As a powerful extension incorporated into the MSIannotator tool, our strategy facilitates the rapid and accurate annotation of lipids, providing new research avenues for probing spatially resolved heterogeneous metabolic changes in response to diseases.

Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.

Identification of Metabolomic Markers in Frozen or Formalin-Fixed and Paraffin-Embedded Samples of Diffuse Glioma from Adults

The aim of this study was to identify metabolomic signatures associated with the gliomagenesis pathway (IDH-mutant or IDH-wt) and tumor grade of diffuse gliomas (DGs) according to the 2021 WHO classification on frozen samples and to evaluate the diagnostic performances of these signatures in tumor samples that are formalin-fixed and paraffin-embedded (FFPE). An untargeted metabolomic study was performed using liquid chromatography/mass spectrometry on a cohort of 213 DG samples. Logistic regression with LASSO penalization was used on the frozen samples to build classification models in order to identify IDH-mutant vs. IDH-wildtype DG and high-grade vs low-grade DG samples. 2-Hydroxyglutarate (2HG) was a metabolite of interest to predict IDH mutational status and aminoadipic acid (AAA) and guanidinoacetic acid (GAA) were significantly associated with grade. The diagnostic performances of the models were 82.6% AUC, 70.6% sensitivity and 80.4% specificity for 2HG to predict IDH status and 84.7% AUC, 78.1% sensitivity and 73.4% specificity for AAA and GAA to predict grade from FFPE samples. Thus, this study showed that AAA and GAA are two novel metabolites of interest in DG and that metabolomic data can be useful in the classification of DG, both in frozen and FFPE samples.

Metabolic Rewiring in Tea Plants in Response to Gray Blight Disease Unveiled by Multi-Omics Analysis

Gray blight disease, which is caused by Pestalotiopsis-like species, poses significant challenges to global tea production. However, the comprehensive metabolic responses of tea plants during gray blight infection remain understudied. Here, we employed a multi-omics strategy to characterize the temporal transcriptomic and metabolomic changes in tea plants during infection by Pseudopestalotiopsis theae, the causal agent of gray blight. Untargeted metabolomic profiling with ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS) revealed extensive metabolic rewiring over the course of infection, particularly within 24 h post-inoculation. A total of 64 differentially accumulated metabolites were identified, including elevated levels of antimicrobial compounds such as caffeine and (-)-epigallocatechin 3-gallate, as well as oxidative catechin polymers like theaflavins, theasinensins and theacitrins. Conversely, the synthesis of (+)-catechin, (-)-epicatechin, oligomeric proanthocyanidins and flavonol glycosides decreased. Integrated omics analyses uncovered up-regulation of phenylpropanoid, flavonoid, lignin biosynthesis and down-regulation of photosynthesis in response to the pathogen stress. This study provides novel insights into the defense strategies of tea plants against gray blight disease, offering potential targets for disease control and crop improvement.

Acod1 Expression in Cancer Cells Promotes Immune Evasion through the Generation of Inhibitory Peptides

Targeting PD-1 is an important component of many immune checkpoint blockade (ICB) therapeutic approaches. However, ICB is not an efficacious strategy in a variety of cancer types, in part due to immunosuppressive metabolites in the tumor microenvironment (TME). Here, we find that αPD-1-resistant cancer cells produce abundant itaconate (ITA) due to enhanced levels of aconitate decarboxylase (Acod1). Acod1 has an important role in the resistance to αPD-1, as decreasing Acod1 levels in αPD-1 resistant cancer cells can sensitize tumors to αPD-1 therapy. Mechanistically, cancer cells with high Acod1 inhibit the proliferation of naïve CD8+ T cells through the secretion of inhibitory factors. Surprisingly, inhibition of CD8+ T cell proliferation is not dependent on secretion of ITA, but is instead a consequence of the release of small inhibitory peptides. Our study suggests that strategies to counter the activity of Acod1 in cancer cells may sensitize tumors to ICB therapy.

Evaluation of Data-Dependent MS/MS Acquisition Parameters for Non-Targeted Metabolomics and Molecular Networking of Environmental Samples: Focus on the Q Exactive Platform

Non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.

Identification of potential salivary biomarkers for Sjögren's syndrome with an untargeted metabolomic approach

INTRODUCTION

Despite the rise of metabolomics over the past years, and particularly salivary metabolomics, little research on Sjögren's syndrome (SS) biomarkers has focused on the salivary metabolome.

OBJECTIVES

This study aims to identify metabolites that could be used as biomarkers for SS.

METHODS

Using the software called XCMS online, the salivary metabolic profiles obtained with liquid chromatography coupled to high-resolution mass spectrometry for 18 female SS patients were compared to those obtained for 22 age-matched female healthy controls.

RESULTS AND CONCLUSION

A total of 91 metabolites showed differential expression in SS patients. A putative identification was proposed with the use of a database for 37 of these metabolites and, of these, 16 identifications were confirmed. Given the identified metabolites, some important metabolic pathways, such as amino acid metabolism, purine metabolism, or even the citric acid cycle seem to be affected. Through the analyses of the ROC (receiver operating characteristic) curves, three metabolites, namely alanine, isovaleric acid, and succinic acid, showed both good sensitivity (respectively 1.000, 1.000, and 0.750) and specificity (respectively 0.692, 0.615, and 0.692) for identifying SS and could then be interesting biomarkers for a potential salivary diagnosis test.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Prediction model for day 3 embryo implantation potential based on metabolites in spent embryo culture medium

BACKGROUND

Metabolites in spent embryo culture medium correlate with the embryo's viability. However, there is no widely accepted method using metabolite dada to predict successful implantation. We sought to combine metabolomic profiling of spent embryo culture medium and clinical variables to create an implantation prediction model as an adjunct to morphological screening of day 3 embryos.

METHODS

This investigation was a prospective, nested case-control study. Forty-two day 3 embryos from 34 patients were transferred, and the spent embryo culture medium was collected. Twenty-two embryos implanted successfully, and the others failed. Metabolites in the medium relevant to implantation were detected and measured by Liquid Chromatography-Mass Spectrometry. Clinical signatures relevant to embryo implantation were subjected to univariate analysis to select candidates for a prediction model. Multivariate logistical regression of the clinical and metabolomic candidates was used to construct a prediction model for embryo implantation potential.

RESULTS

The levels of 13 metabolites were significantly different between the successful and failed groups, among which five were most relevant and interpretable selected by Least Absolute Shrinkage and Selection Operator regression analysis. None of the clinical variables significantly affected day 3 embryo implantation. The most relevant and interpretable set of metabolites was used to construct a prediction model for day 3 embryo implantation potential with an accuracy of 0.88.

CONCLUSIONS

Day 3 embryos'implantation potential could be noninvasively predicted by the spent embryo culture medium's metabolites measured by LC-MS. This approach may become a useful adjunct to morphological evaluation of day 3 embryos.

Comparative Transcriptome and Targeted Metabolome Profiling Unravel the Key Role of Phenylpropanoid and Glucosinolate Pathways in Defense against Alternaria brassicicola in Broccoli

Alternaria brassicicola (Ab) can cause a major yield and quality-limiting disease of Brassica oleracea called black spot, and the genetic resources conferring complete resistance against Ab have not been identified to date. Here, comparative transcriptome and targeted metabolome analysis were performed utilizing a newly identified resistant (R) line and a broccoli susceptible (S) line at 6, 24, and 72 h post-inoculation (hpi). Kyoto encyclopedia of genes and genomes pathway enrichment and the weighted gene co-expression network analyses showed that the phenylpropanoid pathway regulates the resistance to Ab in broccoli. One metabolite, cinnamic acid, was significantly upregulated in the Ab_inoculated R line compared with the mock treatment but no significant difference in the S line, indicating that the cinnamic acid may cause the resistance difference between R and S lines. Our results also revealed that three indolic glucosinolates of I3G, 4MI3G, and 1MI3G were significantly increased in the Ab_inoculated R line compared with the mock treatment, and some related genes were differentially expressed between the R and S lines. These results provided new insights into the mechanism of Ab defense in B. oleracea and have laid a theoretical foundation for effectively utilizing resistant germplasm resources in broccoli breeding.

Plant and microbial sciences as key drivers in the development of metabolomics research

This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Molecular Networking and On-Tissue Chemical Derivatization for Enhanced Identification and Visualization of Steroid Glycosides by MALDI Mass Spectrometry Imaging

Spatial metabolomics describes the spatially resolved analysis of interconnected pathways, biochemical reactions, and transport processes of small molecules in the spatial context of tissues and cells. However, a broad range of metabolite classes (e.g., steroids) show low intrinsic ionization efficiencies in mass spectrometry imaging (MSI) experiments, thus restricting the spatial characterization of metabolic networks. Additionally, decomposing complex metabolite networks into chemical compound classes and molecular annotations remains a major bottleneck due to the absence of repository-scaled databases. Here, we describe a multimodal mass-spectrometry-based method combining computational metabolome mining tools and high-resolution on-tissue chemical derivatization (OTCD) MSI for the spatially resolved analysis of metabolic networks at the low micrometer scale. Applied to plant toxin sequestration in Danaus plexippus as a model system, we first utilized liquid chromatography (LC)-MS-based molecular networking in combination with artificial intelligence (AI)-driven chemical characterization to facilitate the structural elucidation and molecular identification of 32 different steroidal glycosides for the host-plant Asclepias curassavica. These comprehensive metabolite annotations guided the subsequent matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) analysis of cardiac-glycoside sequestration in D. plexippus. We developed a spatial-context-preserving OTCD protocol, which improved cardiac glycoside ion yields by at least 1 order of magnitude compared to results with untreated samples. To illustrate the potential of this method, we visualized previously inaccessible (sub)cellular distributions (2 and 5 μm pixel size) of steroidal glycosides in D. plexippus, thereby providing a novel insight into the sequestration of toxic metabolites and guiding future metabolomics research of other complex sample systems.

Untargeted Metabolomic Approach to Study the Impact of Aging on Salivary Metabolome in Women

Despite the growing interest in salivary metabolomics, few studies have investigated the impact of aging on the salivary metabolome. The alterations in metabolic pathways that occur with aging are likely to be observed in pathologies affecting older people and may interfere with the search for salivary biomarkers. It is therefore important to investigate the age-related changes occurring in the salivary metabolome. Using reversed phase liquid chromatography and hydrophilic interaction chromatography coupled to mass spectrometry used in positive and negative ionization modes, the salivary metabolic profiles of young (22 to 45 years old) and older people (55 to 92 years old) were obtained. Those profiles were compared with the use of XCMS online to highlight the under or overexpression of some metabolites with aging. A total of 60 metabolites showed differential expression with age. The identification of 26 of them was proposed by the METLIN database and, among them, 17 were validated by standard injections. Aging seemed to affect most of the main metabolic pathways (amino acid metabolism, Krebs cycle, fatty acid synthesis, and nucleic acid synthesis). Moreover, most of the metabolites that were over- or under-expressed with age in this study have already been identified as being potential biomarkers of diseases affecting older people, such as in Alzheimer's disease. Special attention should be paid in the search for biomarkers of pathologies affecting the elderly to differentiate age-related changes from disease-related changes.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Artifactual FA dimers mimic FAHFA signals in untargeted metabolomics pipelines

FA esters of hydroxy FAs (FAHFAs) are lipokines with extensive structural and regional isomeric diversity that impact multiple physiological functions, including insulin sensitivity and glucose homeostasis. Because of their low molar abundance, FAHFAs are typically quantified using highly sensitive LC-MS/MS methods. Numerous relevant MS databases house in silico-spectra that allow identification and speciation of FAHFAs. These provisional chemical feature assignments provide a useful starting point but could lead to misidentification. To address this possibility, we analyzed human serum with a commonly applied high-resolution LC-MS untargeted metabolomics platform. We found that many chemical features are putatively assigned to the FAHFA lipid class based on exact mass and fragmentation patterns matching spectral databases. Careful validation using authentic standards revealed that many investigated signals provisionally assigned as FAHFAs are in fact FA dimers formed in the LC-MS pipeline. These isobaric FA dimers differ structurally only by the presence of an olefinic bond. Furthermore, stable isotope-labeled oleic acid spiked into human serum at subphysiological concentrations showed concentration-dependent formation of a diverse repertoire of FA dimers that analytically mimicked FAHFAs. Conversely, validated FAHFA species did not form spontaneously in the LC-MS pipeline. Together, these findings underscore that FAHFAs are endogenous lipid species.  However, nonbiological FA dimers forming in the setting of high concentrations of FFAs can be misidentified as FAHFAs. Based on these results, we assembled a FA dimer database to identify nonbiological FA dimers in untargeted metabolomics datasets.

Non-targeted metabolomics and associations with per- and polyfluoroalkyl substances (PFAS) exposure in humans: A scoping review

OBJECTIVE

To summarize the application of non-targeted metabolomics in epidemiological studies that assessed metabolite and metabolic pathway alterations associated with per- and polyfluoroalkyl substances (PFAS) exposure.

RECENT FINDINGS

Eleven human studies published before April 1st, 2021 were identified through database searches (PubMed, Dimensions, Web of Science Core Collection, Embase, Scopus), and citation chaining (Citationchaser). The sample sizes of these studies ranged from 40 to 965, involving children and adolescents (n = 3), non-pregnant adults (n = 5), or pregnant women (n = 3). High-resolution liquid chromatography-mass spectrometry was the primary analytical platform to measure both PFAS and metabolome. PFAS were measured in either plasma (n = 6) or serum (n = 5), while metabolomic profiles were assessed using plasma (n = 6), serum (n = 4), or urine (n = 1). Four types of PFAS (perfluorooctane sulfonate(n = 11), perfluorooctanoic acid (n = 10), perfluorohexane sulfonate (n = 9), perfluorononanoic acid (n = 5)) and PFAS mixtures (n = 7) were the most studied. We found that alterations to tryptophan metabolism and the urea cycle were most reported PFAS-associated metabolomic signatures. Numerous lipid metabolites were also suggested to be associated with PFAS exposure, especially key metabolites in glycerophospholipid metabolism which is critical for biological membrane functions, and fatty acids and carnitines which are relevant to the energy supply pathway of fatty acid oxidation. Other important metabolome changes reported included the tricarboxylic acid (TCA) cycle regarding energy generation, and purine and pyrimidine metabolism in cellular energy systems.

CONCLUSIONS

There is growing interest in using non-targeted metabolomics to study the human physiological changes associated with PFAS exposure. Multiple PFAS were reported to be associated with alterations in amino acid and lipid metabolism, but these results are driven by one predominant type of pathway analysis thus require further confirmation. Standardizing research methods and reporting are recommended to facilitate result comparison. Future studies should consider potential differences in study methodology, use of prospective design, and influence from confounding bias and measurement errors.

Antioxidant and Cytotoxic Activities of Kudzu Roots and Soy Molasses against Pediatric Tumors and Phytochemical Analysis of Isoflavones Using HPLC-DAD-ESI-HRMS

Pediatric solid tumors (PSTs) are life-threatening and can lead to high morbidity and mortality rates in children. Developing novel remedies to treat these tumors, such as glioblastoma multiforme and sarcomas, such as osteosarcoma, and rhabdomyosarcoma, is challenging, despite immense attempts with chemotherapeutic or radiotherapeutic interventions. Soy (Glycine max) and kudzu roots (KR) (Pueraria spp.) are well-known phytoestrogenic botanical sources that contain high amounts of naturally occurring isoflavones. In the present study, we investigated the antioxidant and cytotoxic effects of the extracts of KR and soy molasses (SM) against PSTs. The green extraction of isoflavones from KR and SM was performed using natural deep eutectic solvents. The extracts were subsequently analyzed by high-performance liquid chromatography (HPLC)-diode array detector (DAD) coupled with high-resolution (HR) mass spectrometry (MS), which identified 10 isoflavones in KR extracts and 3 isoflavones in the SM extracts. Antioxidant and cytotoxic activities of KR and SM extracts were assessed against glioblastoma multiforme (A-172), osteosarcoma (HOS), and rhabdomyosarcoma (Rd) cancer cell lines. The KR and SM extracts showed satisfactory cytotoxic effects (IC₅₀) against the cancer cell lines tested, particularly against Rd cancer cell lines, in a dose-dependent manner. Antioxidant activity was found to be significantly (p ≤ 0.05) higher in KR than in SM, which was consistent with the results of the cytotoxic activity observed with KR and SM extracts against glioblastoma and osteosarcoma cells. The total flavonoid content and antioxidant activities of the extracts were remarkably attributed to the isoflavone content in the KR and SM extracts. This study provides experimental evidence that HPLC-ESI-HRMS is a suitable analytical approach to identify isoflavones that exhibit potent antioxidant and anticancer potential against tumor cells, and that KR and SM, containing many isoflavones, can be a potential alternative for health care in the food and pharmaceutical industries.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners

Background

Responses of the metabolome to acute aerobic exercise may predict maximum oxygen consumption (VO₂max) and longer-term outcomes, including the development of diabetes and its complications.

Methods

Serum samples were collected from overweight/obese trained (OWT) and normal-weight trained (NWT) runners prior to and immediately after a supervised 90-minute treadmill run at 60% VO₂max (NWT = 14, OWT = 11) in a cross-sectional study. We applied a liquid chromatography high-resolution–mass spectrometry–based untargeted metabolomics platform to evaluate the effect of acute aerobic exercise on the serum metabolome.

Results

NWT and OWT metabolic profiles shared increased circulating acylcarnitines and free fatty acids (FFAs) with exercise, while intermediates of adenine metabolism, inosine, and hypoxanthine were strongly correlated with body fat percentage and VO₂max. Untargeted metabolomics-guided follow-up quantitative lipidomic analysis revealed that baseline levels of fatty acid esters of hydroxy fatty acids (FAHFAs) were generally diminished in the OWT group. FAHFAs negatively correlated with visceral fat mass and HOMA-IR. Strikingly, a 4-fold decrease in FAHFAs was provoked by acute aerobic running in NWT participants, an effect that negatively correlated with circulating IL-6; these effects were not observed in the OWT group. Machine learning models based on a preexercise metabolite profile that included FAHFAs, FFAs, and adenine intermediates predicted VO₂max.

Conclusion

These findings in overweight human participants and healthy controls indicate that exercise-provoked changes in FAHFAs distinguish normal-weight from overweight participants and could predict VO₂max. These results support the notion that FAHFAs could modulate the inflammatory response, fuel utilization, and insulin resistance.

Trial registration

ClinicalTrials.gov, NCT02150889.

Funding

NIH DK091538, AG069781, DK098203, TR000114, UL1TR002494.

HMDB 5.0: the Human Metabolome Database for 2022

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

UPLC-MS/MS and Network Pharmacology-Based Analysis of Bioactive Anti-Depression Compounds in Betel Nut

Background

Betel nuts have long been used in traditional Chinese medicine. In our study, the bioactive components of betel nut were systematically investigated, and the main components and their target genes in the treatment of depression were predicted.

Methods

The metabolites of the kernels and peels were analyzed with a UPLC–MS/MS system. Mass spectrometry outcomes were annotated by MULTIAQUANT. “Compound‐disease targets” were utilized to construct a pharmacology network.

Results

A total of 873 metabolites were identified, with a high abundance of flavonoids, alkaloids, and phenols. Moreover, the abundance of flavonoids, alkaloids, and phenols in the kernel was significantly higher than that in the peel. A high abundance of catechin, arginine, and phenylalanine was detected in the kernel, while a high abundance of arginine, arecoline, and aminobutyric acid was detected in the peel. Catechins and cyanoside were the most abundant flavonoids in the kernel and peel, respectively. Arecoline was the most abundant alkaloid. A total of 111 metabolites showed a significant difference between the kernels and peels. The relative abundance of 40 differential metabolites was higher than 100,000, including 14 primary metabolites, 12 flavonoids, 4 phenols, and 4 alkaloids. Among the 40 high abundance metabolites, 20 were higher in the kernel and 20 in the peel. In addition, the enrichment of metabolic pathways found that the kernel and peel of the fruit adopted different metabolic pathways for the synthesis of flavonoids and alkaloids. Network pharmacology prediction showed that 93 metabolites could target 141 depression-related genes. The main components of betel nut intervention in depression were predicted to include L-phenylalanine, protocatechuic acid, okanin, nicotinic acid, L-tyrosine, benzocaine, syringic acid, benzocaine, phloretic acid, cynaroside, and 3,4-dihydroxybenzaldehyde.

Conclusion

Betel nuts are rich in natural metabolites, and some of these metabolites can participate in the intervention of depression. In addition, the metabolites showed distinct characteristics between the kernel and peel. Therefore, it is necessary to comprehensively and rationally use betel nuts.

Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics

Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.

MCF-7 Drug Resistant Cell Lines Switch Their Lipid Metabolism to Triple Negative Breast Cancer Signature

Obesity and adipose tissue have been closely related to a poor cancer prognosis, especially in prostate and breast cancer patients. The ability of transferring lipids from the adipose tissue to the tumor cells is actively linked to tumor progression. However, different types of breast tumor seem to use these lipids in different ways and metabolize them in different pathways. In this study we have tracked by mass spectrometry how palmitic acid from the adipocytes is released to media being later incorporated in different breast cancer cell lines (MDA-MB-231, SKBR3, BT474, MCF-7 and its resistant MCF-7 EPI^R and MCF-7 TAX^R). We have observed that different lines metabolize the palmitic acid in a different way and use their carbons in the synthesis of different new lipid families. Furthermore, we have observed that the lipid synthesis pattern varied according to the cell line. Surprisingly, the metabolic pattern of the resistant cells was more related to the TNBC cell line compared to their sensitive cell line MCF-7. These results allow us to determine a specific lipid pattern in different cell lines that later might be used in breast cancer diagnosis and to find a better treatment according to the cancer molecular type.