Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing

Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web

Stable isotope tracing is a crucial technique for understanding the metabolic wiring of biological systems, determining metabolic flux through pathways of interest, and detecting novel metabolites and pathways. Despite the potential insights provided by this technique, its application remains limited to a small number of targeted molecules and pathways. Because previous software tools usually require chemical formulas to find relevant features, and the data are highly complex, especially in untargeted metabolomics and when the downstream reactions and metabolites are poorly characterized. We report here Khipu version 2 and its new user-friendly web application. New functions are added to enhance analyzing stable isotope tracing data including metrics that evaluate peak enrichment in labeled samples, scoring methods to facilitate robust detection of intensity patterns and integrated natural abundance correction. We demonstrate that this approach can be applied to untargeted metabolomics to systematically extract isotope-labeled compounds and annotate the unidentified metabolites.

High-Fat Feeding Alters Circulating Triglyceride Composition: Roles of FFA Desaturation and ω-3 Fatty Acid Availability

Hypertriglyceridemia is a risk factor for type 2 diabetes and cardiovascular disease (CVD). Plasma triglycerides (TGs) are a key factor for assessing the risk of diabetes or CVD. However, previous lipidomics studies have demonstrated that not all TG molecules behave the same way. Individual TGs with different fatty acid compositions are regulated differentially under various conditions. In addition, distinct groups of TGs were identified to be associated with increased diabetes risk (TGs with lower carbon number [C#] and double-bond number [DB#]), or with decreased risk (TGs with higher C# and DB#). In this study, we examined the effects of high-fat feeding in rats on plasma lipid profiles with special attention to TG profiles. Wistar rats were maintained on either a low-fat (control) or high-fat diet (HFD) for 2 weeks. Plasma samples were obtained before and 2.5 h after a meal (n = 10 each) and subjected to lipidomics analyses. High-fat feeding significantly impacted circulating lipid profiles, with the most significant effects observed on TG profile. The effects of an HFD on individual TG species depended on DB# in their fatty acid chains; an HFD increased TGs with low DB#, associated with increased diabetes risk, but decreased TGs with high DB#, associated with decreased risk. These changes in TGs with an HFD were associated with decreased indices of hepatic stearoyl-CoA desaturase (SCD) activity, assessed from hepatic fatty acid profiles. Decreased SCD activity would reduce the conversion of saturated to monounsaturated fatty acids, contributing to the increases in saturated TGs or TGs with low DB#. In addition, an HFD selectively depleted ω-3 polyunsaturated fatty acids (PUFAs), contributing to the decreases in TGs with high DB#. Thus, an HFD had profound impacts on circulating TG profiles. Some of these changes were at least partly explained by decreased hepatic SCD activity and depleted ω-3 PUFA.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies

The annotation of metabolites detected in LC-MS-based untargeted metabolomics studies routinely applies accurate m/z of the intact metabolite (MS1) as well as chromatographic retention time and MS/MS data. Electrospray ionization and transfer of ions through the mass spectrometer can result in the generation of multiple "features" derived from the same metabolite with different m/z values but the same retention time. The complexity of the different charged and neutral adducts, in-source fragments, and charge states has not been previously and deeply characterized. In this paper, we report the first large-scale characterization using publicly available data sets derived from different research groups, instrument manufacturers, LC assays, sample types, and ion modes. 271 m/z differences relating to different metabolite feature pairs were reported, and 209 were annotated. The results show a wide range of different features being observed with only a core 32 m/z differences reported in >50% of the data sets investigated. There were no patterns reporting specific m/z differences that were observed in relation to ion mode, instrument manufacturer, LC assay type, and mammalian sample type, although some m/z differences were related to study group (mammal, microbe, plant) and mobile phase composition. The results provide the metabolomics community with recommendations of adducts, in-source fragments, and charge states to apply in metabolite annotation workflows.

Unveiling the "hidden quality" of the walnut pellicle: a precious source of bioactive lipids

Tree nut consumption has been widely associated with various health benefits, with walnuts, in particular, being linked with improved cardiovascular and neurological health. These benefits have been attributed to walnuts' vast array of phenolic antioxidants and abundant polyunsaturated fatty acids. However, recent studies have revealed unexpected clinical outcomes related to walnut consumption, which cannot be explained simply with the aforementioned molecular hallmarks. With the goal of discovering potential molecular sources of these unexplained clinical outcomes, an exploratory untargeted metabolomics analysis of the isolated walnut pellicle was conducted. This analysis revealed a myriad of unusual lipids, including oxylipins and endocannabinoids. These lipid classes, which are likely present in the pellicle to enhance the seeds' defenses due to their antimicrobial properties, also have known potent bioactivities as mammalian signaling molecules and homeostatic regulators. Given the potential value of this tissue for human health, with respect to its "bioactive" lipid fraction, we sought to quantify the amounts of these compounds in pellicle-enriched waste by-products of mechanized walnut processing in California. An impressive repertoire of these compounds was revealed in these matrices, and in notably significant concentrations. This discovery establishes these low-value agriculture wastes promising candidates for valorization and translation into high-value, health-promoting products; as these molecules represent a potential explanation for the unexpected clinical outcomes of walnut consumption. This "hidden quality" of the walnut pellicle may encourage further consumption of walnuts, and walnut industries may benefit from a revaluation of abundant pellicle-enriched waste streams, leading to increased sustainability and profitability through waste upcycling.

Ninjurin 2 Modulates Tumorigenesis, Inflammation, and Metabolism via Pyroptosis

The nerve injury-induced protein 2 (NINJ2) belongs to a family of homophilic adhesion molecules and was initially found to be involved in nerve regeneration. However, the role of NINJ2 in other cellular processes is not well studied. The Ninj2-deficient mice generated in the current study had a short lifespan and were prone to spontaneous tumors, systemic inflammation, and metabolic defects. Comprehensive carbohydrate and lipid metabolic analyses were performed to better understand the metabolic traits that contribute to these phenotypes. Carbohydrate metabolic analyses showed that NINJ2 deficiency led to defects in monosaccharide metabolism along with accumulation of multiple disaccharides and sugar alcohols. Lipidomic analyses showed that Ninj2 deficiency altered patterns of several lipids, including triglycerides, phospholipids, and ceramides. To identify a cellular process that associated with these metabolic defects, the role of NINJ2 in pyroptosis, a programmed cell death that links cancer, inflammation, and metabolic disorders, was examined. Loss of NINJ2 promoted pyroptosis by activating the NOD-like receptor protein 3 (NLRP3) inflammasome. Taken together, these data reveal a critical role of NINJ2 in tumorigenesis, inflammatory response, and metabolism via pyroptosis.

The Metabolic and Lipidomic Fingerprint of Torin1 Exposure in Mouse Embryonic Fibroblasts Using Untargeted Metabolomics

Torin1, a selective kinase inhibitor targeting the mammalian target of rapamycin (mTOR), remains widely used in autophagy research due to its potent autophagy-inducing abilities, regardless of its unspecific properties. Recognizing the impact of mTOR inhibition on metabolism, our objective was to develop a reliable and thorough untargeted metabolomics workflow to study torin1-induced metabolic changes in mouse embryonic fibroblast (MEF) cells. Crucially, our quality assurance and quality control (QA/QC) protocols were designed to increase confidence in the reported findings by reducing the likelihood of false positives, including a validation experiment replicating all experimental steps from sample preparation to data analysis. This study investigated the metabolic fingerprint of torin1 exposure by using liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics platforms. Our workflow identified 67 altered metabolites after torin1 exposure, combining univariate and multivariate statistics and the implementation of a validation experiment. In particular, intracellular ceramides, diglycerides, phosphatidylcholines, phosphatidylethanolamines, glutathione, and 5'-methylthioadenosine were downregulated. Lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, glycerophosphocholine, triglycerides, inosine, and hypoxanthine were upregulated. Further biochemical pathway analyses provided deeper insights into the reported changes. Ultimately, our study provides a valuable workflow that can be implemented for future investigations into the effects of other compounds, including more specific autophagy modulators.

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.

Optimization of bronchoalveolar lavage fluid volume for untargeted lipidomic method and application in influenza A virus infection

Bronchoalveolar lavage (BAL) has been widely applied for the diagnosis of pulmonary diseases in clinical as it was recognized as a minimally invasive, well-tolerated and easily performed procedure. Lipid analysis of BAL fluid is a comprehensive strategy to observe lipid phenotypes, explore potential biomarkers, and elucidate the biological mechanisms of respiratory diseases. However, the highly diverse concentration of lipids in BAL fluid due to the deviation between the retrieved and injected aliquot volumes during lavage raised a challenge in obtaining high-quality lipidomic data. Here, this study aims to investigate what volume of BAL fluid is suitable for lipidomic analysis. Specifically, the BAL fluid harvested from H1N1 infected mice and controls was concentrated to varying degrees by freeze-drying technique before preparation for lipidomic analysis. The optimal concentration multiple of BAL fluid was approved by comparing the coverage and quality of identified lipids, as well as the number of differentially expressed lipids in the H1N1 infection model. Sixty-two differential lipids were identified respectively in the positive and negative modes when the BAL fluid was condensed five times, and they were classified into glycerolipids, phospholipids and fatty acids. This study focuses on the alterations of phospholipids, since they are the main constituents of pulmonary surfactants. Several phospholipids significantly accumulated in the BAL fluid of H1N1-infected mice, while most of them contained omega-3 polyunsaturated fatty acids, indicating disrupted inflammatory homeostasis in lungs. This study recommends freeze-drying/reconstitution prior to lipid extraction from BAL fluid for lipidomic analysis, as this procedure increased the richness and abundance of lipids.

Lipidomics analysis reveals the effect of Sirex noctilio infestation on the lipid metabolism in Pinus radiata needles

The Sirex noctilio's climatic adaption and rapid proliferation have caused Pinus mortality worldwide. The infestation combines the early effect of female S. noctilio gland secretion and the spreading symbiotic fungus Amylostereum areolatum. 'Lipidomics' is the study of all non-water-soluble components of the metabolome. Most of these non-water-soluble compounds correspond to lipids which can provide information about a biological activity, an organelle, an organism, or a disease. Using HPLC-MS/MS based lipidomics, 122 lipids were identified in P. radiata needles during S. noctilio infestation. Phosphatidic acids, N-acylethanolamines, and phosphatidylinositol-ceramides accumulated in infested trees could suggest a high level of phospholipases activities. The phosphatidylcholines were the most down-regulated species during infection, which could also suggest that they may be used as a substrate for up-regulated lipids. The accumulation of very long-chain fatty acids and long-chain fatty acids during the infestation could imply the tree defense response to create a barrier in the drilled zone to avoid larvae development and fungus proliferation. Also, the growth arrest phase of the trees during the prolonged infestation suggests a resistance response, regulated by the accumulation of NAE, which potentially shifts the tree energy to respond to the infestation.

[Application progress of innovative high resolution separation and analysis technology in food safety detection field]

In the last decade, mass spectrometry (MS) has been widely used in food inspection science, and the continuous improvement of instruments is the key to the breakthrough of analytical chemistry technology. This paper discusses the latest progress and application of high resolution separation and analysis technology in food safety field (2013-2023), focusing on the innovative development technologies such as on-line MS coupling technology, high resolution screening technology and micro mass spectrometer. Finally, the development of new device in the field of food safety detection is prospected.

Untargeted hair lipidomics: comprehensive evaluation of the hair-specific lipid signature and considerations for retrospective analysis

Lipidomics investigates the composition and function of lipids, typically employing blood or tissue samples as the primary study matrices. Hair has recently emerged as a potential complementary sample type to identify biomarkers in early disease stages and retrospectively document an individual's metabolic status due to its long detection window of up to several months prior to the time of sampling. However, the limited coverage of lipid profiling presented in previous studies has hindered its exploitation. This study aimed to evaluate the lipid coverage of hair using an untargeted liquid chromatography-high-resolution mass spectrometry lipidomics platform. Two distinct three-step exhaustive extraction experiments were performed using a hair metabolomics one-phase extraction technique that has been recently optimized, and the two-phase Folch extraction method which is recognized as the gold standard for lipid extraction in biological matrices. The applied lipidomics workflow improved hair lipid coverage, as only 99 species could be annotated using the one-phase extraction method, while 297 lipid species across six categories were annotated with the Folch method. Several lipids in hair were reported for the first time, including N-acyl amino acids, diradylglycerols, and coenzyme Q10. The study suggests that hair lipids are not solely derived from de novo synthesis in hair, but are also incorporated from sebum and blood, making hair a valuable matrix for clinical, forensic, and dermatological research. The improved understanding of the lipid composition and analytical considerations for retrospective analysis offers valuable insights to contextualize untargeted hair lipidomic analysis and facilitate the use of hair in translational studies.

Improving Quantitative Accuracy in Nontargeted Lipidomics by Evaluating Adduct Formation

For large-scale lipidomic analyses, accurate and reproducible quantification of endogenous lipids is crucial for comparing results within and across studies. Many lipids present in liquid chromatography-electrospray ionization-mass spectrometry form various adducts with buffer components. The mechanisms and conditions that dictate adduct formation are still poorly understood. In a positive mode, neutral lipids like mono-, di-, and triacylglycerides and cholesteryl esters typically generate [M + NH4]+ adduct ions, although [M + Na]+, [M + K]+, and other (more complex) species can also be significantly abundant in MS1 precursor ion spectra. Variations in the ratios of these adducts (within and between matrices) can lead to dramatic inaccuracies during quantification. Here, we examine 48 unique diacylglycerol (DAG) species across 2366 mouse samples for eight matrix-specific data sets of plasma, liver, kidney, brain, heart muscle, gastrocnemius muscle, gonadal, and inguinal fat. Typically, no single adduct ion species accounted for more than 60% of the total observed abundance across each data set. Even within a single matrix, DAGs showed a high variability of adduct ratios. The ratio of [M + NH4]+ adduct ions was increased for longer-chain DAGs and for polyunsaturated DAGs, at the expense of reduced ratios of [M + Na]+ adducts. When using three deuterated internal DAG standards, we found that absolute concentrations were estimated with up to 70% error when only one adduct ion was used instead of all adducts combined. Importantly, when combining [M + NH4]+ and [M + Na]+ adduct ions, quantification results were within 5% accuracy compared to all adduct ions combined. Additional variance can be caused by other factors, such as instrument conditions or matrix effects.

Untargeted Metabolomic Analysis of Lactation-Stage-Matched Human and Bovine Milk Samples at 2 Weeks Postnatal

Epidemiological data demonstrate that bovine whole milk is often substituted for human milk during the first 12 months of life and may be associated with adverse infant outcomes. The objective of this study is to interrogate the human and bovine milk metabolome at 2 weeks of life to identify unique metabolites that may impact infant health outcomes. Human milk (n = 10) was collected at 2 weeks postpartum from normal-weight mothers (pre-pregnant BMI < 25 kg/m2) that vaginally delivered term infants and were exclusively breastfeeding their infant for at least 2 months. Similarly, bovine milk (n = 10) was collected 2 weeks postpartum from normal-weight primiparous Holstein dairy cows. Untargeted data were acquired on all milk samples using high-resolution liquid chromatography-high-resolution tandem mass spectrometry (HR LC-MS/MS). MS data pre-processing from feature calling to metabolite annotation was performed using MS-DIAL and MS-FLO. Our results revealed that more than 80% of the milk metabolome is shared between human and bovine milk samples during early lactation. Unbiased analysis of identified metabolites revealed that nearly 80% of milk metabolites may contribute to microbial metabolism and microbe-host interactions. Collectively, these results highlight untargeted metabolomics as a potential strategy to identify unique and shared metabolites in bovine and human milk that may relate to and impact infant health outcomes.

Differences in the Stool Metabolome between Vegans and Omnivores: Analyzing the NIST Stool Reference Material

To gain confidence in results of omic-data acquisitions, methods must be benchmarked using validated quality control materials. We report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments, as follows: firstly, homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids. A total of 3563 compounds were annotated by mass spectrometry, including 353 compounds that were annotated in more than one assay. Almost half of all compounds were annotated using hydrophilic interaction chromatography/accurate mass spectrometry, followed by the lipidomics and the polyphenol assays. In total, 910 metabolites were found in at least 4-fold different levels in fecal matter from vegans versus omnivores, specifically for peptides, amino acids and lipids. In comparison, only 251 compounds showed 4-fold differences between lyophilized and aqueous fecal samples, including DG O-34:0 and methionine sulfoxide. A range of diet-specific metabolites were identified to be significantly different between vegans and omnivores, exemplified by citrinin and C17:0-acylcarnitine for omnivores, and curcumin and lenticin for vegans. Bioactive molecules like acyl alpha-hydroxy-fatty acids (AAHFA) were differentially regulated in vegan versus omnivore fecal materials, highlighting the importance of diet-specific reference materials for dietary biomarker studies.

The Impacts of Slc19a3 Deletion and Intestinal SLC19A3 Insertion on Thiamine Distribution and Brain Metabolism in the Mouse

The Thiamine Transporter 2 (THTR2) encoded by SLC19A3 plays an ill-defined role in the maintenance of tissue thiamine, thiamine monophosphate, and thiamine diphosphate (TDP) levels. To evaluate the impact of THTR2 on tissue thiamine status and metabolism, we expressed the human SLC19A3 transgene in the intestine of total body Slc19a3 knockout (KO) mice. Male and female wildtype (WT) and transgenic (TG) mice were fed either 17 mg/kg (1×) or 85 mg/kg (5×) thiamine hydrochloride diet, while KOs were only fed the 5× diet. Thiamine vitamers in plasma, red blood cells, duodenum, brain, liver, kidney, heart, and adipose tissue were measured. Untargeted metabolomics were performed on the brain tissues of groups with equivalent plasma thiamine. KO mice had ~two- and ~three-fold lower plasma and brain thiamine levels than WT on the 5× diet. Circulating vitamers were sensitive to diet and equivalent in TG and WT mice. However, TG had 60% lower thiamine but normal brain TDP levels regardless of diet, with subtle differences in the heart and liver. The loss of THTR2 reduced levels of nucleic acid and amino acid derivatives in the brain. Therefore, mutation or inhibition of THTR2 may alter the brain metabolome and reduce the thiamine reservoir for TDP biosynthesis.

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets

Poor chemical annotation of high-resolution mass spectrometry data limits applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics─Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of high-resolution mass spectrometry coupled with liquid chromatography peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R-CRAN repository at https://cran.r-project.org/package=IDSL.CSA. Detailed documentation and tutorials are provided at https://github.com/idslme/IDSL.CSA.

Metabolomic analysis of maternal mid-gestation plasma and cord blood in autism spectrum disorders

The discovery of prenatal and neonatal molecular biomarkers has the potential to yield insights into autism spectrum disorder (ASD) and facilitate early diagnosis. We characterized metabolomic profiles in ASD using plasma samples collected in the Norwegian Autism Birth Cohort from mothers at weeks 17-21 gestation (maternal mid-gestation, MMG, n = 408) and from children on the day of birth (cord blood, CB, n = 418). We analyzed associations using sex-stratified adjusted logistic regression models with Bayesian analyses. Chemical enrichment analyses (ChemRICH) were performed to determine altered chemical clusters. We also employed machine learning algorithms to assess the utility of metabolomics as ASD biomarkers. We identified ASD associations with a variety of chemical compounds including arachidonic acid, glutamate, and glutamine, and metabolite clusters including hydroxy eicospentaenoic acids, phosphatidylcholines, and ceramides in MMG and CB plasma that are consistent with inflammation, disruption of membrane integrity, and impaired neurotransmission and neurotoxicity. Girls with ASD have disruption of ether/non-ether phospholipid balance in the MMG plasma that is similar to that found in other neurodevelopmental disorders. ASD boys in the CB analyses had the highest number of dysregulated chemical clusters. Machine learning classifiers distinguished ASD cases from controls with area under the receiver operating characteristic (AUROC) values ranging from 0.710 to 0.853. Predictive performance was better in CB analyses than in MMG. These findings may provide new insights into the sex-specific differences in ASD and have implications for discovery of biomarkers that may enable early detection and intervention.

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets

Poor chemical annotation of high-resolution mass spectrometry data limit applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics - Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of LC/HRMS peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.CSA . Detailed documentation and tutorials are provided at https://github.com/idslme/IDSL.CSA .

For Table of Contents Only

Effects of Perfluorooctanoic Acid on Gut Microbiota and Microbial Metabolites in C57BL/6J Mice

Perfluorooctanoic acid (PFOA) represents an increasing public health concern due to its persistence in the environment and its toxic effects. The gut microbiota is known to produce various metabolites that assist the host to maintain metabolic homeostasis. However, few studies have explored the effects of PFOA on gut-microbiota-related metabolites. In the present study, male C57BL/6J mice were exposed to 1 ppm of PFOA in drinking water for four weeks and integrative analysis of the gut microbiome and metabolome was performed to reveal the health effects of PFOA. Our results showed that PFOA disturbed both the gut microbiota composition and the metabolic profiles of the feces, serum, and liver in mice. A correlation was found between Lachnospiraceae UCG004, Turicibacter, Ruminococcaceae, and different fecal metabolites. Significant alterations of gut-microbiota-related metabolites were induced by PFOA exposure, including bile acids and tryptophan metabolites such as 3-indoleacrylic acid and 3-indoleacetic acid. The findings of this study are helpful to improve the understanding of the health effects of PFOA, which might be mediated through the gut microbiota and its related metabolites.

Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe Eggerthella lenta

Human gut bacteria perform diverse metabolic functions with consequences for host health. The prevalent and disease-linked Actinobacterium Eggerthella lenta performs several unusual chemical transformations, but it does not metabolize sugars and its core growth strategy remains unclear. To obtain a comprehensive view of the metabolic network of E. lenta, we generated several complementary resources: defined culture media, metabolomics profiles of strain isolates, and a curated genome-scale metabolic reconstruction. Stable isotope-resolved metabolomics revealed that E. lenta uses acetate as a key carbon source while catabolizing arginine to generate ATP, traits which could be recapitulated in silico by our updated metabolic model. We compared these in vitro findings with metabolite shifts observed in E. lenta-colonized gnotobiotic mice, identifying shared signatures across environments and highlighting catabolism of the host signaling metabolite agmatine as an alternative energy pathway. Together, our results elucidate a distinctive metabolic niche filled by E. lenta in the gut ecosystem. Our culture media formulations, atlas of metabolomics data, and genome-scale metabolic reconstructions form a freely available collection of resources to support further study of the biology of this prevalent gut bacterium.

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data

In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions in relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu) and provides a JSON format for easy data exchange and software interoperability. By generalized preannotation, khipu makes it feasible to connect metabolomics data with common data science tools and supports flexible experimental designs.

Aqueous extract of Platycodon grandiflorus attenuates lipopolysaccharide-induced apoptosis and inflammatory cell infiltration in mouse lungs by inhibiting PI3K/Akt signaling

BACKGROUND

Acute lung injury (ALI), an acute inflammatory lung disease, can cause a rapid inflammatory response in clinic, which endangers the patient's life. The components of platycodon grandiflorum, such as platycodins have a wide range of pharmacological activities such as expectorant, anti-apoptotic, anti-inflammatory, anti-tumor and anti-oxidant properties, and can be used for improving human immunity. Previous studies have shown that aqueous extract of platycodon grandiflorum (PAE) has a certain protective effect on ALI, but the main pharmacodynamic components and the mechanism of action are not clear.

METHODS

The anti-inflammatory properties of PAE were studied using the lipopolysaccharide (LPS)-induced ALI animal model. Hematoxylin and eosin stains were used to assess the degree of acute lung damage. Changes in RNA levels of pro-inflammatory cytokines in the lungs were measured using quantitative RT-qPCR. The potential molecular mechanism of PAE preventing ALI was predicted by lipidomics and network pharmacology. To examine the anti-apoptotic effects of PAE, TdT-mediated dUTP nick-end labelling (TUNEL) was employed to determine apoptosis-related variables. The amounts of critical pathway proteins and apoptosis-related proteins were measured using Western blotting.

RESULTS

Twenty-six chemical components from the PAE were identified, and their related pathways were obtained by the network pharmacology. Combined with the analysis of network pharmacology and literature, it was found that the phosphatidylinositol 3 kinase (PI3K)/protein kinase B (AKT) signaling pathway is related to ALI. The results of lipidomics show that PAE alleviates ALI via regulating lung lipids especially phosphatidylinositol (PI). Finally, the methods of molecular biology were used to verify the mechanism of PAE. It can be found that PAE attenuates the inflammatory response to ALI by inhibiting apoptosis through PI3K/Akt signaling pathway.

CONCLUSION

The study revealed that the PAE attenuates lipopolysaccharide-induced apoptosis and inflammatory cell infiltration in mouse lungs by inhibiting PI3K/Akt signaling. Furthermore, our findings provide a novel strategy for the application of PAE as a potential agent for preventing patients with ALI.

In utero metabolomic signatures of refined grain intake and risk of gestational diabetes: A metabolome-wide association study

BACKGROUND

Epidemiologic evidence has linked refined grain intake to a higher risk of gestational diabetes (GDM), but the biological underpinnings remain unclear.

OBJECTIVES

We aimed to identify and validate refined grain-related metabolomic biomarkers for GDM risk.

METHODS

In a metabolome-wide association study of 91 cases with GDM and 180 matched controls without GDM (discovery set) nested in the prospective Pregnancy Environment and Lifestyle Study (PETALS), refined grain intake during preconception and early pregnancy and serum untargeted metabolomics were assessed at gestational weeks 10-13. We identified refined grain-related metabolites using multivariable linear regression and examined their prospective associations with GDM risk using conditional logistic regression. We further examined the predictivity of refined grain-related metabolites selected by least absolute shrinkage and selection operator regression in the discovery set and validation set (a random PETALS subsample of 38 individuals with and 336 without GDM).

RESULTS

Among 821 annotated serum (87.4% fasting) metabolites, 42 were associated with refined grain intake, of which 17 (70.6% in glycerolipids, glycerophospholipids, and sphingolipids clusters) were associated with subsequent GDM risk (all false discovery rate-adjusted P values <0.05). Adding 7 of 17 metabolites to a conventional risk factor-based prediction model increased the C-statistic for GDM risk in the discovery set from 0.71 (95% CI: 0.64, 0.77) to 0.77 (95% CI: 0.71, 0.83) and in the validation set from 0.77 (95% CI: 0.69, 0.86) to 0.81 (95% CI: 0.74, 0.89), both with P-for-difference <0.05.

CONCLUSIONS

Clusters of glycerolipids, glycerophospholipids, and sphingolipids may be implicated in the association between refined grain intake and GDM risk, as demonstrated by the significant associations of these metabolites with both refined grains and GDM risk and the incremental predictive value of these metabolites for GDM risk beyond the conventional risk factors. These findings provide evidence on the potential biological underpinnings linking refined grain intake to the risk of GDM and help identify novel disease-related dietary biomarkers to inform diet-related preventive strategies for GDM.

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

INTRODUCTION

There is still no community consensus regarding strategies for data quality review in liquid chromatography mass spectrometry (LC-MS)-based untargeted metabolomics. Assessing the analytical robustness of data, which is relevant for inter-laboratory comparisons and reproducibility, remains a challenge despite the wide variety of tools available for data processing.

OBJECTIVES

The aim of this study was to provide a model to describe the sources of variation in LC-MS-based untargeted metabolomics measurements, to use it to build a comprehensive curation pipeline, and to provide quality assessment tools for data quality review.

METHODS

Human serum samples (n=392) were analyzed by ultraperformance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) using an untargeted metabolomics approach. The pipeline and tools used to process this dataset were implemented as part of the open source, publicly available TidyMS Python-based package.

RESULTS

The model was applied to understand data curation practices used by the metabolomics community. Sources of variation, which are often overlooked in untargeted metabolomic studies, were identified in the analysis. New tools were used to characterize certain types of variations.

CONCLUSION

The developed pipeline allowed confirming data robustness by comparing the experimental results with expected values predicted by the model. New quality control practices were introduced to assess the analytical quality of data.

Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system

Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month's samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch.

Metabolic signature of HepaRG cells exposed to ethanol and tumor necrosis factor alpha to study alcoholic steatohepatitis by LC-MS-based untargeted metabolomics

Despite the high prevalence of alcoholic liver disease, its identification and characterization remain poor, especially in early stages such as alcoholic fatty liver disease and alcoholic steatohepatitis. This latter implies diagnostic difficulties, few therapeutic options and unclear mechanisms of action. To elucidate the metabolic alterations and pinpoint affected biochemical pathways, alcoholic steatohepatitis was simulated in vitro by exposing HepaRG cells to ethanol (IC₁₀, 368 mM) and tumor necrosis factor alpha (TNF-α, 50 ng/mL) for 24 h. This combined exposure was compared to solely ethanol-exposed as well as -nonexposed cells. Four different metabolomics platforms were used combining liquid chromatography, high-resolution mass spectrometry and drift tube ion mobility to elucidate both intracellular and extracellular metabolic alterations. Some of the key findings include the influence of TNF-α in the upregulation of hepatic triglycerides and the downregulation of hepatic phosphatidylethanolamines and phosphatidylcholines. S-Adenosylmethionine showed to play a central role in the progression of alcoholic steatohepatitis. In addition, fatty acyl esters of hydroxy fatty acid (FAHFA)-containing triglycerides were detected for the first time in human hepatocytes and their alterations showed a potentially important role during the progression of alcoholic steatohepatitis. Ethoxylated phosphorylcholine was identified as a potential new biomarker of ethanol exposure.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Generalized tree structure to annotate untargeted metabolomics and stable isotope tracing data

In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions to relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu), and provides a JSON format for easy data exchange and software interoperability. By generalized pre-annotation, khipu makes it feasible to connect metabolomics data with common data science tools, and supports flexible experimental designs.

Comprehensive lipidomic profiling by plasma separation cards

Large-scale lipidomic analyses have been limited by the cost and accessibility of traditional venipuncture sampling. Microsampling techniques offer a less-invasive and more accessible alternative. From a single drop of blood, plasma separation cards (PSC) deliver two volumetric dried plasma samples which are studied here for profiling endogenous blood lipids. Six lots of EDTA-treated human whole blood were used to compare PSC, dried blood spot analyses (DBS), and classic wet plasma extractions. Six replicate extractions were performed for each lot. Nontargeted lipidomics was performed by liquid chromatography-high resolution tandem mass spectrometry. Lipids were annotated by accurate mass/retention time matching and MS/MS spectral library matching using peak intensities for quantitation. Four hundred ninety-eight compounds covering 24 lipid subclasses were annotated. Inter-lot repeatability was evaluated by the percent relative standard deviation (%RSD) for each lot, giving median %RSD values across the lots at 14.6% for PSC, 9.3% for DBS, and 8.6% for wet plasma. Strong correlations of lipid peak intensities between wet plasma and PSCs were observed, but less for DBS. Lipid recovery and stability were comparable between the PSC and DBS samples, with roughly 60% of annotated lipids stable at room temperature after 28 days. Overall, PSCs provide a better alternative for quantitative blood lipidomic analyses compared to dried blood spots. However, problems with lipid stability for samples handled and shipped at room temperature are currently unavoidable outside of a clinical setting. Data transferability and comparability to standard plasma is lipid and lipid class dependent.

Multiple mitochondria-targeted components screened from Sini decoction improved cardiac energetics and mitochondrial dysfunction to attenuate doxorubicin-induced cardiomyopathy

Rationale: Sini decoction (SND) is an efficient formula against DOX-induced cardiomyopathy (DCM), but the active ingredient combination (AIC) and mechanisms of SND remain unclear. Therefore, the present study aimed to identify the AIC and elucidate the underlying mechanism of AIC on DCM. Methods: The AIC were screened by a novel comprehensive two-dimensional cardiac mitochondrial membrane chromatography (CMMC)-TOFMS analysis system and further validated by cell viability, reactive oxygen species (ROS) generation, ATP level, and mitochondrial membrane potential in DOX-induced H9c2 cell injury model. Then, an integrated model of cardiac mitochondrial metabolomics and proteomics were applied to clarify the underlying mechanism in vitro. Results: The CMMC column lifespan was significantly improved to more than 10 days. Songorine (S), neoline, talatizamine, 8-gingerol (G) and isoliquiritigenin (I), exhibiting stronger retention on the first-dimension CMMC column, were screened to have protective effects against DOX cardiotoxicity in the H9c2 cell model. S, G and I were selected as an AIC from SND according to the bioactivity evaluation and the compatibility theory of SND. The combined in vitro use of S, G and I produced more profound therapeutic effects than any component used individually on increasing ATP levels and mitochondrial membrane potential and suppressing intracellular ROS production. Moreover, SGI attenuated DCM might via regulating mitochondrial energy metabolism and mitochondrial dysfunction. Conclusions: The provided scientific evidence to support that SGI combination from SND could be used as a prebiotic agent for DCM. Importantly, the proposed two-dimensional CMMC-TOFMS analytical system provides a high-throughput screening strategy for mitochondria-targeted compounds from natural products, which could be applied to other subcellular organelle models for drug discovery.

Unraveling the Thermal Oxidation Products and Peroxidation Mechanisms of Different Chemical Structures of Lipids: An Example of Molecules Containing Oleic Acid

Lipid structures affect lipid oxidation, causing differences in types and contents of volatiles and nonvolatiles in various foods. In this study, the oxidation differences of monoacylglycerol (MAG), triacylglycerol (TAG), phosphatidylethanolamine (PE), and phosphatidylcholine (PC) with oleoyl residues and oleic acid (FA) during thermal treatment were investigated. Volatiles and nonvolatiles were monitored by gas chromatography-mass spectrometry and ultrahigh-performance liquid chromatography-Q-Exactive HF-X Orbitrap Mass Spectrometer, respectively. The results showed that the structures of MAG and TAG could delay the chain initiation reaction. The polar heads of PC and PE remarkably influenced the oxidation rate and the formation of the oxidation products probably due to the hydrogen bonds formed with free radicals. Among the volatile oxidation products, aldehydes, acids, and furans with eight or nine carbon atoms accounted for the majority in FA, MAG, TAG, and PC samples, but PE samples mainly generated ketones with nine or 10 carbon atoms. The formation of nonvolatile products in TAG samples possessed significant stage-specific changes. Fatty acid esters of hydroxy fatty acids were only produced in the free fatty acid oxidation model. The activity of chemical bonds participating in the truncation reaction decreased to both sides from the double bond position.

Perfluorooctanoic Acid-Disturbed Serum and Liver Lipidome in C57BL/6 Mice

Perfluorooctanoic acid is a manufactured material extensively utilized in industrial and consumer products. As a persistent organic pollutant, perfluorooctanoic acid has raised increasing public health concerns recently. Although perfluorooctanoic acid is known to induce lipid accumulation in the liver, the impact of perfluorooctanoic acid on different lipid classes has not been fully evaluated. In this study, we performed untargeted lipidomics analysis to investigate the impact of perfluorooctanoic acid on the lipid homeostasis in C57BL/6 male mice. Perfluorooctanoic acid disturbed the lipid profiles in serum and liver, with a variety of lipid classes significantly altered. Greater impacts were observed in the liver lipidome than the serum lipidome. In particular, some lipid clusters in the liver were altered by both high- and low-dose perfluorooctanoic acid exposure, including the increase of unsaturated triglycerides and the decrease of sphingomyelins, saturated phosphatidylcholines, saturated lysophosphatidylcholines, and phospholipid ethers. In parallel with an increase in the liver, a decrease of saturated phosphatidylcholines was found in the serum of high-dose perfluorooctanoic acid-treated mice. The findings from this study are helpful to improve the understanding of perfluorooctanoic acid-induced dysregulation of lipid metabolism and perfluorooctanoic acid-associated health effects in liver.

The RNA-binding protein RBM24 regulates lipid metabolism and SLC7A11 mRNA stability to modulate ferroptosis and inflammatory response

Lipids play a critical role in many cellular processes by serving as structural components of cell membranes or functioning as energy fuel and signaling molecules. The RNA-binding proteins RBM24 and RBM38 share an identical RNA-binding domain and thereby, regulate a group of same targets, such as p21. However, it is not certain whether RBM24 and RBM38 participates in lipid homeostasis. Here, lipidomic analysis showed that a deficiency in RBM24 or RBM38 leads to altered lipid metabolism, with more profound alteration by loss of RBM24 in MCF7 cells. We also showed that mice deficient in RBM24 were prone to chronic inflammation and liver steatosis, but not spontaneous tumors. These data let us speculate whether RBM24 regulates ferroptosis, a programmed cell death that links inflammation and liver steatosis via lipid peroxidation. Indeed, we found that over-expression of RBM24 protected, whereas knockout of RBM24 sensitized, cells to Erastin-induced ferroptosis by modulating the mRNA stability of SLC7A11, a ferroptosis inhibitor. Moreover, we showed that knockdown of SLC7A11 reversed the effect of RBM24 on ferroptosis. Together, our study revealed that RBM24 regulates lipid metabolism and SLC7A11 mRNA stability to modulate ferroptosis and inflammatory response.

IDSL.UFA Assigns High-Confidence Molecular Formula Annotations for Untargeted LC/HRMS Data Sets in Metabolomics and Exposomics

Untargeted liquid chromatography/high-resolution mass spectrometry (LC/HRMS) assays in metabolomics and exposomics aim to characterize the small molecule chemical space in a biospecimen. To gain maximum biological insights from these data sets, LC/HRMS peaks should be annotated with chemical and functional information including molecular formula, structure, chemical class, and metabolic pathways. Among these, molecular formulas may be assigned to LC/HRMS peaks through matching theoretical and observed isotopic profiles (MS1) of the underlying ionized compound. For this, we have developed the Integrated Data Science Laboratory for Metabolomics and Exposomics-United Formula Annotation (IDSL.UFA) R package. In the untargeted metabolomics validation tests, IDSL.UFA assigned 54.31-85.51% molecular formula for true positive annotations as the top hit and 90.58-100% within the top five hits. Molecular formula annotations were also supported by tandem mass spectrometry data. We have implemented new strategies to (1) generate formula sources and their theoretical isotopic profiles, (2) optimize the formula hits ranking for the individual and aligned peak lists, and (3) scale IDSL.UFA-based workflows for studies with larger sample sizes. Annotating the raw data for a publicly available pregnancy metabolome study using IDSL.UFA highlighted hundreds of new pregnancy-related compounds and also suggested the presence of chlorinated perfluorotriether alcohols (Cl-PFTrEAs) in human specimens. IDSL.UFA is useful for human metabolomics and exposomics studies where we need to minimize the loss of biological insights in untargeted LC/HRMS data sets. The IDSL.UFA package is available in the R CRAN repository https://cran.r-project.org/package=IDSL.UFA. Detailed documentation and tutorials are also provided at www.ufa.idsl.me.

Using Metabolomics to Identify the Exposure and Functional Biomarkers of Ginger

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has become an important tool to increase our understanding of how diet affects human health. However, public and commercial mass spectral libraries of dietary metabolites are limited, resulting in the greatest challenge in converting mass spectrometry data into biological insights. In this study, we constructed an LC-MS/MS ginger library as an example to demonstrate the importance of dietary libraries for discovering food biomarkers. The functional and exposure biomarkers of ginger were investigated using plasma samples from mice treated with control and ginger extract diets. Our results showed clear discrimination between the metabolome of mice on normal and ginger extract diets. Using the in-house ginger library, we identified 20 ginger metabolites that can be used as exposure biomarkers of ginger. However, without the LC-MS/MS ginger library, none of the ginger metabolites could be accurately identified based on online mass databases. In addition, ginger treatment significantly impacts the endogenous metabolome, especially the purine metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis. Overall, we demonstrated that the construction of LC-MS/MS spectra dietary libraries would enhance the ability to identify potential dietary biomarkers and correlate potential health benefits associated with food consumption.

Metabolomic Evidence for Peroxisomal Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating disease characterized by unexplained physical fatigue, cognitive and sensory dysfunction, sleeping disturbances, orthostatic intolerance, and gastrointestinal problems. People with ME/CFS often report a prodrome consistent with infections. Using regression, Bayesian and enrichment analyses, we conducted targeted and untargeted metabolomic analysis of plasma from 106 ME/CFS cases and 91 frequency-matched healthy controls. Subjects in the ME/CFS group had significantly decreased levels of plasmalogens and phospholipid ethers (p < 0.001), phosphatidylcholines (p < 0.001) and sphingomyelins (p < 0.001), and elevated levels of dicarboxylic acids (p = 0.013). Using machine learning algorithms, we were able to differentiate ME/CFS or subgroups of ME/CFS from controls with area under the receiver operating characteristic curve (AUC) values up to 0.873. Our findings provide the first metabolomic evidence of peroxisomal dysfunction, and are consistent with dysregulation of lipid remodeling and the tricarboxylic acid cycle. These findings, if validated in other cohorts, could provide new insights into the pathogenesis of ME/CFS and highlight the potential use of the plasma metabolome as a source of biomarkers for the disease.

Lipidomics profiling of zebrafish liver through untargeted liquid chromatography-high resolution mass spectrometry

Lipidomics analysis of zebrafish tissues has shown promising results to understand disease-related outcomes of exposure to toxic substances at molecular level. However, knowledge about their lipidome is limited, as most untargeted studies only identify the lipids that are statistically significant in their setup. In this work, liquid chromatography-high resolution mass spectrometry was used to study different aspects of the analytical workflow, i.e., extraction solvents (methanol/chloroform/water (3/2/2, v/v/v), methanol/dichloromethane/water (2/3/2, v/v/v) and methanol/methyl-tert-butyl ether/water (3/10/2.5, v/v/v), instrumental response, and strategies used for lipid annotation. The number of high-quality features (relative standard deviation of the intensity values ≤ 10% in the range 10³ -10⁷ counts) was affected by the dilution of lipid extracts, indicating that it is an important parameter for developing untargeted methods. The workflows used allowed the selection of a dilution factor to annotate 712 lipid species (507 bulk lipids) in zebrafish liver using four software (LipidMatch, LipidHunter, MS-DIAL and Lipostar). Retention time mapping was a valuable tool to filter lipid annotations obtained from automatic software annotations. The lipid profiling of zebrafish livers will help in a better understanding of the true constitution of their lipidome at the species level, as well as in the use of zebrafish in toxicological studies. This article is protected by copyright. All rights reserved.

The metabolome of Mexican cavefish shows a convergent signature highlighting sugar, antioxidant, and Ageing-Related metabolites

Insights from organisms, which have evolved natural strategies for promoting survivability under extreme environmental pressures, may help guide future research into novel approaches for enhancing human longevity. The cave-adapted Mexican tetra, Astyanax mexicanus, has attracted interest as a model system for metabolic resilience, a term we use to denote the property of maintaining health and longevity under conditions that would be highly deleterious in other organisms (Figure 1). Cave-dwelling populations of Mexican tetra exhibit elevated blood glucose, insulin resistance and hypertrophic visceral adipocytes compared to surface-dwelling counterparts. However, cavefish appear to avoid pathologies typically associated with these conditions, such as accumulation of advanced-glycation-end-products (AGEs) and chronic tissue inflammation. The metabolic strategies underlying the resilience properties of A. mexicanus cavefish, and how they relate to environmental challenges of the cave environment, are poorly understood. Here, we provide an untargeted metabolomics study of long- and short-term fasting in two A. mexicanus cave populations and one surface population. We find that, although the metabolome of cavefish bears many similarities with pathological conditions such as metabolic syndrome, cavefish also exhibit features not commonly associated with a pathological condition, and in some cases considered indicative of an overall robust metabolic condition. These include a reduction in cholesteryl esters and intermediates of protein glycation, and an increase in antioxidants and metabolites associated with hypoxia and longevity. This work suggests that certain metabolic features associated with human pathologies are either not intrinsically harmful, or can be counteracted by reciprocal adaptations. We provide a transparent pipeline for reproducing our analysis and a Shiny app for other researchers to explore and visualize our dataset.

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

Maternal Pyrroloquinoline Quinone Supplementation Improves Offspring Liver Bioactive Lipid Profiles throughout the Lifespan and Protects against the Development of Adult NAFLD

Maternal obesity and consumption of a high-fat diet significantly elevate risk for pediatric nonalcoholic fatty liver disease (NAFLD), affecting 10% of children in the US. Almost half of these children are diagnosed with nonalcoholic steatohepatitis (NASH), a leading etiology for liver transplant. Animal models show that signs of liver injury and perturbed lipid metabolism associated with NAFLD begin in utero; however, safe dietary therapeutics to blunt developmental programming of NAFLD are unavailable. Using a mouse model of maternal Western-style diet (WD), we previously showed that pyrroloquinoline quinone (PQQ), a potent dietary antioxidant, protected offspring of WD-fed dams from development of NAFLD and NASH. Here, we used untargeted mass spectrometry-based lipidomics to delineate lipotoxic effects of WD on offspring liver and identify lipid targets of PQQ. PQQ exposure during pregnancy altered hepatic lipid profiles of WD-exposed offspring, upregulating peroxisome proliferator-activated receptor (PPAR) α signaling and mitochondrial fatty acid oxidation to markedly attenuate triglyceride accumulation beginning in utero. Surprisingly, the abundance of very long-chain ceramides, important in promoting gut barrier and hepatic function, was significantly elevated in PQQ-treated offspring. PQQ exposure reduced the hepatic phosphatidylcholine/phosphatidylethanolamine (PC/PE) ratio in WD-fed offspring and improved glucose tolerance. Notably, levels of protective n - 3 polyunsaturated fatty acids (PUFAs) were elevated in offspring exposed to PQQ, beginning in utero, and the increase in n - 3 PUFAs persisted into adulthood. Our findings suggest that PQQ supplementation during gestation and lactation augments pathways involved in the biosynthesis of long-chain fatty acids and plays a unique role in modifying specific bioactive lipid species critical for protection against NAFLD risk in later life.

Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder

Alcohol-related liver disease is a public health care burden globally. Only 10-20% of patients with alcohol use disorder have progressive liver disease. This study aimed to identify lipid biomarkers for the early identification of progressive alcohol-related liver disease, which is a key step for early intervention. We performed untargeted lipidomics analysis in serum and fecal samples for a cohort of 49 subjects, including 17 non-alcoholic controls, 16 patients with non-progressive alcohol-related liver disease, and 16 patients with progressive alcohol-related liver disease. The serum and fecal lipidome profiles in the two patient groups were different from that in the controls. Nine lipid biomarkers were identified that were significantly different between patients with progressive liver disease and patients with non-progressive liver disease in both serum and fecal samples. We further built a random forest model to predict progressive alcohol-related liver disease using nine lipid biomarkers. Fecal lipids performed better (Area Under the Curve, AUC = 0.90) than serum lipids (AUC = 0.79). The lipid biomarkers identified are promising candidates for the early identification of progressive alcohol-related liver disease.

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.

TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network

Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consecutive days (trend) reveals important aspects such as discharges from agricultural, industrial or domestic activities. This study presents a validated computational framework based on deep learning conventional neural network to classify trends of chemicals over 30 consecutive days of sampling in two sampling sites (upstream and downstream of a river). From trend analysis and factor analysis, the chemicals could be classified into periodic, spill, increasing, decreasing and false trend. The developed method was validated with list of 42 reference standards (target screening) and applied to samples. 25 compounds were selected by the deep learning and identified via non-target screening. Three classes of surfactants were identified for the first time in river water and two of them were never reported in the literature. Overall, 21 new homologous series of the newly identified surfactants were tentatively identified. The aquatic toxicity of the identified compounds was estimated by in silico tools and a few compounds along with their homologous series showed potential risk to aquatic environment.

Metabolic Signature of Ethanol-Induced Hepatotoxicity in HepaRG Cells by Liquid Chromatography-Mass Spectrometry-Based Untargeted Metabolomics

Alcoholic liver disease is highly prevalent but poorly identified and characterized, leading to knowledge gaps, which impairs early diagnosis. Excessive alcohol consumption is known to alter lipid metabolism, followed by progressive intracellular lipid accumulation, resulting in alcoholic fatty liver disease. In this study, HepaRG cells were exposed to ethanol at IC₁₀ and 1/10 IC₁₀ for 24 and 48 h. Metabolic alterations were investigated intra- and extracellularly with liquid chromatography-high-resolution mass spectrometry. Ion mobility was added as an extra separation dimension for untargeted lipidomics to improve annotation confidence. Distinctive patterns between exposed and control cells were consistently observed, with intracellular upregulation of di- and triglycerides, downregulation of phosphatidylcholines and phosphatidylethanolamines, sphingomyelins, and S-adenosylmethionine, among others. Several intracellular metabolic patterns could be related to changes in the extracellular environment, such as increased intracellular hydrolysis of sphingomyelins, leading to increased phosphorylcholine secretion. Carnitines showed alterations depending on the size of their carbon chain, which highlights the interplay between β-oxidation in mitochondria and peroxisomes. Potential new biomarkers of ethanol-induced hepatotoxicity have been observed, such as ceramides with a sphingadienine backbone, octanoylcarnitine, creatine, acetylcholine, and ethoxylated phosphorylcholine. The combination of the metabolic fingerprint and footprint enabled a comprehensive investigation of the pathophysiology behind ethanol-induced hepatotoxicity.

Integrated LC-MS and GC-MS-Based Metabolomics Reveal the Effects of Plant Competition on the Rye Metabolome

Plants compete with their neighbors about the limited resources available to them. Plants under induced stress resulting from competition may alter their metabolome to increase their resilience or enhance their defense mechanisms. In the present study, rye (Secale cereale) plants were cocultivated with different densities (3, 12, and 18 plants per pot) of Austrian pea (Pisum sativum subsp. arvense), hairy vetch (Vicia villosa), and Alexandrian clover (Trifolium alexandrinum L.) to elucidate the changes in the rye metabolome in response to the different levels of competition. Global metabolic profiling by liquid chromatography triple quadrupole tandem mass spectrometry (LC-QqQ-MS), liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS), and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) was performed on rye plants, and the acquired data were analyzed using uni- and multivariate statistics. Targeted analysis showed that a high level of competition reduced the concentration of aglycone benzoxazinoids (BXs) and increased glycoside BXs in rye roots. Untargeted metabolomics analysis indicated an increase in the rye root content of the allelopathic compounds 4-hydroxybenzoic acid and uracil in response to competition. Untargeted analysis of rye shoots revealed that the plant competition increased the d-pyroglutamic acid, which is an elicitor of reactive oxygen species (ROS). Our results have enhanced the knowledge of the biochemical response of plant species to cocultivation.

Systematic Evaluation of HILIC Stationary Phases for Global Metabolomics of Human Plasma

Polar hydrophilic metabolites have been identified as important actors in many biochemical pathways. Despite continuous improvement and refinement of hydrophilic interaction liquid chromatography (HILIC) platforms, its application in global polar metabolomics has been underutilized. In this study, we aimed to systematically evaluate polar stationary phases for untargeted metabolomics by using HILIC columns (neutral and zwitterionic) that have been exploited widely in targeted approaches. To do so, high-resolution mass spectrometry was applied to thoroughly investigate selectivity, repeatability and matrix effect at three pH conditions for 9 classes of polar compounds using 54 authentic standards and plasma matrix. The column performance for utilization in untargeted metabolomics was assessed using plasma samples with diverse phenotypes. Our results indicate that the ZIC-c HILIC column operated at neutral pH exhibited several advantages, including superior performance for different classes of compounds, better isomer separation, repeatability and high metabolic coverage. Regardless of the column type, the retention of inorganic ions in plasma leads to extensive adduct formation and co-elution with analytes, which results in ion-suppression as part of the overall plasma matrix effect. In ZIC-c HILIC, the sodium chloride ion effect was particularly observed for amino acids and amine classes. Successful performance of HILIC for separation of plasma samples with different phenotypes highlights this mode of separation as a valuable approach in global profiling of plasma sample and discovering the metabolic changes associated with health and disease.

Metabolomics study of COVID-19 patients in four different clinical stages

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the coronavirus strain causing the respiratory pandemic COVID-19 (coronavirus disease 2019). To understand the pathobiology of SARS-CoV-2 in humans it is necessary to unravel the metabolic changes that are produced in the individuals once the infection has taken place. The goal of this work is to provide new information about the altered biomolecule profile and with that the altered biological pathways of patients in different clinical situations due to SARS-CoV-2 infection. This is done via metabolomics using HPLC-QTOF-MS analysis of plasma samples at COVID-diagnose from a total of 145 adult patients, divided into different clinical stages based on their subsequent clinical outcome (25 negative controls (non-COVID); 28 positive patients with asymptomatic disease not requiring hospitalization; 27 positive patients with mild disease defined by a total time in hospital lower than 10 days; 36 positive patients with severe disease defined by a total time in hospital over 20 days and/or admission at the ICU; and 29 positive patients with fatal outcome or deceased). Moreover, follow up samples between 2 and 3 months after hospital discharge were also obtained from the hospitalized patients with mild prognosis. The final goal of this work is to provide biomarkers that can help to better understand how the COVID-19 illness evolves and to predict how a patient could progress based on the metabolites profile of plasma obtained at an early stage of the infection. In the present work, several metabolites were found as potential biomarkers to distinguish between the end-stage and the early-stage (or non-COVID) disease groups. These metabolites are mainly involved in the metabolism of carnitines, ketone bodies, fatty acids, lysophosphatidylcholines/phosphatidylcholines, tryptophan, bile acids and purines, but also omeprazole. In addition, the levels of several of these metabolites decreased to "normal" values at hospital discharge, suggesting some of them as early prognosis biomarkers in COVID-19 at diagnose.