Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling

Multimodal Omics Approaches to Aging and Age-Related Diseases

Aging is associated with a progressive decline in physiological capacities and an increased risk of aging-associated disorders. An increasing body of experimental evidence shows that aging is a complex biological process coordinately regulated by multiple factors at different molecular layers. Thus, it is difficult to delineate the overall systematic aging changes based on single-layer data. Instead, multimodal omics approaches, in which data are acquired and analyzed using complementary omics technologies, such as genomics, transcriptomics, and epigenomics, are needed for gaining insights into the precise molecular regulatory mechanisms that trigger aging. In recent years, multimodal omics sequencing technologies that can reveal complex regulatory networks and specific phenotypic changes have been developed and widely applied to decode aging and age-related diseases. This review summarizes the classification and progress of multimodal omics approaches, as well as the rapidly growing number of articles reporting on their application in the field of aging research, and outlines new developments in the clinical treatment of age-related diseases based on omics technologies.

Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges

Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations.

Metabolomic Signatures of Brainstem in Mice following Acute and Subchronic Hydrogen Sulfide Exposure

Hydrogen sulfide (H2S) is an environmental toxicant of significant health concern. The brain is a major target in acute H2S poisoning. This study was conducted to test the hypothesis that acute and subchronic ambient H2S exposures alter the brain metabolome. Male 7-8-week-old C57BL/6J mice were exposed by whole-body inhalation to 1000 ppm H2S for 45 min and euthanized at 5 min or 72 h for acute exposure. For subchronic study, mice were exposed to 5 ppm H2S 2 h/day, 5 days/week for 5 weeks. Control mice were exposed to room air. The brainstem was removed for metabolomic analysis. Enrichment analysis showed that the metabolomic profiles in acute and subchronic H2S exposures matched with those of cerebral spinal fluid from patients with seizures or Alzheimer's disease. Acute H2S exposure decreased excitatory neurotransmitters, aspartate, and glutamate, while the inhibitory neurotransmitter, serotonin, was increased. Branched-chain amino acids and glucose were increased by acute H2S exposure. Subchronic H2S exposure within OSHA guidelines surprisingly decreased serotonin concentration. In subchronic H2S exposure, glucose was decreased, while polyunsaturated fatty acids, inosine, and hypoxanthine were increased. Collectively, these results provide important mechanistic clues for acute and subchronic ambient H2S poisoning and show that H2S alters brainstem metabolome.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Microbiome function and neurodevelopment in Black infants: vitamin B12 emerges as a key factor

The early life gut microbiome affects the developing brain, and therefore may serve as a target to support neurodevelopment of children living in stressful and under-resourced environments, such as Black youth living on the South Side of Chicago, for whom we observe racial disparities in health. Microbiome compositions/functions key to multiple neurodevelopmental facets have not been studied in Black children, a vulnerable population due to racial disparities in health; thus, a subsample of Black infants living in urban, low-income neighborhoods whose mothers participated in a prenatal nutrition study were recruited for testing associations between composition and function of the gut microbiome (16S rRNA gene sequencing, shotgun metagenomics, and targeted metabolomics of fecal samples) and neurodevelopment (developmental testing, maternal report of temperament, and observed stress regulation). Two microbiome community types, defined by high Lachnospiraceae or Enterobacteriaceae abundance, were discovered in this cohort from 16S rRNA gene sequencing analysis; the Enterobacteriaceae-dominant community type was significantly negatively associated with cognition and language scores, specifically in male children. Vitamin B12 biosynthesis emerged as a key microbiome function from shotgun metagenomics sequencing analysis, showing positive associations with all measured developmental skills (i.e., cognition, language, motor, surgency, effortful control, and observed stress regulation). Blautia spp. also were identified as substantial contributors of important microbiome functions, including vitamin B12 biosynthesis and related vitamin B12-dependent microbiome functions, anti-inflammatory microbial surface antigens, competitive mechanisms against pathobionts, and production of antioxidants. The results are promising with respect to the potential for exploring therapeutic candidates, such as vitamin B12 nutritional or Blautia spp. probiotic supplementation, to support the neurodevelopment of infants at risk for experiencing racial disparities in health.

Precision Medicine in Depression: The Role of Proteomics and Metabolomics in Personalized Treatment Approaches

Depression, or major depressive disorder (MDD), is a widespread mental health condition marked by enduring feelings of sorrow and loss of interest. Treatment of depression frequently combines psychotherapy, medication, and lifestyle modifications. However, the occurrence of treatment resistance in certain individuals makes it difficult for physicians to effectively manage this disorder, calling for the implementation of alternative therapeutic strategies. Recently, precision medicine has gained increased attention in the field of mental health, paving the way for more personalized and effective therapeutic interventions in depression. Also known as personalized medicine, this approach relies on genetic composition, molecular profiles, and environmental variables to customize therapies to individual patients. In particular, precision medicine has offered novel viewpoints on depression through two specific domains: proteomics and metabolomics. On one hand, proteomics is the thorough study of proteins in a biological system, while metabolomics focuses on analyzing the complete set of metabolites in a living being. In the past few years, progress in research has led to the identification of numerous depression-related biomarkers using proteomics and metabolomics techniques, allowing for early identification, precise diagnosis, and improved clinical outcome. However, despite significant progress in these techniques, further efforts are required for advancing precision medicine in the diagnosis and treatment of depression. The overarching goal of this chapter is to provide the current state of knowledge regarding the use of proteomics and metabolomics in identifying biomarkers related to depression. It also highlights the potential of proteomics and metabolomics in elucidating the intricate processes underlying depression, opening the door for tailored therapies that could eventually enhance clinical outcome in depressed patients. This chapter finally discusses the main challenges in the use of proteomics and metabolomics and discusses potential future research directions.

Microbe-derived uremic solutes enhance thrombosis potential in the host

IMPORTANCE

Alterations in gut microbial composition and function have been linked to numerous diseases. Identifying microbial pathways responsible for producing molecules that adversely impact the host is an important first step in the development of therapeutic interventions. Here, we first use large-scale clinical observations to link blood levels of defined microbial products to cardiovascular disease risks. Notably, the previously identified uremic toxins p-cresol sulfate and indoxyl sulfate were shown to predict 5-year mortality risks. After identifying the microbes and microbial enzymes involved in the generation of these uremic toxins, we used bioengineering technologies coupled with colonization of germ-free mice to show that the gut microbial genes that generate p-cresol and indole are sufficient to confer p-cresol sulfate and indoxyl sulfate formation, and a pro-thrombotic phenotype in vivo. The findings and tools developed serve as a critical step in both the study and targeting of these gut microbial pathways in vivo.

Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update

The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.

Unraveling the immune and metabolic changes associated with metritis in dairy cows

The objective was to unravel the peripartum immune and metabolic changes associated with metritis in Holstein cows. Holstein cows (n = 128) had blood collected at -14, 0, 3, and 7 d relative to parturition (DRP). Flow cytometry was used to evaluate blood leukocyte counts, proportions, and activation. Total cells, live cells, single cells, monocytes (CD172α+/CD14+), polymorphonuclears (CD172α+/CD14-/SSChigh), B-cells (CD21+/MHCII+), CD4+ T-cells (CD4+), CD8+ T-cells (CD8+), and γδ T-cells (γδTCR+) were evaluated. Both CD62L and CD11b were used as markers of cell activation. Major histocompatibility complex class II was used as a marker of antigen presentation in monocytes. A Milliplex Bovine Cytokine/Chemokine 08-plex kit was used to evaluate plasma concentrations of IFN-γ, IL-1α, IL-1β, IL-4, IL-6, IL-8, IL-10, and tumor necrosis factor-α. The body weight (BW) change prepartum was calculated as the difference between calving BW and prepartum BW divided by the number of days between measurements. Plasma fatty acids (FA) were measured at -14 and 0 DRP using untargeted gas chromatography with time-of-flight mass spectrometry. Data were analyzed by ANOVA for repeated measures. Cows that developed metritis (n = 57) had greater prepartum BW, prepartum BW loss, and greater FA concentrations at calving. Plasma FA at calving was positively correlated with IL-1β. Cows that developed metritis had persistent systemic inflammation, which was demonstrated by greater B-cell activation, greater pro-inflammatory cytokine concentrations, and greater cell damage pre- and postpartum. Postpartum, we observed greater polymorphonuclear cell activation and extravasation but lesser monocytes and CD4+ T-cells activation and extravasation, which suggests postpartum immune tolerance. Greater prepartum adiposity in cows that developed metritis may lead to systemic inflammation pre- and postpartum and immune tolerance postpartum, which may lead to failure to prevent bacterial infection, and development of puerperal metritis.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

Dietary restriction of isoleucine increases healthspan and lifespan of genetically heterogeneous mice

Low-protein diets promote health and longevity in diverse species. Restriction of the branched-chain amino acids (BCAAs) leucine, isoleucine, and valine recapitulates many of these benefits in young C57BL/6J mice. Restriction of dietary isoleucine (IleR) is sufficient to promote metabolic health and is required for many benefits of a low-protein diet in C57BL/6J males. Here, we test the hypothesis that IleR will promote healthy aging in genetically heterogeneous adult UM-HET3 mice. We find that IleR improves metabolic health in young and old HET3 mice, promoting leanness and glycemic control in both sexes, and reprograms hepatic metabolism in a sex-specific manner. IleR reduces frailty and extends the lifespan of male and female mice, but to a greater degree in males. Our results demonstrate that IleR increases healthspan and longevity in genetically diverse mice and suggests that IleR, or pharmaceuticals that mimic this effect, may have potential as a geroprotective intervention.

Blood metabolomics and impacted cellular mechanisms during transition into lactation in dairy cows that develop metritis

The objective of this study was to identify metabolites associated with metritis and use them for identification of cellular mechanisms affected during transition into lactation. Holstein cows (n = 104) had blood collected in the prepartum period (d -14 ± 6 relative to calving), at calving (d 0), and at the day of metritis diagnosis (d 7 ± 2 after calving). Cows with reddish or brownish, watery, and fetid discharge were diagnosed with metritis (n = 52). Cows with metritis were paired with herdmates without metritis (n = 52) based on days in milk. The metabolome of plasma samples was evaluated using untargeted gas chromatography time-of-flight mass spectrometry. Univariate analyses included t-tests and fold change analyses. Metabolites with false discovery rate adjusted P ≤ 0.10 on t-tests were used for partial least squares discriminant analysis coupled with permutational analysis using 2,000 permutations. Metabolites with false discovery rate adjusted P ≤ 0.10 on t-tests were also used for enriched pathway analyses and identification of cellular processes. Cows that developed metritis had affected cellular processes associated with lower amino acid metabolism in the prepartum period, greater lipolysis, cell death, and oxidative stress at calving and at metritis diagnosis, and greater leukocyte activation at calving, but lower immune cell activation at metritis diagnosis. In summary, cows that developed metritis had plasma metabolomic changes associated with greater lipolysis, oxidative stress, and a dysregulated immune response which may predispose cows to metritis development.

Deeper insights into the effects of low dietary levels of polychlorinated biphenyls on pig metabolism using gas chromatography-high resolution mass spectrometry metabolomics

Polychlorinated biphenyls (PCBs) are a class of contaminants of great concern, linked to the development of many chronic diseases. Adverse effects of PCBs have been documented in humans after accidental and massive exposure. However, little is known about the effect of chronic exposure to low-dose PCB mixtures, and studies regarding scattered lifetime exposures to non-dioxin-like (NDL)-PCBs are especially missing. In this work, serum samples from pigs chronically exposed through their diet during 22 days to Aroclor 1260 (i.e. a commercially available mixture of NDL-PCBs) underwent a metabolomics analysis using gas chromatography-high resolution mass spectrometry (GC-HRMS), with the objective to investigate the effect of exposure to low doses of NDL-PCBs (few ng/kg body weight (b.w.) per day). The study showed that the serum profiles of 84 metabolites are significantly altered by the administration of Aroclor 1260, of which 40 could be identified at level 1. The aggregate interpretation of the results of this study, together with the outcome of a previous one involving LC-HRMS profiling, provided a substantial and concise overview of the effect of low dose exposure to NDL-PCBs, reflecting the hepatotoxic and neurotoxic effects already reported in literature at higher and longer exposures. These results are intended to contribute to the debate on the current toxicological reference values for these substances.

Untargeted metabolomics analysis of four date palm (Phoenix dactylifera L.) cultivars using MS and NMR

Since ancient times, the inhabitants of dry areas have depended on the date palm (Phoenix dactylifera L.) as a staple food and means of economic security. For example, dates have been a staple diet for the inhabitants of the Arabian Peninsula and Sahara Desert in North Africa for millennia and the local culture is rich in knowledge and experience with the benefits of dates, suggesting that dates contain many substances essential for the human body. Madinah dates are considered one of the most important types of dates in the Arabian Peninsula, with Ajwa being one of the most famous types and grown only in Madinah, Saudi Arabia. Date seeds are traditionally used for animal feed, seed oil production, cosmetics, and as a coffee substitute. Phytochemical compounds that have been detected in date fruits and date seeds include phenolic acids, carotenoids, and flavonoids. Phenolic acids are the most prevalent bioactive constituents that contribute to the antioxidant activity of date fruits. The bioactive properties of these phytochemicals are believed to promote human health by reducing the risk of diseases such as chronic inflammation. Ajwa dates especially are thought to have superior bioactivity properties. To investigate these claims, in this study, we compare the metabolic profiles of Ajwa with different types of dates collected from Saudi Arabia and Tunisia. We show by UHPLC-MS that date seeds contain several classes of flavonoids, phenolic acids, and amino acid derivatives, including citric acid, malic acid, lactic acid, and hydroxyadipic acid. Additionally, GC-MS profiling showed that date seeds are richer in metabolite classes, such as hydrocinnamic acids (caffeic, ferulic and sinapic acids), than flesh samples. Deglet N fruit extract (minimum inhibitory concentration: 27 MIC/μM) and Sukkari fruit extract (IC50: 479 ± 0.58μg /mL) have higher levels of antibacterial and antioxidative activity than Ajwa fruits. However, the seed analysis showed that seed extracts have better bioactivity effects than fruit extracts. Specifically, Ajwa extract showed the best MIC and strongest ABTS radical-scavenging activity among examined seed extracts (minimum inhibitory concentration: 20 μM; IC50: 54 ± 3.61μg /mL). Our assays are a starting point for more advanced in vitro antibacterial models and investigation into the specific molecules that are responsible for the antioxidative and anti-bacterial activities of dates.

Optimising a urinary extraction method for non-targeted GC-MS metabolomics

Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.

Phytocompound screening, antioxidant activity and molecular docking studies of pomegranate seed: a preventive approach for SARS-CoV-2 pathogenesis

A global hazard to public health has been generated by the coronavirus infection 2019 (COVID-19), which is spreading quickly. Pomegranate is a strong source of antioxidants and has demonstrated a number of pharmacological characteristics. This work was aimed to analyze the phytochemicals present in ethanolic pomegranate seed extract (PSE) and their in vitro antioxidant potential and further in-silico evaluation for antiviral potential against crystal structure of two nucleocapsid proteins i.e., N-terminal RNA binding domain (NRBD) and C-terminal Domain (CTD) of SARS-CoV-2. The bioactive components from ethanolic extract of PSE were assessed by gas chromatography-mass spectroscopy (GC-MS). Free radical scavenging activity of PSE was determined using DPPH dye. Molecular docking was executed through the Glide module of Maestro software. Lipinski's 5 rule was applied for drug-likeness characteristics using cheminformatics Molinspiration software while OSIRIS Data Warrior V5.5.0 was used to predict possible toxicological characteristics of components. Thirty-two phytocomponents was detected in PSE by GC-MS technique. Free radical scavenging assay revealed the high antioxidant capacity of PSE. Docking analysis showed that twenty phytocomponents from PSE exhibited good binding affinity (Docking score ≥ - 1.0 kcal/mol) towards NRBD and CTD nucleocapsid protein. This result increases the possibility that the top 20 hits could prevent the spread of SARS-CoV-2 by concentrating on both nucleocapsid proteins. Moreover, molecular dynamics (MD) simulation using GROMACS was used to check their binding efficacy and internal dynamics of top complexes with the lowest docking scores. The metrics root mean square deviation (RMSD), root mean square fluctuation (RMSF), intermolecular hydrogen bonding (H-bonds) and radius of gyration (Rg) revealed that the lead phytochemicals form an energetically stable complex with the target protein. Majority of the phytoconstituents exhibited drug-likeness with non-tumorigenic properties. Thus, the PSE phytoconstituents could be useful source of drug or nutraceutical development in SARS-CoV-2 pathogenesis.

Measurement of gluconeogenesis by 2H2O labeling and mass isotopomer distribution analysis

The gluconeogenesis pathway, which converts nonsugar molecules into glucose, is critical for maintaining glucose homeostasis. Techniques that measure flux through this pathway are invaluable for studying metabolic diseases such as diabetes that are associated with dysregulation of this pathway. We introduce a new method that measures fractional gluconeogenesis by heavy water labeling and gas chromatographic-mass spectrometric analysis. This technique circumvents cumbersome benchwork or inference of positionality from mass spectra. The enrichment and pattern of deuterium label on glucose is quantified by use of mass isotopomer distribution analysis, which informs on how much of glucose-6-phosphate-derived glucose comes from the gluconeogenesis (GNG) pathway. We use an in vivo model of the GNG pathway that is based on previously published models but offers a new approach to calculating GNG pathway and subpathway contributions using combinatorial probabilities. We demonstrated that this method accurately quantifies fractional GNG through experiments that perturb flux through the pathway and by probing analytical sensitivity. While this method was developed in mice, the results suggest that it is translatable to humans in a clinical setting.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Isotopomer analyses with the tricarboxylic acid cycle intermediates and exchanging metabolites from the rat kidney

Renal metabolism is essential for kidney functions and energy homeostasis in the body. The TCA cycle is the hub of metabolism, but the metabolic activities of the cycle in the kidney have rarely been investigated. This study is to assess metabolic processes at the level of the TCA cycle in the kidney based on isotopomer distributions in multiple metabolites. Isolated rat kidneys were perfused with media containing common substrates including lactate and alanine for an hour. One group of kidneys received [U-13 C3 ]lactate instead of natural abundance lactate while the other group received [U-13 C3 ]alanine instead of natural abundance alanine. Perfused kidneys and effluent were prepared for analysis using NMR spectroscopy. 13 C-labeling patterns in glutamate, fumarate, aspartate and succinate from the kidney extracts showed that pyruvate carboxylase and oxidative metabolism through the TCA cycle were comparably very active, but pyruvate cycling and pyruvate dehydrogenase were relatively less active. Isotopomer analyses with fumarate and malate from effluent, however, indicated that pyruvate carboxylase was much more active than the TCA cycle and other metabolic processes. The reverse equilibrium of oxaloacetate with four-carbon intermediates of the cycle was nearly complete (92%), based on the ratio of [2,3,4-13 C3 ]/[1,2,3-13 C3 ] in aspartate or malate. 13 C enrichment in glucose with 13 C-lactate supply was higher than that with 13 C-alanine. Isotopomer analyses with multiple metabolites (i.e., glutamate, fumarate, aspartate, succinate and malate) allowed us to assess relative metabolic processes in the TCA cycle in the kidney supplied with [U-13 C3 ]lactate. Data from the analytes were generally consistent, indicating highly active pyruvate carboxylase and oxidative metabolism through the TCA cycle. Different 13 C-labeling patterns in analytes from the kidney extracts versus effluent suggested metabolic compartmentalization.

Isocitrate dehydrogenase 1 sustains a hybrid cytoplasmic-mitochondrial tricarboxylic acid cycle that can be targeted for therapeutic purposes in prostate cancer

The androgen receptor (AR) is an established orchestrator of cell metabolism in prostate cancer (PCa), notably by inducing an oxidative mitochondrial program. Intriguingly, AR regulates cytoplasmic isocitrate dehydrogenase 1 (IDH1), but not its mitochondrial counterparts IDH2 and IDH3. Here, we aimed to understand the functional role of IDH1 in PCa. Mouse models, in vitro human PCa cell lines, and human patient-derived organoids (PDOs) were used to study the expression and activity of IDH enzymes in the normal prostate and PCa. Genetic and pharmacological inhibition of IDH1 was then combined with extracellular flux analyses and gas chromatography-mass spectrometry for metabolomic analyses and cancer cell proliferation in vitro and in vivo. In PCa cells, more than 90% of the total IDH activity is mediated through IDH1 rather than its mitochondrial counterparts. This profile seems to originate from the specialized prostate metabolic program, as observed using mouse prostate and PDOs. Pharmacological and genetic inhibition of IDH1 impaired mitochondrial respiration, suggesting that this cytoplasmic enzyme contributes to the mitochondrial tricarboxylic acid cycle (TCA) in PCa. Mass spectrometry-based metabolomics confirmed this hypothesis, showing that inhibition of IDH1 impairs carbon flux into the TCA cycle. Consequently, inhibition of IDH1 decreased PCa cell proliferation in vitro and in vivo. These results demonstrate that PCa cells have a hybrid cytoplasmic-mitochondrial TCA cycle that depends on IDH1. This metabolic enzyme represents a metabolic vulnerability of PCa cells and a potential new therapeutic target.

Differential Metabolites in Osteoarthritis: A Systematic Review and Meta-Analysis

(1) Many studies have attempted to utilize metabolomic approaches to explore potential biomarkers for the early detection of osteoarthritis (OA), but consistent and high-level evidence is still lacking. In this study, we performed a systematic review and meta-analysis of differential small molecule metabolites between OA patients and healthy individuals to screen promising candidates from a large number of samples with the aim of informing future prospective studies. (2) Methods: We searched the EMBASE, the Cochrane Library, PubMed, Web of Science, Wan Fang Data, VIP Date, and CNKI up to 11 August 2022, and selected relevant records based on inclusion criteria. The risk of bias was assessed using the Newcastle-Ottawa quality assessment scale. We performed qualitative synthesis by counting the frequencies of changing directions and conducted meta-analyses using the random effects model and the fixed-effects model to calculate the mean difference and 95% confidence interval. (3) Results: A total of 3798 records were identified and 13 studies with 495 participants were included. In the 13 studies, 132 kinds of small molecule differential metabolites were extracted, 58 increased, 57 decreased and 17 had direction conflicts. Among them, 37 metabolites appeared more than twice. The results of meta-analyses among four studies showed that three metabolites increased, and eight metabolites decreased compared to healthy controls (HC). (4) Conclusions: The main differential metabolites between OA and healthy subjects were amino acids (AAs) and their derivatives, including tryptophan, lysine, leucine, proline, phenylalanine, glutamine, dimethylglycine, citrulline, asparagine, acetylcarnitine and creatinine (muscle metabolic products), which could be potential biomarkers for predicting OA.

Short-Chain Fatty Acid Levels after Fecal Microbiota Transplantation in a Pediatric Cohort with Recurrent Clostridioides difficile Infection

Though antibiotics are the mainstay treatment for Clostridioides difficile, a large population of individuals infected will experience recurrence. In turn, fecal microbiota transplantation (FMT) has emerged as a promising treatment for recurrent C. difficile infection (rCDI). Mechanistically, by providing a healthy, diverse flora to the infected individual, FMT "resets" the underlying gut microbiome dysbiosis associated with rCDI. A proposed mechanism through which this occurs is via microbiome metabolites such as short-chain fatty acids (SCFAs); however, this has not been previously studied in pediatric patients. Using mass spectroscopy, we quantified pre- and post-transplant levels of acetate, isovalerate, butyrate, formate, and propionate in pediatric patients diagnosed with rCDI (n = 9). We compared pre- and post-transplant levels within the rCDI cohort at 1, 3, 6, and 12 months post-transplant and correlated these levels with healthy controls (n = 19). We witnessed a significant difference in the combined SCFA levels and the individual levels of acetate, butyrate, isovalerate, and propionate in the pre-treatment rCDI cohort compared to the healthy controls. In addition, there was a significant increase in combined SCFA levels at 12 months post-transplant within the rCDI group compared to that of their pre-transplant levels, and, more specifically, acetate, propionate, and isovalerate increased from pre-transplant to 12 months post-transplant. The longitudinal aspect of this study allowed us to identify mechanisms that contribute to the durability of responses to FMT, as well as characterize the unique patterns of short-chain fatty acid level recovery in rCDI pediatric patients.

Mass spectrometry-based analysis of gut microbial metabolites of aromatic amino acids

Small molecules derived from gut microbiota have been increasingly investigated to better understand the functional roles of the human gut microbiome. Microbial metabolites of aromatic amino acids (AAA) have been linked to many diseases, such as metabolic disorders, chronic kidney diseases, inflammatory bowel disease, diabetes, and cancer. Important microbial AAA metabolites are often discovered via global metabolite profiling of biological specimens collected from humans or animal models. Subsequent metabolite identity confirmation and absolute quantification using targeted analysis enable comparisons across different studies, which can lead to the establishment of threshold concentrations of potential metabolite biomarkers. Owing to their excellent selectivity and sensitivity, hyphenated mass spectrometry (MS) techniques are often employed to identify and quantify AAA metabolites in various biological matrices. Here, we summarize the developments over the past five years in MS-based methodology for analyzing gut microbiota-derived AAA. Sample preparation, method validation, analytical performance, and statistical methods for correlation analysis are discussed, along with future perspectives.

Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer

Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.

Recent Advances and Perspectives in Relation to the Metabolomics-Based Study of Diabetic Retinopathy

Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is a major cause of acquired blindness in adults. Currently, a clinical diagnosis of DR primarily relies on fundus fluorescein angiography, with a limited availability of effective biomarkers. Metabolomics, a discipline dedicated to scrutinizing the response of various metabolites within living organisms, has shown noteworthy advancements in uncovering metabolic disorders and identifying key metabolites associated with DR in recent years. Consequently, this review aims to present the latest advancements in metabolomics techniques and comprehensively discuss the principal metabolic outcomes derived from analyzing blood, vitreous humor, aqueous humor, urine, and fecal samples.

Optimization of the first extraction protocol for metabolomic studies of Brucella abortus

Brucellosis is a zoonosis prevalent worldwide and very recurrent in less developed or developing regions. This zoonosis affects livestock, generating high financial losses to producers, in addition to transmitting diseases to humans through meat consumption or handling contaminated products and animals. In this study, five extraction methods for Brucella abortus intracellular metabolites, using different solvent compositions and cell membrane disruption procedures, were evaluated. Derivatized extracts were analyzed by GC-HRMS. Raw data were processed in XCMS Online and the results were evaluated through multivariate statistical analysis using the MetaboAnalyst platform. The identification of the extracted metabolites was performed by the Unknowns software using the NIST 17.L library. The extraction performance of each method was evaluated for thirteen representative metabolites, comprising four different chemical classes. Most of these compounds are reported in the cell membrane composition of Gram-negative bacteria. The method based on extraction with methanol/chloroform/water presented the best performance in the evaluation of the extracted compounds and in the statistical results. Therefore, this method was selected for extracting intracellular metabolites from cultures of Brucella abortus for untargeted metabolomics analysis.

Unravelling the anti-inflammatory and antioxidant effects of standardized green and black caffeinated coffee, tea, and their mixtures in an obese male rat model: Insights from biochemical, metabolomic, and histopathological analyses

Obesity is one of the major metabolic syndrome risk factors upon which altered metabolic pathways follow. This study aimed to discern altered metabolic pathways associated with obesity and to pinpoint metabolite biomarkers in serum of obese rats fed on high fructose diet using metabolomics. Further, the effect of standardized green versus black caffeinated aqueous extracts (tea and coffee) in controlling obesity and its comorbidities through monitoring relevant serum biomarkers viz. Leptin, adiponectin, spexin, malondialdehyde, total antioxidant capacity. Liver tissue oxidative stress (catalase, super oxide dismutase and glutathione) and inflammation (IL-1β and IL-6) markers were assessed for green coffee and its mixture with green tea. Results revealed improvement of all parameters upon treatments with more prominence for those treated with green caffeinated extract (coffee and tea) especially in mixture. Upon comparing with obese rat group, the green mixture of coffee and tea exhibited anti-hyperlipidemic action through lowering serum triglycerides by 35.0% and elevating high density lipoprotein by 71.0%. Black tea was likewise effective in lowering serum cholesterol and low density lipoprotein by 28.0 and 50.6%, respectively. GC-MS- based metabolomics of rat serum led to the identification of 34 metabolites with obese rat serum enriched in fatty acids (oleamide).

Circulating biomarkers in perioperative management of cancer patients

Owing to the advances in surgical technology, most solid tumours can be controlled by surgical excision. The priority should be tumour control, while some routine perioperative management might influence cancer progression in an unnoticed way. Moreover, it is increasingly recognized that effective perioperative management should include techniques to improve postoperative outcomes. These influences are elucidated by the different functions of circulating biomarkers in cancer patients. Here, circulating biomarkers with two types of clinical functions were reviewed: (i) circulating biomarkers for cancer progression monitoring, for instance, those related to cancer cell malignancy, tumour microenvironment formation, and early metastasis, and (ii) circulating biomarkers with relevance to postoperative outcomes, including systemic inflammation, immunosuppression, cognitive dysfunction, and pain management. This review aimed to provide new perspectives for the perioperative management of patients with cancer and highlight the potential clinical translation value of circulating biomarkers in improving outcomes.

Differential Volatile Organic Compound Expression in the Interaction of Daldinia eschscholtzii and Mycena citricolor

Fungi exhibit a wide range of ecological guilds, but those that live within the inner tissues of plants (also known as endophytes) are particularly relevant due to the benefits they sometimes provide to their hosts, such as herbivory deterrence, disease protection, and growth promotion. Recently, endophytes have gained interest as potential biocontrol agents against crop pathogens, for example, coffee plants (Coffea arabica). Published results from research performed in our laboratory showed that endophytic fungi isolated from wild Rubiaceae plants were effective in reducing the effects of the American leaf spot of coffee (Mycena citricolor). One of these isolates (GU11N) from the plant Randia grandifolia was identified as Daldinia eschscholtzii (Xylariales). Its antagonism mechanisms, effects, and chemistry against M. citricolor were investigated by analyzing its volatile profile alone and in the presence of the pathogen in contactless and dual culture assays. The experimental design involved direct sampling of agar plugs in vials for headspace (HS) and headspace solid-phase microextraction (HS-SPME) gas chromatography-mass spectrometry (GC-MS) analysis. Additionally, we used ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS/MS) to identify nonvolatile compounds from organic extracts of the mycelia involved in the interaction. Results showed that more volatile compounds were identified using HS-SPME (39 components) than those by the HS technique (13 components), sharing only 12 compounds. Statistical tests suggest that D. eschscholtzii inhibited the growth of M. citricolor through the release of VOCs containing a combination of 1,8-dimethoxynapththalene and terpene compounds affecting M. citricolor pseudopilei. The damaging effects of 1,8-dimethoxynaphthalene were corroborated in an in vitro test against M. citricolor pseudopilei; scanning electron microscopy (SEM) photographs confirmed structural damage. After analyzing the UHPLC-HRMS/MS data, a predominance of fatty acid derivatives was found among the putatively identified compounds. However, a considerable proportion of features (37.3%) remained unannotated. In conclusion, our study suggests that D. eschscholtzii has potential as a biocontrol agent against M. citricolor and that 1,8-dimethoxynaphthalene contributes to the observed damage to the pathogen's reproductive structures.

Automatic Assignment of Molecular Ion Species to Elemental Formulas in Gas Chromatography/Methane Chemical Ionization Accurate Mass Spectrometry

Gas chromatography-mass spectrometry (GC-MS) usually employs hard electron ionization, leading to extensive fragmentations that are suitable to identify compounds based on library matches. However, such spectra are less useful to structurally characterize unknown compounds that are absent from libraries, due to the lack of readily recognizable molecular ion species. We tested methane chemical ionization on 369 trimethylsilylated (TMS) derivatized metabolites using a quadrupole time-of-flight detector (QTOF). We developed an algorithm to automatically detect molecular ion species and tested SIRIUS software on how accurate the determination of molecular formulas was. The automatic workflow correctly recognized 289 (84%) of all 345 detected derivatized standards. Specifically, strong [M - CH3]+ fragments were observed in 290 of 345 derivatized chemicals, which enabled the automatic recognition of molecular adduct patterns. Using Sirius software, correct elemental formulas were retrieved in 87% of cases within the top three hits. When investigating the cases for which the automatic pattern analysis failed, we found that several metabolites showed a previously unknown [M + TMS]+ adduct formed by rearrangement. Methane chemical ionization with GC-QTOF mass spectrometry is a suitable avenue to identify molecular formulas for abundant unknown peaks.

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.

Research of Pesticide Metabolites in Human Brain Tumor Tissues by Chemometrics-Based Gas Chromatography-Mass Spectrometry Analysis for a Hypothetical Correlation between Pesticide Exposure and Risk Factor of Central Nervous System Tumors

Pesticides are widely used, resulting in continuing human exposure with potential health impacts. Some exposures related to agricultural works have been associated with neurological disorders. Since the 2000s, the hypothesis of the role of pesticides in the occurrence of central nervous system (CNS) tumors has been better documented in the literature. However, the etiology of childhood brain cancers still remains largely unknown. The major objective of this work was to assess the potential role of pesticide exposure as a risk factor for CNS tumors based on questionnaires and statistical analysis of information collected from patients hospitalized in the Neurosurgery Department of the Habib Bourguiba Hospital Medium in Sfax, Tunisia, during the period from January 1, 2022, to May 31, 2023. It also aimed to develop a simple and rapid analytical method by the gas chromatography-mass spectrometry technique for the research traces of pesticide metabolites in some collected human brain tumor tissues in order to more emphasize our hypothesis for such a correlation between pesticide exposure and brain tumor development. Patients with a history of high-risk exposure were selected to conduct further analysis. Chemometric methods were adapted to discern intrinsic variation between pathological and control groups and ascertain effective separation with the identification of differentially expressed metabolites accountable for such variations. Three samples revealed traces of pesticide metabolites that were mostly detected at an early age. The histopathological diagnosis was medulloblastoma for a 10-year-old child and high-grade gliomas for 27- and 35-year-old adults. The bivariate analyses (odds ratio >1 and P value <5%) confirmed the great probability of developing cancer by an exposure case. The Cox proportional hazards model revealed the risk of carcinogenicity beyond the age of 50 as a long-term effect of pesticide toxicity. Our study supports the correlation between pesticide exposure and the risk of development of human brain tumors, suggesting that preconception pesticide exposure, and possibly exposure during pregnancy, is associated with an increased childhood brain tumor risk. This hypothesis was enhanced in identifying traces of metabolites from the carbamate insecticide class known for their neurotoxicity and others from pyridazinone, organochlorines (OCs), triazole fungicide, and N-nitroso compounds known for their carcinogenicity. The 2D-OXYBLOT analysis confirmed the neurotoxicity effect of insecticides to induce oxidative damage in CNS cells. Aldicarb was implicated in brain carcinogenicity confirmed by the identification of oxime metabolites in a stress degradation study. Revealing "aziridine" metabolites from the OC class may better emphasize the theory of detecting traces of pesticide metabolites at an early age. Overall, our findings lead to the recommendation of limiting the residential use of pesticides and the support of public health policies serving this objective that we need to be vigilant in the postmarketing surveillance of human health impacts.

Sexual Dimorphism of the Mouse Plasma Metabolome Is Associated with Phenotypes of 30 Gene Knockout Lines

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions.

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.

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.

Research progress of metabolomics in psoriasis

ABSTRACT

Psoriasis is a chronic inflammatory skin disease with significant physical and psychological burdens. The interplay between the innate and adaptive immune systems is thought to contribute to the pathogenesis; however, the details of the pathogenesis remain unclear. In addition, reliable biomarkers for diagnosis, assessment of disease activity, and monitoring of therapeutic response are limited. Metabolomics is an emerging science that can be used to identify and analyze low molecular weight molecules in biological systems. During the past decade, metabolomics has been widely used in psoriasis research, and substantial progress has been made. This review summarizes and discusses studies that applied metabolomics to psoriatic disease. These studies have identified dysregulation of amino acids, carnitines, fatty acids, lipids, and carbohydrates in psoriasis. The results from these studies have advanced our understanding of: (1) the molecular mechanisms of psoriasis pathogenesis; (2) diagnosis of psoriasis and assessment of disease activity; (3) the mechanism of treatment and how to monitor treatment response; and (4) the link between psoriasis and comorbid diseases. We discuss common research strategies and progress in the application of metabolomics to psoriasis, as well as emerging trends and future directions.

Glycerol as a precursor for hepatic de novo glutathione synthesis in human liver

BACKGROUND

Glycerol is a substrate for gluconeogenesis and fatty acid esterification in the liver, processes which are upregulated in obesity and may contribute to excess fat accumulation. Glycine and glutamate, in addition to cysteine, are components of glutathione, the major antioxidant in the liver. In principle, glycerol could be incorporated into glutathione via the TCA cycle or 3-phosphoglycerate, but it is unknown whether glycerol contributes to hepatic de novo glutathione biosynthesis.

METHODS

Glycerol metabolism to hepatic metabolic products including glutathione was examined in the liver from adolescents undergoing bariatric surgery. Participants received oral [U-13C3]glycerol (50 mg/kg) prior to surgery and liver tissue (0.2-0.7g) was obtained during surgery. Glutathione, amino acids, and other water-soluble metabolites were extracted from the liver tissue and isotopomers were quantified with nuclear magnetic resonance spectroscopy.

RESULTS

Data were collected from 8 participants (2 male, 6 female; age 17.1 years [range 14-19]; BMI 47.4 kg/m2 [range 41.3-63.3]). The concentrations of free glutamate, cysteine, and glycine were similar among participants, and so were the fractions of 13C-labeled glutamate and glycine derived from [U-13C3]glycerol. The signals from all component amino acids of glutathione - glutamate, cysteine and glycine - were strong and analyzed to obtain the relative concentrations of the antioxidant in the liver. The signals from glutathione containing [13C2]glycine or [13C2]glutamate derived from the [U-13C3]glycerol drink were readily detected, and 13C-labelling patterns in the moieties were consistent with the patterns in corresponding free amino acids from the de novo glutathione synthesis pathway. The newly synthesized glutathione with [U-13C3]glycerol trended to be lower in obese adolescents with liver pathology.

CONCLUSIONS

This is the first report of glycerol incorporation into glutathione through glycine or glutamate metabolism in human liver. This could represent a compensatory mechanism to increase glutathione in the setting of excess glycerol delivery to the liver.

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.

Metabolites Potentially Determine the High Antioxidant Properties of Limosilactobacillus fermentum U-21

Many kinds of Lactobacillus are common occupants of humans' digestive tract that support the preservation of a balanced microbial environment that benefits host health. In this study, the unique lactic acid bacterium strain Limosilactobacillus fermentum U-21, which was isolated from the feces of a healthy human, was examined for its metabolite profile in order to compare it to that of the strain L. fermentum 279, which does not have antioxidant (AO) capabilities. By using GC × GC-MS, the metabolite fingerprint of each strain was identified, and the data were then subjected to multivariate bioinformatics analysis. The L. fermentum U-21 strain has previously been shown to possess distinctive antioxidant properties in in vivo and in vitro studies, positioning it as a drug candidate for the treatment of Parkinsonism. The production of multiple distinct compounds is shown by the metabolite analysis, demonstrating the unique characteristics of the L. fermentum U-21 strain. According to reports, some of the L. fermentum U-21 metabolites found in this study have health-promoting properties. The GC × GC-MS-based metabolomic tests defined strain L. fermentum U-21 as a potential postbiotic with significant antioxidant potential.

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.

Serum, Urine, and Fecal Metabolome Alterations in the Gut Microbiota in Response to Lifestyle Interventions in Pediatric Obesity: A Non-Randomized Clinical Trial

Pediatric obesity is associated with alterations in the gut microbiota and its metabolites. However, how they influence obesity and the effect of lifestyle interventions remains unknown.. In this non-randomized clinical trial, we analyzed metabolomes and microbial features to understand the associated metabolic pathways and the effect of lifestyle interventions on pediatric obesity. Anthropometric/biochemical data and fasting serum, urine, and fecal samples were collected at baseline and after an eight-week, weight-reduction lifestyle modification program. Post-intervention, children with obesity were classified into responder and non-responder groups based on changes in total body fat. At baseline, serum L-isoleucine and uric acid levels were significantly higher in children with obesity compared with those in normal-weight children and were positively correlated with obesogenic genera. Taurodeoxycholic and tauromuricholic α + β acid levels decreased significantly with obesity and were negatively correlated with obesogenic genera. Branched-chain amino acid and purine metabolisms were distinguished metabolic pathways in the obese group. Post-intervention, urinary myristic acid levels decreased significantly in the responder group, showing a significant positive correlation with Bacteroides. Fatty acid biosynthesis decreased significantly in the responder group. Thus, lifestyle intervention with weight loss is associated with changes in fatty acid biosynthesis, and myristic acid is a possible therapeutic target for pediatric obesity.

Human metabolome variation along the upper intestinal tract

Most processing of the human diet occurs in the small intestine. Metabolites in the small intestine originate from host secretions, plus the ingested exposome1 and microbial transformations. Here we probe the spatiotemporal variation of upper intestinal luminal contents during routine daily digestion in 15 healthy male and female participants. For this, we use a non-invasive, ingestible sampling device to collect and analyse 274 intestinal samples and 60 corresponding stool homogenates by combining five mass spectrometry assays2,3 and 16S rRNA sequencing. We identify 1,909 metabolites, including sulfonolipids and fatty acid esters of hydroxy fatty acids (FAHFA) lipids. We observe that stool and intestinal metabolomes differ dramatically. Food metabolites display trends in dietary biomarkers, unexpected increases in dicarboxylic acids along the intestinal tract and a positive association between luminal keto acids and fruit intake. Diet-derived and microbially linked metabolites account for the largest inter-individual differences. Notably, two individuals who had taken antibiotics within 6 months before sampling show large variation in levels of bioactive FAHFAs and sulfonolipids and other microbially related metabolites. From inter-individual variation, we identify Blautia species as a candidate to be involved in FAHFA metabolism. In conclusion, non-invasive, in vivo sampling of the human small intestine and ascending colon under physiological conditions reveals links between diet, host and microbial metabolism.

Responses in Nodulated Bean (Phaseolus vulgaris L.) Plants Grown at Elevated Atmospheric CO2

The increase in the concentration of CO₂ in the atmosphere is currently causing metabolomic and physiological changes in living beings and especially in plants. Future climate change may affect crop productivity by limiting the uptake of soil resources such as nitrogen (N) and water. The contribution of legume-rhizobia symbioses to N₂ fixation increases the available biological N reserve. Elevated CO₂ (eCO₂) has been shown to enhance the amount of fixed N₂ primarily by increasing biomass. Greater leaf biomass under eCO₂ levels increases N demand, which can stimulate and increase N₂ fixation. For this reason, bean plants (Phaseolus vulgaris L.) were used in this work to investigate how, in a CO₂-enriched atmosphere, inoculation with rhizobia (Rhizobium leguminosarum) affects different growth parameters and metabolites of carbon and nitrogen metabolism, as well as enzymatic activities of nitrogen metabolism and the oxidative state of the plant, with a view to future scenarios, where the concentration of CO₂ in the atmosphere will increase. The results showed that bean symbiosis with R. leguminosarum improved N₂ fixation, while also decreasing the plant's oxidative stress, and provided the plant with a greater defense system against eCO₂ conditions. In conclusion, the nodulation with rhizobia potentially replaced the chemical fertilization of bean plants (P. vulgaris L.), resulting in more environmentally friendly agricultural practices. However, further optimization of symbiotic activities is needed to improve the efficiency and to also develop strategies to improve the response of legume yields to eCO_2, particularly due to the climate change scenario in which there is predicted to be a large increase in the atmospheric CO₂ concentration.

In situ microwave fixation provides an instantaneous snapshot of the brain metabolome

Brain glucose metabolism is highly heterogeneous among brain regions and continues postmortem. In particular, we demonstrate exhaustion of glycogen and glucose and an increase in lactate production during conventional rapid brain resection and preservation by liquid nitrogen. In contrast, we show that these postmortem changes are not observed with simultaneous animal sacrifice and in situ fixation with focused, high-power microwave. We further employ microwave fixation to define brain glucose metabolism in the mouse model of streptozotocin-induced type 1 diabetes. Using both total pool and isotope tracing analyses, we identified global glucose hypometabolism in multiple brain regions, evidenced by reduced 13C enrichment into glycogen, glycolysis, and the tricarboxylic acid (TCA) cycle. Reduced glucose metabolism correlated with a marked decrease in GLUT2 expression and several metabolic enzymes in unique brain regions. In conclusion, our study supports the incorporation of microwave fixation for more accurate studies of brain metabolism in rodent models.

Transcriptome Analysis of Redox Systems and Polyamine Metabolic Pathway in Hepatoma and Non-Tumor Hepatocyte-like Cells

Reactive oxygen species (ROS) play a major role in the regulation of various processes in the cell. The increase in their production is a factor contributing to the development of numerous pathologies, including inflammation, fibrosis, and cancer. Accordingly, the study of ROS production and neutralization, as well as redox-dependent processes and the post-translational modifications of proteins, is warranted. Here, we present a transcriptomic analysis of the gene expression of various redox systems and related metabolic processes, such as polyamine and proline metabolism and the urea cycle in Huh7.5 hepatoma cells and the HepaRG liver progenitor cell line, that are widely used in hepatitis research. In addition, changes in response to the activation of polyamine catabolism that contribute to oxidative stress were studied. In particular, differences in the gene expression of various ROS-producing and ROS-neutralizing proteins, the enzymes of polyamine metabolisms and proline and urea cycles, as well as calcium ion transporters between cell lines, are shown. The data obtained are important for understanding the redox biology of viral hepatitis and elucidating the influence of the laboratory models used.

Gut Microbiota and Atrial Fibrillation: Pathogenesis, Mechanisms and Therapies

Over the past decade there has been an interest in understanding the role of gut microbiota in the pathogenesis of AF. A number of studies have linked the gut microbiota to the occurrence of traditional AF risk factors such as hypertension and obesity. However, it remains unclear whether gut dysbiosis has a direct effect on arrhythmogenesis in AF. This article describes the current understanding of the effect of gut dysbiosis and associated metabolites on AF. In addition, current therapeutic strategies and future directions are discussed.

Metabolomic Profiling of Asparagine Deprivation in Asparagine Synthetase Deficiency Patient-Derived Cells

The natural amino acid asparagine (Asn) is required by cells to sustain function and proliferation. Healthy cells can synthesize Asn through asparagine synthetase (ASNS) activity, whereas specific cancer and genetically diseased cells are forced to obtain asparagine from the extracellular environment. ASNS catalyzes the ATP-dependent synthesis of Asn from aspartate by consuming glutamine as a nitrogen source. Asparagine Synthetase Deficiency (ASNSD) is a disease that results from biallelic mutations in the ASNS gene and presents with congenital microcephaly, intractable seizures, and progressive brain atrophy. ASNSD often leads to premature death. Although clinical and cellular studies have reported that Asn deprivation contributes to the disease symptoms, the global metabolic effects of Asn deprivation on ASNSD-derived cells have not been studied. We analyzed two previously characterized cell culture models, lymphoblastoids and fibroblasts, each carrying unique ASNS mutations from families with ASNSD. Metabolomics analysis demonstrated that Asn deprivation in ASNS-deficient cells led to disruptions across a wide range of metabolites. Moreover, we observed significant decrements in TCA cycle intermediates and anaplerotic substrates in ASNS-deficient cells challenged with Asn deprivation. We have identified pantothenate, phenylalanine, and aspartate as possible biomarkers of Asn deprivation in normal and ASNSD-derived cells. This work implies the possibility of a novel ASNSD diagnostic via targeted biomarker analysis of a blood draw.

Metabolomics profile of plasma in acute diquat-poisoned patients using gas chromatography-mass spectrometry

Diquat (DQ) has been confirmed to be toxic to humans and responsible for severe health impairment. While to date, very little is known about the toxicological mechanisms of DQ. Thus, investigations to discover the toxic targets and potential biomarkers of DQ poisoning are urgently needed. In this study, a metabolic profiling analysis was conducted to reveal the changes of metabolites of plasma and find out the potential biomarkers of DQ intoxication by GC-MS. First, multivariate statistical analysis demonstrated that acute DQ poisoning can lead to metabolomic changes in human plasma. Then, metabolomics studies showed that 31 of the identified metabolites were significantly altered by DQ. Pathway analysis indicated that three primarily metabolic pathways including phenylalanine, tyrosine and tryptophan biosynthesis, taurine and hypotaurine metabolism, and phenylalanine metabolism were affected by DQ, resulting in the perturbations of phenylalanine, tyrosine, taurine, and cysteine. Finally, the results of receiver operating characteristic analysis showed the above four metabolites could be used as reliable tools for the diagnosis and severity assessments of DQ intoxication. These data provided the theoretical basis for basic research to understand the potential mechanisms of DQ poisoning, and also identified the desirable biomarkers with great potential for clinical applications.

Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review

In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. "Classical" tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent ad hoc treatments.

Comparison of Plasma Metabolome Response to Diets Enriched in Soybean and Partially-Hydrogenated Soybean Oil in Moderately Hypercholesterolemic Adults-A Pilot Study

Partially-hydrogenated fat/trans fatty acid intake has been associated with adverse effects on cardiometabolic risk factors. Comparatively unexplored is the effect of unmodified oil relative to partially-hydrogenated fat on the plasma metabolite profile and lipid-related pathways. To address this gap, we conducted secondary analyses using a subset of samples randomly selected from a controlled dietary intervention trial involving moderately hypercholesterolemic individuals. Participants (N = 10, 63 ± 8 y, BMI, 26.2 ± 4.2 kg/m2, LDL-C, 3.9 ± 0.5 mmol/L) were provided with diets enriched in soybean oil (SO) and partially-hydrogenated soybean oil (PHSO). Plasma metabolite concentrations were determined using an untargeted approach and pathway analysis using LIPIDMAPS. Data were assessed using a volcano plot, receiver operating characteristics curve, partial least square-discrimination analysis and Pearson correlations. Among the known metabolites higher in plasma after the PHSO diet than the SO diet, the majority were phospholipids (53%) and di- and triglycerides (DG/TG, 34%). Pathway analysis indicated upregulation of phosphatidylcholine synthesis from DG and phosphatidylethanolamine. We identified seven metabolites (TG_56:9, TG_54:8, TG_54:7, TG_54:6, TG_48:5, DG_36:5 and benproperine) as potential biomarkers for PHSO intake. These data indicate that TG-related metabolites were the most affected lipid species, and glycerophospholipid biosynthesis was the most active pathway in response to PHSO compared to SO intake.