Evaluation of two-step liquid-liquid extraction protocol for untargeted metabolic profiling of serum samples to achieve broader metabolome coverage by UPLC-Q-TOF-MS

Liquid and gas-chromatography-mass spectrometry methods for exposome analysis

Mass spectrometry-based methods have become fundamental to exposome research, providing the capability to explore a broad spectrum of chemical exposures. Liquid and gas chromatography coupled with low/high-resolution mass spectrometry (MS) are among the most frequently employed platforms due to their sensitivity and accuracy. However, these approaches present challenges, such as the inherent complexity of MS data and the expertise of biologists, chemists, clinicians, and data analysts to integrate and interpret MS data with other datasets effectively. The "omics" era advances rapidly, driven by developments of AI-based algorithms and an increase in accessible data; nevertheless, further efforts are necessary to ensure that exposomics outputs are comparable and reproducible, thus enhancing research findings. This review outlines the principles of MS-based methods for the exposome analytical pipeline, from sample collection to data analysis. We summarize and review both standard and cutting-edge strategies in exposome research, covering sample preparation, focusing on MS-based platforms, data acquisition strategies, and data annotation. The ultimate goal of this review is to highlight applications that enable the simultaneous analysis of endogenous metabolites and xenobiotics, which can help enhance our understanding of the impact of human exposure on health and disease and support personalized healthcare.

α-tocopherol deficiency in follicular ovarian cyst (FOCs) follicular fluid (FF) elevates oxidative stress and impairs oocyte maturation

Follicular ovarian cysts (FOCs) are prevalent reproductive disorders in both humans and animals, especially in livestock, where they cause economic losses by reducing fertility and productivity. FOCs are marked by a dominant follicle that fails to ovulate, disrupting the estrous cycle and reproductive efficiency. Previous studies indicate that the follicular fluid (FF) in cystic ovaries shows oxidative imbalance, affecting oocyte quality by altering glutathione peroxidase (GPX1) and selenium pathways. However, the metabolic profile of FF in cystic ovaries needs further exploration. This study examined oxidative stress and metabolic changes in FOC pathogenesis. Using untargeted metabolomics of goat FF, we found significant differences in 12,741 metabolites between cystic and control FF. Cystic FF had reduced levels of α-tocopherol and 8'-apocaroten-8'-ol, key for oxidative stress management, and increased levels of mycotoxins (e.g., Deoxynivalenol-3-glucoside) and long-chain fatty acids. Adding 200 μM α-tocopherol to FOC FF oocyte cultures doubled oocyte maturation rates and decreased reactive oxygen species (ROS). Metabolomic analysis linked low α-tocopherol to high lipid peroxyl radicals and low glutathione oxidation, emphasizing oxidative stress regulation's importance in the follicular microenvironment. Our findings suggest that α-tocopherol may serve as a biomarker and therapeutic agent to enhance oocyte maturation in FOCs.

Metabolic Fingerprint of Dual Body Fluids Deciphers Diabetic Retinopathy

Diabetic retinopathy (DR) is a microvascular complication of diabetes, affecting 34.6% of diabetes patients worldwide. Early detection and timely treatment can effectively improve the prognosis of DR. Metabolomic analysis provides a powerful tool for studying pathophysiological processes. Conducting metabolomic analyses on DR-related biofluids helps identify differential metabolic expressions during disease progression, thereby discovering potential biomarkers to support clinical diagnosis and treatment. Here, an innovative workflow for vitreous liquid analysis is established, and a machine learning-based DR analysis platform integrating vitreous liquid metabolic fingerprint (VL-MF) and plasma metabolic fingerprint (P-MF) derived via nanoparticle enhanced laser desorption/ionization mass spectrometry is developed. Direct VL-MF and P-MF are obtained with desirable reproducibility (coefficient of variation, CV <5%) and remarkable speed (3 s per sample), and DR patients are distinguished from healthy controls applying dual biofluid-MF with an area under the curve (AUC) of 0.957. Moreover, a biomarker candidate panel from vitreous liquid and plasma with an AUC of 0.945 is constructed and the related metabolic pathways are identified by metabolomics pathway analysis (MetPA). This work offers a powerful multi-biofluid platform that can not only contribute to DR but also provide solid references for other clinical applications.

Investigation of the Molecular Mechanism of Asthma in Meishan Pigs Using Multi-Omics Analysis

Asthma has been extensively studied in humans and animals, but the molecular mechanisms underlying asthma in Meishan pigs, a breed with distinct genetic and physiological characteristics, remain elusive. Understanding these mechanisms could provide insights into veterinary medicine and human asthma research. We investigated asthma pathogenesis in Meishan pigs through transcriptomic and metabolomic analyses of blood samples taken during autumn and winter. Asthma in Meishan pigs is related to inflammation, mitochondrial oxidative phosphorylation, and tricarboxylic acid (TCA) cycle disorders. Related genes include CXCL10, CCL8, CCL22, CCL21, OLR1, and ACKR1, while metabolites include succinic acid, riboflavin-5-phosphate, and fumaric acid. Transcriptomic sequencing was performed on panting and normal Meishan pigs, and differentially expressed genes underwent functional enrichment screening. Metabolomic analysis revealed differential metabolites and pathways between groups. Combined analyses indicated that lung inflammation is influenced by genetic, allergenic, and environmental factors disrupting oxidative phosphorylation in lung mitochondria, affecting the TCA cycle. Mitochondrial reactive oxygen species, glutathione S-transferases, arginase 1 and RORC in immune regulation, the Notch pathway, YPEL4 in cell proliferation, and MARCKS in airway mucus secretion play roles in asthma pathogenesis. This study highlights that many cytokines and signaling pathways contribute to asthma. Further studies are needed to elucidate their complex interactions.

Optimization of metabolomics pretreatment method of cholangiocarcinoma cells based on ultrahigh performance liquid chromatography coupled with mass spectrometry

Metabolomics intends to maximize the quantity of available metabolites for the global metabolome, which largely depends on sample pretreatment protocols. However, there are few studies that comprehensively examined the effects of extraction and reconstitution solvents on metabolome coverage of adherent mammalian cells. In this study, the human cholangiocarcinoma TFK-1 cells were chosen as a cell model, and eight extraction solvents and five reconstitution solvents were used for the pretreatment based on ultrahigh performance liquid chromatography coupled with mass spectrometry (UPLC/MS). The coverage, reproducibility, and stability of the data were norms to evaluate the effectiveness of different extraction solvents and reconstitution solvents. Based on the number of metabolites, the mean Euclidean distance (EDMEAN) in the principal component analysis (PCA) 3D score plots and the relative standard deviation (RSD) distribution of metabolites, it was demonstrated that MeOH-CHCl3-H2O (8:1:1, v/v/v) was the optimal extraction solvent and MeOH-H2O (1:1, v/v) or H2O was superior to other reconstitution solvents for RP column analysis, and the extraction solvent MeOH-ACN-H2O (2:2:1, v/v/v) and the reconstitution solvents ACN-H2O (4:1, v/v) or MeOH-H2O (1:1, v/v) provide the best performance for HILIC column analysis. The optimized pretreatment methods explored in this study expand the coverage of polar and non-polar metabolites and improve the reproducibility and stability of the metabolic data, which can be applied to UPLC/MS-based global metabolomics study on cholangiocarcinoma cells, potentially providing better extraction solvents and reconstitution solvents for other adherent mammalian cells with similar chemical and physical properties.

Polarity Gradient Solvent Confinement Membrane Cartridge to Broaden Metabolite Coverage of Plasma Untargeted Analysis

Various polarity chemicals exist in complex samples, such as plasma; nontargeted comprehensive analysis naturally requires multiple polar-extracted solvents; consequently, the polarity of the solvent plays a crucial role in the extraction efficiency of analytes from complex samples. In the present study, based on the diffusion behavior and nanoconfinement effect of solvents in the nanoconfined space, the polarity gradient solvent confinement liquid-phase nanoextraction (PGSC-NLPNE) protocol aimed to perform a one-step nontargeted analysis of a wide range of metabolites in plasma was established. The continuously wide range of extracted solvent polarities on carbon nanofibers/carbon fiber (CNFs/CF) membranes was achieved using a mixture of hexane, dichloromethane, methanol, and water as nanoconfined solvents. The polarities (Log P) of gradient solvents ranged from -1.38 to 3.94. Correlational analyses indicated that metabolites with Log P values ranging from -1.90 to 3.84 were closely related according to similarity-intermiscibility theory. Coupled with a homemade modified guard column device, CNFs/CF membrane cartridge (CCMC), a PGSC-NLPNE-UHPLC-MS online protocol was established and applied in plasma untargeted analysis. By comparing metabolome coverage, reproducibility, and extraction recovery with protein precipitation and two-step liquid-liquid extraction commonly used in untargeted analysis, the PGSC-NLPNE-CCMC protocol demonstrated higher reproducibility and recovery. This protocol has shown great potential for ultrafast analysis of plasma untargeted metabolomics with broader metabolome coverage. It could be a potential tool to rapidly screen out valuable biomarkers related to diseases in the clinic.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Physicochemical, microbiological and metabolomics changes in yogurt supplemented with lactosucrose

Lactosucrose (LS) is a known prebiotic that has gained recognition for its low caloric content and various health benefits. However, its potential in food applications remains largely unexplored. In this study the effects of adding LS to milk at concentrations (0 %, 2 %, 5 % and 8 % w/v) for yogurt production, and the relevant changes in yogurt texture, microbial composition and metabolomics were investigated. Our findings revealed that LS played a role in promoting the formation of a structured gel during fermentation, resulting in increased elasticity and viscosity while reducing fluidity. Additionally incorporating high doses of LS into yogurt led to reduced post-acidification, enhanced survival of starter bacteria, improved water retention capacity and overall texture throughout a refrigerated storage period of 21 days. Notably higher concentrations of LS (8 % w/v) exhibited effects on enhancing yogurt quality. Furthermore, untargeted metabolomics analysis using UPLC Q TOF MS/MS revealed 45 differentially expressed metabolites, including up-regulated L-arginine, L-proline and L-glutamic acid along with the down-regulated glutathione, L-tyrosine, L-phenylalanyl and L-proline. These differential metabolites were primarily associated with amino acid metabolism such as thiamine metabolism, nicotinic acid salt and nicotinamide metabolism, and pyrimidine metabolism. As a result, the inclusion of LS in yogurt had an impact on the production of various beneficial metabolites in yogurt, highlighting the importance of combining prebiotic LS with probiotics to obtain desired physiological benefits of yogurt.

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

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

Exploring the mechanism of anti-chronic heart failure effect of qiweiqiangxin І granules based on metabolomics

Background: Qiweiqiangxin І granules (QWQX І) is a traditional Chinese medicine preparation based on the basic theory of traditional Chinese medicine, which produces a good curative effect in treating chronic heart failure (CHF). However, its pharmacological effect and potential mechanism for CHF remain unknown. Aim of the study: The purpose of this study is to clarify the efficacy of QWQX І and its possible mechanisms. Materials and methods: A total of 66 patients with CHF were recruited and randomly assigned to the control or QWQX І groups. The primary endpoint was the effect of left ventricular ejection fraction (LVEF) after 4 weeks of treatment. The LAD artery of rats was occluded to establish the model of CHF. Echocardiography, HE and Masson staining were performed to evaluate the pharmacological effect of QWQX І against CHF. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) untargeted metabolomics was to screen endogenous metabolites in rat plasma and heart and elucidate the mechanism of QWQX І against CHF. Results: In the clinical study, a total of 63 heart failure patients completed the 4-week follow-up, including 32 in the control group and 31 in QWQX І group. After 4 weeks of treatment, LVEF was significantly improved in QWQX І group compared with the control group. In addition, the patients in QWQX І group had better quality of life than the control group. In animal studies, QWQX І significantly improved cardiac function, decreased B-type natriuretic peptide (BNP) levels, reduced inflammatory cell infiltration, and inhibited collagen fibril rate. Untargeted metabolomic analysis revealed that 23 and 34 differential metabolites were screened in the plasma and heart of chronic heart failure rats, respectively. 17 and 32 differential metabolites appeared in plasma and heart tissue after QWQX І treatment, which were enriched to taurine and hypotaurine metabolism, glycerophospholipid metabolism and linolenic acid metabolism by KEGG analysis. LysoPC (16:1 (9Z)) is a common differential metabolite in plasma and heart, which is produced by lipoprotein-associated phospholipase A2 (Lp-PLA2), hydrolyzes oxidized linoleic acid to produce pro-inflammatory substances. QWQX І regulates the level of LysoPC (16:1 (9Z)) and Lp-PLA2 to normal. Conclusion: QWQX І combined with western medicine can improve the cardiac function of patients with CHF. QWQX І can effectively improve the cardiac function of LAD-induced CHF rats through regulating glycerophospholipid metabolism and linolenic acid metabolism-mediated inflammatory response. Thus, QWQX I might provide a potential strategy for CHF therapy.

A “One‐Stop Shop” Decision Tree for Diagnosing and Phenotyping Polycystic Ovarian Syndrome on Serum Metabolic Fingerprints

Polycystic ovary syndrome (PCOS) is a common endocrine disease regulated by metabolic disorders, the effective intervention of which depends on diverse phenotypes (e.g., insulin resistance). Serum metabolic fingerprint (SMF) holds promise in characterizing the pathogenesis stress related to diseases; yet, PCOS diagnosis and phenotyping are time‐consuming and challenging due to the lack of an integrated metabolic tool. Here, a nanoparticle‐enhanced laser desorption/ionization mass spectrometry platform is introduced for one‐time serum metabolic fingerprinting and to identify the metabolic heterogeneity associated with obesity in PCOS patients. A decision tree based on the acquired SMFs is constructed, and real‐world simulations on independent internal and external cohorts are performed. The decision tree yields the area under the receiver operating characteristic curves (AUC) of 0.967 for PCOS diagnosis and AUC of 0.898 for phenotyping, respectively. The technical robustness of the “one‐stop shop” decision tree across laboratories is validated for clinical utility. The decision tree aims to improve PCOS management in comparison to clinical assessment, leading to a potential reduction in multiple blood tests and physician workload.

A multi-platform metabolomics reveals possible biomarkers for the early-stage esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent types of upper gastrointestinal malignancies. This work aimed to identify potential biomarkers for early screening for ESCC and characterize the systemic metabolic disturbances underlying ESCC using multi-platform metabolomics analysis.

METHODS

We divided 239 patients (the early-stage ESCC patients, n = 132; Healthy controls, n = 107) into discovery and validation sets after matching age and sex. Integrated statistical and multi-platform serum metabolomics analyses were used to screen and validate significant metabolites linked to ESCC patients.

RESULTS

Multi-platform metabolomics analyses showed that amino acid and lipid metabolism were crucial in the etiology of ESCC. Five metabolites, tryptophan (Trp), citrulline, l-carnitine, lysine, and acetyl-carnitine, were selected as potential biomarkers to establish a diagnosis panel, which showed high accuracy in distinguishing ESCC patients from healthy controls (area under the receiver operating characteristic curve, 0.873, 95% confidence interval [CI]: 0.825-0.925).

CONCLUSIONS

This work laid the groundwork for understanding the etiology of ESCC. The diagnostic panel showed potential usefulness in early-stage ESCC diagnosis in clinical practice.

Two untargeted metabolomics reveals yogurt-associated metabolic alterations in women with multiple metabolic disorders from a randomized controlled study

The beneficial role of yogurt on metabolic profile has been widely reported. Yet, few studies have intended to describe the integrated metabolic alterations in response to yogurt. Yogurt and milk (220 g/d) were given to 48 and 44 obese women with metabolic syndrome and nonalcoholic fatty liver disease for 24 weeks in a randomized controlled trial (registered at http://www.chictr.org.cn as ChiCTR-IPR-15006801). Fasting serum samples were collected before and after intervention for global, untargeted metabolomics based on ¹H nuclear magnetic resonance (NMR) and ultra-high-performance liquid chromatography coupled with electrospray ionization time-of-flight mass spectrometry (UPLC-Q-TOF-MS) (in positive and negative ion modes). Multivariable statistical analysis and pathway analysis were conducted. In both ¹H NMR and UPLC-Q-TOF-MS metabolomics, no clustering was observed between the two groups at baseline. While, a clear clustering was shown after intervention, and the yogurt group had significantly different metabolic status from the milk. The metabolites that contributed mostly to class separation were identified, and involved into pathway analysis. Pathways on amino acids metabolism, fatty acid oxidation, cholesterol catabolism and choline metabolism significantly changed after yogurt intervention. The study revealed the integrated metabolic alterations in response to yogurt via two metabolomics approaches, suggesting the potential mechanisms of yogurt against metabolic disorders. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR-IPR-15006801. Registered 20 July 2015, http://www.chictr.org.cn/ ChiCTR-IPR-15006801. SIGNIFICANCE: Both review from prospective studies and our randomized clinical trial showed the protective role of yogurt against multiple metabolic disorders. However, they were focus on targeted glucose, lipid, and other metabolic indicators, which were only part of human metabolism, failing to show an integrated metabolic feature on yogurt. Therefore, two global, untargeted metabolomics were applied in our current randomized clinical trial, trying to uncover the significant metabolic alterations characterizing the effects of yogurt on obese women with multiple metabolic disorders, and to explore the potential biological mechanisms of yogurt. The finding will shed light on a more comprehensive picture of how yogurt affects host metabolism, and provide theoretical foundation for dietary prevention of chronic diseases.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

Mass-Spectrometry-Based Functional Proteomic and Phosphoproteomic Technologies and Their Application for Analyzing Ex Vivo and In Vitro Models of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disease thought to be principally caused by mutations in sarcomeric proteins. Despite extensive genetic analysis, there are no comprehensive molecular frameworks for how single mutations in contractile proteins result in the diverse assortment of cellular, phenotypic, and pathobiological cascades seen in HCM. Molecular profiling and system biology approaches are powerful tools for elucidating, quantifying, and interpreting dynamic signaling pathways and differential macromolecule expression profiles for a wide range of sample types, including cardiomyopathy. Cutting-edge approaches combine high-performance analytical instrumentation (e.g., mass spectrometry) with computational methods (e.g., bioinformatics) to study the comparative activity of biochemical pathways based on relative abundances of functionally linked proteins of interest. Cardiac research is poised to benefit enormously from the application of this toolkit to cardiac tissue models, which recapitulate key aspects of pathogenesis. In this review, we evaluate state-of-the-art mass-spectrometry-based proteomic and phosphoproteomic technologies and their application to in vitro and ex vivo models of HCM for global mapping of macromolecular alterations driving disease progression, emphasizing their potential for defining the components of basic biological systems, the fundamental mechanistic basis of HCM pathogenesis, and treating the ensuing varied clinical outcomes seen among affected patient cohorts.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Assessment of Biphasic Extraction Methods of Mouse Fecal Metabolites for Liquid Chromatography-Mass Spectrometry-Based Metabolomic Studies

With the increasing knowledge about the important roles of gut microbiota on the biological system, a systematic strategy to profile the fecal metabolome is urgently needed. Thus, an unbiased, efficient, and reproducible fecal metabolite extraction protocol needs to be established; however, the effect of biphasic extraction methods for the fecal samples remains unclear. In this study, five different methods were assessed in the extraction of polar and non-polar metabolites for the liquid chromatography-mass spectrometry (LC-MS)-based mouse fecal metabolomic study. First, the detection coverage of two extraction systems, the Bligh and Dyer extraction method (M1, chloroform/methanol/water, 2/2/1.8) and Matyash method (M2, methyl tert-butyl ether (MTBE)/methanol/water, 10/3/2.5), was compared; then, MTBE/methanol/water system with different solvent ratios (M3, 2.6/2.0/2.4; M4, 4.5/1/2.5; and M5, 3/2.5/2.5) were further evaluated. The results showed that M2 showed higher detection coverage than M1. For the MTBE/methanol/water system with different solvent ratios, M3 showed the largest detection coverage based on peak numbers and numbers of putatively annotated metabolites, while M4 presented the least overlap between two phases, higher peak intensities of metabolites, and superior reproducibility. Based on the above evidence, M4 was recommended for the biphasic extraction of fecal metabolites in the LC-MS-based mouse fecal metabolomic study.

Evaluation of liver tissue extraction protocol for untargeted metabolomics analysis by ultra-high-performance liquid chromatography/tandem mass spectrometry

The aim of the untargeted metabolomics study is to obtain a global metabolome coverage from biological samples. Therefore, a comprehensive and systematic protocol for tissue metabolite extraction is highly desirable. In this study, we evaluated a comprehensive liver pretreatment strategy based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry to obtain more metabolites using four different protocols. These protocols included (A) methanol protein precipitation, (B) two-step extraction of dichloromethane-methanol followed by methanol-water, (C) two-step extraction of methyl tert-butyl ether-methanol followed by methanol-water, and (D) two-step extraction of isopropanol-methanol followed by methanol-water. Our results showed that protocol D was superior to the others due to more extracted features, annotated metabolites, and better reproducibility. And then, the stability and extraction sequence of protocol D were evaluated. The results showed that extraction with isopropanol-methanol followed by methanol-water was the optimum preparation sequence, which offered higher extraction efficiency, satisfactory repeatability, and acceptable stability. Furthermore, the optimal protocol was successfully applied by liver samples of rats after high-fat intervention. In summary, our protocol enabled a comprehensive and systematic evaluation of liver pretreatment to obtain more medium-polar and nonpolar metabolites and was suitable for high-throughput metabolomics analysis.

Towards Standards for Human Fecal Sample Preparation in Targeted and Untargeted LC-HRMS Studies

Gut microbiota and their metabolic products are increasingly being recognized as important modulators of human health. The fecal metabolome provides a functional readout of the interactions between human metabolism and the gut microbiota in health and disease. Due to the high complexity of the fecal matrix, sample preparation often introduces technical variation, which must be minimized to accurately detect and quantify gut bacterial metabolites. Here, we tested six different representative extraction methods (single-phase and liquid–liquid extractions) and compared differences due to fecal amount, extraction solvent type and solvent pH. Our results indicate that a minimum fecal (wet) amount of 0.50 g is needed to accurately represent the complex texture of feces. The MTBE method (MTBE/methanol/water, 3.6/2.8/3.5, v/v/v) outperformed the other extraction methods, reflected by the highest extraction efficiency for 11 different classes of compounds, the highest number of extracted features (97% of the total identified features in different extracts), repeatability (CV < 35%) and extraction recovery (≥70%). Importantly, optimization of the solvent volume of each step to the initial dried fecal material (µL/mg feces) offers a major step towards standardization, which enables confident assessment of the contributions of gut bacterial metabolites to human health.

Metabolomics analysis of serum in pediatric nephrotic syndrome based on targeted and non-targeted platforms

BACKGROUND AND AIMS

Nephrotic syndrome (NS) is a common pediatric urinary system disease. The aim in this work was to investigate the changes in pediatric NS-related metabolites through serum metabolomics, and explore the new potential metabolites and differential metabolic pathways.

METHODS

Serum samples from 40 pediatric patients with nephrotic syndrome and 40 healthy controls were collected. The targeted and non-targeted metabolomics analyses were performed to determine the metabolic changes in pediatric NS. Based on multivariate statistical analysis and the regression model, the serum potential metabolites were screened and different metabolic pathways were explored.

RESULTS

39 differential metabolites in pediatric NS were obtained based on the metabolomics analysis. 12 differential metabolites (serine, C18: 2 (EFA), C18: 2 (FFA), Isonuatigenin 3- [rhamnosyl- (1- > 2) -glucoside], C18: 4 (EFA), C18: 4 (FFA), caprylic acid, citric acid, methylmalonic acid, caproic acid, canavalioside and uroporphyrin were identified to establish the diagnostic model for pediatric NS. Five metabolic pathways including TCA cycle, amino acid metabolism, bile acid biosynthesis, linoleate metabolism and glyoxylate and dicarboxylate metabolism were the key differential metabolic pathways.

CONCLUSION

These data elucidated the metabolic alterations associated with pediatric NS and suggested a new diagnosis model for monitoring pediatric NS. The current study provides the useful information to bridge the gaps in our understanding of the metabolic alterations associated with pediatric NS and might facilitate the characterization of pediatric NS patients by performing serum metabolomics.

Optimization of pretreatment methods for cerebrospinal fluid metabolomics based on ultrahigh performance liquid chromatography/mass spectrometry

Sample pretreatment of cerebrospinal fluid (CSF) in metabolomics plays an important role in metabolic profiling study, especially for samples related to central nervous system diseases. However, there is few study about optimization of CSF metabolomics pretreatment. Therefore, it is an urgent need to optimize CSF pretreatment in order to promote the extraction efficiency of metabolites. In this study, CSF samples were separately subjected to nine different protein precipitation solvents and five different reconstitution solvents to establish the most effective pretreatment method before hydrophilic interaction (HILIC) and reverse-phase (RP) ultrahigh performance liquid chromatography mass spectrometry (UPLC/MS) analysis. The optimal conditions for different sample pretreatment methods were analyzed based on coverage (number of detected potential metabolites), stability (the relative standard deviation (RSD) distribution of metabolites) and the reproducibility of the data. Our results suggested that using EtOH or MeOH-EtOH-ACN (1:1:1, v/v/v) as the protein precipitation solvents and H₂O-MeOH-ACN (2:1:1, v/v/v) as the reconstitution solvent were optimal methods for T3 column analysis. For HILIC column analysis, using EtOH to precipitate protein and H₂O-MeOH-ACN (2:1:1, v/v/v) to reconstitute or MeOH to precipitate and 5 %ACN to reconstitute performed best. This developed UPLC/MS pretreatment method could provide better protein precipitation solvents and reconstitution solvents for global CSF metabolic analysis, potentially facilitating the comprehensive understanding of many central nervous system diseases.

Metabolomics strategy assisted by transcriptomics analysis to identify biomarkers associated with schizophrenia

BACKGROUND

Metabolomics strategy was perform to identify the novel serum biomarkers linked to schizophrenia with the assistance of transcriptomics analysis.

METHODS

Two analytical platforms, UPLC-Q-TOF MS/MS and 1H NMR, were used to acquire the serum fingerprinting profiles from a total of 112 participants (57 healthy controls and 55 schizophrenia patients). The differential metabolites were primarily selected after statistical analyses. Meanwhile, GSE17612 dataset downloaded from GEO database was implemented WGCNA analysis to discover crucial genes and corresponding biological processes. Based on metabolomics analysis, the metabolic distinctions were explored under the aid of transcriptomics. Then using Boruta algorithm identified the biomarkers, and LASSO regression analysis and Random Forest algorithm were used to evaluate the performance of the diagnostic model constructed by biomarkers selected.

RESULTS

A total of four metabolites (α-CEHC, neuraminic acid, glyceraldehyde and asparagine) were selected as the biomarkers to establish diagnosis model. The performance of this model showed a higher accuracy rate to distinguish schizophrenia patients from healthy controls (area under the receive operating characteristic curve, 0.992; precision recall curve, 1.000, the mean accuracy of random forest algorithm, 95.00%).

CONCLUSIONS

A four-biomarker model (α-CEHC, neuraminic acid, glyceraldehyde and asparagine) seems to be a good model for diagnosing schizophrenia patients. It might be helpful to guide the future studies on permitting early intervention designed to prevent disease progression.

Simultaneous Measurement of Tricarboxylic Acid Cycle Intermediates in Different Biological Matrices Using Liquid Chromatography-Tandem Mass Spectrometry; Quantitation and Comparison of TCA Cycle Intermediates in Human Serum, Plasma, Kasumi-1 Cell and Murine Liver Tissue

The tricarboxylic acid (TCA) cycle is a central part of carbon and energy metabolism, also connecting to glycolysis, amino acid, and lipid metabolism. The quantitation of the TCA cycle intermediate within one method is lucrative due to the interest in central carbon metabolism profiling in cells and tissues. In addition, TCA cycle intermediates in serum have been discovered to correspond as biomarkers to various underlying pathological conditions. In this work, an Liquid Chromatography-Mass Spectrometry/Mass Spectrometry-based quantification method is developed and validated, which takes advantage of fast, specific, sensitive, and cost-efficient precipitation extraction. Chromatographic separation is achieved while using Atlantis dC18 2.1 mm × 100 mm, particle size 3-μm of Waters column with a gradient elution mobile phase while using formic acid in water (0.1% v/v) and acetonitrile. Linearity was clearly seen over a calibration range of: 6.25 to 6400 ng/mL (r² > 0.980) for malic acid; 11.72 to 12,000 ng/mL (r² > 0.980) for cis-aconitic acid and L-aspartic acid; 29.30 to 30,000 ng/mL (r² > 0.980) for isocitric acid, l-serine, and l-glutamic acid; 122.07 to 125,000 ng/mL (r² > 0.980) for citric acid, glycine, oxo-glutaric acid, l-alanine, and l-glutamine; 527.34 to 540,000 ng/mL (r² > 0.980) for l-lactic acid; 976.56 to 1,000,000 ng/mL (r² > 0.980) for d-glucose; 23.44 to 24,000 ng/mL (r² > 0.980) for fumaric acid and succinic acid; and, 244.14 to 250,000 ng/mL (r² > 0.980) for pyruvic acid. Validation was carried out, as per European Medicines Agency (EMA) “guidelines on bioanalytical method validation”, for linearity, precision, accuracy, limit of detection (LOD), limit of quantification (LLOQ), recovery, matrix effect, and stability. The recoveries from serum and tissue were 79–119% and 77–223%, respectively. Using this method, we measured TCA intermediates in serum, plasma (NIST 1950 SRM), and in mouse liver samples. The concentration found in NIST SRM 1950 (n = 6) of glycine (246.4 µmol/L), l-alanine (302.4 µmol/L), and serine (92.9 µmol/L).

NMR Spectroscopy-Based Metabolic Profiling of Biospecimens

Metabolomics refers to study of metabolites in biospecimens such as blood serum, tissues, and urine. Nuclear magnetic resonance (NMR) spectroscopy and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS; mass spectrometry coupled with liquid chromatography) are most frequently employed to analyze complex biological/clinical samples. NMR is a relatively insensitive tool compared to UPLC-MS/MS but offers straightforward quantification and identification and easy sample processing. One-dimensional ¹ H NMR spectroscopy is inherently quantitative and can be readily used for metabolite quantification without individual metabolite standards. Two-dimensional spectroscopy is most commonly used for identification of metabolites but can also be used quantitatively. Although NMR experiments are unbiased regarding the chemical nature of the analyte, it is crucial to adhere to the proper metabolite extraction protocol for optimum results. Selection and implementation of appropriate NMR pulse programs are also important. Finally, employment of the correct metabolite quantification strategy is crucial as well. In this unit, step-by-step guidance for running an NMR metabolomics experiment from typical biospecimens is presented. The unit describes an optimized metabolite extraction protocol, followed by implementation of NMR experiments and quantification strategies using the so-called "targeted profiling" technique. This approach relies on an underlying basis set of metabolite spectra acquired under similar conditions. Some strategies for statistical analysis of the data are also presented. Overall, this set of protocols should serve as a guide for anyone who wishes to enter the world of NMR-based metabolomics analysis. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Metabolite extraction from different biospecimens Basic Protocol 2: Preparation of dried upper fraction for NMR analysis Alternate Protocol: Preparation of urine samples for NMR analysis Basic Protocol 3: NMR experiments Basic Protocol 4: Spectral processing and quantification of metabolites Basic Protocol 5: Statistical analysis of the data.