Liquid chromatography-mass spectrometry based global metabolite profiling: a review

A machine vision tool for multi-color H2O2 sensing by MoOx nanoparticles with oxygen vacancies

Improving the ease of operation and portability of hydrogen peroxide (H2O2) detection in daily production and life holds significant application value. However, it remains a challenge to achieve rapid colorimetric detection of H2O2 and color change quantification. In this study, we achieved rapid and visual detection of H2O2 by MoOx (2 ≤ x ≤ 3) nanoparticles with rich oxygen vacancies using machine vision. As the concentration of H2O2 increases, the detection system exhibited a visible multi-color change from blue to green and then yellow and the absorption peak near 680 nm measured by the UV-visible spectrophotometer gradually decreased. With excellent sensitivity, a wide linear range of 0.1-600 μmol/L, concentrations as low as 0.1 μmol/L can be detected with good selectivity towards H2O2. The sensing mechanism of detecting H2O2 by the change of oxygen vacancies in MoOx was revealed through characterization methods such as XPS, EPR, and DFT. In addition, the Hue, Saturation, Value (HSV) visual analysis system based on MoOx was constructed to assist in the rapid, portable, and sensitive monitoring of H2O2 in practical application scenarios. This work offers an easy-to operate, low cost, and convenience for achieving rapid colorimetric determination of H2O2 and has broad application prospects in daily life and industrial production.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Profiling of urinary steroids aided by lithium ion adduction-based ultrahigh-performance liquid chromatography-tandem mass spectrometry

RATIONALE

As 3-OH-containing steroids are prone to dehydration by conventional electrospray ionization, reducing detection sensitivity, Li ion adduction-based ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC/MS/MS), developed to prevent dehydration and effectively detect 3-OH steroids, was applied for profiling total and free steroids in urine.

METHODS

Free urinary steroids were isolated directly from urine by solid-phase extraction (SPE) with 80% acetonitrile. The total steroids were prepared by enzymatic treatment of urine with a cocktail of sulfatase and glucronidase, protein precipitation, and separation with the above SPE. In order to detect as many steroid types as possible, UHPLC/MS/MS (Li method) with Li+ solution added after the column was used for analysis in addition to the conventional method of detecting protonated ions (H method). The 13 3-OH steroids and the remaining 16 steroids were quantified by standard curves prepared using product ion transitions derived from [M + Li]+ and MH+ , respectively.

RESULTS

Two groups of human urine, male and female urine, were analyzed. 3-OH steroids could be detected with greater sensitivity using the Li method than the conventional method. The absolute amounts of each steroid were normalized based on creatinine levels. The difference between the male and female groups are clearly attributable to sex steroids.

CONCLUSIONS

Twenty-nine total steroids and 19 free steroids were identified in a limited volume (240 mL) of urine. Of these, 13 3-OH steroids were better detected by Li+ adduction-based UHPLC/MS/MS.

Decoding the chemical language of Suillus fungi: genome mining and untargeted metabolomics uncover terpene chemical diversity

Ectomycorrhizal fungi establish mutually beneficial relationships with trees, trading nutrients for carbon. Suillus are ectomycorrhizal fungi that are critical to the health of boreal and temperate forest ecosystems. Comparative genomics has identified a high number of non-ribosomal peptide synthetase and terpene biosynthetic gene clusters (BGC) potentially involved in fungal competition and communication. However, the functionality of these BGCs is not known. This study employed co-culture techniques to activate BGC expression and then used metabolomics to investigate the diversity of metabolic products produced by three Suillus species (Suillus hirtellus EM16, Suillus decipiens EM49, and Suillus cothurnatus VC1858), core members of the pine microbiome. After 28 days of growth on solid media, liquid chromatography-tandem mass spectrometry identified a diverse range of extracellular metabolites (exometabolites) along the interaction zone between Suillus co-cultures. Prenol lipids were among the most abundant chemical classes. Out of the 62 unique terpene BGCs predicted by genome mining, 41 putative prenol lipids (includes 37 putative terpenes) were identified across the three Suillus species using metabolomics. Notably, some terpenes were significantly more abundant in co-culture conditions. For example, we identified a metabolite matching to isomers isopimaric acid, sandaracopimaric acid, and abietic acid, which can be found in pine resin and play important roles in host defense mechanisms and Suillus spore germination. This research highlights the importance of combining genomics and metabolomics to advance our understanding of the chemical diversity underpinning fungal signaling and communication.IMPORTANCEUsing a combination of genomics and metabolomics, this study's findings offer new insights into the chemical diversity of Suillus fungi, which serve a critical role in forest ecosystems.

LC-QTOF/MS-based non-targeted metabolomics to explore the toxic effects of di(2-ethylhexyl) phthalate (DEHP) on Brassica chinensis L

Di(2-ethylhexyl) phthalate (DEHP) is a widely used plasticizer known to pose health risks to humans upon exposure. Recognizing the toxic nature of DEHP, our study aimed to elucidate the response mechanisms in Brassica chinensis L. (Shanghai Qing) when subjected to varying concentrations of DEHP (2 mg kg-1, 20 mg kg-1, and 50 mg kg-1), particularly under tissue stress. The findings underscored the substantial impact of DEHP treatment on the growth of Brassica chinensis L., with increased DEHP concentration leading to a notable decrease in chlorophyll levels and alterations in the content of antioxidant enzyme activities, particularly superoxide dismutase (SOD) and peroxidase (POD). Moreover, elevated DEHP concentrations correlated with increased malondialdehyde (MDA) levels. Our analysis detected a total of 507 metabolites in Brassica chinensis L., with 331 in shoots and 176 in roots, following DEHP exposure. There was a significant difference in the number of metabolites in shoots and roots, with 79 and 64 identified, respectively (VIP > 1, p < 0.05). Metabolic pathway enrichment in Brassica chinensis L. shoots revealed significant perturbations in valine, leucine, and isoleucine biosynthesis and degradation, aminoacyl-tRNA, and glucosinolate biosynthesis. In the roots of Brassica chinensis L., varying DEHP levels exerted a substantial impact on the biosynthesis of zeatin, ubiquinone terpenoids, propane, piperidine, and pyridine alkaloids, as well as glutathione metabolic pathways. Notably, DEHP's influence was more pronounced in the roots than in the shoots, with higher DEHP concentrations affecting a greater number of metabolic pathways. This experimental study provides valuable insights into the molecular mechanisms underlying DEHP-induced stress in Brassica chinensis L., with potential implications for human health and food safety.

Comprehensive quality control of Qingjin Yiqi granule based on UHPLC-Q-Orbitrap-MS and UPLC-QQQ-MS

INTRODUCTION

Qingjin Yiqi granule (QYG) is a prescription medicine of traditional Chinese medicine which is widely used clinically for the recovery of coronavirus patients. However, there is currently limited research on the quality control of QYG.

OBJECTIVE

To evaluate the quality of QYG qualitatively and quantitatively by making full use of advanced chromatography-mass spectrometry techniques.

METHODS

Firstly, a multicomponent characterisation of QYG was performed by ultrahigh-performance liquid chromatography coupled with a Q Exactive™ hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Orbitrap-MS) system using a rapid negative/positive switching mode. Secondly, the co-condition fingerprint analysis of constituted herbal medicines of QYG was performed to unveil active ingredients as the quality markers of QYG. Thirdly, the marker compounds in 10 batches of QYG were quantified by ultrahigh-performance liquid chromatography coupled with a Waters Xevo TQ-S triple quadrupole mass spectrometry (UPLC-QQQ-MS) system.

RESULTS

A comprehensive method that combined the inclusion list and data-dependent acquisition (DDA) to achieve a systematic characterisation of QYG was established by UHPLC-Q-Orbitrap-MS. After analysis based on Compound Discoverer software and Global Natural Products Social (GNPS) platform, a total of 332 compounds were detected. Eleven Q-markers were determined for the quality evaluation of QYG by comparison with the fingerprint of nine constituted herbal medicines. An adjusted multiple reaction monitoring (MRM) quantification method was further established to simultaneously determine the 11 Q-markers for holistic quality evaluation of QYG.

CONCLUSION

This is the first study to report comprehensive multicomponent characterisation, identification, and quality assessment of QYG, which could be used for effective guarantee of the quality of QYG.

Decoding the chemical language of Suillus fungi: genome mining and untargeted metabolomics uncover terpene chemical diversity

Ectomycorrhizal fungi establish mutually beneficial relationships with trees, trading nutrients for carbon. Suillus are ectomycorrhizal fungi that are critical to the health of boreal and temperate forest ecosystems. Comparative genomics has identified a high number of non-ribosomal peptide synthetase and terpene biosynthetic gene clusters (BGC) potentially involved in fungal competition and communication. However, the functionality of these BGCs is not known. This study employed co-culture techniques to activate BGC expression and then used metabolomics to investigate the diversity of metabolic products produced by three Suillus species ( S. hirtellus EM16, S. decipiens EM49, and S. cothurnatus VC1858), core members of the Pine microbiome. After 28 days of growth on solid media, liquid chromatography-tandem mass spectrometry identified a diverse range of extracellular metabolites (exometabolites) along the interaction zone between Suillus co-cultures. Prenol lipids were among the most abundant chemical classes. Out of the 62 unique terpene BGCs predicted by genome mining, 116 putative terpenes were identified across the three Suillus species using metabolomics. Notably, some terpenes were significantly more abundant in co-culture conditions. For example, we identified a metabolite matching to isomers isopimaric acid, sandaracopimaric acid, and abietic acid, which can be found in pine resin and play important roles in host defense mechanisms and Suillus spore germination. This research highlights the importance of combining genomics and metabolomics to advance our understanding of the chemical diversity underpinning fungal signaling and communication.

Importance

Using a combination of genomics and metabolomics, this study's findings offer new insights into the signaling and communication of Suillus fungi, which serve a critical role in forest ecosystems.

Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis

BACKGROUND

Sepsis is a life-threatening disease with a poor prognosis, and metabolic disorders play a crucial role in its development. This study aims to identify key metabolites that may be associated with the accurate diagnosis and prognosis of sepsis.

METHODS

Septic patients and healthy individuals were enrolled to investigate metabolic changes using non-targeted liquid chromatography-high-resolution mass spectrometry metabolomics. Machine learning algorithms were subsequently employed to identify key differentially expressed metabolites (DEMs). Prognostic-related DEMs were then identified using univariate and multivariate Cox regression analyses. The septic rat model was established to verify the effect of phenylalanine metabolism-related gene MAOA on survival and mean arterial pressure after sepsis.

RESULTS

A total of 532 DEMs were identified between healthy control and septic patients using metabolomics. The main pathways affected by these DEMs were amino acid biosynthesis, phenylalanine metabolism, tyrosine metabolism, glycine, serine and threonine metabolism, and arginine and proline metabolism. To identify sepsis diagnosis-related biomarkers, support vector machine (SVM) and random forest (RF) algorithms were employed, leading to the identification of four biomarkers. Additionally, analysis of transcriptome data from sepsis patients in the GEO database revealed a significant up-regulation of the phenylalanine metabolism-related gene MAOA in sepsis. Further investigation showed that inhibition of MAOA using the inhibitor RS-8359 reduced phenylalanine levels and improved mean arterial pressure and survival rate in septic rats. Finally, using univariate and multivariate cox regression analysis, six DEMs were identified as prognostic markers for sepsis.

CONCLUSIONS

This study employed metabolomics and machine learning algorithms to identify differential metabolites that are associated with the diagnosis and prognosis of sepsis patients. Unraveling the relationship between metabolic characteristics and sepsis provides new insights into the underlying biological mechanisms, which could potentially assist in the diagnosis and treatment of sepsis.

TRIAL REGISTRATION

This human study was approved by the Ethics Committee of the Research Institute of Surgery (2021-179) and was registered by the Chinese Clinical Trial Registry (Date: 09/12/2021, ChiCTR2200055772).

A Study of Gentianae Radix et Rhizoma Class Differences Based on Chemical Composition and Core Efficacy

(1) Background: Establishment of a method for evaluating Gentianae Radix et Rhizoma (GRR) classes based on chemical composition and core efficacy; (2) Methods: Liquid chromatography-mass spectrometry (LC-MS) was used to determine the chemical constituents of GRR-first class (GF) and GRR-second class (GS). The cell viability, liver function, oxidative stress enzyme activity, and inflammatory factor levels of GF and GS on H2O2-induced HepG2 cells were determined with CCK-8, ELISA, and biochemical methods, and the antioxidant activity of the two was evaluated using bioefficacy; ELISA, biochemical methods, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) method, and Western blot (WB) were used to determine the liver function, oxidative stress enzyme activity, inflammatory factor levels, and expression of related genes and proteins in mice with acute liver injury (ALI) model induced with 0.3% CCl4 olive oil solution after gavage administration; (3) Results: GF and GS had the same types of components, but the cyclic enol ether terpenes such as morinlon goside c, loganin, gentiopicroside, and swertiamarin differed significantly between the two; the effect of GF on CCl4-induced acute hepatic injury in C57BL/6 mice was stronger compared to GS. It helped alleviate weight loss, increase hepatic and splenic indices, improve hepatic lobular structure and hepatocyte status, inhibit collagen deposition, enhance oxidative stress and anti-inflammatory-related genes and protein expression, and decrease apoptotic genes and proteins more significantly than GS; (4) Conclusions: In this study, we established a GRR class evaluation method combining chemical composition and core medicinal effects, which can rapidly determine the differential composition of GF and GS, detect the quality of GRR through antioxidant bioefficacy, and validate it with in vivo experiments, which provides references for the evaluation of the class of GRR and the rational use of medication in the clinic.

Research Progress of SERS Sensors Based on Hydrogen Peroxide and Related Substances

Hydrogen peroxide (H2O2) has an important role in living organisms, and its detection is of great importance in medical, chemical, and food safety applications. This review provides a comparison of different types of Surface-enhanced Raman scattering (SERS) sensors for H2O2 and related substances with respect to their detection limits, which are of interest due to high sensitivity compared to conventional sensors. According to the latest research report, this review focuses on the sensing mechanism of different sensors and summarizes the linear range, detection limits, and cellular applications of new SERS sensors, and discusses the limitations in vivo and future prospects of SERS technology for the detection of H2O2.

Mass spectrometry-based approaches to assess the botanical authenticity of dietary supplements

Dietary supplements are legally considered foods despite frequently including medicinal plants as ingredients. Currently, the consumption of herbal dietary supplements, also known as plant food supplements (PFS), is increasing worldwide and some raw botanicals, highly demanded due to their popularity, extensive use, and/or well-established pharmacological effects, have been attaining high prices in the international markets. Therefore, botanical adulteration for profit increase can occur along the whole PFS industry chain, from raw botanicals to plant extracts, until final PFS. Besides the substitution of high-value species, unintentional mislabeling can happen in morphologically similar species. Both cases represent a health risk for consumers, prompting the development of numerous works to access botanical adulterations in PFS. Among different approaches proposed for this purpose, mass spectrometry (MS)-based techniques have often been reported as the most promising, particularly when hyphenated with chromatographic techniques. Thus, this review aims at describing an overview of the developments in this field, focusing on the applications of MS-based techniques to targeted and untargeted analysis to detect botanical adulterations in plant materials, extracts, and PFS.

Metabolomics: A useful tool for ischemic stroke research

Ischemic stroke (IS) is a multifactorial and heterogeneous disease. Despite years of studies, effective strategies for the diagnosis, management and treatment of stroke are still lacking in clinical practice. Metabolomics is a growing field in systems biology. It is starting to show promise in the identification of biomarkers and in the use of pharmacometabolomics to help patients with certain disorders choose their course of treatment. The development of metabolomics has enabled further and more biological applications. Particularly, metabolomics is increasingly being used to diagnose diseases, discover new drug targets, elucidate mechanisms, and monitor therapeutic outcomes and its potential effect on precision medicine. In this review, we reviewed some recent advances in the study of metabolomics as well as how metabolomics might be used to identify novel biomarkers and understand the mechanisms of IS. Then, the use of metabolomics approaches to investigate the molecular processes and active ingredients of Chinese herbal formulations with anti-IS capabilities is summarized. We finally summarized recent developments in single cell metabolomics for exploring the metabolic profiles of single cells. Although the field is relatively young, the development of single cell metabolomics promises to provide a powerful tool for unraveling the pathogenesis of IS.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Urinary Metabolomic Analysis of Prostate Cancer by UPLC-FTMS and UPLC-Ion Trap MSn

Accumulative evidence suggests metabolic disorders correlate with prostate cancer. Metabolic profiling of urine allows the measurement of numerous metabolites simultaneously. This study set up a metabolomic platform consisting of UPLC-FTMS and UPLC-ion trap MSⁿ for urine metabolome analysis. The platform improved retention time, mass accuracy, and signal stability. Additionally, the product ion spectrum obtained from ion trap MSⁿ facilitated structure elucidation of candidate metabolites, especially when authentic standards were not available. Urine samples from six hernia patients and six BPH patients were used for the initial establishment of the analytic platform. This platform was further employed to analyze the urine samples of 27 PCa and 49 BPH patients. Choosing the upper and lower 16% of metabolites, 258 metabolite candidates were selected. Twenty-four of them with AUC values larger than 0.65 were further selected. Eighteen of the twenty-four features can be matched in METLIN and HMDB. Eleven of the eighteen features can be interpreted by MSⁿ experiments. They were used for the combination achieving the best differential power. Finally, four metabolites were combined to reach the AUC value of 0.842 (CI 95, 0.7559 to 0.9279). This study demonstrates the urinary metabolomic analysis of prostate cancer and sheds light on future research.

Anti-Hypercholesterolemia Effects of Edible Seaweed Extracts and Metabolomic Changes in Hep-G2 and Caco-2 Cell Lines

Hypercholesterolemia is a major risk for the development of cardiovascular diseases (CVDs), the main cause of mortality worldwide, and it is characterized by high levels of circulating cholesterol. The drugs currently available for hypercholesterolemia control have several side effects, so it is necessary to develop new effective and safer therapies. Seaweeds serve as sources of several bioactive compounds with claimed beneficial effects. Eisenia bicyclis (Aramé) and Porphyra tenera (Nori) are edible seaweeds that were previously recognized as rich in bioactive compounds. In the present study, we aim to evaluate the anti-hypercholesterolemia effect of these two seaweed extracts and their health potential. Both extracts, but more efficiently Aramé extract, have liver 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) inhibitory activity as well as the capability to reduce approximately 30% of cholesterol permeation through human Caco-2 cells by simulating the intestinal lining, which is a target for hypercholesterolemia treatments. An untargeted metabolomic assay on human intestinal Caco-2 and liver Hep-G2 cell lines exposed to Aramé and Nori extracts revealed changes in the cells' metabolism, indicating the extracts' health beneficial effects. The metabolic pathways affected by exposure to both extracts were associated with lipid metabolism, such as phospholipids, and fatty acid metabolism, amino acid pathways, cofactors, vitamins, and cellular respiration metabolism. The effects were more profound in Aramé-treated cells, but they were also observed in Nori-exposed cells. The metabolite modifications were associated with the protection against CVDs and other diseases and to the improvement of the cells' oxidative stress tolerance. The results obtained for the anti-hypercholesterolemia properties, in addition to the revelation of the positive impact on cell metabolism, offer an important contribution for further evaluation of these seaweed extracts as functional foods or for CVD prevention.

Clinical diagnostic value of amino acids in laryngeal squamous cell carcinomas

Background

Early diagnosis and treatment can improve the survival rates of patients with laryngeal squamous cell carcinoma (LSCC). Therefore, it is necessary to discover new biomarkers for laryngeal cancer screening and early diagnosis.

Methods

We collected fasting plasma from LSCC patients and healthy volunteers, as well as cancer and para-carcinoma tissues from LSCC patients, and performed quantitative detection of amino acid levels using liquid chromatography-mass spectrometry. We used overall analysis and multivariate statistical analysis to screen out the statistically significant differential amino acids in the plasma and tissue samples, conducted receiver operating characteristic (ROC) analysis of the differential amino acids to evaluate the sensitivity and specificity of the differential amino acids, and finally determined the diagnostic value of amino acids for laryngeal cancer. Additionally, we identified amino acids in the plasma and tissue samples that are valuable for the early diagnosis of laryngeal cancer classified according to the tumor-node-metastasis (TNM) classification.

Results

Asparagine (Asp) and homocysteine (Hcy) were two amino acids of common significance in plasma and tissue samples, and their specificity and sensitivity analysis showed that they may be new biomarkers for the diagnosis and treatment of LSCC. According to the TNM staging system, phenylalanine (Phe) and isoleucine (Ile) were screened out in the plasma of LSCC patients at early (I and II) and advanced (III and IV) stages; ornithine hydrochloride (Orn), glutamic acid (Glu), and Glycine (Gly) were selected in the tissue. These dysregulated amino acids found in LSCC patients may be useful as clinical biomarkers for the early diagnosis and screening of LSCC.

Technological advances for analyzing the content of organ-on-a-chip by mass spectrometry

Three-dimensional (3D) cell cultures, including organ-on-a-chip (OOC) devices, offer the possibility to mimic human physiology conditions better than 2D models. The organ-on-a-chip devices have a wide range of applications, including mechanical studies, functional validation, and toxicology investigations. Despite many advances in this field, the major challenge with the use of organ-on-a-chips relies on the lack of online analysis methods preventing the real-time observation of cultured cells. Mass spectrometry is a promising analytical technique for real-time analysis of cell excretes from organ-on-a-chip models. This is due to its high sensitivity, selectivity, and ability to tentatively identify a large variety of unknown compounds, ranging from metabolites, lipids, and peptides to proteins. However, the hyphenation of organ-on-a-chip with MS is largely hampered by the nature of the media used, and the presence of nonvolatile buffers. This in turn stalls the straightforward and online connection of organ-on-a-chip outlet to MS. To overcome this challenge, multiple advances have been made to pre-treat samples right after organ-on-a-chip and just before MS. In this review, we summarised these technological advances and exhaustively evaluated their benefits and shortcomings for successful hyphenation of organ-on-a-chip with MS.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.

Health outcomes of 100% orange juice and orange flavored beverage: A comparative analysis of gut microbiota and metabolomics in rats

A high intake of sugar-sweetened fruity beverage (FB) is associated with a higher risk of metabolic syndromes, but the health outcome of 100% fruit juice (FJ) intake remains unclear. We aim to reveal health outcomes of diet intervention (FJ or FB) with system profiling via interaction of gut microbiota and metabolomics in a rat (Rattus norvegicus) model. Firstly, the glucose, sucrose, fructose, and bioactive metabolites of FJ and FB were analyzed, and FJ possessed higher sucrose and flavonoids, while FB showed higher glucose and fructose. Secondly, C0 was set as the control group on Day 0, and a 4-week diet invention was performed to control, FJ-intake, and FB-intake groups with normal saline, FJ, and FB, respectively. The results showed that FJ improved alpha diversity and decreased the Firmicutes/Bacteroidota ratio (F/B ratio) of gut microbiota and prevented insulin resistance. However, FB possessed unchanged microbial diversity and enhanced F/B ratio, causing insulin resistance with renal triglyceride accumulation. In summary, FJ, although naturally containing similar amounts of total free sugars as FB, could be a healthier drink choice.

Comparison of the metabolomic and proteomic profiles associated with triterpene and phytosterol accumulation between wild and cultivated ginseng

Wild ginseng is thought to be superior in its medicinal quality to cultivated ginseng, potentially owing to the differences in active components. This study was designed accordingly to assess the differences in secondary metabolite components and their synthesis in wild and cultivated ginseng by using quantitative proteomics combined with secondary metabolomics approaches. A total of 72 secondary metabolites were found to be differentially abundant, of which dominant abundant in wild ginseng primarily included triterpenoid saponins (ginsenosides) and phytosterols. Ginsenoside diversity was increased in wild ginseng, particularly with respect to rare ginsenosides. Ginsenoside Rk1, F1, Rg5, Rh1, PPT, Rh2, and CK enriched in wild ginseng were validated by HPLC. In addition to ginsenosides, stigmasterol and β-sitosterol were accumulated in wild ginseng. 102 differentially expressed proteins between wild and cultivated ginseng were identified using iTRAQ labeling technique. Among them, 25 were related to secondary metabolism, mainly involved in sesquiterpene and triterpene biosynthesis, which was consistent with metabolomics results. Consistently, the activity levels of HMGR, FDPS, SS, SE, DS, CYP450, GT and CAS, which are key enzymes related to ginsenoside and phytosterol biosynthesis, were confirmed to be elevated in wild ginseng.The biosynthesis of ginsenosides and phytosterols in wild ginseng is higher than that in cultivated ginseng, which may be related to natural growth without artificial domestication. To some extent, this study explained the accumulation of pharmacodynamic components and overall quality of ginseng, which could provide reference for the germplasm improvement and planting of ginseng.

An exposome connectivity paradigm for the mechanistic assessment of the effects of prenatal and early life exposure to metals on neurodevelopment

The exposome paradigm through an integrated approach to investigating the impact of perinatal exposure to metals on child neurodevelopment in two cohorts carried out in Slovenia (PHIME cohort) and Greece (HERACLES cohort) respectively, is presented herein. Heavy metals are well-known neurotoxicants with well-established links to impaired neurodevelopment. The links between in utero and early-life exposure to metals, metabolic pathway dysregulation, and neurodevelopmental disorders were drawn through urinary and plasma untargeted metabolomics analysis, followed by the combined application of in silico and biostatistical methods. Heavy metal prenatal and postnatal exposure was evaluated, including parameters indirectly related to exposure and health adversities, such as sociodemographic and anthropometric parameters and dietary factors. The primary outcome of the study was that the identified perturbations related to the TCA cycle are mainly associated with impaired mitochondrial respiration, which is detrimental to cellular homeostasis and functionality; this is further potentiated by the capacity of heavy metals to induce oxidative stress. Insufficient production of energy from the mitochondria during the perinatal period is associated with developmental disorders in children. The HERACLES cohort included more detailed data regarding diet and sociodemographic status of the studied population, allowing the identification of a broader spectrum of effect modifiers, such as the beneficial role of a diet rich in antioxidants such as lycopene and ω-3 fatty acids, the negative effect the consumption of food items such as pork and chicken meat has or the multiple impacts of fish consumption. Beyond diet, several other factors have been proven influential for child neurodevelopment, such as the proximity to pollution sources (e.g., waste treatment site) and the broader living environment, including socioeconomic and demographic characteristics. Overall, our results demonstrate the utility of exposome-wide association studies (EWAS) toward understanding the relationships among the multiple factors that determine human exposure and the underlying biology, reflected as omics markers of effect on neurodevelopment during childhood.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Interactions between microbiota and cervical epithelial, immune, and mucus barrier

The female reproductive tract harbours hundreds of bacterial species and produces numerous metabolites. The uterine cervix is located between the upper and lower parts of the female genital tract. It allows sperm and birth passage and hinders the upward movement of microorganisms into a relatively sterile uterus. It is also the predicted site for sexually transmitted infection (STI), such as Chlamydia, human papilloma virus (HPV), and human immunodeficiency virus (HIV). The healthy cervicovaginal microbiota maintains cervical epithelial barrier integrity and modulates the mucosal immune system. Perturbations of the microbiota composition accompany changes in microbial metabolites that induce local inflammation, damage the cervical epithelial and immune barrier, and increase susceptibility to STI infection and relative disease progression. This review examined the intimate interactions between the cervicovaginal microbiota, relative metabolites, and the cervical epithelial-, immune-, and mucus barrier, and the potent effect of the host-microbiota interaction on specific STI infection. An improved understanding of cervicovaginal microbiota regulation on cervical microenvironment homeostasis might promote advances in diagnostic and therapeutic approaches for various STI diseases.

Comparison analysis of metabolite profiling in seeds and bark of Ulmus parvifolia, a Chinese medicine species

Ulmus parvifolia (U. parvifolia) is a Chinese medicine plant whose bark and leaves are used in the treatment of some diseases such as inflammation, diarrhea and fever. However, metabolic signatures of seeds have not been studied. The seeds and bark of U. parvifolia collected at the seed ripening stage were used for metabolite profiling analysis through the untargeted metabolomics approach. A total of 2,578 and 2,207 metabolites, while 503 and 132 unique metabolites were identified in seeds and bark, respectively. Additionally, 574 differential metabolites (DEMs) were detected in the two different organs of U. parvifolia, which were grouped into 52 classes. Most kinds of metabolites classed into prenol lipids class. The relative content of flavonoids class was the highest. DEMs contained some bioactive compounds (e.g., flavonoids, terpene glycosides, triterpenoids, sesquiterpenoids) with antioxidant, anti-inflammatory, and anti-cancer activities. Most kinds of flavonoids and sesquiterpenes were up-regulated in seeds. There were more varieties of terpene glycosides and triterpenoids showing up-regulated in bark. The pathway enrichment was performed, while flavonoid biosynthesis, flavone and flavonol biosynthesis were worthy of attention. This study identified DEMs with pharmaceutical value between seeds and bark during seed maturation and offered a molecular basis for alternative or complementary use of seeds and bark of U. parvifolia as a Chinese medicinal material.

Thin layer chromatography/desorption flame-induced atmospheric pressure chemical ionization/mass spectrometry for the analysis of volatile and semi-volatile mixtures

Flame-induced atmospheric pressure chemical ionization (FAPCI) has been used to directly characterize chemical compounds on a glass rod and drug tablet surfaces. In this study, FAPCI was further applied to interface thin layer chromatography (TLC) and mass spectrometry (MS) for mixture analysis.

METHODS

A micro-sized oxyacetylene flame was generated using a small concentric tube system. Hot gas flow and primary reactive species from the micro-flame were directed toward a developed TLC gel plate to thermally desorb and ionize analytes on the gel surface. The resulting analyte ions subsequently entered the MS inlet for detection.

RESULTS

A 1-1.5-mm-wide light-brown line was observed on the TLC plate after the desorption FAPCI/MS (DFAPCI/MS) analysis, revealing that the gel surface withstood a high temperature from the impact of the micro-flame. Volatile and semi-volatile chemical compounds, including amine and amide standards, drugs, and aromatherapy oils, were successfully desorbed, ionized, and detected using this TLC/DFAPCI/MS. The limit of detection of TLC-DFAPCI/MS was determined to be 5 ng/spot for dibenzylamine and ethenzamide.

CONCLUSIONS

TLC/DFAPCI/MS is one of the simplest TLC-MS interfaces showing the advantages such as low costs and an easy set up. The technique is useful for characterizing thermally stable volatile and semi-volatile compounds in a mixture.

Microbiome and Metabolomics in Liver Cancer: Scientific Technology

Primary liver cancer is a heterogeneous disease. Liver cancer metabolism includes both the reprogramming of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment and fluctuations in regular tissue metabolism. Currently, metabolomics and metabolite profiling in liver cirrhosis, liver cancer, and hepatocellular carcinoma (HCC) have been in the spotlight in terms of cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecules, chemicals, and metabolites. Metabolomics technologies can provide critical information about the liver cancer state. Here, we review how liver cirrhosis, liver cancer, and HCC therapies interact with metabolism at the cellular and systemic levels. An overview of liver metabolomics is provided, with a focus on currently available technologies and how they have been used in clinical and translational research. We also list scalable methods, including chemometrics, followed by pathway processing in liver cancer. We conclude that important drivers of metabolomics science and scientific technologies are novel therapeutic tools and liver cancer biomarker analysis.

Role of Oxylipins in the Inflammatory-Related Diseases NAFLD, Obesity, and Type 2 Diabetes

Oxygenated polyunsaturated fatty acids (oxylipins) are bioactive molecules established as important mediators during inflammation. Different classes of oxylipins have been found to have opposite effects, e.g., pro-inflammatory prostaglandins and anti-inflammatory resolvins. Production of the different classes of oxylipins occurs during distinct stages of development and resolution of inflammation. Chronic inflammation is involved in the progression of many pathophysiological conditions and diseases such as non-alcoholic fatty liver disease, insulin resistance, diabetes, and obesity. Determining oxylipin profiles before, during, and after inflammatory-related diseases could provide clues to the onset, development, and prevention of detrimental conditions. This review focusses on recent developments in our understanding of the role of oxylipins in inflammatory disease, and outlines novel technological advancements and approaches to study their action.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

A Prospective, Case-Control Study of Serum Metabolomics in Neonates with Late-Onset Sepsis and Necrotizing Enterocolitis

Late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) are major causes of neonatal morbidity and mortality. In this prospective, case-control study, we evaluated the metabolic profile of neonates with LOS and NEC. Blood samples were collected from 15 septic neonates and 17 neonates with NEC at the clinical suspicion of the specific diseases. Sixteen gestational and postnatal age-matched neonates without sepsis/NEC served as controls. Serum metabolic profiles were assessed using liquid chromatography–quadrupole time-of-flight mass spectrometry. Metabolomic analysis revealed significant differences in the metabolic profile of neonates with LOS or NEC compared to controls. More specifically, a number of molecules possibly identified as phosphatidylcholines or lysophosphatidylcholines were found to be significantly reduced both in neonates with LOS and those with NEC compared to controls. Additionally, L-carnitine could efficiently discriminate NEC cases from controls. The results of the current study suggest that certain phospholipids and their derivatives could possibly be used as biomarkers for the early detection of LOS and NEC.

Retrieving Spectra of Pure Components from the DOSY-NMR Experiment via a Comprehensive Approach Involving the 2D Asynchronous Spectrum, 2D Quotient Spectrum, and Genetic Algorithm Refinement

When diffusion coefficients of different components in a mixture are similar, NMR spectra of pure individual components are difficult to be obtained via a diffusion-ordered spectroscopy (DOSY) experiment. Two-dimensional correlation spectroscopy (2D-COS) is used to analyze the data from the DOSY experiment. Through the properties of the systematic absence of cross-peak (SACP) in the 2D asynchronous spectra, spectra of pure components can be obtained even if their diffusion coefficients are similar. However, fluctuations in peak-position and peak-width are often unavoidable in NMR spectra, which makes SACPs unrecognizable. To address the problem, a 2D quotient spectrum is used to identify the masked SACPs. However, undesirable interference peaks due to the fluctuations in peak-position and peak-width are still present when we extract a spectrum of a component by slicing the 2D asynchronous spectrum across the SACP. A genetic algorithm (GA) is used to select a suitable subset of spectra where the diversities of peak-position and peak-width are significantly reduced. Then, we used the selected spectra to construct a refined 2D asynchronous spectrum so that the spectra of pure components with significant attenuated interference can be obtained. The above approach has been proven to be effective on a model system and a real-world example, demonstrating that 2D-COS possesses a bright perspective in the analysis of the bilinear data from DOSY experiments.

Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data

INTRODUCTION

Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis.

OBJECTIVES

Present spectral binning as a pragmatic approach to post-acquisition procession of FIE-HRMS metabolome fingerprinting data.

METHODS

A spectral binning approach was developed that included the elimination of single scan m/z events, the binning of spectra and the averaging of spectra across the infusion profile. The modal accurate m/z was then extracted for each bin. This approach was assessed using four different biological matrices and a mix of 31 known chemical standards analysed by FIE-HRMS using an Exactive Orbitrap. Bin purity and centrality metrics were developed to objectively assess the distribution and position of accurate m/z within an individual bin respectively.

RESULTS

The optimal spectral binning width was found to be 0.01 amu. 80.8% of the extracted accurate m/z matched to predicted ionisation products of the chemical standards mix were found to have an error of below 3 ppm. The open-source R package binneR was developed as a user friendly implementation of the approach. This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB.

CONCLUSION

Spectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open-source R package binneR allows users to efficiently process data from FIE-HRMS experiments with the resources available on a standard desktop computer.

Targeted Metabolomics Using LC-MS in Neurospora crassa

The filamentous fungus Neurospora crassa has historically been a model for understanding the relationship between genes and metabolism-auxotrophic mutants of N. crassa were used by Beadle and Tatum to develop the one-gene-one-enzyme hypothesis for which they earned the Nobel Prize in 1958. In the ensuing decades, several techniques have been developed for the systematic analysis of metabolites in N. crassa and other fungi. Untargeted and targeted approaches have been used, with a focus on secondary metabolites over primary metabolism. Here, we describe a pipeline for sample preparation, metabolite extraction, Liquid Chromatography-Mass Spectrometry (LC-MS), and data analysis that can be used for targeted metabolomics of primary metabolites in N. crassa. Liquid cultures are grown with shaking in a defined minimal medium and then collected using filtration. Samples are lyophilized for 2 days at -80°C, pulverized, and mixed with a solution to extract polar metabolites. The metabolites are separated and identified using LC-MS, with downstream analysis using Skyline interpretive software. Relative levels of hundreds of metabolites can be detected and compared across strains. © 2022 Wiley Periodicals LLC. Basic Protocol: Metabolite extraction and detection from Neurospora crassa cell cultures using Liquid Chromatography-Mass Spectrometry.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Time Course Metabolite Profiling of Fusarium Head Blight-Infected Hard Red Spring Wheat Using Ultra-High-Performance Liquid Chromatography Coupled with Quadrupole Time of Flight/MS

Wheat is an important food crop, yet its value is reduced by fungal infections (ex. Fusarium graminearum). Metabolite profiling is a useful tool for explaining resistance mechanisms. By analyzing near-isogenic lines (NILs) with contrasting Fhb1 alleles and three wheat varieties, a time course resulting in 61 relevant metabolites was studied. The presence of one metabolite as resistant related constitutive late in the time course was detected. Results confirm the presence of hydroxycinnamic acid amides conjugated with polyamine derivatives (hydroxycinnamic acid amides, HCAAs), which have been shown to induce thickening of cell walls. These compounds are shared by resistant and susceptible genotypes with no difference in intensities but vary in time as early- or late-occurring, suggesting that for the NIL studied here, HCAAs were a normal part of the host reaction. Overall, metabolites synthesized as a result of infection were observed regardless of susceptibility but occurred at different times after infection.

Comparison of freshly squeezed, Non-thermally and thermally processed orange juice based on traditional quality characters, untargeted metabolomics, and volatile overview

The NOVA food classification system, divides foods into four categories, namely unprocessed and minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods. With the recently increasing pursuit of healthy diets, special attention to minimally processed foods has become crucial. According to NOVA, freshly squeezed, high pressure processing (HPP) and pasteurized orange juice are minimally processed foods. In this study, the differences in the quality and composition of these minimally processed juice are explored, as it was found that their traditional quality characteristics were too weak to illustrate their difference. However, based on untargeted metabolomics, two differential compounds were identified between freshly squeezed and HPP orange juice, in addition to 15 differential compounds between freshly squeezed and pasteurized orange juice. Moreover, all the pasteurized orange juice in this study was deemed to be out of the acceptance area of freshly squeezed and HPP orange juice in a data-driven soft independent modeling of class analogy (dd-SIMCA) model based on volatile overview. The results of this study provide data for clarifying the compositional differences between minimally processed juice for their further subclassification.

Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data

Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.

Metabolomic Studies in Inner Ear Pathologies

Sensorineural hearing loss is the most common sensory deficit. The etiologies of sensorineural hearing loss have been described and can be congenital or acquired. For congenital non-syndromic hearing loss, mutations that are related to sites of cochlear damage have been discovered (e.g., connexin proteins, mitochondrial genes, etc.). For cytomegalovirus infection or auditory neuropathies, mechanisms are also well known and well researched. Although the etiologies of sensorineural hearing loss may be evident for some patients, the damaged sites and pathological mechanisms remain unclear for patients with progressive post-lingual hearing loss. Metabolomics is an emerging technique in which all metabolites present in a sample at a given time are analyzed, reflecting a physiological state. The objective of this study was to review the literature on the use of metabolomics in hearing loss. The findings of this review suggest that metabolomic studies may help to develop objective tests for diagnosis and personalized treatment.

High-Throughput UHPLC-MS to Screen Metabolites in Feces for Gut Metabolic Health

Feces are the product of our diets and have been linked to diseases of the gut, including Chron’s disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. As sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and non-polar metabolites. Fecal samples (n = 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. MS analyses of the prepared fecal samples have shown reproducibility and coverage of n = 28 metabolites, mostly comprising bile acids and amino acids. We report metabolite-wise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD (limit of detection), LOQ (limit of quantification), range of linearity and method recovery. The average concentrations for 135 healthy participants are reported here for clinical applications. Our high-throughput method provides a novel tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

Dynamic changes of serum metabolites associated with infection and severity of patients with acute hepatitis E infection

Dynamic changes in metabolites may affect liver disease progression, and provide new methods for predicting liver damage. We used ultra-performance liquid chromatography-mass spectroscopy to assess serum metabolites in healthy controls (HC), and patients with acute hepatitis E (AHE) or hepatitis E virus acute liver failure (HEV-ALF). The principal component analysis, partial least squares discriminant analysis, and discriminant analysis of orthogonal projections to latent structures models illustrated significant differences in the metabolite components between AHE patients and HCs, or between HEV-ALF and AHE patients. In pathway enrichment analysis, we further identified two altered pathways, including linoleic acid metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis, when comparing AHE patients with HCs. Linoleic acid metabolism and porphyrin and chlorophyll metabolism pathways were significantly different in HEV-ALF when compared with AHE patients. The discriminative performances of differential metabolites showed that taurocholic acid, glycocholic acid, glycochenodeoxycholate-3-sulfate, and docosahexaenoic acid could be used to distinguish HEV-ALF from AHE patients. The serum levels of glycocholic acid, taurocholic acid, deoxycholic acid glycine conjugate, and docosahexaenoic acid were associated with the prognosis of HEV-ALF patients. Dynamic changes in serum metabolites were associated with AHE infection and severity. The identified metabolites can be used to diagnose and predict the prognosis of HEV-ALF.

Impact of Metabolomics Technologies on the Assessment of Peritoneal Membrane Profiles in Peritoneal Dialysis Patients: A Systematic Review

Peritoneal dialysis (PD) is an effective and frequent dialysis modality in adults, particularly preferred in infants and young children with end-stage renal disease (ESRD). Long-term exposure of the peritoneal membrane to dialysis solutions results in severe morphologic and functional alterations. Peritoneal dialysis effluent biomarkers are based on omics technologies, which could predict the onset or confirm the diagnosis of peritoneal membrane dysfunction, would allow the development of accurate early prognostic tools and, potentially, the identification of future therapeutic targets. The purpose of our study was to critically review the literature on the impact and the effectiveness of metabolomics technologies in peritoneal health. The main search was performed in electronic databases (PubMed/MEDLINE, Embase and Cochrane Central Register of Controlled Trials) from inception to December 2020, using various combinations of Medical Subject Headings (MeSH). The main search highlighted nine studies, of which seven were evaluated in detail. Metabolomics technologies may provide significant input in the recognition of peritoneal membrane dysfunction in PD patients and provide evidence of early intervention strategies that could protect peritoneum health and function.

Stability of Wheat Floret Metabolites during Untargeted Metabolomics Studies

A typical metabolomic analysis consists of a multi-step procedure. Variation can be introduced in any analysis segment if proper care in quality assurance is not taken, thus compromising the final results. Sample stability is one of those factors. Although sophisticated studies addressing sample decay over time have been performed in the medical field, they are emerging in plant metabolomics. Here, we focus on the stability of wheat floret extracts on queue inside an auto-injector held at 25 °C. The objective was to locate an analytical time window from extraction to injection with no significant difference occurring in the sample. Total ion current chromatograms, principal component analysis, and volcano plots were used to measure changes in the samples. Results indicate a maximum work window time of 7:45 h for Steele-ND wheat methanolic extractions in an auto-sampler at 25 °C. Comparisons showed a significant gradual increase in the number and intensity of compounds observed that may be caused by the degradation of other molecules in the sample extract. The approach can be applied as preliminary work in a metabolite profiling study, helping to set the appropriate workload to produce confident results.

Nano liquid chromatography, an updated review

Low flow chromatography has a rich history of innovation but has yet to reach widespread implementation in bioanalytical applications. Improvements in pump technology, microfluidic connections, and nano-electrospray sources for mass spectrometry have laid the groundwork for broader application, and innovation in this space has accelerated in recent years. This article reviews the instrumentation used for nano-flow liquid chromatography , the types of columns employed, and strategies for multi-dimensionality of separations, which is key to the future state of the technique to the high-throughput needs of modern bioanalysis. An update of the current applications where nano-LC is widely used, such as proteomics and metabolomics, is discussed. But the trend towards biopharmaceutical development of increasingly complex, targeted, and potent therapeutics for the safe treatment of disease drives the need for ultimate selectivity and sensitivity of our analytical platforms for targeted quantitation in a regulated space. The selectivity needs are best addressed by mass spectrometric detection, especially at high resolutions, and exquisite sensitivity is provided by nano-electrospray ionization as the technology continues to evolve into an accessible, robust, and easy to use platform.