Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

Plasma metabolomics and lipidomics reveal potential novel biomarkers in early gastric cancer: An explorative study

BACKGROUND

Early identification and therapy can significantly improve the outcome for gastric cancer. However, there is still no perfect biomarker available for the detection of early gastric cancer. This study aimed to investigate the alterations in the plasma metabolites of early gastric cancer using metabolomics and lipidomics based on high-performance liquid chromatography-mass spectrometry (HPLC-MS), which detected potential biomarkers that could be used for clinical diagnosis.

METHODS

To investigate the changes in metabolomics and lipidomics, a total of 30 plasma samples were collected, consisting of 15 patients with early gastric cancer and 15 healthy controls. Extensive HPLC-MS-based untargeted metabolomic and lipidomic investigations were conducted. Differential metabolites and metabolic pathways were uncovered through the utilization of statistical analysis and bioinformatics analysis. Candidate biomarker screening was performed using support vector machine-based multivariate receiver operating characteristic analysis.

RESULTS

A disturbance was observed in a combined total of 19 metabolites and 67 lipids of the early gastric cancer patients. The analysis of KEGG pathways showed that the early gastric cancer patients experienced disruptions in the arginine biosynthesis pathway, the pathway for alanine, aspartate, and glutamate metabolism, as well as the pathway for glyoxylate and dicarboxylate metabolism. Plasma metabolomics and lipidomics have identified multiple biomarker panels that can effectively differentiate early gastric cancer patients from healthy controls, exhibiting an area under the curve exceeding 0.9.

CONCLUSION

These metabolites and lipids could potentially serve as biomarkers for the screening of early gastric cancer, thereby optimizing the strategy for the detection of early gastric cancer. The disrupted pathways implicated in early gastric cancer provide new clues for additional understanding of gastric cancer's pathogenesis. Nonetheless, large-scale clinical data are required to prove our findings.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Integration of targeted metabolome and transcript profiling of Pseudomonas syringae-triggered changes in defence-related phytochemicals in oat plants

MAIN CONCLUSION

A gene-to-metabolite approach afforded new insights regarding defence mechanisms in oat plants that can be incorporated into plant breeding programmes for the selection of markers and genes related to disease resistance. Monitoring metabolite levels and changes therein can complement and corroborate transcriptome (mRNA) data on plant-pathogen interactions, thus revealing mechanisms involved in pathogen attack and host defence. A multi-omics approach thus adds new layers of information such as identifying metabolites with antimicrobial properties, elucidating metabolomic profiles of infected and non-infected plants, and reveals pathogenic requirements for infection and colonisation. In this study, two oat cultivars (Dunnart and SWK001) were inoculated with Pseudomonas syringae pathovars, pathogenic and non-pathogenic on oat. Following inoculation, metabolites were extracted with methanol from leaf tissues at 2, 4 and 6 days post-infection and analysed by multiple reaction monitoring (MRM) on a triple quadrupole mass spectrometer system. Relatedly, mRNA was isolated at the same time points, and the cDNA analysed by quantitative PCR (RT-qPCR) for expression levels of selected gene transcripts associated with avenanthramide (Avn) biosynthesis. The targeted amino acids, hydroxycinnamic acids and Avns were successfully quantified. Distinct cultivar-specific differences in the metabolite responses were observed in response to pathogenic and non-pathogenic strains. Trends in aromatic amino acids and hydroxycinnamic acids seem to indicate stronger activation and flux through these pathways in Dunnart as compared to SWK001. A positive correlation between hydroxycinnamoyl-CoA:hydroxyanthranilate N-hydroxycinnamoyl transferase (HHT) gene expression and the abundance of Avn A in both cultivars was documented. However, transcript profiling of selected genes involved in Avn synthesis did not reveal a clear pattern to distinguish between the tolerant and susceptible cultivars.

Photoperiod-Dependent Nutrient Accumulation in Rice Cultivated in Plant Factories: A Comparative Metabolomic Analysis

Plant factories offer a promising solution to some of the challenges facing traditional agriculture, allowing for year-round rapid production of plant-derived foods. However, the effects of conditions in plant factories on metabolic nutrients remain to be explored. In this study, we used three rice accessions (KongYu131, HuangHuaZhan, and Kam Sweet Rice) as objectives, which were planted in a plant factory with strict photoperiods that are long-day (12 h light/12 h dark) or short-day (8 h light/16 h dark). A total of 438 metabolites were detected in the harvested rice grains. The difference in photoperiod leads to a different accumulation of metabolites in rice grains. Most metabolites accumulated significantly higher levels under the short-day condition than the long-day condition. Differentially accumulated metabolites were enriched in the amino acids and vitamin B6 pathway. Asparagine, pyridoxamine, and pyridoxine are key metabolites that accumulate at higher levels in rice grains harvested from the short-day photoperiod. This study reveals the photoperiod-dependent metabolomic differences in rice cultivated in plant factories, especially the metabolic profiling of taste- and nutrition-related compounds.

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.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

PLA2G4A and ACHE modulate lipid profiles via glycerophospholipid metabolism in platinum-resistant gastric cancer

BACKGROUND

Bioactive lipids involved in the progression of various diseases. Nevertheless, there is still a lack of biomarkers and relative regulatory targets. The lipidomic analysis of the samples from platinum-resistant in gastric cancer patients is expected to help us further improve our understanding of it.

METHODS

We employed LC-MS based untargeted lipidomic analysis to search for potential candidate biomarkers for platinum resistance in GC patients. Partial least squares discriminant analysis (PLS-DA) and variable importance in projection (VIP) analysis were used to identify differential lipids. The possible molecular mechanisms and targets were obtained by metabolite set enrichment analysis and potential gene network screened. Finally, verified them by immunohistochemical of a tissue microarray.

RESULTS

There were 71 differential lipid metabolites identified in GC samples between the chemotherapy-sensitivity group and the chemotherapy resistance group. According to Foldchange (FC) value, VIP value, P values (FC > 2, VIP > 1.5, p < 0.05), a total of 15 potential biomarkers were obtained, including MGDG(43:11)-H, Cer(d18:1/24:0) + HCOO, PI(18:0/18:1)-H, PE(16:1/18:1)-H, PE(36:2) + H, PE(34:2p)-H, Cer(d18:1 + hO/24:0) + HCOO, Cer(d18:1/23:0) + HCOO, PC(34:2e) + H, SM(d34:0) + H, LPC(18:2) + HCOO, PI(18:1/22:5)-H, PG(18:1/18:1)-H, Cer(d18:1/24:0) + H and PC(35:2) + H. Furthermore, we obtained five potential key targets (PLA2G4A, PLA2G3, DGKA, ACHE, and CHKA), and a metabolite-reaction-enzyme-gene interaction network was built to reveal the biological process of how they could disorder the endogenous lipid profile of platinum resistance in GC patients through the glycerophospholipid metabolism pathway. Finally, we further identified PLA2G4A and ACHE as core targets of the process by correlation analysis and tissue microarray immunohistochemical verification.

CONCLUSION

PLA2G4A and ACHE regulated endogenous lipid profile in the platinum resistance in GC patients through the glycerophospholipid metabolism pathway. The screening of lipid biomarkers will facilitate earlier precision medicine interventions for chemotherapy-resistant gastric cancer. The development of therapies targeting PLA2G4A and ACHE could enhance platinum chemotherapy effectiveness.

Metabolomics approach for phenolic compounds profiling of soursop (Annona muricata L.) fruit during postharvest storage

INTRODUCTION

Soursop (Annona muricata L.) is a crop with medicinal properties and numerous bioactive compounds. Ripening is a complex process that regulates fruit quality and changes in metabolite content, such as flavonoids, polyphenols, and organic acids.

OBJECTIVES

This study aimed to analyze the phenolic profiling of soursop fruit ripening.

METHODS

The metabolic changes in different days of storage of soursop fruits were investigated using a semi-metabolomic approach based on ultra-performance liquid chromatography coupled to electrospray ionization quadrupole-time of flight mass spectrometry (UPLC-ESI-QTOF-MS). Further, multivariate analysis such as supervised partial least squares discriminant analysis (PLS-DA) was conducted to identify differential metabolites.

RESULTS

A total of 68 metabolites were identified in soursop fruit during postharvest storage. A higher number of metabolites were identified in the Day zero (D0) compared to the Day one (D1), Day three (D3), and Day five (D5), belonging to flavonoids, other polyphenols, phenolic acids, and organic acids. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the pathways of flavone and flavonol biosynthesis, flavonoid biosynthesis, and biosynthesis of secondary metabolites were mostly enriched. Additionally, we included all the compounds and their postharvest storage in the public Phenolics profile database.

CONCLUSIONS

Here, we show that the stage of ripening has a significant effect on the phenolic content, highlighting the point of cut (D0) and the onset of senescence (D5). The findings of this study provide new insights into the soursop fruit quality and may contribute to the identification of metabolic markers for its storage.

Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.

Improved quantification of carbonyl sub-metabolome by liquid chromatography mass spectrometry using a fragment controlled multiplexed isotopic tag

BACKGROUND

Carbonyl-containing metabolites are a class of key intermediate in metabolism, which has potentials to be biomarkers. Since their poor ionization, derivatization reagents, such as dansylhydrazine, are usually used to improve the sensitivity and/or to facilitate quantification. However, most current carbonyl derivatization reagents only have two channels, one is isotopically labeled and the other one is non-labeled. To quantify more samples in a run and using data-independent acquisition (DIA) mode to get comprehensive and unbiased mass fragmentation, we proposed a fragment-controlled isotopic tag, called DiMe-FP-NHNH2 (FP) which has five channels: Δ0, Δ3, Δ6, Δ9, and Δ12, thus up to 5 samples can be analyzed in a run.

RESULTS

The most important improvement is that the FP tag can produce multiple characteristic signals in tandem mass, diagnostic ions and neutral losses, which helps to selectively detect aldehydes/ketones for targeted and untargeted analysis. To exhibit all capabilities of the FP tag, we mimicked an untargeted metabolomics experiment, which comprises two steps. First, discovery step, using Data-Independent Analysis (SWATH-MS) and the labeling of two channels (Δ0 and Δ3), we picked out aldehyde/ketone from the pooled urine samples based on three characteristic signals, including isotope patterns, diagnostic ions, and neutral losses. Second, five-plex quantification, relative and absolute quantification were achieved in a single LC-MS analysis. Notably, because of different nominal masses, the FP tag can be used on any low or high resolution mass spectrometers.

SIGNIFICANCE

The benefits and performance of the FP tag are demonstrated by the analysis of urine samples collected from patients from a prostate cancer study, in which more than a thousand features were found based on MS1 fingerprint, but only around 120 aldehyde/ketone candidates were confirmed with characteristic signals and nine of which were quantified showing significant differences from healthy and reference urine samples.

Development and comparison of parallel reaction monitoring and data-independent acquisition methods for quantitative analysis of hydrophilic compounds in white tea

In the present work, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) methods were developed for the accurate quantitation of amino acids, alkaloids nucleosides and nucleotides in tea. The quality peaks were significantly enhanced by optimizing the LC elution procedure, HCD voltage, MS resolution, and scanning event. Both methods were validated with good liner linearity (0.004-200 μg/mL), LODs (0.001-0.309 μg/mL for PRM and 0.001-0.564 μg/mL for DIA). Applied to white tea sample, the contents of these hydrophilic compounds were range from 34,655.39 to 70,586.14 mg/kg, and caffeine (32,529.02 mg/kg) and theanine (5483.46 mg/kg) were determined as the most abundant ones. Based on the quantitation data set, the white tea samples from Puer, Lincang and Xishuangbanna were clearly discriminated using multivariate data analysis. The results of the present works show that PRM and DIA have great potential in quantitative analysis of multiple hydrophilic compounds in food samples.

Targeted Analysis of Permethylated N-Glycans Using MRM/PRM Approaches

Targeted mass spectrometric analysis is widely employed across various omics fields as a validation strategy due to its high sensitivity and accuracy. The approach has been successfully employed for the structural analysis of proteins, glycans, lipids, and metabolites. Multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) have been the methods of choice for targeted structural studies of biomolecules. These target analyses simplify the analytical workflow, reduce background interference, and increase selectivity/specificity, allowing for a reliable quantification of permethylated N-glycans in complex biological matrices.

Integrated metabolomics and proteomics analysis of plasma lipid metabolism in Parkinson's disease

INTRODUCTION

Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

AREAS COVERED

In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

EXPERT OPINION

Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

Unravelling the brain metabolome: A review of liquid chromatography - mass spectrometry strategies for extracellular brain metabolomics

The analysis of the brain extracellular metabolome is of interest for numerous subdomains within neuroscience. Not only does it provide information about normal physiological functions, it is even more of interest for biomarker discovery and target discovery in disease. The extracellular analysis of the brain is particularly interesting as it provides information about the release of mediators in the brain extracellular fluid to look at cellular signaling and metabolic pathways through the release, diffusion and re-uptake of neurochemicals. In vivo samples are obtained through microdialysis, cerebral open-flow microperfusion or solid-phase microextraction. The analytes of potential interest are typically low in concentration and can have a wide range of physicochemical properties. Liquid chromatography coupled to mass spectrometry has proven its usefulness in brain metabolomics. It allows sensitive and specific analysis of low sample volumes, obtained through different approaches. Several strategies for the analysis of the extracellular fluid have been proposed. The most widely used approaches apply sample derivatization, specific stationary phases and/or hydrophilic interaction liquid chromatography. Miniaturization of these methods allows an even higher sensitivity. The development of chiral metabolomics is indispensable, as it allows to compare the enantiomeric ratio of compounds and provides even more challenges. Some limitations continue to exist for the previously developed methods and the development of new, more sensitive methods remains needed. This review provides an overview of the methods developed for sampling and liquid chromatography-mass spectrometry analysis of the extracellular metabolome.

The metabolomic profile of endometrial receptivity in recurrent miscarriage

BACKGROUND

Endometrial receptivity has been the focus of continuous research for over eight decades; however, current clinical practice lacks an accurate test of endometrial receptivity to allow the prediction of successful pregnancy. We aimed to characterize the endometrial metabolomic profiles of women who suffered recurrent miscarriage using discovery metabolomics and to set the foundation for the development of an endometrial receptivity test.

METHODS

This was a prospective multicenter cohort study led by the Tommy's National Centre for Miscarriage Research in Birmingham. Endometrial biopsies were obtained during the window of implantation from 24 women aged 18-35 years, who were not pregnant and regularly menstruating, diagnosed with unexplained recurrent miscarriage. The metabolite composition and relative concentrations of samples were analyzed applying ultra-high performance liquid chromatography-mass spectrometry to investigate water-soluble and lipid metabolites.

RESULTS

Various metabolic perturbations are associated with observation of increased numbers of miscarriages. They relate to fatty acid metabolism including increased lipolysis and decreased medium chain fatty acid beta-oxidation, poorer mitochondrial health, and redox-active co-factors which are present at higher oxidative levels. Other metabolic perturbations are associated with observation of live birth following miscarriages. They relate to perturbed cholesterol-cholesterol sulphate metabolism, fatty acid metabolism including increased diacylglyceride lipolysis and decreased medium chain fatty acid beta-oxidation, and improved mitochondrial health.

CONCLUSIONS

The present endometrial metabolomics discovery studies have implicated a small number of metabolic pathways and biological functions which are biologically important in miscarriage mechanisms.

Integration of non-targeted multicomponent profiling, targeted characteristic chromatograms and quantitative to accomplish systematic quality evaluation strategy of Huo-Xiang-Zheng-Qi oral liquid

Huo-Xiang-Zheng-Qi oral liquid (HXZQOL) is a well-known traditional Chinese medicine formula for the treatment of gastrointestinal diseases, with the pharmacologic effects of antiinflammatory, immune protection and gastrointestinal motility regulation. More significantly, HXZQOL is recommended for the treatment of COVID-19 patients with gastrointestinal symptoms, and it has been clinically proven to reduce the inflammatory response in patients with COVID-19. However, the effective and overall quality control of HXZQOL is currently limited due to its complex composition, especially the large amount of volatile and non-volatile active components involved. In this study, aimed to fully develop a comprehensive strategy based on non-targeted multicomponent identification, targeted authentication and quantitative analysis for quality evaluation of HXZQOL from different batches. Firstly, the non-targeted high-definition MSE (HDMSE) approach is established based on UHPLC/IM-QTOF-MS, utilized for multicomponent comprehensive characterization of HXZQOL. Combined with in house library-driven automated peak annotation and comparison of 47 reference compounds, 195 components were initially identified. In addition, HS-SPME-GC-MS was employed to analyze the volatile organic compounds (VOCs) in HXZQOL, and a total of 61 components were identified by comparison to the NIST database, reference compounds as well as retention indices. Secondly, based on the selective ion monitoring (SIM) of 24 "identity markers" (involving each herbal medicine), characteristic chromatograms (CCs) were established on LC-MS and GC-MS respectively, to authenticate 15 batches of HXZQOL samples. The targeted-SIM CCs showed that all marker compounds in 15 batches of samples could be accurately monitored, which could indicate preparations authenticity. Finally, a parallel reaction monitoring (PRM) method was established and validated to quantify the nine compounds in 15 batches of HXZQOL. Conclusively, this study first reports chemical-material basis, SIM CCs and quality evaluation of HXZQOL, which is of great implication to quality control and ensuring the authenticity of the preparation.

Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection

Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of Swertia chirayita (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.

A large-scale, targeted metabolomics method for the analysis and quantification of metabolites in human plasma via liquid chromatography-mass spectrometry

Metabolomics is the study of small molecules, primarily metabolites, that are produced during metabolic processes. Analysis of the composition of an organism's metabolome can yield useful information about an individual's health status at any given time. In recent years, the development of large-scale, targeted metabolomic methods has allowed for the analysis of biological samples using analytical techniques such as LC-MS/MS. This paper presents a large-scale metabolomics method for analysis of biological samples, with a focus on quantification of metabolites found in blood plasma. The method comprises a 10-min chromatographic separation using HILIC and RP stationary phases combined with positive and negative electrospray ionization in order to maximize metabolome coverage. Complete analysis of a single sample can be achieved in as little as 40 min using the two columns and dual modes of ionization. With 540 metabolites and the inclusion of over 200 analytical standards, this method is comprehensive and quantitatively robust when compared to current targeted metabolomics methods. This study uses a large-scale evaluation of metabolite recovery from plasma that enables absolute quantification of metabolites by correcting for analyte loss throughout processes such as extraction, handling, or storage. In addition, the method was applied to plasma collected from adjuvant breast cancer patients to confirm the suitability of the method to clinical samples.

Profiling Analysis of Tryptophan Metabolites in the Urine of Patients with Parkinson's Disease Using LC-MS/MS

Although Parkinson's disease (PD) is a representative neurodegenerative disorder and shows characteristic motor impediments, the pathophysiological mechanisms and treatment targets for PD have not yet been clearly identified. Since several tryptophan metabolites produced by gut microbiota could pass the blood-brain barrier and, furthermore, might influence the central nervous system, tryptophan metabolites within the indole, kynurenine, and serotonin metabolic pathways might be the most potent targets for PD development. Furthermore, most metabolites are circulated via the blood, play roles in and/or are metabolized via the host organs, and finally are excreted into the urine. Therefore, profiling the overall tryptophan metabolic pathways in urine samples of patients with PD is important to understanding the pathological mechanisms, finding biomarkers, and discovering therapeutic targets for PD. However, the development of profiling analysis based on tryptophan metabolism pathways in human urine samples is still challenging due to the wide physiological ranges, the varied signal response, and the structural diversity of tryptophan metabolites in complicated urine matrices. In this study, an LC-MS/MS method was developed to profile 21 tryptophan metabolites within the indole, kynurenine, and serotonin metabolic pathways in human urine samples using ion-pairing chromatography and multiple reaction monitoring determination. The developed method was successfully applied to urine samples of PD patients (n = 41) and controls (n = 20). Further, we investigated aberrant metabolites to find biomarkers for PD development and therapeutic targets based on the quantitative results. Unfortunately, most tryptophan metabolites in the urine samples did not present significant differences between control and PD patients, except for indole-3-acetic acid. Nonetheless, indole-3-acetic acid was reported for the first time for its aberrant urinary levels in PD patients and tentatively selected as a potential biomarker for PD. This study provides accurate quantitative results for 21 tryptophan metabolites in biological samples and will be helpful in revealing the pathological mechanisms of PD development, discovering biomarkers for PD, and further providing therapeutic targets for various PD symptoms. In the near future, to further investigate the relationship between gut microbial metabolites and PD, we will employ studies on microbial metabolites using plasma and stool samples from control and PD patients.

Uric acid metabolism promotes apoptosis against Bombyx mori nucleopolyhedrovirus in silkworm, Bombyx mori

The white epidermis of silkworms is due to the accumulation of uric acid crystals. Abnormal silkworm uric acid metabolism decreases uric acid production, leading to a transparent or translucent phenotype. The oily silkworm op50 is a mutant strain with a highly transparent epidermis derived from the p50 strain. It shows more susceptibility to Bombyx mori nucleopolyhedrovirus (BmNPV) infection than the wild type; however, the underlying mechanism is unknown. This study analysed the changes in 34 metabolites in p50 and op50 at different times following BmNPV infection based on comparative metabolomics. The differential metabolites were mainly clustered in six metabolic pathways. Of these, the uric acid pathway was identified as critical for resistance in silkworms, as feeding with inosine significantly enhanced larval resistance compared to other metabolites and modulated other metabolic pathways. Additionally, the increased level of resistance to BmNPV in inosine-fed silkworms was associated with the regulation of apoptosis, which is mediated by the reactive oxygen species produced during uric acid synthesis. Furthermore, feeding the industrial strain Jingsong (JS) with inosine significantly increased the level of larval resistance to BmNPV, indicating its potential application in controlling the virus in sericulture. These results lay the foundation for clarifying the resistance mechanism of silkworms to BmNPV and provide new strategies and methods for the biological control of pests.

Characterization of chemical constituents and metabolites in vivo and in vitro after oral administration of Wuteng tablets in rats by UHPLC-Q/TOF-MS

Waste medicinal plants are widely used in drug production. With the increasing demand for botanical drugs, there is an urgent need to identify new and effective drugs and improve the utilization of medicinal plant resources. Wuteng tablets (WTP) are extracted from the stem of Schisandra chinensis and have a good therapeutic effect on Alzheimer's disease. In this study, a holistic identification strategy based on UHPLC-Q/TOF-MS was developed for the first time to investigate the metabolites and metabolic pathways involved in the in vitro metabolism and liver microsomal incubation and in the in vivo metabolic system of rats after WTP administration. After the oral administration of WTP, 21 metabolites were identified in the serum and 25 metabolites were identified in the urine, of which six were new metabolites; 33 metabolites were inferred from the microsomal metabolites in vitro. The metabolic pathways related to WTP mainly involve demethylation, hydroxylation, dehydroxylation and dehydrogenation. In this study, the metabolites and metabolic pathways of WTP were elucidated via UHPLC-Q/TOF-MS, which provided a basis for an in-depth study of the pharmacodynamic and pharmacotoxicological effects of WTP.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Dysregulated lipid metabolism in TMZ-resistant glioblastoma: pathways, proteins, metabolites and therapeutic opportunities

Glioblastoma (GBM) is a highly aggressive and lethal brain tumor with limited treatment options, such as the chemotherapeutic agent, temozolomide (TMZ). However, many GBM tumors develop resistance to TMZ, which is a major obstacle to effective therapy. Recently, dysregulated lipid metabolism has emerged as an important factor contributing to TMZ resistance in GBM. The dysregulation of lipid metabolism is a hallmark of cancer and alterations in lipid metabolism have been linked to multiple aspects of tumor biology, including proliferation, migration, and resistance to therapy. In this review, we aimed to summarize current knowledge on lipid metabolism in TMZ-resistant GBM, including key metabolites and proteins involved in lipid synthesis, uptake, and utilization, and recent advances in the application of metabolomics to study lipid metabolism in GBM. We also discussed the potential of lipid metabolism as a target for novel therapeutic interventions. Finally, we highlighted the challenges and opportunities associated with developing these interventions for clinical use, and the need for further research to fully understand the role of lipid metabolism in TMZ resistance in GBM. Our review suggests that targeting dysregulated lipid metabolism may be a promising approach to overcome TMZ resistance and improve outcomes in patients with GBM.

Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics

Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

Robust quantitation of gangliosides and sulfatides in human brain using UHPLC-MRM-MS: Method development and application in Alzheimer's disease

Gangliosides (GAs) and sulfatides (STs) are acidic glycosphingolipids that are particularly abundant in the nervous system and are closely related to aging and neurodegenerative disorders. To explore their roles in brain diseases, in-depth molecular profiling, including structural variations of sphingoid backbone, fatty acyl group, and sugar chain of GAs and STs was performed. A total of 210 GAs and 38 STs were characterized in the inferior frontal gyrus (IFG) of human brain, with 90 GAs discovered in brain tissues for the first time. Influential MS parameters for detecting GAs and STs in multiple reaction monitoring (MRM) mode were systematically examined and optimized to minimize in-source fragmentation, resulting in remarkable signal intensity enhancement for GAs and STs, especially for polysialylated species. To eliminate analytical variations, isotopic interference-free internal standards were prepared by simple and fast reduction reaction. The final established method facilitated the simultaneous quantitation of 184 GAs and 30 STs from 25 subtypes, which represents the highest number of GAs quantitated among all quantitation methods recorded in literature so far. The method was further validated and applied to reveal the aberrant change of GAs and STs in the IFG of 12 Alzheimer's disease (AD) patients. Four GAs exhibited high classification capacity for AD (AUC ≥0.80) and were thereby considered the most promising signatures for AD. These findings suggested the close correlation between GAs and the pathogenesis of AD, highlighting the achievements of our robust method for investigating the roles of GAs and STs in various physiological states and diseases.

Comparison of chromatographic conditions for the targeted tandem mass spectrometric determination of 354 mammalian metabolites

Metabolomics is becoming increasingly popular in livestock research, but no single analytical method can cover the entire metabolome. As such, we compared similar and complementary chromatographic methods with respect to analyte coverage and chromatographic properties of mammalian metabolites. We investigated 354 biologically relevant primary metabolites from 19 compound classes including amino acids, bile acids, biogenic amines, carboxylic acids, lipids, nucleotides and sugars. A total of 2063 selected reaction monitoring transitions were optimized on a triple quadrupole mass spectrometer. We then determined the retention profiles and peak parameters of our compounds using an anion exchange chromatography (AIC), three reversed-phase (RP) and three hydrophilic interaction liquid chromatography (HILIC) methods. On average, HILIC methods covered 54% of all metabolites with retention factors >1, while average RP coverage was 41%. In contrast to RP, HILIC methods could also retain polar metabolites such as amino acids and biogenic amines. Carboxylic acids, nucleotides, and sugar related compounds were best separated by AIC or zwitterionic pHILIC with alkaline eluents. Combining two complementary HILIC and RP methods increased the library coverage to 92%. By further including important short chain fatty acids, a combination of HILIC, RP and AIC methods achieved a coverage of 97%. The resulting dataset of LC and MS/MS parameters will facilitate the development of tailor-made quantitative targeted LC-MS/MS methods to investigate the mammalian metabolome.

Phytotoxicity of microplastics to the floating plant Spirodela polyrhiza (L.): Plant functional traits and metabolomics

Freshwater ecosystems are gradually becoming sinks for terrestrial microplastics (MPs), posing a potential ecological risk. Although the effects of MPs on plankton and aquatic animals in freshwater ecosystems have been given increasing attention, the toxicity of MPs to the metabolism of aquatic plants remains unclear. Here, the model aquatic plant Spirodela polyrhiza (L.) Schleid. (S. polyrhiza) was exposed to polyvinyl chloride (PVC; 0, 10, 100 and 1000 mg/L) MPs, and changes in the plant functional traits and physiological metabolism were monitored. The results showed that the high dose of PVC MPs decreased the adventitious root elongation ratio by 41.68% and leaf multiplication ratio by 61.03% of S. polyrhiza, and resulted in the decrease in anthocyanin and nitrogen contents to 63.45% and 84.21% of the control group, respectively. Moreover, the widely targeted metabolomics analysis results showed 37 differential metabolites in the low-dose treatment and 119 differential metabolites in the high-dose treatment. PVC MPs interfered with organic matter accumulation by affecting carbon metabolism, nitrogen metabolism, amino acid metabolism and lipid metabolism, and S. polyrhiza resists PVC MP stress by regulating the synthesis and metabolism of secondary metabolites. PVC MPs had concentration-related toxicological effects on plant functional traits, inhibited plant growth and reproduction, affected plant nutrient metabolism, and exhibited profound effects on the nitrogen fate of aquatic plant habitats. Overall, we systematically summarized the metabolic response mechanisms of aquatic plants to PVC MP stress, providing a new perspective for studying the effects of MPs on plant trait function and ecological risks.

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Association of Metabolomic Biomarkers with Sleeve Gastrectomy Weight Loss Outcomes

This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG.

Quantitative multiple fragment monitoring with enhanced in-source fragmentation/annotation mass spectrometry

Analytical techniques with high sensitivity and selectivity are essential to the quantitative analysis of clinical samples. Liquid chromatography coupled to tandem mass spectrometry is the gold standard in clinical chemistry. However, tandem mass spectrometers come at high capital expenditure and maintenance costs. We recently showed that it is possible to generate very similar results using a much simpler single mass spectrometry detector by performing enhanced in-source fragmentation/annotation (EISA) combined with correlated ion monitoring. Here we provide a step-by-step protocol for optimizing the analytical conditions for EISA, so anyone properly trained in liquid chromatography-mass spectrometry can follow and apply this technique for any given analyte. We exemplify the approach by using 2-hydroxyglutarate (2-HG) which is a clinically relevant metabolite whose D-enantiomer is considered an 'oncometabolite', characteristic of cancers associated with mutated isocitrate dehydrogenases 1 or 2 (IDH1/2). We include procedures for determining quantitative robustness, and show results of these relating to the analysis of DL-2-hydroxyglutarate in cells, as well as in serum samples from patients with acute myeloid leukemia that contain the IDH1/2 mutation. This EISA-mass spectrometry protocol is a broadly applicable and low-cost approach for the quantification of small molecules that has been developed to work well for both single-quadrupole and time-of-flight mass analyzers.

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.

Gastric cancer detection by non-blood-based liquid biopsies: A systematic review looking into the last decade of research

Gastric cancer (GC) screening is arguable in most Western countries. Liquid biopsies are a great promise to answer the unmet need for less invasive diagnostic biomarkers in GC. Thus, we aimed at systematically reviewing the current knowledge on liquid biopsy-based biomarkers in GC screening. A systematic search on PubMed/MEDLINE and Scopus databases was performed on published articles reporting the use of non-blood specimen (saliva, gastric juice [GJ], urine and stool) on GC diagnosis. 3208 records were retrieved by June 2022. After removal of duplicate records, 2379 abstracts were screened, and 84 full texts included in this systematic review. More than 90% of studies were reported on Asian populations. Overall, 9 studies explored stool-, 12 saliva-, and 29 urine-derived biomarkers for GC detection. Additionally, 37 studies, representing the majority, analyzed GJ, focusing on nucleic acid molecules. Several miRNAs and lncRNA molecules have been associated with GC risk, particularly miR-21 (area under the curve [AUC] = 0.97, 95% CI: 0.94-1.00). Considering salivary biomarkers, the best described model in validation sets included the soybean agglutinin and Vicia villosa agglutinin lectins (AUC = 0.89, 95% CI: 0.80-0.99). Most studies in urine carried out metabolomic approaches, with two discriminatory models presenting AUC values superior to 0.97. This systematic review emphasizes the potential role of non-blood-based biomarkers, although further validation, particularly in Western countries, is mandatory, namely for non-invasive screening and/or monitoring, as well as the use of GJ as a tool to enhance upper gastrointestinal endoscopy accuracy.

Psychological distress and metabolomic markers: A systematic review of posttraumatic stress disorder, anxiety, and subclinical distress

Psychological distress can be conceptualized as an umbrella term encompassing symptoms of depression, anxiety, posttraumatic stress disorder (PTSD), or stress more generally. A systematic review of metabolomic markers associated with distress has the potential to reveal underlying molecular mechanisms linking distress to adverse health outcomes. The current systematic review extends prior reviews of clinical depressive disorders by synthesizing 39 existing studies that examined metabolomic markers for PTSD, anxiety disorders, and subclinical psychological distress in biological specimens. Most studies were based on small sets of pre-selected candidate metabolites, with few metabolites overlapping between studies. Vast heterogeneity was observed in study design and inconsistent patterns of association emerged between distress and metabolites. To gain a more robust understanding of distress and its metabolomic signatures, future research should include 1) large, population-based samples and longitudinal assessments, 2) replication and validation in diverse populations, 3) and agnostic metabolomic strategies profiling hundreds of targeted and nontargeted metabolites. Addressing these research priorities will improve the scope and reproducibility of future metabolomic studies of psychological distress.

How to employ metabolomic analysis to research on functions of prebiotics and probiotics in poultry gut health?

Gut health can be considered one of the major, manageable constituents of the animal immunity and performance. The fast spread of intestinal diseases, and increase of antimicrobial resistance have been observed, therefore the intestinal health has become not only economically relevant, but also highly important subject addressing the interest of public health. It is expected, that the strategies to control infections should be based on development of natural immunity in animals and producing resilient flocks using natural solutions, whilst eliminating antibiotics and veterinary medicinal products from action. Probiotics and prebiotics have been favored, because they have potential to directly or indirectly optimize intestinal health by manipulating the metabolism of the intestinal tract, including the microbiota. Studying the metabolome of probiotics and gut environment, both in vivo, or using the in vitro models, is required to attain the scientific understanding about the functions of bioactive compounds in development of gut health and life lasting immunity. There is a practical need to identify new metabolites being the key bioactive agents regulating biochemical pathways of systems associated with gut (gut-associated axes). Technological advancement in metabolomics studies, and increasing access to the powerful analytical platforms have paved a way to implement metabolomics in exploration of the effects of prebiotics and probiotics on the intestinal health of poultry. In this article, the basic principles of metabolomics in research involving probiotics and probiotics are introduced, together with the overview of existing strategies and suggestions of their use to study metabolome in poultry.

Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine

Metabolomics is a fast-developing technique used in biomedical researches focusing on pathological mechanism illustration or novel biomarker development for diseases. The ability of simultaneously quantifying thousands of metabolites in samples makes metabolomics a promising technique in predictive or personalized medicine-oriented researches and applications. Liquid chromatography-mass spectrometry is the most widely employed analytical strategy for metabolomics. In this current mini-review, we provide a brief update on the recent developments and novel applications of LC-MS based metabolomics in the predictive and personalized medicine sector, such as early diagnosis, molecular phenotyping or prognostic evaluation. COVID-19 related metabolomic studies are also summarized. We also discuss the prospects of metabolomics in precision medicine-oriented researches, as well as critical issues that need to be addressed when employing metabolomic strategy in clinical applications.

Metabolomic Study of Urine from Workers Exposed to Low Concentrations of Benzene by UHPLC-ESI-QToF-MS Reveals Potential Biomarkers Associated with Oxidative Stress and Genotoxicity

Benzene is a human carcinogen whose exposure to concentrations below 1 ppm (3.19 mg·m^-3) is associated with myelotoxic effects. The determination of biomarkers such as trans-trans muconic acid (AttM) and S-phenylmercapturic acid (SPMA) show exposure without reflecting the toxic effects of benzene. For this reason, in this study, the urinary metabolome of individuals exposed to low concentrations of benzene was investigated, with the aim of understanding the biological response to exposure to this xenobiotic and identifying metabolites correlated with the toxic effects induced by it. Ultra-efficient liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer (UHPLC-ESI-Q-ToF-MS) was used to identify metabolites in the urine of environmentally (n = 28) and occupationally exposed (n = 32) to benzene (mean of 22.1 μg·m^-3 and 31.8 μg·m^-3, respectively). Non-targeted metabolomics analysis by PLS-DA revealed nine urinary metabolites discriminating between groups and statistically correlated with oxidative damage (MDA, thiol) and genetic material (chromosomal aberrations) induced by the hydrocarbon. The analysis of metabolic pathways revealed important alterations in lipid metabolism. These results point to the involvement of alterations in lipid metabolism in the mechanisms of cytotoxic and genotoxic action of benzene. Furthermore, this study proves the potential of metabolomics to provide relevant information to understand the biological response to exposure to xenobiotics and identify early effect biomarkers.

Evaluation of Metabolism of a Defined Pesticide Mixture through Multiple In Vitro Liver Models

The evaluation of exposure to multiple contaminants in a mixture presents a number of challenges. For example, the characterization of chemical metabolism in a mixture setting remains a research area with critical knowledge gaps. Studies of chemical metabolism typically utilize suspension cultures of primary human hepatocytes; however, this model is not suitable for studies of more extended exposures and donor-to-donor variability in a metabolic capacity is unavoidable. To address this issue, we utilized several in vitro models based on human-induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep) to characterize the metabolism of an equimolar (1 or 5 µM) mixture of 20 pesticides. We used iHep suspensions and 2D sandwich cultures, and a microphysiological system OrganoPlate^® 2-lane 96 (Mimetas^TM) that also included endothelial cells and THP-1 cell-derived macrophages. When cell culture media were evaluated using gas and liquid chromatography coupled to tandem mass spectrometry methods, we found that the parent molecule concentrations diminished, consistent with metabolic activity. This effect was most pronounced in iHep suspensions with a 1 µM mixture, and was lowest in OrganoPlate^® 2-lane 96 for both mixtures. Additionally, we used ion mobility spectrometry-mass spectrometry (IMS-MS) to screen for metabolite formation in these cultures. These analyses revealed the presence of five primary metabolites that allowed for a more comprehensive evaluation of chemical metabolism in vitro. These findings suggest that iHep-based suspension assays maintain higher metabolic activity compared to 2D sandwich and OrganoPlate^® 2-lane 96 model. Moreover, this study illustrates that IMS-MS can characterize in vitro metabolite formation following exposure to mixtures of environmental contaminants.

Stability of Metabolomic Content during Sample Preparation: Blood and Brain Tissues

Thermal and enzymatic reactions can significantly change the tissue metabolomic content during the sample preparation. In this work, we evaluated the stability of metabolites in human whole blood, serum, and rat brain, as well as in metabolomic extracts from these tissues. We measured the concentrations of 63 metabolites in brain and 52 metabolites in blood. We have shown that metabolites in the extracts from biological tissues are stable within 24 h at 4 °C. Serum and whole blood metabolomes are also rather stable, changes in metabolomic content of the whole blood homogenate become apparent only after 1–2 h of incubation at 4 °C, and become strong after 24 h. The most significant changes correspond to energy metabolites: the concentrations of ATP and ADP decrease fivefold, and the concentrations of NAD, NADH, and NADPH decrease below the detectable level. A statistically significant increase was observed for AMP, IMP, hypoxanthine, and nicotinamide. The brain tissue is much more metabolically active than human blood, and significant metabolomic changes occur already within the first several minutes during the brain harvest and sample homogenization. At a longer timescale (hours), noticeable changes were observed for all classes of compounds, including amino acids, organic acids, alcohols, amines, sugars, nitrogenous bases, nucleotides, and nucleosides.

Plasma lipidomics in subjects with combat posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) is complex neuropsychiatric disorder triggered by a traumatic event and characterized by the symptoms that represent large burden to patients, as well as to society. Lipidomic approach can be applied as a useful tool for discovery of novel diagnostic, prognostic and therapeutic lipid biomarkers of various disorders, whose etiology is complex and still unknown, including PTSD. Since changes in the levels of lipid metabolites might indicate impairments in various metabolic pathways and cellular processes, the aim of this lipidomic study was to determine altered levels of lipid compounds in PTSD. The study enrolled 235 male patients with combat PTSD and 241 healthy male control subjects. Targeted lipidomic analysis of plasma samples was conducted using reverse-phase liquid chromatography coupled with mass spectrometry. Lipids that have been analyzed belong to the group of ceramides, cholesterol esters, diacylglycerols, lysophosphatidylcholines, lysophosphatidylethanolamines, phosphatidylcholines, phosphatidylethanolamines, sphingomyelins and triglycerides. The levels of fifteen lipid compounds were found to be significantly different between PTSD patients and healthy control subjects, including four phosphatidylcholines, two phosphatidylethanolamines, five sphingomyelins, two cholesterol esters and two ceramides. The lipid metabolites whose levels significantly differed between patients with PTSD and control subjects are associated with various biological processes, including impairments of membrane integrity and function, mitochondrial dysfunction, inflammation and oxidative stress. As these processes might be associated with development and progression of PTSD, altered lipid compounds represent potential biomarkers that could facilitate the diagnosis of PTSD, prediction of the disease, as well as identification of novel treatment approaches in PTSD.

Metabolic Fingerprinting of Fabry Disease: Diagnostic and Prognostic Aspects

Fabry disease (FD) is an X-linked lysosomal disease due to a deficiency in the activity of the lysosomal-galactosidase A (GalA), a key enzyme in the glycosphingolipid degradation pathway. FD is a complex disease with a poor genotype–phenotype correlation. In the early stages, FD could involve the peripheral nervous system (acroparesthesias and dysautonomia) and the ski (angiokeratoma), but later kidney, heart or central nervous system impairment may significantly decrease life expectancy. The advent of omics technologies offers the possibility of a global, integrated and systemic approach well-suited for the exploration of this complex disease. In this narrative review, we will focus on the main metabolomic studies, which have underscored the importance of detecting biomarkers for a diagnostic and prognostic purpose in FD. These investigations are potentially useful to explain the wide clinical, biochemical and molecular heterogeneity found in FD patients. Moreover, the quantitative mass spectrometry methods developed to evaluate concentrations of these biomarkers in urine and plasma will be described. Finally, the complex metabolic biomarker profile depicted in FD patients will be reported, which varies according to gender, types of mutations, and therapeutic treatment.

Deubiquitinase OTUD3 regulates metabolism homeostasis in response to nutritional stresses

The ovarian-tumor-domain-containing deubiquitinases (OTUDs) block ubiquitin-dependent protein degradation and are involved in diverse signaling pathways. We discovered a rare OTUD3 c.863G>A mutation in a family with an early age of onset of diabetes. This mutation reduces the stability and catalytic activity of OTUD3. We next constructed an experiment with Otud3^-/- mice and found that they developed worse obesity, dyslipidemia, and insulin resistance than wild-type mice when challenged with a high-fat diet (HFD). We further found that glucose and fatty acids stimulate CREB-binding-protein-dependent OTUD3 acetylation, promoting its nuclear translocation, where OTUD3 regulates various genes involved in glucose and lipid metabolism and oxidative phosphorylation by stabilizing peroxisome-proliferator-activated receptor delta (PPARδ). Moreover, targeting PPARδ using a specific agonist can partially rescue the phenotype of HFD-fed Otud3^-/- mice. We propose that OTUD3 is an important regulator of energy metabolism and that the OTUD3 c.863G>A is associated with obesity and a higher risk of diabetes.

Ring Trial on Quantitative Assessment of Bile Acids Reveals a Method- and Analyte-Specific Accuracy and Reproducibility

(1) Background: Bile acids are a key mediator of the molecular microbiome-host interaction, and various mass spectrometry-based assays have been developed in the recent decade to quantify a wide range of bile acids. We compare existing methodologies to harmonize them. (2) Methods: Methodology for absolute quantification of bile acids from six laboratories in Europe were compared for the quantification of the primary bile acids cholic acid (CA) and chenodeoxycholic acid (CDCA) and conjugated products glycocholic acid (GCA) and taurocholic acid (TCA). For the bacterially modified secondary bile acids, the quantification of deoxycholic acid (DCA) and lithocholic acid (LCA) was compared. For the murine bile acids, we used the primary muricholic acids (α-MCA and, β-MCA) and the intestinally produced secondary bile acid muricholic (ω-MCA). The standards were spiked into methanol:water (1:1) mix as well as in human and murine serum at either low concentration range (150–3000 nM) or high concentration range (1500–40,000 nM). (3) Results: The precision was better for higher concentrations. Measurements for the hydrophobic unconjugated bile acids LCA and ω-MCA were the most challenging. (4) Conclusions: The quality assessments were generally very similar, and the comprehensive analyses demonstrated that data from chosen locations can be used for comparisons between studies.

Evaluation of Untargeted Metabolomic Strategy for the Discovery of Biomarker of Breast Cancer

Discovery of disease biomarker based on untargeted metabolomics is informative for pathological mechanism studies and facilitates disease early diagnosis. Numerous of metabolomic strategies emerge due to different sample properties or experimental purposes, thus, methodological evaluation before sample analysis is essential and necessary. In this study, sample preparation, data processing procedure and metabolite identification strategy were assessed aiming at the discovery of biomarker of breast cancer. First, metabolite extraction by different solvents, as well as the necessity of vacuum-dried and re-dissolution, was investigated. The extraction efficiency was assessed based on the number of eligible components (components with MS/MS data acquired), which was more reasonable for metabolite identification. In addition, a simplified data processing procedure was proposed involving the OPLS-DA, primary screening for eligible components, and secondary screening with constraints including VIP, fold change and p value. Such procedure ensured that only differential candidates were subjected to data interpretation, which greatly reduced the data volume for database search and improved analysis efficiency. Furthermore, metabolite identification and annotation confidence were enhanced by comprehensive consideration of mass and MS/MS errors, isotope similarity, fragmentation match, and biological source confirmation. On this basis, the optimized strategy was applied for the analysis of serum samples of breast cancer, according to which the discovery of differential metabolites highly encouraged the independent biomarkers/indicators used for disease diagnosis and chemotherapy evaluation clinically. Therefore, the optimized strategy simplified the process of differential metabolite exploration, which laid a foundation for biomarker discovery and studies of disease mechanism.

Early Metabolomic Markers of Acute Low-Dose Exposure to Uranium in Rats

Changes in metabolomics over time were studied in rats to identify early biomarkers and highlight the main metabolic pathways that are significantly altered in the period immediately following acute low-dose uranium exposure. A dose response relationship study was established from urine and plasma samples collected periodically over 9 months after the exposure of young adult male rats to uranyl nitrate. LC-MS and biostatistical analysis were used to identify early discriminant metabolites. As expected, low doses of uranium lead to time-based non-toxic biological effects, which can be used to identify early and delayed markers of exposure in both urine and plasma samples. A combination of surrogate markers for uranium exposure was validated from the most discriminant early markers for making effective predictions. N-methyl-nicotinamide, kynurenic acid, serotonin, tryptophan, tryptamine, and indole acetic acid associated with the nicotinate–nicotinamide and tryptophan pathway seem to be one of the main biological targets, as shown previously for chronic contaminations and completed, among others, by betaine metabolism. This study can be considered as a proof of concept for the relevance of metabolomics in the field of low-dose internal contamination by uranium, for the development of predictive diagnostic tests usable for radiotoxicological monitoring.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.