Integration of GC–MS and LC–MS for untargeted metabolomics profiling

Optimized high-throughput protocols for comprehensive metabolomic and lipidomic profiling of brain sample

Establishing direct causal and functional links between genotype and phenotype requires thoroughly analyzing metabolites and lipids in systems biology. Tissue samples, which provide localized and direct information and contain unique compounds, play a significant role in objectively classifying diseases, predicting prognosis, and deciding personalized therapeutic strategies. Comprehensive metabolomic and lipidomic analyses in tissue samples need efficient sample preparation steps, optimized analysis conditions, and the integration of orthogonal analytical platforms because of the physicochemical diversities of biomolecules. Here, we propose simple, rapid, and robust high-throughput analytical protocols based on the design of experiment (DoE) strategies, with the various parameters systematically tested for comprehensively analyzing the heterogeneous brain samples. The suggested protocols present a systematically DoE-based strategy for performing the most comprehensive analysis for integrated GC-MS and LC-qTOF-MS from brain samples. The five different DoE models, including D-optimal, full factorial, fractional, and Box-Behnken, were applied to increase extraction efficiency for metabolites and lipids and optimize instrumental parameters, including sample preparation and chromatographic parameters. The superior simultaneous extraction of metabolites and lipids from brain samples was achieved by the methanol-water-dichloromethane (2:1:3, v/v/v) mixture. For GC-MS based metabolomics analysis, incubation time, temperature, and methoxyamine concentration (10 mg/mL) affected metabolite coverage significantly. For LC-qTOF-MS based metabolomics analysis, the extraction solvent (methanol-water; 2:1, v/v) and the reconstitution solvent (%0.1 FA in acetonitrile) were superior on the metabolite coverage. On the other hand, the ionic strength and column temperature were critical and significant parameters for high throughput metabolomics and lipidomics studies using LC-qTOF-MS. In conclusion, DoE-based optimization strategies for a three-in-one single-step extraction enabled rapid, comprehensive, high-throughput, and simultaneous analysis of metabolites, lipids, and even proteins from a 10 mg brain sample. Under optimized conditions, 475 lipids and 158 metabolites were identified in brain samples.

Identification and Structural Elucidation of Acylsugars in Tomato Leaves Using Liquid Chromatography-Ion Mobility-Tandem Mass Spectrometry (LC-IM-MS/MS)

Leaves of tomato plants contain various glandular trichomes that produce a wide range of metabolic products including acylsugars, which may serve as a defense mechanism against various insect pests. Acylsugars exhibit significant structural diversity, differing in their sugar cores, acylated positions, and type of acyl chains. This work demonstrated a comprehensive approach using multidimensional separation techniques, specifically liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), for structural characterization, and the discrimination of different tomato plants (one cultivar and five accessions) was demonstrated using tomato leaf extracts; six genotypes from five species of Solanum were represented. As a result, we identified 16 acylsugars through their molecular formulas and annotations using LC and MS analyses. The incorporation of ion mobility (IM) analysis revealed an additional 9 isomeric forms, resulting in a comprehensive total of 25 isomeric acylsugars identified. Furthermore, the experimental collision cross section (CCSexp) values agreed reasonably well with the corresponding predicted values (CCSpred), with an overall estimated error of less than 2%. These findings pave the way for research into how the different structural isomers of acylsugars might influence the self-defense mechanism in plants. Moreover, this work demonstrated that the investigated cultivar and accessions of tomatoes can be distinguished from each other based on their metabolite profile, e.g., acylsugars, with principal component analysis (PCA) and linear discriminant analysis (LDA) statistical models, yielding a prediction rate of 98.3%.

Unbiased Metabolomic and Chemometric profiles of three Sargassum polycystum extracts using GCMS and LCMS/MS: content analysis, correlation analysis and molecular docking

Sargassum polycystum (S. polycystum) is a brown macroalga with a high phytochemical content, making it a nutritious and bioactive food source. However, information on factors contributing to health benefits, like antioxidants and cytotoxicity, is less explored for Malaysian S. polycystum. In this study, three extracts of S. polycystum were characterized using a combination of analytical techniques. Despite similar carbohydrate content across all extracts, water extract exhibited the highest protein [21.90 ± 1.01 albumin equivalent (μg/mg)] and phenolic [7.73 ± 1.95 gallic acid equivalent (μg/mg)] contents. However, it displayed the lowest antioxidant and anticancer activities [half-maximal inhibitory concentration (IC50) of > 2000 μg/mL]. Interestingly, ethanolic extract demonstrated the strongest scavenging activity (IC50 of 397.90 ± 20.43 μg/mL) and selective anticancer activity against MCF7 breast cancer cells (IC50 of 338.63 ± 48.98 μg/mL). Untargeted metabolomic profiling confirmed the differences in the chemical composition of the extracts. Subsequently, correlation and docking analyses were used to identify the potential bioactive compounds within the extracts. The ethanolic extract is a rich source of these bioactive compounds with superior antioxidant and anticancer properties, highlighting the need for further research on its potential utility in the food industry.

Development and thorough evaluation of a multi-omics sample preparation workflow for comprehensive LC-MS/MS-based metabolomics, lipidomics and proteomics datasets

The importance of sample preparation selection if often overlooked particularly for untargeted multi-omics approaches that gained popularity in recent years. To minimize issues with sample heterogeneity and additional freeze-thaw cycles during sample splitting, multiple -omics datasets (e.g. metabolomics, lipidomics and proteomics) should ideally be generated from the same set of samples. For sample extraction, commonly biphasic organic solvent systems are used that require extensive multi-step protocols. Individual studies have recently also started to investigate monophasic (all-in-one) extraction procedures. The aim of the current study was to develop and systematically compare ten different mono- and biphasic extraction solvent mixtures for their potential to aid in the most comprehensive metabolomics, lipidomics and proteomics datasets. As the focus was on human postmortem tissue samples (muscle and liver tissue), four tissue homogenization parameters were also evaluated. Untargeted liquid chromatography mass spectrometry-based metabolomics, lipidomic and proteomics methods were utilized along with 1D sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and bicinchoninic acid (BCA) assay results. Optimal homogenization was found to be achieved by bead-homogenizing 20 mg of muscle or liver tissue with 200 μL (1:10 ratio) Water:Methanol (1:2) using 3 × 30 s pulses. The supernatant of the homogenate was further extracted. Comprehensive ranking, taking nine different processing parameters into account, showed that the monophasic extraction solvents, overall, showed better scores compared to the biphasic solvent systems, despite their recommendation for one or all of the -omics extractions. The optimal extraction solvent was found to be Methanol:Acetone (9:1), resulting in the most comprehensive metabolomics, lipidomics and proteomics datasets, showing the potential to be automated, hence, allowing for high-throughput analysis of samples and opening the door for comprehensive multi-omics results from routine clinical cases in the future.

Metabolomic heterogeneity of ageing with ethnic diversity: a step closer to healthy ageing

INTRODUCTION

Outside of case-control settings, ethnicity specific changes in the human metabolome are understudied especially in community dwelling, ageing men. Characterising serum for age and ethnicity specific features can enable tailored therapeutics research and improve our understanding of the interplay between age, ethnicity, and metabolism in global populations.

OBJECTIVE

A metabolomics approach was adopted to profile serum metabolomes in middle-aged and elderly men of different ethnicities from the Northwest of England, UK.

METHODS

Serum samples from 572 men of White European (WE), South Asian (SA), and African-Caribbean (AC) ethnicities, ranging between 40 and 86 years were analysed. A combination of liquid chromatography (LC) and gas chromatography (GC) coupled to high-resolution mass spectrometry (MS) was used to generate the metabolomic profiles. Partial Least Squares Discriminant Analysis (PLS-DA) based classification models were built and validated using resampling via bootstrap analysis and permutation testing. Features were putatively annotated using public Human Metabolome Database (HMDB) and Golm Metabolite Database (GMD). Variable Importance in Projection (VIP) scores were used to determine features of interest, after which pathway enrichment analysis was performed.

RESULTS

Using profiles from our analysis we classify subjects by their ethnicity with an average correct classification rate (CCR) of 90.53% (LC-MS data) and 85.58% (GC-MS data). Similar classification by age (< 60 vs. ≥ 60 years) returned CCRs of 90.20% (LC-MS) and 71.13% (GC-MS). VIP scores driven feature selection revealed important compounds from putatively annotated lipids (subclasses including fatty acids and carboxylic acids, glycerophospholipids, steroids), organic acids, amino acid derivatives as key contributors to the classifications. Pathway enrichment analysis using these features revealed statistically significant perturbations in energy metabolism (TCA cycle), N-Glycan and unsaturated fatty acid biosynthesis linked pathways amongst others.

CONCLUSION

We report metabolic differences measured in serum that can be attributed to ethnicity and age in healthy population. These results strongly emphasise the need to consider confounding effects of inherent metabolic variations driven by ethnicity of participants in population-based metabolic profiling studies. Interpretation of energy metabolism, N-Glycan and fatty acid biosynthesis should be carefully decoupled from the underlying differences in ethnicity of participants.

The Potential of Metabolomics in Colorectal Cancer Prognosis

Colorectal cancer (CRC) is one of the most common cancers worldwide, posing a serious threat to human health. Metabolic reprogramming represents a critical feature in the process of tumor development and progression, encompassing alterations in sugar metabolism, lipid metabolism, amino acid metabolism, and other pathways. Metabolites hold promise as innovative prognostic biomarkers for cancer patients, which is crucial for targeted follow-up care and interventions. This review aims to provide an overview of the progress in research on metabolic biomarkers for predicting the prognosis of CRC. We also discuss the future trends and challenges in this area.

Advances and challenges in non-targeted analysis: An insight into sample preparation and detection by liquid chromatography-mass spectrometry

Unknown impurities, metabolites and harmful pollutants present in pharmaceutical products, biological and environmental samples, respectively are of high concern in terms of their detection and quantification. The targeted analysis aims to quantify known chemical entities, but it lacks the ability to identify unknown components present in a sample. Non-targeted analysis is an analytical approach that can be made applicable to various disciplines of science to effectively search for unknown chemical, biological, or environmental entities that can answer various baffling mysteries of research. It employs various high-end analytical techniques that can specifically screen out multiple unknown compounds from complex mixtures. Non-targeted analysis is also applicable for complex studies such as metabolomics to search unidentified metabolites of new chemical entities. This review critically discusses the current advancements in non-targeted analysis related to the analysis of pharmaceutical, biological, and environmental samples. Various steps like sample collection, handling, preparation, extraction, its analysis using advanced techniques like high-resolution mass spectrometry, liquid chromatography mass spectrometry, and lastly interpretation of the huge amounts of complex data obtained upon analysis of complex matrices have been discussed broadly in this article. Besides the advantages of non-targeted analysis over targeted analysis, limitations, bioinformatics tools, sources of error, and research gaps have been critically analyzed.

Identification of the Biomarkers for Chronic Gastritis with TCM Damp Phlegm Pattern by Using Tongue Coating Metabolomics

Objective

This study aimed to establish a model for identifying chronic gastritis with the traditional Chinese medicine damp phlegm pattern by examining metabolite changes in the tongue coating of patients. It also explored the role of metabolic pathways in the pathogenesis of this condition.

Methods

This cross-sectional study involved 300 patients diagnosed with chronic gastritis. Of these, 200 patients exhibited the damp phlegm pattern, while 100 did not. Metabolomic methods employing GC-TOF-MS and UHPLC-QE-MS were utilized to identify various metabolites in the tongue coating of patients. An identification model for chronic gastritis with the damp phlegm pattern was created based on ROC curves derived from differential biomarkers. Additionally, 50 samples not included in model construction were collected for external validation.

Results

Comparison of the damp phlegm pattern group with the non-damp phlegm pattern group revealed a total of 116 differential metabolites. Among these, lipids and lipid-like compounds were most abundant, comprising 27 types, which included four lipid metabolites related to sphingomyelin metabolism. The ROC model, which included phenol, 2.6-diaminoheptanedioic acid, and N-hexadecanoyl pyrrolidine, demonstrated the highest accuracy, with accuracy, sensitivity, and specificity metrics of 94.0%, 91.0%, and 87.0%, respectively. Furthermore, external validation using tongue coating metabolites from 50 patients revealed accuracy, sensitivity, and specificity in the validation set of 93.9%, 90.6%, and 83.3%, respectively.

Conclusion

Differential metabolites between patients with the damp phlegm pattern and those without are primarily lipids and lipid-like compounds. N-hexadecanoyl pyrrolidine, phenol, and 2.6-diaminoheptanedioic acid may serve as potential biomarkers for chronic gastritis characterized by the damp phlegm pattern.

The interaction between Lactobacillus delbrueckii ssp. bulgaricus M-58 and Streptococcus thermophilus S10 can enhance the texture and flavor profile of fermented milk: Insights from metabolomics analysis

Lactobacillus delbrueckii ssp. bulgaricus M-58 (M58) and Streptococcus thermophilus S10 (S10) are both dairy starter strains known for their favorable fermentation characteristics. Therefore, this research aimed to study the effects of 1-d low-temperature ripening on the physicochemical properties and metabolomics of fermented milk. Initially, the performance of single (M58 or S10) and dual (M58+S10) strain fermentation was assessed, revealing that the M58+S10 combination resulted in a shortened fermentation time, a stable gel structure, and desirable viscosity, suggesting positive strain interactions. Subsequently, nontargeted metabolomics analyses using liquid chromatography-MS and GC-MS were performed to comparatively analyze M58+S10 fermented milk samples collected at the end of fermentation and after 1 d of low-temperature ripening. The results showed a significant increase in almost all small peptides and dodecanedioic acid in the samples after 1 d of ripening, although there was a substantial decrease in indole and amino acid metabolites. Moreover, notable increases were observed in high-quality flavor compounds, such as geraniol, delta-nonalactone, 1-hexanol,2-ethyl-, methyl jasmonate, and undecanal. This study provides valuable insights into the fermentation characteristics of the dual bacterial starter consisting of M58 and S10 strains and highlights the specific contribution of the low-temperature ripening step to the overall quality of fermented milk.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Differential serum metabolites in patients with pregnancy-associated venous thromboembolism analyzed using GC-MS/LC-MS untargeted metabolomics

Untargeted metabolomics can be used for the comprehensive analysis of metabolite profiles in biological samples without preset targets, making them particularly suitable for exploring metabolic characteristics and potential mechanisms in complex diseases. Therefore, in this study, we employed gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) techniques to analyze the serum metabolic characteristics of patients with pregnancy-associated venous thromboembolism (PA-VTE). In this study, 11 pregnant women with VTE and 11 healthy pregnant women were included in the experimental and control groups, respectively. Using GC-MS, we identified 325 metabolites, with the highest proportion being organic oxygen compounds. Using LC-MS, we identified 3104 metabolites, with the highest proportion being acylcarnitine. The results revealed significant differences in the levels of lipids, organic compounds, and other metabolites between patients compared to healthy pregnant women. Pathways such as pyrimidine metabolism, linoleic acid metabolism, and mineral absorption differed between patients with PA-VTE and controls. Furthermore, we identified biomarkers associated with metabolic processes, such as fatty acids and amino acids (2-hydroxyhexanedioic acid, hexadecenal, palmitoylethanolamide, glycerol-1-phosphate, and N-acetyl-beta-D-glucosamine). These findings revealed the metabolic characteristics of PA-VTE and provided important clues for further research on its pathophysiological mechanisms. Our findings may contribute to the development of new diagnostic markers and support early diagnosis and treatment of PA-VTE.

Chemical analysis of different parts from agarwood columns by artificially agarwood-inducing method based on GC-MS and UPLC-TOF-MS

Agarwood is resin-containing wood produced by plants that have been injured. It is widely used in herbal medicine, incense, decorative items, and so on. In this study, we conducted resin area statistical analysis, determined starch particle and reducing sugar contents, and performed multivariate statistical analysis of chemical composition by GC-MS and UPLC-Q-TOF-MS to explore the different components in sections cut from an agarwood column, designated as A1-A4. The results showed that after stimulation by Agar-Bit inducer, the internal phloem parenchyma cells of the column started to form agarwood, and then starch granules were converted into soluble reducing sugars and agarwood resin. Section A1 showed rapid loss of starch granules, resulting in higher contents of reducing sugars and resin. The resin areas of agarwood in the respective sections were different, gradually decreasing on going from A1 to A4. Total numbers of metabolites of 87 and 63 were identified by GC-MS and UPLC-Q-TOF-MS, respectively. Of these, 10 and 16 metabolites with significant differences (variable importance projection >1) were selected through multivariate statistical analysis. These metabolites included chromones, sesquiterpenes, alkanes, and fatty acids. Among them, 6-methoxy-2-(2-phenylethyl)chromone and 6,7-dimethoxy-2-(2-phenylethyl)chromone were significant markers detected by both GC-MS and UPLC-Q-TOF-MS, which may be essential substances responsible for differences in the agarwood-forming capacities of the cut sections. In conclusion, there has been limited research on the different agarwood-forming capacities of agarwood columns. Here, we explored the differences in various sections of agarwood through chemical analysis to provide a more comprehensive and in-depth understanding of its constitution.

Impact of three different peak picking software tools on the quality of untargeted metabolomics data

Data quality and control parameters are becoming more important in metabolomics. For peak picking, open-source or commercial solutions are used. Other publications consider different software solutions or data acquisition types for peak picking, a combination, including proposed and new quality parameters for the process of peak picking, does not exist. This study tries to examine the performance of three different software in terms of reproducibility and quality of their output while also considering new quality parameters to gain a better understanding of resulting feature lists in metabolomics data. We saw best recovery of spiked analytes in MS-DIAL. Reproducibility over multiple projects was good among all software. The total number of features found was consistent for DDA and full scan acquisition in MS-DIAL but full scan data leading to considerably more features in MZmine and Progenesis Qi. Feature linearity proved to be a good quality parameter. Features in MS-DIAL and MZmine, showed good linearity while Progenesis Qi produced large variation, especially in full scan data. Peak width proved to be a very powerful filtering criteria revealing many features in MZmine and Progenesis Qi to be of questionable peak width. Additionally, full scan data appears to produce a disproportionally higher number of short features. This parameter is not yet available in MS-DIAL. Finally, the manual classification of true positive features proved MS-DIAL to perform significantly better in DDA data (62 % true positive) than the two other software in either mode. We showed that currently popular solutions MS-DIAL and MZmine perform well in targeted analysis of spiked analytes as well as in classic untargeted analysis. The commercially available solution Progenesis Qi does not hold any advantage over the two in terms of quality parameters, of which we proposed peak width as a new parameter and showed that already proposed parameters such as feature linearity in samples of increasing concentration are advisable to use.

Metabolic and Pharmacokinetic Profiling Studies of N, N-Dimethylaniline-Heliamine in Rats by UHPLC-Q-Orbitrap MS/MS

Cardiovascular disease is the first cause of death worldwide and kills more people each year than any other cause of death. N, N-dimethylaniline-heliamine (DH), a synthetic tetrahydroisoquinoline alkaloid, has shown notable antiarrhythmic activity. However, the metabolic processes and pharmacokinetic characteristics of DH in rats have not been studied. This study aims to identify its metabolites, as well as develop and validate a rapid and efficient bioanalytical method for quantifying DH in rat plasma over a wide range of concentrations. Its metabolites were characterized in silico, in vitro, and in vivo. A series of 16 metabolites were identified, of which 12 were phase I metabolites and 4 were phase II metabolites. A low probability of DH binding to DNA, protein, and glutathione is predicted by the in silico model. The main metabolic processes of DH were demethylation, dehydrogenation, glucuronidation, and sulfation. Concentration-time profiles were generated by analyzing the plasma, and the outcomes were analyzed via non-compartmental analysis to identify the pharmacokinetic parameters. Among the detected parameters were the volume of distribution, estimated at 126,728.09 ± 56,867.09 mL/kg, clearance at 30,148.65 ± 15,354.27 mL/h/kg, and absolute oral bioavailability at 16.11%. The plasma distribution volume of DH was substantially higher than the overall plasma volume of rats, which suggests that DH has a specific tissue distribution in rats. This study suggests that DH is appropriately bioavailable and excreted via a variety of routes and has low toxicity.

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognostic accuracy for various pathological conditions. This study explored the synergistic potential of dual-platform metabolomics (specifically, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)) to expand the detection of metabolites and improve the precision of DVT diagnosis. Sixty-one differential metabolites were identified in serum from DVT patients: 22 from GC-MS and 39 from LC-MS. Among these, five key metabolites were highlighted by SHapley Additive exPlanations (SHAP)-guided feature engineering and then used to develop a stacking diagnostic model. Additionally, a user-friendly interface application system was developed to streamline and automate the application of the diagnostic model, enhancing its practicality and accessibility for clinical use. This work showed that the integration of dual-platform metabolomics with a stacking machine learning model enables faster and more accurate diagnosis of DVT in clinical environments.

Application of omics technologies in studies on antitumor effects of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is considered to be one of the most comprehensive and influential form of traditional medicine. It plays an important role in clinical treatment and adjuvant therapy for cancer. However, the complex composition of TCM presents challenges to the comprehensive and systematic understanding of its antitumor mechanisms, which hinders further development of TCM with antitumor effects. Omics technologies can immensely help in elucidating the mechanism of action of drugs. They utilize high-throughput sequencing and detection techniques to provide deeper insights into biological systems, revealing the intricate mechanisms through which TCM combats tumors. Multi-omics approaches can be used to elucidate the interrelationships among different omics layers by integrating data from various omics disciplines. By analyzing a large amount of data, these approaches further unravel the complex network of mechanisms underlying the antitumor effects of TCM and explain the mutual regulations across different molecular levels. In this study, we presented a comprehensive overview of the recent progress in single-omics and multi-omics research focused on elucidating the mechanisms underlying the antitumor effects of TCM. We discussed the significance of omics technologies in advancing research on the antitumor properties of TCM and also provided novel research perspectives and methodologies for further advancing this research field.

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Mass spectrometric approaches in discovering lipid biomarkers for COVID-19 by lipidomics: Future challenges and perspectives

Coronavirus disease 2019 (COVID-19) has emerged as a global health threat and has rapidly spread worldwide. Significant changes in the lipid profile before and after COVID-19 confirmed the significance of lipid metabolism in regulating the response to viral infection. Therefore, understanding the role of lipid metabolism may facilitate the development of new therapeutics for COVID-19. Owing to their high sensitivity and accuracy, mass spectrometry (MS)-based methods are widely used for rapidly identifying and quantifying of thousands of lipid species present in a small amount of sample. To enhance the capabilities of MS for the qualitative and quantitative analysis of lipids, different platforms have been combined to cover a wide range of lipidomes with high sensitivity, specificity, and accuracy. Currently, MS-based technologies are being established as efficient methods for discovering potential diagnostic biomarkers for COVID-19 and related diseases. As the lipidome of the host cell is drastically affected by the viral replication process, investigating lipid profile alterations in patients with COVID-19 and targeting lipid metabolism pathways are considered to be crucial steps in host-directed drug targeting to develop better therapeutic strategies. This review summarizes various MS-based strategies that have been developed for lipidomic analyzes and biomarker discoveries to combat COVID-19 by integrating various other potential approaches using different human samples. Furthermore, this review discusses the challenges in using MS technologies and future perspectives in terms of drug discovery and diagnosis of COVID-19.

Integrated GC-MS and UPLC-ESI-QTOF-MS based untargeted metabolomics analysis of in vitro raised tissues of Digitalis purpurea L

Digitalis purpurea L. is one of the important plant species of Nilgiris, Kashmir and Darjeeling regions of India, belonging to the family Plantaginaceae, with well-known pharmacological applications. In the present investigation, an in vitro culture technique of indirect shoot organogenesis of D. purpurea is being explored; the biochemical attributes, the antioxidant activities and the metabolomic analyses were made by utilizing untargeted Gas Chromatography-Mass Spectrometry (GC-MS) and Ultra Performance Liquid Chromatography coupled with electronspray ionization/quadrupole-time-of-flight-mass spectrometry (UPLC-ESI-QTOF-MS) approaches. Initially, the leaf explants were used for callus induction and proliferation and maximum callusing frequency (94.44%) and fresh biomass (4.9 g) were obtained on MS, fortified with 8.8 µM BAP (6-benzyl amino purine) + 0.9 µM 2,4-D (2,4-dichlorophenoxyacetic acid), subsequently shoot formation (indirect organogenesis) was noted on the same MS medium with a shoot induction frequency of 83.33%. Later on, the biochemical and antioxidant potential of in vivo-, in vitro grown leaf and leaf derived callus were assessed. Significantly higher total phenol, flavonoid, DPPH (2,2-diphenyl-1-picrylhydrazyl), POD (peroxidase) and SOD (superoxide dismutase) activities were noticed in in vitro grown callus and leaf tissues compared with field grown leaf. The GC-MS analysis of each methanolic extract (in vivo-, in vitro derived leaf and leaf derived callus) displayed the presence of more than 75 bioactive compounds viz loliolide, stigmasterin, alpha-tocopherol, squalene, palmitic acid, linoleic acid, beta-amyrin, campesterol etc. possessing immense therapeutic importance. The UPLC-MS based metabolite fingerprinting of each methanolic extracts were conducted in both positive and negative ionization mode. The obtained results revealed variation in phytochemical composition in field - and laboratory grown tissues, indicating the impact of in vitro culture conditions on plant tissues. The detected phytocompounds belongs to various classes such as flavonoids, steroids, terpenoids, carbohydrates, tannins, lignans etc. The medicinally important metabolites identified were 20, 22-dihydrodigoxigenin, digoxigenin monodigitoxoside, apigenin, luteolin, kaempferide, rosmarinic acid, nepitrin and others. The results of the present study suggest that in vitro culture of D. purpurea could successfully be utilized for the novel drug discovery by producing such important phytocompounds of commercial interest in shorter duration without harming the plants' natural population.

Multi-omics and chemical profiling approaches to understand the material foundation and pharmacological mechanism of sophorae tonkinensis radix et rhizome-induced liver injury in mice

ETHNOPHARMACOLOGICAL RELEVANCE

Sophorae tonkinensis Radix et Rhizoma (STR) is an extensively applied traditional Chinese medicine (TCM) in southwest China. However, its clinical application is relatively limited due to its hepatotoxicity effects.

AIM OF THE STUDY

To understand the material foundation and liver injury mechanism of STR.

MATERIALS AND METHODS

Chemical compositions in STR and its prototypes in mice were profiled by ultra-performance liquid chromatography coupled quadrupole-time of flight mass spectrometry (UPLC-Q/TOF MS). STR-induced liver injury (SILI) was comprehensively evaluated by STR-treated mice mode. The histopathologic and biochemical analyses were performed to evaluate liver injury levels. Subsequently, network pharmacology and multi-omics were used to analyze the potential mechanism of SILI in vivo. And the target genes were further verified by Western blot.

RESULTS

A total of 152 compounds were identified or tentatively characterized in STR, including 29 alkaloids, 21 organic acids, 75 flavonoids, 1 quinone, and 26 other types. Among them, 19 components were presented in STR-medicated serum. The histopathologic and biochemical analysis revealed that hepatic injury occurred after 4 weeks of intragastric administration of STR. Network pharmacology analysis revealed that IL6, TNF, STAT3, etc. were the main core targets, and the bile secretion might play a key role in SILI. The metabolic pathways such as taurine and hypotaurine metabolism, purine metabolism, and vitamin B6 metabolism were identified in the STR exposed groups. Among them, taurine, hypotaurine, hypoxanthine, pyridoxal, and 4-pyridoxate were selected based on their high impact value and potential biological function in the process of liver injury post STR treatment.

CONCLUSIONS

The mechanism and material foundation of SILI were revealed and profiled by a multi-omics strategy combined with network pharmacology and chemical profiling. Meanwhile, new insights were taken into understand the pathological mechanism of SILI.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

The metabolites mainly composed of lipids in tongue coating are non-invasive potential biomarkers for chronic gastritis

The changes in tongue coating metabolites in patients with chronic gastritis (CG) under different gastroscopy indicators were analyzed, and these metabolites were screened for potential non-invasive biomarkers to assist in the diagnosis of chronic gastritis. The technology of gas chromatography and liquid chromatography combined with mass spectrometry has been used to more comprehensively detect tongue coating metabolites of 350 CG patients. Spearman correlation analysis and random forest algorithm were used to screen metabolites that can serve as potential biomarkers. Compared with healthy individuals, CG group showed significant changes in the content of 101 metabolites, with an increase in the content of 54 metabolites and a decrease in the content of 47 metabolites. These differential metabolites are mainly composed of 47 lipids and lipid like substances. 1 metabolite was associated with bile reflux, 1 metabolite was associated with gastric mucosal erosion, 10 metabolites were associated with atrophy, 10 metabolites were associated with intestinal metaplasia, and 3 metabolites were associated with Helicobacter pylori infection. The ROC model composed of 5 metabolites can distinguish between CG group and healthy individuals, with an accuracy of 95.4%. The ROC model composed of 5,6-Dihydroxyindole can distinguish between chronic superficial gastritis group and chronic atrophic gastritis group, with an accuracy of 75.3%. The lipids and lipid like metabolites were the main abnormal metabolites in patients with chronic gastritis. It was worth noting that the content of Sphinganine 1-phase, 4-Ipomenol, and Nervonic acid in tongue coating increased, and the content of 1-Methyladenosine and 3-Hydroxycapric acid decreased, which helped to identify CG patients. The decrease in the content of 5,6-dihydroxyindole reminded patients that the development trend of CG was shifting from superficial to atrophic or even intestinal metaplasia. The detection of these metabolic markers of tongue coating was expected to be developed as a non-invasive and convenient technology in the future to assist us in monitoring and diagnosing the occurrence and development of CG.

Lipophilic and Hydrophilic Metabolites as Descriptors of Different Coffee Beverages

Coffee is a widely consumed beverage rich in bioactive phytochemicals. This study investigated the effect of brewing method on the profile of potential bioactive compounds in different coffee beverages using metabolomics and lipidomics based on UHPLC-MS/QTOF. The oil contents of the espresso coffee (EC), pot coffee (PC), instant coffee (IC), and filter coffee (FC) beverages studied were 0.13% ± 0.002, 0.12% ± 0.001, 0.04% ± 0.002, and 0.03% ± 0.003, respectively. Univariate analysis indicated significant differences (P < 0.001) in oil content when EC and PC beverages were compared with IC and FC beverages. Principal component analysis revealed similarities in the lipid profiles of FC and EC beverages and the hydrophilic profiles of PC and FC beverages. The EC beverage had the highest intensity of hydrophilic compounds such as adenine, theobromine, chlorogenic acid, and caffeine. The PC beverage was the most abundant in triglycerides, phosphatidylcholine, and diterpenes. Cafestol and kahweol esters, but not their free forms, were the most abundant diterpenes in the PC beverage. This work provides information on the differences in the profile of potentially bioactive compounds in four commonly consumed coffee beverage types and, thus, on the possible differences in the health effects of these coffee beverage types.

Polar metabolomics using trichloroacetic acid extraction and porous graphitic carbon stationary phase

INTRODUCTION

Accurately identifying and quantifying polar metabolites using untargeted metabolomics has proven challenging in comparison to mid to non-polar metabolites. Hydrophilic interaction chromatography and gas chromatography-mass spectrometry are predominantly used to target polar metabolites.

OBJECTIVES

This study aims to demonstrate a simple one-step extraction combined with liquid chromatography-mass spectrometry (LC-MS) that reliably retains polar metabolites.

METHODS

The method involves a MilliQ + 10% trichloroacetic acid extraction from 6 healthy individuals serum, combined with porous graphitic carbon liquid chromatography-mass spectrometry (LC-MS). The coefficient of variation (CV) assessed retention reliability of polar metabolites with logP as low as - 9. QreSS (Quantification, Retention, and System Suitability) internal standards determined the method's consistency and recovery efficiency.

RESULTS

The method demonstrated reliable retention (CV < 0.30) of polar metabolites within a logP range of - 9.1 to 5.6. QreSS internal standards confirmed consistent performance (CV < 0.16) and effective recovery (70-130%) of polar to mid-polar metabolites. Quality control dilution series demonstrated that ~ 80% of annotated metabolites could be accurately quantified (Pearson's correlation coefficient > 0.80) within their concentration range. Repeatability was demonstrated through clustering of repeated extractions from a single sample.

CONCLUSION

This LC-MS method is better suited to covering the polar segment of the metabolome than current methods, offering a reliable and efficient approach for accurate quantification of polar metabolites in untargeted metabolomics.

Impact of four different extraction methods and three different reconstitution solvents on the untargeted metabolomics analysis of human and rat urine samples

Unsuitable sample preparation may result in loss of important analytes and consequently affect the outcome of untargeted metabolomics. Due to species differences, different sample preparations may be required within the same biological matrix. The study aimed to compare the in-house sample preparation method for urine with methods from literature and to investigate the transferability of sample preparation from human urine to rat urine. A total of 12 different conditions for protein precipitation were tested, combining four different extraction solvents and three different reconstitution solvents using an untargeted liquid-chromatography high resolution mass spectrometry (LC-HRMS) metabolomics analysis. Evaluation was done based on the impact on feature count, their detectability, as well as the reproducibility of selected compounds. Results showed that a combination of methanol as extraction and acetonitrile/water (75/25) as reconstitution solvent provided improved results at least regarding the total feature count. Additionally, it was found that a higher amount of methanol was most suitable for extraction of rat urine among the tested conditions. In comparison, human urine requires significantly less volume of extraction solvent. Overall, it is recommended to systematically optimize both, the extraction method, and the reconstitution solvent for the used biofluid and the individual analytical settings.

Loss of Volatile Metabolites during Concentration of Metabolomic Extracts

Volatile metabolites can be lost during the preanalytical stage of metabolomic analysis. This work is aimed at the experimental and theoretical study of mechanisms of volatile substance evaporation and retention in the residues during the drying of extract solutions. We demonstrate that solvent evaporation leads to the unavoidable loss of nondissociating volatile metabolites with low boiling points and high vapor pressures (such as acetone and ethanol). The retention of dissociating volatile compounds (primarily organic acids RH) during the evaporation depends on the presence of buffer salts in solution, which are responsible for maintaining the neutral pH. An acid remains in the solution as long as it is present predominantly in the dissociated R- state. At the very last stage of solvent evaporation, buffer salts precipitate, forming a solid matrix for metabolite trapping in the residue. At the same time, buffer precipitation leads to a decrease of the solution pH, increase of the portion of RH in associated state, and acceleration of RH volatilization. The RH recovery is thus determined by the competition between the solute volatilization in the associated RH form and metabolite trapping in the solid matrix. The retention of volatile acids in the residue after extract drying can be improved either by adding buffer salts to maintain high pH or by incomplete sample drying.

A pseudo-targeted metabolomics for discovery of potential biomarkers of cardiac hypertrophy in rats

Cardiac hypertrophy (CH) is one of the stages in the occurrence and development of severe cardiovascular diseases, and exploring its biomarkers is beneficial for delaying the progression of severe cardiovascular diseases. In this research, we established a comprehensive and highly efficient pseudotargeted metabolomics method, which demonstrated a superior capacity to identify differential metabolites when compared to traditionaluntargeted metabolomics. The intra/inter-day precision and reproducibility results proved the method is reliable and precise. The established method was then applied to seek the potential differentiated metabolic biomarkers of cardiac hypertrophy (CH) rats, and oxylipins, phosphorylcholine (PC), lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE), Krebs cycle intermediates, carnitines, amino acids, and bile acids were disclosed to be the possible differentiate components. Their metabolic pathway analysis revealed that the potential metabolic alterations in CH rats were mainly associated with phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, citrate cycle, glyoxylate and dicarboxylate metabolism, and tyrosine metabolism. In sum, this research provided a comprehensiveand reliable LC-MS/MS MRM platform for pseudo-targeted metabolomics investigation of disease condition, and some interesting potential biomarkers were disclosed for CH, which merit further exploration in the future.

Phospholipidomics in Clinical Trials for Brain Disorders: Advancing our Understanding and Therapeutic Potentials

Phospholipidomics is a specialized branch of lipidomics that focuses on the characterization and quantification of phospholipids. By using sensitive analytical techniques, phospholipidomics enables researchers to better understand the metabolism and activities of phospholipids in brain disorders such as Alzheimer's and Parkinson's diseases. In the brain, identifying specific phospholipid biomarkers can offer valuable insights into the underlying molecular features and biochemistry of these diseases through a variety of sensitive analytical techniques. Phospholipidomics has emerged as a promising tool in clinical studies, with immense potential to advance our knowledge of neurological diseases and enhance diagnosis and treatment options for patients. In the present review paper, we discussed numerous applications of phospholipidomics tools in clinical studies, with a particular focus on the neurological field. By exploring phospholipids' functions in neurological diseases and the potential of phospholipidomics in clinical research, we provided valuable insights that could aid researchers and clinicians in harnessing the full prospective of this innovative practice and improve patient outcomes by providing more potent treatments for neurological diseases.

Determination of amino acid metabolic diseases from dried blood spots with a rapid extraction method coupled with nanoelectrospray ionization mass spectrometry

In this work, a rapid extraction method of methanol/water (95:5 v/v) with 0.1% formic acid was developed for extraction of amino acids from dried blood spots (DBS) for inherited metabolic diseases (IMDs). The combination of this extraction procedure with nanoelectrospray ionization mass spectrometry (nESI-MS) was used for the rapid analysis of amino acids. This approach with eliminating the chromatographic separation required only 2 min for the extraction of amino acids from DBS, which simplified the configuration and improved the timeliness. Dependence of the sensitivity on the operating parameters was systematically investigated. The LOD of 91.2-262.5 nmol/L and LOQ of 304-875 nmol/L which were lower than the cut-off values were obtained for amino acids within DBS. The accuracy was determined to be 93.82%-103.07% and the precision was determined to be less than 8.30%. The effectiveness of this method was also compared with the gold standard method (e.g., LC-MS/MS). The desalination mechanism was explored with interference mainly originated from the blood. These findings indicated that the rapid extraction procedure coupled with nESI-MS is capable of screening indicators for IMDs in complex biological samples.

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.

Global status and trends of metabolomics in diabetes: A literature visualization knowledge graph study

BACKGROUND

Diabetes is a metabolic disease characterized by hyperglycemia, which has increased the global medical burden and is also the main cause of death in most countries.

AIM

To understand the knowledge structure of global development status, research focus, and future trend of the relationship between diabetes and metabolomics in the past 20 years.

METHODS

The articles about the relationship between diabetes and metabolomics in the Web of Science Core Collection were retrieved from 2002 to October 23, 2023, and the relevant information was analyzed using CiteSpace6.2.2R (CiteSpace), VOSviewer6.1.18 (VOSviewer), and Bibliometrix software under R language.

RESULTS

A total of 3123 publications were included from 2002 to 2022. In the past two decades, the number of publications and citations in this field has continued to increase. The United States, China, Germany, the United Kingdom, and other relevant funds, institutions, and authors have significantly contributed to this field. Scientific Reports and PLoS One are the journals with the most publications and the most citations. Through keyword co-occurrence and cluster analysis, the closely related keywords are "insulin resistance", "risk", "obesity", "oxidative stress", "metabolomics", "metabolites" and "biomarkers". Keyword clustering included cardiovascular disease, gut microbiota, metabonomics, diabetic nephropathy, molecular docking, gestational diabetes mellitus, oxidative stress, and insulin resistance. Burst detection analysis of keyword depicted that "Gene", "microbiota", "validation", "kidney disease", "antioxidant activity", "untargeted metabolomics", "management", and "accumulation" are knowledge frontiers in recent years.

CONCLUSION

The relationship between metabolomics and diabetes is receiving extensive attention. Diabetic nephropathy, diabetic cardiovascular disease, and kidney disease are key diseases for future research in this field. Gut microbiota, molecular docking, and untargeted metabolomics are key research directions in the future. Antioxidant activity, gene, validation, mass spectrometry, management, and accumulation are at the forefront of knowledge frontiers in this field.

Postmortem metabolomics: influence of time since death on the level of endogenous compounds in human femoral blood. Necessary to be considered in metabolome study planning?

INTRODUCTION

The (un)targeted analysis of endogenous compounds has gained interest in the field of forensic postmortem investigations. The blood metabolome is influenced by many factors, and postmortem specimens are considered particularly challenging due to unpredictable decomposition processes.

OBJECTIVES

This study aimed to systematically investigate the influence of the time since death on endogenous compounds and its relevance in designing postmortem metabolome studies.

METHODS

Femoral blood samples of 427 authentic postmortem cases, were collected at two time points after death (854 samples in total; t1: admission to the institute, 1.3-290 h; t2: autopsy, 11-478 h; median ∆t = 71 h). All samples were analyzed using an untargeted metabolome approach, and peak areas were determined for 38 compounds (acylcarnitines, amino acids, phospholipids, and others). Differences between t2 and t1 were assessed by Wilcoxon signed-ranked test (p < 0.05). Moreover, all samples (n = 854) were binned into time groups (6 h, 12 h, or 24 h intervals) and compared by Kruskal-Wallis/Dunn's multiple comparison tests (p < 0.05 each) to investigate the effect of the estimated time since death.

RESULTS

Except for serine, threonine, and PC 34:1, all tested analytes revealed statistically significant changes between t1 and t2 (highest median increase 166%). Unpaired analysis of all 854 blood samples in-between groups indicated similar results. Significant differences were typically observed between blood samples collected within the first and later than 48 h after death, respectively.

CONCLUSIONS

To improve the consistency of comprehensive data evaluation in postmortem metabolome studies, it seems advisable to only include specimens collected within the first 2 days after death.

Behavior, antioxidant, and metabolomics effects of Allium tuncelianum

Allium species are consumed extensively as folkloric medicine and dietary elements, but limited studies have been conducted on them. In this study, the effects of an ethanol-water extract obtained from the underground bulb of Allium tuncelianum (Kollmann) Özhatay, B. Mathew & Şiraneci (AT) on the behavioral, antioxidant, and metabolite parameters in rats were evaluated. AT was administered orally once a day at doses of 100 and 400 mg/kg to male Wistar albino rats for 10 consecutive days. The elevated plus maze, rotarod, and hotplate tests were used to examine anxiety-like behaviors, locomotor activities, and pain perception in the rats, respectively. Additionally, untargeted metabolomic analyses were performed on plasma samples and AT extracts using two orthogonal analytical platforms. The phenolic components, mainly fumaric acid, malic acid, vanillic acid, quercetin-3-arabinoside, hydrocinnamic acid, and gallocatechin, were determined in the extract. In addition, arbutin, salicylic acid, trehalose, and nicotinic acid were analyzed in the extract for the first time. The AT extract did not decrease the catalase, glutathione peroxidase, or superoxide dismutase levels; however, diazepam decreased some of those parameters significantly in the brain, liver, and kidney. Although both the AT and diazepam treatments resulted in an increase in anxiolytic-like effects compared to the control group, no significant differences were observed (p > .05). In the metabolomic analysis, significant changes were observed in the rats treated with AT and diazepam, and they caused significant changes in some metabolic pathways, including amino acid and fatty acid metabolism, compared to the control.

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.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40-60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

Liquid chromatography-electrospray ionization-mass spectrometry/mass spectrometry characterization of depsides and depsidones from the Chilean lichen Parmotrema perlatum

Lichens are recognized by their unique compounds and diverse applications in food, medicines, and cosmetics. Using ultra-high pressure liquid chromatography, coupled with a high-resolution mass spectrometer, metabolomic profiling of the lichen Parmotrema perlatum, from a methanolic extract, was performed. Based on characteristic fragmentation patterns, twenty-five lichenic substances were tentatively identified including 5 depsides, 12 depsidones, 2 diphenyl ethers, 1 aromatic considered as possible artifact, 1 dibenzofuran, 1 carbohydrate, 1 organic acid, and 2 undefined compounds. To the best of our knowledge, this is a more complete report of their phytochemistry from P perlatum. Our findings of the P perlatum profile may contribute and complement the current data of the Parmotrema genus.

Advancements in CHO metabolomics: techniques, current state and evolving methodologies

Background: Investigating the metabolic behaviour of different cellular phenotypes, i.e., good/bad grower and/or producer, in production culture is important to identify the key metabolite(s)/pathway(s) that regulate cell growth and/or recombinant protein production to improve the overall yield. Currently, LC-MS, GC-MS and NMR are the most used and advanced technologies for investigating the metabolome. Although contributed significantly in the domain, each technique has its own biasness towards specific metabolites or class of metabolites due to various reasons including variability in the concept of working, sample preparation, metabolite-extraction methods, metabolite identification tools, and databases. As a result, the application of appropriate analytical technique(s) is very critical. Purpose and scope: This review provides a state-of-the-art technological insights and overview of metabolic mechanisms involved in regulation of cell growth and/or recombinant protein production for improving yield from CHO cultures. Summary and conclusion: In this review, the advancements in CHO metabolomics over the last 10 years are traced based on a bibliometric analysis of previous publications and discussed. With the technical advancement in the domain of LC-MS, GC-MS and NMR, metabolites of glycolytic and nucleotide biosynthesis pathway (glucose, fructose, pyruvate and phenylalanine, threonine, tryptophan, arginine, valine, asparagine, and serine, etc.) were observed to be upregulated in exponential-phase thereby potentially associated with cell growth regulation, whereas metabolites/intermediates of TCA, oxidative phosphorylation (aspartate, glutamate, succinate, malate, fumarate and citrate), intracellular NAD+/NADH ratio, and glutathione metabolic pathways were observed to be upregulated in stationary-phase and hence potentially associated with increased cell-specific productivity in CHO bioprocess. Moreover, each of technique has its own bias towards metabolite identification, indicating their complementarity, along with a number of critical gaps in the CHO metabolomics pipeline and hence first time discussed here to identify their potential remedies. This knowledge may help in future study designs to improve the metabolomic coverage facilitating identification of the metabolites/pathways which might get missed otherwise and explore the full potential of metabolomics for improving the CHO bioprocess performances.

In Silico and Chromatographic Methods for Analysis of Biotransformation of Prospective Neuroprotective Pyrrole-Based Hydrazone in Isolated Rat Hepatocytes

In the current study, chromatographic and in silico techniques were applied to investigate the biotransformation of ethyl 5-(4-bromophenyl)-1-(2-(2-(2-hydroxybenzylidene) hydrazinyl)-2-oxoethyl)-2-methyl-1H-pyrrole-3-carboxylate (11b) in hepatocytic media. The initial chromatographic procedure was based on the employment of the conventional octadecyl stationary phase method for estimation of the chemical stability. Subsequently, a novel and rapid chromatographic approach based on a phenyl-hexyl column was developed, aiming to separate the possible metabolites. Both methods were performed on a Dionex 3000 ThermoScientific (ACM 2, Sofia, Bulgaria) device equipped with a diode array detector set up at 272 and 279 nm for analytes detection. An acetonitrile: phosphate buffer of pH 3.5: methanol (17:30:53 v/v/v) was eluted isocratically as a mobile phase with a 1 mL/min flow rate. A preliminary purification from the biological media was achieved by protein precipitation with methanol. A validation procedure was carried out, where the method was found to correspond to all ICH (Q2) and M10 set criteria. Additionally, an in silico-based approach with the online server BioTransformer 3.0 was applied in an attempt to predict the possible metabolites of the title compound 11b. It was hypothesized that four CYP450 isoforms (1A2, 2C9, 3A4, and 2C8) were involved in the phase I metabolism, resulting in the formation of 12 metabolites. Moreover, docking studies were conducted to evaluate the formation of stable complexes between 11b and the aforementioned isoforms. The obtained data indicated three metabolites as the most probable products, two of which (M9_11b and M10_11b) were synthesized by a classical approach for verification. Finally, liquid chromatography with a mass detector was implemented for comprehensive and summarized analysis, and the obtained results revealed that the metabolism of the 11b proceeds possibly with the formation of glucuronide and glycine conjugate of M11_11b.

Metabolomics approach to understand molecular mechanisms involved in fungal pathogen-citrus pathosystems

Citrus is a crucial crop with a significant economic impact globally. However, postharvest decay caused by fungal pathogens poses a considerable threat, leading to substantial financial losses. Penicillium digitatum, Penicillium italicum, Geotrichum citri-aurantii and Phyllosticta citricarpa are the main fungal pathogens, causing green mold, blue mold, sour rot and citrus black spot diseases, respectively. The use of chemical fungicides as a control strategy in citrus raises concerns about food and environmental safety. Therefore, understanding the molecular basis of host-pathogen interactions is essential to find safer alternatives. This review highlights the potential of the metabolomics approach in the search for bioactive compounds involved in the pathogen-citrus interaction, and how the integration of metabolomics and genomics contributes to the understanding of secondary metabolites associated with fungal virulence and the fungal infection mechanisms. Our goal is to provide a pipeline combining metabolomics and genomics that can effectively guide researchers to perform studies aiming to contribute to the understanding of the fundamental chemical and biochemical aspects of pathogen-host interactions, in order to effectively develop new alternatives for fungal diseases in citrus cultivation. We intend to inspire the scientific community to question unexplored biological systems, and to employ diverse analytical approaches and metabolomics techniques to address outstanding questions about the non-studied pathosystems from a chemical biology perspective.

High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 μL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983-1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Metabolomics-driven investigation of plant defense response against pest and pathogen attack

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Untargeted Metabolomics Based on UPLC-Q-Exactive-Orbitrap-MS/MS Revealed the Differences and Correlations between Different Parts of the Root of Paeonia lactiflora Pall

BACKGROUND

Paeonia lactiflora Pall. (PLP) is a plant with excellent ornamental and therapeutic value that can be utilized in traditional Chinese medicine as Paeoniae Radix Alba (PRA) and Paeoniae Radix Rubra (PRR). PRA must undergo the "peeling" process, which involves removing the cork and a portion of the phloem. PLP's biological function is strongly linked to its secondary metabolites, and the distribution of metabolites in different regions of the PLP rhizome causes changes in efficacy when PLP is processed into various therapeutic compounds.

METHODS

The metabolites of the cork (cor), phloem (phl), and xylem (xyl) were examined in the roots of PLP using a metabolomics approach based on UPLC-Q-Exactive-Orbitrap-MS/MS (UPLC-MS/MS), and the differential metabolites were evaluated using multivariate analysis.

RESULTS

Significant changes were observed among the cor, phl, and xyl samples. In both positive and negative ion modes, a total of 15,429 peaks were detected and 7366 metabolites were identified. A total of 525 cor-phl differential metabolites, 452 cor-xyl differential metabolites, and 328 phl-xyl differential metabolites were evaluated. Flavonoids, monoterpene glycosides, fatty acids, sugar derivatives, and carbohydrates were among the top 50 dissimilar chemicals. The key divergent metabolic pathways include linoleic acid metabolism, galactose metabolism, ABC transporters, arginine biosynthesis, and flavonoid biosynthesis.

CONCLUSION

The cor, phl, and xyl of PLP roots exhibit significantly different metabolite types and metabolic pathways; therefore, "peeling" may impact the pharmaceutical effect of PLP. This study represents the first metabolomics analysis of the PLP rhizome, laying the groundwork for the isolation and identification of PLP pharmacological activity, as well as the quality evaluation and efficacy exploration of PLP.

Genome-Wide Characterization of Differentially Expressed Scent Genes in the MEP Control Network of the Flower of Lilium 'Sorbonne'

Fragrance is an important feature of ornamental lilies. Components of volatile substances and important genes for monoterpene synthesis in the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway were examined in this study. Twenty volatile compounds (2 in the budding stage, 3 in the initial flowering stage, 7 in the semi-flowering stage, 17 in the full-flowering stage, and 5 in withering stage) were detected in the Oriental lily 'Sorbonne' using gas chromatography-mass spectrometry. The semi- and full-flowering stages were key periods for volatile substance production and enzyme function. Sequence assembly from samples collected during all flowering stages resulted in the detection of 274,849 genes and 129,017 transcripts. RNA sequencing and heatmapping led to the detection of genes in the MEP monoterpene metabolism pathway. Through gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, we extracted key genes (LiDXS2, LiLIS, and LiMYS) and transcription factors (in the bHLH, MYB, HD-ZIP, and NAC families) associated with the MEP pathway. Tissue localization revealed that LiDXS2, LiLIS, and LiMYS were expressed in Lilium 'Sorbonne' petals in the full-flowering stage. Genes regulating the 1-deoxy-D-X-lignone-5-phosphate synthase family of rate-limiting enzymes, involved in the first step of monoterpene synthesis, showed high expression in the semi- and full-flowering stages. LiDXS2 was cloned and localized in chloroplast subcells. The relative expression of terpene-related genes in the MEP and mevalonic acid pathways of wild-type and LiLIS/LiMYS transgenic Arabidopsis thaliana, and changes in chemical composition, confirmed that LiLIS/LiMYS regulates the monoterpene synthesis pathway. The results of this study provide a theoretical basis for the synthesis of lily aromatic substances and the cultivation of new garden flower varieties.

Thyroid cancer cell metabolism: A glance into cell culture system-based metabolomics approaches

Thyroid cancer is the most common malignancy of the endocrine system and the seventh most prevalent cancer in women worldwide. It is a complex and diverse disease characterized by heterogeneity, underscoring the importance of understanding the underlying metabolic alterations within tumor cells. Metabolomics technologies offer a powerful toolset to explore and identify endogenous and exogenous biochemical reaction products, providing crucial insights into the intricate metabolic pathways and processes within living cells. Metabolism plays a central role in cell function, making metabolomics a valuable reflection of a cell's phenotype. In the OMICs era, metabolomics analysis of cells brings numerous advantages over existing methods, propelling cell metabolomics as an emerging field with vast potential for investigating metabolic pathways and their perturbation in pathophysiological conditions. This review article aims to look into recent developments in applying metabolomics for characterizing and interpreting the cellular metabolome in thyroid cancer cell lines, exploring their unique metabolic characteristics. Understanding the metabolic alterations in tumor cells can lead to the identification of critical nodes in the metabolic network that could be targeted for therapeutic intervention.

Novel Ag-modified vanadate nanosheets for determination of small organic molecules with laser desorption ionization mass spectrometry

Laser desorption ionization mass spectrometry (LDI-MS) aroused intensive concerns for the merits of label-free and high-throughput analysis. Here, we designed a silver nanoparticles (AgNP)-modified indium vanadate nanosheets with doping samarium (AgNP@InVO4:Sm) nanosheets. The developed AgNP@InVO4:Sm nanosheets (AIVON) were synthesized based on the microemulsion-mediated solvothermal method and ultraviolet-assisted in situ formation of AgNP, then for the first time applied as a matrix in LDI-MS analysis. With the advantages including enhanced MS signal, little matrix-related background, high reproducibility, and good salt tolerance, AIVON exhibited much better prospect than non-modified indium vanadate nanosheets with doping samarium (IVON) and traditional organic matrix, thus allowing sensitive MS detection for a wide range of low-molecular-weight (LMW) molecules. Moreover, by coupling with headspace sampling thin-film microextraction (TFME), a kind of representative pollutant chlorophenols were identified and quantified via AIVON-assisted LDI-MS in environmental and biological samples. Volatile LMW pollutants could be preconcentrated after TFME, hence a sensitive and rapid assay with negligible sample matrix effect was realized by using AIVON-assisted LDI-MS. It is anticipated that this novel nano-matrix AIVON and the proposed TFME coupling detection strategy were of competitive merits for LDI-MS analysis in the fields of environment, biomedicine, and agriculture.

Exploiting volatile fingerprints for bladder cancer diagnosis: A scoping review of metabolomics and sensor-based approaches

Bladder cancer (BC) represents a significant global health concern, for which early detection is essential to improve patient outcomes. This review evaluates the potential of the urinary volatile organic compounds (VOCs) as biomarkers for detecting and staging BC. The methods used include gas chromatography-mass spectrometry (GC-MS)-based metabolomics and electronic-nose (e-nose) sensors. The GC-MS studies that have been published reveal diverse results in terms of diagnostic performance. The sensitivities range from 27 % to an impressive 97 %, while specificities vary between 43 % and 94 %. Furthermore, the accuracies reported in these studies range from 80 to 89 %. In the urine of BC patients, a total of 80 VOCs were discovered to be significantly altered when compared to controls. These VOCs encompassed a variety of chemical classes such as alcohols, aldehydes, alkanes, aromatic compounds, fatty acids, ketones, and terpenoids, among others. Conversely, e-nose-based studies displayed sensitivities from 60 to 100 %, specificities from 53 to 96 %, and accuracies from 65 to 97 %. Interestingly, conductive polymer-based sensors performed better, followed by metal oxide semiconductor and optical sensors. GC-MS studies have shown improved performance in detecting early stages and low-grade tumors, providing valuable insights into staging. Based on these findings, VOC-based diagnostic tools hold great promise for early BC detection and staging. Further studies are needed to validate biomarkers and their classification performance. In the future, advancements in VOC profiling technologies may significantly contribute to improving the overall survival and quality of life for BC patients.

Metabolomic profiling of human bladder tissue extracts

INTRODUCTION

Bladder cancer is a common malignancy affecting the urinary tract and effective biomarkers and for which monitoring therapeutic interventions have yet to be identified.

OBJECTIVES

Major aim of this work was to perform metabolomic profiling of human bladder cancer and adjacent normal tissue and to evaluate cancer biomarkers.

METHODS

This study utilized nuclear magnetic resonance (NMR) and high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS) methods to investigate polar metabolite profiles in tissue samples from 99 bladder cancer patients.

RESULTS

Through NMR spectroscopy, six tissue metabolites were identified and quantified as potential indicators of bladder cancer, while LDI-MS allowed detection of 34 compounds which distinguished cancer tissue samples from adjacent normal tissue. Thirteen characteristic tissue metabolites were also found to differentiate bladder cancer tumor grades and thirteen metabolites were correlated with tumor stages. Receiver-operating characteristics analysis showed high predictive power for all three types of metabolomics data, with area under the curve (AUC) values greater than 0.853.

CONCLUSION

To date, this is the first study in which bladder human normal tissues adjacent to cancerous tissues are analyzed using both NMR and MS method. These findings suggest that the metabolite markers identified in this study may be useful for the detection and monitoring of bladder cancer stages and grades.

Predicting Retention Time in Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography High-Resolution Tandem Mass Spectrometry (Unified-HILIC/AEX/HRMS/MS) for Comprehensive Structural Annotation of Polar Metabolome

The accuracy of the structural annotation of unidentified peaks obtained in metabolomic analysis using liquid chromatography/tandem mass spectrometry (LC/MS/MS) can be enhanced using retention time (RT) information as well as precursor and product ions. Unified-hydrophilic-interaction/anion-exchange liquid chromatography high-resolution tandem mass spectrometry (unified-HILIC/AEX/HRMS/MS) has been recently developed as an innovative method ideal for nontargeted polar metabolomics. However, the RT prediction for unified-HILIC/AEX has not been developed because of the complex separation mechanism characterized by the continuous transition of the separation modes from HILIC to AEX. In this study, we propose an RT prediction model of unified-HILIC/AEX/HRMS/MS, which enables the comprehensive structural annotation of polar metabolites. With training data for 203 polar metabolites, we ranked the feature importance using a random forest among 12,420 molecular descriptors (MDs) and constructed an RT prediction model with 26 selected MDs. The accuracy of the RT model was evaluated using test data for 51 polar metabolites, and 86.3% of the ΔRTs (difference between measured and predicted RTs) were within ±1.50 min, with a mean absolute error of 0.80 min, indicating high RT prediction accuracy. Nontargeted metabolomic data from the NIST SRM 1950-Metabolites in frozen human plasma were analyzed using the developed RT model and in silico MS/MS prediction, resulting in a successful structural estimation of 216 polar metabolites, in addition to the 62 identified based on standards. The proposed model can help accelerate the structural annotation of unknown hydrophilic metabolites, which is a key issue in metabolomic research.

GC/MS and LC/MS serum metabolomic analysis of Chinese LN patients

China, being a densely populated nation, faces a substantial economic burden due to a high incidence of lupus nephritis (LN) cases. The concealed onset of LN has resulted in many individuals have missed the optimal timing for treatment. The aim of the research is to study the serum metabolomics of Chinese LN patients using gas chromatography (GC)/mass spectrometry (MS) and liquid chromatography (LC)/MS to identify potential diagnostic markers. Fifty LN patients and fifty normal controls, matched for Body Mass Index (BMI) and age, were selected. Serum analysis was conducted using GC/MS and LC/MS, followed by multivariate statistical analysis. Various multidimensional analyses, including principal component analysis, partial least squares discrimination analysis, and orthogonal partial least squares discrimination analysis, along with one-dimensional analyses such as t-tests, were performed. Metabolites with variable importance in projection value > 1 and a p-value < 0.05 were considered critical biomarkers for LN. Furthermore, identified biomarkers delineated relevant metabolic pathways, and a metabolic pathway map was obtained from the database. Forty-one metabolites were identified as potential LN biomarkers, primarily associated with immune regulation, energy metabolism, intestinal microbial metabolism, renal damage, and oxidative stress. The potential for diagnosing LN and other diseases through metabolomics is demonstrated. Future research should explore larger sample sizes, metabolomic comparisons across different diseases and health states, and integration of metabolomics with clinical diagnostics. Such studies will enhance the understanding of metabolomics in medical diagnosis and provide robust support for its practical application.

An omics review and perspective of researches on intrahepatic cholestasis of pregnancy

Intrahepatic cholestasis of pregnancy (ICP) is one of the common pregnancy complications that may threaten the health of both pregnant women and their fetuses. Hence, it is of vital importance to identify key moleculars and the associated functional pathways of ICP, which will help us to better understand the pathological mechanisms as well as to develop precise clinical biomarkers. The emerging and developing of multiple omics approaches enable comprehensive studies of the genome, transcriptome, proteome and metabolome of clinical samples. The present review collected and summarized the omics based studies of ICP, aiming to provide an overview of the current progress, limitations and future directions. Briefly, these studies covered a broad range of research contents by the comparing of different experimental groups including ICP patients, ICP subtypes, ICP fetuses, ICP models and other complications. Correspondingly, the studied samples contain various types of clinical samples, in vitro cultured tissues, cell lines and the samples from animal models. According to the main research objectives, we further categorized these studies into two groups: pathogenesis and diagnosis analyses. The pathogenesis studies identified tens of functional pathways that may represent the key regulatory events for the occurrence, progression, treatment and fetal effects of ICP. On the other hand, the diagnosis studies tested more than 40 potential models for the early-prediction, diagnosis, grading, prognosis or differential diagnosis of ICP. Apart from these achievements, we also evaluated the limitations of current studies, and emphasized that many aspects of clinical characteristics, sample processing, and analytical method can greatly affect the reliability and repeatability of omics results. Finally, we also pointed out several new directions for the omics based analyses of ICP and other perinatal associated conditions in the future.