Metabolite identification and quantitation in LC-MS/MS-based metabolomics

High-coverage identification of hydroxyl compounds based on pyridine derivatization-assisted liquid chromatography mass spectrometry

Hydroxyl compounds are widely present in plants and play essential roles in plant growth and development. High-coverage detection of hydroxyl compounds is crucial for understanding the physiological processes of plants. Despite the prevalence of chemical derivatization-assisted liquid chromatography-high resolution mass spectrometry (CD-LC-HRMS) in high-coverage detection of compounds with diverse functional groups, the confident identification of these compounds after derivatization remains a significant challenge. Herein, a novel method was developed for the identification of pyridine (PY)-derivatized hydroxyl compounds by comparing the MS/MS similarity of derivatized and corresponding underivatized compounds. Fragmentation rules of standards were summarized, and theoretical calculations have demonstrated the MS/MS similarity of PY-derivatized hydroxyl compounds with their underivatized counterparts. The effectiveness of the developed method was demonstrated by identifying PY-derivatized authentic standards. A total of 90 hydroxyl compounds were putatively identified in maize using the proposed method. This method can significantly enhance ionization efficiency with minimal impact on the quality of the MS/MS spectra, enabling the effective utilization of mass spectra databases for the identification of hydroxyl compounds.

Ex vivo T cell differentiation in adoptive immunotherapy manufacturing: Critical process parameters and analytical technologies

Adoptive immunotherapy shows great promise as a treatment for cancer and other diseases. Recent evidence suggests that the therapeutic efficacy of these cell-based therapies can be enhanced by the enrichment of less-differentiated T cell subpopulations in the therapeutic product, giving rise to a need for advanced manufacturing technologies capable of enriching of these subpopulations through regulation of T cell differentiation. Studies have shown that modifying certain critical process control parameters, such as cytokines, metabolites, amino acids, and culture environment, can effectively manipulate T cell differentiation in ex vivo cultures. Advanced process analytical technologies (PATs) are crucial for monitoring these parameters and the assessment of T cell differentiation during culture. In this review, we examine such critical process parameters and PATs, with an emphasis on their impact on enriching less-differentiated T cell population. We also discuss the limitations of current technologies and advocate for further efforts from the community to establish more stringent critical process parameters (CPPs) and develop more at-line/online PATs that are specific to T cell differentiation. These advancements will be essential to enable the manufacturing of more efficacious adoptive immunotherapy products.

Aspergillus Species from the Sabkha Marsh: Potential Antimicrobial and Anticancer Agents Revealed Through Molecular and Pharmacological Analysis

Introduction

This study aimed to investigate the fungal growth and diversity in the Sabkha marsh. The anti-bacterial properties of the isolated fungi were assessed using an agar disk diffusion assay, and the crude extracts were tested for their anticancer activities. Liquid chromatography-mass spectrometry was employed to identify the active compounds of the fungal secondary metabolites. In-silico studies were conducted to predict the toxicity, pharmacokinetic properties, and safety profiles of the identified compounds.

Results

The analysis revealed that the isolated fungi belonged to the Aspergillus species, specifically Aspergillus flavus and Aspergillus niger. The crude extract of A. flavus exhibited significant anticancer activity against various cancer cell lines, while the antifungal activities against pathogenic bacteria varied between the two fungi. Liquid chromatography-mass spectrometry analysis identified several compounds in the fungal isolates. In Aspergillus flavus, the compounds included Aflavinine, Dihydro-24-hydroxyaflavinine, Phomaligin A, Hydroxysydonic acid, Gregatin B, Pulvinulin A, Chrysogine, Aspergillic acid, Aflatoxin B1, and Aflatoxin G1. In Aspergillus niger, the compounds identified were atromentin, fonsecin B, firalenone, rubrofusarin, aurasperone E, aurasperone D, aurasperone C, nigerone, and αβ-dehydrocurvularin.

Conclusion

This study demonstrated promising fungal growth and diversity in the Sabkha marsh, with Aspergillus species being the most prevalent. The fungal crude extract showed anticancer activities against various cancer cell lines, while the antifungal activities against pathogenic bacteria varied between the two fungi. Future research should focus on investigating the antimicrobial activities of these fungi against multidrug-resistant bacteria and exploring the genetic changes in bacteria and cancer cells treated with these fungal extracts. Additionally, it is important to test the anticancer activity of the active compounds separately to determine which one is the active agent against cancer cells. This information can be used in drug development trials.

Cysteamine Nanoemulsion Delivery by Inhalation to Attenuate Adverse Effects of Exposure to Cigarette Smoke: A Metabolomics Study in Wistar Rats

There is a pressing need for novel pharmacological interventions and drug delivery innovations to attenuate the cigarette smoke-associated oxidative stress and lung disease. We report here on the attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) and metabolomics of Wistar rats exposed to cigarette smoke for 28 days. The animals were treated for 15 days with plain cysteamine given orally or cysteamine as nanoemulsion given orally or via inhalation. The study design also included two control groups as follows: rats exposed to cigarette smoke but did not receive a treatment (diseased control group) and rats neither exposed to cigarette smoke nor a treatment (normal control group). The targeted metabolomics using Parallel Reaction Monitoring showed that in the diseased control group, ornithine, nicotinamide, xanthine, hypoxanthine, and caprolactam were increased compared with the normal control group. In addition, (±)8(9)-DiHET, which was initially downregulated in the diseased control group, exhibited a reversal of this trend with cysteamine nanoemulsion given via inhalation. The cysteamine nanoemulsion delivered by inhalation highlighted the importance of the route of drug administration for targeting the lungs. To the best of our knowledge, this is the first work to use ATR-FTIR and metabolomics in Wistar rat lung tissues, suggesting how cysteamine nanoemulsion can potentially reduce cigarette smoke-induced oxidative damage. The metabolites reported herein have potential implications for discovery of novel theranostics and, thus, to cultivate diagnostic and therapeutic innovation for early prevention and treatment of cigarette smoke-associated lung diseases.

Metabolomics for dental caries diagnosis: Past, present, and future

Dental caries, a prevalent global infectious condition affecting over 95% of adults, remains elusive in its precise etiology. Addressing the complex dynamics of caries demands a thorough exploration of taxonomic, potential, active, and encoded functions within the oral ecosystem. Metabolomic profiling emerges as a crucial tool, offering immediate insights into microecosystem physiology and linking directly to the phenotype. Identified metabolites, indicative of caries status, play a pivotal role in unraveling the metabolic processes underlying the disease. Despite challenges in metabolite variability, the use of metabolomics, particularly via mass spectrometry and nuclear magnetic resonance spectroscopy, holds promise in caries research. This review comprehensively examines metabolomics in caries prevention, diagnosis, and treatment, highlighting distinct metabolite expression patterns and their associations with disease-related bacterial communities. Pioneering in approach, it integrates singular and combinatory metabolomics methodologies, diverse biofluids, and study designs, critically evaluating prior limitations while offering expert insights for future investigations. By synthesizing existing knowledge, this review significantly advances our comprehension of caries, providing a foundation for improved prevention and treatment strategies.

Metabolomics reveals changes in soil metabolic profiles during vegetation succession in karst area

Soil metabolites are critical in regulating the dynamics of ecosystem structure and function, particularly in fragile karst ecosystems. Clarification of response of soil metabolism to vegetation succession in karst areas will contribute to the overall understanding and management of karst soils. Here, we investigated the metabolite characteristics of karst soils with different vegetation stages (grassland, brushwood, secondary forest and primary forest) based on untargeted metabolomics. We confirmed that the abundance and composition of soil metabolites altered with vegetation succession. Of the 403 metabolites we found, 157 had significantly varied expression levels across vegetation soils, including mainly lipids and lipid-like molecules, phenylpropanoids and polyketides, organic acids and derivatives. Certain soil metabolites, such as maltotetraose and bifurcose, were sensitive to vegetation succession, increasing significantly from grassland to brushwood and then decreasing dramatically in secondary and primary forests, making them possible indicators of karst vegetation succession. In addition, soil metabolic pathways, such as galactose metabolism and biosynthesis of unsaturated fatty acids, also changed with vegetation succession. This study characterized the soil metabolic profile in different vegetation stages during karst secondary succession, which would provide new insights for the management of karst soils.

Fecal microbiota transplantation: current challenges and future landscapes

SUMMARYGiven the importance of gut microbial homeostasis in maintaining health, there has been considerable interest in developing innovative therapeutic strategies for restoring gut microbiota. One such approach, fecal microbiota transplantation (FMT), is the main "whole gut microbiome replacement" strategy and has been integrated into clinical practice guidelines for treating recurrent Clostridioides difficile infection (rCDI). Furthermore, the potential application of FMT in other indications such as inflammatory bowel disease (IBD), metabolic syndrome, and solid tumor malignancies is an area of intense interest and active research. However, the complex and variable nature of FMT makes it challenging to address its precise functionality and to assess clinical efficacy and safety in different disease contexts. In this review, we outline clinical applications, efficacy, durability, and safety of FMT and provide a comprehensive assessment of its procedural and administration aspects. The clinical applications of FMT in children and cancer immunotherapy are also described. We focus on data from human studies in IBD in contrast with rCDI to delineate the putative mechanisms of this treatment in IBD as a model, including colonization resistance and functional restoration through bacterial engraftment, modulating effects of virome/phageome, gut metabolome and host interactions, and immunoregulatory actions of FMT. Furthermore, we comprehensively review omics technologies, metagenomic approaches, and bioinformatics pipelines to characterize complex microbial communities and discuss their limitations. FMT regulatory challenges, ethical considerations, and pharmacomicrobiomics are also highlighted to shed light on future development of tailored microbiome-based therapeutics.

Soil metabolomics: Deciphering underground metabolic webs in terrestrial ecosystems

Soil metabolomics is an emerging approach for profiling diverse small molecule metabolites, i.e., metabolomes, in the soil. Soil metabolites, including fatty acids, amino acids, lipids, organic acids, sugars, and volatile organic compounds, often contain essential nutrients such as nitrogen, phosphorus, and sulfur and are directly linked to soil biogeochemical cycles driven by soil microorganisms. This paper presents an overview of methods for analyzing soil metabolites and the state-of-the-art of soil metabolomics in relation to soil nutrient cycling. We describe important applications of metabolomics in studying soil carbon cycling and sequestration, and the response of soil organic pools to changing environmental conditions. This includes using metabolomics to provide new insights into the close relationships between soil microbiome and metabolome, as well as responses of soil metabolome to plant and environmental stresses such as soil contamination. We also highlight the advantage of using soil metabolomics to study the biogeochemical cycles of elements and suggest that future research needs to better understand factors driving soil function and health.

Comparative Metabolomics and Network Pharmacology Analysis Reveal Shared Neuroprotective Mechanisms of Bacopa monnieri (L.) Wettst and Centella asiatica (L.) Urb

Bacopa monnieri (L.) Wettst and Centella asiatica (L.) Urb., two nootropics, are recognized in Indian Ayurvedic texts. Studies have attempted to understand their action as memory enhancers and neuroprotectants, but many molecular aspects remain unknown. We propose that Bacopa monnieri (L.) Wettst and Centella asiatica (L.) Urb. share common neuroprotective mechanisms. Mass spectrometry-based untargeted metabolomics and network pharmacology approach were used to identify potential protein targets for the metabolites from each extract. Phytochemical analyses and cell culture validation studies were also used to assess apoptosis and ROS activity using aqueous extracts prepared from both herbal powders. Further, docking studies were also performed using the LibDock protocol. Untargeted metabolomics and network pharmacology approach unveiled 2751 shared metabolites and 3439 and 2928 non-redundant metabolites from Bacopa monnieri and Centella asiatica extracts, respectively, suggesting a potential common neuroprotective mechanism among these extracts. Protein-target prediction highlighted 92.4% similarity among the proteins interacting with metabolites for these extracts. Among them, kinases mapped to MAPK, mTOR, and PI3K-AKT signaling pathways represented a predominant population. Our results highlight a significant similarity in the metabolome of Bacopa monnieri (L.) Wettst and Centella asiatica (L.) Urb., and their potential protein targets may be attributed to their common neuroprotective functions.

Methionine inducing carbohydrate esterase secretion of Trichoderma harzianum enhances the accessibility of substrate glycosidic bonds

BACKGROUND

The conversion of plant biomass into biochemicals is a promising way to alleviate energy shortage, which depends on efficient microbial saccharification and cellular metabolism. Trichoderma spp. have plentiful CAZymes systems that can utilize all-components of lignocellulose. Acetylation of polysaccharides causes nanostructure densification and hydrophobicity enhancement, which is an obstacle for glycoside hydrolases to hydrolyze glycosidic bonds. The improvement of deacetylation ability can effectively release the potential for polysaccharide degradation.

RESULTS

Ammonium sulfate addition facilitated the deacetylation of xylan by inducing the up-regulation of multiple carbohydrate esterases (CE3/CE4/CE15/CE16) of Trichoderma harzianum. Mainly, the pathway of ammonium-sulfate's cellular assimilates inducing up-regulation of the deacetylase gene (Thce3) was revealed. The intracellular metabolite changes were revealed through metabonomic analysis. Whole genome bisulfite sequencing identified a novel differentially methylated region (DMR) that existed in the ThgsfR2 promoter, and the DMR was closely related to lignocellulolytic response. ThGsfR2 was identified as a negative regulatory factor of Thce3, and methylation in ThgsfR2 promoter released the expression of Thce3. The up-regulation of CEs facilitated the substrate deacetylation.

CONCLUSION

Ammonium sulfate increased the polysaccharide deacetylation capacity by inducing the up-regulation of multiple carbohydrate esterases of T. harzianum, which removed the spatial barrier of the glycosidic bond and improved hydrophilicity, and ultimately increased the accessibility of glycosidic bond to glycoside hydrolases.

Unveiling the Nexus: Cellular Metabolomics Unravels the Impact of Estrogen on Nicotinamide Metabolism in Mitigating Rheumatoid Arthritis Pathogenesis

Rheumatoid arthritis (RA) is a metabolic joint disorder influenced by hormonal regulation, notably estrogen, which plays a cytoprotective role against inflammation. While estrogen's impact on RA pathogenesis has been studied, the altered metabolite expression under estrogen's influence remains unexplored. This study investigated the changes in the metabolome of synovial fibroblasts isolated from RA patients under 17β-estradiol (E2) using the liquid chromatography with tandem mass spectrometry (LC-MS/MS) approach followed by multivariate and biological pathway analysis along with in vitro validation. Results identified 3624 m/z, among which eight metabolites were significant (p < 0.05). Nicotinate and nicotinamide metabolism was found to be highly correlated with the treatment of E2, with metabolites NAD+ and 1-methynicotinamide (1-MNA) upregulated by E2 induction in RA-FLS. PharmMapper analysis identified potential gene targets of 1-MNA, which were further matched with RA gene targets, and thus, STAT1, MAPK14, MMP3, and MMP9 were concluded to be the common targets. E2 treatment affected the expression of these gene targets and ameliorated the development of oxidative stress associated with RA inflammation, which can be attributed to increased concentration of 1-MNA. Thus, an LC-MS/MS-based metabolomics study revealed the prominent role of estrogen in preventing inflammatory progression in RA by altering metabolite concentration, which can support its therapeutic capacity in remitting RA.

A current review of U.S. beef flavor I: Measuring beef flavor

Historically, consumer acceptance of beef was determined by tenderness. Developments in genetics and management over the last couple of decades have improved tenderness to the point that it is secondary to other factors in beef's taste. Flavor, however, is an extraordinarily complex taste attribute dependent on biological sensors in the mouth, sinus cavity, and jaws. The culinary industry has recently focused on innovative ways to give consumers new products satisfying their curiosity about different foods, especially proteins. Competition from plant-based, cell-based, and even other animal-based proteins provides diversity in consumers' ability to select a protein that satisfies their desire to include unique products in their diet. Consequently, the beef industry has focused on flavor for the last 10 to 15 years to determine whether it can provide the guardrails for beef consumption in the future. The U.S. beef industry formed a Flavor Working Group in 2012 composed of the authors listed here to investigate new and innovative ways to manage and measure beef flavor. The results of this working group have resulted in dozens of papers, presentations, abstracts, and symposia. The objective of this manuscript is to summarize the research developed by this working group and by others worldwide that have investigated methodologies that measure beef flavor. This paper will describe the strengths of the research in beef flavor measurement and point out future needs that might be identified as technology advances.

Metabolomics revealed more deleterious toxicity induced by the combined exposure of ammonia and nitrite on Ruditapes philippinarum compared to single exposure

NH3-N and NO2-N always co-exist in the aquatic environment, but there is not a clear opinion on their joint toxicities to the molluscs. Presently, clams Ruditapes philippinarum were challenged by environmental concentrations of NH3-N and NO2-N, singly or in combination, and analyzed by metabolomics approaches, enzyme assays and transmission electron microscope (TEM) observation. Results showed that some same KEGG pathways with different enriched-metabolites were detected in the three exposed groups within one day, and completely different profiles of metabolites were found in the rest of the exposure period. The combined exposure induced heavier and more lasting toxicities to the clams compared with their single exposure. ACP activity and the number of secondary lysosomes were significantly increased after the combined exposure. The present study shed light on the joint-toxicity mechanism of NH3-N and NO2-N, and provided fundamental data for the toxicity research on inorganic nitrogen.

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.

Metabolomic Profiles and Pathways in Osteoarthritic Human Cartilage: A Comparative Analysis with Healthy Cartilage

Osteoarthritis (OA) is a chronic joint disease with heterogenous metabolic pathology. To gain insight into OA-related metabolism, metabolite extracts from healthy (n = 11) and end-stage osteoarthritic cartilage (n = 35) were analyzed using liquid chromatography-mass spectrometry metabolomic profiling. Specific metabolites and metabolic pathways, including lipid and amino acid pathways, were differentially regulated in osteoarthritis-derived and healthy cartilage. The detected alterations in amino acids and lipids highlighted key differences in bioenergetic resources, matrix homeostasis, and mitochondrial alterations in OA-derived cartilage compared to healthy cartilage. Moreover, the metabolomic profiles of osteoarthritic cartilage separated into four distinct endotypes, highlighting the heterogenous nature of OA metabolism and the diverse landscape within the joint in patients. The results of this study demonstrate that human cartilage has distinct metabolomic profiles in healthy and end-stage OA patients. By taking a comprehensive approach to assess metabolic differences between healthy and osteoarthritic cartilage and within osteoarthritic cartilage alone, several metabolic pathways with distinct regulation patterns were detected. Additional investigation may lead to the identification of metabolites that may serve as valuable indicators of disease status or potential therapeutic targets.

Selected Ion Monitoring for Orbitrap-Based Metabolomics

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage.

Evaluation of urinary limonene metabolites as biomarkers of exposure to greenness

Exposure to plants is known to improve physical and mental health and living in areas of high vegetation is associated with better health. The addition of quantitative measures of greenness exposure at individual-level to other objective and subjective study measures will help establish cause-and-effect relationships between greenspaces and human health. Because limonene is one of the most abundant biogenic volatile organic compounds emitted by plants, we hypothesized that urinary metabolites of inhaled limonene can serve as biomarkers of exposure to greenness. To test our hypothesis, we analyzed urine samples collected from eight human volunteers after limonene inhalation or after greenness exposure using liquid chromatography-high resolution mass spectrometry-based profiling. Eighteen isomers of nine metabolites were detected in urine after limonene inhalation, and their kinetic parameters were estimated using nonlinear mixed effect models. Urinary levels of most abundant limonene metabolites were elevated after brief exposure to a forested area, and the ratio of urinary limonene metabolites provided evidence of recent exposure. The identities and structures of these metabolites were validated using stable isotope tracing and tandem mass spectral comparison. Together, these data suggest that urinary metabolites of limonene, especially uroterpenol glucuronide and dihydroperillic acid glucuronide, could be used as individualized biomarkers of greenness exposure.

Harnessing Schistosoma-associated metabolite changes in the human host to identify biomarkers of infection and morbidity: Where are we and what should we do next?

Schistosomiasis is the second most widespread parasitic disease affecting humans. A key component of today's infection control measures is the diagnosis and monitoring of infection, informing individual- and community-level treatment. However, newly acquired infections and/or low parasite burden are still difficult to diagnose reliably. Furthermore, even though the pathological consequence of schistosome egg sequestration in host tissues is well described, the evidence linking egg burden to morbidity is increasingly challenged, making it inadequate for pathology monitoring. In the last decades, omics-based instruments and methods have been developed, adjusted, and applied in parasitic research. In particular, the profiling of the most reliable determinants of phenotypes, metabolites by metabolomics, emerged as a powerful boost in the understanding of basic interactions within the human host during infection. As such, the fine detection of host metabolites produced upon exposure to parasites such as Schistosoma spp. and the ensuing progression of the disease are believed to enable the identification of Schistosoma spp. potential biomarkers of infection and associated pathology. However, attempts to provide such a comprehensive understanding of the alterations of the human metabolome during schistosomiasis are rare, limited in their design when performed, and mostly inconclusive. In this review, we aimed to briefly summarize the most robust advances in knowledge on the changes in host metabolic profile during Schistosoma infections and provide recommendations for approaches to optimize the identification of metabolomic signatures of human schistosomiasis.

Analytical techniques for screening of cannabis and derivatives from human hair specimens

Cannabis and associated substances are some of the most frequently abused drugs across the globe, mainly due to their anxiolytic and euphorigenic properties. Nowadays, the analysis of hair samples has been given high importance in forensic and analytical sciences and in clinical studies because they are associated with a low risk of infection, do not require complicated storage conditions, and offer a broad window of non-invasive detection. Analysis of hair samples is very easy compared to the analysis of blood, urine, and saliva samples. This review places particular emphasis on methodologies of analyzing hair samples containing cannabis, with a special focus on the preparation of samples for analysis, which involves screening and extraction techniques, followed by confirmatory assays. Through this manuscript, we have presented an overview of the available literature on the screening of cannabis using mass spectroscopy techniques. We have presented a detailed overview of the advantages and disadvantages of this technique, to establish it as a suitable method for the analysis of cannabis from hair samples.

Determination of alkaloid-inspired molecule vindeburnol in rabbit plasma by UPLC-HRMS and its application to pharmacokinetic studies and preliminary metabolite identification

The eburnamine-vincamine alkaloids exhibit a range of pharmacological activities. There is a limited understanding of the pharmacokinetics and pharmacodynamics of vindeburnol, a synthetic derivative of this chemical class of alkaloids. A fast and reliable UPLC-HRMS method was developed and validated to quantify vindeburnol in Soviet Chinchilla rabbit plasma from pharmacokinetics studies. An ultra-performance liquid chromatography system equipped with a Waters Acquity UPLC HSS T3 column was used for chromatographic separation by gradient elution with 0.1% (v/v) formic acid in water and acetonitrile. An Impact II QqTOF high-resolution mass spectrometer equipped with an Apollo II electrospray ionization source was used for analysis in positive mode; the ions [M+H]+m/z 269.1648 ± 0.003 and m/z 351.2067 ± 0.003 were monitored for vindeburnol and internal standard (vinpocetine), respectively. Preliminary metabolite profiling was also performed, and the pharmacokinetics of the identified metabolites were evaluated. The mean retention times for vindeburnol and vinpocetine were 2.0 and 3.5 min. The UPLC-HRMS method was validated with accuracy and precision within the 15% acceptance limit (8.2% and 11.0%, respectively). The mean extraction recovery value of vindeburnol from rabbit plasma was 77%. Pharmacokinetic evaluation of vindeburnol revealed that the compound is distributed rapidly with a short elimination half-life. Vindeburnol undergoes extensive first-pass metabolism and is metabolized into hydroxyvindeburnol and vindeburnol glucuronide.

The development and optimisation of an HPLC-based in vitro serum stability assay for a calcitonin gene-related peptide receptor antagonist peptide

Evaluation of the stability of peptide drug candidates in biological fluids, such as blood serum, is of high importance during the lead optimisation phase. Here, we describe the optimisation and validation of a method for the evaluation of the stability of a lead calcitonin gene-related peptide antagonist peptide (P006) in blood serum. After initially determining appropriate peptide and human serum concentrations and selection of the quenching reagent, the HPLC method optimisation used two experimental designs, Plackett-Burman design and Taguchi design. The analytical method was validated as complying with the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guidelines. The optimised method allowed the successful resolution of the parent peptide from its metabolites using RP-HPLC and identification of the major metabolites of P006 by mass spectrometry. This paradigm may be widely adopted as a robust early-stage platform for screening peptide stability to rule out candidates with low in vitro stability, which would likely translate into poor in vivo pharmacokinetics.

Non-target influence of imidacloprid residues on grape global metabolome and berry quality with the identification of metabolite biomarkers

This paper illustrates the non-target impact of imidacloprid (IM) residues on the grape global metabolome and biomarker identification with high-resolution mass spectrometry. IM was applied at the recommended dose (SD), and ten times SD (10 RD). The global metabolome analysis revealed that 21 metabolites were up- and down-regulated with IM SD treatment. In 10 RD, 9 metabolites were upregulated, and 28 were downregulated. Pathway enrichment analysis revealed the primary and secondary pathway disruption in grapes. Berry quality was affected with decrease in flavonoids by 32.97% in 10 RD; phenols were reduced by 53.93 in SD, 50.8% in 10 RD. The non-target and target study revealed the degradation of IM in grapes to desnitro-IM and IM-urea which were identified as a potential biomarker for IM residues in grapes, which would benefit the authentication of organic product. Overall, imidacloprid showed a significant impact on the grape metabolome and quality.

Physicochemical, microbiological and metabolomics changes in yogurt supplemented with lactosucrose

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

Metabolomic Profiles and Pathways in Osteoarthritic Human Cartilage: A Comparative Analysis with Healthy Cartilage

Objective

Osteoarthritis (OA) is a chronic joint disease with heterogenous metabolic pathology. To gain insight into OA-related metabolism, healthy and end-stage osteoarthritic cartilage were compared metabolically to uncover disease-associated profiles, classify OA-specific metabolic endotypes, and identify targets for intervention for the diverse populations of individuals affected by OA.

Design

Femoral head cartilage (n=35) from osteoarthritis patients were collected post-total joint arthroplasty. Healthy cartilage (n=11) was obtained from a tissue bank. Metabolites from all cartilage samples were extracted and analyzed using liquid chromatography-mass spectrometry metabolomic profiling. Additionally, cartilage extracts were pooled and underwent fragmentation analysis for biochemical identification of metabolites.

Results

Specific metabolites and metabolic pathways, including lipid- and amino acid pathways, were differentially regulated between osteoarthritis-derived and healthy cartilage. The detected alterations of amino acids and lipids highlight key differences in bioenergetic resources, matrix homeostasis, and mitochondrial alterations in osteoarthritis-derived cartilage compared to healthy. Moreover, metabolomic profiles of osteoarthritic cartilage separated into four distinct endotypes highlighting the heterogenous nature of OA metabolism and diverse landscape within the joint between patients.

Conclusions

The results of this study demonstrate that human cartilage has distinct metabolomic profiles between healthy and end-stage osteoarthritis patients. By taking a comprehensive approach to assess metabolic differences between healthy and osteoarthritic cartilage, and within osteoarthritic cartilage alone, several metabolic pathways with distinct regulation patterns were detected. Additional investigation may lead to the identification of metabolites that may serve as valuable indicators of disease status or potential therapeutic targets.

Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium

INTRODUCTION

Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility.

OBJECTIVE

The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework.

METHODS

We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure.

RESULTS

Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction.

CONCLUSIONS

Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications

Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.

Hallmarks of the metabolic secretome

The identification of novel secreted factors is advancing at an unprecedented pace. However, there is a critical need to consolidate and integrate this knowledge to provide a framework of their diverse mechanisms, functional significance, and inter-relationships. Complicating this effort are challenges related to nonstandardized methods, discrepancies in sample handling, and inconsistencies in the annotation of unknown molecules. This Review aims to synthesize the rapidly expanding field of the metabolic secretome, encompassing the five major types of secreted factors: proteins, peptides, metabolites, lipids, and extracellular vesicles. By systematically defining the functions and detection of the components within the metabolic secretome, this Review provides a primer into the advances of the field, and how integration of the techniques discussed can provide a deeper understanding of the mechanisms underlying metabolic homeostasis and its disorders.

Quantification of Nitric Oxide in Single Cells Using the Single-Probe Mass Spectrometry Technique

Nitric oxide (NO) is a small molecule that plays important roles in biological systems and human diseases. The abundance of intracellular NO is tightly related to numerous biological processes. Due to cell heterogeneity, the intracellular NO amounts significantly vary from cell to cell, and therefore, any meaningful studies need to be conducted at the single-cell level. However, measuring NO in single cells is very challenging, primarily due to the extremely small size of single cells and reactive nature of NO. In the current studies, the quantitative reaction between NO and amlodipine, a compound containing the Hantzsch ester group, was performed in live cells. The product dehydro amlodipine was then detected by the Single-probe single-cell mass spectrometry technique to quantify NO in single cells. The experimental results indicated heterogeneous distributions of intracellular NO amounts in single cells with the existence of subpopulations.

Application of Skyline software for detecting prohibited substances in doping control analysis

As the number of prohibited drugs has been progressively increasing and analytical methods for detecting such substances are renewed continuously for doping control, the need for more sensitive and accurate doping analysis has increased. To address the urgent need for high throughput and accurate analysis, liquid chromatography with tandem mass spectrometry is actively utilized in case of most of the newly designated prohibited substances. However, because all mass spectrometer vendors provide data processing software that is incapable of handling other instrumental data, it is difficult to cover all doping analysis procedures, from method development to result reporting, on one platform. Skyline is an open-source and vendor-neutral software program invented for the method development and data processing of targeted proteomics. Recently, the utilization of Skyline has been expanding for the quantitative analysis of small molecules and lipids. Herein, we demonstrated Skyline as a simple platform for unifying overall doping control, including the optimization of analytical methods, monitoring of data quality, discovery of suspected doping samples, and validation of analytical methods for detecting newly prohibited substances. For method optimization, we selected the optimal collision energies for 339 prohibited substances. Notably, 195 substances exhibited a signal intensity increase of >110% compared with the signal intensity of the original collision energy. All data related to method validation and quantitative analysis were efficiently visualized, extracted, or calculated using Skyline. Moreover, a comparison of the time consumed and the number of suspicious samples screened in the initial test procedure highlighted the advantages of using Skyline over the commercially available software TraceFinder in doping control.

Untargeted metabolomics to expand the chemical space of the marine diatom Skeletonema marinoi

Diatoms (Bacillariophyceae) are aquatic photosynthetic microalgae with an ecological role as primary producers in the aquatic food web. They account substantially for global carbon, nitrogen, and silicon cycling. Elucidating the chemical space of diatoms is crucial to understanding their physiology and ecology. To expand the known chemical space of a cosmopolitan marine diatom, Skeletonema marinoi, we performed High-Resolution Liquid Chromatography-Tandem Mass Spectrometry (LC-MS2) for untargeted metabolomics data acquisition. The spectral data from LC-MS2 was used as input for the Metabolome Annotation Workflow (MAW) to obtain putative annotations for all measured features. A suspect list of metabolites previously identified in the Skeletonema spp. was generated to verify the results. These known metabolites were then added to the putative candidate list from LC-MS2 data to represent an expanded catalog of 1970 metabolites estimated to be produced by S. marinoi. The most prevalent chemical superclasses, based on the ChemONT ontology in this expanded dataset, were organic acids and derivatives, organoheterocyclic compounds, lipids and lipid-like molecules, and organic oxygen compounds. The metabolic profile from this study can aid the bioprospecting of marine microalgae for medicine, biofuel production, agriculture, and environmental conservation. The proposed analysis can be applicable for assessing the chemical space of other microalgae, which can also provide molecular insights into the interaction between marine organisms and their role in the functioning of ecosystems.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

High-throughput MALDI-MSI metabolite analysis of plant tissue microarrays

A novel metabolomics analysis technique, termed matrix-assisted laser desorption/ionization mass spectrometry imaging-based plant tissue microarray (MALDI-MSI-PTMA), was successfully developed for high-throughput metabolite detection and imaging from plant tissues. This technique completely overcomes the disadvantage that metabolites cannot be accessible on an intact plant tissue due to the limitations of the special structures of plant cells (e.g. epicuticular wax, cuticle and cell wall) through homogenization of plant tissues, preparation of PTMA moulds and matrix spraying of PTMA sections. Our study shows several properties of MALDI-MSI-PTMA, including no need of sample separation and enrichment, high-throughput metabolite detection and imaging (>1000 samples per day), high-stability mass spectrometry data acquisition and imaging reconstruction and high reproducibility of data. This novel technique was successfully used to quickly evaluate the effects of two plant growth regulator treatments (i.e. 6-benzylaminopurine and N-phenyl-N'-1,2,3-thiadiazol-5-ylurea) on endogenous metabolite expression in plant tissue culture specimens of Dracocephalum rupestre Hance (D. rupestre). Intra-day and inter-day evaluations indicated that the metabolite data detected on PTMA sections had good reproducibility and stability. A total of 312 metabolite ion signals in leaves tissues of D. rupestre were detected, of which 228 metabolite ion signals were identified, they were composed of 122 primary metabolites, 90 secondary metabolites and 16 identified metabolites of unknown classification. The results demonstrated the advantages of MALDI-MSI-PTMA technique for enhancing the overall detection ability of metabolites in plant tissues, indicating that MALDI-MSI-PTMA has the potential to become a powerful routine practice for high-throughput metabolite study in plant science.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Targeted quantitative metabolic profiling of brain-derived cell cultures by semi-automated MEPS and LC-MS/MS

The accurate characterisation of metabolic profiles is an important prerequisite to determine the rate and the efficiency of the metabolic pathways taking place in the cells. Changes in the balance of metabolites involved in vital processes such as glycolysis, tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), as well as in the biochemical pathways related to amino acids, lipids, nucleotides, and their precursors reflect the physiological condition of the cells and may contribute to the development of various human diseases. The feasible and reliable measurement of a wide array of metabolites and biomarkers possesses great potential to elucidate physiological and pathological mechanisms, aid preclinical drug development and highlight potential therapeutic targets. An effective, straightforward, sensitive, and selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach was developed for the simultaneous quali-quantitative analysis of 41 compounds in both cell pellet and cell growth medium obtained from brain-derived cell cultures. Sample pretreatment miniaturisation was achieved thanks to the development and optimisation of an original extraction/purification approach based on digitally programmed microextraction by packed sorbent (eVol®-MEPS). MEPS allows satisfactory and reproducible clean-up and preconcentration of both low-volume homogenate cell pellet lysate and cell growth medium with advantages including, but not limited to, minimal sample handling and method sustainability in terms of sample, solvents, and energy consumption. The MEPS-LC-MS/MS method showed good sensitivity, selectivity, linearity, and precision. As a proof of concept, the developed method was successfully applied to the analysis of both cell pellet and cell growth medium obtained from a line of mouse immortalised oligodendrocyte precursor cells (OPCs; Oli-neu cell line), leading to the unambiguous determination of all the considered target analytes. This method is thus expected to be suitable for targeted, quantitative metabolic profiling in most brain cell models, thus allowing accurate investigations on the biochemical pathways that can be altered in central nervous system (CNS) neuropathologies, including e.g., mitochondrial respiration and glycolysis, or use of specific nutrients for growth and proliferation, or lipid, amino acid and nucleotide metabolism.

UHPLC-MS/MS metabolomics analysis of sea cucumber (Apostichopus japonicus) processed using different methods

The effects of different processing methods on the nutritional components of sea cucumber (Apostichopus japonicus) are of concern to consumers who select sea cucumber products. This study employed liquid chromatography tandem mass spectrometry to examine the metabolites in fresh, unsoaked salted, soaked salted, and instant sea cucumber body wall samples sourced from Dalian, China. Metabolites were evaluated utilizing partial least squares discriminant analysis (PLS-DA) and subsequently subjected to KEGG metabolic pathway analysis for further investigation. PLS-DA effectively discriminated the body wall metabolites of sea cucumbers obtained via various processing techniques. The differential metabolites identified predominantly encompassed amino acids, lipids, and carbohydrates. Subsequent KEGG metabolic pathway analysis demonstrated a significant association between lipid, carbohydrate, and amino acid metabolism and the specific processing methods employed. The assessment of nutritional differences corresponding to the various A. japonicus processing methods was conducted. The findings of this study can assist in the choice of sea cucumber products and the selection of suitable processing methods.

Activity labelled molecular networking fuels the antioxidation active molecules profile of Ginger

Ginger has been used as consumed food spice and folk medicine in daily life for thousands of years in various regions of the world. Considerable antioxidation is one of the major activities for Ginger to exhibit health-promoting effects. In this study, a bioinformatic workflow was developed to generate activity labelled molecular networking (ALMN) to fuel the antioxidation active molecules profile of Ginger. In ALMN, antioxidation activity data, which was defined as correlation (r and p value) between the relative abundance of a molecule in fractions and the activity level of each fraction, was labelled to feature-based molecular network to profile out antioxidation active molecules visually. Fragmentation tree was further computed as a complementary way to conduct high confidence structure annotations of antioxidation active molecules. Consequently, 48 molecules were prioritized as antioxidation active molecules from 11,720 metabolite molecules of Ginger in a systematical way.

Bioactive Metabolites of Serratia sp. NhPB1 Isolated from Pitcher of Nepenthes and its Application to Control Pythium aphanidermatum

Plant-associated bacteria have already been considered as the store house of bioactive compounds that confer the plant growth promotion and disease protection. Hence, the unique plant parts have already been expected to harbor diverse microbial communities with multi-beneficial properties. Based on this, the current study has been designed to identify the potential of Serratia sp. NhPB1 isolated from the pitcher of Nepenthes plant for its activity against the infamous pathogen Pythium aphanidermatum. The in vitro antifungal, plant growth promoting and enzymatic activities of the isolate indicated its promises for agricultural application. The isolate NhPB1 was also demonstrated to have positive effect on Solanum lycopersicum and Capsicum annuum, due to its plant beneficial metabolites. From the results of LC-MS/MS analysis, the isolate has also been revealed to have the ability to synthesize bioactive compounds including salicylic acid, cyclodipeptides, acyl homoserine lactone, indole-3-acetic acid, and serrawettin W1. These identified compounds and their known biological properties make the isolate characterized in the study to have significant promises as an eco-friendly solution for the improvement of agricultural productivity.

Modulation of Caecal Microbiota and Metabolome Profile in Salmonella-Infected Broilers by Phage Therapy

Bacteriophage therapy is considered one of the most promising tools to control zoonotic bacteria, such as Salmonella, in broiler production. Phages exhibit high specificity for their targeted bacterial hosts, causing minimal disruption to the niche microbiota. However, data on the gut environment's response to phage therapy in poultry are limited. This study investigated the influence of Salmonella phage on host physiology through caecal microbiota and metabolome modulation using high-throughput 16S rRNA gene sequencing and an untargeted metabolomics approach. We employed 24 caecum content samples and 24 blood serum samples from 4-, 5- and 6-week-old broilers from a previous study where Salmonella phages were administered via feed in Salmonella-infected broilers, which were individually weighed weekly. Phage therapy did not affect the alpha or beta diversity of the microbiota. Specifically, we observed changes in the relative abundance of 14 out of the 110 genera using the PLS-DA and Bayes approaches. On the other hand, we noted changes in the caecal metabolites (63 up-accumulated and 37 down-accumulated out of the 1113 caecal metabolites). Nevertheless, the minimal changes in blood serum suggest a non-significant physiological response. The application of Salmonella phages under production conditions modulates the caecal microbiome and metabolome profiles in broilers without impacting the host physiology in terms of growth performance.

Polarity-extended liquid chromatography-triple quadrupole mass spectrometry for simultaneous hydrophilic and hydrophobic metabolite analysis

Although various metabolomic methods have been reported in recent years, simultaneous detection of hydrophilic and hydrophobic metabolites in a single analysis remains a technical challenge. In this study, based on the combination of hydrophilic interaction liquid chromatography (HILIC) and reversed phase liquid chromatography (RPLC), an online two-dimensional liquid chromatography/triple quadrupole mass spectrometry method (2D-LC/TQMS) was developed for the simultaneous analysis of hydrophilic and hydrophobic metabolites of various biological samples. The method can measure 417 biologically important metabolites (e.g., amino acids and peptides, pyrimidines, purines, monosaccharides, fatty acids and conjugates, organic dicarboxylic acids, and others) with logP values ranging from -10.3 to 21.9. The metabolites are involved in a variety of metabolic pathways (e.g., purine metabolism, pyrimidine metabolism, tyrosine metabolism, galactose metabolism, gluconeogenesis, and TCA cycle). The developed method has good intra- and inter-day reproducibility (RSD of retention time <2%, RSD of peak area <30%), good linearity (R2 > 0.9) and wide linear range (from 0.0025 μg/mL to 5 μg/mL). The applicability of the method was tested using different biological samples (i.e., plasma, serum, urine, fecal, seminal plasma and liver) and it was found that 208 (out of 417) identical metabolites were detected in all biological samples. Furthermore, the metabolomic method was applied to a case/control study of urinary of bladder cancer. Thirty differential metabolites were identified that were involved in carbohydrate and amino acid metabolism.

Integrated Analysis of Metabolome and Transcriptome Reveals Insights for Low Phosphorus Tolerance in Wheat Seedling

Low phosphorus (LP) stress leads to a significant reduction in wheat yield, primarily in the reduction of biomass, the number of tillers and spike grains, the delay in heading and flowering, and the inhibition of starch synthesis and grouting. However, the differences in regulatory pathway responses to low phosphorus stress among different wheat genotypes are still largely unknown. In this study, metabolome and transcriptome analyses of G28 (LP-tolerant) and L143 (LP-sensitive) wheat varieties after 72 h of normal phosphorus (CK) and LP stress were performed. A total of 181 and 163 differentially accumulated metabolites (DAMs) were detected for G28CK vs. G28LP and L143CK vs. L143LP, respectively. Notably, the expression of pilocarpine (C07474) in G28CK vs. G28LP was significantly downregulated 4.77-fold, while the expression of neochlorogenic acid (C17147) in L143CK vs. L143LP was significantly upregulated 2.34-fold. A total of 4023 differentially expressed genes (DEGs) were acquired between G28 and L143, of which 1120 DEGs were considered as the core DEGs of LP tolerance of wheat after LP treatment. The integration of metabolomics and transcriptomic data further revealed that the LP tolerance of wheat was closely related to 15 metabolites and 18 key genes in the sugar and amino acid metabolism pathway. The oxidative phosphorylation pathway was enriched to four ATPases, two cytochrome c reductase genes, and fumaric acid under LP treatment. Moreover, PHT1;1, TFs (ARFA, WRKY40, MYB4, MYB85), and IAA20 genes were related to the Pi starvation stress of wheat roots. Therefore, the differences in LP tolerance of different wheat varieties were related to energy metabolism, amino acid metabolism, phytohormones, and PHT proteins, and precisely regulated by the levels of various molecular pathways to adapt to Pi starvation stress. Taken together, this study may help to reveal the complex regulatory process of wheat adaptation to Pi starvation and provide new genetic clues for further study on improving plant Pi utilization efficiency.

Linking MS1 and MS2 signals in positive and negative modes of LC-HRMS in untargeted metabolomics using the ROIMCR approach

Data-independent acquisition (DIA) mode in liquid chromatography (LC) high-resolution mass spectrometry (HRMS) has emerged as a powerful strategy in untargeted metabolomics for detecting a broad range of metabolites. However, the use of this approach also represents a challenge in the analysis of the large datasets generated. The regions of interest (ROI) multivariate curve resolution (MCR) approach can help in the identification and characterization of unknown metabolites in their mixtures by linking their MS1 and MS2 DIA spectral signals. In this study, it is proposed for the first time the analysis of MS1 and MS2 DIA signals in positive and negative electrospray ionization modes simultaneously to increase the coverage of possible metabolites present in biological systems. In this work, this approach has been tested for the detection and identification of the amino acids present in a standard mixture solution and in fish embryo samples. The ROIMCR analysis allowed for the identification of all amino acids present in the analyzed mixtures in both positive and negative modes. The methodology allowed for the direct linking and correspondence between the MS signals in their different acquisition modes. Overall, this approach confirmed the advantages and possibilities of performing the proposed ROIMCR simultaneous analysis of mass spectrometry signals in their differing acquisition modes in untargeted metabolomics studies.

RT-Ensemble Pred: A tool for retention time prediction of metabolites on different LC-MS systems

Liquid chromatography-mass spectrometry (LC-MS) could provide a large amount of information to assist in metabolites identification. Different liquid chromatographic methods (CMs) could produce different retention times to the same metabolite. To predict the retention time of local dataset by online datasets has become a trend, but the datasets downloaded from different databases were differences in quantity levels. And the imbalanced data could produce bad influence in model prediction. Thus, based on quantitative structure-retention relationships (QSRRs), an ensemble model, named RT-Ensemble Pred, has been successfully built to predict retention time of different LC-MS systems in this study. A total of 76, 807 metabolites (76, 909 retention times) have been collected across 9 CMs, and 19 natural products and 1 antifungal drug (20 retention times) have been collected to test the model applicability. An ensemble sampling was applied for the preprocessing procedure to solve the problem of imbalanced data. Based on the ensemble sampling, RT-Ensemble Pred could better utilize online datasets for the prediction of retention time. RT-Ensemble Pred was built based on the online datasets and tested by local dataset. The predictive accuracy of RT-Ensemble Pred was higher than the models without any sampling methods. The results showed that RT-Ensemble Pred could predict the metabolites which was not included in the database and the metabolites which were from new CMs. It could also be used for the prediction of other compounds beside metabolites. Furthermore, a tool of RT-Ensemble Pred was packed and can be freely downloaded at https://gitlab.com/mikic93/rt-ensemble-pred. It provides convenience for the users who need to predict the retention time of metabolites.

Study on the targeted regulation of Scutellaria baicalensis leaf on glutamine-glutamate metabolism and glutathione synthesis in the liver of d-gal ageing rats

OBJECTIVES

Scutellaria baicalensis leaf (SLE), the above-ground part of the traditional Chinese medicine Scutellaria baicalensis Georgi, is rich in resources and contains a large number of flavonoids with anti-inflammatory, antioxidant and neuroprotective functions. The present study evaluated the ameliorative effects and related mechanisms of SLE on d-gal-induced ageing rats, providing a theoretical basis for the exploitation of SLE.

METHODS

This experiment investigated the mechanism of SLE for anti-ageing by non-targeted metabonomics technology combined with targeted quantitative analysis and molecular biology technology.

KEY FINDINGS

Non-targeted metabonomics analysis showed that 39 different metabolites were screened out. Among them, 38 metabolites were regulated by SLE (0.4 g/kg), and 33 metabolites were regulated by SLE (0.8 g/kg). Through enrichment analysis, glutamine-glutamate metabolic pathway was identified as the key metabolic pathway. Subsequently, the results of targeted quantitative and biochemical analysis displayed that the contents of key metabolites and the activities of enzymes in glutamine-glutamate metabolic pathway and glutathione synthesis could be regulated by SLE. Furthermore, the results of Western blotting indicated that SLE significantly modulated the expression of Nrf2, GCLC, GCLM, HO-1, and NQO1 proteins.

CONCLUSION

To sum up, the anti-ageing mechanism of SLE was related to glutamine-glutamate metabolism pathway and Nrf2 signalling pathway.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Metabolomics in archaeological science: A review of their advances and present requirements

Metabolomics, the study of metabolites (small molecules of <1500 daltons), has been posited as a potential tool to explore the past in a comparable manner to other omics, e.g., genomics or proteomics. Archaeologists have used metabolomic approaches for a decade or so, mainly applied to organic residues adhering to archaeological materials. Because of advances in sensitivity, resolution, and the increased availability of different analytical platforms, combined with the low mass/volume required for analysis, metabolomics is now becoming a more feasible choice in the archaeological sector. Additional approaches, as presented by our group, show the versatility of metabolomics as a source of knowledge about the human past when using human osteoarchaeological remains. There is tremendous potential for metabolomics within archaeology, but further efforts are required to position it as a routine technique.

Emerging role of metabolomics for biomarker discovery in obstructive sleep apnea

Obstructive sleep apnea (OSA) is characterized by the complete or partial blockage of the upper airway passage during sleep which causes repetitive breaks in sleep and may result in excessive daytime sleepiness. OSA has been linked to various metabolic disorders and chronic health conditions, such as obesity, diabetes, hypertension, and depression. Profiling of alterations in metabolites and their regulation in OSA has been hypothesized to be an effective approach for early diagnosis and prognosis of OSA. Several studies have characterized metabolic fingerprints associated with sleep disorders. There is a lack of understanding of metabolite contents and their alterations in OSA that may help to identify specific biomarkers. The information provided in this review will help update new methodologies and interventions of high throughput advanced molecular/metabolomics tools which may clarify the metabolic aspects and mechanisms for improved management and treatment of OSA.

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

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