LC-MS-based methodology for global metabolite profiling in metabonomics/metabolomics

Seed priming with graphene oxide improves salinity tolerance and increases productivity of peanut through modulating multiple physiological processes

Graphene oxide (GO), beyond its specialized industrial applications, is rapidly gaining prominence as a nanomaterial for modern agriculture. However, its specific effects on seed priming for salinity tolerance and yield formation in crops remain elusive. Under both pot-grown and field-grown conditions, this study combined physiological indices with transcriptomics and metabolomics to investigate how GO affects seed germination, seedling salinity tolerance, and peanut pod yield. Peanut seeds were firstly treated with 400 mg L⁻¹ GO (termed GO priming). At seed germination stage, GO-primed seeds exhibited higher germination rate and percentage of seeds with radicals breaking through the testa. Meanwhile, omics analyses revealed significant enrichment in pathways associated with carbon and nitrogen metabolisms in GO-primed seeds. At seedling stage, GO priming contributed to strengthening plant growth, enhancing photosynthesis, maintaining the integrity of plasma membrane, and promoting the nutrient accumulation in peanut seedlings under 200 mM NaCl stress. Moreover, GO priming increased the activities of antioxidant enzymes, along with reduced the accumulation of reactive oxygen species (ROS) in response to salinity stress. Furthermore, the differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) of peanut seedlings under GO priming were mainly related to photosynthesis, phytohormones, antioxidant system, and carbon and nitrogen metabolisms in response to soil salinity. At maturity, GO priming showed an average increase in peanut pod yield by 12.91% compared with non-primed control. Collectively, our findings demonstrated that GO plays distinguish roles in enhancing seed germination, mitigating salinity stress, and boosting pod yield in peanut plants via modulating multiple physiological processes.

Metabolomic and Transcriptomic Analyses Reveal the Potential Mechanisms of Dynamic Ovarian Development in Goats during Sexual Maturation

The ovary is a crucial reproductive organ in mammals, and its development directly influences an individual's sexual maturity and reproductive capacity. To comprehensively describe ovarian sexual maturation in goats, we integrated phenotypic, hormonal, metabolomic, and transcriptomic data from four specific time points: after birth (D1), at 2 months old (M2), at 4 months old (M4), and at 6 month old (M6). The study showed that during the early stage (D1-M2), ovarian growth was the most rapid, with weight and morphology increasing by 284% and 65%, respectively, and hormone levels rose significantly, with estradiol increasing by 57%. Metabolomic analysis identified 1231 metabolites, primarily lipids, lipid molecules, and organic acids, which can support hormone balance and follicle development by providing energy and participating in signaling transduction. Transcriptomic analysis identified 543 stage-specific differentially expressed genes, mainly enriched in steroid biosynthesis, amino acid metabolism, and the PI3K/AKT pathway, which are key factors influencing ovarian cell proliferation, apoptosis, hormone secretion, and metabolism. The integrated analysis revealed the key processes in the ovarian steroid hormone biosynthesis pathway and gene/metabolite networks associated with ovarian phenotypes and hormone levels, ultimately highlighting scavenger receptor class B type 1 (SCARB1), Cytochrome P450 Family 1 Subfamily A Member 1 (CYP11A1), 3beta-hydroxysteroid dehydrogenase (3BHSD), progesterone, estradiol, and L-phenylalanine as key regulators of ovarian morphological and functional changes at different developmental stages. This study is the first to reveal the metabolic changes and molecular regulatory mechanisms during ovarian sexual maturation in goats, providing valuable insights for understanding reproductive system development and optimizing reproductive performance and breeding efficiency.

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.

Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial

BACKGROUND

Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration.

AIM OF REVIEW

In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.

Metabolome and transcriptome analyses reveal changes of rapeseed in response to ABA signal during early seedling development

Seed germination is an important development process in plant growth. The phytohormone abscisic acid (ABA) plays a critical role during seed germination. However, the mechanism of rapeseed in response to ABA is still elusive. In order to understand changes of rapeseed under exogenous ABA treatment, we explored differentially expressed metabolites (DEMs) and the differentially expressed genes (DEGs) between mock- and ABA-treated seedlings. A widely targeted LC-MS/MS based metabolomics were used to identify and quantify metabolic changes in response to ABA during seed germination, and a total of 186 significantly DEMs were identified. There are many compounds which are involved in ABA stimuli, especially some specific ABA transportation-related metabolites such as starches and lipids were screened out. Meanwhile, a total of 4440 significantly DEGs were identified by transcriptomic analyses. There was a significant enrichment of DEGs related to phenylpropanoid and cell wall organization. It suggests that exogenous ABA mainly affects seed germination by regulating cell wall loosening. Finally, the correlation analysis of the key DEMs and DEGs indicates that many DEGs play a direct or indirect regulatory role in DEMs metabolism. The integrative analysis between DEGs and DEMs suggests that the starch and sucrose pathways were the key pathway in ABA responses. The two metabolites from starch and sucrose pathways, levan and cellobiose, both were found significantly down-regulated in ABA-treated seedlings. These comprehensive metabolic and transcript analyses provide useful information for the subsequent post-transcriptional modification and post germination growth of rapeseed in response to ABA signals and stresses.

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.

Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides

Achieving untargeted chemical identification, isomeric differentiation, and quantification is critical to most scientific and technological problems but remains challenging. Here, we demonstrate an integrated SERS-based chemical taxonomy machine learning framework for untargeted structural elucidation of 11 epimeric cerebrosides, attaining >90% accuracy and robust single epimer and multiplex quantification with <10% errors. First, we utilize 4-mercaptophenylboronic acid to selectively capture the epimers at molecular sites of isomerism to form epimer-specific SERS fingerprints. Corroborating with in-silico experiments, we establish five spectral features, each corresponding to a structural characteristic: (1) presence/absence of epimers, (2) monosaccharide/cerebroside, (3) saturated/unsaturated cerebroside, (4) glucosyl/galactosyl, and (5) GlcCer or GalCer's carbon chain lengths. Leveraging these insights, we create a fully generalizable framework to identify and quantify cerebrosides at concentrations between 10-4 to 10-10 M and achieve multiplex quantification of binary mixtures containing biomarkers GlcCer24:1, and GalCer24:1 using their untrained spectra in the models.

User-friendly relative quantification procedure for gas chromatography/mass spectrometry-based plasma metabolome analysis

RATIONALE

Recently, metabolome analysis has been applied to a variety of research fields, but differences between batches or facilities can cause discrepancies in the results of such analyses. To resolve these issues using comprehensive metabolome analysis, in which it is difficult to perform quantitative analyses of all detected metabolites, internal standard compounds are used to obtain relative metabolite levels. This study investigated gas chromatography/mass spectrometry-based plasma metabolome analysis methods that are superior to relative quantification using internal standard compounds.

METHODS

In experiment I, four analyses were performed under different analytical conditions at one facility, and then the data from the four analyses were compared. In experiment II, the same samples were analyzed at three facilities, and then the data from the three facilities were compared.

RESULTS

Regarding the relative values obtained through comparisons with the internal standard compound, differences in the analytical results were observed among the four analytical conditions in experiment I and among the three facilities in experiment II, and the differences observed among the three facilities (experiment II) were larger. When correction was performed using plasma as a quality control, which is the procedure suggested in this study, these differences were markedly ameliorated.

CONCLUSION

The suggested procedure involves the analysis of a plasma standard as a quality control for each batch and the calculation of relative target plasma to quality-control plasma values for each metabolite. This is an easy and low-cost method and could be readily employed by researchers during comprehensive plasma metabolome analysis.

Genome scale metabolic network modelling for metabolic profile predictions

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

Discovery of candidate biomarkers to discriminate between Korean and Japanese red seabream (Pagrus major) using metabolomics

Red seabream (Pagrus major) is widely consumed in East Asia. As nuclear wastewater is discharged into Japanese waterbodies, the country of origin of marine products must be accurately labeled. Here, we aimed to discover candidate metabolite biomarkers to discriminate between Korean and Japanese red seabream using LC-Orbitrap mass spectrometry. In total, 95 and 138 putative metabolites were detected via chromatographic separation of fish sampled in the warm and cold seasons, respectively. The spectrometric and chromatographic data were analyzed using principal component analysis and orthogonal partial least squares discriminant analysis. We identified 12 and 19 influential metabolites to discriminate between each origin fish in the warm and cold seasons, respectively, using variable importance in projection scores and p values. Anserine was further selected as a candidate biomarker based on receiver operating characteristic curve analysis. This study provides a basis for using anserine to determine the geographic origin of red seabream.

Optimizing the QuEChERS method for efficient monitoring of fipronil, thiobencarb, and cartap residues in paddy soils with varying properties

This study aims to optimize the QuEChERS methodology for extracting three pesticides (fipronil, thiobencarb, and cartap) from two paddy soils with distinct characteristics. Various modifications were explored to enhance extraction efficiency, employing acetonitrile (MeCN) or ethyl acetate (EtOAc) for extraction and primary-secondary amine (PSA) and graphitized carbon black (GCB) for the clean-up. Assessment criteria included accuracy, precision, linearity, detection limits, uncertainty, and matrix effects. Results revealed that the clayey soil with lower organic carbon (OC) content (1.26%) and 100% moisture yielded the highest pesticide recoveries (113.72%, 115.73%, and 116.41% for FIP, THIO, and CART, respectively). In contrast, the silty clayey soil with higher OC content (2.91%) and 20% water content exhibited poor recoveries (< 60%). FIP and CART demonstrated better recoveries with MeCN, while THIO performed better with EtOAc under specific moisture conditions. Clean-up sorbents significantly reduced FIP and CART recoveries, with THIO recoveries less affected. Acidifying with HCl substantially improved CART recovery. EtOAc introduced a moderate to strong matrix effect for FIP and THIO, while MeCN in soils with 100% moisture resulted in a strong matrix effect for CART. The study highlighted the substantial impact of extraction conditions, pesticide properties, and soil conditions on the outcomes of the QuEChERS method. A comprehensive understanding of these interplays was deemed crucial for accurately quantifying pesticide residues in agricultural soils.

Technology Invention and Mechanism Analysis of Rapid Rooting of Taxus × media Rehder Branches Induced by Agrobacterium rhizogenes

Taxus, a vital source of the anticancer drug paclitaxel, grapples with a pronounced supply-demand gap. Current efforts to alleviate the paclitaxel shortage involve expanding Taxus cultivation through cutting propagation. However, traditional cutting propagation of Taxus is difficult to root and time-consuming. Obtaining the roots with high paclitaxel content will cause tree death and resource destruction, which is not conducive to the development of the Taxus industry. To address this, establishing rapid and efficient stem rooting systems emerges as a key solution for Taxus propagation, facilitating direct and continuous root utilization. In this study, Agrobacterium rhizogenes were induced in the 1-3-year-old branches of Taxus × media Rehder, which has the highest paclitaxel content. The research delves into the rooting efficiency induced by different A. rhizogenes strains, with MSU440 and C58 exhibiting superior effects. Transcriptome and metabolome analyses revealed A. rhizogenes' impact on hormone signal transduction, amino acid metabolism, zeatin synthesis, and secondary metabolite synthesis pathways in roots. LC-MS-targeted quantitative detection showed no significant difference in paclitaxel and baccatin III content between naturally formed and induced roots. These findings underpin the theoretical framework for T. media rapid propagation, contributing to the sustainable advancement of the Taxus industry.

Recent developments and applications of ambient mass spectrometry imaging in pharmaceutical research: an overview

The application of ambient mass spectrometry imaging "MSI" is expanding in the areas of fundamental research on drug delivery and multiple phases of the process of identifying and developing drugs. Precise monitoring of a drug's pharmacological workflows, such as intake, distribution, metabolism, and discharge, is made easier by MSI's ability to determine the concentrations of the initiating drug and its metabolites across dosed samples without losing spatial data. Lipids, glycans, and proteins are just a few of the many phenotypes that MSI may be used to concurrently examine. Each of these substances has a particular distribution pattern and biological function throughout the body. MSI offers the perfect analytical tool for examining a drug's pharmacological features, especially in vitro and in vivo effectiveness, security, probable toxic effects, and putative molecular pathways, because of its high responsiveness in chemical and physical environments. The utilization of MSI in the field of pharmacy has further extended from the traditional tissue examination to the early stages of drug discovery and development, including examining the structure-function connection, high-throughput capabilities in vitro examination, and ex vivo research on individual cells or tumor spheroids. Additionally, an enormous array of endogenous substances that may function as tissue diagnostics can be scanned simultaneously, giving the specimen a highly thorough characterization. Ambient MSI techniques are soft enough to allow for easy examination of the native sample to gather data on exterior chemical compositions. This paper provides a scientific and methodological overview of ambient MSI utilization in research on pharmaceuticals.

The nematicide emamectin benzoate increases ROS accumulation in Pinus massoniana and poison Monochamus alternatus

Pine wilt disease (PWD) is caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus) and transmitted by a vector insect, the Monochamus alternatus. The PWN has caused much extensive damage to pine-dominated forest ecosystems. Trunk injection of emamectin benzoate (EB) has been found to be the most useful protective measure against the PWN, due to its low effective dose and long residence time in the field. However, the interactions between EB and the host or the environment remain largely unknown, which limits the efficacy and stability of EB in practical field settings. In this study, we investigated the impact on PWN from EB injection for both adult and young host plants (Pinus massoniana) by taking a multi-omics (phenomics, transcriptomics, microbiome, and metabolomics) approach. We found that EB injection can significantly reduce the amount of PWN in both living adult and young pine trees. Additionally, EB was able to activate the genetic response of P. massoniana against PWN, promotes P. massoniana growth and development and resistance to Pine wilt disease, which requires the presence of PWN. Further, the presence of EB greatly increased the accumulation of reactive oxygen species (ROS) in the host plant in a PWN-dependent manner, possibly by affecting ROS-related microbes and metabolites. Moreover, we uncovered the function of EB limiting the consumption of P. massoniana by the JPS. Based on biochemical and gut microbial data, we found that EB can significantly reduces cellulase activity in JPS, whose transcription factors, sugar metabolism, and the phosphotransferase system are also affected. These results document the impact of EB on the entire PWD transmission chain through multi-omics regarding the dominant pine (P. massoniana) in China and provide a novel perspective for controlling PWD outbreaks in the field.

A multiplatform metabolomics approach for comprehensive analysis of GIST xenografts with various KIT mutations

Metabolites in biological matrices belong to diverse chemical groups, ranging from non-polar long-chain fatty acids to small polar molecules. The goal of untargeted metabolomic analysis is to measure the highest number of metabolites in the sample. Nevertheless, from an analytical point of view, no single technique can measure such a broad spectrum of analytes. Therefore, we selected a method based on GC-MS and LC-MS with two types of stationary phases for the untargeted profiling of gastrointestinal stromal tumours. The procedure was applied to GIST xenograft samples (n = 71) representing four different mutation models, half of which were treated with imatinib. We aimed to verify the method coverage and advantages of applying each technique. RP-LC-MS measured most metabolites due to a significant fraction of lipid components of the tumour tissue. What is unique and worth noting is that all applied techniques were able to distinguish between different mutation models. However, for detecting imatinib-induced alterations in the GIST metabolome, RP-LC-MS and GC-MS proved to be more relevant than HILIC-LC-MS, resulting in a higher number of significantly changed metabolites in four treated models. Undoubtedly, the inclusion of all mentioned techniques makes the method more comprehensive. Nonetheless, for green chemistry and time and labour saving, we assume that RP-LC-MS and GC-MS analyses are sufficient to cover the global GIST metabolome.

Analysis of metabolites of bactrain camel milk in Alxa of China before and after fermentation with fermenting agent TR1 based on untargeted LC-MS/MS based metabolomics

Camel milk produces many beneficial functional compounds and affects body health through metabolism. The differential metabolites of bactrain camel milk in Alxa before and after fermentation were identified by liquid chromatography-tandem mass spectrometry based metabolomics (LC-MS/MS). The differential metabolite pathway types were also identified in this paper. We obtained the following results that 148 and 82 differential metabolites were detected in positive and negative ion mode respectively, 85 differential metabolites were shown a significant upward trend and 63 with downward trend after fermentation in positive ion mode. Meanwhile, 32 differential metabolites characterized upward trend and 50 characterized downward trend in negative ion mode. The differential metabolites were mainly organic acids, amino acids, esters, vitamins and other substances contained in camel milk. Among them, most up-regulated substances had the functions of lowering blood pressure, lowering blood sugar, treatment of inflammation, antibiosis and other effects. Many harmful substances were significantly down-regulated after camel milk fermentation. However, there were also some metabolites whose prebiotic functions have been weakened by camel milk fermentation, which may provide reference values for healthcare function, exploitation and application of camel milk.

Metabolomics-Based Mechanistic Insights into Revealing the Adverse Effects of Pesticides on Plants: An Interactive Review

In plant biology, metabolomics is often used to quantitatively assess small molecules, metabolites, and their intermediates in plants. Metabolomics has frequently been applied to detect metabolic alterations in plants exposed to various biotic and abiotic stresses, including pesticides. The widespread use of pesticides and agrochemicals in intensive crop production systems is a serious threat to the functionality and sustainability of agroecosystems. Pesticide accumulation in soil may disrupt soil-plant relationships, thereby posing a pollution risk to agricultural output. Application of metabolomic techniques in the assessment of the biological consequences of pesticides at the molecular level has emerged as a crucial technique in exposome investigations. State-of-the-art metabolomic approaches such as GC-MS, LC-MS/MS UHPLC, UPLC-IMS-QToF, GC/EI/MS, MALDI-TOF MS, and ¹H-HR-MAS NMR, etc., investigating the harmful effects of agricultural pesticides have been reviewed. This updated review seeks to outline the key uses of metabolomics related to the evaluation of the toxicological impacts of pesticides on agronomically important crops in exposome assays as well as bench-scale studies. Overall, this review describes the potential uses of metabolomics as a method for evaluating the safety of agricultural chemicals for regulatory applications. Additionally, the most recent developments in metabolomic tools applied to pesticide toxicology and also the difficulties in utilizing this approach are discussed.

The multifaceted roles of Arbuscular Mycorrhizal Fungi in peanut responses to salt, drought, and cold stress

BACKGROUND

Arbuscular Mycorrhizal Fungi (AMF) are beneficial microorganisms in soil-plant interactions; however, the underlying mechanisms regarding their roles in legumes environmental stress remain elusive. Present trials were undertaken to study the effect of AMF on the ameliorating of salt, drought, and cold stress in peanut (Arachis hypogaea L.) plants. A new product of AMF combined with Rhizophagus irregularis SA, Rhizophagus clarus BEG142, Glomus lamellosum ON393, and Funneliformis mosseae BEG95 (1: 1: 1: 1, w/w/w/w) was inoculated with peanut and the physiological and metabolomic responses of the AMF-inoculated and non-inoculated peanut plants to salt, drought, and cold stress were comprehensively characterized, respectively.

RESULTS

AMF-inoculated plants exhibited higher plant growth, leaf relative water content (RWC), net photosynthetic rate, maximal photochemical efficiency of photosystem II (PSII) (Fv/Fm), activities of antioxidant enzymes, and K⁺: Na⁺ ratio while lower leaf relative electrolyte conductivity (REC), concentration of malondialdehyde (MDA), and the accumulation of reactive oxygen species (ROS) under stressful conditions. Moreover, the structures of chloroplast thylakoids and mitochondria in AMF-inoculated plants were less damaged by these stresses. Non-targeted metabolomics indicated that AMF altered numerous pathways associated with organic acids and amino acid metabolisms in peanut roots under both normal-growth and stressful conditions, which were further improved by the osmolytes accumulation data.

CONCLUSION

This study provides a promising AMF product and demonstrates that this AMF combination could enhance peanut salt, drought, and cold stress tolerance through improving plant growth, protecting photosystem, enhancing antioxidant system, and regulating osmotic adjustment.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

Integrative Analysis of Transcriptomic and Metabolomic Data for Identification of Pathways Related to Matrine-Induced Hepatotoxicity

Matrine (MT) is a major bioactive compound extracted from Sophorae tonkinensis. However, the clinical application of MT is relatively restricted due to its potentially toxic effects, especially hepatotoxicity. Although MT-induced liver injury has been reported, little is known about the underlying molecular mechanisms. In this study, transcriptomics and metabolomics were applied to investigate the hepatotoxicity of MT in mice. The results indicated that liver injury occurred when the administration of MT (30 or 60 mg/kg, i.g) lasted for 2 weeks, including dramatically increased alanine aminotransferase (ALT), aspartate aminotransferase (AST), etc. The metabolomic results revealed that steroid biosynthesis, purine metabolism, glutathione metabolism, and pyruvate metabolism were involved in the occurrence and development of MT-induced hepatotoxicity. Further, the transcriptomic data indicated that the downregulation of NSDHL with CYP51, FDFT1, and DHCR7, involved in steroid biosynthesis, resulted in a lower level of cholic acid. Besides, Gstps and Nat8f1 were related to the disorder of glutathione metabolism, and HMGCS1 could be treated as the marker gene of the development of MT-induced hepatotoxicity. In addition, other metabolites, such as taurine, flavin mononucleotide (FMN), and inosine monophosphate (IMP), also made a contribution to the boosting of MT-induced hepatotoxicity. In this work, our results provide clues for the mechanism investigation of MT-induced hepatotoxicity, and several biomarkers (metabolites and genes) closely related to the liver injury caused by MT are also provided. Meanwhile, new insights into the understanding of the development of MT-induced hepatotoxicity or other monomer-induced hepatotoxicity were also provided.

Untargeted Metabolomics Pilot Study Using UHPLC-qTOF MS Profile in Sows' Urine Reveals Metabolites of Bladder Inflammation

Urinary tract infections (UTI) of sows (characterized by ascending infections of the urinary bladder (cyst), ureters, and renal pelvis), are major health issues with a significant economic impact to the swine industry. The current detection of UTI incidents lacks sensitivity; thus, UTIs remain largely under-diagnosed. The value of metabolomics in unraveling the mechanisms of sow UTI has not yet been established. This study aims to investigate the urine metabolome of sows for UTI biomarkers. Urine samples were collected from 58 culled sows from a farrow-to-finish herd in Greece. Urine metabolomic profiles in 31 healthy controls and in 27 inflammatory ones were evaluated. UHPLC-qTOF MS/MS was applied for the analysis with a combination of multivariate and univariate statistical analysis. Eighteen potential markers were found. The changes in several urine metabolites classes (nucleosides, indoles, isoflavones, and dipeptides), as well as amino-acids allowed for an adequate discrimination between the study groups. Identified metabolites were involved in purine metabolism; phenylalanine; tyrosine and tryptophan biosynthesis; and phenylalanine metabolism. Through ROC analysis it was shown that the 18 identified metabolite biomarkers exhibited good predictive accuracy. In summary, our study provided new information on the potential targets for predicting early and accurate diagnosis of UTI. Further, this information also sheds light on how it could be applied in live animals.

Application of Metabolomics in Fungal Research

Metabolomics is an essential method to study the dynamic changes of metabolic networks and products using modern analytical techniques, as well as reveal the life phenomena and their inherent laws. Currently, more and more attention has been paid to the development of metabolic histochemistry in the fungus field. This paper reviews the application of metabolomics in fungal research from five aspects: identification, response to stress, metabolite discovery, metabolism engineering, and fungal interactions with plants.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Metabolomics Research in Periodontal Disease by Mass Spectrometry

Periodontology is a newer field relative to other areas of dentistry. Remarkable progress has been made in recent years in periodontology in terms of both research and clinical applications, with researchers worldwide now focusing on periodontology. With recent advances in mass spectrometry technology, metabolomics research is now widely conducted in various research fields. Metabolomics, which is also termed metabolomic analysis, is a technology that enables the comprehensive analysis of small-molecule metabolites in living organisms. With the development of metabolite analysis, methods using gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis–mass spectrometry, etc. have progressed, making it possible to analyze a wider range of metabolites and to detect metabolites at lower concentrations. Metabolomics is widely used for research in the food, plant, microbial, and medical fields. This paper provides an introduction to metabolomic analysis and a review of the increasing applications of metabolomic analysis in periodontal disease research using mass spectrometry technology.

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

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

Urinary metabolomics for discovering metabolic biomarkers of bladder cancer by UPLC-MS

Bladder cancer (BC) is one of the most frequent cancer in the world, and its incidence is rising worldwide, especially in developed countries. Urine metabolomics is a powerful approach to discover potential biomarkers for cancer diagnosis. In this study, we applied an ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) method to profile the metabolites in urine from 29 bladder cancer patients and 15 healthy controls. The differential metabolites were extracted and analyzed by univariate and multivariate analysis methods. Together, 19 metabolites were discovered as differently expressed biomarkers in the two groups, which mainly related to the pathways of phenylacetate metabolism, propanoate metabolism, fatty acid metabolism, pyruvate metabolism, arginine and proline metabolism, glycine and serine metabolism, and bile acid biosynthesis. In addition, a subset of 11 metabolites of those 19 ones were further filtered as potential biomarkers for BC diagnosis by using logistic regression model. The results revealed that the area under the curve (AUC) value, sensitivity and specificity of receiving operator characteristic (ROC) curve were 0.983, 95.3% and 100%, respectively, indicating an excellent discrimination power for BC patients from healthy controls. It was the first time to reveal the potential diagnostic markers of BC by metabolomics, and this will provide a new sight for exploring the biomarkers of the other disease in the future work.

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

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

Serum metabolomic profiling reveals potential biomarkers in assessing the management of women with polycystic ovary syndrome: a randomized controlled trial

BACKGROUND

As one of the most common endocrinal disorders for women at childbearing age, the diagnostic criteria of polycystic ovary syndrome (PCOS) have been defined differently among different international health organizations. Phenotypic heterogeneity of PCOS also brings about difficulties for its diagnosis and management assessment. Therefore, more efficient biomarkers representing the progression of PCOS are expected to be integrated into the monitoring of management process using metabolomic approaches.

METHODS

In this prospective randomized controlled trial, 117 PCOS patients were enrolled from December 2016 to September 2017. Classical diagnostic parameters, blood glucose, and metabolome were measured in these patients before and at 2 months and 3 months of different medical interventions. The receiver operating characteristic (ROC) curves were built based on multivariate statistical analysis using data at baseline and 3 months' management, and combinational biomarkers with appreciable sensitivity and specificity were selected, which then validated with data collected at 2 months.

RESULTS

A set of metabolites including glutamic acid, aspartic acid, 1-methylnicotinamide, acetylcarnitine, glycerophosphocholine, and oleamide were filtered out with high performance in representing the improvement through 3-month management of PCOS with high sensitivity and specificity in ROC analysis and validation with other two groups showed an appreciable area under the curve over 0.96.

CONCLUSIONS

The six metabolites were representative of the remission of PCOS through medical intervention, making them a set of potential biomarkers for assessing the outcome of PCOS management.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03264638.

Multi-Omics of Pine Wood Nematode Pathogenicity Associated With Culturable Associated Microbiota Through an Artificial Assembly Approach

Pinewood nematode (PWN), the causal agent of pine wilt disease (PWD), causes massive global losses of Pinus species each year. Bacteria and fungi existing in symbiosis with PWN are closely linked with the pathogenesis of PWD, but the relationship between PWN pathogenicity and the associated microbiota is still ambiguous. This study explored the relationship between microbes and the pathogenicity of PWN by establishing a PWN-associated microbe library, and used this library to generate five artificial PWN-microbe symbiont (APMS) assemblies with gnotobiotic PWNs. The fungal and bacterial communities of different APMSs (the microbiome) were explored by next-generation sequencing. Furthermore, different APMSs were used to inoculate the same Masson pine (Pinus massoniana) cultivar, and multi-omics (metabolome, phenomics, and transcriptome) data were obtained to represent the pathogenicity of different APMSs at 14 days post-inoculation (dpi). Significant positive correlations were observed between microbiome and transcriptome or metabolome data, but microbiome data were negatively correlated with the reactive oxygen species (ROS) level in the host. Five response genes, four fungal genera, four bacterial genera, and nineteen induced metabolites were positively correlated with the ROS level, while seven induced metabolites were negatively correlated. To further explore the function of PWN-associated microbes, single genera of functional microbes (Mb1-Mb8) were reloaded onto gnotobiotic PWNs and used to inoculate pine tree seedlings. Three of the genera (Cladophialophora, Ochroconis, and Flavobacterium) decreased the ROS level of the host pine trees, while only one genus (Penicillium) significantly increased the ROS level of the host pine tree seedlings. These results demonstrate a clear relationship between associated microbes and the pathogenicity of PWN, and expand the knowledge on the interaction between PWD-induced forest decline and the PWN-associated microbiome.

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

Rhubarb granule promotes diethylnitrosamine-induced liver tumorigenesis by activating the oxidative branch of pentose phosphate pathway via G6PD in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Rhubarb is a natural herbal medicine widely used clinically with numerous pharmacological activities including anti-cancer. Specifically, several studies reported that free anthraquinones from Rhubarb suppressed the proliferation of hepatoma cells. Nonetheless, recent studies revealed that Rhubarb caused hepatotoxicity in vivo, confirming its "two-way" effect on the liver. Therefore, the efficacy and safety of Rhubarb in the in vivo treatment of liver cancer should be further elucidated.

AIM OF THE STUDY

This study investigated the presence of hepatoprotection or hepatotoxicity of Rhubarb in diethylnitrosamine (DEN)-induced hepatocarcinogenesis.

MATERIAL AND METHODS

A total of 112 male Sprague-Dawley rats weighing 190-250 g were enrolled. The rats were induced hepatocarcinogenesis using diethylnitrosamine (0.002 g/rat) until 17 weeks. Starting at week 11, Rhubarb granules (4 g/kg and 8 g/kg) were intragastrically administered daily for 7 weeks. All rats were euthanized at week 20 and the livers were analyzed via non-targeted metabolomics analysis. We established hepatic glucose 6 phosphate (6PG) levels and glucose 6 phosphate dehydrogenase (G6PD) activities to assess the pentose phosphate pathway (PPP). And the liver injuries of rats were analyzed via histological changes, hepatic function, as well as hepatic protein levels of alpha-fetoprotein (AFP), pyruvate kinase isozyme type M2 (PKM2), and proliferating cell nuclear antigen (PCNA). Furthermore, polydatin (0.1 g/kg/d) as a specific inhibitor of G6PD was used to treat rats. Notably, their histological changes, hepatic function, hepatic 6PG levels, hepatic G6PD activities, PCNA levels, and PKM2 levels were recorded.

RESULTS

Non-targeted metabolomics revealed that Rhubarb regulated the PPP in the liver of Rhubarb-DEN-treated rats. Besides, Rhubarb activated the oxidative branch of the PPP by activating G6PD (a rate-limiting enzyme in the oxidative PPP) in the liver of Rhubarb-DEN-treated rats. Meanwhile, Rhubarb promoted DEN-induced hepatocarcinogenesis. Moreover, polydatin attenuated the promoting effect of Rhubarb on DEN-induced hepatocarcinogenesis.

CONCLUSIONS

Rhubarb promoted DEN-induced hepatocarcinogenesis by activating the PPP, indicating that the efficacy and safety of Rhubarb in the treatment of liver cancer deserve to be deliberated.

Resistance to a nonselective 4-hydroxyphenylpyruvate dioxygenase-inhibiting herbicide via novel reduction-dehydration-glutathione conjugation in Amaranthus tuberculatus

Metabolic resistance to 4-hydroxyphenylpyruvate dioxygenase (HPPD)-inhibiting herbicides is a threat in controlling waterhemp (Amaranthus tuberculatus) in the USA. We investigated resistance mechanisms to syncarpic acid-3 (SA3), a nonselective, noncommercial HPPD-inhibiting herbicide metabolically robust to Phase I oxidation, in multiple-herbicide-resistant (MHR) waterhemp populations (SIR and NEB) and HPPD inhibitor-sensitive populations (ACR and SEN). Dose-response experiments with SA3 provided ED₅₀ -based resistant : sensitive ratios of at least 18-fold. Metabolism experiments quantifying parent SA3 remaining in excised leaves during a time course indicated MHR populations displayed faster rates of SA3 metabolism compared to HPPD inhibitor-sensitive populations. SA3 metabolites were identified via LC-MS-based untargeted metabolomics in whole plants. A Phase I metabolite, likely generated by cytochrome P450-mediated alkyl hydroxylation, was detected but was not associated with resistance. A Phase I metabolite consistent with ketone reduction followed by water elimination was detected, creating a putative α,β-unsaturated carbonyl resembling a Michael acceptor site. A Phase II glutathione-SA3 conjugate was associated with resistance. Our results revealed a novel reduction-dehydration-GSH conjugation detoxification mechanism. SA3 metabolism in MHR waterhemp is thus atypical compared to commercial HPPD-inhibiting herbicides. This previously uncharacterized detoxification mechanism presents a unique opportunity for future biorational design by blocking known sites of herbicide metabolism in weeds.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.

Integrative analyses of metabolome and transcriptome reveals metabolomic variations and candidate genes involved in sweet cherry (Prunus avium L.) fruit quality during development and ripening

Sweet cherry (Prunus avium L.), one of the most appreciated and most important commercial temperate fruits, has high sensory quality and nutritional value. Investigating its metabolic variations provides valuable information on the formation of fruit quality. In this study, widely targeted LC-MS/MS based metabolomics was used to identify and quantify metabolic changes during 'Black Pearl' sweet cherry development and ripening. A total of 263 significant differentially expressed metabolites (DEMs) were detected during the four fruit-development stages. Significant differences were observed in the composition and content of compounds in the four stages of cherry development, especially sugars, organic acids, and flavonoids. Moreover, transcriptome analysis provided a molecular basis for metabolic variations during fruit development. A total of 6724 significant differentially expressed genes (DEGs) were identified. Further correlation analysis of major DEMs and DEGs showed that 19 key DEGs were involved in sugar metabolism, 23 key DEGs in organic acid metabolism, and 13 key DEGs in flavonoid metabolism. The upregulated genes involved in the flavonoid pathway probably play an important role in regulating the rapid increase of anthocyanin content during fruit development. These comprehensive analysis data provide a better understanding to improve fruit quality traits based on molecular and metabolic levels.

Metabolic Profiling and Metabolites Fingerprints in Human Hypertension: Discovery and Potential

Early detection of pathogenesis through biomarkers holds the key to controlling hypertension and preventing cardiovascular complications. Metabolomics profiling acts as a potent and high throughput tool offering new insights on disease pathogenesis and potential in the early diagnosis of clinical hypertension with a tremendous translational promise. This review summarizes the latest progress of metabolomics and metabolites fingerprints and mainly discusses the current trends in the application in clinical hypertension. We also discussed the associated mechanisms and pathways involved in hypertension's pathogenesis and explored related research challenges and future perspectives. The information will improve our understanding of the development of hypertension and inspire the clinical application of metabolomics in hypertension and its associated cardiovascular complications.

Thermal degradation of metabolites in urine using multiple isotope-labelled internal standards for off-line GC metabolomics - effects of injector and oven temperatures

Thermal processes are widely used in small molecule chemical analysis and metabolomics for derivatization, vaporization, chromatography, and ionization, especially in gas chromatography mass spectrometry (GC/MS). An optimized derivatization protocol has been successfully applied using multiple isotope labelled analytical internal standards of selected deuterated and ¹³C selected compounds, covering a range of different groups of metabolites for non-automated GC metabolomics (off-line). Moreover, the study was also realized in a pooled urine sample, following metabolic profiling. A study of thermal degradation of metabolites due to GC inlet and oven programs (fast, slow) was performed, where the results indicated that both GC oven programs (fast and slow) negatively affected the thermal stability of the metabolites, while the fast-ramp GC program also suppressed MS signals. However, the use of multiple internal standards can overcome this drawback. The application of extended temperature ramp GC program presented identical behaviour on metabolite stability and better chromatographic separation combined with much lower signal suppression, compared to a short temperature ramp program. No effects were observed for organic acids, fatty acids, sugars and sugar alcohols, while significant differences were observed for amino acids. GC metabolomics is a strong tool that can facilitate analysis, but special attention is required for sampling handling and heating, before and during the GC analysis. The use and application of multiple multi-group internal standards is highly recommended.

IBD metabonomics predicts phenotype, disease course, and treatment response

Metabonomics in inflammatory bowel disease (IBD) characterizes the effector molecules of biological systems and thus aims to describe the molecular phenotype, generate insight into the pathology, and predict disease course and response to treatment. Nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and integrated NMR and MS platforms coupled with multivariate analyses have been applied to create such metabolic profiles. Recent advances have identified quiescent ulcerative colitis as a distinct molecular phenotype and demonstrated metabonomics as a promising clinical tool for predicting relapse and response to treatment with biologics as well as fecal microbiome transplantation, thus facilitating much needed precision medicine. However, understanding this complex research field and how it translates into clinical settings is a challenge. This review aims to describe the current workflow, analytical strategies, and associated bioinformatics, and translate current IBD metabonomic knowledge into new potential clinically applicable treatment strategies, and outline future key translational perspectives.

Evaluation of Cocaine Effect on Endogenous Metabolites of HepG2 Cells Using Targeted Metabolomics

Cocaine toxicity has been a subject of study because cocaine is one of the most common and potent drugs of abuse. In the current study the effect of cocaine on human liver cancer cell line (HepG2) was assessed. Cocaine toxicity (IC50) on HepG2 cells was experimentally calculated using an XTT assay at 2.428 mM. The metabolic profile of HepG2 cells was further evaluated to investigate the cytotoxic activity of cocaine at 2 mM at three different time points. Cell medium and intracellular material samples were analyzed with a validated HILIC-MS/MS method for targeted metabolomics on an ACQUITY Amide column in gradient mode with detection on a triple quadrupole mass spectrometer in multiple reaction monitoring. About 106 hydrophilic metabolites from different metabolic pathways were monitored. Multivariate analysis clearly separated the studied groups (cocaine-treated and control samples) and revealed potential biomarkers in the extracellular and intracellular samples. A predominant effect of cocaine administration on alanine, aspartate, and glutamate metabolic pathway was observed. Moreover, taurine and hypotaurine metabolism were found to be affected in cocaine-treated cells. Targeted metabolomics managed to reveal metabolic changes upon cocaine administration, however deciphering the exact cocaine cytotoxic mechanism is still challenging.

Recent Progress in Metabolic Syndrome Research and Therapeutics

Metabolic syndrome (MetS) is a well-defined yet difficult-to-manage disease entity. Both the precipitous rise in its incidence due to contemporary lifestyles and the growing heterogeneity among affected populations present unprecedented challenges. Moreover, the predisposed risk for developing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in populations with MetS, and the viral impacts on host metabolic parameters, underscores the need to investigate this mechanism thoroughly. Recent investigations of metabolomics and proteomics have revealed not only differentially expressed substances in MetS, but also the consequences of diet consumption and physical activity on energy metabolism. These variations in metabolites, as well as protein products, also influence a wide spectrum of host characteristics, from cellular behavior to phenotype. Research on the dysregulation of gut microbiota and the resultant inflammatory status has also contributed to our understanding of the underlying pathogenic mechanisms. As for state-of-the-art therapies, advancing depictions of the bio-molecular landscape of MetS have emerged and now play a key role in individualized precision medicine. Fecal microbiota transplantation, aiming to restore the host's homeostasis, and targeting of the bile acid signaling pathway are two approaches to combatting MetS. Comprehensive molecular inquiries about MetS by omics measures are mandatory to facilitate the development of novel therapeutic modalities.

Advances in the Study of Metabolomics and Metabolites in Some Species Interactions

In the natural environment, interactions between species are a common natural phenomena. The mechanisms of interaction between different species are mainly studied using genomic, transcriptomic, proteomic, and metabolomic techniques. Metabolomics is a crucial part of system biology and is based on precision instrument analysis. In the last decade, the emerging field of metabolomics has received extensive attention. Metabolomics not only provides a qualitative and quantitative method for studying the mechanisms of interactions between different species, but also helps clarify the mechanisms of defense between the host and pathogen, and to explore new metabolites with various biological activities. This review focuses on the methods and progress of interspecies metabolomics. Additionally, the prospects and challenges of interspecies metabolomics are discussed.

Garlic skin induces shifts in the rumen microbiome and metabolome of fattening lambs

Garlic (Allium sativum L.) and its constituents have been shown to modify rumen fermentation and improve growth performance. Garlic skin, a by-product of garlic processing, contains similar bioactive components as garlic bulb. This study aimed to investigate the effects of garlic skin supplementation on growth performance, ruminal microbes, and metabolites in ruminants. Twelve Hu lambs were randomly assigned to receive a basal diet (CON) or a basal diet supplemented with 80 g/kg DM of garlic skin (GAS). The experiment lasted for 10 weeks, with the first 2 weeks serving as the adaptation period. The results revealed that the average daily gain and volatile fatty acid concentration were higher (P < 0.05) in lambs fed GAS than those in the CON group. Garlic skin supplementation did not significantly (P > 0.10) affect the α-diversity indices, including the Chao1 index, the abundance-based coverage estimator value, and the Shannon and Simpson indices. At the genus level, garlic skin supplementation altered the ruminal bacterial composition by increasing (P < 0.05) the relative abundances of Prevotella, Bulleidia, Howardella, and Methanosphaera and decreasing (P < 0.05) the abundance of Fretibacterium. Concentrations of 139 metabolites significantly differed (P < 0.05) between the GAS and the CON groups. Among them, substrates for rumen microbial protein synthesis were enriched in the GAS group. The pathways of pyrimidine metabolism, purine metabolism, and vitamin B6 metabolism were influenced (P < 0.05) by garlic skin supplementation. Integrated correlation analysis also provided a link between the significantly altered rumen microbiota and metabolites. Thus, supplementation of garlic skin improved the growth performance of lambs by modifying rumen fermentation through shifts in the rumen microbiome and metabolome.

Effects of Bisphenol A and Bisphenol S Exposure at Low Doses on the Metabolome of Adolescent Male Sprague-Dawley Rats

Toxic effects induced upon exposure to low-dose bisphenol A (BPA) or bisphenol S (BPS) remains controversial. In this study, metabolomics was used to examine the metabolomic perturbation arising from 28 days of exposure to BPA or BPS at 50 μg/kg bw/day in Sprague-Dawley (SD) rats. Endogenous metabolite profiling revealed a clear discrimination of metabolome in the rat plasma among BPA-treatment, BPS-treatment, and control groups. BPA exposure induced the up-regulation of 19 metabolites and down-regulation of 32 metabolites in plasma of SD rats, compared with the control. BPS exposure induced the up-regulation of 15 metabolites and the down-regulation of 33 metabolites in the plasma of SD rats, compared with the control. Joint pathway analysis suggested marked perturbations in the citrate cycle, butanoate metabolism, and alanine, aspartate, and glutamate metabolism for BPA-exposed rats as well as glycerophospholipid metabolism for BPS-exposed rats. These findings provide novel insights into associations between the metabolomic perturbation and phenotypic changes arising from BPA and BPS exposure.

Mass Spectrometry-based Metabolomics in Translational Research

Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.

A perspective on the standards describing mass spectrometry-based metabolic phenotyping (metabolomics/metabonomics) studies in publications

As metabolic phenotyping (metabolomics, metabonomics and also lipidomics) gains in popularity and new investigators enter the field, the need to maintain and improve standards in publication is ever more pressing. In this perspective the requirements for information that should be included in manuscripts published in the Journal of Chromatography B, to ensure that the work is both credible and repeatable, are discussed. These include aspects such as study design, ethics, quality assurance (QA), quality control (QC) and data processing. In addition, aspects such as the level of confidence required for reporting metabolite identification (to a level where they could be subsequently used to develop hypotheses) are discussed.

Temporal Regulation of the Metabolome and Proteome in Photosynthetic and Photorespiratory Pathways Contributes to Maize Heterosis

Heterosis or hybrid vigor is widespread in plants and animals. Although the molecular basis for heterosis has been extensively studied, metabolic and proteomic contributions to heterosis remain elusive. Here we report an integrative analysis of time-series metabolome and proteome data in maize (Zea mays) hybrids and their inbred parents. Many maize metabolites and proteins are diurnally regulated, and many of these show nonadditive abundance in the hybrids, including key enzymes and metabolites involved in carbon assimilation. Compared with robust trait heterosis, metabolic heterosis is relatively mild. Interestingly, most amino acids display negative mid-parent heterosis (MPH), i.e., having lower values than the average of the parents, while sugars, alcohols, and nucleoside metabolites show positive MPH. From the network perspective, metabolites in the photosynthetic pathway show positive MPH, whereas metabolites in the photorespiratory pathway show negative MPH, which corresponds to nonadditive protein abundance and enzyme activities of key enzymes in the respective pathways in the hybrids. Moreover, diurnally expressed proteins that are upregulated in the hybrids are enriched in photosynthesis-related gene-ontology terms. Hybrids may more effectively remove toxic metabolites generated during photorespiration, and thus maintain higher photosynthetic efficiency. These metabolic and proteomic resources provide unique insight into heterosis and its utilization for high yielding maize and other crop plants.

Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection

MOTIVATION

Liquid chromatography-mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning.

RESULTS

Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output.

AVAILABILITY AND IMPLEMENTATION

An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.