Environmental metabolomics: new insights into earthworm ecotoxicity and contaminant bioavailability in soil

Environmental metabolomics is a growing and emerging sub-discipline of metabolomics. Studies with earthworms have progressed from the initial stages of simple contact exposure tests to detailed studies of earthworm responses in soil. Over the past decade, a variety of endogenous metabolites have been identified as potential biomarkers of contaminant exposure. Furthermore, metabolomic methods have delineated responses from sub-lethal exposure of earthworms to polycyclic aromatic hydrocarbons and metals in soil suggesting that environmental metabolomics may be used as a direct measure of contaminant bioavailability in soil. Environmental metabolomics has the potential to fill knowledge gaps related to earthworm toxicity and contaminant bioavailability. However, challenges with metabolite quantification and limited systems-level models of metabolic data require improvement before detailed models of “normal” responses can be developed and used routinely in assessment of contaminated sites. Nonetheless, environmental metabolomics is poised to improve our fundamental understanding of earthworm responses and toxicity to contaminants in soil. Figure Principal component analysis (PCA) scores plots of earthworm metabolic profiles measured by ¹H NMR spectroscopy after exposure to sub-lethal concentrations of phenanthrene in soil.

Volatile profiling reveals intracellular metabolic changes in <Emphasis Type="Italic">Aspergillus parasiticus</Emphasis>: <Emphasis Type="Italic">veA</Emphasis> regulates branched chain amino acid and ethanol metabolism

Background  

Filamentous fungi in the genus Aspergillus produce a variety of natural products, including aflatoxin, the most potent naturally occurring carcinogen known. Aflatoxin biosynthesis, one of the most highly characterized secondary metabolic pathways, offers a model system to study secondary metabolism in eukaryotes. To control or customize biosynthesis of natural products we must understand how secondary metabolism integrates into the overall cellular metabolic network. By applying a metabolomics approach we analyzed volatile compounds synthesized by Aspergillus parasiticus in an attempt to define the association of secondary metabolism with other metabolic and cellular processes.     

Lactate determination in human vitreous humour by capillary electrophoresis and time of death investigation

Forensic inquests, particularly, in assessing time since death currently recognize the importance of the analysis of vitreous humour (VH) biomarkers. Present research, studies, and validates the determination of lactate (La) in VH by CZE with indirect UV detection. The BGE (pH 8.9) consisted of Tris buffer (37 mM) containing 4-methoxybenzoic acid (4 mM) and alkyl-trimethyl-ammonium bromide (1.2 mM). Each VH specimen was diluted with a butyric acid solution (internal standard 0.057 mM) and La and butyrate were separated within 3–5 min (30 kV). The La LOQ and LOD were 4 and 2 mM, respectively. The calibration curve linearity ranged from 4 to 80 mM; intra- and interruns precisions were less than 10% for standard as well as for VH specimen, respectively. To investigate postmortem interval (PMI) and VH lactate level correlation, human VH specimens were collected during autopsy (n = 40) and stored at −20°C until assay. La levels ranged from 16 to 42 mM; PMI values ranged from 10 to 141 h. La (mM) and PMI (h) correlation was statistically significant (r² = 0.527; p < 0.05). In conclusion, the present CZE analysis is efficacious to determine VH La as a biomarker for PMI investigation.     

High-performance isotope-labeling liquid chromatography mass spectrometry for investigating the effect of drinking Goji tea on urine metabolome profiling

Urine is a human biofluid that is widely used for metabolomics research on disease biomarker discovery.Ideally,the metabolome profiles generated from comparative groups of individuals should mainly consist of the endogenous human metabolites that reflect the healthy states of the individuals.However,external factors,such as diet,may alter the urine metabolome profile by either introducing a significant amount or variety of exogenous metabolites to urine or inducing changes of the metabolome profile.Thus,strict control of the external factors during the sample collection process is critical for urine metabolomics aimed at discovery of disease biomarkers.In this work,we describe a study to determine the effect of drinking Goji tea,which is considered a nutritional supplement drink in some regions of the world,on urine metabolome profile.The purpose of this work is not to determine the nutritional values of Goji tea,but to investigate whether drinking a moderate amount of Goji tea 1-3 h(short-term effect)or 12 h(longer-term effect)before urine collection can cause significant variations of urine metabolome profiles.A highly sensitive dansylation isotope labeling liquid chromatography mass spectrometry(LC-MS)method was used to determine the urine metabolomes before and after drinking Goji tea.From the studies of the short term(<3 h)and longer term(12 h)effects of drinking Goji tea,it is clear that the consumption of a moderate amount of Goji tea does not affect the urine metabolome significantly.Fasting for 12 h should be sufficient to remove any potential interference of Goji metabolites from the human urine metabolome profile.     

Metabolic footprinting of tumorigenic and nontumorigenic uroepithelial cells using two-dimensional gas chromatography time-of-flight mass spectrometry

In this study, gas chromatography mass spectrometry (GC-MS) and two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) were employed for the metabolic footprinting of a pair of immortalized human uroepithelial cells namely HUC-1 (nontumorigenic) and HUC T-2 (tumorigenic). Both HUC-1 and HUC T-2 cell lines were cultivated in 1 mL of Ham’s F-12 media. Subsequent to 48 h of incubation, 200 μL of cell culture supernatant was protein-precipitated using 1.7 mL of methanol and an aliquot of 1.5 mL of the mixture was separated, dried, trimethylsilyl-derivatized, and analyzed using GC-MS and GC×GC-TOFMS. Metabolic profiles were analyzed using multivariate data analysis techniques to evaluate the changes of the metabolomes. Both GC-MS and GC×GC-TOFMS analyses showed distinct differences in metabolic phenotypes of the normal and tumorigenic human bladder cells (partial least squares-discriminant analysis (PLS-DA) of GC×GC-TOFMS data; two latent variables, R ² X = 0.418, R ² Y = 0.977 and Q ² (cumulative) = 0.852). Twenty metabolites were identified as being statistically different between the two cell types. These metabolites revealed that several key metabolic pathways were perturbed in tumorigenic urothelial cells as compared to the normal cells. Application of GC×GC-TOFMS offered several advantages compared to classical one-dimensional GC-MS which include enhanced chromatographic resolution (without increase in analytical run time), increase in sensitivity, improved identification of metabolites, and also separation of reagent artifacts from the metabolite peaks. Our results reinforced the advantages of GC×GC-TOFMS and the role of metabolomics in characterizing bladder cancer biology using in vitro cell culture models.     

Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.     

Influence of different processing times on the quality of Polygoni Multiflora Radix by metabolomics based on ultra high performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry

A metabolomics method based on ultra high performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry was developed to evaluate the influence of processing times on the quality of raw and processed Polygoni Multiflora Radix . Principal component analysis and partial least‐squares discriminant analysis was used to screen the potential maker metabolites that were contributed to the quality changes. Then these marker metabolites were selected as variables in Fisher's discriminant analysis to establish the models that were used to distinguish the raw and processed Polygoni Multiflora Radix in the markets. Additionally, 36 compounds were identified. Twelve raw Polygoni Multiflora Radix samples and 23 processed Polygoni Multiflora Radix samples were distinguished. The results showed that the 12 raw Polygoni Multiflora Radix samples belonged to the group of processing time of 0 h, and two processed Polygoni Multiflora Radix samples were part of the group of processing times of 4 h, 12 samples belonged to group of processing times of 8 to 16 h, and nine samples were the group of processing times of 24 to 48 h. The results demonstrated that the method could provide scientific support for the processing standardization of Polygoni Multiflora Radix .     

A SNaPshot assay for detection of 45 mutations in the SCN5A gene in the Chinese Han Population

Sudden cardiac death (SCD) occurs frequently in forensic practice and results in no visible pathological changes that can be detected in an autopsy. In recent years, the genetic background has been emphasized when examining SCD cases. The aim of this study is to establish a feasible system to detect SCD‐related genes for forensic DNA laboratories. Forty‐five reported SCD‐associated SNPs from sodium voltage‐gated channel alpha subunit 5 (SCN5A) were considered in our experiment. We established a SNaPshot assay for the typing of 45 SNPs using multiplex PCR and the minisequencing technique. Two multiplex PCRs were performed and optimized to cover 14 and 16 DNA fragments. The SCD victims came from the Chinese Han population residing in Shanxi and Chongqing provinces and were examined and compared with a non‐SCD group and with normal healthy individuals. A missense mutation at rs1805124 (H558R) was detected in the Chinese Han population in this study. A SNaPshot assay can be performed in any forensic DNA laboratory and would be capable of meeting the increasing demand for SCD detection. This method would also be beneficial for screening at‐risk in family members of SCD victims.     

Metabolic responses of BV-2 cells to puerarin on its polarization using ultra-performance liquid chromatography–mass spectrometry

Microglia are the primary immune cells in the central nervous system with functional plasticity. They can be activated into M1 and M2 phenotypes when neuroinflammation-related diseases occur. M1 phenotype cells produce pro-inflammatory mediators that cause neuroinflammation and the M2 phenotype can secrete anti-inflammatory cytokines that protect neurons from damage. Therefore, inhibiting the M1 phenotype while stimulating the M2 phenotype has been suggested as a potential therapeutic approach for treating neuroinflammation-related diseases. Puerarin has been demonstrated to exert anti-inflammatory and neuroprotective effects. However, the role of puerarin in regulating microglia polarization and its reaction mechanism has not been fully elucidated. In this paper, a metabolomics approach with ultra-performance liquid chromatography–mass spectrometry was performed to investigate the metabolic changes of BV-2 cells in different phenotypes and test the effects of puerarin on polarization. Thirty-nine metabolites were identified as the biomarkers related to the polarization of BV-2 cells and puerarin intervention reverted the content of most of the biomarkers. Our study demonstrated that puerarin could play a key role in M1/M2 polarization of BV-2 cells from a perspective of metabolomics, and it could regulate the balance between promotion and suppression of inflammation.     

Metabolic fingerprinting of high-fat plasma samples processed by centrifugation- and filtration-based protein precipitation delineates significant differences in metabolite information coverage

Metabolomics and metabolic fingerprinting are being extensively employed for improved understanding of biological changes induced by endogenous or exogenous factors. Blood serum or plasma samples are often employed for metabolomics studies. Plasma protein precipitation (PPP) is currently performed in most laboratories before LC–MS analysis. However, the impact of fat content in plasma samples on metabolite coverage has not previously been investigated. Here, we have studied whether PPP procedures influence coverage of plasma metabolites from high-fat plasma samples. An optimized UPLC-QTOF/MS metabolic fingerprinting approach and multivariate modeling (PCA and OPLS-DA) were utilized for finding characteristic metabolite changes induced by two PPP procedures; centrifugation and filtration. We used 12-h fasting samples and postprandial samples collected at 2 h after a standardized high-fat protein-rich meal in obese non-diabetic subjects recruited in a dietary intervention. The two PPP procedures as well as external and internal standards (ISs) were used to track errors in response normalization and quantification. Remarkably and sometimes uniquely, the fPPP, but not the cPPP approach, recovered not only high molecular weight (HMW) lipophilic metabolites, but also small molecular weight (SMW) relatively polar metabolites. Characteristic SMW markers of postprandial samples were aromatic and branched-chain amino acids that were elevated (p < 0.001) as a consequence of the protein challenge. In contrast, some HMW lipophilic species, e.g. acylcarnitines, were moderately lower (p < 0.001) in postprandial samples. LysoPCs were largely unaffected. In conclusion, the fPPP procedure is recommended for processing high-fat plasma samples in metabolomics studies. While method improvements presented here were clear, use of several ISs revealed substantial challenges to untargeted metabolomics due to large and variable matrix effects.     

Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models

Metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors: diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation of the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. In current study, we proposed a metabolomics approach for analyzing MetS based on GC–MS profiling and random forest models. The serum samples from healthy controls and MetS patients were characterized by GC–MS. Then, random forest (RF) models were used to visually discriminate the serum changes in MetS based on these GC–MS profiles. Simultaneously, some informative metabolites or potential biomarkers were successfully discovered by means of variable importance ranking in random forest models. The metabolites such as 2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that the combining GC–MS profiling with random forest models was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis.     

Effects of Tao‐Hong‐Si‐Wu decoction on acute blood stasis in rats based on a LC‐Q/TOF‐MS metabolomics and network approach

A novel approach using metabolomics coupled with a metabolic network was used to investigate the effects of Tao‐Hong‐Si‐Wu decoction (THSWD) on the rat model of acute blood stasis syndrome. Acute blood stasis syndrome was induced by placing the rats in ice‐cold water following two injections with epinephrine. The hemorheological indicators [whole blood viscosity (WBV) and plasma viscosity (PV)] and the blood coagulation indicators [thrombin time (TT), prothrombin time (PT), activated partial thromboplastin time (APTT) and fibrinogen (FIB)] were detected. The nonparametric univariate method and multivariate statistical analysis were performed for determining the potential biomarkers. A correlation map was structured between biochemical indicators and hub metabolites to explain the effects mechanism of THSWD. After the administration of THSWD, the levels of WBV, PV, TT, APTT and FIB returned to levels observed in the control group. According to metabolomics coupled with metabolic network analysis, the intervention of THSWD in rats with acute blood stasis syndrome induced substantial and characteristic changes in their metabolic profiles. Fifteen metabolites were screened, which mainly involved 10 pathways and five hub metabolites, namely, l ‐glutamate, l ‐phenylalanine, N‐acylsphingosine, arachidonic acid and phosphatidate. The biochemical indicators and hub metabolites could be adjusted to close to normal levels by THSWD. Therefore, combining metabolomics and metabolic network helped to evaluate the effects of THSWD on acute blood stasis.     

Chemical composition analysis of Angelica sinensis (Oliv.) Diels and its four processed products by ultra-high-performance liquid chromatography coupled with quadrupole-orbitrap mass spectrometry combining with nontargeted metabolomics

Angelica sinensis (Oliv.) Diels. has been used for women to enrich the blood, prevent and treat blood deficiency syndrome in Traditional Chinese Medicine for thousands of years. Wine-processed Angelica sinensis, soil-processed Angelica sinensis, oil-processed Angelica sinensis, and charred-processed Angelica sinensis are the most significant four processed products used in Chinese clinic. However, there have been few studies aimed at comparing their chemical differences. Ultra-high-performance liquid chromatography coupled with quadrupole-orbitrap mass spectrometry combining with nontargeted metabolomics was applied to investigate the diversity of processed products of Angelica sinensis. A total of 74 compounds with the variable importance in the projection value more than 1.5 and P less than 0.05 in ANOVA were highlighted as the compounds that contribute most to the discrimination of Angelica sinensis and four processed products. The results showed the metabolic changes between Angelica sinensis and its four processed products, there were 19 metabolites, 3 metabolites, 6 metabolites, and 45 metabolites were tentatively assigned in soil-processed Angelica sinensis, wine-processed Angelica sinensis, oil-processed Angelica sinensis, and charred-processed Angelica sinensis, respectively. These results suggested that the proposed metabolomics approach was useful for the quality evaluation and control of processed products of Angelica sinensis.     

Untargeted metabolomic study on the insomnia effect of Suan‐Zao‐Ren decoction in the rat serum and brain using ultra‐high‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry combined with data processing analysis

Insomnia is a common clinical disease that can seriously damage the normal lives of sufferers. Suan‐Zao‐Ren decoction has been used to treat insomnia for a long time. However, the underlying molecular mechanism of Suan‐Zao‐Ren decoction is still not clear. In this study, the nontargeted metabolomics based on high‐resolution mass spectrometry and multiple statistical approaches were initially used to investigate the changes of potential serum and brain biomarkers and metabolic pathways in the insomnia model rat. Principal component analysis‐discriminate analysis indicated that the Suan‐Zao‐Ren decoction treatment improved the metabolic phenotype insomnia. Moreover, the heatmap analysis identified the most important biomarkers involved in insomnia. According to the pathway analysis, phenylalanine metabolism, tryptophan metabolism, and so on were recognized as the most affected metabolic pathways associated with insomnia disease. These findings provided a comprehensive understanding of the regulative effects of Suan‐Zao‐Ren decoction on the host metabolic phenotype of the insomnia rats. Our work demonstrated that the metabolomics approach is a promising tool that could help us to conduct the exploration of the therapeutic effects and mechanism of traditional Chinese medicines.     

Metabolomics of meat exudate: Its potential to evaluate beef meat conservation and aging

In this study we analyzed the exudate of beef to evaluate its potential as non invasive sampling for nuclear magnetic resonance (NMR) based metabolomic analysis of meat samples. Exudate, as the natural juice from raw meat, is an easy to obtain matrix that it is usually collected in small amounts in commercial meat packages. Although meat exudate could provide complete and homogeneous metabolic information about the whole meat piece, this sample has been poorly studied. Exudates from 48 beef samples of different breeds, cattle and storage times have been studied by ¹H NMR spectroscopy. The liquid exudate spectra were compared with those obtained by High Resolution Magic Angle Spinning (HRMAS) of the original meat pieces. The close correlation found between both spectra (>95% of coincident peaks in both registers; Spearman correlation coefficient = 0.945) lead us to propose the exudate as an excellent alternative analytical matrix with a view to apply meat metabolomics. 60 metabolites could be identified through the analysis of mono and bidimensional exudate spectra, 23 of them for the first time in NMR meat studies. The application of chemometric tools to analyze exudate dataset has revealed significant metabolite variations associated with meat aging. Hence, NMR based metabolomics have made it possible both to classify meat samples according to their storage time through Principal Component Analysis (PCA), and to predict that storage time through Partial Least Squares (PLS) regression.     

Serum metabolomics study of patients with allergic rhinitis

Allergic rhinitis (AR) negatively affects the healthy lives of many individuals. Most previous studies on AR focused on the expression of cytokines, with only a few analyzing cytokine expression from a metabolomics viewpoint. Therefore, it is worthwhile to study AR at the metabolic level. Consequently, we aimed to identify differential serum biomarkers by metabolomics. In this study, the orthogonal partial least squares discriminant analysis (OPLS-DA) model was applied to characterize the differences in serum samples collected from patients with AR and healthy volunteers. Ten metabolites (except hexadecanoic acid) were found to be altered significantly (p < .05) in the former group, according to results of principal component analysis and OPLS-DA, indicating that these metabolites could be potential biomarkers. MetaboAnalyst 4.0 and pathway enrichment analysis showed that these changes in metabolites mainly involved three pathways, namely, porphyrin and chlorophyll metabolism, arachidonic acid metabolism, and purine metabolism. Our findings may contribute to a better understanding of the potential pathogenesis mechanisms and provide a metabolic evidence for in-depth studies of AR.     

Application of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry method to identify potential biomarkers of perinatal asphyxia in a non-human primate model

Perinatal asphyxia is a leading cause of brain injury in infants, occurring in 2-4 per 1000 live births. The clinical response to asphyxia is variable and difficult to predict with current diagnostic tests. Reliable biomarkers are needed to help predict the timing and severity of asphyxia, as well as response to treatment. Two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC×GC-TOFMS) was used herein, in conjunction with chemometric data analysis approaches for metabolomic analysis in order to identify significant metabolites affected by birth asphyxia. Blood was drawn before and after 15 or 18 min of cord occlusion in a Macaca nemestrina model of perinatal asphyxia. Postnatal samples were drawn at 5 min of age (n=20 subjects). Metabolomic profiles of asphyxiated animals were compared to four controls delivered at comparable gestational age. Fifty metabolites with the greatest change pre- to post-asphyxia were identified and quantified. The metabolic profile of post-asphyxia samples showed marked variability compared to the pre-asphyxia samples. Fifteen of the 50 metabolites showed significant elevation in response to asphyxia, ten of which remained significant upon comparison to the control animals. This metabolomic analysis confirmed lactate and creatinine as markers of asphyxia and discovered new metabolites including succinic acid and malate (intermediates in the Krebs cycle) and arachidonic acid (a brain fatty acid and inflammatory marker) as potential biomarkers. GC×GC-TOFMS coupled with chemometric data analysis are useful tools to identify acute biomarkers of brain injury. Further study is needed to correlate these metabolites with severity of disease, and response to treatment.     

Accelerated,untargeted metabolomics analysis of cutaneous T‐cell lymphoma reveals metabolic shifts in plasma and tumor adjacent skins of xenograft mice

Cutaneous T‐cell lymphoma (CTCL) is a heterogeneous group of skin‐homing T‐cell neoplasms. Clinical management is stage based but diagnosis and prognosis could be extremely challenging. The presented study aims to explore the metabolic profiling of CTCL by an accelerated untargeted metabolomics data analysis tool “Mummichog” to facilitate the discoveries of potential biomarkers for clinical early stage diagnosis, prognosis, and treatments in CTCL. Ultra high‐performance liquid chromatography–quadrupole time‐of‐flight–based untargeted metabolomics were conducted on the skin and plasma of CTCL mice. It showed that the metabolism of skin changed greatly versus control samples in the development of CTCL. Increased l ‐glutamate and decreased adenosine monophosphate were the most essential metabolic features of CTCL tumor and tumor adjacent skins. Unique metabolism changes in tumor adjacent non‐involved skin tissues (ANIT) occurred in the progress of carcinogenesis, including upregulated cytidine‐5′‐triphosphate, aberrant biosynthesis of prostaglandins, pyrimidine, mevalonate pathway, and tryptophan degradation. Sharply elevated 5‐phospho‐α‐d ‐ribose 1‐diphosphate (PRPP) marked the final state of tumor in CTCL. In the plasma, systematic shifts in corticosterone, sphingolipid, and ceramide metabolism were found. These uncovered aberrant metabolites and metabolic pathways suggested that the metabolic reprogramming of PRPP in tumor tissues may cause the disturbance of cytidine and uridine metabolic homeostasis in ANIT. Accumulative cytidine‐5′‐triphosphate in ANIT may exert positive feedback on the PRPP level and leads to CTCL further development. In addition, the accelerated data analysis tool “Mummichog” showed good practicability and can be widely used in high‐resolution liquid chromatography mass spectrometry–based untargeted metabolomics.     

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.     

微生物代谢物组学的研究方法与进展*

代谢物组学、基因组学、转录组学和蛋白质组学是系统生物学研究的重要组成部分。近年来,在代谢物组学领域,微生物代谢物组学的研究受到人们的重视,成为研究的热点。本文综述了微生物代谢物组学的研究方法,包括样品处理、分析平台、数据处理和生物学解释等,并讨论了微生物代谢物组学在代谢工程方面的应用潜力,以及微生物代谢物组学的研究前景和所面临的挑战。