Experiment-originated variations, and multi-peak and multi-origination phenomena in derivatization-based GC-MS metabolomics

Metabolomics Analysis of Sporulation-Associated Metabolites of Metarhizium anisopliae Based on Gas Chromatography-Mass Spectrometry

Metarhizium anisopliae, an entomopathogenic fungus, has been widely used for the control of agricultural and forestry pests. However, sporulation degeneration occurs frequently during the process of successive culture, and we currently lack a clear understanding of the underlying mechanisms. In this study, the metabolic profiles of M. anisopliae were comparatively analyzed based on the metabolomics approach of gas chromatography-mass spectrometry (GC-MS). A total of 74 metabolites were detected in both normal and degenerate strains, with 40 differential metabolites contributing significantly to the model. Principal component analysis (PCA) and potential structure discriminant analysis (PLS-DA) showed a clear distinction between the sporulation of normal strains and degenerate strains. Specifically, 23 metabolites were down-regulated and 17 metabolites were up-regulated in degenerate strains compared to normal strains. The KEGG enrichment analysis identified 47 significant pathways. Among them, the alanine, aspartate and glutamate metabolic pathways and the glycine, serine and threonine metabolism had the most significant effects on sporulation, which revealed that significant changes occur in the metabolic phenotypes of strains during sporulation and degeneration processes. Furthermore, our subsequent experiments have substantiated that the addition of amino acids could improve M. anisopliae's spore production. Our study shows that metabolites, especially amino acids, which are significantly up-regulated or down-regulated during the sporulation and degeneration of M. anisopliae, may be involved in the sporulation process of M. anisopliae, and amino acid metabolism (especially glutamate, aspartate, serine, glycine, arginine and leucine) may be an important part of the sporulation mechanism of M. anisopliae. This study provides a foundation and technical support for rejuvenation and production improvement strategies for M. anisopliae.

High-Throughput Profiling of Metabolic Phenotypes Using High-Resolution GC-MS

Metabolite profiling provides insights into the metabolic signatures, which themselves are considered as phonotypes closely related to the agronomic and phenotypic traits such as yield, nutritional values, stress resistance, and nutrient use efficiency. GC-MS is a sensitive and high-throughput analytical platform and has been proved to be a vital tool for the analysis of primary metabolism to provide an overview of cellular and organismal metabolic status. The potential of GC-MS metabolite profiling as a tool for detecting metabolic changes in plants grown in a high-throughput plant phenotyping platform was explored. In this chapter, we describe an integrated workflow of semi-targeted GC-high-resolution (HR)-time-of-flight (TOF)-MS metabolomics with both the analytical and computational steps, focusing mainly on the sample preparation, GC-HR-TOF-MS analysis part, and data analysis for plant phenotyping efforts.

Subcellular metabolomics: Isolation, measurement, and applications

Metabolomics, a technique that profiles global small molecules in biological samples, has been a pivotal tool for disease diagnosis and mechanism research. The sample type in metabolomics covers a wide range, including a variety of body fluids, tissues, and cells. However, little attention was paid to the smaller, relatively independent partition systems in cells, namely the organelles. The organelles are specific compartments/places where diverse metabolic activities are happening in an orderly manner. Metabolic disorders of organelles were found to occur in various pathological conditions such as inherited metabolic diseases, diabetes, cancer, and neurodegenerative diseases. However, at the cellular level, the metabolic outcomes of organelles and cytoplasm are superimposed interactively, making it difficult to describe the changes in subcellular compartments. Therefore, characterizing the metabolic pool in the compartmentalized system is of great significance for understanding the role of organelles in physiological functions and diseases. So far, there are very few research articles or reviews related to subcellular metabolomics. In this review, subcellular fractionation and metabolite analysis methods, as well as the application of subcellular metabolomics in the physiological and pathological studies are systematically reviewed, as a practical reference to promote the continued advancement in subcellular metabolomics.

Analytical Platforms for Mass Spectrometry-Based Metabolomics of Polar and Ionizable Metabolites

Metabolomics studies rely on the availability of suitable analytical platforms to determine a vast collection of chemically diverse metabolites in complex biospecimens. Liquid chromatography-mass spectrometry operated under reversed-phase conditions is the most commonly used platform in metabolomics, which offers extensive coverage for nonpolar and moderately polar compounds. However, complementary techniques are required to obtain adequate separation of polar and ionic metabolites, which are involved in several fundamental metabolic pathways. This chapter focuses on the main mass-spectrometry-based analytical platforms used to determine polar and/or ionizable compounds in metabolomics (GC-MS, HILIC-MS, CE-MS, IPC-MS, and IC-MS). Rather than comprehensively describing recent applications related to GC-MS, HILIC-MS, and CE-MS, which have been covered in a regular basis in the literature, a brief discussion focused on basic principles, main strengths, limitations, as well as future trends is presented in this chapter, and only key applications with the purpose of illustrating important analytical aspects of each platform are highlighted. On the other hand, due to the relative novelty of IPC-MS and IC-MS in the metabolomics field, a thorough compilation of applications for these two techniques is presented here.

Lipid Metabolite Biomarkers in Cardiovascular Disease: Discovery and Biomechanism Translation from Human Studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites.

Strategy for Global Profiling and Identification of 2- and 3-Hydroxy Fatty Acids in Plasma by UPLC-MS/MS

2-Hydroxy fatty acids (2-OHFAs) and 3-hydroxy fatty acids (3-OHFAs) with the same carbon backbone are isomers, both of which are closely related to diseases involving fatty acid oxidation disorder. However, the comprehensive profiling of 2- and 3-OHFAs remains an ongoing challenge due to their high structure similarity, few structure-informative product ions, and limited availability of standards. Here, we developed a new strategy to profile and identify 2- and 3-OHFAs according to structure-dependent retention time prediction models using ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Both accurate MS and MS/MS spectra were collected for peak annotation by comparison with an in-house database of theoretically possible 2- and 3-OHFAs. The structures were further confirmed by the validated structure-dependent retention time prediction models, taking advantage of the correlation between the retention time, carbon chain length and number of double bonds, as well as the hydroxyl position-induced isomeric retention time shift rule. With the use of this strategy, 18 2-OHFAs and 32 3-OHFAs were identified in the pooled plasma, of which 7 2-OHFAs and 20 3-OHFAs were identified for the first time in this work, furthering our understanding of OHFA metabolism. Subsequent quantitation method was developed by scheduled multiple reaction monitoring (MRM) and then applied to investigate the alteration of 2- and 3-OHFAs in esophageal squamous cell carcinoma (ESCC) patients. Finally, a potential biomarker panel consisting of six OHFAs with good diagnostic performance was achieved. Our study provides a new strategy for isomer identification and analysis, showing great potential for targeted metabolomics in clinical biomarker discovery.

Metabolite Transformation and Enzyme Activities of Hainan Vanilla Beans During Curing to Improve Flavor Formation

This paper compares the differences in metabolites of vanilla beans at five different curing stages. Key vanilla flavors, vanillin precursors and main enzymes during the curing process of Hainan vanilla beans were also analyzed. Hundreds of metabolites were detected based on metabolic analyses of a widely targeted metabolome technique, compared with blanched vanilla beans (BVB), sweating vanilla beans (SVB) and drying vanilla beans (DVB), the total peak intensity of cured vanilla beans (CVB) is on the rise. The score plots of principal component analysis indicated that the metabolites were generally similar at the same curing stages, but for the different curing stages, they varied substantially. During processing, vanillin content increased while glucovanillin content decreased, and vanillic acid was present in sweating beans, but its content was reduced in drying beans. Both p-hydroxybenzaldehyde and p-hydroxybenzoic acid showed the maximum contents in cured beans. Ferulic acid was mainly produced in drying beans and reduced in cured beans. p-coumaric acid increased during the curing process. Vanillyl alcohol in drying beans (0.22%) may be formed by the hydrolysis of glucoside, whose conversion into vanillin may explain its decrease during the curing stage. β-Glucosidase enzymatic activity was not detected in blanched and sweating beans, but was observed after drying. Peroxidase activity decreased during curing by 94% in cured beans. Polyphenol oxidase activity was low in earlier stages, whereas cellulase activity in processed beans was higher than in green beans, except for cured beans. This study contributes to revealing the formation of flavor components and the biosynthesis pathway of vanillin.

Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases

Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidney disease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidney disease and a control. These metabolites are likely to be robust indicators of kidney disease processes, and therefore potential biomarkers, warranting further investigation.

Review of recent developments in GC-MS approaches to metabolomics-based research

BACKGROUND

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available.

AIM OF REVIEW

This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation

Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 14 metabolites were uniquely identified by NMR, and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism, leading to fatty-acid and complex-lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques: the improved detection and annotation of metabolites.

Comparative metabolomics in vanilla pod and vanilla bean revealing the biosynthesis of vanillin during the curing process of vanilla

High-performance liquid chromatography–mass spectrometry (LC–MS) was used for comprehensive metabolomic fingerprinting of vanilla fruits prepared from the curing process. In this study, the metabolic changes of vanilla pods and vanilla beans were characterized using MS-based metabolomics to elucidate the biosynthesis of vanillin. The vanilla pods were significantly different from vanilla beans. Seven pathways of vanillin biosynthesis were constructed, namely, glucovanillin, glucose, cresol, capsaicin, vanillyl alcohol, tyrosine, and phenylalanine pathways. Investigations demonstrated that glucose, cresol, capsaicin, and vanillyl alcohol pathway were detected in a wide range of distribution in microbial metabolism. Thus, microorganisms might have participated in vanillin biosynthesis during vanilla curing. Furthermore, the ion strength of glucovanillin was stable, which indicated that glucovanillin only participated in the vanillin biosynthesis during the curing of vanilla.

Gas Chromatography-Mass Spectrometry for Metabolite Profiling of Japanese Black Cattle Naturally Contaminated with Zearalenone and Sterigmatocystin

The objective of this study was to evaluate the metabolic profile of cattle fed with or without zearalenone (ZEN) and sterigmatocystin (STC)-contaminated diets using a gas chromatography-mass spectrometry metabolomics approach. Urinary samples were collected from individual animals (n = 6 per herd) from fattening female Japanese Black (JB) cattle herds (23 months old, 550–600 kg). Herd 1 had persistently high urinary ZEN and STC concentrations due to the presence of contaminated rice straw. Herd 2, the second female JB fattening herd (23 months old, 550–600 kg), received the same dietary feed as Herd 1, with non-contaminated rice straw. Urine samples were collected from Herd 1, two weeks after the contaminated rice straw was replaced with uncontaminated rice straw (Herd 1N). Identified metabolites were subjected to principal component analysis (PCA) and ANOVA. The PCA revealed that the effects on cattle metabolites depended on ZEN and STC concentrations. The contamination of cattle feed with multiple mycotoxins may alter systemic metabolic processes, including metabolites associated with ATP generation, amino acids, glycine-conjugates, organic acids, and purine bases. The results obtained from Herd 1N indicate that a two-week remedy period was not sufficient to improve the levels of urinary metabolites, suggesting that chronic contamination with mycotoxins may have long-term harmful effects on the systemic metabolism of cattle.

Gas chromatographic-mass spectrometric determination of glycosides without prior hydrolysis

The article presents for the first time the linear temperature programmed retention indices on a column with stationary phases of 5% phenylpolydimethyl silicone and the mass spectra of trimethylsilyl (TMS) derivatives of 71 glycosides (both commercial preparations and compounds extracted from plant tissues) which were not characterized earlier by these parameters. Converted to their TMS derivatives, the glycosides were thermally stable: they exhibited single peaks on their chromatograms without products of thermal decomposition. Therefore this work demonstrates the suitability of high resolution-high temperature gas chromatography (HR-HT/GC) to analyse different groups of glycosides including compounds with disaccharide moieties without the necessity of their hydrolyses. Since a limited number of commercial and plant-isolated glycosides were available, an attempt was made to assess their retention indices using the known "structure-retention relationships" approach. It was demonstrated that the retention indices of silanised glycosides and their aglycones were characterized by a linear dependence.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Metabolomic signature of brain cancer

Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.

Influences of Normalization Method on Biomarker Discovery in Gas Chromatography-Mass Spectrometry-Based Untargeted Metabolomics: What Should Be Considered?

Data reduction techniques in gas chromatography-mass spectrometry-based untargeted metabolomics has made the following workflow of data analysis more lucid. However, the normalization process still perplexes researchers, and its effects are always ignored. In order to reveal the influences of normalization method, five representative normalization methods (mass spectrometry total useful signal, median, probabilistic quotient normalization, remove unwanted variation-random, and systematic ratio normalization) were compared in three real data sets with different types. First, data reduction techniques were used to refine the original data. Then, quality control samples and relative log abundance plots were utilized to evaluate the unwanted variations and the efficiencies of normalization process. Furthermore, the potential biomarkers which were screened out by the Mann-Whitney U test, receiver operating characteristic curve analysis, random forest, and feature selection algorithm Boruta in different normalized data sets were compared. The results indicated the determination of the normalization method was difficult because the commonly accepted rules were easy to fulfill but different normalization methods had unforeseen influences on both the kind and number of potential biomarkers. Lastly, an integrated strategy for normalization method selection was recommended.

Changes in fatty acid composition in the giant clam Tridacna maxima in response to thermal stress

Temperature can modify membrane fluidity and thus affects cellular functions and physiological activities. This study examines lipid remodelling in the marine symbiotic organism, Tridacna maxima, during a time series of induced thermal stress, with an emphasis on the morphology of their symbiont Symbiodinium First, we show that the French Polynesian giant clams harbour an important proportion of saturated fatty acids (SFA), which reflects their tropical location. Second, in contrast to most marine organisms, the total lipid content in giant clams remained constant under stress, though some changes in their composition were shown. Third, the stress-induced changes in fatty acid (FA) diversity were accompanied by an upregulation of genes involved in lipids and ROS pathways. Finally, our microscopic analysis revealed that for the giant clam's symbiont, Symbiodinium, thermal stress led to two sequential cell death processes. Our data suggests that the degradation of Symbiodinium cells could provide an additional source of energy to T maxima in response to heat stress.

HILIC-MS for metabolomics: An attractive and complementary approach to RPLC-MS

Hydrophilic interaction chromatography (HILIC) is an emerging separation mode of liquid chromatography (LC). Using highly hydrophilic stationary phases capable of retaining polar/ionic metabolites, and accompany with high organic content mobile phase that offer readily compatibility with mass spectrometry (MS) has made HILIC an attractive complementary tool to the widely used reverse-phase (RP) chromatographic separations in metabolomic studies. The combination of HILIC and RPLC coupled with an MS detector expands the number of detected analytes and provides more comprehensive metabolite coverage than use of only RP chromatography. This review describes the recent applications of HILIC-MS/MS in metabolomic studies, ranging from amino acids, lipids, nucleotides, organic acids, pharmaceuticals, and metabolites of specific nature. The biological systems investigated include microbials, cultured cell line, plants, herbal medicine, urine, and serum as well as tissues from animals and humans. Owing to its unique capability to measure more-polar biomolecules, the HILIC separation technique would no doubt enhance the comprehensiveness of metabolite detection, and add significant value for metabolomic investigations. © 2014 Wiley Periodicals, Inc. Mass Spec Rev 35:574-600, 2016.

Challenges of analyzing different classes of metabolites by a single analytical method

Complex biological samples include thousands of metabolites that range widely in both physiochemical properties and concentration. Simultaneously analyzing metabolites with different properties using a single analytical method is very challenging. The analytical process for metabolites comprises multiple steps including sampling, quenching, sample preparation, separation and detection. Each step can have a significant effect on the reliability and precision of ultimate analytic results. The aim of review is a discussion of considerations and challenges for the simultaneous analysis of metabolites using LC– and GC–MS systems. The review discusses available methodology for each analytical step, and presents the limitations and advantages of each method for the large-scale targeted metabolomics analysis of human and animal biological samples.

Development of a GC/Quadrupole-Orbitrap mass spectrometer, part II: new approaches for discovery metabolomics

Identification of unknown peaks in gas chromatography/mass spectrometry (GC/MS)-based discovery metabolomics is challenging, and remains necessary to permit discovery of novel or unexpected metabolites that may elucidate disease processes and/or further our understanding of how genotypes relate to phenotypes. Here, we introduce two new technologies and an analytical workflow that can facilitate the identification of unknown peaks. First, we report on a GC/Quadrupole-Orbitrap mass spectrometer that provides high mass accuracy, high resolution, and high sensitivity analyte detection. Second, with an "intelligent" data-dependent algorithm, termed molecular-ion directed acquisition (MIDA), we maximize the information content generated from unsupervised tandem MS (MS/MS) and selected ion monitoring (SIM) by directing the MS to target the ions of greatest information content, that is, the most-intact ionic species. We combine these technologies with (13)C- and (15)N-metabolic labeling, multiple derivatization and ionization types, and heuristic filtering of candidate elemental compositions to achieve (1) MS/MS spectra of nearly all intact ion species for structural elucidation, (2) knowledge of carbon and nitrogen atom content for every ion in MS and MS/MS spectra, (3) relative quantification between alternatively labeled samples, and (4) unambiguous annotation of elemental composition.

A metabolomic study of low estimated GFR in non-proteinuric type 2 diabetes mellitus

AIMS/HYPOTHESIS

We carried out a urinary metabolomic study to gain insight into low estimated GFR (eGFR) in patients with non-proteinuric type 2 diabetes.

METHODS

Patients were identified as being non-proteinuric using multiple urinalyses. Cases (n = 44) with low eGFR and controls (n = 46) had eGFR values <60 and ≥60 ml min(-1) 1.73 m(-2), respectively, as calculated using the Modification of Diet in Renal Disease formula. Urine samples were analysed by liquid chromatography/mass spectrometry (LC/MS) and GC/MS. False discovery rates were used to adjust for multiple hypotheses testing, and selection of metabolites that best predicted low eGFR status was achieved using least absolute shrinkage and selection operator logistic regression.

RESULTS

Eleven GC/MS metabolites were strongly associated with low eGFR after correction for multiple hypotheses testing (smallest adjusted p value = 2.62 × 10(-14), largest adjusted p value = 3.84 × 10(-2)). In regression analysis, octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5-dimethoxymandelic amide and N-acetylglutamine were selected as the best subset for prediction and allowed excellent classification of low eGFR (AUC = 0.996). In LC/MS, 19 metabolites remained significant after multiple hypotheses testing had been taken into account (smallest adjusted p value = 2.04 × 10(-4), largest adjusted p value = 4.48 × 10(-2)), and several metabolites showed stronger evidence of association relative to the uraemic toxin, indoxyl sulphate (adjusted p value = 3.03 × 10(-2)). The potential effect of confounding on the association between metabolites was excluded.

CONCLUSIONS/INTERPRETATION

Our study has yielded substantial new insight into low eGFR and provided a collection of potential urinary biomarkers for its detection.

Metabonomic study on the anti-osteoporosis effect of Rhizoma Drynariae and its action mechanism using ultra-performance liquid chromatography-tandem mass spectrometry

ETHNOPHARMACOLOGICAL RELEVANCE

Rhizoma Drynariae (RD) is an effectively traditional Chinese medicine which is usually used in treating osteoporosis, bone fracture, streptomycin ototoxicity and hyperlipemia. Up to now, studies on pharmacological mechanism of RD mostly focus on cell and gene level, little is known about its metabonomics study. The aim of this study is to establish the rats plasma metabonomic profiles of control, model and treatment group, then to investigate the anti-osteoporosis effect of RD and its action mechanism.

METHOD

A total of 21 Wistar rats was divided into three groups: control group, model group and treatment group. The model and treatment rats were injected prednisolone for 12 weeks, at the same time the treatment rats were orally administered RD extract at a therapeutic dose (10g/kg, expressed as the weight of raw material) once daily throughout the experimental period, control group and model group were orally gavaged approximately volume normal saline solution. After 12 weeks, all plasma samples of three groups were collected and their metabolic profiling changes were analyzed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The resulting dataset was analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The identification of all potential biomarkers was performed using reference standard by comparing their mass spectra, MS/MS fragmentation and retention time. Furthermore, clinical biochemistry and biomechanics study were also carried out to ensure the success of the osteoporosis model and to investigate the anti-osteoporosis effect of RD.

RESULTS

Obvious separation trend between control and model group was found in PCA score plot, the anti-osteoporosis effect of RD can be indicated in PLS-DA score plot among these three groups. Six potential metabolite biomarkers, Lysophosphatidylcholines (C16:0 LPC, C18:0 LPC, C18:1 LPC and C18:2 LPC), tryptophane and phenylalanine, which were proved to be related with osteoporosis, were identified in the rats plasma. Compared with control group, level of all biomarkers increased significantly in model group, while that was much closer to normal in treatment group.

CONCLUSION

The anti-osteoporosis effect of RD has been reliably confirmed by the metabonomics method. The osteoporosis might be prevented by RD via intervening antioxidant-oxidation balance, tryptophane metabolism and phenylalanine metabolism in vivo in rats.

Applications of mass spectrometry to metabolomics and metabonomics: detection of biomarkers of aging and of age-related diseases

Every 5 years or so new technologies, or new combinations of old ones, seemingly burst onto the science scene and are then sought after until they reach the point of becoming commonplace. Advances in mass spectrometry instrumentation, coupled with the establishment of standardized chemical fragmentation libraries, increased computing power, novel data-analysis algorithms, new scientific applications, and commercial prospects have made mass spectrometry-based metabolomics the latest sought-after technology. This methodology affords the ability to dynamically catalogue and quantify, in parallel, femtomole quantities of cellular metabolites. The study of aging, and the diseases that accompany it, has accelerated significantly in the last decade. Mutant genes that alter the rate of aging have been found that increase lifespan by up to 10-fold in some model organisms, and substantial progress has been made in understanding fundamental alterations that occur at both the mRNA and protein level in tissues of aging organisms. The application of metabolomics to aging research is still relatively new, but has already added significant insight into the aging process. In this review we summarize these findings. We have targeted our manuscript to two audiences: mass spectrometrists interested in applying their technical knowledge to unanswered questions in the aging field, and gerontologists interested in expanding their knowledge of both mass spectrometry and the most recent advances in aging-related metabolomics.

Enhancement of the capabilities of liquid chromatography-mass spectrometry with derivatization: general principles and applications

The integration of liquid chromatography-mass spectrometry (LC-MS) with derivatization is a relatively new and unique strategy that could add value and could enhance the capabilities of LC-MS-based technologies. The derivatization process could be carried out in various analytical steps, for example, sampling, storage, sample preparation, HPLC separation, and MS detection. This review presents an overview of derivatization-based LC-MS strategy over the past 10 years and covers both the general principles and applications in the fields of pharmaceutical and biomedical analysis, biomarker and metabolomic research, environmental analysis, and food-safety evaluation. The underlying mechanisms and theories for derivative reagent selection are summarized and highlighted to guide future studies.

Characterization of the human tear metabolome by LC-MS/MS

The tear film overlying the epithelial cells of the eye's surface is vital to visual function, and its composition is reflective of ocular surface health. The ultrasmall volume of tears poses challenges in its analysis, contributing to the limited number of reports on the tear metabolome. In addition, using a standard clinical method of tear collection posed some confounding factors in metabonomic analysis. We sought to establish an analytical platform for the global characterization of human tear metabolites. Following information dependent acquisition (IDA) directed liquid chromatography-tandem mass spectrometry (LC-MS/MS), isotope pattern matched peak mining was performed using Extracted Ion Chromatogram (XIC) manager within the PeakView software. Sixty metabolites representing diverse compound classes were identified in human tears, most of which have not been previously reported. Selected metabolites were verified using pure standards. Unsupervised chemometric analysis showed good separation between tear samples and blanks (PC1 = 87%, R(2) = 0.91, Q(2) = 0.87). The results demonstrated the potential of our platform for untargeted metabonomic studies of eye diseases.

Gas chromatography-mass spectrometry for metabolite profiling of Japanese medaka (Oryzias latipes) juveniles exposed to malathion

PURPOSE

We evaluate malathion toxicity to Japanese medaka (Oryzias latipes) juveniles by using a mass spectrometry combined with gas chromatography (GC/MS) metabolomics approach.

METHODS

Medaka were exposed to low (L) and high (H) concentrations (nominally 20 and 2,000 μg/L, respectively) of water-borne malathion. Metabolites were extracted from the fish, derivatized, and analyzed by GC/MS. Identified metabolites were subjected to one-way analysis of variance and principal component analysis (PCA). We examined the variations in the amounts of the metabolites during the exposure period.

RESULTS AND DISCUSSION

At 24 h, control, L, and H groups were separated along PC1, suggesting that the effects of malathion depended on exposure concentration. The PCA results at 96 h suggest that the metabolite profiles variations of the L and H groups differed, and thus that the effects of malathion in groups differed. At 24 h, the amounts of amino acids in both exposed groups were lower than the control group amounts, perhaps owing to accelerated protein synthesis. At 96 h, the amounts of almost all the amino acids increased in the L group but decreased in the H group relative to the control group amounts, suggesting the proteolysis occurred in the L group while protein synthesis continued in the H group, that the high malathion exposure affected the fish. In addition, at 96 h, gluconeogenesis may have been induced in the L group but not in H group.

CONCLUSIONS

Malathion exposure may have altered the balance between protein synthesis and degradation and induced gluconeogenesis in medaka. Our results suggest that metabolomics will be useful for comprehensive evaluation of toxicity.

Gas chromatography-mass spectrometric method for metabolic profiling of tobacco leaves

A gas chromatography-mass spectrometric method was developed for profiling of tobacco leaves. The differentiation among tobacco leaves planted in two different regions was investigated. Prior to analysis, the extraction solvent formulation was optimized and a combination of water, methanol and acetonitrile with a volume ratio of 3:1:1 was found to be optimal. The reproducibility of the method was satisfactory. Kendall tau-b rank correlation coefficients were equal to 1 (p<0.05) for 82% of the resolved peaks (up to 95% of the overall peak areas), indicating the good response correlation. Forty-four compounds including 9 saccharides, 9 alcohols, 9 amino acids, 16 organic acids and phosphoric acid were identified based on standard compounds. The method was successfully applied for profiling of tobacco leaves from Zimbabwe and Yunnan of China. Our result revealed that levels of saccharides and their derivatives including xylose, ribose, fructose, glucose, turanose, xylitol and glyceric acid were more abundant while sucrose, glucitol and D-gluconic acid were less abundant in tobacco leaves from Yunnan as compared to those from Zimbabwe. Amino acids such as L-alanine, L-tyrosine and L-threonine were found to be richer in Zimbabwe tobacco than in Yunnan tobacco.