Mass spectrometry-based technologies for high-throughput metabolomics

Unraveling biomarkers of exposure for tenuazonic acid through urinary metabolomics

Mycotoxins are secondary metabolites produced by fungi such as Aspergillus, Alternaria, and Penicillium, affecting nearly 80% of global food crops. Tenuazonic acid (TeA) is the major mycotoxin produced by Alternaria alternata, a prevalent pathogen affecting plants, fruits, and vegetables. TeA is notably prevalent in European diets, however, TeA biomarkers of exposure and metabolites remain unknown. This research aims to bridge this knowledge-gap by gaining insights about human TeA exposure and metabolization. Nine subjects were divided into two groups. The first group received a single bolus of TeA at the Threshold of Toxicological Concern (TTC) to investigate the presence of TeA urinary biomarkers, while the second group served as a control. Sixty-nine urinary samples were prepared and analyzed using UPLC-Xevo TQ-XS for TeA quantification and UPLC-Orbitrap Exploris for polar metabolome acquisition. TeA was rapidly excreted during the first 13 h and the fraction extracted was 0.39 ± 0.22. The polar metabolome compounds effectively discriminating the two groups were filtered using Orthogonal Partial Least Squares-Discriminant Analysis and subsequently annotated (n = 122) at confidence level 4. Finally, the urinary metabolome was compared to in silico predicted TeA metabolites. Nine metabolites, including oxidized, N-alkylated, desaturated, glucuronidated, and sulfonated forms of TeA were detected.

Quasi-continuous infrared matrix-assisted laser desorption electrospray ionization source coupled to a quadrupole time-of-flight mass spectrometer for direct analysis from well plates

High-throughput screening (HTS) is a technique mostly used by pharmaceutical companies to rapidly screen multiple libraries of compounds to find drug hits with biological or pharmaceutical activity. Mass spectrometry (MS) has become a popular option for HTS given that it can simultaneously resolve hundreds to thousands of compounds without additional chemical derivatization. For this application, it is convenient to do direct analysis from well plates. Herein, we present the development of an infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source coupled directly to an Agilent 6545 for direct analysis from well plates. The source is coupled to a quadrupole time-of-flight (Q-TOF) mass spectrometer to take advantage of the high acquisition rates without sacrificing resolving power as required with Orbitrap or Fourier-transform ion cyclotron resonance (FTICR) instruments. The laser used for this source operates at 100 Hz, firing 1 pulse-per-burst, and delivers around 0.7 mJ per pulse. Continuously firing this laser for an extended duration makes it a quasi-continuous ionization source. Additionally, a metal capillary was constructed to extend the inlet of the mass spectrometer, increase desolvation of electrospray charged droplets, improve ion transmission, and increase sensitivity. Its efficiency was compared with the conventional dielectric glass capillary by measured signal and demonstrated that the metal capillary increased ionization efficiency due to its more uniformly distributed temperature gradient. Finally, we present the functionality of the source by analyzing tune mix directly from well plates. This source is a proof of concept for HTS applications using IR-MALDESI coupled to a different MS platform.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

UPLC-MS metabolomics method provides valuable insights into the effect and underlying mechanisms of Rhizoma Drynariae protecting osteoporosis

Osteoporosis (OP) is a metabolic bone disease in which that volume of bone tissue per unit volume decrease, which is a common disease disturbing the elderly or postmenopausal women. Rhizoma Drynariae (RD) is a kind of herb widely used in thousands of years of clinical practice in China to tonify kidney and prevent osteoporosis, with reliable curative effect. However, the mechanism of its anti-osteoporosis action is still unclear. This study is dedicated to exploration the therapeutic effect of RD on retinoic acid solution-induced OP model rats based on high-throughput metabolomics technology platform, and reveal its influence on metabolomics level, so as to find effective potential biomarkers and therapeutic targets for diagnosing OP. OP model was established by intragastric administration of retinoic acid solution for 21 days, and then the treatment group was treated by intragastric administration of RD solution for 60 days. Blood samples of all groups were collected and analyzed based on UPLC-MS metabolomics and combined with EZinfo 3.0 data analysis, 32 potential biomarkers were identified, including 22 in ESI+ and 10 in ESI-, these biomarkers are related to 9 metabolic pathways. After treatment with RD solution, 21 biomarkers were obviously regulated, these mainly affected linoleic acid metabolic, glycerophospholipid metabolism and arachidonic acid metabolism pathway. The results show that RD can reduce the risk of OP disease, which may be related to the metabolic pathway mentioned above, and provides the foundation for the administer prophylaxis and treatment of OP with natural products.

Canine metabolomics advances

INTRODUCTION

Canis lupus familiaris is a domestic dog and many owners consider their pets as a family member. Medical bills with dogs are overcame only by the health care received by humans. Medical care is constantly progressing, and so is veterinary care. Metabolomics is the ''omic" technique aimed to the study of metabolome, low-molecular weight molecules, through biofluids or tissue samples. And it also allows to evaluate disease diagnosis and prognosis, therapeutic evaluation and toxicological studies.

OBJECTIVES

The goal of this paper is to review the current and potential applications of metabolomics in domestic dogs.

METHOD

ScienceDirect, Scopus, Reaxys and PubMed were searched for papers that performed canine metabolomics in any research area.

RESULTS

We analysed 38 papers, published until April 2019 in canine metabolomics approach. Metabolomic research in dogs so far can be divided into three areas: (a) Metabolomics studies in veterinary science, such as improving pet dogs health and welfare. (b) Diet, breeds and species discrimination. (c) Use of dogs as animal model in different diseases and drug development (evaluation toxicity and effect).

CONCLUSIONS

The results of this review showed that interest in metabolomics is growing in veterinary research. Several canine diseases have been evaluated with some promise for potential biomarker and/or disease mechanism discovery. Because canine metabolomics is a relatively new area, the researches spread across different research areas and with few studies in each area.

Comparative study of five different amine-derivatization methods for metabolite analyses by liquid chromatography-tandem mass spectrometry

Current metabolomics research utilizes liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses to handle biological samples that contain thousands of quantifiable metabolites. However, no LC-MS/MS condition is suitable for directly analyzing all metabolites. An alternative approach is to derivatize metabolites to impart desirable properties such as better chromatographic separation, enhanced ionization efficiency, or fluorescence detection. An important category of metabolites is amine-containing compounds, which includes amino acids, neurotransmitters, alkaloids, biogenic amines, etc. Various derivatization methods have been developed for amine groups, but few studies have compared their relative strengths and weaknesses. We chose Dansyl-Cl, o-phthalaldehyde (OPA), Fmoc-Cl, Dabsyl-Cl, and Marfey's reagent to systematically compare their reactivity, absorbance, fluorescence, chromatographic separation, and ionization efficiencies under three pH conditions-2.6, 5.0, and 8.0. Their MS/MS fragmentation patterns were also examined under different collision energies. Overall, Dansyl-Cl is a very versatile derivatization method, generating products with fluorescence and high ionization efficiency. Fmoc-Cl is similarly useful under highly acidic chromatography conditions. Dabsyl-Cl may be a good alternative at weakly acidic and weakly basic conditions. OPA is a versatile fluorogenic reagent and its chemistry may be fine-tuned by incorporating different thiol molecules. Marfey's reagent is suboptimal in general, but its chiral property is useful for the separation of enantiomers. All five were applied to the analyses of Coptis chinensis, a Chinese medical herb, identifying hundreds of amine-containing metabolites through MS/MS analyses. None of the five methods is clearly superior, and their compound coverage profiles are rather distinct. A combination of multiple derivatization reagents is required for comprehensive coverage. Our comparative data provide useful guidelines for designing more efficient metabolomics experiments for different analytical goals.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

Surface fitting for calculating the second dimension retention index in comprehensive two-dimensional gas chromatography mass spectrometry

Comprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS) has been widely used for analysis of volatile compounds. However, the second dimension retention index (I) of each compound is not widely used to aid compound identification owing to the limited accuracy of I calculation. We report a surface fitting approach to the calculation of I using n-alkanes (C7-C30) as references, where the second dimension retention time (2tR) and the second dimension column temperature (2Te) formed the X-Y plane and the I was the Z-axis to form the I surface. Compared to the conventional approach for calculating I using isovolatility curves, the surface fitting approach eliminated the construction of isovolatility curves for the reference compounds and gives better reproducibility. The goodness of the proposed surface fitting achieved R2 = 0.9999 and RMSE = 6.1 retention index units (iu). Ten-fold cross validation demonstrated the surface fitting approach had a good predictability with average R2 = 0.9999 and RMSE = 6.6 iu. The developed method was also applied to calculate the second dimension retention indices of compound standards in two commercial mixtures MegaMix A and MegaMix B. The mean standard deviation of the calculated I was only 1.6 iu for compounds in MegaMix A and 3.4 iu for compounds in MegaMix B. Compared with the literature results, the small value of standard deviation in the calculated retention index using surface fitting method shows that the surface fitting method has less measurement variability than the conventional isovolatility curve approach.

Metabolic phenotyping for understanding the gut microbiome and host metabolic interplay

There is growing interest in the role of the gut microbiome in human health and disease. This unique complex ecosystem has been implicated in many health conditions, including intestinal disorders, inflammatory skin diseases and metabolic syndrome. However, there is still much to learn regarding its capacity to affect host health. Many gut microbiome research studies focus on compositional analysis to better understand the causal relationships between microbial communities and disease phenotypes. Yet, microbial diversity and complexity is such that community structure alone does not provide full understanding of microbial function. Metabolic phenotyping is an exciting field in systems biology that provides information on metabolic outputs taking place in the system at a given moment in time. These readouts provide information relating to by-products of endogenous metabolic pathways, exogenous signals arising from diet, drugs and other lifestyle and environmental stimuli, as well as products of microbe-host co-metabolism. Thus, better understanding of the gut microbiome and host metabolic interplay can be gleaned using such analytical approaches. In this review, we describe research findings focussed on gut microbiota-host interactions, for functional insights into the impact of microbiome composition on host health. We evaluate different analytical approaches for capturing metabolic activity and discuss analytical methodological advancements that have made a contribution to the field. This information will aid in developing novel approaches to improve host health in the future, and therapeutic modulation of the microbiome may soon augment conventional clinical strategies.

Ambient mass spectrometry in metabolomics

Since the introduction of desorption electrospray ionization (DESI) mass spectrometry (MS), ambient MS methods have seen increased use in a variety of fields from health to food science. Increasing its popularity in metabolomics, ambient MS offers limited sample preparation, rapid and direct analysis of liquids, solids, and gases, in situ and in vivo analysis, and imaging. The metabolome consists of a constantly changing collection of small (<1.5 kDa) molecules. These include endogenous molecules that are part of primary metabolism pathways, secondary metabolites with specific functions such as signaling, chemicals incorporated in the diet or resulting from environmental exposures, and metabolites associated with the microbiome. Characterization of the responsive changes of this molecule cohort is the principal goal of any metabolomics study. With adjustments to experimental parameters, metabolites with a range of chemical and physical properties can be selectively desorbed and ionized and subsequently analyzed with increased speed and sensitivity. This review covers the broad applications of a variety of ambient MS techniques in four primary fields in which metabolomics is commonly employed.

Growth Hormone Deficiency in Prepubertal Children: Predictive Markers of Cardiovascular Disease

BACKGROUND

Cardiovascular (CV) risk factors have been identified in adults with untreated growth hormone deficiency (GHD). Existing evidence suggests that the development of the atheromatous plaque begins early in childhood. Previous reports have shown that GHD children are prone to increased CV risks including impaired cardiac function, dyslipidemia and abnormalities in body composition. Recent studies in epigenetics and metabolomics have defined specific fingerprints that might be associated with an increased risk of CV disease.

AIM

The aim of this review is to point out the most significant biochemical and clinical predictive markers of CV disease in prepubertal children and to evaluate the effect of recombinant human growth hormone therapy on most of these alterations. The novel findings in epigenetics and metabolomics are also reviewed, with a particular focus on translating them into clinical practice.

Actinobacteria: Current research and perspectives for bioremediation of pesticides and heavy metals

Actinobacteria exhibit cosmopolitan distribution since their members are widely distributed in aquatic and terrestrial ecosystems. In the environment they play relevant ecological roles including recycling of substances, degradation of complex polymers, and production of bioactive molecules. Biotechnological potential of actinobacteria in the environment was demonstrated by their ability to remove organic and inorganic pollutants. This ability is the reason why actinobacteria have received special attention as candidates for bioremediation, which has gained importance because of the widespread release of contaminants into the environment. Among organic contaminants, pesticides are widely used for pest control, although the negative impact of these chemicals in the environmental balance is increasingly becoming apparent. Similarly, the extensive application of heavy metals in industrial processes lead to highly contaminated areas worldwide. Several studies focused in the use of actinobacteria for cleaning up the environment were performed in the last 15 years. Strategies such as bioaugmentation, biostimulation, cell immobilization, production of biosurfactants, design of defined mixed cultures and the use of plant-microbe systems were developed to enhance the capabilities of actinobacteria in bioremediation. In this review, we compiled and discussed works focused in the study of different bioremediation strategies using actinobacteria and how they contributed to the improvement of the already existing strategies. In addition, we discuss the importance of omic studies to elucidate mechanisms and regulations that bacteria use to cope with pollutant toxicity, since they are still little known in actinobacteria. A brief account of sources and harmful effects of pesticides and heavy metals is also given.

The emergence of metabolomics as a key discipline in the drug discovery process

Metabolomics is a recent science that could be defined as the comprehensive qualitative and quantitative analysis of all small molecular weight compounds present in a cell, organ (including biofluids) or organism at a specific time point. More and more applications have been found these past years to metabolomics in the pharmaceutical field. Specifically in the drug discovery process, metabolomics open new perspectives, in new targets identification, in toxicological studies and in bioactive natural products discovery. The challenge in metabolomics is to find a technological approach allowing the reproducible identification and quantitation of as much metabolites as possible. In this context, mass spectrometry and NMR are emerging as key and complementary technologies.

Current state-of-the-art of nontargeted metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications

Metabolomics, as a part of systems biology, has been widely applied in different fields of life science by studying the endogenous metabolites. The development and applications of liquid chromatography (LC) coupled with high resolution mass spectrometry (MS) greatly improve the achievable data quality in non-targeted metabolic profiling. However, there are still some emerging challenges to be covered in LC-MS based metabolomics. Here, recent approaches about sample collection and preparation, instrumental analysis, and data handling of LC-MS based metabolomics are summarized, especially in the analysis of clinical samples. Emphasis is put on the improvement of analytical techniques including the combination of different LC columns, isotope coded derivatization methods, pseudo-targeted LC-MS method, new data analysis algorithms and structural identification of important metabolites.

Review: toxicometabolomics

Metabolomics use in toxicology is rapidly increasing, particularly owing to advances in mass spectroscopy, which is widely used in the life sciences for phenotyping disease states. Toxicology has the advantage of having the disease agent, the toxicant, available for experimental induction of metabolomics changes monitored over time and dose. This review summarizes the different technologies employed and gives examples of their use in various areas of toxicology. A prominent use of metabolomics is the identification of signatures of toxicity - patterns of metabolite changes predictive of a hazard manifestation. Increasingly, such signatures indicative of a certain hazard manifestation are identified, suggesting that certain modes of action result in specific derangements of the metabolism. This might enable the deduction of underlying pathways of toxicity, which, in their entirety, form the Human Toxome, a key concept for implementing the vision of Toxicity Testing for the 21st century. This review summarizes the current state of metabolomics technologies and principles, their uses in toxicology and gives a thorough overview on metabolomics bioinformatics, pathway identification and quality assurance. In addition, this review lays out the prospects for further metabolomics application also in a regulatory context.

QTL validation and stability for volatile organic compounds (VOCs) in apple

The aroma trait in apple is a key factor for fruit quality strongly affecting the consumer appreciation, and its detection and analysis is often an extremely laborious and time consuming procedure. Molecular markers associated to this trait can to date represent a valuable selection tool to overcome these limitations. QTL mapping is the first step in the process of targeting valuable molecular markers to be employed in marker-assisted breeding programmes (MAB). However, a validation step is usually required before a newly identified molecular marker can be implemented in marker-assisted selection. In this work the position of a set of QTLs associated to volatile organic compounds (VOCs) was confirmed and validated in three different environments in Switzerland, namely Wädenswil, Conthey and Cadenazzo, where the progeny 'Fiesta×Discovery' was replicated. For both QTL identification and validation, the phenotypic data were represented by VOCs produced by mature apple fruit and assessed with a Proton Transfer Reaction-Mass Spectrometer (PTR-MS) instrument. The QTL-VOC combined analysis performed among these three locations validated the presence of important QTLs in three specific genomic regions, two located in the linkage group 2 and one in linkage group 15, respectively, for compounds related to esters (m/z 43, 61 and 131) and to the hormone ethylene (m/z 28). The QTL set presented here confirmed that in apple some compounds are highly genetically regulated and stable across environments.

Flux analysis and metabolomics for systematic metabolic engineering of microorganisms

Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and (13)C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology.

Computational mass spectrometry for small molecules

: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.

Multivariate Analysis in Metabolomics

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

Full utilization of a mass spectrometer using on-demand sharing with multiple LC units

The applicability of liquid chromatography-mass spectrometry (LC/MS) is often limited by throughput. The sharing of a mass spectrometer with multiple LCs significantly improves throughput; however, the reported systems have not been designed to fully utilize the MS duty cycle, and as a result to achieve maximum throughput. To fully utilize the mass spectrometer, the number of LC units that a MS will need to recruit is application dependent and could be significantly larger than the current commercial or published implementations. For the example of a single analyte, the number may approach the peak capacity to a first degree approximation. Here, the construction of a MS system that flexibly recruits any number of LC units demanded by the application is discussed, followed by the method to port a previously developed LC/MS method to the system to fully utilize a mass spectrometer. To demonstrate the performance and operation, a prototypical MS system of eight LC units was constructed. When 1-min chromatographic separations were performed in parallel on the eight LCs of the system, the average LC/MS analysis time per sample was 10.5 s when applied to the analysis of samples in 384-well plate format. This system has been successfully used to conduct large-volume biochemical assays with the analysis of a variety of molecular entities in support of drug discovery efforts. Allowing the recruitment of the number of LC units appropriate for a given application, this system has the potential to be a plug-and-play system to fully utilize a mass spectrometer.

On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies

Background

Genome-wide association studies (GWAS) with metabolic traits and metabolome-wide association studies (MWAS) with traits of biomedical relevance are powerful tools to identify the contribution of genetic, environmental and lifestyle factors to the etiology of complex diseases. Hypothesis-free testing of ratios between all possible metabolite pairs in GWAS and MWAS has proven to be an innovative approach in the discovery of new biologically meaningful associations. The p-gain statistic was introduced as an ad-hoc measure to determine whether a ratio between two metabolite concentrations carries more information than the two corresponding metabolite concentrations alone. So far, only a rule of thumb was applied to determine the significance of the p-gain.

Results

Here we explore the statistical properties of the p-gain through simulation of its density and by sampling of experimental data. We derive critical values of the p-gain for different levels of correlation between metabolite pairs and show that B/(2*α) is a conservative critical value for the p-gain, where α is the level of significance and B the number of tested metabolite pairs.

Conclusions

We show that the p-gain is a well defined measure that can be used to identify statistically significant metabolite ratios in association studies and provide a conservative significance cut-off for the p-gain for use in future association studies with metabolic traits.

Exploring the metabolic state of microorganisms using metabolomics

Microorganisms depend on their ability to modulate their metabolic composition according to specific circumstances, such as different phases of the growth cycle and circadian rhythms, fluctuations in environmental conditions, as well as experimental perturbations. A thorough understanding of these metabolic adaptations requires the ability to comprehensively identify and quantify the metabolome of bacterial cells in different states. In this review, we present an overview of the diverse metabolomics approaches recently adopted to explore the metabolism of a wide variety of microorganisms. Focusing on a selection of illustrative case studies, we assess the different experimental designs used and explore the major achievements and remaining challenges in the field. We conclude by discussing the important complementary information provided by computational methods such as genome-scale metabolic modeling, which enable an integrated analysis of metabolic state changes in the context of overall cellular physiology.

Metabonomic profiles delineate the effect of traditional Chinese medicine sini decoction on myocardial infarction in rats

Background

In spite of great advances in target-oriented Western medicine for treating myocardial infarction (MI), it is still a leading cause of death in a worldwide epidemic. In contrast to Western medicine, Traditional Chinese medicine (TCM) uses a holistic and synergistic approach to restore the balance of Yin-Yang of body energy so the body's normal function can be restored. Sini decoction (SND) is a well-known formula of TCM which has been used to treat MI for many years. However, its holistic activity evaluation and mechanistic understanding are still lacking due to its complex components.

Methodology/Principal Findings

A urinary metabonomic method based on nuclear magnetic resonance and ultra high-performance liquid chromatography coupled to mass spectrometry was developed to characterize MI-related metabolic profiles and delineate the effect of SND on MI. With Elastic Net for classification and selection of biomarkers, nineteen potential biomarkers in rat urine were screened out, primarily related to myocardial energy metabolism, including the glycolysis, citrate cycle, amino acid metabolism, purine metabolism and pyrimidine metabolism. With the altered metabolism pathways as possible drug targets, we systematically analyze the therapeutic effect of SND, which demonstrated that SND administration could provide satisfactory effect on MI through partially regulating the perturbed myocardial energy metabolism.

Conclusions/Significance

Our results showed that metabonomic approach offers a useful tool to identify MI-related biomarkers and provides a new methodological cue for systematically dissecting the underlying efficacies and mechanisms of TCM in treating MI.

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.