Comparative evaluation of eight software programs for alignment of gas chromatography–mass spectrometry chromatograms in metabolomics experiments

Critical Review of Selected Analytical Platforms for GC-MS Metabolomics Profiling-Case Study: HS-SPME/GC-MS Analysis of Blackberry's Aroma

Data processing and data extraction are the first, and most often crucial, steps in metabolomics and multivariate data analysis in general. There are several software solutions for these purposes in GC-MS metabolomics. It becomes unclear which platform offers what kind of data and how that information influences the analysis's conclusions. In this study, selected analytical platforms for GC-MS metabolomics profiling, SpectConnect and XCMS as well as MestReNova software, were used to process the results of the HS-SPME/GC-MS aroma analyses of several blackberry varieties. In addition, a detailed analysis of the identification of the individual components of the blackberry aroma club varieties was performed. In total, 72 components were detected in the XCMS platform, 119 in SpectConnect, and 87 and 167 in MestReNova, with automatic integral and manual correction, respectively, as well as 219 aroma components after manual analysis of GC-MS chromatograms. The obtained datasets were fed, for multivariate data analysis, to SIMCA software, and underwent the creation of PCA, OPLS, and OPLS-DA models. The results of the validation tests and VIP-pred. scores were analyzed in detail.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Research on floral volatiles has grown substantially in the last 20 years, which has generated insights into their diversity and prevalence. These studies have paved the way for new research that explores the evolutionary origins and ecological consequences of different types of variation in floral scent, including community-level, functional, and environmentally induced variation. However, to address these types of questions, novel approaches are needed that can handle large sample sizes, provide quality control measures, and make volatile research more transparent and accessible, particularly for scientists without prior experience in this field. Drawing upon a literature review and our own experiences, we present a set of best practices for next-generation research in floral scent. We outline methods for data collection (experimental designs, methods for conducting field collections, analytical chemistry, compound identification) and data analysis (statistical analysis, database integration) that will facilitate the generation and interpretation of quality data. For the intermediate step of data processing, we created the R package bouquet, which provides a data analysis pipeline. The package contains functions that enable users to convert chromatographic peak integrations to a filtered data table that can be used in subsequent statistical analyses. This package includes default settings for filtering out non-floral compounds, including background contamination, based on our best-practice guidelines, but functions and workflows can be easily customized as necessary. Next-generation research into the ecology and evolution of floral scent has the potential to generate broadly relevant insights into how complex traits evolve, their genomic architecture, and their consequences for ecological interactions. In order to fulfill this potential, the methodology of floral scent studies needs to become more transparent and reproducible. By outlining best practices throughout the lifecycle of a project, from experimental design to statistical analysis, and providing an R package that standardizes the data processing pipeline, we provide a resource for new and seasoned researchers in this field and in adjacent fields, where high-throughput and multi-dimensional datasets are common.

Using metabolomic profiling to inform use of surrogate species in ecological risk assessment practices

The U.S. EPA frequently uses avian or fish toxicity data to set protective standards for amphibians in ecological risk assessments. However, this approach does not always adequately represent aquatic-dwelling and terrestrial-phase amphibian exposure data. For instance, it is accepted that early life stage tests for fish are typically sensitive enough to protect larval amphibians, however, metamorphosis from tadpole to a terrestrial-phase adult relies on endocrine cues that are less prevalent in fish but essential for amphibian life stage transitions. These differences suggest that more robust approaches are needed to adequately elucidate the impacts of pesticide exposure in amphibians across critical life stages. Therefore, in the current study, methodology is presented that can be applied to link the perturbations in the metabolomic response of larval zebrafish (Danio rerio), a surrogate species frequently used in ecotoxicological studies, to those of African clawed frog (Xenopus laevis) tadpoles following exposure to three high-use pesticides, bifenthrin, chlorothalonil, or trifluralin. Generally, D. rerio exhibited greater metabolic perturbations in both number and magnitude across the pesticide exposures as opposed to X. laevis. This suggests that screening ecological risk assessment surrogate toxicity data would sufficiently protect amphibians at the single life stage studied but care needs to be taken to understand the suite of metabolic requirements of each developing species. Ultimately, methodology presented, and data gathered herein will help inform the applicability of metabolomic profiling in establishing the risk pesticide exposure poses to amphibians and potentially other non-target species.

Integrated Omic Analyses Identify Pathways and Transcriptomic Regulators Associated With Chemical Alterations of In Vitro Neural Network Formation

Development of in vitro new approach methodologies has been driven by the need for developmental neurotoxicity (DNT) hazard data on thousands of chemicals. The network formation assay characterizes DNT hazard based on changes in network formation but provides no mechanistic information. This study investigated nervous system signaling pathways and upstream physiological regulators underlying chemically induced neural network dysfunction. Rat primary cortical neural networks grown on microelectrode arrays were exposed for 12 days in vitro to cytosine arabinoside, 5-fluorouracil, domoic acid, cypermethrin, deltamethrin, or haloperidol as these exposures altered network formation in previous studies. RNA-seq from cells and gas chromatography/mass spectrometry analysis of media extracts collected on days in vitro 12 provided gene expression and metabolomic identification, respectively. The integration of differentially expressed genes and metabolites for each neurotoxicant was analyzed using ingenuity pathway analysis. All 6 compounds altered gene expression that linked to developmental disorders and neurological diseases. Other enriched canonical pathways overlapped among compounds of the same class; eg, genes and metabolites altered by both cytosine arabinoside and 5-fluorouracil exposures are enriched in axonal guidance pathways. Integrated analysis of upstream regulators was heterogeneous across compounds, but identified several transcriptomic regulators including CREB1, SOX2, NOTCH1, and PRODH. These results demonstrate that changes in network formation are accompanied by transcriptomic and metabolomic changes and that different classes of compounds produce differing responses. This approach can enhance information obtained from new approach methodologies and contribute to the identification and development of adverse outcome pathways associated with DNT.

Route of exposure influences pesticide body burden and the hepatic metabolome in post-metamorphic leopard frogs

Pesticides are being applied at a greater extent than in the past. Once pesticides enter the ecosystem, many environmental factors can influence their residence time. These interactions can result in processes such as translocation, environmental degradation, and metabolic activation facilitating exposure to target and non-target species. Most anurans start off their life cycle in aquatic environments and then transition into terrestrial habitats. Their time in the aquatic environment is generally short; however, many important developmental stages occur during this tenure. Post-metamorphosis, most species spend many years on land but migrate back to the aquatic environment for breeding. Due to the importance of both the aquatic and terrestrial environments to the life stages of amphibians, we investigated how the route of exposure (i.e., uptake from contaminated soils vs. uptake from contaminated surface water) influences pesticide bioavailability and body burden for four pesticides (bifenthrin (BIF), chlorpyrifos (CPF), glyphosate (GLY), and trifloxystrobin (TFS)) as well as the impact on the hepatic metabolome of adult leopard frogs (Gosner stage 46 with 60-90 days post-metamorphosis). Body burden concentrations for amphibians exposed in water were significantly higher (ANOVA p < 0.0001) compared to amphibians exposed to contaminated soil across all pesticides studied. Out of 80 metabolites that were putatively identified, the majority expressed a higher abundance in amphibians that were exposed in pesticide contaminated water compared to soil. Ultimately, this research will help fill regulatory data gaps, aid in the creation of more accurate amphibian dermal uptake models and inform continued ecological risk assessment efforts.

Application of untargeted volatile profiling and data driven approaches in wine flavoromics research

Traditional flavor chemistry research usually makes use of targeted approaches by focusing on the detection and quantification of key flavor active metabolites that are present in food and beverages. In the last decade, flavoromics has emerged as an alternative to targeted methods where non-targeted and data driven approaches have been used to determine as many metabolites as possible with the aim to establish relationships among the chemical composition of foods and their sensory properties. Flavoromics has been successfully applied in wine research to gain more insights into the impact of a wide range of flavor active metabolites on wine quality. In this review, we aim to provide an overview of the applications of flavoromics approaches in wine research based on existing literature mainly by focusing on untargeted volatile profiling of wines and how this can be used as a powerful tool to generate novel insights. We highlight the fact that untargeted volatile profiling used in flavoromics approaches ultimately can assist the wine industry to produce different wine styles and to market existing wines appropriately based on consumer preference. In addition to summarizing the main steps involved in untargeted volatile profiling, we also provide an outlook about future perspectives and challenges of wine flavoromics research.

Discrimination of white wine ageing based on untarget peak picking approach with multi-class target coupled with machine learning algorithms

The complexity of the chemical reactions occurring during white wine storage, such as oxidation turns the capacity of prediction and consequently the capacity to avoid it extremely difficult. This study proposes an untarget methodology based on machine learning algorithms capable to classify wines according to their "oxidative-status". Instead of the most common approach in statistics using one class for classification, in this work eight classes were selected based on target oxidation markers for the extraction of relevant compounds. VIPS from OPLS-DA and mean decrease accuracy from random forest were used as feature selection parameters. Fifty-one molecules correlated with 5 classes, from which 23 were selected has having higher sensitivities (AUC > 0.85). For the first time to our knowledge hydroxy esters ethyl-2-hydroxy-3-methylbutanal and ethyl-2-hydroxy-4-methylpentanal were found to be correlated with oxidation markers and consequently to be discriminant of the wine oxidative status.

Data handling and data analysis in metabolomic studies of essential oils using GC-MS

Gas chromatography electron impact ionization mass spectrometry (GC-EI-MS) has been, and remains, the most widely applied analytical technique for metabolomic studies of essential oils. GC-EI-MS analysis of complex samples, such as essential oils, creates a large volume of data. Creating predictive models for such samples and observing patterns within complex data sets presents a significant challenge and requires application of robust data handling and data analysis methods. Accordingly, a wide variety of software and algorithms has been investigated and developed for this purpose over the years. This review provides an overview and summary of that research effort, and attempts to classify and compare different data handling and data analysis procedures that have been reported to-date in the metabolomic study of essential oils using GC-EI-MS.

patRoon: open source software platform for environmental mass spectrometry based non-target screening

Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon, a new R based open-source software platform, which provides comprehensive, fully tailored and straightforward non-target analysis workflows. This platform makes the use, evaluation and mixing of well-tested algorithms seamless by harmonizing various common (primarily open) software tools under a consistent interface. In addition, patRoon offers various functionality and strategies to simplify and perform automated processing of complex (environmental) data effectively. patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform time-efficient and automated non-target data annotation of environmental samples is demonstrated with a simple and reproducible workflow using open-access data of spiked samples from a drinking water treatment plant study. In addition, the ability to easily use, combine and evaluate different algorithms was demonstrated for three commonly used feature finding algorithms. This article, combined with already published works, demonstrate that patRoon helps make comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers.

A unique data analysis framework and open source benchmark data set for the analysis of comprehensive two-dimensional gas chromatography software

Comprehensive two-dimensional gas chromatography (GC × GC) is amongst the most powerful separation technologies currently existing. Since its advent in early 1990, it has become an established method which is readily available. However, one of its most challenging aspects, especially in hyphenation with mass spectrometry is the high amount of chemical information it provides for each measurement. The GC × GC community agrees that there, the highest demand for action is found. In response, the number of software packages allowing for in-depth data processing of GC × GC data has risen over the last couple of years. These packages provide sophisticated tools and algorithms allowing for more streamlined data evaluation. However, these tools/algorithms and their respective specific functionalities differ drastically within the available software packages and might result in various levels of findings if not appropriately implemented by the end users. This study focuses on two main objectives. First, to propose a data analysis framework and second to propose an open-source dataset for benchmarking software options and their specificities. Thus, allowing for an unanimous and comprehensive evaluation of GC × GC software. Thereby, the benchmark data includes a set of standard compound measurements and a set of chocolate aroma profiles. On this foundation, eight readily available GC × GC software packages were anonymously investigated for fundamental and advanced functionalities such as retention and detection device derived parameters, revealing differences in the determination of e.g. retention times and mass spectra.

Gonad metabolomics and blood biochemical analysis reveal differences associated with testicular oocytes in wild largemouth bass (Micropterus salmoides)

Adverse reproductive effects associated with gonadal intersex among freshwater fish could hold considerable implications for population sustainability. Presence of testicular oocytes (TO) is the most common form of intersex and is widespread among centrarchids (sunfishes) of North America and other freshwater teleosts. Placing TO within the toxicological context of adverse outcome pathways (AOPs) to assess ecological risk is a priority for ecotoxicologists due to the association of TO with harmful chemical exposure and adverse reproductive effects in some cases. However, key event relationships between EDC exposure, incidence of TO, and apical outcomes have yet to be fully elucidated - in part due to a lack of knowledge of relationships between intersex gonad physiology and fish health. Understanding the physiological status of intersex fish is critical to assess ecological risk, understand mechanisms of induction, and to establish biomarkers of intersex in fish. In the present study, features of gonad metabolite profiles associated with TO in largemouth bass (LMB, Micropterus salmoides) from an impoundment in Georgia (USA) were determined using GC-MS-based metabolomics. Clinical blood biochemical screens were used to evaluate markers of fish health associated with TO. Results suggest that physiological changes in energy expenditure as well as relatively 'feminized' gonad lipid and protein metabolism may be related to the occurrence of TO in male LMB, and highlight the need to understand relationships between intersex and physical stressors such as elevated temperature and hypoxia. These results provide novel insight to AOPs associated with TO and identify candidate analytes for biomarker discovery.

Metabolomic Investigations of the Temporal Effects of Exposure to Pharmaceuticals and Personal Care Products and Their Mixture in the Eastern Oyster (Crassostrea virginica)

The eastern oyster (Crassostrea virginica) supports a large aquaculture industry and is a keystone species along the Atlantic seaboard. Native oysters are routinely exposed to a complex mixture of contaminants that increasingly includes pharmaceuticals and personal care products (PPCPs). Unfortunately, the biological effects of chemical mixtures on oysters are poorly understood. Untargeted gas chromatography-mass spectrometry metabolomics was utilized to quantify the response of oysters exposed to fluoxetine, N,N-diethyl-meta-toluamide, 17α-ethynylestradiol, diphenhydramine, and their mixture. Oysters were exposed to 1 µg/L of each chemical or mixture for 10 d, followed by an 8-d depuration period. Adductor muscle (n = 14/treatment) was sampled at days 0, 1, 5, 10, and 18. Trajectory analysis illustrated that metabolic effects and class separation of the treatments varied at each time point and that, overall, the oysters were only able to partially recover from these exposures post-depuration. Altered metabolites were associated with cellular energetics (i.e., Krebs cycle intermediates), as well as amino acid metabolism and fatty acids. Exposure to these PPCPs also affected metabolic pathways associated with anaerobic metabolism, osmotic stress, and oxidative stress, in addition to the physiological effects of each chemical's postulated mechanism of action. Following depuration, fewer metabolites were altered, but none of the treatments returned them to their initial control values, indicating that metabolic disruptions were long-lasting. Interestingly, the mixture did not directly cluster with individual treatments in the scores plot from partial least squares discriminant analysis, and many of its affected metabolic pathways were not well predicted from the individual treatments. The present study highlights the utility of untargeted metabolomics in developing exposure biomarkers for compounds with different modes of action in bivalves. Environ Toxicol Chem 2020;39:419-436. © 2019 SETAC.

Utilization of GC-MS untargeted metabolomics to assess the delayed response of glufosinate treatment of transgenic herbicide resistant (HR) buffalo grasses (Stenotaphrum secundatum L.)

INTRODUCTION

Herbicide resistant (HR) buffalo grasses were genetically engineered to resist the non-selective herbicide, glufosinate in order to facilitate a modern, 'weeding program' which is highly effective in terms of minimizing costs and labor. The resistant trait was conferred by an insertion of the pat gene to allow for the production of the enzyme phosphinothricin acetyltransferase (PAT) to detoxify the glufosinate inhibitive effect. To date, there are only a few reports using metabolomics as well as molecular characterizations published for glufosinate-resistant crops with no reports on HR turfgrass. Therefore, for the first time, this study examines the metabolome of glufosinate-resistant buffalo grasses which not only will be useful to future growers but also the scientific community.

OBJECTIVE

A major aim of this present work is to characterize and evaluate the metabolic alterations which may arise from a genetic transformation of HR buffalo grasses by comprehensively using gas chromatography-mass spectrometry (GC-MS) based untargeted metabolomics.

METHODS

Eight-week old plants of 4 HR buffalo grasses, (93-1A, 93-2B, 93-3C and 93-5A) and 3 wild type varieties (WT 8-4A, WT 9-1B and WT 9-1B) were selected for physiological, molecular and metabolomics experiments. Plants were either sprayed with 1, 5, 10 and 15% v/v of glufosinate to evaluate the visual injuries or submerged in 5% v/v of glufosinate 3 days prior to a GC-MS based untargeted metabolomics analysis. In contrast, the control group was treated with distilled water. Leaves were extracted in 1:1 methanol:water and then analysed, using an in-house GC-MS untargeted workflow.

RESULTS

Results identified 199 metabolites with only 6 of them (cis-aconitic acid, allantoin, cellobiose, glyceric acid, maltose and octadecanoic acid) found to be statistically significant (p < 0.05) between the HR and wild type buffalo grass varieties compared to the control experiment. Among these metabolites, unusual accumulation of allantoin was prominent and was an unanticipated effect of the pat gene insertion. As expected, glufosinate treatment caused significant metabolic alterations in the sensitive wild type, with the up-regulation of several amino acids (e.g. phenylalanine and isoleucine) which was likely due to glufosinate-induced senescence. The aminoacyl-tRNA biosynthetic pathway was identified as the most significant enriched pathway as a result of glufosinate effects because a number of its intermediates were amino acids.

CONCLUSION

HR buffalo grasses were very similar to its wild type comparator based on a comprehensive GC-MS based untargeted metabolomics and therefore, should guarantee the safe use of these HR buffalo grasses. The current metabolomics analyses not only confirmed the effects of glufosinate to up-regulate free amino acid pools in the sensitive wild type but also several alterations in sugar, sugar phosphate and organic acid metabolism have been reported.

Non-targeted Screening in Environmental Monitoring Programs

Contaminant monitoring programs have been tasked with understanding the fate and transport of toxic chemicals in the environment. Mass spectrometry based methods have traditionally been developed to maximize sensitivity and accuracy of a select set of target compounds. As mass spectrometry methods have advanced, so has the breadth of questions proposed by environmental chemists. Incorporating these methods in chemical monitoring programs provides large data sets to explore the effects of complex mixtures on environmental systems.

Untargeted and Targeted Fingerprinting of Extra Virgin Olive Oil Volatiles by Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Challenges in Long-Term Studies

Comprehensive two-dimensional gas chromatography coupled with mass spectrometric detection (GC × GC-MS) offers an information-rich basis for effective chemical fingerprinting of food. However, GC × GC-MS yields 2D-peak patterns (i.e., sample 2D fingerprints) whose consistency may be affected by variables related to either the analytical platform or to the experimental parameters adopted for the analysis. This study focuses on the complex volatile fraction of extra-virgin olive oil and addresses 2D-peak patterns variations, including MS signal fluctuations, as they may occur in long-term studies where pedo-climatic, harvest year, or shelf life changes are studied. The 2D-pattern misalignments are forced by changing chromatographic settings and MS acquisition. All procedural steps, preceding pattern recognition by template matching, are analyzed and a rational workflow defined to accurately realign patterns and analytes metadata. Signal-to-noise ratio (SNR) detection threshold, reference spectra extraction, and similarity match factor threshold are critical to avoid false-negative matches. Distance thresholds and polynomial transform parameters are key for effective template matching. In targeted analysis (supervised workflow) with optimized parameters, method accuracy reaches 92.5% (i.e., % of true-positive matches) while for combined untargeted and targeted ( UT) fingerprinting (unsupervised workflow), accuracy reaches 97.9%. Response normalization also is examined, evidencing good performance of multiple internal standard normalization that effectively compensates for discriminations occurring during injection of highly volatile compounds. The resulting workflow is simple, effective, and time efficient.

Endogenous and exogenous biomarker analysis in terrestrial phase amphibians (Lithobates sphenocephala) following dermal exposure to pesticide mixtures

Pesticide mixtures are frequently co-applied throughout an agricultural growing season to maximize crop yield. Therefore, non-target ecological species (e.g., amphibians) may be exposed to several pesticides at any given time on these agricultural landscapes. The objectives of this study were to quantify body burdens in terrestrial phase amphibians and translate perturbed metabolites to their corresponding biochemical pathways affected by exposure to pesticides as both singlets and in combination. Southern leopard frogs (Lithobates sphenocephala) were exposed either at maximum or 1/10^th maximum application rate to single, double, or triple pesticide mixtures of bifenthrin (insecticide), metolachlor (herbicide), and triadimefon (fungicide). Tissue concentrations demonstrate both facilitated and competitive uptake of pesticides when in mixtures. Metabolomic profiling of amphibian livers identified metabolites of interest for both application rates, however; magnitude of changes varied for the two exposure rates. Exposure to lower concentrations demonstrated down regulation in amino acids, potentially due to their being utilized for glutathione metabolism and/or increased energy demands. Amphibians exposed to the maximum application rate resulted in up regulation of amino acids and other key metabolites likely due to depleted energy resources. Coupling endogenous and exogenous biomarkers of pesticide exposure can be utilized to form vital links in an ecological risk assessment by relating internal dose to pathophysiological outcomes in non-target species.

Scaled traumatic brain injury results in unique metabolomic signatures between gray matter, white matter, and serum in a piglet model

Traumatic brain injury (TBI) is a leading cause of death and long-term disability in the United States. The heterogeneity of the disease coupled with the lack of comprehensive, standardized scales to adequately characterize multiple types of TBI remain to be major challenges facing effective therapeutic development. A systems level approach to TBI diagnosis through the use of metabolomics could lead to a better understanding of cellular changes post-TBI and potential therapeutic targets. In the current study, we utilize a GC-MS untargeted metabolomics approach to demonstrate altered metabolism in response to TBI in a translational pig model, which possesses many neuroanatomical and pathophysiologic similarities to humans. TBI was produced by controlled cortical impact (CCI) in Landrace piglets with impact velocity and depth of depression set to 2m/s;6mm, 4m/s;6mm, 4m/s;12mm, or 4m/s;15mm resulting in graded neural injury. Serum samples were collected pre-TBI, 24 hours post-TBI, and 7 days post-TBI. Partial least squares discriminant analysis (PLS-DA) revealed that each impact parameter uniquely influenced the metabolomic profile after TBI, and gray and white matter responds differently to TBI on the biochemical level with evidence of white matter displaying greater metabolic change. Furthermore, pathway analysis revealed unique metabolic signatures that were dependent on injury severity and brain tissue type. Metabolomic signatures were also detected in serum samples which potentially captures both time after injury and injury severity. These findings provide a platform for the development of a more accurate TBI classification scale based unique metabolomic signatures.

Influence of exposure to pesticide mixtures on the metabolomic profile in post-metamorphic green frogs (Lithobates clamitans)

Pesticide use in agricultural areas requires the application of numerous chemicals to control target organisms, leaving non-target organisms at risk. The present study evaluates the hepatic metabolomic profile of one group of non-target organisms, amphibians, after exposure to a single pesticide and pesticide mixtures. Five common-use pesticide active ingredients were used in this study, three herbicides (atrazine, metolachlor and 2,4-d), one insecticide (malathion) and one fungicide (propiconazole). Juvenile green frogs (Lithobates clamitans) were reared for 60-90days post-metamorphosis then exposed to a single pesticide or a combination of pesticides at the labeled application rate on soil. Amphibian livers were excised for metabolomic analysis and pesticides were quantified for whole body homogenates. Based on the current study, metabolomic profiling of livers support both individual and interactive effects where pesticide exposures altered biochemical processes, potentially indicating a different response between active ingredients in pesticide mixtures, among these non-target species. Amphibian metabolomic response is likely dependent on the pesticides present in each mixture and their ability to perturb biochemical networks, thereby confounding efforts with risk assessment.

Mass Spectrometry-Based Metabolomics

Metabolomics based on mass spectrometry can provide quantitative and qualitative information of the pool of metabolites (metabolome) present intracellularly or extracellularly in a given biological system. A typical metabolomics workflow requires several key steps such as quick and robust sample preparations with quenching of metabolism, chemical derivatization if needed, instrumental measurement, data-processing with/without database information and further statistical analysis and interpretation. Here, we introduce general metabolomics workflows for global and targeted analyses using gas chromatography or liquid chromatography coupled with mass spectrometers.

Comparative assessment of software for non-targeted data analysis in the study of volatile fingerprint changes during storage of a strawberry beverage

Five free software packages were compared to assess their utility for the non-targeted study of changes in the volatile profile during the storage of a novel strawberry beverage. AMDIS coupled to Gavin software turned out to be easy to use, required the minimum handling for subsequent data treatment and its results were the most similar to those obtained by manual integration. However, AMDIS coupled to SpectConnect software provided more information for the study of volatile profile changes during the storage of strawberry beverage. During storage, volatile profile changed producing the differentiation among the strawberry beverage stored at different temperatures, and this difference increases as time passes; these results were also supported by PCA. As expected, it seems that cold temperature is the best way of preservation for this product during long time storage. Variable Importance in the Projection (VIP) and correlation scores pointed out four volatile compounds as potential markers for shelf-life of our strawberry beverage: 2-phenylethyl acetate, decanoic acid, γ-decalactone and furfural.

Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine

In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.

Navigating freely-available software tools for metabolomics analysis

INTRODUCTION

The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.

OBJECTIVES

To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.

METHODS

The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC-MS, GC-MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.

RESULTS

A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.

CONCLUSION

This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools' abilities to perform specific data analysis tasks e.g. peak picking.

eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics

Gas chromatography coupled to mass spectrometry (GC/MS) has been a long-standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex data sets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information on metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC/MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-time-of-flight (TOF) MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign, and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available at http://CRAN.R-project.org/package=erah .

Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future

Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright © 2016 John Wiley & Sons, Ltd.

Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis

In the context of the Human Toxome project, mass spectroscopy-based metabolomics characterization of estrogen-stimulated MCF-7 cells was studied in order to support the untargeted deduction of pathways of toxicity. A targeted and untargeted approach using overrepresentation analysis (ORA), quantitative enrichment analysis (QEA) and pathway analysis (PA) and a metabolite network approach were compared. Any untargeted approach necessarily has some noise in the data owing to artifacts, outliers and misidentified metabolites. Depending on the chemical analytical choices (sample extraction, chromatography, instrument and settings, etc.), only a partial representation of all metabolites will be achieved, biased by both the analytical methods and the database used to identify the metabolites. Here, we show on the one hand that using a data analysis approach based exclusively on pathway annotations has the potential to miss much that is of interest and, in the case of misidentified metabolites, can produce perturbed pathways that are statistically significant yet uninformative for the biological sample at hand. On the other hand, a targeted approach, by narrowing its focus and minimizing (but not eliminating) misidentifications, renders the likelihood of a spurious pathway much smaller, but the limited number of metabolites also makes statistical significance harder to achieve. To avoid an analysis dependent on pathways, we built a de novo network using all metabolites that were different at 24 h with and without estrogen with a p value <0.01 (53) in the STITCH database, which links metabolites based on known reactions in the main metabolic network pathways but also based on experimental evidence and text mining. The resulting network contained a "connected component" of 43 metabolites and helped identify non-endogenous metabolites as well as pathways not visible by annotation-based approaches. Moreover, the most highly connected metabolites (energy metabolites such as pyruvate and alpha-ketoglutarate, as well as amino acids) showed only a modest change between proliferation with and without estrogen. Here, we demonstrate that estrogen has subtle but potentially phenotypically important alterations in the acyl-carnitine fatty acids, acetyl-putrescine and succinoadenosine, in addition to likely subtle changes in key energy metabolites that, however, could not be verified consistently given the technical limitations of this approach. Finally, we show that a network-based approach combined with text mining identifies pathways that would otherwise neither be considered statistically significant on their own nor be identified via ORA, QEA, or PA.

Alterations of the volatile metabolome in mouse models of Alzheimer's disease

In the present study, we tested whether the volatile metabolome was altered by mutations of the Alzheimer's disease (AD)-implicated amyloid precursor protein gene (APP) and comprehensively examined urinary volatiles that may potentially serve as candidate biomarkers of AD. Establishing additional biomarkers in screening populations for AD will provide enhanced diagnostic specificity and will be critical in evaluating disease-modifying therapies. Having strong evidence of gross changes in the volatile metabolome of one line of APP mice, we utilized three unique mouse lines which over-express human mutations of the APP gene and their respective non-transgenic litter-mates (NTg). Head-space gas chromatography/mass spectrometry (GC/MS) of urinary volatiles uncovered several aberrant chromatographic peak responses. We later employed linear discrimination analysis and found that the GC/MS peak responses provide accurate (>84%) genotype classification of urinary samples. These initial data in animal models show that mutant APP gene expression entails a uniquely identifiable urinary odor, which if uncovered in clinical AD populations, may serve as an additional biomarker for the disease.

Analytical methods in untargeted metabolomics: state of the art in 2015

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.