Compliance with minimum information guidelines in public metabolomics repositories

Extracting Knowledge from MS Clinical Metabolomic Data: Processing and Analysis Strategies

Assessing potential alterations of metabolic pathways using large-scale approaches plays today a central role in clinical research. Because several thousands of mass features can be measured for each sample with separation techniques hyphenated to mass spectrometry (MS) detection, adapted strategies have to be implemented to detect altered pathways and help to elucidate the mechanisms of pathologies. These procedures include peak detection, sample alignment, normalization, statistical analysis, and metabolite annotation. Interestingly, considerable advances have been made over the last years in terms of analytics, bioinformatics, and chemometrics to help massive and complex metabolomic data to be more adequately handled with automated processing and data analysis workflows. Recent developments and remaining challenges related to MS signal processing, metabolite annotation, and biomarker discovery based on statistical models are illustrated in this chapter in light of their application to clinical research.

Untargeted Metabolomic Biomarker Discovery for the Detection of Ectopic Pregnancy

Ectopic pregnancy (EP) is the leading cause of maternal morbidity and mortality in the first trimester. Using an untargeted metabolomic approach, we sought to identify putative plasma biomarkers using tandem liquid chromatography-mass spectrometry for the detection of tubal EP. This case-control study included the prospective recruitment of 50 tubal EP cases and 50 early intrauterine pregnancy controls. To avoid over-fitting, logistic regression models were developed in a randomly selected discovery group (30 cases vs. 30 controls) and validated in the test group (20 cases vs. 20 controls). In total, 585 mass spectral features were detected, of which 221 molecular features were significantly altered in EP plasma (p < 0.05). Molecular networking and metabolite identification was employed using the Global Natural Products Social Molecular Networking (GNPS) database, which identified 97 metabolites at a high confidence level. Top significant metabolites include subclasses of sphingolipids, carnitines, glycerophosphocholines, and tryptophan metabolism. The top regression model, consisting of D-erythro-sphingosine and oleoyl-carnitine, was validated in a test group and achieved an area under receiving operating curve (AUC) (95% CI) = 0.962 (0.910-1) with a sensitivity of 100% and specificity of 95.9%. Metabolite alterations indicate alterations related to inflammation and abnormal placentation in EP. The validation of these metabolite biomarkers in the future could potentially result in improved early diagnosis.

Sources of biases in the in vitro testing of nanomaterials: the role of the biomolecular corona

The biological fate of nanomaterials (NMs) is driven by specific interactions through which biomolecules, naturally adhering onto their surface, engage with cell membrane receptors and intracellular organelles. The molecular composition of this layer, called the biomolecular corona (BMC), depends on both the physical-chemical features of the NMs and the biological media in which the NMs are dispersed and cells grow. In this work, we demonstrate that the widespread use of 10% fetal bovine serum in an in vitro assay cannot recapitulate the complexity of in vivo systemic administration, with NMs being transported by the blood. For this purpose, we undertook a comparative journey involving proteomics, lipidomics, high throughput multiparametric in vitro screening, and single molecular feature analysis to investigate the molecular details behind this in vivo/in vitro bias. Our work indirectly highlights the need to introduce novel, more physiological-like media closer in composition to human plasma to produce realistic in vitro screening data for NMs. We also aim to set the basis to reduce this in vitro-in vivo mismatch, which currently limits the formulation of NMs for clinical settings.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

An evaluation of untargeted metabolomics methods to characterize inborn errors of metabolism

Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

Animal Metabolite Database: Metabolite Concentrations in Animal Tissues and Convenient Comparison of Quantitative Metabolomic Data

The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.

Lipopolysaccharides from Ralstonia solanacearum induce a broad metabolomic response in Solanum lycopersicum

Ralstonia solanacearum, one of the most destructive crop pathogens worldwide, causes bacterial wilt disease in a wide range of host plants. The major component of the outer membrane of Gram-negative bacteria, lipopolysaccharides (LPS), has been shown to function as elicitors of plant defense leading to the activation of signaling and defense pathways in several plant species. LPS from a R. solanacearum strain virulent on tomato (LPSR. sol.), were purified, chemically characterized, and structurally elucidated. The lipid A moiety consisted of tetra- to hexa-acylated bis-phosphorylated disaccharide backbone, also decorated by aminoarabinose residues in minor species, while the O-polysaccharide chain consisted of either linear tetrasaccharide or branched pentasaccharide repeating units containing α-L-rhamnose, N-acetyl-β-D-glucosamine, and β-L-xylose. These properties might be associated with the evasion of host surveillance, aiding the establishment of the infection. Using untargeted metabolomics, the effect of LPSR. sol. elicitation on the metabolome of Solanum lycopersicum leaves was investigated across three incubation time intervals with the application of UHPLC-MS for metabolic profiling. The results revealed the production of oxylipins, e.g., trihydroxy octadecenoic acid and trihydroxy octadecadienoic acid, as well as several hydroxycinnamic acid amide derivatives, e.g., coumaroyl tyramine and feruloyl tyramine, as phytochemicals that exhibit a positive correlation to LPSR. sol. treatment. Although the chemical properties of these metabolite classes have been studied, the functional roles of these compounds have not been fully elucidated. Overall, the results suggest that the features of the LPSR. sol. chemotype aid in limiting or attenuating the full deployment of small molecular host defenses and contribute to the understanding of the perturbation and reprogramming of host metabolism during biotic immune responses.

Untargeted Metabolomics for Integrative Taxonomy: Metabolomics, DNA Marker-Based Sequencing, and Phenotype Bioimaging

Integrative taxonomy is a fundamental part of biodiversity and combines traditional morphology with additional methods such as DNA sequencing or biochemistry. Here, we aim to establish untargeted metabolomics for use in chemotaxonomy. We used three thallose liverwort species Riccia glauca, R. sorocarpa, and R. warnstorfii (order Marchantiales, Ricciaceae) with Lunularia cruciata (order Marchantiales, Lunulariacea) as an outgroup. Liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) were integrated with DNA marker-based sequencing of the trnL-trnF region and high-resolution bioimaging. Our untargeted chemotaxonomy methodology enables us to distinguish taxa based on chemophenetic markers at different levels of complexity: (1) molecules, (2) compound classes, (3) compound superclasses, and (4) molecular descriptors. For the investigated Riccia species, we identified 71 chemophenetic markers at the molecular level, a characteristic composition in 21 compound classes, and 21 molecular descriptors largely indicating electron state, presence of chemical motifs, and hydrogen bonds. Our untargeted approach revealed many chemophenetic markers at different complexity levels that can provide more mechanistic insight into phylogenetic delimitation of species within a clade than genetic-based methods coupled with traditional morphology-based information. However, analytical and bioinformatics analysis methods still need to be better integrated to link the chemophenetic information at multiple scales.

Critical assessment of chromatographic metadata in publicly available metabolomics data repositories

INTRODUCTION

The structural identification of metabolites represents one of the current bottlenecks in non-targeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics. The Metabolomics Standard Initiative has developed a multilevel system to report confidence in metabolite identification, which involves the use of MS, MS/MS and orthogonal data. Limitations due to similar or same fragmentation pattern (e.g. isomeric compounds) can be overcome by the additional orthogonal information of the retention time (RT), since it is a system property that is different for each chromatographic setup.

OBJECTIVES

In contrast to MS data, sharing of RT data is not as widespread. The quality of data and its (re-)useability depend very much on the quality of the metadata. We aimed to evaluate the coverage and quality of this metadata from public metabolomics repositories.

METHODS

We acquired an overview on the current reporting of chromatographic separation conditions. For this purpose, we defined the following information as important details that have to be provided: column name and dimension, flow rate, temperature, composition of eluents and gradient.

RESULTS

We found that 70% of descriptions of the chromatographic setups are incomplete (according to our definition) and an additional 10% of the descriptions contained ambiguous and/or incorrect information. Accordingly, only about 20% of the descriptions allow further (re-)use of the data, e.g. for RT prediction. Therefore, we have started to develop a unified and standardized notation for chromatographic metadata with detailed and specific description of eluents, columns and gradients.

CONCLUSION

Reporting of chromatographic metadata is currently not unified. Our recommended suggestions for metadata reporting will enable more standardization and automatization in future reporting.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Comprehensive two-dimensional gas chromatographic platforms comparison for exhaled breath metabolites analysis

The high potential of exhaled breath for disease diagnosis has been highlighted in numerous studies. However, exhaled breath analysis is suffering from a lack of standardized sampling and analysis procedures, impacting the robustness of inter-laboratory results, and thus hampering proper external validation. The aim of this work was to verify compliance and validate the performance of two different comprehensive two-dimensional gas chromatography coupled to mass spectrometry platforms in different laboratories by monitoring probe metabolites in exhaled breath following the Peppermint Initiative guidelines. An initial assessment of the exhaled breath sampling conditions was performed, selecting the most suitable sampling bag material and volume. Then, a single sampling was performed using Tedlar bags, followed by the trapping of the volatile organic compounds into thermal desorption tubes for the subsequent analysis using two different analytical platforms. The thermal desorption tubes were first analyzed by a (cryogenically modulated) comprehensive two-dimensional gas chromatography system coupled to high-resolution time-of-flight mass spectrometry. The desorption was performed in split mode and the split part was recollected in the same tube and further analyzed by a different (flow modulated) comprehensive two-dimensional gas chromatography system with a parallel detection, specifically using a quadrupole mass spectrometer and a vacuum ultraviolet detector. Both the comprehensive two-dimensional gas chromatography platforms enabled the longitudinal tracking of the peppermint oil metabolites in exhaled breath. The increased sensitivity of comprehensive two-dimensional gas chromatography enabled to successfully monitor over a 6.5 h period a total of 10 target compounds, namely α-pinene, camphene, β-pinene, limonene, cymene, eucalyptol, menthofuran, menthone, isomenthone, and neomenthol.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Non-targeted metabolomics and associations with per- and polyfluoroalkyl substances (PFAS) exposure in humans: A scoping review

OBJECTIVE

To summarize the application of non-targeted metabolomics in epidemiological studies that assessed metabolite and metabolic pathway alterations associated with per- and polyfluoroalkyl substances (PFAS) exposure.

RECENT FINDINGS

Eleven human studies published before April 1st, 2021 were identified through database searches (PubMed, Dimensions, Web of Science Core Collection, Embase, Scopus), and citation chaining (Citationchaser). The sample sizes of these studies ranged from 40 to 965, involving children and adolescents (n = 3), non-pregnant adults (n = 5), or pregnant women (n = 3). High-resolution liquid chromatography-mass spectrometry was the primary analytical platform to measure both PFAS and metabolome. PFAS were measured in either plasma (n = 6) or serum (n = 5), while metabolomic profiles were assessed using plasma (n = 6), serum (n = 4), or urine (n = 1). Four types of PFAS (perfluorooctane sulfonate(n = 11), perfluorooctanoic acid (n = 10), perfluorohexane sulfonate (n = 9), perfluorononanoic acid (n = 5)) and PFAS mixtures (n = 7) were the most studied. We found that alterations to tryptophan metabolism and the urea cycle were most reported PFAS-associated metabolomic signatures. Numerous lipid metabolites were also suggested to be associated with PFAS exposure, especially key metabolites in glycerophospholipid metabolism which is critical for biological membrane functions, and fatty acids and carnitines which are relevant to the energy supply pathway of fatty acid oxidation. Other important metabolome changes reported included the tricarboxylic acid (TCA) cycle regarding energy generation, and purine and pyrimidine metabolism in cellular energy systems.

CONCLUSIONS

There is growing interest in using non-targeted metabolomics to study the human physiological changes associated with PFAS exposure. Multiple PFAS were reported to be associated with alterations in amino acid and lipid metabolism, but these results are driven by one predominant type of pathway analysis thus require further confirmation. Standardizing research methods and reporting are recommended to facilitate result comparison. Future studies should consider potential differences in study methodology, use of prospective design, and influence from confounding bias and measurement errors.

The minimum information required for a glycomics experiment (MIRAGE): reporting guidelines for capillary electrophoresis

The Minimum Information Required for a Glycomics Experiment (MIRAGE) is an initiative to standardize the reporting of glycoanalytical methods and to assess their reproducibility. To date, the MIRAGE Commission has published several reporting guidelines that describe what information should be provided for sample preparation methods, mass spectrometry methods, liquid chromatography (LC) analysis, exoglycosidase digestions, glycan microarray methods and nuclear magnetic resonance methods. Here we present the first version of reporting guidelines for glyco(proteo)mics analysis by capillary electrophoresis (CE) for standardized and high-quality reporting of experimental conditions in the scientific literature. The guidelines cover all aspects of a glyco(proteo)mics CE experiment including sample preparation, CE operation mode (CZE, CGE, CEC, MEKC, cIEF, cITP), instrument configuration, capillary separation conditions, detection, data analysis, and experimental descriptors. These guidelines are linked to other MIRAGE guidelines and are freely available through the project website https://www.beilstein-institut.de/en/projects/mirage/guidelines/#ce_analysis (doi:10.3762/mirage.7).

Development of RIKEN Plant Metabolome MetaDatabase

The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. gas chromatography-MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data based on the Resource Description Framework using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology, which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format) and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

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

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

Metabolomics Data Treatment: Basic Directions of the Full Process

The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.

Altered metabolomic states elicited by Flg22 and FlgII-28 in Solanum lycopersicum: intracellular perturbations and metabolite defenses

BACKGROUND

Surveillance of potential pathogens is a key feature of plant innate immunity. For non-self-recognition plants rely on the perception of pathogen-derived molecules. Early post-perception events activate signaling cascades, leading to the synthesis of defense-related proteins and specialized metabolites, thereby providing a broad-spectrum antimicrobial coverage. This study was concerned with tracking changes in the tomato plant metabolome following perception of the flagellum-derived elicitors (Flg22 and FlgII-28).

RESULTS

Following an untargeted metabolomics workflow, the metabolic profiles of a Solanum lycopersicum cultivar were monitored over a time range of 16-32 h post-treatment. Liquid chromatography was used to resolve the complex mixture of metabolites and mass spectrometry for the detection of differences associated with the elicitor treatments. Stringent data processing and multivariate statistical tools were applied to the complex dataset to extract relevant metabolite features associated with the elicitor treatments. Following perception of Flg22 and FlgII-28, both elicitors triggered an oxidative burst, albeit with different kinetic responses. Signatory biomarkers were annotated from diverse metabolite classes which included amino acid derivatives, lipid species, steroidal glycoalkaloids, hydroxybenzoic acids, hydroxycinnamic acids and derivatives, as well as flavonoids.

CONCLUSIONS

An untargeted metabolomics approach adequately captured the subtle and nuanced perturbations associated with elicitor-linked plant defense responses. The shared and unique features characterizing the metabolite profiles suggest a divergence of signal transduction events following perception of Flg22 vs. FlgII-28, leading to a differential reorganization of downstream metabolic pathways.

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.

Specialized Metabolites from Ribosome Engineered Strains of Streptomyces clavuligerus

Bacterial specialized metabolites are of immense importance because of their medicinal, industrial, and agricultural applications. Streptomyces clavuligerus is a known producer of such compounds; however, much of its metabolic potential remains unknown, as many associated biosynthetic gene clusters are silent or expressed at low levels. The overexpression of ribosome recycling factor (frr) and ribosome engineering (induced rpsL mutations) in other Streptomyces spp. has been reported to increase the production of known specialized metabolites. Therefore, we used an overexpression strategy in combination with untargeted metabolomics, molecular networking, and in silico analysis to annotate 28 metabolites in the current study, which have not been reported previously in S. clavuligerus. Many of the newly described metabolites are commonly found in plants, further alluding to the ability of S. clavuligerus to produce such compounds under specific conditions. In addition, the manipulation of frr and rpsL led to different metabolite production profiles in most cases. Known and putative gene clusters associated with the production of the observed compounds are also discussed. This work suggests that the combination of traditional strain engineering and recently developed metabolomics technologies together can provide rapid and cost-effective strategies to further speed up the discovery of novel natural products.

MassBase: A large-scaled depository of mass spectrometry datasets for metabolome analysis

Depository of low-molecular-weight compounds or metabolites detected in various organisms in a non-targeted manner is indispensable for metabolomics research. Due to the diverse chemical compounds, various mass spectrometry (MS) setups with state-of-the-art technologies have been used. Over the past two decades, we have analyzed various biological samples by using gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, or capillary electrophoresis-mass spectrometry, and archived the datasets in the depository MassBase (http://webs2.kazusa.or.jp/massbase/). As the format of MS datasets depends on the MS setup used, we converted each raw binary dataset of the mass chromatogram to text file format, and thereafter, information of the chromatograph peak was extracted in the text file from the converted file. In total, the depository comprises 46,493 datasets, of which 38,750 belong to the plant species and 7,743 are authentic or mixed chemicals as well as other sources (microorganisms, animals, and foods), as on August 1, 2020. All files in the depository can be downloaded in bulk from the website. Mass chromatograms of 90 plant species obtained by LC-Fourier transform ion cyclotron resonance MS or Orbitrap MS, which detect the ionized molecules with high accuracy allowing speculation of chemical compositions, were converted to text files by the software PowerGet, and the chemical annotation of each peak was added. The processed datasets were deposited in the annotation database KomicMarket2 (http://webs2.kazusa.or.jp/km2/). The archives provide fundamental resources for comparative metabolomics and functional genomics, which may result in deeper understanding of living organisms.

Untargeted In Silico Compound Classification-A Novel Metabolomics Method to Assess the Chemodiversity in Bryophytes

In plant ecology, biochemical analyses of bryophytes and vascular plants are often conducted on dried herbarium specimen as species typically grow in distant and inaccessible locations. Here, we present an automated in silico compound classification framework to annotate metabolites using an untargeted data independent acquisition (DIA)–LC/MS–QToF-sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) ecometabolomics analytical method. We perform a comparative investigation of the chemical diversity at the global level and the composition of metabolite families in ten different species of bryophytes using fresh samples collected on-site and dried specimen stored in a herbarium for half a year. Shannon and Pielou’s diversity indices, hierarchical clustering analysis (HCA), sparse partial least squares discriminant analysis (sPLS-DA), distance-based redundancy analysis (dbRDA), ANOVA with post-hoc Tukey honestly significant difference (HSD) test, and the Fisher’s exact test were used to determine differences in the richness and composition of metabolite families, with regard to herbarium conditions, ecological characteristics, and species. We functionally annotated metabolite families to biochemical processes related to the structural integrity of membranes and cell walls (proto-lignin, glycerophospholipids, carbohydrates), chemical defense (polyphenols, steroids), reactive oxygen species (ROS) protection (alkaloids, amino acids, flavonoids), nutrition (nitrogen- and phosphate-containing glycerophospholipids), and photosynthesis. Changes in the composition of metabolite families also explained variance related to ecological functioning like physiological adaptations of bryophytes to dry environments (proteins, peptides, flavonoids, terpenes), light availability (flavonoids, terpenes, carbohydrates), temperature (flavonoids), and biotic interactions (steroids, terpenes). The results from this study allow to construct chemical traits that can be attributed to biogeochemistry, habitat conditions, environmental changes and biotic interactions. Our classification framework accelerates the complex annotation process in metabolomics and can be used to simplify biochemical patterns. We show that compound classification is a powerful tool that allows to explore relationships in both molecular biology by “zooming in” and in ecology by “zooming out”. The insights revealed by our framework allow to construct new research hypotheses and to enable detailed follow-up studies.

Mass spectrometry-based forest tree metabolomics

Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long-life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)-based analytical platforms. In this review, advances in MS-based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS-based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re-usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126-157, 2021.

Cancers in Agreement? Exploring the Cross-Talk of Cancer Metabolomic and Transcriptomic Landscapes Using Publicly Available Data

One of the major hallmarks of cancer is the derailment of a cell's metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics data of eight cancer types (breast, lung, gastric, renal, liver, colorectal, prostate, and multiple myeloma) to find and investigate differences and commonalities on a pathway level among different cancer types. Topological analysis of inferred graphical Gaussian association networks showed that cancer was strongly defined in genetic networks, but not in metabolic networks. Using different statistical approaches to find significant differences between cancer and control cases, we highlighted the difficulties of high-level data-merging and in using statistical association networks. Cancer transcriptomics and metabolomics and landscapes were characterized by changed macro-molecule production, however, only major metabolic deregulations with highly impacted pathways were found in liver cancer. Cell cycle was enriched in breast, liver, and colorectal cancer, while breast and lung cancer were distinguished by highly enriched oncogene signaling pathways. A strong inflammatory response was observed in lung cancer and, to some extent, renal cancer. This study highlights the necessity of combining different omics levels to obtain a better description of cancer characteristics.

Use of Untargeted Metabolomics to Explore the Air Pollution-Related Disease Continuum

PURPOSE OF REVIEW

The purpose of this review is to summarize the application of untargeted metabolomics to identify the perturbation of metabolites or metabolic pathways associated with air pollutant exposures.

RECENT FINDINGS

Twenty-three studies were included in this review, in adults, children, or pregnant women. The most commonly measured air pollutant is particulate matter smaller than 2.5 μm. Size-fractioned particles, particle chemical species, gas pollutants, or organic compounds were also investigated. The reviewed studies used a wide range of air pollution measurement techniques and metabolomics analyses. Identified metabolites were primarily related to oxidative stress and inflammatory responses, and a few were related to the alterations of steroid metabolic pathways. The observed metabolic perturbations can differ by disease status, sex, and age. Air pollution-related metabolic changes were also associated with health outcomes in some studies. Our review shows that air pollutant exposures are associated with metabolic pathways primarily related to oxidative stress, inflammation, as assessed through untargeted metabolomics in 23 studies. More metabolomic studies with larger sample sizes are needed to identify air pollution components most responsible for adverse health effects, elaborate on mechanisms for subpopulation susceptibility, and link air pollution exposure to specific adverse health effects.

A benchmarking protocol for breath analysis: the peppermint experiment

Sampling of volatile organic compounds (VOCs) has shown promise for detection of a range of diseases but results have proved hard to replicate due to a lack of standardization. In this work we introduce the 'Peppermint Initiative'. The initiative seeks to disseminate a standardized experiment that allows comparison of breath sampling and data analysis methods. Further, it seeks to share a set of benchmark values for the measurement of VOCs in breath. Pilot data are presented to illustrate the standardized approach to the interpretation of results obtained from the Peppermint experiment. This pilot study was conducted to determine the washout profile of peppermint compounds in breath, identify appropriate sampling time points, and formalise the data analysis. Five and ten participants were recruited to undertake a standardized intervention by ingesting a peppermint oil capsule that engenders a predictable and controlled change in the VOC profile in exhaled breath. After collecting a pre-ingestion breath sample, five further samples are taken at 2, 4, 6, 8, and 10 h after ingestion. Samples were analysed using ion mobility spectrometry coupled to multi-capillary column and thermal desorption gas chromatography mass spectrometry. A regression analysis of the washout data was used to determine sampling times for the final peppermint protocol, and the time for the compound measurement to return to baseline levels was selected as a benchmark value. A measure of the quality of the data generated from a given technique is proposed by comparing data fidelity. This study protocol has been used for all subsequent measurements by the Peppermint Consortium (16 partners from seven countries). So far 1200 breath samples from 200 participants using a range of sampling and analytical techniques have been collected. The data from the consortium will be disseminated in subsequent technical notes focussing on results from individual platforms.

Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

PURPOSE OF REVIEW

Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer.

RECENT FINDINGS

Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

Data normalization strategies in metabolomics: Current challenges, approaches, and tools

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from - either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.

Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology

Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.

Metabolic Biomarkers of Squamous Cell Carcinoma of the Aerodigestive Tract: A Systematic Review and Quality Assessment

Introduction. Aerodigestive squamous cell carcinomas (ASCC) constitute a major source of global cancer deaths. Patients typically present with advanced, incurable disease, so new means of detecting early disease are a research priority. Metabolite quantitation is amenable to point-of-care analysis and can be performed in ASCC surrogates such as breath and saliva. The purpose of this systematic review is to summarise progress of ASCC metabolomic studies, with an emphasis on the critical appraisal of methodological quality and reporting.

METHOD

A systematic online literature search was performed to identify studies reporting metabolic biomarkers of ASCC. This review was conducted in accordance with the recommendations of the Cochrane Library and MOOSE guidelines.

RESULTS

Thirty studies comprising 2117 patients were included in the review. All publications represented phase-I biomarker discovery studies, and none validated their findings in an independent cohort. There was heterogeneity in study design and methodological and reporting quality. Sensitivities and specificities were higher in oesophageal and head and neck squamous cell carcinomas compared to those in lung squamous cell carcinoma. The metabolic phenotypes of these cancers were similar, as was the kinetics of metabolite groups when comparing blood, tissue, and breath/saliva concentrations. Deregulation of amino acid metabolism was the most frequently reported theme.

CONCLUSION

Metabolite analysis has shown promising diagnostic performance, especially for oesophageal and head and neck ASCC subtypes, which are phenotypically similar. However, shortcomings in study design have led to inconsistencies between studies. To support future studies and ultimately clinical adoption, these limitations are discussed.

Crowd-Sourced Chemistry: Considerations for Building a Standardized Database to Improve Omic Analyses

Mass spectrometry (MS) is used in multiple omics disciplines to generate large collections of data. This data enables advancements in biomedical research by providing global profiles of a given system. One of the main barriers to generating these profiles is the inability to accurately annotate omics data, especially small molecules. To complement pre-existing large databases that are not quite complete, research groups devote efforts to generating personal libraries to annotate their data. Scientific progress is impeded during the generation of these personal libraries because the data contained within them is often redundant and/or incompatible with other databases. To overcome these redundancies and incompatibilities, we propose that communal, crowd-sourced databases be curated in a standardized fashion. A small number of groups have shown this model is feasible and successful. While the needs of a specific field will dictate the functionality of a communal database, we discuss some features to consider during database development. Special emphasis is made on standardization of terminology, documentation, format, reference materials, and quality assurance practices. These standardization procedures enable a field to have higher confidence in the quality of the data within a given database. We also discuss the three conceptual pillars of database design as well as how crowd-sourcing is practiced. Generating open-source databases requires front-end effort, but the result is a well curated, high quality data set that all can use. Having a resource such as this fosters collaboration and scientific advancement.

Experimental Design and Sample Preparation in Forest Tree Metabolomics

Appropriate experimental design and sample preparation are key steps in metabolomics experiments, highly influencing the biological interpretation of the results. The sample preparation workflow for plant metabolomics studies includes several steps before metabolite extraction and analysis. These include the optimization of laboratory procedures, which should be optimized for different plants and tissues. This is particularly the case for trees, whose tissues are complex matrices to work with due to the presence of several interferents, such as oleoresins, cellulose. A good experimental design, tree tissue harvest conditions, and sample preparation are crucial to ensure consistency and reproducibility of the metadata among datasets. In this review, we discuss the main challenges when setting up a forest tree metabolomics experiment for mass spectrometry (MS)-based analysis covering all technical aspects from the biological question formulation and experimental design to sample processing and metabolite extraction and data acquisition. We also highlight the importance of forest tree metadata standardization in metabolomics studies.

Methods and Challenges for Computational Data Analysis for DNA Adductomics

Frequent exposure to chemicals in the environment, diet, and endogenous electrophiles leads to chemical modification of DNA and the formation of DNA adducts. Some DNA adducts can induce mutations during cell division and, when occurring in critical regions of the genome, can lead to the onset of disease, including cancer. The targeted analysis of DNA adducts over the past 30 years has revealed that the human genome contains many types of DNA damages. However, a long-standing limitation in conducting DNA adduct measurements has been the inability to screen for the total complement of DNA adducts derived from a wide range of chemicals in a single assay. With the advancement of high-resolution mass spectrometry (MS) instrumentation and new scanning technologies, nontargeted "omics" approaches employing data-dependent acquisition and data-independent acquisition methods have been established to simultaneously screen for multiple DNA adducts, a technique known as DNA adductomics. However, notable challenges in data processing must be overcome for DNA adductomics to become a mature technology. DNA adducts occur at low abundance in humans, and current softwares do not reliably detect them when using common MS data acquisition methods. In this perspective, we discuss contemporary computational tools developed for feature finding of MS data widely utilized in the disciplines of proteomics and metabolomics and highlight their limitations for conducting nontargeted DNA-adduct biomarker discovery. Improvements to existing MS data processing software and new algorithms for adduct detection are needed to develop DNA adductomics into a powerful tool for the nontargeted identification of potential cancer-causing agents.

Comparative Genomics and Metabolomics Analyses of Clavulanic Acid-Producing Streptomyces Species Provides Insight Into Specialized Metabolism

Clavulanic acid is a bacterial specialized metabolite, which inhibits certain serine β-lactamases, enzymes that inactivate β-lactam antibiotics to confer resistance. Due to this activity, clavulanic acid is widely used in combination with penicillin and cephalosporin (β-lactam) antibiotics to treat infections caused by β-lactamase-producing bacteria. Clavulanic acid is industrially produced by fermenting Streptomyces clavuligerus, as large-scale chemical synthesis is not commercially feasible. Other than S. clavuligerus, Streptomyces jumonjinensis and Streptomyces katsurahamanus also produce clavulanic acid along with cephamycin C, but information regarding their genome sequences is not available. In addition, the Streptomyces contain many biosynthetic gene clusters thought to be "cryptic," as the specialized metabolites produced by them are not known. Therefore, we sequenced the genomes of S. jumonjinensis and S. katsurahamanus, and examined their metabolomes using untargeted mass spectrometry along with S. clavuligerus for comparison. We analyzed the biosynthetic gene cluster content of the three species to correlate their biosynthetic capacities, by matching them with the specialized metabolites detected in the current study. It was recently reported that S. clavuligerus can produce the plant-associated metabolite naringenin, and we describe more examples of such specialized metabolites in extracts from the three Streptomyces species. Detailed comparisons of the biosynthetic gene clusters involved in clavulanic acid (and cephamycin C) production were also performed, and based on our analyses, we propose the core set of genes responsible for producing this medicinally important metabolite.

Metabolomic identification of novel diagnostic biomarkers in ectopic pregnancy

INTRODUCTION

Ectopic pregnancy (EP) is a potentially life-threatening condition and early diagnosis still remains a challenge, causing a delay in management leading to tubal rupture.

OBJECTIVES

To identify putative plasma biomarkers for the detection of tubal EP and elucidate altered biochemical pathways in EP compared to intrauterine pregnancies.

METHODS

This case-control study included prospective recruitment of 39 tubal EP cases and 89 early intrauterine pregnancy controls. Plasma samples were biochemically profiled using proton nuclear magnetic resonance spectroscopy (¹H NMR). To avoid over-fitting, datasets were randomly divided into a discovery group (26 cases vs 60 controls) and a test group (13 cases and 29 controls). Logistic regression models were developed in the discovery group and validated in the independent test group. Area under the receiver operating characteristics curve (AUC), 95% confidence interval (CI), sensitivity, and specificity values were calculated.

RESULTS

In total 13 of 43 (30.3%) metabolite concentrations were significantly altered in EP plasma (p < 0.05). Metabolomic profiling yielded significant separation between EP and controls (p < 0.05). Independent validation of a two-metabolite model consisting of lactate and acetate, achieved an AUC (95% CI) = 0.935 (0.843-1.000) with a sensitivity of 92.3% and specificity of 96.6%. The second metabolite model (D-glucose, pyruvate, acetoacetate) performed well with an AUC (95% CI) = 0.822 (0.657-0.988) and a sensitivity of 84.6% and specificity of 86.2%.

CONCLUSION

We report novel metabolomic biomarkers with a high accuracy for the detection of EP. Accurate biomarkers could potentially result in improved early diagnosis of tubal EP cases.

Metabolomic Profiling of the Host Response of Tomato (Solanum lycopersicum) Following Infection by Ralstonia solanacearum

Tomato (Solanum lycopersicum) is an important dietary source of bioactive phytochemicals and active breeding programs constantly produce new cultivars possessing superior and desirable traits. The phytopathogenic Ralstonia solanacearum, the causal agent of bacterial wilt, is a highly destructive bacterial disease with a high economic impact on tomato production. This study followed an untargeted metabolomic approach involving four tomato cultivars and aimed at the identification of secondary metabolites involved in plant defense after infection with R. solanacearum. Liquid chromatography coupled to mass spectrometry (LC-MS) in combination with multivariate data analysis and chemometric modelling were utilized for the identification of discriminant secondary metabolites. The total of 81 statistically selected features were annotated belonging to the metabolite classes of amino acids, organic acids, fatty acids, various derivatives of cinnamic acid and benzoic acids, flavonoids and steroidal glycoalkaloids. The results indicate that the phenylpropanoid pathway, represented by flavonoids and hydroxycinnamic acids, is of prime importance in the tomato defense response. The hydroxycinnamic acids esters of quinic acid, hexoses and glucaric acids were identified as signatory biomarkers, as well as the hydroxycinnamic acid amides to polyamines and tyramine. Interestingly, the rapid and differential accumulation of putrescine, dopamine, and tyramine derivatives, along with the presence of a newly documented metabolite, feruloyl serotonin, were documented in the infected plants. Metabolite concentration variability in the different cultivar tissues point to cultivar-specific variation in the speed and manner of resource redistribution between the host tissues. These metabolic phenotypes provide insights into the differential metabolic signatures underlying the defense metabolism of the four cultivars, defining their defensive capabilities to R. solanacearum.

Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: a systematic review

INTRODUCTION

To date, there is no robust enough test to predict small-for-gestational-age (SGA) infants, who are at increased lifelong risk of morbidity and mortality.

OBJECTIVE

To determine the accuracy of metabolomics in predicting SGA babies and elucidate which metabolites are predictive of this condition.

DATA SOURCES

Two independent researchers explored 11 electronic databases and grey literature in February 2018 and November 2018, covering publications from 1998 to 2018. Both researchers performed data extraction and quality assessment independently. A third researcher resolved discrepancies.

STUDY ELIGIBILITY CRITERIA

Cohort or nested case-control studies were included which investigated pregnant women and performed metabolomics analysis to evaluate SGA infants. The primary outcome was birth weight <10th centile-as a surrogate for fetal growth restriction-by population-based or customised charts.

STUDY APPRAISAL AND SYNTHESIS METHODS

Two independent researchers extracted data on study design, obstetric variables and sampling, metabolomics technique, chemical class of metabolites, and prediction accuracy measures. Authors were contacted to provide additional data when necessary.

RESULTS

A total of 9181 references were retrieved. Of these, 273 were duplicate, 8760 were removed by title or abstract, and 133 were excluded by full-text content. Thus, 15 studies were included. Only two studies used the fifth centile as a cut-off, and most reports sampled second-trimester pregnant women. Liquid chromatography coupled to mass spectrometry was the most common metabolomics approach. Untargeted studies in the second trimester provided the largest number of predictive metabolites, using maternal blood or hair. Fatty acids, phosphosphingolipids and amino acids were the most prevalent predictive chemical subclasses.

CONCLUSIONS AND IMPLICATIONS

Significant heterogeneity of participant characteristics and methods employed among studies precluded a meta-analysis. Compounds related to lipid metabolism should be validated up to the second trimester in different settings.

PROSPERO REGISTRATION NUMBER

CRD42018089985.

Ontology-based metabolomics data integration with quality control

Aim: The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. Results & methodology: This paper presents an integrated system of quality control (QC) methods to assess metabolomics results by evaluating the data acquisition strategies and metabolite identification process when integrating datasets for meta-analysis. An ontology knowledge base and a rule-based system representing the experiment and chemical background information direct the processes involved in data integration and QC verification. A diabetes meta-analysis study using these QC methods finds putative biomarkers that differ between cohorts. Conclusion: The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.

A Tool to Encourage Minimum Reporting Guideline Uptake for Data Analysis in Metabolomics

Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis' sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.

RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies

INTRODUCTION

The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The "putatively annotated compound" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of "putatively annotated compound" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data.

OBJECTIVES

To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to "putatively annotated compound" assignments in NMR-based metabolic profiling studies.

METHODS

The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment.

RESULTS

Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to "putatively annotated compounds" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories.

CONCLUSION

RANCM is intended to reduce the ambiguity in "putatively annotated compound" assignments, to facilitate effective communication of the degree of confidence in "putatively annotated compound" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.

Balancing sufficiency and impact in reporting standards for mass spectrometry imaging experiments

Reproducibility, or a lack thereof, is an increasingly important topic across many research fields. A key aspect of reproducibility is accurate reporting of both experiments and the resulting data. Herein, we propose a reporting guideline for mass spectrometry imaging (MSI). Previous standards have laid out guidelines sufficient to guarantee a certain quality of reporting; however, they set a high bar and as a consequence can be exhaustive and broad, thus limiting uptake.To help address this lack of uptake, we propose a reporting supplement-Minimum Information About a Mass Spectrometry Imaging Experiment (MIAMSIE)-and its abbreviated reporting standard version, MSIcheck. MIAMSIE is intended to improve author-driven reporting. It is intentionally not exhaustive, but is rather designed for extensibility and could therefore eventually become analogous to existing standards that aim to guarantee reporting quality. Conversely, its abbreviated form MSIcheck is intended as a diagnostic tool focused on key aspects in MSI reporting.We discuss how existing standards influenced MIAMSIE/MSIcheck and how these new approaches could positively impact reporting quality, followed by test implementation of both standards to demonstrate their use. For MIAMSIE, we report on author reviews of four articles and a dataset. For MSIcheck, we show a snapshot review of a one-month subset of the MSI literature that indicated issues with data provision and the reporting of both data analysis steps and calibration settings for MS systems. Although our contribution is MSI specific, we believe the underlying approach could be considered as a general strategy for improving scientific reporting.

Computational workflow to study the seasonal variation of secondary metabolites in nine different bryophytes

In Eco-Metabolomics interactions are studied of non-model organisms in their natural environment and relations are made between biochemistry and ecological function. Current challenges when processing such metabolomics data involve complex experiment designs which are often carried out in large field campaigns involving multiple study factors, peak detection parameter settings, the high variation of metabolite profiles and the analysis of non-model species with scarcely characterised metabolomes. Here, we present a dataset generated from 108 samples of nine bryophyte species obtained in four seasons using an untargeted liquid chromatography coupled with mass spectrometry acquisition method (LC/MS). Using this dataset we address the current challenges when processing Eco-Metabolomics data. Here, we also present a reproducible and reusable computational workflow implemented in Galaxy focusing on standard formats, data import, technical validation, feature detection, diversity analysis and multivariate statistics. We expect that the representative dataset and the reusable processing pipeline will facilitate future studies in the research field of Eco-Metabolomics.

Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics

Accurate annotation is vital for data interpretation; however, metabolite identification is a major bottleneck in untargeted metabolomics. Although community guidelines for metabolite identification were published over a decade ago, adaptation of the recommended standards has been limited. The complexity of LC-MS data due to combinations of various chromatographic and mass spectrometric acquisition methods has resulted in the advent of diverse workflows, which often involve non-standardized manual curation. Herein, we review the parameters involved in metabolite reporting and provide a workflow to estimate the level of confidence in reported metabolite annotation. The future of metabolite identification will be heavily based upon the use of metabolome data repositories and associated data analysis tools, which will enable data to be shared, re-analyzed and re-annotated in an automated fashion.