Tidy-Direct-to-MS: An Open-Source Data-Processing Pipeline for Direct Mass Spectrometry-Based Metabolomics Experiments.

Direct-to-Mass Spectrometry and ambient ionization techniques can be used for biochemical fingerprinting in a fast way. Data processing is typically accomplished with vendor-provided software tools. Here, a novel, open-source functionality, entitled Tidy-Direct-to-MS, was developed for data processing of direct-to-MS data sets. It allows for fast and user-friendly processing using different modules for optional sample position detection and separation, mass-to-charge ratio drift detection and correction, consensus spectra calculation, and bracketing across sample positions as well as feature abundance calculation. The tool also provides functionality for the automated comparison of different sets of parameters, thereby assisting the user in the complex task of finding an optimal combination to maximize the total number of detected features while also checking for the detection of user-provided reference features. In addition, Tidy-Direct-to-MS has the capability for data quality review and subsequent data analysis, thereby simplifying the workflow of untargeted ambient MS-based metabolomics studies. Tidy-Direct-to-MS is implemented in the Python programming language as part of the TidyMS library and can thus be easily extended. Capabilities of Tidy-Direct-to-MS are showcased in a data set acquired in a marine metabolomics study reported in MetaboLights (MTBLS1198) using a transmission mode Direct Analysis in Real Time-Mass Spectrometry (TM-DART-MS)-based method.

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard.

Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive Python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two-dimensional t-distributed stochastic neighbor embedding embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of state-of-the-art ms2deepscore pairwise spectral similarities as a quantitative backbone while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part of mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.

Metabolic changes in tomato plants caused by psychrotolerant Antarctic endophytic bacteria might be implicated in cold stress mitigation.

Climate change is responsible for mild winters and warm springs that can induce premature plant development, increasing the risk of exposure to cold stress with a severe reduction in plant growth. Tomato plants are sensitive to cold stress and beneficial microorganisms can increase their tolerance. However, scarce information is available on mechanisms stimulated by bacterial endophytes in tomato plants against cold stress. This study aimed to clarify metabolic changes stimulated by psychrotolerant endophytic bacteria in tomato plants exposed to cold stress and annotate compounds possibly associated with cold stress mitigation. Tomato seeds were inoculated with two bacterial endophytes isolated from Antarctic Colobanthus quitensis plants (Ewingella sp. S1.OA.A_B6 and Pseudomonas sp. S2.OTC.A_B10) or with Paraburkholderia phytofirmans PsJN, while mock-inoculated seeds were used as control. The metabolic composition of tomato plants was analyzed immediately after cold stress exposure (4°C for seven days) or after two and four days of recovery at 25°C. Under cold stress, the content of malondialdehyde, phenylalanine, ferulic acid, and p-coumaric acid was lower in bacterium-inoculated compared to mock-inoculated plants, indicating a reduction of lipid peroxidation and the stimulation of phenolic compound metabolism. The content of two phenolic compounds, five putative phenylalanine-derived dipeptides, and three further phenylalanine-derived compounds was higher in bacterium-inoculated compared to mock-inoculated samples under cold stress. Thus, psychrotolerant endophytic bacteria can reprogram polyphenol metabolism and stimulate the accumulation of secondary metabolites, like 4-hydroxybenzoic and salicylic acid, which are presumably involved in cold stress mitigation, and phenylalanine-derived dipeptides possibly involved in plant stress responses.

PeakBot: machine-learning-based chromatographic peak picking.

MOTIVATION: Chromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task. RESULTS: A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time versus m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot's architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs. AVAILABILITY AND IMPLEMENTATION: The package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CPExtract, a Software Tool for the Automated Tracer-Based Pathway Specific Screening of Secondary Metabolites in LC-HRMS Data.

The use of stable isotopically labeled tracers is a long-proven way of specifically detecting and tracking derived metabolites through a metabolic network of interest. While the recently developed stable isotope-assisted methods and associated, supporting data analysis tools have greatly improved untargeted metabolomics approaches, no software tool is currently available that allows us to automatically and flexibly search liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) chromatograms for user-definable isotopolog patterns expected for the metabolism of labeled tracer substances. Here, we present Custom Pattern Extract (CPExtract), a versatile software tool that allows for the first time the high-throughput search for user-defined isotopolog patterns in LC-HRMS data. The patterns can be specified via a set of rules including the presence or absence of certain isotopologs, their relative intensity ratios as well as chromatographic coelution. Each isotopolog pattern satisfying the respective rules is verified on an MS scan level and also in the chromatographic domain. The CPExtract algorithm allows the use of both labeled tracer compounds in nonlabeled biological samples as well as a reversed tracer approach, employing nonlabeled tracer compounds along with globally labeled biological samples. In a proof-of-concept study, we searched for metabolites specifically arising from the malonate pathway of the filamentous fungi Fusarium graminearum and Trichoderma reesei. 1,2,3-13C3-malonic acid diethyl ester and native malonic acid monomethyl ester were used as tracers. We were able to reliably detect expected fatty acids and known polyketides. In addition, up to 46 and 270 further, unknown metabolites presumably including novel polyketides were detected in the F. graminearum and T. reesei culture samples, respectively, all of which exhibited the user-predicted isotopolog patterns originating from the malonate tracer incorporation. The software can be used for every conceivable tracer approach. Furthermore, the rule sets can be easily adapted or extended if necessary. CPExtract is available free of charge for noncommercial use at https://metabolomics-ifa.boku.ac.at/CPExtract.

Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools.

BACKGROUND: Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. AIM OF REVIEW: We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. KEY SCIENTIFIC CONCEPTS OF REVIEW: This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.

Towards a broader view of the metabolome: untargeted profiling of soluble and bound polyphenols in plants.

Phenylalanine (Phe) is a central precursor for numerous secondary plant metabolites with a multitude of biological functions. Recent studies on the fungal disease Fusarium head blight in wheat showed numerous Phe-derived defence metabolites to be induced in the presence of the pathogen. These studies also suggest a partial incorporation of Phe-derived secondary metabolites into the cell wall. To broaden the view of the metabolome to bound Phe derivatives, an existing approach using 13C-labelled Phe as tracer was extended. The developed workflow consists of three successive extractions with an acidified acetonitrile-methanol-water mixture to remove the soluble plant metabolites, followed by cell wall hydrolysis with 4M aqueous NaOH, acidification with aqueous HCl, and liquid-liquid extraction of the hydrolysate with ethyl acetate. The untargeted screening of Phe-derived metabolites revealed 156 soluble compounds and 90 compounds in the hydrolysed samples including known cell wall constituents like ferulic acid, coumaric acid, and tricin. Forty-nine metabolites were found exclusively in the hydrolysate. The average cumulative extraction yield of the soluble metabolites was 99.6%, with a range of 91.8 to 100%. Repeatability coefficients of variation of the protocol ranged from 10.5 to 25.9%, with a median of 16.3%. To demonstrate the suitability of the proposed method for a typical metabolomics application, mock-treated and Fusarium graminearum-treated wheat samples were compared. The study revealed differences between the hydrolysates of the two sample types, confirming the differential incorporation of Phe-derived metabolites into the cell wall under infection conditions.

Trichoderma spp. volatile organic compounds protect grapevine plants by activating defense-related processes against downy mildew.

Volatile organic compounds (VOCs) are produced by soil-borne microorganisms and play crucial roles in fungal interactions with plants and phytopathogens. Although VOCs have been characterized in Trichoderma spp., the mechanisms against phytopathogens strongly differ according to the strain and pathosystem. This study aimed at characterizing VOCs produced by three Trichoderma strains used as biofungicides and to investigate their effects against grapevine downy mildew (caused by Plasmopara viticola). A VOC-mediated reduction of downy mildew severity was found in leaf disks treated with Trichoderma asperellum T34 (T34), T. harzianum T39 (T39), and T. atroviride SC1 (SC1) and 31 compounds were detected by head space-solid phase microextraction gas chromatography-mass spectrometry. Among the Trichoderma VOCs annotated, α-farnesene, cadinene, 1,3-octadiene, 2-pentylfuran, and 6-pentyl-2H-pyran-2-one reduced downy mildew severity on grapevine leaf disks. In particular, 6-pentyl-2H-pyran-2-one and 2-pentylfuran increased the accumulation of callose and enhanced the modulation of defense-related genes after P. viticola inoculation, indicating an induction of grapevine defense mechanisms. Moreover, 6-pentyl-2H-pyran-2-one activated the hypersensitive response after P. viticola inoculation, possibly to reinforce the grapevine defense reaction. These results indicate that Trichoderma VOCs can induce grapevine resistance, and these molecules could be further applied to control grapevine downy mildew.

Transcription factor Xpp1 is a switch between primary and secondary fungal metabolism.

Fungi can produce a wide range of chemical compounds via secondary metabolism. These compounds are of major interest because of their (potential) application in medicine and biotechnology and as a potential source for new therapeutic agents and drug leads. However, under laboratory conditions, most secondary metabolism genes remain silent. This circumstance is an obstacle for the production of known metabolites and the discovery of new secondary metabolites. In this study, we describe the dual role of the transcription factor Xylanase promoter binding protein 1 (Xpp1) in the regulation of both primary and secondary metabolism of Trichoderma reesei Xpp1 was previously described as a repressor of xylanases. Here, we provide data from an RNA-sequencing analysis suggesting that Xpp1 is an activator of primary metabolism. This finding is supported by our results from a Biolog assay determining the carbon source assimilation behavior of an xpp1 deletion strain. Furthermore, the role of Xpp1 as a repressor of secondary metabolism is shown by gene expression analyses of polyketide synthases and the determination of the secondary metabolites of xpp1 deletion and overexpression strains using an untargeted metabolomics approach. The deletion of Xpp1 resulted in the enhanced secretion of secondary metabolites in terms of diversity and quantity. Homologs of Xpp1 are found among a broad range of fungi, including the biocontrol agent Trichoderma atroviride, the plant pathogens Fusarium graminearum and Colletotrichum graminicola, the model organism Neurospora crassa, the human pathogen Sporothrix schenckii, and the ergot fungus Claviceps purpurea.

Volatiles from the Mandibular Gland Reservoir Content of Colobopsis explodens Laciny and Zettel, 2018, Worker Ants (Hymenoptera: Formicidae).

Forty-five volatile organic compounds (VOCs) were identified or annotated in the mandibular gland reservoir content (MGRC) of the Southeast Asian ant Colobopsis explodens Laciny and Zettel, 2018 (Hymenoptera: Formicidae), using headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography mass spectrometry (GC-MS) and liquid extraction combined with GC-MS. In extension of previous reports on VOCs of C. explodens, members of different compound classes, such as alkanes, aliphatic and aromatic carboxylic acids, and phenolics, were detected. The ketone 2-heptanone and the biochemically related phenolics benzene-1,3,5-triol (phloroglucinol, PG), 1-(2,4,6-trihydroxyphenyl)ethanone (monoacetylphloroglucinol, MAPG), 5,7-dihydroxy-2-methylchromen-4-one (noreugenin), and 1-(3-Acetyl-2,4,6-trihydroxyphenyl)ethanone (2,4-diacetylphloroglucinol, DAPG) dominated the GC-MS chromatograms. The identities of the main phenolics MAPG and noreugenin were further verified by liquid chromatography-high resolution-tandem mass spectrometry (LC-HRMS/MS). A comparative study of MGRC samples originating from three distinct field expeditions revealed differences in the VOC profiles, but the presence and relative abundances of the dominating constituents were largely consistent in all samples. Our study considerably extends the knowledge about the number and type of VOCs occurring in the MGRC of C. explodens. Based on the type of the detected compounds, we propose that the likely irritant and antibiotic phenolic constituents play a role in defense against arthropod opponents or in protection against microbial pathogens.

Partially 13C-labeled mouse tissue as reference for LC-MS based untargeted metabolomics.

The inclusion of stable isotope-labeled reference standards in the sample is an established method for the detection and relative quantification of metabolic features in untargeted metabolomics. In order to quantify as many metabolites as possible, the reference should ideally include the same metabolites in their stable isotope-labeled form as the sample under investigation. We present here an attempt to use partially 13C-labeled mouse material as internal standard for relative metabolite quantification of mouse and human samples in untargeted metabolomics. We fed mice for 14 days with a13C-labeled Ralstonia eutropha based diet. Tissue and blood amino acids from these mice showed 13C enrichment levels that ranged from 6% to 75%. We used MetExtract II software to automatically detect native and labeled peak pairs in an untargeted manner. In a dilution series and with the implementation of a correction factor, partially 13C-labeled mouse plasma resulted in accurate relative quantification of human plasma amino acids using liquid chromatography coupled to mass spectrometry, The coefficient of variation for the relative quantification is reduced from 27% without internal standard to 10% with inclusion of partially 13C-labeled internal standard. We anticipate the method to be of general use for the relative metabolite quantification of human specimens.

MetExtract II: A Software Suite for Stable Isotope-Assisted Untargeted Metabolomics.

Stable isotope labeling (SIL) techniques have the potential to enhance different aspects of liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics methods including metabolite detection, annotation of unknown metabolites, and comparative quantification. In this work, we present MetExtract II, a software toolbox for detection of biologically derived compounds. It exploits SIL-specific isotope patterns and elution profiles in LC-HRMS(/MS) data. The toolbox consists of three complementary modules: M1 (AllExtract) uses mixtures of uniformly highly isotope-enriched and native biological samples for selective detection of the entire accessible metabolome. M2 (TracExtract) is particularly suited to probe the metabolism of endogenous or exogenous secondary metabolites and facilitates the untargeted screening of tracer derivatives from concurrently metabolized native and uniformly labeled tracer substances. With M3 (FragExtract), tandem mass spectrometry (MS/MS) fragments of corresponding native and uniformly labeled ions are evaluated and automatically assigned with putative sum formulas. Generated results can be graphically illustrated and exported as a comprehensive data matrix that contains all detected pairs of native and labeled metabolite ions that can be used for database queries, metabolome-wide internal standardization, and statistical analysis. The software, associated documentation, and sample data sets are freely available for noncommercial use at http://metabolomics-ifa.boku.ac.at/metextractII .

The Profile and Dynamics of RNA Modifications in Animals.

More than a hundred distinct modified nucleosides have been identified in RNA, but little is known about their distribution across different organisms, their dynamic nature and their response to cellular and environmental stress. Mass-spectrometry-based methods have been at the forefront of identifying and quantifying modified nucleosides. However, they often require synthetic reference standards, which do not exist in the case of many modified nucleosides, and this therefore impedes their analysis. Here we use a metabolic labelling approach to achieve rapid generation of bio-isotopologues of the complete Caenorhabditis elegans transcriptome and its modifications and use them as reference standards to characterise the RNA modification profile in this multicellular organism through an untargeted liquid-chromatography tandem high-resolution mass spectrometry (LC-HRMS) approach. We furthermore show that several of these RNA modifications have a dynamic response to environmental stress and that, in particular, changes in the tRNA wobble base modification 5-methoxycarbonylmethyl-2-thiouridine (mcm5 s2 U) lead to codon-biased gene-expression changes in starved animals.

Stable Isotope-Assisted Evaluation of Different Extraction Solvents for Untargeted Metabolomics of Plants.

The evaluation of extraction protocols for untargeted metabolomics approaches is still difficult. We have applied a novel stable isotope-assisted workflow for untargeted LC-HRMS-based plant metabolomics , which allows for the first time every detected feature to be considered for method evaluation. The efficiency and complementarity of commonly used extraction solvents, namely 1 + 3 (v/v) mixtures of water and selected organic solvents (methanol, acetonitrile or methanol/acetonitrile 1 + 1 (v/v)), with and without the addition of 0.1% (v/v) formic acid were compared. Four different wheat organs were sampled, extracted and analysed by LC-HRMS. Data evaluation was performed with the in-house-developed MetExtract II software and R. With all tested solvents a total of 871 metabolites were extracted in ear, 785 in stem, 733 in leaf and 517 in root samples, respectively. Between 48% (stem) and 57% (ear) of the metabolites detected in a particular organ were found with all extraction mixtures, and 127 of 996 metabolites were consistently shared between all extraction agent/organ combinations. In aqueous methanol, acidification with formic acid led to pronounced pH dependency regarding the precision of metabolite abundance and the number of detectable metabolites, whereas extracts of acetonitrile-containing mixtures were less affected. Moreover, methanol and acetonitrile have been found to be complementary with respect to extraction efficiency. Interestingly, the beneficial properties of both solvents can be combined by the use of a water-methanol-acetonitrile mixture for global metabolite extraction instead of aqueous methanol or aqueous acetonitrile alone.

QCScreen: a software tool for data quality control in LC-HRMS based metabolomics.

BACKGROUND: Metabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process. RESULTS: The software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection. CONCLUSIONS: QCScreen is an open source software tool which provides a useful basis for assessing the suitability of LC-HRMS data prior to time consuming, detailed data processing and subsequent statistical analysis. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple quality control sample types as well as experimental samples in one or more measurement sequences.

Tracing the metabolism of HT-2 toxin and T-2 toxin in barley by isotope-assisted untargeted screening and quantitative LC-HRMS analysis.

An extensive study of the metabolism of the type A trichothecene mycotoxins HT-2 toxin and T-2 toxin in barley using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is reported. A recently developed untargeted approach based on stable isotopic labelling, LC-Orbitrap-MS analysis with fast polarity switching and data processing by MetExtract software was combined with targeted LC-Q-TOF-MS(/MS) analysis for metabolite structure elucidation and quantification. In total, 9 HT-2 toxin and 13 T-2 toxin metabolites plus tentative isomers were detected, which were successfully annotated by calculation of elemental formulas and further LC-HRMS/MS measurements as well as partly identified with authentic standards. As a result, glucosylated forms of the toxins, malonylglucosides, and acetyl and feruloyl conjugates were elucidated. Additionally, time courses of metabolite formation and mass balances were established. For absolute quantification of those compounds for which standards were available, the method was validated by determining apparent recovery, signal suppression, or enhancement and extraction recovery. Most importantly, T-2 toxin was rapidly metabolised to HT-2 toxin and for both parent toxins HT-2 toxin-3-O-ß-glucoside was identified (confirmed by authentic standard) as the main metabolite, which reached its maximum already 1 day after toxin treatment. Graphical Abstract Isotope-assisted untargeted screening of HT-2 toxin and T-2 toxin metabolites in barley.

Automated LC-HRMS(/MS) approach for the annotation of fragment ions derived from stable isotope labeling-assisted untargeted metabolomics.

Structure elucidation of biological compounds is still a major bottleneck of untargeted LC-HRMS approaches in metabolomics research. The aim of the present study was to combine stable isotope labeling and tandem mass spectrometry for the automated interpretation of the elemental composition of fragment ions and thereby facilitate the structural characterization of metabolites. The software tool FragExtract was developed and evaluated with LC-HRMS/MS spectra of both native (12)C- and uniformly (13)C (U-(13)C)-labeled analytical standards of 10 fungal substances in pure solvent and spiked into fungal culture filtrate of Fusarium graminearum respectively. Furthermore, the developed approach is exemplified with nine unknown biochemical compounds contained in F. graminearum samples derived from an untargeted metabolomics experiment. The mass difference between the corresponding fragment ions present in the MS/MS spectra of the native and U-(13)C-labeled compound enabled the assignment of the number of carbon atoms to each fragment signal and allowed the generation of meaningful putative molecular formulas for each fragment ion, which in turn also helped determine the elemental composition of the precursor ion. Compared to laborious manual analysis of the MS/MS spectra, the presented algorithm marks an important step toward efficient fragment signal elucidation and structure annotation of metabolites in future untargeted metabolomics studies. Moreover, as demonstrated for a fungal culture sample, FragExtract also assists the characterization of unknown metabolites, which are not contained in databases, and thus exhibits a significant contribution to untargeted metabolomics research.

Untargeted profiling of tracer-derived metabolites using stable isotopic labeling and fast polarity-switching LC-ESI-HRMS.

An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope-assisted LC-HRMS-based metabolomics workflow for the global annotation of biological samples has been further developed and extended. For untargeted detection of metabolites arising from labeled tracer substances, isotope pattern recognition has been adjusted to account for nonlabeled moieties conjugated to the native and labeled tracer molecules. Furthermore, the workflow has been extended by (i) an optional ion intensity ratio check, (ii) the automated combination of positive and negative ionization mode mass spectra derived from fast polarity switching, and (iii) metabolic feature annotation. These extensions enable the automated, unbiased, and global detection of tracer-derived metabolites in complex biological samples. The workflow is demonstrated with the metabolism of (13)C9-phenylalanine in wheat cell suspension cultures in the presence of the mycotoxin deoxynivalenol (DON). In total, 341 metabolic features (150 in positive and 191 in negative ionization mode) corresponding to 139 metabolites were detected. The benefit of fast polarity switching was evident, with 32 and 58 of these metabolites having exclusively been detected in the positive and negative modes, respectively. Moreover, for 19 of the remaining 49 phenylalanine-derived metabolites, the assignment of ion species and, thus, molecular weight was possible only by the use of complementary features of the two ion polarity modes. Statistical evaluation showed that treatment with DON increased or decreased the abundances of many detected metabolites.

MetExtract: a new software tool for the automated comprehensive extraction of metabolite-derived LC/MS signals in metabolomics research.

MOTIVATION: Liquid chromatography-mass spectrometry (LC/MS) is a key technique in metabolomics. Since the efficient assignment of MS signals to true biological metabolites becomes feasible in combination with in vivo stable isotopic labelling, our aim was to provide a new software tool for this purpose. RESULTS: An algorithm and a program (MetExtract) have been developed to search for metabolites in in vivo labelled biological samples. The algorithm makes use of the chromatographic characteristics of the LC/MS data and detects MS peaks fulfilling the criteria of stable isotopic labelling. As a result of all calculations, the algorithm specifies a list of m/z values, the corresponding number of atoms of the labelling element (e.g. carbon) together with retention time and extracted adduct-, fragment- and polymer ions. Its function was evaluated using native (12)C- and uniformly (13)C-labelled standard substances. AVAILABILITY: MetExtract is available free of charge and warranty at http://code.google.com/p/metextract/. Precompiled executables are available for Windows operating systems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stable isotopic labelling-assisted untargeted metabolic profiling reveals novel conjugates of the mycotoxin deoxynivalenol in wheat.

An untargeted screening strategy for the detection of biotransformation products of xenobiotics using stable isotopic labelling (SIL) and liquid chromatography-high resolution mass spectrometry (LC-HRMS) is reported. The organism of interest is treated with a mixture of labelled and non-labelled precursor and samples are analysed by LC-HRMS. Raw data are processed with the recently developed MetExtract software for the automated extraction of corresponding peak pairs. The SIL-assisted approach is exemplified by the metabolisation of the Fusarium mycotoxin deoxynivalenol (DON) in planta. Flowering ears were inoculated with 100 µg of a 1 + 1 (v/v) mixture of non-labelled and fully labelled DON. Subsequent sample preparation, LC-HRMS measurements and data processing revealed a total of 57 corresponding peak pairs, which originated from ten metabolites. Besides the known DON and DON-3-glucoside, which were confirmed by measurement of authentic standards, eight further DON-biotransformation products were found by the untargeted screening approach. Based on a mass deviation of less than ±5 ppm and MS/MS measurements, one of these products was annotated as DON-glutathione (GSH) conjugate, which is described here for the first time for wheat. Our data further suggest that two DON-GSH-related metabolites, the processing products DON-S-cysteine and DON-S-cysteinyl-glycine and five unknown DON conjugates were formed in planta. Future MS/MS measurements shall reveal the molecular structures of the detected conjugates in more detail.