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.

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.

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.

Light-Induced Changes in Secondary Metabolite Production of Trichoderma atroviride.

Many studies aim at maximizing fungal secondary metabolite production but the influence of light during cultivation has often been neglected. Here, we combined an untargeted isotope-assisted liquid chromatography-high-resolution mass spectrometry-based metabolomics approach with standardized cultivation of Trichoderma atroviride under three defined light regimes (darkness (PD), reduced light (RL) exposure, and 12/12 h light/dark cycle (LD)) to systematically determine the effect of light on secondary metabolite production. Comparative analyses revealed a similar metabolite profile upon cultivation in PD and RL, whereas LD treatment had an inhibiting effect on both the number and abundance of metabolites. Additionally, the spatial distribution of the detected metabolites for PD and RL was analyzed. From the more than 500 detected metabolites, only 25 were exclusively produced upon fungal growth in darkness and 85 were significantly more abundant in darkness. The majority were detected under both cultivation conditions and annotation revealed a cluster of substances whose production followed the pattern observed for the well-known T. atroviride metabolite 6-pentyl-alpha-pyrone. We conclude that cultivation of T. atroviride under RL can be used to maximize secondary metabolite production.

Untargeted Plant Metabolomics: Evaluation of Lyophilization as a Sample Preparation Technique.

Lyophilization is a common method used for stabilizing biological samples prior to storage or to concentrate extracts. However, it is possible that this process may alter the metabolic composition or lead to the loss of metabolites. In this study, the performance of lyophilization is investigated in the example of wheat roots. To this end, native and 13C-labelled, fresh or already lyophilized root samples, and (diluted) extracts with dilution factors up to 32 and authentic reference standards were investigated. All samples were analyzed using RP-LC-HRMS. Results show that using lyophilization for the stabilization of plant material altered the metabolic sample composition. Overall, 7% of all wheat metabolites detected in non-lyophilized samples were not detected in dried samples anymore, and up to 43% of the remaining metabolites exhibited significantly increased or decreased abundances. With respect to extract concentration, less than 5% of the expected metabolites were completely lost by lyophilization and the recovery rates of the remaining metabolites were slightly reduced with increasing concentration factors to an average of 85% at an enrichment factor of 32. Compound annotation did not indicate specific classes of wheat metabolites to be affected.

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.

Gramiketides, Novel Polyketide Derivatives of Fusarium graminearum, Are Produced during the Infection of Wheat.

The plant pathogen Fusarium graminearum is a proficient producer of mycotoxins and other in part still unknown secondary metabolites, some of which might act as virulence factors on wheat. The PKS15 gene is expressed only in planta, so far hampering the identification of an associated metabolite. Here we combined the activation of silent gene clusters by chromatin manipulation (kmt6) with blocking the metabolic flow into the competing biosynthesis of the two major mycotoxins deoxynivalenol and zearalenone. Using an untargeted metabolomics approach, two closely related metabolites were found in triple mutants (kmt6 tri5 pks4,13) deficient in production of the major mycotoxins deoxynivalenol and zearalenone, but not in strains with an additional deletion in PKS15 (kmt6 tri5 pks4,13 pks15). Characterization of the metabolites, by LC-HRMS/MS in combination with a stable isotope-assisted tracer approach, revealed that they are likely hybrid polyketides comprising a polyketide part consisting of malonate-derived acetate units and a structurally deviating part. We propose the names gramiketide A and B for the two metabolites. In a biological experiment, both gramiketides were formed during infection of wheat ears with wild-type but not with pks15 mutants. The formation of the two gramiketides during infection correlated with that of the well-known virulence factor deoxynivalenol, suggesting that they might play a role in virulence.

Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets.

Covalent or non-covalent heterogeneous multimerization of molecules associated with extracts from biological samples analyzed via LC-MS are quite difficult to recognize/annotate and therefore the prevalence of multimerization remains largely unknown. In this study, we utilized 13C labeled and unlabeled Pichia pastoris extracts to recognize heterogeneous multimers. More specifically, between 0.8% and 1.5% of the biologically-derived features detected in our experiments were confirmed to be heteromers, about half of which we could successfully annotate with monomeric partners. Interestingly, we found specific chemical classes such as nucleotides to disproportionately contribute to heteroadducts. Furthermore, we compiled these compounds into the first MS/MS library that included data from heteromultimers to provide a starting point for other labs to improve the annotation of such ions in other metabolomics data sets. Then, the detected heteromers were also searched in publicly accessible LC-MS datasets available in Metabolights, Metabolomics WB and GNPS/MassIVE to demonstrate that these newly annotated ions are also relevant to other public datasets. Furthermore, in additional datasets (Triticum aestivum, Fusarium graminearum, and Trichoderma reesei) our developed workflow also detected 0.5%-4.9% of metabolite features to originate from heterodimers, demonstrating heteroadducts to be present in metabolomics studies at a low percentage.

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.

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.

A novel method combining stable isotopic labeling and high-resolution mass spectrometry to trace the quinone reaction products in wines.

Quinone formation is a key initial step of wine oxidation. Nucleophiles sacrificially react with quinones to sustain color and aroma, but due to the complexity of wine, determining the identity of the constitutive nucleophile has been challenging. Here we apply a novel stable-isotope labelling approach combined with high-resolution mass spectrometry, using 13C6-labelled ortho-quinone. This allows for the specific detection of quinone reaction products with M and M + 6x peak feature-pairs in real wines. Analysis using MetExtract II successfully identified 225 quinone reaction suspects in negative mode in Sauvignon blanc wine, and 120 in Cabernet Sauvignon. Ten quinone reaction products with the most abundant peak areas were tentatively identified using a mass/structure workflow. It appears that sulfides largely quench quinones in white wines, whereas flavonoids are the dominant reactants in red wines. The latter result demonstrates how skin/seed extraction preserves red wine.

Elucidation of xenoestrogen metabolism by non-targeted, stable isotope-assisted mass spectrometry in breast cancer cells.

Environmental exposure to xenoestrogens, i.e., chemicals that imitate the hormone 17ß-estradiol, has the potential to influence hormone homeostasis and action. Detailed knowledge of xenobiotic biotransformation processes in cell models is key when transferring knowledge learned from in vitro models to in vivo relevance. This study elucidated the metabolism of two naturally-occurring phyto- and mycoestrogens; namely genistein and zearalenone, in an estrogen receptor positive breast cancer cell line (MCF-7) with the aid of stable isotope-assisted metabolomics and the bioinformatic tool MetExtract II. Metabolism was studied in a time course experiment after 2 h, 6 h and 24 h incubation. Twelve and six biotransformation products of zearalenone and genistein were detected, respectively, clearly demonstrating the abundant xenobiotic biotransformation capability of the cells. Zearalenone underwent extensive phase-I metabolism resulting in α-zearalenol (α-ZEL), a molecule known to possess a significantly higher estrogenicity, and several phase-II metabolites (sulfo- and glycoconjugates) of the native compound and the major phase I metabolite α-ZEL. Moreover, potential adducts of zearalenone with a vitamin and several hydroxylated metabolites were annotated. Genistein metabolism resulted in sulfation, combined sulfation and hydroxylation, acetylation, glucuronidation and unexpectedly adduct formation with pentose- and hexose sugars. Kinetics of metabolite formation and subsequent excretion into the extracellular medium revealed a time-dependent increase in most biotransformation products. The untargeted elucidation of biotransformation products formed during cell culture experiments enables an improved and more meaningful interpretation of toxicological assays and has the potential to identify unexpected or unknown metabolites.

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.

The Comprehensive and Reliable Detection of Secondary Metabolites in Trichoderma reesei: A Tool for the Discovery of Novel Substances.

A method based on reversed phase high-performance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (RP-HPLC-ESI-HRMS) for the comprehensive and reliable detection of secondary metabolites of Trichoderma reesei cultured in synthetic minimal liquid medium is presented. A stable isotope-assisted (SIA) workflow is used, which allows the automated, comprehensive extraction of truly fungal metabolite-derived LC-MS signals from the acquired chromatographic data. The subsequent statistical data analysis and a typical outcome of such a metabolomics data evaluation are shown by way of example in a previously published study on the influence of the pleiotropic regulator transcription factor Xylanase promoter binding protein 1 (Xpp1) in T. reesei on secondary metabolism.

Enhanced Metabolome Coverage and Evaluation of Matrix Effects by the Use of Experimental-Condition-Matched 13C-Labeled Biological Samples in Isotope-Assisted LC-HRMS Metabolomics.

Stable isotope-assisted approaches can improve untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally 13C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve the detection of treatment-relevant metabolites and can aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly 13C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched 13C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.

Stable Isotope-Assisted Metabolomics for Deciphering Xenobiotic Metabolism in Mammalian Cell Culture.

Xenobiotics are ubiquitous in the environment and modified in the human body by phase I and II metabolism. Liquid chromatography coupled to high resolution mass spectrometry is a powerful tool to investigate these biotransformation products. We present a workflow based on stable isotope-assisted metabolomics and the bioinformatics tool MetExtract II for deciphering xenobiotic metabolites produced by human cells. Its potential was demonstrated by the investigation of the metabolism of deoxynivalenol (DON), an abundant food contaminant, in a liver carcinoma cell line (HepG2) and a model for colon carcinoma (HT29). Detected known metabolites included DON-3-sulfate, DON-10-sulfonate 2, and DON-10-glutathione as well as DON-cysteine. Conjugation with amino acids and an antibiotic was confirmed for the first time. The approach allows the untargeted elucidation of human xenobiotic products in tissue culture. It may be applied to other fields of research including drug metabolism, personalized medicine, exposome research, and systems biology to better understand the relevance of in vitro experiments.

Stable Isotope-Assisted Plant Metabolomics: Combination of Global and Tracer-Based Labeling for Enhanced Untargeted Profiling and Compound Annotation.

Untargeted approaches and thus biological interpretation of metabolomics results are still hampered by the reliable assignment of the global metabolome as well as classification and (putative) identification of metabolites. In this work we present an liquid chromatography-mass spectrometry (LC-MS)-based stable isotope assisted approach that combines global metabolome and tracer based isotope labeling for improved characterization of (unknown) metabolites and their classification into tracer derived submetabolomes. To this end, wheat plants were cultivated in a customized growth chamber, which was kept at 400 ± 50 ppm 13CO2 to produce highly enriched uniformly 13C-labeled sample material. Additionally, native plants were grown in the greenhouse and treated with either 13C9-labeled phenylalanine (Phe) or 13C11-labeled tryptophan (Trp) to study their metabolism and biochemical pathways. After sample preparation, liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis and automated data evaluation, the results of the global metabolome- and tracer-labeling approaches were combined. A total of 1,729 plant metabolites were detected out of which 122 respective 58 metabolites account for the Phe- and Trp-derived submetabolomes. Besides m/z and retention time, also the total number of carbon atoms as well as those of the incorporated tracer moieties were obtained for the detected metabolite ions. With this information at hand characterization of unknown compounds was improved as the additional knowledge from the tracer approaches considerably reduced the number of plausible sum formulas and structures of the detected metabolites. Finally, the number of putative structure formulas was further reduced by isotope-assisted annotation tandem mass spectrometry (MS/MS) derived product ion spectra of the detected metabolites. A major innovation of this paper is the classification of the metabolites into submetabolomes which turned out to be valuable information for effective filtering of database hits based on characteristic structural subparts. This allows the generation of a final list of true plant metabolites, which can be characterized at different levels of specificity.

Stable Isotope-Assisted Plant Metabolomics: Investigation of Phenylalanine-Related Metabolic Response in Wheat Upon Treatment With the Fusarium Virulence Factor Deoxynivalenol.

The major Fusarium mycotoxin deoxynivalenol (DON) is a virulence factor in wheat and has also been shown to induce defense responses in host plant tissue. In this study, global and tracer labeling with 13C were combined to annotate the overall metabolome of wheat spikes and to evaluate the response of phenylalanine-related pathways upon treatment with DON. At anthesis, spikes of resistant and susceptible cultivars as well as two related near isogenic wheat lines (NILs) differing in the presence/absence of the major resistance QTL Fhb1 were treated with 1 mg DON or water (control), and samples were collected at 0, 12, 24, 48, and 96 h after treatment (hat). A total of 172 Phe-derived wheat constituents were detected with our untargeted approach employing 13C-labeled phenylalanine and subsequently annotated as flavonoids, lignans, coumarins, benzoic acid derivatives, hydroxycinnamic acid amides (HCAAs), as well as peptides. Ninety-six hours after the DON treatment, up to 30% of the metabolites biosynthesized from Phe showed significantly increased levels compared to the control samples. Major metabolic changes included the formation of precursors of compounds implicated in cell wall reinforcement and presumed antifungal compounds. In addition, also dipeptides, which presumably are products of proteolytic degradation of truncated proteins generated in the presence of the toxin, were significantly more abundant upon DON treatment. An in-depth comparison of the two NILs with correlation clustering of time course profiles revealed some 70 DON-responsive Phe derivatives. While several flavonoids had constitutively different abundance levels between the two NILs differing in resistance, other Phe-derived metabolites such as HCAAs and hydroxycinnamoyl quinates were affected differently in the two NILs after treatment with DON. Our results suggest a strong activation of the general phenylpropanoid pathway and that coumaroyl-CoA is mainly diverted towards HCAAs in the presence of Fhb1, whereas the metabolic route to monolignol(-conjugates), lignans, and lignin seems to be favored in the absence of the Fhb1 resistance quantitative trait loci.

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.