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BACKGROUND AND AIMS Diabetic kidney disease (DKD) remains the most common cause of the end-stage renal disease (ESRD) worldwide. Kidney fibrosis predicts functional decline and ESRD in many, but not all patients [1]. Autotaxin (ATX) is the enzyme converting lysophosphatidylcholine to Lysophosphatidic acid (LPA), which is a key factor in the development of fibrosis and inflammation. Preclinical studies have shown that inhibition of ATX may be beneficial in DKD [2, 3]. In this study, we assessed the relationship between systemic/circulating ATX, LPA and fibrosis markers of collagen type III degradation (C3M), type VI formation (PRO-C6) and connective tissue growth factor (CTGF) with kidney disease stage in a retrospective DKD cohort. METHOD We measured ATX, LPA 18:2, LPA 20:4, pPRO-C6 and pCTGF in plasma, and uPRO-C6, uC3M in urine in 203 patients (including DKD and control) at recruitment and at a follow-up (FU) visit (n = 108). After adjusting for missingness, skewness, highly correlated confounders and excluding patients on dialysis, the cohort resulted in n = 155 patients at baseline, n = 53 at FU and n = 144 for the time to adverse outcome. To assess the relationship between the markers of interest with CKD stage and estimated glomerular filtration rate (eGFR), Bayes ordinal logistic regression and linear regression were applied, respectively. A random forest (RF) algorithm was used also to predict both outcomes and identify the most relevant predictors. Cox regression analysis was used to assess the relationship between the markers at baseline and time to adverse outcomes, defined as a decline in eGFR >30% or death. A sensitivity analysis including relevant confounding covariates was applied to all regression models. Benjamini & Hochberg adjustment was used to account for multiplicity. RESULTS pPRO-C6 and uC3M levels showed consistently significant associations with CKD stage (Figure 1a-b) also after adjusting for confounding variables both at baseline and FU. Higher CKD stages or lower eGFR values were associated with higher pPRO-C6 and lower uC3M levels. RF (10-fold cross-validated mean AUC = 0.75) also selected these markers as the most relevant predictors for CKD stage (Figure 1c). Similar results were obtained when using eGFR. During a median follow-up time of 4.00 years, 14.58% of the patients had an adverse outcome. Both baseline pPRO-C6 [hazard ratio (HR) 2.68 (95% confidence interval, CI 1.47–4.88, P -adjusted < 0.05 per 2-fold ng/mL increase] (Figure 2) and uPRO-C6 levels [HR 1.43 (1.08–1.89, P -adjusted< 0.05) per 2-fold ng/mmol increase] were significantly associated with development of adverse outcomes, whereas only a trend was found for uC3M level [HR 0.50 (0.27–0.91, P -adjusted< 0.06) per 2-fold µg/mmol increase]. After adjusting for confounders, none of the markers showed a significant association with time to adverse outcome. CONCLUSION pPRO-C6 and uC3M showed consistent associations with CKD stage and pPRO-C6, uPRO-C6 and uC3M (trend) also an association with the risk of adverse outcomes, which is in alignment with previous studies [4]. In contrast, no associations were observed for circulating ATX or LPAs. However, results should be interpreted carefully due to the relatively low number of patients in this cohort and missing histology to verify fibrosis or local ATX/LPA action.

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 .

Butyrate, a small fatty acid, has an important role in the colon of ruminants and mammalians including the inhibition of inflammation and the regulation of cell proliferation. There is also growing evidence that butyrate is influencing the histone structure in mammalian cells by inhibition of histone deacetylation. Butyrate shows furthermore an antimicrobial activity against fungi, yeast and bacteria, which is linked to its toxicity at a high concentration. In fungi there are indications that butyrate induces the production of secondary metabolites potentially via inhibition of histone deacetylases. However, information about the influence of butyrate on growth, primary metabolite production and metabolism, besides lipid catabolism, in fungi is scarce. We have identified the filamentous fungus Penicillium (P.) restrictum as a susceptible target for butyrate treatment in an antimicrobial activity screen. The antimicrobial activity was detected only in the mycelium of the butyrate treated culture. We investigated the effect of butyrate ranging from low (0.001mM) to high (30mM), potentially toxic, concentrations on biomass and antimicrobial activity. Butyrate at high concentrations (3 and 30mM) significantly reduced the fungal biomass. In contrast P. restrictum treated with 0.03mM of butyrate showed the highest antimicrobial activity. We isolated three antimicrobial active compounds, active against Staphylococcus aureus, from P. restrictum cellular extracts treated with butyrate: adenine, its derivate hypoxanthine and the nucleoside derivate adenosine. Production of all three compounds was increased at low butyrate concentrations. Furthermore we found that butyrate influences the intracellular level of the adenine nucleoside derivate cAMP, an important signalling molecule in fungi and various organisms. In conclusion butyrate treatment increases the intracellular levels of adenine and its respective derivatives.

Background Filamentous fungi have evolved to succeed in nature by efficient growth and degradation of substrates, but also due to the production of secondary metabolites including mycotoxins. For Trichoderma reesei, as a biotechnological workhorse for homologous and heterologous protein production, secondary metabolite secretion is of particular importance for industrial application. Recent studies revealed an interconnected regulation of enzyme gene expression and carbon metabolism with secondary metabolism. Results Here, we investigated gene regulation by YPR2, one out of two transcription factors located within the SOR cluster of T. reesei, which is involved in biosynthesis of sorbicillinoids. Transcriptome analysis showed that YPR2 exerts its major function in constant darkness upon growth on cellulose. Targets (direct and indirect) of YPR2 overlap with induction specific genes as well as with targets of the carbon catabolite repressor CRE1 and a considerable proportion is regulated by photoreceptors as well. Functional category analysis revealed both effects on carbon metabolism and secondary metabolism. Further, we found indications for an involvement of YPR2 in regulation of siderophores. In agreement with transcriptome data, mass spectrometric analyses revealed a broad alteration in metabolite patterns in ∆ypr2. Additionally, YPR2 positively influenced alamethicin levels along with transcript levels of the alamethicin synthase tex1 and is essential for production of orsellinic acid in darkness. Conclusions YPR2 is an important regulator balancing secondary metabolism with carbon metabolism in darkness and depending on the carbon source. The function of YPR2 reaches beyond the SOR cluster in which ypr2 is located and happens downstream of carbon catabolite repression mediated by CRE1. Electronic supplementary material The online version of this article (10.1186/s12864-019-5574-8) contains supplementary material, which is available to authorized users.

We report the identification of deoxynivalenol-3-sulfate and deoxynivalenol-15-sulfate as two novel metabolites of the trichothecene mycotoxin deoxynivalenol in wheat. Wheat ears which were either artificially infected with Fusarium graminearum or directly treated with the major Fusarium toxin deoxynivalenol (DON) were sampled 96 h after treatment. Reference standards, which have been chemically synthesized and confirmed by NMR, were used to establish a liquid chromatography-electrospray ionization (LC-ESI)-MS/MS-based “dilute and shoot” method for the detection, unambiguous identification, and quantification of both sulfate conjugates in wheat extracts. Using this approach, detection limits of 0.003 mg/kg for deoxynivalenol-3-sulfate and 0.002 mg/kg for deoxynivalenol-15-sulfate were achieved. Matrix-matched calibration was used for the quantification of DON-sulfates in the investigated samples. In DON-treated samples, DON-3-sulfate was detected in the range of 0.29–1.4 mg/kg fresh weight while DON-15-sulfate concentrations were significantly lower (range 0.015–0.061 mg/kg fresh weight). In Fusarium-infected wheat samples, DON-3-sulfate was the only detected sulfate conjugate (range 0.022–0.059 mg/kg fresh weight). These results clearly demonstrate the potential of wheat to form sulfate conjugates of DON. In order to test whether sulfation is a detoxification reaction in planta, we determined the ability of the sulfated DON derivatives to inhibit in vitro protein synthesis of wheat ribosomes. The results demonstrate that both DON-sulfates can be regarded as detoxification products. DON-15-sulfate was about 44× less inhibitory than the native toxin, and no toxicity was observed for DON-3-sulfate in the tested range. Graphical abstract ᅟ Electronic supplementary material The online version of this article (doi:10.1007/s00216-014-8340-4) contains supplementary material, which is available to authorized users.

Fusariumgraminearum and related species commonly infest grains causing the devastating plant disease Fusarium head blight (FHB) and the formation of trichothecene mycotoxins. The most relevant toxin is deoxynivalenol (DON), which acts as a virulence factor of the pathogen. FHB is difficult to control and resistance to this disease is a polygenic trait, mainly mediated by the quantitative trait loci (QTL) Fhb1 and Qfhs.ifa-5A. In this study we established a targeted GC–MS based metabolomics workflow comprising a standardized experimental setup for growth, treatment and sampling of wheat ears and subsequent GC–MS analysis followed by data processing and evaluation of QC measures using tailored statistical and bioinformatics tools. This workflow was applied to wheat samples of six genotypes with varying levels of Fusarium resistance, treated with either DON or water, and harvested 0, 12, 24, 48 and 96 h after treatment. The results suggest that the primary carbohydrate metabolism and transport, the citric acid cycle and the primary nitrogen metabolism of wheat are clearly affected by DON treatment. Most importantly significantly elevated levels of amino acids and derived amines were observed. In particular, the concentrations of the three aromatic amino acids phenylalanine, tyrosine, and tryptophan increased. No clear QTL specific difference in the response could be observed except a generally faster increase in shikimate pathway intermediates in genotypes containing Fhb1. The overall workflow proved to be feasible and facilitated to obtain a more comprehensive picture on the effect of DON on the central metabolism of wheat. Electronic supplementary material The online version of this article (doi:10.1007/s11306-014-0731-1) contains supplementary material, which is available to authorized users.

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.

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.

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.

Metabolomics has emerged as the latest of the so-called “omics” disciplines and has great potential to provide deeper understanding of fundamental biochemical processes at the biological system level. Among recent technological developments, LC–HRMS enables determination of hundreds to thousands of metabolites over a wide range of concentrations and has developed into one of the most powerful techniques in non-targeted metabolomics. The analysis of mixtures of in-vivo-stable isotopic-labeled samples or reference substances with un-labeled samples leads to specific LC–MS data patterns which can be systematically exploited in practically all data-processing steps. This includes recognition of true metabolite-derived analytical features in highly complex LC–MS data and characterization of the global biochemical composition of biological samples. In addition, stable-isotopic labeling can be used for more accurate quantification (via internal standardization) and identification of compounds in different organisms.

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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0783-x) contains supplementary material, which is available to authorized users.

Introduction Volatile organic compounds (VOCs) occurring in leaves of plants carry information about the physiological state of the plant. Monitoring of VOCs assists in detecting plant stress before visible signs are present. Objective To establish and apply a simple workflow for the automated extraction, measurement and annotation/identification of Vitis vinifera cv. Pinot Noir leaf metabolites. Methodology Leaf samples were harvested, cooled with liquid nitrogen and homogenised under cooled conditions. VOCs were extracted and enriched by solid phase microextraction (SPME) and analysed by GC-MS. Samples were measured on two columns with different polarity of stationary phases. Mass spectral deconvolution and identification was done by AMDIS software. Strict identification criteria were applied: match factor ≥ 90; relative retention index deviation ≤ 2% from reference value on both columns. Data of two sampling dates were analysed with multivariate statistics. Results We found ~600 components in a single chromatogram. Applying the mentioned criteria resulted in annotation of 63 metabolites of which 47 were confirmed with authentic standards. For the majority of the compounds technical variability was

In this study, a total of nine different biotransformation products of the Fusarium mycotoxin deoxynivalenol (DON) formed in wheat during detoxification of the toxin are characterized by liquid chromatography—high resolution mass spectrometry (LC-HRMS). The detected metabolites suggest that DON is conjugated to endogenous metabolites via two major metabolism routes, namely 1) glucosylation (DON-3-glucoside, DON-di-hexoside, 15-acetyl-DON-3-glucoside, DON-malonylglucoside) and 2) glutathione conjugation (DON-S-glutathione, “DON-2H”-S-glutathione, DON-S-cysteinyl-glycine and DON-S-cysteine). Furthermore, conjugation of DON to a putative sugar alcohol (hexitol) was found. A molar mass balance for the cultivar ‘Remus’ treated with 1 mg DON revealed that under the test conditions approximately 15% of the added DON were transformed into DON-3-glucoside and another 19% were transformed to the remaining eight biotransformation products or irreversibly bound to the plant matrix. Additionally, metabolite abundance was monitored as a function of time for each DON derivative and was established for six DON treated wheat lines (1 mg/ear) differing in resistance quantitative trait loci (QTL) Fhb1 and/or Qfhs.ifa-5A. All cultivars carrying QTL Fhb1 showed similar metabolism kinetics: Formation of DON-Glc was faster, while DON-GSH production was less efficient compared to cultivars which lacked the resistance QTL Fhb1. Moreover, all wheat lines harboring Fhb1 showed significantly elevated D3G/DON abundance ratios.

Driven by significant technical developments in analytical instrumentation and the tremendous advances in biological sciences, a change in paradigm from reductionist to holistic approaches for the study of filamentous fungi can be observed currently. This development is also reflected by the emergence of metabolomics as the latest of the so-called -omics disciplines. Metabolomics , the scientific discipline dealing with the determination of the low-molecular-weight complement of biological systems is increasingly being used to investigate the biochemical composition of fungi and their biological interactions. This chapter introduces the general concept of metabolomics and summarizes the analytical approaches used for the study of fungal exo- and endo-metabolomes. Current applications in fungal metabolomics and metabolite profiling such as chemotaxonomical classification, the search and production of novel beneficial secondary metabolites as well the dissection of host–fungus interactions are presented. Finally, novel emerging approaches for the improved fungal metabolomics, such as the use of stable isotope labeled biological samples and tracer metabolites and novel techniques, that enable spatial and temporal dissection of metabolite production, are briefly summarized.

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.

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 12C- and uniformly 13C-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. Contact: rainer.schuhmacher@boku.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.

A glutathione (GSH) adduct of the mycotoxin 4-deoxynivalenol (DON), together with a range of related conjugates, has recently been tentatively identified by LC-MS of DON-treated wheat spikelets. In this study, we prepared samples of DON conjugated at the 10- and 13-positions with GSH, Cys, CysGly, γ-GluCys and N-acetylcysteine (NAC). The mixtures of conjugates were used as standards for LC-HRMS analysis of one of the DON-treated wheat spikelet samples, as well as 19 Norwegian grain samples of spring wheat and 16 grain samples of oats that were naturally-contaminated with DON at concentrations higher than 1 mg/kg. The artificially-contaminated wheat spikelets contained conjugates of GSH, CysGly and Cys coupled at the olefinic 10-position of DON, whereas the naturally-contaminated harvest-ripe grain samples contained GSH, CysGly, Cys, and NAC coupled mainly at the 13-position on the epoxy group. The identities of the conjugates were confirmed by LC-HRMS comparison with authentic standards, oxidation to the sulfoxides with hydrogen peroxide, and examination of product-ion spectra from LC-HRMS/MS analysis. No γ-GluCys adducts of DON were detected in any of the samples. The presence of 15-O-acetyl-DON was demonstrated for the first time in Norwegian grain. The results indicate that a small but significant proportion of DON is metabolized via the GSH-conjugation pathway in plants. To our knowledge, this is the first report of in vivo conjugation of trichothecenes via their epoxy group, which has generally been viewed as unreactive. Because conjugation at the 13-position of DON and other trichothecenes has been shown to be irreversible, this type of conjugate may prove useful as a biomarker of exposure to DON and other 12,13-epoxytrichothecenes. View Full-Text

A method based on solid phase microextraction coupled to gas chromatography–mass spectrometry (GC–MS) for the detection and identification of microbial volatile organic compounds (MVOCs) in the headspace of filamentous fungi is presented. MVOCs are identified by comparison of mass spectra and linear temperature programmed retention indices (LTPRIs) with database entries and LTPRIs published in literature. The presented method enables the monitoring of the formation of volatile metabolites for defined time intervals during cultivation of the investigated fungus. The experimental procedure is exemplified with Fusarium graminearum and Trichoderma atroviride but can also be used to detect, identify and profile MVOCs produced by other filamentous fungi.

In the present study we describe a method, which is based on solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS) and which can be used for the profiling of microbial volatile organic compounds (MVOCs) in the headspace (HS) of cultures of filamentous fungi. The method comprises the following successive steps: 1. growth of the fungus on a solid culture medium directly in headspace vials, 2. measurement of volatiles by HS-SPME-GC-MS, 3. deconvolution of mass spectra, 4. identification of volatiles by comparison of measured, deconvoluted mass spectra and linear temperature programmed retention indices (LTPRI) on two stationary GC phases with database entries and LTPRI published in the literature, and 5. profiling of the identified MVOCs. The developed method was successfully applied to cultures of the biocontrol fungus Trichoderma atroviride. An in-house library consisting of mass spectra and LTPRI values of fungal VOCs was established and used to study the profiles of MVOCs of this fungus. In total, 25 different MVOCs were identified by applying strict criteria (spectral match factor at least 90% and a maximum relative deviation of LTPRI of +/-2% from literature values). The MVOCs were assigned to the compound classes of alcohols, ketones, alkanes, furanes, pyrones (mainly the bioactive 6-pentyl-alpha-pyrone), mono- and sesquiterpenes, 13 of which have never been reported to be produced by Trichoderma spp. before. Eleven of these volatiles have been additionally confirmed using authentic standards. Finally, time course experiments and cultivation of T. atroviride in the presence of the mycotoxin fusaric acid demonstrated the potential of the method to study the dynamics of MVOC profiles as well as the effect of different environmental/biological conditions on the expression of MVOCs of filamentous fungi.

Many untargeted LC–ESI–HRMS based metabolomics studies are still hampered by the large proportion of non-biological sample derived signals included in the generated raw data. Here, a novel, powerful stable isotope labelling (SIL)-based metabolomics workflow is presented, which facilitates global metabolome extraction, improved metabolite annotation and metabolome wide internal standardisation (IS). The general concept is exemplified with two different cultivation variants, (1) co-cultivation of the plant pathogenic fungi Fusarium graminearum on non-labelled and highly 13C enriched culture medium and (2) experimental cultivation under native conditions and use of globally U-13C labelled biological reference samples as exemplified with maize and wheat. Subsequent to LC–HRMS analysis of mixtures of labelled and non-labelled samples, two-dimensional data filtering of SIL specific isotopic patterns is performed to better extract truly biological derived signals together with the corresponding number of carbon atoms of each metabolite ion. Finally, feature pairs are convoluted to feature groups each representing a single metabolite. Moreover, the correction of unequal matrix effects in different sample types and the improvement of relative metabolite quantification with metabolome wide IS are demonstrated for the F. graminearum experiment. Data processing employing the presented workflow revealed about 300 SIL derived feature pairs corresponding to 87–135 metabolites in F. graminearum samples and around 800 feature pairs corresponding to roughly 350 metabolites in wheat samples. SIL assisted IS, by the use of globally U-13C labelled biological samples, reduced the median CV value from 7.1 to 3.6 % for technical replicates and from 15.1 to 10.8 % for biological replicates in the respective F. graminearum samples.