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.

YPR2 is a regulator of light modulated carbon and secondary metabolism in Trichoderma reesei.

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.

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 .

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.

Deoxynivalenol-sulfates: identification and quantification of novel conjugated (masked) mycotoxins in wheat.

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.

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.

Establishment and application of a metabolomics workflow for identification and profiling of volatiles from leaves of Vitis vinifera by HS-SPME-GC-MS.

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 < < 40% (RSD), biological variability among plants was 7-119%. Principal component analysis (PCA) scores plot of leaf samples from two different sampling dates showed two clearly separated clusters. The presented workflow enabled for the first time the detection and identification of 19 metabolites that have so far not been described for Vitis spp. CONCLUSION: The developed workflow enabled the identification of grapevine leaf metabolites, which allowed the separation of leaves from two sampling dates by PCA.

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.

Butyrate influences intracellular levels of adenine and adenine derivatives in the fungus Penicillium restrictum.

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.

Glutathione-Conjugates of Deoxynivalenol in Naturally Contaminated Grain Are Primarily Linked via the Epoxide Group.

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.

Valproic Acid Induces Antimicrobial Compound Production in Doratomyces microspores.

One of the biggest challenges in public health is the rising number of antibiotic resistant pathogens and the lack of novel antibiotics. In recent years there is a rising focus on fungi as sources of antimicrobial compounds due to their ability to produce a large variety of bioactive compounds and the observation that virtually every fungus may still contain yet unknown so called "cryptic," often silenced, compounds. These putative metabolites could include novel bioactive compounds. Considerable effort is spent on methods to induce production of these "cryptic" metabolites. One approach is the use of small molecule effectors, potentially influencing chromatin landscape in fungi. We observed that the supernatant of the fungus Doratomyces (D.) microsporus treated with valproic acid (VPA) displayed antimicrobial activity against Staphylococcus (S.) aureus and two methicillin resistant clinical S. aureus isolates. VPA treatment resulted in enhanced production of seven antimicrobial compounds: cyclo-(L-proline-L-methionine) (cPM), p-hydroxybenzaldehyde, cyclo-(phenylalanine-proline) (cFP), indole-3-carboxylic acid, phenylacetic acid (PAA) and indole-3-acetic acid. The production of the antimicrobial compound phenyllactic acid was exclusively detectable after VPA treatment. Furthermore three compounds, cPM, cFP, and PAA, were able to boost the antimicrobial activity of other antimicrobial compounds. cPM, for the first time isolated from fungi, and to a lesser extent PAA, are even able to decrease the minimal inhibitory concentration of ampicillin in MRSA strains. In conclusion we could show in this study that VPA treatment is a potent tool for induction of "cryptic" antimicrobial compound production in fungi, and that the induced compounds are not exclusively linked to the secondary metabolism. Furthermore this is the first discovery of the rare diketopiperazine cPM in fungi. Additionally we could demonstrate that cPM and PAA boost antibiotic activity against antibiotic resistant strains, suggesting a possible application in combinatorial antibiotic treatment against resistant pathogens.

GC-MS based targeted metabolic profiling identifies changes in the wheat metabolome following deoxynivalenol treatment.

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.

Biotransformation of the mycotoxin deoxynivalenol in fusarium resistant and susceptible near isogenic wheat lines.

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.

A novel stable isotope labelling assisted workflow for improved untargeted LC-HRMS based metabolomics research.

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.

Evaluation of settled floor dust for the presence of microbial metabolites and volatile anthropogenic chemicals in indoor environments by LC-MS/MS and GC-MS methods.

This study reports on detection of a large number of biological and anthropogenic pollutants using LC-MS/MS and GC-MS technologies in settled floor dust (SFD). The latter technique was applied to obtain a general picture on the presence of microbial as well as non-microbial volatile organic compounds, whereas the targeted LC-MS/MS analysis focused on identification of species specific secondary metabolites. In the absence of moisture monitoring data the relevance of finding of stachybotrylactam and other metabolites of tertiary colonizers are confined only to accidental direct exposure to SFD. To the best of our knowledge 30 of the 71 identified volatile organic compounds (VOCs) are newly reported in SFD matrix. Coordinated application of "AMDIS and Spectconnect" was found beneficial for the evaluation and identification of prime volatile pollutants in complex environmental samples. Principal component analysis (PCA) of peak areas of 18 microbial volatile organic compounds (MVOCs) resulted in identification of nonanal as potential MVOC marker. Two more volatiles toluene and 1-tetradecanol though had discriminative influence, are not regarded as MVOC markers, considering their probable alternate origin from paints and cosmetics, respectively.