Bernhard Kluger

Isotopic labeling-assisted metabolomics using LC–MS

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.

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 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.

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.

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 13C9-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.

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

Significance Fungi produce a vast number of different chemical compounds via secondary metabolism. These compounds are of great interest because of their potential applicability in medicine, pharmacy, and biotechnology. In addition, a number of such compounds are toxins that potentially represent severe threats to human and animal health. However, under standard cultivation conditions, fungal secondary metabolism remains largely inactive. Here, we show that the deletion of the regulator Xylanase promoter binding protein 1 (Xpp1) results in the production of significantly more secondary metabolites in terms of both number and concentration. Because homologs of Xpp1 exist in fungi with numerous bioactive secondary metabolites, our results can lead to the discovery of secondary metabolites. 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.

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 12C- and uniformly 13C (U-13C)-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-13C-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.

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

BackgroundMetabolomics 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.ResultsThe 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.ConclusionsQCScreen 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.

Detection and Identification of Fungal Microbial Volatile Organic Compounds by HS-SPME-GC–MS

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.

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.

Metabolomics and Secondary Metabolite Profiling of Filamentous Fungi

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.