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Publications

Hoffmann, N.; Rein, J.; Sachsenberg, T.; Hartler, J.; Haug, K.; Mayer, G.; Alka, O.; Dayalan, S.; Pearce, J. T. M.; Rocca-Serra, P.; Qi, D.; Eisenacher, M.; Perez-Riverol, Y.; Vizcaíno, J. A.; Salek, R. M.; Neumann, S.; Jones, A. R. mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics Anal Chem 91, 3302-3310, (2019) DOI: 10.1021/acs.analchem.8b04310

Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.
Publications

Peters, K.; Bradbury, J.; Bergmann, S.; Capuccini, M.; Cascante, M.; de Atauri, P.; Ebbels, T. M. D.; Foguet, C.; Glen, R.; Gonzalez-Beltran, A.; Günther, U. L.; Handakas, E.; Hankemeier, T.; Haug, K.; Herman, S.; Holub, P.; Izzo, M.; Jacob, D.; Johnson, D.; Jourdan, F.; Kale, N.; Karaman, I.; Khalili, B.; Emami Khonsari, P.; Kultima, K.; Lampa, S.; Larsson, A.; Ludwig, C.; Moreno, P.; Neumann, S.; Novella, J. A.; O'Donovan, C.; Pearce, J. T. M.; Peluso, A.; Piras, M. E.; Pireddu, L.; Reed, M. A. C.; Rocca-Serra, P.; Roger, P.; Rosato, A.; Rueedi, R.; Ruttkies, C.; Sadawi, N.; Salek, R. M.; Sansone, S.-A.; Selivanov, V.; Spjuth, O.; Schober, D.; Thévenot, E. A.; Tomasoni, M.; van Rijswijk, M.; van Vliet, M.; Viant, M. R.; Weber, R. J. M.; Zanetti, G.; Steinbeck, C. PhenoMeNal: processing and analysis of metabolomics data in the cloud GigaScience 8, giy149, (2019) DOI: 10.1093/gigascience/giy149

BackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution.FindingsPhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm.ConclusionsPhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and ‘omics research domains.
Printed publications

Emami Khoonsari, P.; Moreno, P.; Bergmann, S.; Burman, J.; Capuccini, M.; Carone, M.; Cascante, M.; de Atauri, P.; Foguet, C.; Gonzalez-Beltran, A.; Hankemeier, T.; Haug, K.; He, S.; Herman, S.; Johnson, D.; Kale, N.; Larsson, A.; Neumann, S.; Peters, K.; Pireddu, L.; Rocca-Serra, P.; Roger, P.; Rueedi, R.; Ruttkies, C.; Sadawi, N.; Salek, R. M.; Sansone, S.-A.; Schober, D.; Selivanov, V.; Thévenot, E. A.; van Vliet, M.; Zanetti, G.; Steinbeck, C.; Kultima, K.; Spjuth, O. Interoperable and scalable data analysis with microservices: Applications in Metabolomics Bioinformatics (2019) DOI: 10.1093/bioinformatics/btz160

MotivationDeveloping a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator.ResultsWe developed a virtual research environment which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics, and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.Availability and ImplementationThe PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the virtual research environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects.
Publications

Matern, A.; Böttcher, C.; Eschen-Lippold, L.; Westermann, B.; Smolka, U.; Döll, S.; Trempel, F.; Aryal, B.; Scheel, D.; Geisler, M.; Rosahl, S. A substrate of the ABC transporter PEN3 stimulates bacterial flagellin (flg22)-induced callose deposition in Arabidopsis thaliana J Biol Chem 294, 6857-6870, (2019) DOI: 10.1074/jbc.RA119.007676

Nonhost resistance of Arabidopsis thaliana against Phytophthora infestans, a filamentous eukaryotic microbe and the causal agent of potato late blight, is based on a multilayered defense system. Arabidopsis thaliana controls pathogen entry through the penetration-resistance genes PEN2 and PEN3, encoding an atypical myrosinase and an ABC transporter, respectively, required for synthesis and export of unknown indole compounds. To identify pathogen-elicited leaf surface metabolites and further unravel nonhost resistance in Arabidopsis, we performed untargeted metabolite profiling by incubating a P. infestans zoospore suspension on leaves of WT or pen3 mutant Arabidopsis plants. Among the plant-secreted metabolites, 4-methoxyindol-3-yl-methanol and S-(4-methoxy-indol-3-yl-methyl) cysteine were detected in spore suspensions recollected from WT plants, but at reduced levels from the pen3 mutant plants. In both whole-cell and microsome-based assays, 4-methoxyindol-3-yl-methanol was transported in a PEN3-dependent manner, suggesting that this compound is a PEN3 substrate. The syntheses of both compounds were dependent on functional PEN2 and phytochelatin synthase 1. None of these compounds inhibited mycelial growth of P. infestans in vitro. Of note, exogenous application of 4-methoxyindol-3-yl methanol slightly elevated cytosolic Ca2+ levels and enhanced callose deposition in hydathodes of seedlings treated with a bacterial pathogen-associated molecular pattern (PAMP), flagellin (flg22). Loss of flg22-induced callose deposition in leaves of pen3 seedlings was partially reverted by the addition of 4-methoxyindol-3-yl methanol. In conclusion, we have identified a specific indole compound that is a substrate for PEN3 and contributes to the plant defense response against microbial pathogens.
Publications

Westphal, L.; Strehmel, N.; Eschen-Lippold, L.; Bauer, N.; Westermann, B.; Rosahl, S.; Scheel, D.; Lee, J. pH effects on plant calcium fluxes: lessons from acidification-mediated calcium elevation induced by the γ-glutamyl-leucine dipeptide identified from Phytophthora infestans Sci Rep 9, 4733, (2019) DOI: 10.1038/s41598-019-41276-0

Cytosolic Ca2+ ([Ca2+]cyt) elevation is an early signaling response upon exposure to pathogen-derived molecules (so-called microbe-associated molecular patterns, MAMPs) and has been successfully used as a quantitative read-out in genetic screens to identify MAMP receptors or their associated components. Here, we isolated and identified by mass spectrometry the dipeptide γ-Glu-Leu as a component of a Phytophthora infestans mycelium extract that induces [Ca2+]cyt elevation. Treatment of Arabidopsis seedlings with synthetic γ-Glu-Leu revealed stimulatory effects on defense signaling, including a weak enhancement of the expression of some MAMP-inducible genes or affecting the refractory period to a second MAMP elicitation. However, γ-Glu-Leu is not a classical MAMP since pH adjustment abolished these activities and importantly, the observed effects of γ-Glu-Leu could be recapitulated by mimicking extracellular acidification. Thus, although γ-Glu-Leu can act as a direct agonist of calcium sensing receptors in animal systems, the Ca2+-mobilizing activity in plants reported here is due to acidification. Low pH also shapes the Ca2+ signature of well-studied MAMPs (e.g. flg22) or excitatory amino acids such as glutamate. Overall, this work serves as a cautionary reminder that in defense signaling studies where Ca2+ flux measurements are concerned, it is important to monitor and consider the effects of pH.
Publications

Dietz, S.; Herz, K.; Döll, S.; Haider, S.; Jandt, U.; Bruelheide, H.; Scheel, D. Semi‐polar root exudates in natural grassland communities Ecol Evol 9, 5526-5541, (2019) DOI: 10.1002/ece3.5043

In the rhizosphere, plants are exposed to a multitude of different biotic and abiotic factors, to which they respond by exuding a wide range of secondary root metabolites. So far, it has been unknown to which degree root exudate composition is species‐specific and is affected by land use, the local impact and local neighborhood under field conditions. In this study, root exudates of 10 common grassland species were analyzed, each five of forbs and grasses, in the German Biodiversity Exploratories using a combined phytometer and untargeted liquid chromatography‐mass spectrometry (LC‐MS) approach. Redundancy analysis and hierarchical clustering revealed a large set of semi‐polar metabolites common to all species in addition to species‐specific metabolites. Chemical richness and exudate composition revealed that forbs, such as Plantago lanceolata and Galium species, exuded more species‐specific metabolites than grasses. Grasses instead were primarily affected by environmental conditions. In both forbs and grasses, plant functional traits had only a minor impact on plant root exudation patterns. Overall, our results demonstrate the feasibility of obtaining and untargeted profiling of semi‐polar metabolites under field condition and allow a deeper view in the exudation of plants in a natural grassland community.
Printed publications

Goslin, K.; Eschen-Lippold, L.; Naumann, C.; Linster, E.; Sorel, M.; Klecker, M.; de Marchi, R.; Kind, A.; Wirtz, M.; Lee, J.; Dissmeyer, N.; Graciet, E. Differential N-end rule degradation of RIN4/NOI fragments generated by the AvrRpt2 effector protease Plant Physiol (2019) DOI: 10.1104/pp.19.00251

In plants, the protein RPM1-INTERACTING PROTEIN4 (RIN4) is a central regulator of both pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). RIN4 is targeted by several effectors, including the Pseudomonas syringae protease effector AvrRpt2. Cleavage of RIN4 by AvrRpt2 generates potentially unstable RIN4 fragments, whose degradation leads to the activation of the resistance protein RESISTANT TO P. SYRINGAE2 (RPS2). Hence, identifying the determinants of RIN4 degradation is key to understanding RPS2-mediated ETI, as well as virulence functions of AvrRpt2. In addition to RIN4, AvrRpt2 cleaves host proteins from the nitrate-induced (NOI) domain family. Although cleavage of NOI domain proteins by AvrRpt2 may contribute to PTI regulation, the (in)stability of these proteolytic fragments and the determinants regulating their stability remain unexamined. Notably, a common feature of RIN4, and of many NOI domain protein fragments generated by AvrRpt2 cleavage, is the exposure of a new N-terminal residue that is destabilizing according to the N-end rule. Using antibodies raised against endogenous RIN4, we show that the destabilization of AvrRpt2-cleaved RIN4 fragments is independent of the N-end rule pathway (recently renamed the N-degron pathway). By contrast, several NOI domain protein fragments are bona fide substrates of the N-degron pathway. The discovery of this set of substrates considerably expands the number of known proteins targeted for degradation by this ubiquitin-dependent pathway in plants. These results advance our current understanding of the role of AvrRpt2 in promoting bacterial virulence.
Publications

Ruttkies, C.; Neumann, S.; Posch, S. Improving MetFrag with statistical learning of fragment annotations BMC Bioinformatics 20, 376, (2019) DOI: 10.1186/s12859-019-2954-7

BackgroundMolecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.ResultsThe results on the 87 MS/MS spectra from positive and negative mode show a substantial improvement of the results compared to submissions made by the former MetFrag approach. Top1 rankings increased from 5 to 21 and Top10 rankings from 39 to 55 both showing higher values than for CSI:IOKR, the winner of the CASMI 2016 contest. For the negative mode spectra, MetFrag’s statistical scoring outperforms all other participants which submitted results for this type of spectra.ConclusionsThis study shows how statistical learning can improve molecular structure identification based on MS/MS data compared on the same method using combinatorial in silico fragmentation only. MetFrag2.4.5 shows especially in negative mode a better performance compared to the other participating approaches.
Publications

Moreno-Pedraza, A.; Gabriel, J.; Treutler, H.; Winkler, R.; Vergara, F. Effects of Water Availability in the Soil on Tropane Alkaloid Production in Cultivated Datura stramonium Metabolites 9, 131, (2019) DOI: 10.3390/metabo9070131

Background: different Solanaceae and Erythroxylaceae species produce tropane alkaloids. These alkaloids are the starting material in the production of different pharmaceuticals. The commercial demand for tropane alkaloids is covered by extracting them from cultivated plants. Datura stramonium is cultivated under greenhouse conditions as a source of tropane alkaloids. Here we investigate the effect of different levels of water availability in the soil on the production of tropane alkaloids by D. stramonium. Methods: We tested four irrigation levels on the accumulation of tropane alkaloids. We analyzed the profile of tropane alkaloids using an untargeted liquid chromatography/mass spectrometry method. Results: Using a combination of informatics and manual interpretation of mass spectra, we generated several structure hypotheses for signals in D. stramonium extracts that we assign as putative tropane alkaloids. Quantitation of mass spectrometry signals for our structure hypotheses across different anatomical organs allowed us to identify patterns of tropane alkaloids associated with different levels of irrigation. Furthermore, we identified anatomic partitioning of tropane alkaloid isomers with pharmaceutical applications. Conclusions: Our results show that soil water availability is an effective method for maximizing the production of specific tropane alkaloids for industrial applications.
Books and chapters

Doell, S.; Arens, N.; Mock, H. Liquid Chromatography and Liquid Chromatography–Mass Spectrometry of Plants: Techniques and Applications (Meyers, R. A., ed.). (2019) ISBN: 9780470027318 DOI: 10.1002/9780470027318.a9912.pub2

Mass spectrometry coupled with LC (liquid chromatography) separation has developed into a technique routinely applied for targeted as well as for nontargeted analysis of complex biological samples, not only in plant biochemistry. Earlier on, LC‐MS (liquid chromatography–mass spectrometry) was mostly part of the efforts for identification of one or few unknown metabolites of interest as part of a phytochemical study. As a major strategy, unknown compounds had to be purified in sufficient quantities. The purified fractions were then subjected to LC‐MS/MS as part of the structural elucidation, mostly complemented by NMR (nuclear magnetic resonance) analysis. With the advance of mass spectrometry instrumentation, LC‐MS is now widely applied for analysis of crude plant extracts and large numbers (100s to 1000s) of samples. It has become an essential part of metabolomic studies (see Metabolomics), aiming at the comprehensive coverage of the metabolite profiles of cells, tissues, or organs. Owing to the huge chemical diversity of small molecules, conditions for the extraction will restrict the subfraction of the metabolome, which can be actually analyzed. The conditions for LC have to be adjusted to allow good separation of the particular metabolites from the respective extract. Major consideration will be the selection of an appropriate column and suitable eluents, the establishment of gradient profiles, temperature conditions, and so on.
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