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Publications - Stress and Develop Biology

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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.
Books and chapters

Neumann, S.; Yanes, O.; Mumm, R.; Franceschi, P. Mass Spectrometry Data Processing (Wehrens, R. & Salek, R., eds.). (2019) ISBN: 9781498725262 DOI: 10.1201/9781315370583-4

The chapter “Mass Spectrometry Data Processing” focuses on the mass spectrometry data processing workflow. The first step consists of processing the raw MS data using conversion of vendor formats to open standards, followed by feature detection, optionally retention time correction and grouping of features across samples leading to a feature matrix amenable for statistical analysis. The metabolomics community has developed several open source software packages capable of processing large-scale data commonly occurring in metabolomics studies. In the second stage, features of interest are identified, i.e., annotated with names of metabolites, or compound classes. Tandem MS or LC-MS/MS fragmentation data provides structural hints. The MS/MS spectra can be used to search in open and commercial spectral libraries. If no reference spectra are available, in-silico annotation tools or more recently machine learning approaches can be used.
Publications

Zembek, P.; Danilecka, A.; Hoser, R.; Eschen-Lippold, L.; Benicka, M.; Grech-Baran, M.; Rymaszewski, W.; Barymow-Filoniuk, I.; Morgiewicz, K.; Kwiatkowski, J.; Piechocki, M.; Poznanski, J.; Lee, J.; Hennig, J.; Krzymowska, M. Two Strategies of Pseudomonas syringae to Avoid Recognition of the HopQ1 Effector in Nicotiana Species Front Plant Sci 9, 978, (2018) DOI: 10.3389/fpls.2018.00978

Pseudomonas syringae employs a battery of type three secretion effectors to subvert plant immune responses. In turn, plants have developed receptors that recognize some of the bacterial effectors. Two strain-specific HopQ1 effector variants (for Hrp outer protein Q) from the pathovars phaseolicola 1448A (Pph) and tomato DC3000 (Pto) showed considerable differences in their ability to evoke disease symptoms in Nicotiana benthamiana. Surprisingly, the variants differ by only six amino acids located mostly in the N-terminal disordered region of HopQ1. We found that the presence of serine 87 and leucine 91 renders PtoHopQ1 susceptible to N-terminal processing by plant proteases. Substitutions at these two positions did not strongly affect PtoHopQ1 virulence properties in a susceptible host but they reduced bacterial growth and accelerated onset of cell death in a resistant host, suggesting that N-terminal mutations rendered PtoHopQ1 susceptible to processing in planta and, thus, represent a mechanism of recognition avoidance. Furthermore, we found that co-expression of HopR1, another effector encoded within the same gene cluster masks HopQ1 recognition in a strain-dependent manner. Together, these data suggest that HopQ1 is under high host-pathogen co-evolutionary selection pressure and P. syringae may have evolved differential effector processing or masking as two independent strategies to evade HopQ1 recognition, thus revealing another level of complexity in plant – microbe interactions.
Publications

Strehmel, N.; Hoehenwarter, W.; Mönchgesang, S.; Majovsky, P.; Krüger, S.; Scheel, D.; Lee, J. Stress-reated mitogen-activated protein kinases stimulate the accumulation of small molecules and proteins in Arabidopsis thaliana root exudates. Front Plant Sci 8 , 1292, (2017) DOI: 10.3389/fpls.2017.01292

A delicate balance in cellular signaling is required for plants to respond to microorganisms or to changes in their environment. Mitogen-activated protein kinase (MAPK) cascades are one of the signaling modules that mediate transduction of extracellular microbial signals into appropriate cellular responses. Here, we employ a transgenic system that simulates activation of two pathogen/stress-responsive MAPKs to study release of metabolites and proteins into root exudates. The premise is based on our previous proteomics study that suggests upregulation of secretory processes in this transgenic system. An advantage of this experimental set-up is the direct focus on MAPK-regulated processes without the confounding complications of other signaling pathways activated by exposure to microbes or microbial molecules. Using non-targeted metabolomics and proteomics studies, we show that MAPK activation can indeed drive the appearance of dipeptides, defense-related metabolites and proteins in root apoplastic fluid. However, the relative levels of other compounds in the exudates were decreased. This points to a bidirectional control of metabolite and protein release into the apoplast. The putative roles for some of the identified apoplastic metabolites and proteins are discussed with respect to possible antimicrobial/defense or allelopathic properties. Overall, our findings demonstrate that sustained activation of MAPKs alters the composition of apoplastic root metabolites and proteins, presumably to influence the plant-microbe interactions in the rhizosphere. The reported metabolomics and proteomics data are available via Metabolights (Identifier: MTBLS441) and ProteomeXchange (Identifier: PXD006328), respectively.
Publications

Hettwer, K.; Böttcher, C.; Frolov, A.; Mittasch, J.; Albert, A.; von Roepenack-Lahayeb, E.; Strack, D.; Milkowski, C. Dynamic metabolic changes in seeds and seedlings of Brassica napus (oilseed rape) suppressing UGT84A9 reveal plasticity and molecular regulation of the phenylpropanoid pathway 124, 46–57, (2016) DOI: 10.1016/j.phytochem.2016.01.014

In Brassica napus, suppression of the key biosynthetic enzyme UDP-glucose:sinapic acid glucosyltransferase (UGT84A9) inhibits the biosynthesis of sinapine (sinapoylcholine), the major phenolic component of seeds. Based on the accumulation kinetics of a total of 158 compounds (110 secondary and 48 primary metabolites), we investigated how suppression of the major sink pathway of sinapic acid impacts the metabolome of developing seeds and seedlings. In UGT84A9-suppressing (UGT84A9i) lines massive alterations became evident in late stages of seed development affecting the accumulation levels of 58 secondary and 7 primary metabolites. UGT84A9i seeds were characterized by decreased amounts of various hydroxycinnamic acid (HCA) esters, and increased formation of sinapic and syringic acid glycosides. This indicates glycosylation and β-oxidation as metabolic detoxification strategies to bypass intracellular accumulation of sinapic acid. In addition, a net loss of sinapic acid upon UGT84A9 suppression may point to a feedback regulation of HCA biosynthesis. Surprisingly, suppression of UGT84A9 under control of the seed-specific NAPINC promoter was maintained in cotyledons during the first two weeks of seedling development and associated with a reduced and delayed transformation of sinapine into sinapoylmalate. The lack of sinapoylmalate did not interfere with plant fitness under UV-B stress. Increased UV-B radiation triggered the accumulation of quercetin conjugates whereas the sinapoylmalate level was not affected.
Publications

Lee, J.; Eschen-Lippold, L.; Lassowskat, I.; Böttcher, C.; Scheel, D. Cellular reprogramming through mitogen-activated protein kinases Front Plant Sci 6, 940, (2015) DOI: 10.3389/fpls.2015.00940

Mitogen-activated protein kinase (MAPK) cascades are conserved eukaryote signaling modules where MAPKs, as the final kinases in the cascade, phosphorylate protein substrates to regulate cellular processes. While some progress in the identification of MAPK substrates has been made in plants, the knowledge on the spectrum of substrates and their mechanistic action is still fragmentary. In this focused review, we discuss the biological implications of the data in our original paper (Sustained mitogen-activated protein kinase activation reprograms defense metabolism and phosphoprotein profile in Arabidopsis thaliana; Frontiers in Plant Science 5: 554) in the context of related research. In our work, we mimicked in vivo activation of two stress-activated MAPKs, MPK3 and MPK6, through transgenic manipulation of Arabidopsis thaliana and used phosphoproteomics analysis to identify potential novel MAPK substrates. Here, we plotted the identified putative MAPK substrates (and downstream phosphoproteins) as a global protein clustering network. Based on a highly stringent selection confidence level, the core networks highlighted a MAPK-induced cellular reprogramming at multiple levels of gene and protein expression—including transcriptional, post-transcriptional, translational, post-translational (such as protein modification, folding, and degradation) steps, and also protein re-compartmentalization. Additionally, the increase in putative substrates/phosphoproteins of energy metabolism and various secondary metabolite biosynthesis pathways coincides with the observed accumulation of defense antimicrobial substances as detected by metabolome analysis. Furthermore, detection of protein networks in phospholipid or redox elements suggests activation of downstream signaling events. Taken in context with other studies, MAPKs are key regulators that reprogram cellular events to orchestrate defense signaling in eukaryotes.
Books and chapters

Hummel, J.; Strehmel, N.; Bölling, C.; Schmidt, S.; Walther D.; Kopka, J. Mass spectral search and analysis using the Golm metabolome. (Weckwerth, W.; Kahl, G.). 321-343, (2013) ISBN: 978-3-527-32777-5 DOI: 10.1002/9783527669882.ch18

The novel “omics” technologies of the postgenomic era generate large multiplexed phenotyping datasets, which can only inadequately be published in the traditional journal and supplemental formats. For this reason, public databases have been developed that utilize the efficient communication of knowledge through the World Wide Web. This trend also applies to the metabolomics field, which is, after genomics, transcriptomics, and proteomics, the fourth major systems-level phenotyping platform. Each different analytical technology used in metabolomics studies requires specific reference data for metabolite identification and optimal data formats for reporting the complex metabolite profiling data features. Therefore, we envision that every technology platform or even each high-throughput metabolomic laboratory will establish dedicated databases, which will communicate between each other and will be integrated by meta-databases and web services. The Golm Metabolome Database (GMD) (http://gmd.mpimp-golm.mpg.de/) is a metabolomic database, maintained by the Max Planck Institute of Molecular Plant Physiology, that was initiated around a nucleus of reference data from gas chromatography–mass spectrometry metabolite profiling data and is now developing toward a general mass spectrometry-based repository of reference metabolite profiles for essential plant tissues and typical variations of growth conditions. This chapter describes the mass spectral searches and analyses currently supported by the GMD. We specifically address the searches for the different chemical entities within GMD, namely the metabolites, reference substances, and the chemically derivatized analytes. We report the diverse options for mass spectral analyses and highlight the decision tree-supported prediction of chemical substructures, a feature of GMD that currently appears to be a unique among the many tools for the analysis of gas chromatography–electron ionization mass spectra.
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