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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

Schüler, J.-A.; Neumann, S.; Müller-Hannemann, M.; Brandt, W. ChemFrag: Chemically meaningful annotation of fragment ion mass spectra J Mass Spectrom 53, 1104-1115, (2018) DOI: 10.1002/jms.4278

Identification and structural determination of small molecules by mass spectrometry is an important step in chemistry and biochemistry. However, the chemically realistic annotation of a fragment ion spectrum can be a difficult challenge. We developed ChemFrag, for the detection of fragmentation pathways and the annotation of fragment ions with chemically reasonable structures. ChemFrag combines a quantum chemical with a rule‐based approach. For different doping substances as test instances, ChemFrag correctly annotates fragment ions. In most cases, the predicted fragments are chemically more realistic than those from purely combinatorial approaches, or approaches based on machine learning. The annotation generated by ChemFrag often coincides with spectra that have been manually annotated by experts. This is a major advance in peak annotation and allows a more precise automatic interpretation of mass spectra.
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

Gerlich, M.; Neumann, S. MetFusion: integration of compound identification strategies<!--[if gte mso 9]><![endif]--><!--[if gte mso 9]><xml> Normal 0 21 false false false DE X-NONE X-NONE</xml><![endif]--><!--[if gte mso 9]><![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normale Tabelle"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Arial","sans-serif"; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;}</style> <![endif]--> J Mass Spectrom 48(3), 291-298, (2013) DOI: 10.1002/jms.3123

Mass spectrometry (MS) is an important analytical technique for the detection and identification of small compounds. The main bottleneck in the interpretation of metabolite profiling or screening experiments is the identification of unknown compounds from tandem mass spectra.Spectral libraries for tandem MS, such as MassBank or NIST, contain reference spectra for many compounds, but their limited chemical coverage reduces the chance for a correct and reliable identification of unknown spectra outside the database domain.On the other hand, compound databases like PubChem or ChemSpider have a much larger coverage of the chemical space, but they cannot be queried with spectral information directly. Recently, computational mass spectrometry methods and in silico fragmentation prediction allow users to search such databases of chemical structures.We present a new strategy called MetFusion to combine identification results from several resources, in particular, from the in silico fragmenter MetFrag with the spectral library MassBank to improve compound identification. We evaluate the performance on a set of 1062 spectra and achieve an improved ranking of the correct compound from rank 28 using MetFrag alone, to rank 7 with MetFusion, even if the correct compound and similar compounds are absent from the spectral library. On the basis of the evaluation, we extrapolate the performance of MetFusion to the KEGG compound database.
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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