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Publikation

Peters, K.; Blatt-Janmaat, K. L.; Tkach, N.; Dam, N. M.; Neumann, S.; Untargeted metabolomics for integrative taxonomy: Metabolomics, DNA marker-based sequencing, and phenotype bioimaging Plants 12, 881, (2023) DOI: 10.3390/plants12040881

Integrative taxonomy is a fundamental part of biodiversity and combines traditional morphology with additional methods such as DNA sequencing or biochemistry. Here, we aim to establish untargeted metabolomics for use in chemotaxonomy. We used three thallose liverwort species Riccia glauca, R. sorocarpa, and R. warnstorfii (order Marchantiales, Ricciaceae) with Lunularia cruciata (order Marchantiales, Lunulariacea) as an outgroup. Liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) were integrated with DNA marker-based sequencing of the trnL-trnF region and high-resolution bioimaging. Our untargeted chemotaxonomy methodology enables us to distinguish taxa based on chemophenetic markers at different levels of complexity: (1) molecules, (2) compound classes, (3) compound superclasses, and (4) molecular descriptors. For the investigated Riccia species, we identified 71 chemophenetic markers at the molecular level, a characteristic composition in 21 compound classes, and 21 molecular descriptors largely indicating electron state, presence of chemical motifs, and hydrogen bonds. Our untargeted approach revealed many chemophenetic markers at different complexity levels that can provide more mechanistic insight into phylogenetic delimitation of species within a clade than genetic-based methods coupled with traditional morphology-based information. However, analytical and bioinformatics analysis methods still need to be better integrated to link the chemophenetic information at multiple scales.
Publikation

Parks, N. A.; Fischer, T. G.; Blankenburg, C.; Scalfani, V. F.; McEwen, L. R.; Herres-Pawlis, S.; Neumann, S.; The current landscape of author guidelines in chemistry through the lens of research data sharing Pure and Applied Chemistry 95, 439-450, (2023) DOI: 10.1515/pac-2022-1001

As the primary method of communicating research results, journals garner an enormous impact on community behavior. Publishing the underlying research data alongside journal articles is widely considered good scientific practice. Ideally, journals and their publishers place these recommendations or requirements in their author guidelines and data policies. Several efforts are working to improve the infrastructure, processes, and uptake of research data sharing, including the NFDI4Chem consortium, working groups within the RDA, and IUPAC, including the WorldFAIR Chemistry project. In this article, we present the results of a large-scale analysis of author guidelines from several publishers and journals active in chemistry research, showing how well the publishing landscape supports different criteria and where there is room for improvement. While the requirement for deposition of X-ray diffraction data is commonplace, guidelines rarely mention machine-readable chemical structures and metadata/minimum information standards. Further evaluation criteria included recommendations on persistent identifiers, data availability statements, data deposition into repositories as well as of open analytical data formats. Our survey shows that publishers and journals are starting to include aspects of research data in their guidelines. We as authors should accept and embrace the guidelines with increasing requirements for data availability, data interoperability, and re-usability to improve chemistry research.
Publikation

Walker, T. W. N.; Schrodt, F.; Allard, P.-M.; Defossez, E.; Jassey, V. E. J.; Schuman, M. C.; Alexander, J. M.; Baines, O.; Baldy, V.; Bardgett, R. D.; Capdevila, P.; Coley, P. D.; Dam, N. M.; David, B.; Descombes, P.; Endara, M.; Fernandez, C.; Forrister, D.; Gargallo-Garriga, A.; Glauser, G.; Marr, S.; Neumann, S.; Pellissier, L.; Peters, K.; Rasmann, S.; Roessner, U.; Salguero‐Gómez, R.; Sardans, J.; Weckwerth, W.; Wolfender, J.; Peñuelas, J.; Leaf metabolic traits reveal hidden dimensions of plant form and function Sci. Adv. 9, eadi4029, (2023) DOI: 10.1126/sciadv.adi4029

The metabolome is the biochemical basis of plant form and function, but we know little about its macroecological variation across the plant kingdom. Here, we used the plant functional trait concept to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five metabolic functional traits reveals that plants vary on two major axes of leaf metabolic specialization—a leaf chemical defense spectrum and an expression of leaf longevity. Axes are similar for tropical and temperate species, with many trait combinations being viable. However, metabolic traits vary orthogonally to life-history strategies described by widely used functional traits. The metabolome thus expands the functional trait concept by providing additional axes of metabolic specialization for examining plant form and function.
Publikation

Martens, M.; Stierum, R.; Schymanski, E. L.; Evelo, C. T.; Aalizadeh, R.; Aladjov, H.; Arturi, K.; Audouze, K.; Babica, P.; Berka, K.; Bessems, J.; Blaha, L.; Bolton, E. E.; Cases, M.; Damalas, D. ?.; Dave, K.; Dilger, M.; Exner, T.; Geerke, D. P.; Grafström, R.; Gray, A.; Hancock, J. M.; Hollert, H.; Jeliazkova, N.; Jennen, D.; Jourdan, F.; Kahlem, P.; Klanova, J.; Kleinjans, J.; Kondić, T.; Kone, B.; Lynch, I.; Maran, U.; Martinez Cuesta, S.; Ménager, H.; Neumann, S.; Nymark, P.; Oberacher, H.; Ramirez, N.; Remy, S.; Rocca-Serra, P.; Salek, R. M.; Sallach, B.; Sansone, S.-A.; Sanz, F.; Sarimveis, H.; Sarntivijai, S.; Schulze, T.; Slobodnik, J.; Spjuth, O.; Tedds, J.; Thomaidis, N.; Weber, R. J.; van Westen, G. J.; Wheelock, C. E.; Williams, A. J.; Witters, H.; Zdrazil, B.; Županič, A.; Willighagen, E. L.; ELIXIR and Toxicology: a community in development F1000Research 10, 1129, (2023) DOI: 10.12688/f1000research.74502.2

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology, and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.
Publikation

Dumschott, K.; Dörpholz, H.; Laporte, M.-A.; Brilhaus, D.; Schrader, A.; Usadel, B.; Neumann, S.; Arnaud, E.; Kranz, A.; Ontologies for increasing the FAIRness of plant research data Front. Plant Sci. 14, 1279694, (2023) DOI: 10.3389/fpls.2023.1279694

The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human- and machine- interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.
Publikation

Deutsch, E. W.; Vizcaíno, J. A.; Jones, A. R.; Binz, P.-A.; Lam, H.; Klein, J.; Bittremieux, W.; Perez-Riverol, Y.; Tabb, D. L.; Walzer, M.; Ricard-Blum, S.; Hermjakob, H.; Neumann, S.; Mak, T. D.; Kawano, S.; Mendoza, L.; Van Den Bossche, T.; Gabriels, R.; Bandeira, N.; Carver, J.; Pullman, B.; Sun, Z.; Hoffmann, N.; Shofstahl, J.; Zhu, Y.; Licata, L.; Quaglia, F.; Tosatto, S. C. E.; Orchard, S. E.; Proteomics standards initiative at twenty years: Current activities and future work J. Proteome Res. 22, 287-301, (2023) DOI: 10.1021/acs.jproteome.2c00637

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.
Publikation

Blatt-Janmaat, K.; Neumann, S.; Schmidt, F.; Ziegler, J.; Qu, Y.; Peters, K.; Impact of in vitro phytohormone treatments on the metabolome of the leafy liverwort Radula complanata (L.) Dumort Metabolomics 19, 17, (2023) DOI: 10.1007/s11306-023-01979-y

Introduction Liverworts are a group of non-vascular plants that possess unique metabolism not found in other plants. Many liverwort metabolites have interesting structural and biochemical characteristics, however the fluctuations of these metabolites in response to stressors is largely unknown. Objectives To investigate the metabolic stress-response of the leafy liverwort Radula complanata. Methods Five phytohormones were applied exogenously to in vitro cultured R. complanata and an untargeted metabolomic analysis was conducted. Compound classification and identification was performed with CANOPUS and SIRIUS while statistical analyses including PCA, ANOVA, and variable selection using BORUTA were conducted to identify metabolic shifts.Results It was found that R. complanata was predominantly composed of carboxylic acids and derivatives, followed by benzene and substituted derivatives, fatty acyls, organooxygen compounds, prenol lipids, and flavonoids. The PCA revealed that samples grouped based on the type of hormone applied, and the variable selection using BORUTA (Random Forest) revealed 71 identified and/or classified features that fluctuated with phytohormone application. The stress-response treatments largely reduced the production of the selected primary metabolites while the growth treatments resulted in increased production of these compounds. 4-(3-Methyl-2-butenyl)-5-phenethylbenzene-1,3-diol was identified as a biomarker for the growth treatments while GDP-hexose was identified as a biomarker for the stress-response treatments. Conclusion Exogenous phytohormone application caused clear metabolic shifts in Radula complanata that deviate from the responses of vascular plants. Further identification of the selected metabolite features can reveal metabolic biomarkers unique to liverworts and provide more insight into liverwort stress responses.
Publikation

Blatt-Janmaat, K. L.; Neumann, S.; Ziegler, J.; Peters, K.; Host tree and geography induce metabolic shifts in the epiphytic liverwort Radula complanata Plants 12, 571, (2023) DOI: 10.3390/plants12030571

Bryophytes are prolific producers of unique, specialized metabolites that are not found in other plants. As many of these unique natural products are potentially interesting, for example, pharmacological use, variations in the production regarding ecological or environmental conditions have not often been investigated. Here, we investigate metabolic shifts in the epiphytic Radula complanata L. (Dumort) with regard to different environmental conditions and the type of phorophyte (host tree). Plant material was harvested from three different locations in Sweden, Germany, and Canada and subjected to untargeted liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) and data-dependent acquisition (DDA-MS). Using multivariate statistics, variable selection methods, in silico compound identification, and compound classification, a large amount of variation (39%) in the metabolite profiles was attributed to the type of host tree and 25% to differences in environmental conditions. We identified 55 compounds to vary significantly depending on the host tree (36 on the family level) and 23 compounds to characterize R. complanata in different environments. Taken together, we found metabolic shifts mainly in primary metabolites that were associated with the drought response to different humidity levels. The metabolic shifts were highly specific to the host tree, including mostly specialized metabolites suggesting high levels of ecological interaction. As R. complanata is a widely distributed generalist species, we found it to flexibly adapt its metabolome according to different conditions. We found metabolic composition to also mirror the constitution of the habitat, which makes it interesting for conservation measures.
Publikation

Blatt-Janmaat, K.; Neumann, S.; Schmidt, F.; Ziegler, J.; Peters, K.; Qu, Y.; Impact of in vitro hormone treatments on the bibenzyl production of Radula complanata Botany 101, 232 - 242, (2022) DOI: 10.1139/cjb-2022-0048

Bibenzyls are a specialized metabolite class found throughout the plant kingdom. One of the most prolific producers of bibenzyls are liverworts, specifically plants of the Radula genera. These plants possess an incredible diversity of bibenzyls, prenylated bibenzyls, and a few (bis)bibenzyls, several of which have medicinal properties, including perrottetinene, an analog of tetrahydrocannabinol from cannabis. To provide insight into the bibenzyls’ biosynthesis in planta, exogenous phytohormones were applied to in vitro grown Radula complanata and bibenzyl metabolite production was monitored with targeted and untargeted metabolomics. The targeted metabolomic analysis of six prenylated bibenzyls revealed that production of these metabolites was largely reduced when plants were treated with abscisic acid (AA), salicylic acid (SA), 1-naphthaleneacetic acid (NAA), or 6-benzylaminopurine (BAP). The reduction of these metabolites in the BAP and NAA treatment suggests that prenylated bibenzyl production is negatively correlated with vegetative plant growth. The reduction of bibenzyls at low AA and SA concentrations and mild increase at higher AA and SA concentrations suggest that their production is regulated by these stress hormones. In addition, six other bibenzyl metabolites were tentatively identified from the untargeted analysis. These results provide insight into the influence of phytohormones on the bioactive bibenzyl content of R. complanata.
Publikation

Amara, A.; Frainay, C.; Jourdan, F.; Naake, T.; Neumann, S.; Novoa-del-Toro, E. M.; Salek, R. M.; Salzer, L.; Scharfenberg, S.; Witting, M.; Networks and graphs discovery in metabolomics data analysis and interpretation Frontiers in Molecular Biosciences 9, 841373, (2022) DOI: 10.3389/fmolb.2022.841373

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.
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