zur Suche springenzur Navigation springenzum Inhalt springen

Sortieren nach: Erscheinungsjahr Typ der Publikation

Zeige Ergebnisse 1 bis 10 von 123.

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

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

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

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

Rauh, D.; Blankenburg, C.; Fischer, T. G.; Jung, N.; Kuhn, S.; Schatzschneider, U.; Schulze, T.; Neumann, S.; Data format standards in analytical chemistry Pure and Applied Chemistry 94, 725-736, (2022) DOI: 10.1515/pac-2021-3101

Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.
Publikation

Rainer, J.; Vicini, A.; Salzer, L.; Stanstrup, J.; Badia, J. M.; Neumann, S.; Stravs, M. A.; Verri Hernandes, V.; Gatto, L.; Gibb, S.; Witting, M.; A modular and expandable ecosystem for metabolomics data annotation in R Metabolites 12, 173, (2022) DOI: 10.3390/metabo12020173

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.
IPB Mainnav Search