Geschmack ist vorhersagbar: Mit FlavorMiner. FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…
Seit Februar 2021 bietet Wolfgang Brandt, ehemaliger Leiter der Arbeitsgruppe Computerchemie am IPB, sein Citizen Science-Projekt zur Pilzbestimmung an. Dafür hat er in regelmäßigen Abständen öffentliche Vorträge zur Vielfalt…
Zulfiqar, M.; Crusoe, M. R.; König-Ries, B.; Steinbeck, C.; Peters, K.; Gadelha, L.;Implementation of FAIR practices in computational metabolomics workflows—A case studyMetabolites14118(2024)DOI: 10.3390/metabo14020118
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.
Publikation
Morgan, I.; Rennert, R.; Berger, R.; Jelača, S.; Maksimović-Ivanić, D.; Dunđerović, D.; Mijatović, S.; Kaluđerović, G. N.; Wessjohann, L. A.;The impact of 9-azaglycophymine and phenylguanidine derivatives on the proliferation of various breast cancer cell lines in vitro and in vivoSci. Rep.1428126(2024)DOI: 10.1038/s41598-024-71624-8
Quinazolinones, particularly 9-azaglycophymines, and closely related derivatives and precursors were tested in vitro against various breast cancer cell lines representing the major types of breast tumors. Among the 49 compounds tested, azaglycophymine derivative 19 with an electron-withdrawing substituent demonstrated the most significant anti-proliferative effects, with IC50 values of around 4 µM. Extensive cell-based investigations revealed that compound 19 induced caspase-dependent apoptosis in HCC1937 (human TNBC), BT-474 (human HER2+/HR+), and 4T1 (mouse TNBC) cells. In contrast, in MDA-MB-468 (human TNBC) and MCF-7 (human HR+) cells, the cell death was induced via a non-apoptotic pathway. The in vivo efficacy of compound 19 was validated using a syngeneic orthotopic 4T1 model in BALB/c mice, resulting in significant reduction of 4T1 breast tumor growth upon intraperitoneal (i.p.) application of doses of 5 or 20 mg/kg. These findings highlight the potential of compound 19 as a promising scaffold for the development of new therapeutic agents for various types of breast cancer and a first structure-activity insight.
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 bioimagingPlants12881(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
Zulfiqar, M.; Stettin, D.; Schmidt, S.; Nikitashina, V.; Pohnert, G.; Steinbeck, C.; Peters, K.; Sorokina, M.;Untargeted metabolomics to expand the chemical space of the marine diatom Skeletonema marinoiFront. Microbiol.141295994(2023)DOI: 10.3389/fmicb.2023.1295994
Diatoms (Bacillariophyceae) are aquatic photosynthetic microalgae with an ecological role as primary producers in the aquatic food web. They account substantially for global carbon, nitrogen, and silicon cycling. Elucidating the chemical space of diatoms is crucial to understanding their physiology and ecology. To expand the known chemical space of a cosmopolitan marine diatom, Skeletonema marinoi, we performed High-Resolution Liquid Chromatography-Tandem Mass Spectrometry (LC-MS2) for untargeted metabolomics data acquisition. The spectral data from LC-MS2 was used as input for the Metabolome Annotation Workflow (MAW) to obtain putative annotations for all measured features. A suspect list of metabolites previously identified in the Skeletonema spp. was generated to verify the results. These known metabolites were then added to the putative candidate list from LC-MS2 data to represent an expanded catalog of 1970 metabolites estimated to be produced by S. marinoi. The most prevalent chemical superclasses, based on the ChemONT ontology in this expanded dataset, were organic acids and derivatives, organoheterocyclic compounds, lipids and lipid-like molecules, and organic oxygen compounds. The metabolic profile from this study can aid the bioprospecting of marine microalgae for medicine, biofuel production, agriculture, and environmental conservation. The proposed analysis can be applicable for assessing the chemical space of other microalgae, which can also provide molecular insights into the interaction between marine organisms and their role in the functioning of ecosystems.