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…
Schymanski, E. L.; Kondić, T.; Neumann, S.; Thiessen, P. A.; Zhang, J.; Bolton, E. E.;Empowering large chemical knowledge bases for exposomics: PubChemLite meets MetFragJ. Cheminform.1319(2021)DOI: 10.1186/s13321-021-00489-0
Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much—yet not enough—information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput “big data” services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments.
Publikation
Peters, K.; Balcke, G.; Kleinenkuhnen, N.; Treutler, H.; Neumann, S.;Untargeted in silico compound classification—A novel metabolomics method to assess the chemodiversity in bryophytesInt. J. Mol. Sci.223251(2021)DOI: 10.3390/ijms22063251
In plant ecology, biochemical analyses of bryophytes and vascular plants are often conducted on dried herbarium specimen as species typically grow in distant and inaccessible locations. Here, we present an automated in silico compound classification framework to annotate metabolites using an untargeted data independent acquisition (DIA)–LC/MS–QToF-sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) ecometabolomics analytical method. We perform a comparative investigation of the chemical diversity at the global level and the composition of metabolite families in ten different species of bryophytes using fresh samples collected on-site and dried specimen stored in a herbarium for half a year. Shannon and Pielou’s diversity indices, hierarchical clustering analysis (HCA), sparse partial least squares discriminant analysis (sPLS-DA), distance-based redundancy analysis (dbRDA), ANOVA with post-hoc Tukey honestly significant difference (HSD) test, and the Fisher’s exact test were used to determine differences in the richness and composition of metabolite families, with regard to herbarium conditions, ecological characteristics, and species. We functionally annotated metabolite families to biochemical processes related to the structural integrity of membranes and cell walls (proto-lignin, glycerophospholipids, carbohydrates), chemical defense (polyphenols, steroids), reactive oxygen species (ROS) protection (alkaloids, amino acids, flavonoids), nutrition (nitrogen- and phosphate-containing glycerophospholipids), and photosynthesis. Changes in the composition of metabolite families also explained variance related to ecological functioning like physiological adaptations of bryophytes to dry environments (proteins, peptides, flavonoids, terpenes), light availability (flavonoids, terpenes, carbohydrates), temperature (flavonoids), and biotic interactions (steroids, terpenes). The results from this study allow to construct chemical traits that can be attributed to biogeochemistry, habitat conditions, environmental changes and biotic interactions. Our classification framework accelerates the complex annotation process in metabolomics and can be used to simplify biochemical patterns. We show that compound classification is a powerful tool that allows to explore relationships in both molecular biology by “zooming in” and in ecology by “zooming out”. The insights revealed by our framework allow to construct new research hypotheses and to enable detailed follow-up studies.
Publikation
Peters, K.; Herman, S.; Khoonsari, P. E.; Burman, J.; Neumann, S.; Kultima, K.;Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluidSci. Rep.1118822(2021)DOI: 10.1038/s41598-021-97491-1
Chronic diseases affecting the central nervous system (CNS) like
Alzheimer’s or Parkinson’s disease typically develop with advanced
chronological age. Yet, aging at the metabolic level has been explored
only sporadically in humans using biofluids in close proximity to the
CNS such as the cerebrospinal fluid (CSF). We have used an untargeted
liquid chromatography high-resolution mass spectrometry (LC-HRMS) based
metabolomics approach to measure the levels of metabolites in the CSF of
non-neurological control subjects in the age of 20 up to 74. Using a
random forest-based feature selection strategy, we extracted 69 features
that were strongly related to age (page
Publikation
Trogisch, S.; Liu, X.; Rutten, G.; Xue, K.; Bauhus, J.; Brose, U.; Bu, W.; Cesarz, S.; Chesters, D.; Connolly, J.; Cui, X.; Eisenhauer, N.; Guo, L.; Haider, S.; Härdtle, W.; Kunz, M.; Liu, L.; Ma, Z.; Neumann, S.; Sang, W.; Schuldt, A.; Tang, Z.; van Dam, N. M.; von Oheimb, G.; Wang, M.-Q.; Wang, S.; Weinhold, A.; Wirth, C.; Wubet, T.; Xu, X.; Yang, B.; Zhang, N.; Zhu, C.-D.; Ma, K.; Wang, Y.; Bruelheide, H.;The significance of tree-tree interactions for forest ecosystem functioningBasic and Applied Ecology5533-52(2021)DOI: 10.1016/j.baae.2021.02.003
Global change exposes forest ecosystems to many risks including novel climatic conditions, increased frequency of climatic extremes and sudden emergence and spread of pests and pathogens. At the same time, forest landscape restoration has regained global attention as an integral strategy for climate change mitigation. Owing to unpredictable future risks and the need for new forests that provide multiple ecosystem services, mixed-species forests have been advocated for this purpose. However, the successful establishment of mixed forests requires intrinsic knowledge of biodiversity\'s role for forest ecosystem functioning. In this respect, a better understanding of tree-tree interactions and how they contribute to observed positive tree species richness effects on key ecosystem functions is critical. Here, we review the current knowledge of the underlying mechanisms of tree-tree interactions and argue that positive net biodiversity effects at the community scale may emerge from the dominance of positive over negative interactions at the local neighbourhood scale. In a second step, we demonstrate how tree-tree interactions and the immediate tree neighbourhood\'s role can be systematically assessed in a tree diversity experiment. The expected results will improve predictions about the effects of tree interactions on ecosystem functioning based on general principles. We argue that this knowledge is urgently required to guide the design of tree species mixtures for the successful establishment of newly planted forests.