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…
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.
Publikation
Marr, S.; Hageman, J. A.; Wehrens, R.; van Dam, N. M.; Bruelheide, H.; Neumann, S.;LC-MS based plant metabolic profiles of thirteen grassland species grown in diverse neighbourhoodsSci. Data852(2021)DOI: 10.1038/s41597-021-00836-8
In plants, secondary metabolite profiles provide a unique opportunity to explore seasonal variation and responses to the environment. These include both abiotic and biotic factors. In field experiments, such stress factors occur in combination. This variation alters the plant metabolic profiles in yet uninvestigated ways. This data set contains trait and mass spectrometry data of thirteen grassland species collected at four time points in the growing season in 2017. We collected above-ground vegetative material of seven grass and six herb species that were grown in plant communities with different levels of diversity in the Jena Experiment. For each sample, we recorded visible traits and acquired shoot metabolic profiles on a UPLC-ESI-Qq-TOF-MS. We performed the raw data pre-processing in Galaxy-W4M and prepared the data for statistical analysis in R by applying missing data imputation, batch correction, and validity checks on the features. This comprehensive data set provides the opportunity to investigate environmental dynamics across diverse neighbourhoods that are reflected in the metabolomic profile.
Publikation
Chutia, R.; Scharfenberg, S.; Neumann, S.; Abel, S.; Ziegler, J.;Modulation of phosphate deficiency-induced metabolic changes by iron availability in Arabidopsis thalianaInt. J. Mol. Sci.227609(2021)DOI: 10.3390/ijms22147609
Concurrent suboptimal supply of several nutrients requires the coordination of nutrient-specific transcriptional, phenotypic, and metabolic changes in plants in order to optimize growth and development in most agricultural and natural ecosystems. Phosphate (Pi) and iron (Fe) deficiency induce overlapping but mostly opposing transcriptional and root growth responses in Arabidopsis thaliana. On the metabolite level, Pi deficiency negatively modulates Fe deficiency-induced coumarin accumulation, which is controlled by Fe as well as Pi deficiency response regulators. Here, we report the impact of Fe availability on seedling growth under Pi limiting conditions and on Pi deficiency-induced accumulation of amino acids and organic acids, which play important roles in Pi use efficiency. Fe deficiency in Pi replete conditions hardly changed growth and metabolite profiles in roots and shoots of Arabidopsis thaliana, but partially rescued growth under conditions of Pi starvation and severely modulated Pi deficiency-induced metabolic adjustments. Analysis of T-DNA insertion lines revealed the concerted coordination of metabolic profiles by regulators of Fe (FIT, bHLH104, BRUTUS, PYE) as well as of Pi (SPX1, PHR1, PHL1, bHLH32) starvation responses. The results show the interdependency of Pi and Fe availability and the interplay between Pi and Fe starvation signaling on the generation of plant metabolite profiles.