Omanische Heilpflanze im Fokus der Phytochemie IPB-Wissenschaftler und Partner aus Dhofar haben jüngst die omanische Heilpflanze Terminalia dhofarica unter die phytochemische Lupe genommen. Die Pflanze ist reich an…
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…
Jasmonic acid (JA) signaling can be switched off by metabolism of JA. The master regulator MYC2, interacting with MED25, has been shown to be deactivated by the bHLH transcription factors MTB1, MTB2, and MTB3. An autoregulatory negative feedback loop has been proposed for this termination in JA signaling.
Publikation
Ruttkies, C.; Neumann, S.; Posch, S.;Improving MetFrag with statistical learning of fragment annotationsBMC Bioinformatics20376(2019)DOI: 10.1186/s12859-019-2954-7
BackgroundMolecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.ResultsThe results on the 87 MS/MS spectra from positive and negative mode show a substantial improvement of the results compared to submissions made by the former MetFrag approach. Top1 rankings increased from 5 to 21 and Top10 rankings from 39 to 55 both showing higher values than for CSI:IOKR, the winner of the CASMI 2016 contest. For the negative mode spectra, MetFrag’s statistical scoring outperforms all other participants which submitted results for this type of spectra.ConclusionsThis study shows how statistical learning can improve molecular structure identification based on MS/MS data compared on the same method using combinatorial in silico fragmentation only. MetFrag2.4.5 shows especially in negative mode a better performance compared to the other participating approaches.
Publikation
Wasternack, C.;New Light on Local and Systemic Wound SignalingTrends Plant Sci.24102-105(2019)DOI: 10.1016/j.tplants.2018.11.009
Electric signaling and Ca2+ waves were discussed to occur in systemic wound responses. Two new overlapping scenarios were identified: (i) membrane depolarization in two special cell types followed by an increase in systemic cytoplasmic Ca2+ concentration ([Ca2+]cyt), and (ii) glutamate sensed by GLUTAMATE RECEPTOR LIKE proteins and followed by Ca2+-based defense in distal leaves.
Publikation
Ferlian, O.; Biere, A.; Bonfante, P.; Buscot, F.; Eisenhauer, N.; Fernandez, I.; Hause, B.; Herrmann, S.; Krajinski-Barth, F.; Meier, I. C.; Pozo, M. J.; Rasmann, S.; Rillig, M. C.; Tarkka, M. T.; van Dam, N. M.; Wagg, C.; Martinez-Medina, A.;Growing Research Networks on Mycorrhizae for Mutual BenefitsTrends Plant Sci.23975-984(2018)DOI: 10.1016/j.tplants.2018.08.008
Research on mycorrhizal interactions has traditionally developed into separate disciplines addressing different organizational levels. This separation has led to an incomplete understanding of mycorrhizal functioning. Integration of mycorrhiza research at different scales is needed to understand the mechanisms underlying the context dependency of mycorrhizal associations, and to use mycorrhizae for solving environmental issues. Here, we provide a road map for the integration of mycorrhiza research into a unique framework that spans genes to ecosystems. Using two key topics, we identify parallels in mycorrhiza research at different organizational levels. Based on two current projects, we show how scientific integration creates synergies, and discuss future directions. Only by overcoming disciplinary boundaries, we will achieve a more comprehensive understanding of the functioning of mycorrhizal associations.