University of Marburg

About: University of Marburg is a education organization based out in Marburg, Germany. It is known for research contribution in the topics: Population & Gene. The organization has 23195 authors who have published 42907 publications receiving 1506069 citations. The organization is also known as: Philipps University of Marburg & Philipps-Universität.

Metabolic priming by a secreted fungal effector

Abstract: Maize smut caused by the fungus Ustilago maydis is a widespread disease characterized by the development of large plant tumours. U. maydis is a biotrophic pathogen that requires living plant tissue for its development and establishes an intimate interaction zone between fungal hyphae and the plant plasma membrane. U. maydis actively suppresses plant defence responses by secreted protein effectors. Its effector repertoire comprises at least 386 genes mostly encoding proteins of unknown function and expressed exclusively during the biotrophic stage. The U. maydis secretome also contains about 150 proteins with probable roles in fungal nutrition, fungal cell wall modification and host penetration as well as proteins unlikely to act in the fungal-host interface like a chorismate mutase. Chorismate mutases are key enzymes of the shikimate pathway and catalyse the conversion of chorismate to prephenate, the precursor for tyrosine and phenylalanine synthesis. Root-knot nematodes inject a secreted chorismate mutase into plant cells likely to affect development. Here we show that the chorismate mutase Cmu1 secreted by U. maydis is a virulence factor. The enzyme is taken up by plant cells, can spread to neighbouring cells and changes the metabolic status of these cells through metabolic priming. Secreted chorismate mutases are found in many plant-associated microbes and might serve as general tools for host manipulation.

Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains

Abstract: In the realm of multilabel classification (MLC), it has become an opinio communis that optimal predictive performance can only be achieved by learners that explicitly take label dependence into account. The goal of this paper is to elaborate on this postulate in a critical way. To this end, we formalize and analyze MLC within a probabilistic setting. Thus, it becomes possible to look at the problem from the point of view of risk minimization and Bayes optimal prediction. Moreover, inspired by our probabilistic setting, we propose a new method for MLC that generalizes and outperforms another approach, called classifier chains, that was recently introduced in the literature.