The ability of plants to monitor their surroundings, for instance the perception of bacteria, is of crucial importance. The perception of microorganism-derived molecules and their effector proteins is the best understood of these monitoring processes. In addition, plants perceive bacterial quorum sensing (QS) molecules used for cell-to-cell communication between bacteria. Here, we propose a mechanism for how N-acyl-homoserine lactones (AHLs), a group of QS molecules, influence host defense and fortify resistance in Arabidopsis thaliana against bacterial pathogens. N-3-oxo-tetradecanoyl-Lhomoserine lactone (oxo-C14-HSL) primed plants for enhanced callose deposition, accumulation of phenolic compounds, and lignification of cell walls. Moreover, increased levels of oxylipins and salicylic acid favored closure of stomata in response to Pseudomonas syringae infection. The AHL-induced resistance seems to differ from the systemic acquired and the induced systemic resistances, providing new insight into inter-kingdom communication. Consistent with the observation that shortchain AHLs, unlike oxo-C14-HSL, promote plant growth, treatments with C6-HSL, oxo-C10-HSL, or oxo-C14-HSL resulted in different transcriptional profiles in Arabidopsis. Understanding the priming induced by bacterial QS molecules augments our knowledge of plant reactions to bacteria and suggests strategies for using beneficial bacteria in plant protection.

Bacterial quorum sensing molecules not only grant the communication within bacterial communities, but also influence eukaryotic hosts. N-acyl-homoserine lactones (AHLs) produced by pathogenic or beneficial bacteria were shown to induce diverse reactions in animals and plants. In plants, the reaction to AHLs depends on the length of the lipid side chain. Here we investigated the impact of two bacteria on Arabidopsis thaliana, which usually enter a close symbiosis with plants from the Fabaceae (legumes) family and produce a long-chain AHL (Sinorhizobium meliloti) or a short-chain AHL (Rhizobium etli). We demonstrate that, similarly to the reaction to pure AHL molecules, the impact, which the inoculation with rhizosphere bacteria has on plants, depends on the type of the produced AHL. The inoculation with oxo-C14-HSL-producing S. meliloti strains enhanced plant resistance towards pathogenic bacteria, whereas the inoculation with an AttM lactonase-expressing S. meliloti strain did not. Inoculation with the oxo-C8-HSL-producing R. etli had no impact on the resistance, which is in agreement with our previous hypothesis. In addition, plants seem to influence the availability of AHLs in the rhizosphere. Taken together, this report provides new insights in the role of N-acyl-homoserine lactones in the inter-kingdom communication at the root surface.

Abstract. Shape optimization is a problem which arises in numerous computer vision problems such as image segmentation and multiview reconstruction. In this paper, we focus on a certain class of binary labeling problems which can be globally optimized both in a spatially discrete setting and in a spatially continuous setting. The main contribution of this paper is to present a quantitative comparison of the reconstruction accuracy and computation times which allows to assess some of the strengths and limitations of both approaches. We also present a novel method to approximate length regularity in a graph cut based framework: Instead of using pairwise terms we introduce higher order terms. These allow to represent a more accurate discretization of the L2-norm in the length term.

BackgroundThe enteric pathogen Salmonella is the causative agent of the majority of food-borne bacterial poisonings. Resent research revealed that colonization of plants by Salmonella is an active infection process. Salmonella changes the metabolism and adjust the plant host by suppressing the defense mechanisms. In this report we developed an automatic algorithm to quantify the symptoms caused by Salmonella infection on Arabidopsis.ResultsThe algorithm is designed to attribute image pixels into one of the two classes: healthy and unhealthy. The task is solved in three steps. First, we perform segmentation to divide the image into foreground and background. In the second step, a support vector machine (SVM) is applied to predict the class of each pixel belonging to the foreground. And finally, we do refinement by a neighborhood-check in order to omit all falsely classified pixels from the second step. The developed algorithm was tested on infection with the non-pathogenic E. coli and the plant pathogen Pseudomonas syringae and used to study the interaction between plants and Salmonella wild type and T3SS mutants. We proved that T3SS mutants of Salmonella are unable to suppress the plant defenses. Results obtained through the automatic analyses were further verified on biochemical and transcriptome levels.ConclusionThis report presents an automatic pixel-based classification method for detecting “unhealthy” regions in leaf images. The proposed method was compared to existing method and showed a higher accuracy. We used this algorithm to study the impact of the human pathogenic bacterium Salmonella Typhimurium on plants immune system. The comparison between wild type bacteria and T3SS mutants showed similarity in the infection process in animals and in plants. Plant epidemiology is only one possible application of the proposed algorithm, it can be easily extended to other detection tasks, which also rely on color information, or even extended to other features.