An image classification approach to analyze the suppression of plant immunity by the human pathogen SalmonellaTyphimurium

Abstract

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.

Interaction between Salmonella and Plants: Potential Hosts and Vectors for Human Infection

“…Similar to animals or humans, plants respond to colonization of pathogenic bacteria inducing numerous immune responses, among others are oxidative burst and enhanced expression of Pathogenesis Related (PR) genes. Both were observed after inoculation with Salmonella and both were stronger if the inoculation was performed using mutants in T3SS [59,85,86]. Those results suggest that the wild-type strain is able to suppress the immune response.…”

“…The inoculation with the wild-type Salmonella strains and mutants in one or both of the T3SSs showed that functional secretion systems are required for eicient plant colonization [59,85,93]. Two observations allow such a conclusion: (1) The mutants had lower proliferation rates when compared to the respective wild type, which suggests that a functional T3SS helps with the colonization of plants; and (2) T3SS mutants induced stronger immune response of the host plant.…”

“…Firstly, the genetic disposition of the strain, for example, the presence of type III secretion system (T3SS), the ability to form bioilms, chemotaxis, or motility are important. Studies using strains with mutations inluencing these characteristics usually resulted in reduced survival [57][58][59]. Salmonella can also produce an O-antigen capsule, which improves survival under desiccation stress [60].…”

“…Salmonella uses two T3SS and more than 40 efectors in order to manipulate the immune system (perception mechanisms and signaling cascades) as well as the cytoskeleton of animal cells at diferent stages of the infection process [91]. Recent discoveries from others and our group imply that the mechanisms used in animal and plant hosts may resemble each other [59,85,86,[92][93][94][95].…”

Common Duckweed (Lemna minor) Is a Versatile High-Throughput Infection Model For the Burkholderia cepacia Complex and Other Pathogenic Bacteria

“…However, the bleaching effect we observed in the dead plants was also the result of chlorophyll degradation, as measured following ethanol extraction of infected plants (data not shown). Although the use of a qualitative visual endpoint determination in the current method results in a simple and inexpensive assay, the sensitivity of this assay could potentially be improved by automating the quantification of the extent of plant morbidity and chlorophyll degradation by a method similar to that described by Schikora et al [36], who produced a pixellation algorithm to monitor Salmonella Typhimurium infection of Arabidopsis .…”