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

Marek Schikora,Marek Schikora,Wolfgang Koch,Heribert Hirt,Daniel Cremers,Satish Madhogaria,Balram Neupane,Adam Schikora,Karl-Heinz Kogel

doi:10.1186/1471-2105-13-171

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.

Highlights

The enteric pathogen Salmonella is the causative agent of the majority of food-borne bacterial poisonings
In this report we developed an automatic algorithm to quantify the symptoms caused by Salmonella infection on Arabidopsis plants
Arabidopsis plants were observed during 5 days after infiltration (DAI), detached leaves were photographed and without any further processing sent to the computing algorithm

Summary

Introduction

The enteric pathogen Salmonella is the causative agent of the majority of food-borne bacterial poisonings. In this report we developed an automatic algorithm to quantify the symptoms caused by Salmonella infection on Arabidopsis. Salmonella, a genus of Gram-negative enteropathogenic bacteria, are the causal agents of both gastroenteritis and typhoid fever They are responsible for an estimated one Plants can be the source of infection Many reports have linked food poisoning with the consumption of Salmonella-contaminated raw vegetables and fruits (for review see [2,6]). In this report we developed an automatic algorithm to quantify the symptoms caused by Salmonella infection on Arabidopsis plants. We show that it outperforms other algorithms developed for this task It was tested on infection with the non-pathogenic E. coli and the plant pathogen Pseudomonas syringae and subsequently used to study the interaction between plant host and Salmonella wild type and T3SS mutants. Results obtained through the automatic analyses were further verified on biochemical and transcriptome levels

Methods

Results

Discussion

Conclusion