Computational approach to predict species-specific type III secretion system (T3SS) effectors using single and multiple genomes.

Christopher K Hobbs,Vanessa L Porter,Ka Yin Leung,Maxwell L S Stow,Herbert H Tsang,Bupe A Siame

doi:10.1186/s12864-016-3363-1

Christopher K Hobbs, Vanessa L Porter + Show 4 more

Open Access

https://doi.org/10.1186/s12864-016-3363-1

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Abstract

BackgroundMany gram-negative bacteria use type III secretion systems (T3SSs) to translocate effector proteins into host cells. T3SS effectors can give some bacteria a competitive edge over others within the same environment and can help bacteria to invade the host cells and allow them to multiply rapidly within the host. Therefore, developing efficient methods to identify effectors scattered in bacterial genomes can lead to a better understanding of host-pathogen interactions and ultimately to important medical and biotechnological applications.ResultsWe used 21 genomic and proteomic attributes to create a precise and reliable T3SS effector prediction method called Genome Search for Effectors Tool (GenSET). Five machine learning algorithms were trained on effectors selected from different organisms and a trained (voting) algorithm was then applied to identify other effectors present in the genome testing sets from the same (GenSET Phase 1) or different (GenSET Phase 2) organism. Although a select group of attributes that included the codon adaptation index, probability of expression in inclusion bodies, N-terminal disorder, and G + C content (filtered) were better at discriminating between positive and negative sets, algorithm performance was better when all 21 attributes (unfiltered) were used. Performance scores (sensitivity, specificity and area under the curve) from GenSET Phase 1 were better than those reported for six published methods. More importantly, GenSET Phase 1 ranked more known effectors (70.3%) in the top 40 ranked proteins and predicted 10–80% more effectors than three available programs in three of the four organisms tested. GenSET Phase 2 predicted 43.8% effectors in the top 40 ranked proteins when tested on four related or unrelated organisms. The lower prediction rates from GenSET Phase 2 may be due to the presence of different translocation signals in effectors from different T3SS families.ConclusionsThe species-specific GenSET Phase 1 method offers an alternative approach to T3SS effector prediction that can be used with other published programs to improve effector predictions. Additionally, our approach can be applied to predict effectors of other secretion systems as long as these effectors have translocation signals embedded in their sequences.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3363-1) contains supplementary material, which is available to authorized users.

Highlights

Many gram-negative bacteria use type III secretion systems (T3SSs) to translocate effector proteins into host cells
Attribute selection Genome Search for Effectors Tool (GenSET) Phase 1 generated data sets based on the genomes of the four test organisms (Fig. 1)
In GenSET Phase 1, four attributes gave good separation between the positive and negative sets were selected for each organism, except for Pseudomonas syringae where eight attributes gave good separation and were selected using Waikato Environment for Knowledge Analysis (WEKA) (Table 2; Additional file 1)

Summary

Introduction

Many gram-negative bacteria use type III secretion systems (T3SSs) to translocate effector proteins into host cells. Developing efficient methods to identify effectors scattered in bacterial genomes can lead to a better understanding of host-pathogen interactions and to important medical and biotechnological applications. Many gram-negative bacteria possess contact-dependent type III secretion systems (T3SSs) that translocate diverse effector proteins from the bacterial cytosol into host cells [1, 2]. Effectors secreted by T3SSs play essential roles in the host-pathogen interactions by mimicking host proteins in structure and function, and by suppressing the host innate immunity [3]. Use of computational approaches to accurately predict novel effectors scattered in the genomes of T3SScontaining bacteria is an exciting area of research [4, 6]. Machine learning is an area of Artificial Intelligence in which computer programs are used to identify patterns and distinguish between different classes of objects resulting in phylogenetic tree construction, protein function prediction, and recognition of translocation signals in effectors [4, 7, 8]

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