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Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.

ABSTRACT: Motivation:Many Gram-negative bacteria use type VI secretion systems (T6SS) to export effector proteins into adjacent target cells. These secreted effectors (T6SEs) play vital roles in the competitive survival in bacterial populations, as well as pathogenesis of bacteria. Although various computational analyses have been previously applied to identify effectors secreted by certain bacterial species, there is no universal method available to accurately predict T6SS effector proteins from the growing tide of bacterial genome sequence data. Results:We extracted a wide range of features from T6SE protein sequences and comprehensively analyzed the prediction performance of these features through unsupervised and supervised learning. By integrating these features, we subsequently developed a two-layer SVM-based ensemble model with fine-grain optimized parameters, to identify potential T6SEs. We further validated the predictive model using an independent dataset, which showed that the proposed model achieved an impressive performance in terms of ACC (0.943), F-value (0.946), MCC (0.892) and AUC (0.976). To demonstrate applicability, we employed this method to correctly identify two very recently validated T6SE proteins, which represent challenging prediction targets because they significantly differed from previously known T6SEs in terms of their sequence similarity and cellular function. Furthermore, a genome-wide prediction across 12 bacterial species, involving in total 54 212 protein sequences, was carried out to distinguish 94 putative T6SE candidates. We envisage both this information and our publicly accessible web server will facilitate future discoveries of novel T6SEs. Availability and implementation:http://bastion6.erc.monash.edu/. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Wang J

PROVIDER: S-EPMC6061801 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.

Wang Jiawei J Yang Bingjiao B Leier André A Marquez-Lago Tatiana T TT Hayashida Morihiro M Rocker Andrea A Zhang Yanju Y Akutsu Tatsuya T Chou Kuo-Chen KC Strugnell Richard A RA Song Jiangning J Lithgow Trevor T

Bioinformatics (Oxford, England) 20180801 15

<h4>Motivation</h4>Many Gram-negative bacteria use type VI secretion systems (T6SS) to export effector proteins into adjacent target cells. These secreted effectors (T6SEs) play vital roles in the competitive survival in bacterial populations, as well as pathogenesis of bacteria. Although various computational analyses have been previously applied to identify effectors secreted by certain bacterial species, there is no universal method available to accurately predict T6SS effector proteins from ...[more]

PMID: 29547915

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Project description:BackgroundMany pathogens use a type III secretion system to translocate virulence proteins (called effectors) in order to adapt to the host environment. To date, many prediction tools for effector identification have been developed. However, these tools are insufficiently accurate for producing a list of putative effectors that can be applied directly for labor-intensive experimental verification. This also suggests that important features of effectors have yet to be fully characterized.ResultsIn this study, we have constructed an accurate approach to predicting secreted virulence effectors from Gram-negative bacteria. This consists of a support vector machine-based discriminant analysis followed by a simple criteria-based filtering. The accuracy was assessed by estimating the average number of true positives in the top-20 ranking in the genome-wide screening. In the validation, 10 sets of 20 training and 20 testing examples were randomly selected from 40 known effectors of Salmonella enterica serovar Typhimurium LT2. On average, the SVM portion of our system predicted 9.7 true positives from 20 testing examples in the top-20 of the prediction. Removal of the N-terminal instability, codon adaptation index and ProtParam indices decreased the score to 7.6, 8.9 and 7.9, respectively. These discrimination features suggested that the following characteristics of effectors had been uncovered: unstable N-terminus, non-optimal codon usage, hydrophilic, and less aliphathic. The secondary filtering process represented by coexpression analysis and domain distribution analysis further refined the average true positive counts to 12.3. We further confirmed that our system can correctly predict known effectors of P. syringae DC3000, strongly indicating its feasibility.ConclusionsWe have successfully developed an accurate prediction system for screening effectors on a genome-wide scale. We confirmed the accuracy of our system by external validation using known effectors of Salmonella and obtained the accurate list of putative effectors of the organism. The level of accuracy was sufficient to yield candidates for gene-directed experimental verification. Furthermore, new features of effectors were revealed: non-optimal codon usage and instability of the N-terminal region. From these findings, a new working hypothesis is proposed regarding mechanisms controlling the translocation of virulence effectors and determining the substrate specificity encoded in the secretion system.

Project description:UnlabelledThe type VI secretion system (T6SS) is widespread in Gram-negative bacteria and can deliver toxic effector proteins into eukaryotic cells or competitor bacteria. Antibacterial T6SSs are increasingly recognized as key mediators of interbacterial competition and may contribute to the outcome of many polymicrobial infections. Multiple antibacterial effectors can be delivered by these systems, with diverse activities against target cells and distinct modes of secretion. Polymorphic toxins containing Rhs repeat domains represent a recently identified and as-yet poorly characterized class of T6SS-dependent effectors. Previous work had revealed that the potent antibacterial T6SS of the opportunistic pathogen Serratia marcescens promotes intraspecies as well as interspecies competition (S. L. Murdoch, K. Trunk, G. English, M. J. Fritsch, E. Pourkarimi, and S. J. Coulthurst, J Bacteriol 193:6057-6069, 2011, http://dx.doi.org/10.1128/JB.05671-11). In this study, two new Rhs family antibacterial effectors delivered by this T6SS have been identified. One of these was shown to act as a DNase toxin, while the other contains a novel, cytoplasmic-acting toxin domain. Importantly, using S. marcescens, it has been demonstrated for the first time that Rhs proteins, rather than other T6SS-secreted effectors, can be the primary determinant of intraspecies competition. Furthermore, a new family of accessory proteins associated with T6SS effectors has been identified, exemplified by S. marcescens EagR1, which is specifically required for deployment of its associated Rhs effector. Together, these findings provide new insight into how bacteria can use the T6SS to deploy Rhs-family effectors and mediate different types of interbacterial interactions.ImportanceInfectious diseases caused by bacterial pathogens represent a continuing threat to health and economic prosperity. To counter this threat, we must understand how such organisms survive and prosper. The type VI secretion system is a weapon that many pathogens deploy to compete against rival bacterial cells by injecting multiple antibacterial toxins into them. The ability to compete is vital considering that bacteria generally live in mixed communities. We aimed to identify new toxins and understand their deployment and role in interbacterial competition. We describe two new type VI secretion system-delivered toxins of the Rhs class, demonstrate that this class can play a primary role in competition between closely related bacteria, and identify a new accessory factor needed for their delivery.

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Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.

Publications

Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.

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