Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria

Features and algorithms: facilitating investigation of secreted effectors in Gram-negative bacteria

Gram-negative bacteria deliver effector proteins through type III, IV, or VI secretion systems (T3SSs, T4SSs, and T6SSs) into host cells, causing infections and diseases. In general, effector proteins for each of these distinct secretion systems lack homology and are difficult to identify. Sequence analysis has disclosed many common features, helping us to understand the evolution, function, and secretion mechanisms of the effectors. In combination with various algorithms, the known common features have facilitated accurate prediction of new effectors. Ensemblers or integrated pipelines achieve a better prediction of performance, which combines multiple computational models or modules with multidimensional features. Natural language processing (NLP) models also show the merits, which could enable discovery of novel features and, in turn, facilitate more precise effector prediction, extending our knowledge about each secretion mechanism.

Natural language processing approach to model the secretion signal of type III effectors

Type III effectors are proteins injected by Gram-negative bacteria into eukaryotic hosts. In many plant and animal pathogens, these effectors manipulate host cellular processes to the benefit of the bacteria. Type III effectors are secreted by a type III secretion system that must "classify" each bacterial protein into one of two categories, either the protein should be translocated or not. It was previously shown that type III effectors have a secretion signal within their N-terminus, however, despite numerous efforts, the exact biochemical identity of this secretion signal is generally unknown. Computational characterization of the secretion signal is important for the identification of novel effectors and for better understanding the molecular translocation mechanism. In this work we developed novel machine-learning algorithms for characterizing the secretion signal in both plant and animal pathogens. Specifically, we represented each protein as a vector in high-dimensional space using Facebook's protein language model. Classification algorithms were next used to separate effectors from non-effector proteins. We subsequently curated a benchmark dataset of hundreds of effectors and thousands of non-effector proteins. We showed that on this curated dataset, our novel approach yielded substantially better classification accuracy compared to previously developed methodologies. We have also tested the hypothesis that plant and animal pathogen effectors are characterized by different secretion signals. Finally, we integrated the novel approach in Effectidor, a web-server for predicting type III effector proteins, leading to a more accurate classification of effectors from non-effectors.

DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework

Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram-negative pathogens. Current computational methods can predict type III secreted effectors (T3SEs) from amino acid sequences, but due to algorithmic constraints, reliable and large-scale prediction of T3SEs in Gram-negative bacteria remains a challenge. Here, we present DeepT3 2.0 (http://advintbioinforlab.com/deept3/), a novel web server that integrates different deep learning models for genome-wide predicting T3SEs from a bacterium of interest. DeepT3 2.0 combines various deep learning architectures including convolutional, recurrent, convolutional-recurrent and multilayer neural networks to learn N-terminal representations of proteins specifically for T3SE prediction. Outcomes from the different models are processed and integrated for discriminating T3SEs and non-T3SEs. Because it leverages diverse models and an integrative deep learning framework, DeepT3 2.0 outperforms existing methods in validation datasets. In addition, the features learned from networks are analyzed and visualized to explain how models make their predictions. We propose DeepT3 2.0 as an integrated and accurate tool for the discovery of T3SEs.

Computational prediction of secreted proteins in gram-negative bacteria

Gram-negative bacteria harness multiple protein secretion systems and secrete a large proportion of the proteome. Proteins can be exported to periplasmic space, integrated into membrane, transported into extracellular milieu, or translocated into cytoplasm of contacting cells. It is important for accurate, genome-wide annotation of the secreted proteins and their secretion pathways. In this review, we systematically classified the secreted proteins according to the types of secretion systems in Gram-negative bacteria, summarized the known features of these proteins, and reviewed the algorithms and tools for their prediction.

T3SEpp: an Integrated Prediction Pipeline for Bacterial Type III Secreted Effectors

Many Gram-negative bacteria infect hosts and cause diseases by translocating a variety of type III secreted effectors (T3SEs) into the host cell cytoplasm. However, despite a dramatic increase in the number of available whole-genome sequences, it remains challenging for accurate prediction of T3SEs. Traditional prediction models have focused on atypical sequence features buried in the N-terminal peptides of T3SEs, but unfortunately, these models have had high false-positive rates. In this research, we integrated promoter information along with characteristic protein features for signal regions, chaperone-binding domains, and effector domains for T3SE prediction. Machine learning algorithms, including deep learning, were adopted to predict the atypical features mainly buried in signal sequences of T3SEs, followed by development of a voting-based ensemble model integrating the individual prediction results. We assembled this into a unified T3SE prediction pipeline, T3SEpp, which integrated the results of individual modules, resulting in high accuracy (i.e., ∼0.94) and >1-fold reduction in the false-positive rate compared to that of state-of-the-art software tools. The T3SEpp pipeline and sequence features observed here will facilitate the accurate identification of new T3SEs, with numerous benefits for future studies on host-pathogen interactions.IMPORTANCE Type III secreted effector (T3SE) prediction remains a big computational challenge. In practical applications, current software tools often suffer problems of high false-positive rates. One of the causal factors could be the relatively unitary type of biological features used for the design and training of the models. In this research, we made a comprehensive survey on the sequence-based features of T3SEs, including signal sequences, chaperone-binding domains, effector domains, and transcription factor binding promoter sites, and assembled a unified prediction pipeline integrating multi-aspect biological features within homology-based and multiple machine learning models. To our knowledge, we have compiled the most comprehensive biological sequence feature analysis for T3SEs in this research. The T3SEpp pipeline integrating the variety of features and assembling different models showed high accuracy, which should facilitate more accurate identification of T3SEs in new and existing bacterial whole-genome sequences.

An account of in silico identification tools of secreted effector proteins in bacteria and future challenges

Bacterial pathogens secrete numerous effector proteins via six secretion systems, type I to type VI secretion systems, to adapt to new environments or to promote virulence by bacterium-host interactions. Many computational approaches have been used in the identification of effector proteins before the subsequent experimental verification because they tolerate laborious biological procedures and are genome scale, automated and highly efficient. Prevalent examples include machine learning methods and statistical techniques. In this article, we summarize the computational progress toward predicting secreted effector proteins in bacteria, with an opening of an introduction of features that are used to discriminate effectors from non-effectors. The mechanism, contribution and deficiency of previous developed detection tools are presented, which are further benchmarked based on a curated testing data set. According to the results of benchmarking, potential improvements of the prediction performance are discussed, which include (1) more informative features for discriminating the effectors from non-effectors; (2) the construction of comprehensive training data set of the machine learning algorithms; (3) the advancement of reliable prediction methods and (4) a better interpretation of the mechanisms behind the molecular processes. The future of in silico identification of bacterial secreted effectors includes both opportunities and challenges.

Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI

Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance. Here, we provide a comprehensive survey and benchmarking of currently available tools for the prediction of effector proteins of bacterial types III, IV and VI secretion systems (T3SS, T4SS and T6SS, respectively). We review core algorithms, feature selection techniques, tool availability and applicability and evaluate the prediction performance based on carefully curated independent test data sets. In an effort to improve predictive performance, we constructed three ensemble models based on ML algorithms by integrating the output of all individual predictors reviewed. Our benchmarks demonstrate that these ensemble models outperform all the reviewed tools for the prediction of effector proteins of T3SS and T4SS. The webserver of the proposed ensemble methods for T3SS and T4SS effector protein prediction is freely available at http://tbooster.erc.monash.edu/index.jsp. We anticipate that this survey will serve as a useful guide for interested users and that the new ensemble predictors will stimulate research into host-pathogen relationships and inspiration for the development of new bioinformatics tools for predicting effector proteins of T3SS, T4SS and T6SS.

A protein that controls the onset of a Salmonella virulence program

The mechanism of action and contribution to pathogenesis of many virulence genes are understood. By contrast, little is known about anti-virulence genes, which contribute to the start, progression, and outcome of an infection. We now report how an anti-virulence factor in Salmonella enterica serovar Typhimurium dictates the onset of a genetic program that governs metabolic adaptations and pathogen survival in host tissues. Specifically, we establish that the anti-virulence protein CigR directly restrains the virulence protein MgtC, thereby hindering intramacrophage survival, inhibition of ATP synthesis, stabilization of cytoplasmic pH, and gene transcription by the master virulence regulator PhoP. We determine that, like MgtC, CigR localizes to the bacterial inner membrane and that its C-terminal domain is critical for inhibition of MgtC. As in many toxin/anti-toxin genes implicated in antibiotic tolerance, the mgtC and cigR genes are part of the same mRNA. However, cigR is also transcribed from a constitutive promoter, thereby creating a threshold of CigR protein that the inducible MgtC protein must overcome to initiate a virulence program critical for pathogen persistence in host tissues.

Characterization and Expression Profiling of Camellia sinensis Cinnamate 4-hydroxylase Genes in Phenylpropanoid Pathways

Cinnamate 4-hydroxylase (C4H), a cytochrome P450-dependent monooxygenase, participates in the synthesis of numerous polyphenoid compounds, such as flavonoids and lignins. However, the C4H gene number and function in tea plants are not clear. We screened all available transcriptome and genome databases of tea plants and three C4H genes were identified and named CsC4Ha, CsC4Hb, and CsC4Hc, respectively. Both CsC4Ha and CsC4Hb have 1518-bp open reading frames that encode 505-amino acid proteins. CsC4Hc has a 1635-bp open reading frame that encodes a 544-amino acid protein. Enzymatic analysis of recombinant proteins expressed in yeast showed that the three enzymes catalyzed the formation of p-coumaric acid (4-hydroxy trans-cinnamic acid) from trans-cinnamic acid. Quantitative real-time PCR (qRT-PCR) analysis showed that CsC4Ha was highly expressed in the 4th leaf, CsC4Hb was highly expressed in tender leaves, while CsC4Hc was highly expressed in the young stems. The three CsC4Hs were induced with varying degrees by abiotic stress treatments. These results suggest they may have different subcellular localization and different physiological functions.

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

BACKGROUND

Many 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.

RESULTS

We 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.

CONCLUSIONS

The 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.

[Molecular targets of bacterial effectors and toxins that underlie vulnerability to diseases]

Pathogenic bacteria produce a variety of effectors and/or toxins, which subvert target cell/tissue functions in the infected hosts. Some of those effectors/toxins also perturb host defense mechanism, thereby making up more complicated pathophysiological conditions. Such bacterial effectors/toxins may have been positively selected during evolution because they directly strike vulnerable points in the host system. In turn, this indicates that systemic exploration of molecules and signaling pathways targeted by bacterial effectors/toxins provides a powerful tool in digging up an unexpected Achilles' heel(s), malfunctioning of which gives rise to disorders not restricted to infectious diseases. Based on this viewpoint, this review shows molecular basis underlying host susceptibility and vulnerability to diseases through the studies of host molecules targeted by bacterial effectors and toxins.

Crimean-Congo Hemorrhagic Fever Virus Gn Bioinformatic Analysis and Construction of a Recombinant Bacmid in Order to Express Gn by Baculovirus Expression System

Background

Crimean-Congo hemorrhagic fever virus (CCHFV) is a member of the nairovirus, a genus in the Bunyaviridae family, which causes a life threatening disease in human. Currently, there is no vaccine against CCHFV and detailed structural analysis of CCHFV proteins remains undefined. The CCHFV M RNA segment encodes two viral surface glycoproteins known as Gn and Gc. Viral glycoproteins can be considered as key targets for vaccine development.

Objectives

The current study aimed to investigate structural bioinformatics of CCHFV Gn protein and design a construct to make a recombinant bacmid to express by baculovirus system.

Materials and Methods

To express the Gn protein in insect cells that can be used as antigen in animal model vaccine studies. Bioinformatic analysis of CCHFV Gn protein was performed and designed a construct and cloned into pFastBacHTb vector and a recombinant Gn-bacmid was generated by Bac to Bac system.

Results

Primary, secondary, and 3D structure of CCHFV Gn were obtained and PCR reaction with M13 forward and reverse primers confirmed the generation of recombinant bacmid DNA harboring Gn coding region under polyhedron promoter.

Conclusions

Characterization of the detailed structure of CCHFV Gn by bioinformatics software provides the basis for development of new experiments and construction of a recombinant bacmid harboring CCHFV Gn, which is valuable for designing a recombinant vaccine against deadly pathogens like CCHFV.

The SdiA-regulated gene srgE encodes a type III secreted effector

Salmonella enterica serovar Typhimurium is a food-borne pathogen that causes severe gastroenteritis. The ability of Salmonella to cause disease depends on two type III secretion systems (T3SSs) encoded in two distinct Salmonella pathogenicity islands, 1 and 2 (SPI1 and SPI2, respectively). S. Typhimurium encodes a solo LuxR homolog, SdiA, which can detect the acyl-homoserine lactones (AHLs) produced by other bacteria and upregulate the rck operon and the srgE gene. SrgE is predicted to encode a protein of 488 residues with a coiled-coil domain between residues 345 and 382. In silico studies have provided conflicting predictions as to whether SrgE is a T3SS substrate. Therefore, in this work, we tested the hypothesis that SrgE is a T3SS effector by two methods, a β-lactamase activity assay and a split green fluorescent protein (GFP) complementation assay. SrgE with β-lactamase fused to residue 40, 100, 150, or 300 was indeed expressed and translocated into host cells, but SrgE with β-lactamase fused to residue 400 or 488 was not expressed, suggesting interference by the coiled-coil domain. Similarly, SrgE with GFP S11 fused to residue 300, but not to residue 488, was expressed and translocated into host cells. With both systems, translocation into host cells was dependent upon SPI2. A phylogenetic analysis indicated that srgE is found only within Salmonella enterica subspecies. It is found sporadically within both typhoidal and nontyphoidal serovars, although the SrgE protein sequences found within typhoidal serovars tend to cluster separately from those found in nontyphoidal serovars, suggesting functional diversification.

Molecular cloning and yeast expression of cinnamate 4-hydroxylase from Ornithogalum saundersiae baker

OSW-1, isolated from the bulbs of Ornithogalum saundersiae Baker, is a steroidal saponin endowed with considerable antitumor properties. Biosynthesis of the 4-methoxybenzoyl group on the disaccharide moiety of OSW-1 is known to take place biochemically via the phenylpropanoid biosynthetic pathway, but molecular biological characterization of the related genes has been insufficient. Cinnamic acid 4-hydroxylase (C4H, EC 1.14.13.11), catalyzing the hydroxylation of trans-cinnamic acid to p-coumaric acid, plays a key role in the ability of phenylpropanoid metabolism to channel carbon to produce the 4-methoxybenzoyl group on the disaccharide moiety of OSW-1. Molecular isolation and functional characterization of the C4H genes, therefore, is an important step for pathway characterization of 4-methoxybenzoyl group biosynthesis. In this study, a gene coding for C4H, designated as OsaC4H, was isolated according to the transcriptome sequencing results of Ornithogalum saundersiae. The full-length OsaC4H cDNA is 1,608-bp long, with a 1,518-bp open reading frame encoding a protein of 505 amino acids, a 55-bp 5′ non-coding region and a 35-bp 3'-untranslated region. OsaC4H was functionally characterized by expression in Saccharomyces cerevisiae and shown to catalyze the oxidation of trans-cinnamic acid to p-coumaric acid, which was identified by high performance liquid chromatography with diode array detection (HPLC-DAD), HPLC-MS and nuclear magnetic resonance (NMR) analysis. The identification of the OsaC4H gene was expected to open the way to clarification of the biosynthetic pathway of OSW-1.

Effective identification of Gram-negative bacterial type III secreted effectors using position-specific residue conservation profiles

BACKGROUND

Type III secretion systems (T3SSs) are central to the pathogenesis and specifically deliver their secreted substrates (type III secreted proteins, T3SPs) into host cells. Since T3SPs play a crucial role in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T3SSs. This study reports a novel and effective method for identifying the distinctive residues which are conserved different from other SPs for T3SPs prediction. Moreover, the importance of several sequence features was evaluated and further, a promising prediction model was constructed.

RESULTS

Based on the conservation profiles constructed by a position-specific scoring matrix (PSSM), 52 distinctive residues were identified. To our knowledge, this is the first attempt to identify the distinct residues of T3SPs. Of the 52 distinct residues, the first 30 amino acid residues are all included, which is consistent with previous studies reporting that the secretion signal generally occurs within the first 30 residue positions. However, the remaining 22 positions span residues 30-100 were also proven by our method to contain important signal information for T3SP secretion because the translocation of many effectors also depends on the chaperone-binding residues that follow the secretion signal. For further feature optimisation and compression, permutation importance analysis was conducted to select 62 optimal sequence features. A prediction model across 16 species was developed using random forest to classify T3SPs and non-T3 SPs, with high receiver operating curve of 0.93 in the 10-fold cross validation and an accuracy of 94.29% for the test set. Moreover, when performing on a common independent dataset, the results demonstrate that our method outperforms all the others published to date. Finally, the novel, experimentally confirmed T3 effectors were used to further demonstrate the model's correct application. The model and all data used in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/T3SPs.zip.

Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles

MOTIVATION

Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species; however, widely applicable prediction approaches are still unavailable

RESULTS

In this work, four types of distinctive features, namely, amino acid composition, dipeptide composition, .position-specific scoring matrix composition and auto covariance transformation of position-specific scoring matrix, were calculated from primary sequences. A classifier, T4EffPred, was developed using the support vector machine with these features and their different combinations for effector prediction. Various theoretical tests were performed in a newly established dataset, and the results were measured with four indexes. We demonstrated that T4EffPred can discriminate IVA and IVB effectors in benchmark datasets with positive rates of 76.7% and 89.7%, respectively. The overall accuracy of 95.9% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences. A classifier ensemble was designed to synthesize all single classifiers. Notable performance improvement was observed using this ensemble system in benchmark tests. To demonstrate the model's application, a genome-scale prediction of effectors was performed in Bartonella henselae, an important zoonotic pathogen. A number of putative candidates were distinguished.

AVAILABILITY

A web server implementing the prediction method and the source code are both available at http://bioinfo.tmmu.edu.cn/T4EffPred.

Exoproteomics: exploring the world around biological systems

The term 'exoproteome' describes the protein content that can be found in the extracellular proximity of a given biological system. These proteins arise from cellular secretion, other protein export mechanisms or cell lysis, but only the most stable proteins in this environment will remain in abundance. It has been shown that these proteins reflect the physiological state of the cells in a given condition and are indicators of how living systems interact with their environments. High-throughput proteomic approaches based on a shotgun strategy, and high-resolution mass spectrometers, have modified the authors' view of exoproteomes. In the present review, the authors describe how these new approaches should be exploited to obtain the maximum useful information from a sample, whatever its origin. The methodologies used for studying secretion from model cell lines derived from eukaryotic, multicellular organisms, virulence determinants of pathogens and environmental bacteria and their relationships with their habitats are illustrated with several examples. The implication of such data, in terms of proteogenomics and the discovery of novel protein functions, is discussed.

Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes

Background

As one of the most important virulence factor types in gram-negative pathogenic bacteria, type-III effectors (TTEs) play a crucial role in pathogen-host interactions by directly influencing immune signaling pathways within host cells. Based on the hypothesis that type-III secretion signals may be comprised of some weakly conserved sequence motifs, here we used profile-based amino acid pair information to develop an accurate TTE predictor.

Results

For a TTE or non-TTE, we first used a hidden Markov model-based sequence searching method (i.e., HHblits) to detect its weakly homologous sequences and extracted the profile-based k-spaced amino acid pair composition (HH-CKSAAP) from the N-terminal sequences. In the next step, the feature vector HH-CKSAAP was used to train a linear support vector machine model, which we designate as BEAN (Bacterial Effector ANalyzer). We compared our method with four existing TTE predictors through an independent test set, and our method revealed improved performance. Furthermore, we listed the most predictive amino acid pairs according to their weights in the established classification model. Evolutionary analysis shows that predictive amino acid pairs tend to be more conserved. Some predictive amino acid pairs also show significantly different position distributions between TTEs and non-TTEs. These analyses confirmed that some weakly conserved sequence motifs may play important roles in type-III secretion signals. Finally, we also used BEAN to scan one plant pathogen genome and showed that BEAN can be used for genome-wide TTE identification. The webserver and stand-alone version of BEAN are available at http://protein.cau.edu.cn:8080/bean/.

Learning virulent proteins from integrated query networks

Background

Methods of weakening and attenuating pathogens’ abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categories.

Results

A lightweight method for data integration is used, which links information regarding a protein via a path-based query graph. A method of weighting is then applied to query graphs that can serve as input to various statistical classification methods for discrimination, and the combined usage of both data integration and learning methods are tested against the problem of both generalized and specific virulence function prediction.

Conclusions

This approach improves coverage of functional data over a protein. Moreover, while depending largely on noisy and potentially non-curated data from public sources, we find it outperforms other techniques to identification of general virulence factors and baseline remote homology detection methods for specific virulence categories.

Identification of novel type III effectors using latent Dirichlet allocation

Among the six secretion systems identified in Gram-negative bacteria, the type III secretion system (T3SS) plays important roles in the disease development of pathogens. T3SS has attracted a great deal of research interests. However, the secretion mechanism has not been fully understood yet. Especially, the identification of effectors (secreted proteins) is an important and challenging task. This paper adopts machine learning methods to identify type III secreted effectors (T3SEs). We extract features from amino acid sequences and conduct feature reduction based on latent semantic information by using latent Dirichlet allocation model. The experimental results on Pseudomonas syringae data set demonstrate the good performance of the new methods.

SrfJ, a Salmonella type III secretion system effector regulated by PhoP, RcsB, and IolR

Virulence-related type III secretion systems are present in many Gram-negative bacterial pathogens. These complex devices translocate proteins, called effectors, from the bacterium into the eukaryotic host cell. Here, we identify the product of srfJ, a Salmonella enterica serovar Typhimurium gene regulated by SsrB, as a new substrate of the type III secretion system encoded by Salmonella pathogenicity island 2. The N-terminal 20-amino-acid segment of SrfJ was recognized as a functional secretion and translocation signal specific for this system. Transcription of srfJ was positively regulated by the PhoP/PhoQ system in an SsrB-dependent manner and was negatively regulated by the Rcs system in an SsrB-independent manner. A screen for regulators of an srfJ-lacZ transcriptional fusion using the T-POP transposon identified IolR, the regulator of genes involved in myo-inositol utilization, as an srfJ repressor. Our results suggest that SrfJ is synthesized both inside the host, in response to intracellular conditions, and outside the host, in myo-inositol-rich environments.