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

Sato, Yoshiharu; Takaya, Akiko; Yamamoto, Tomoko

doi:10.1186/1471-2105-12-442

Cited by 28 publications

(30 citation statements)

References 44 publications

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“…This is a hurdle in the identification of effector proteins, in spite of the increasing availability of whole-genome sequences. Proteomic and genetic screens have led to the identification of many effectors in different bacteria but the catalogue of effectors is still growing, indicating that it is not completed yet (12,20,43,46,51,52,55).…”

Section: Discussionmentioning

confidence: 99%

SrfJ, a Salmonella Type III Secretion System Effector Regulated by PhoP, RcsB, and IolR

2012

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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 SsrBdependent 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.T ype III secretion systems (T3SS) are complex devices that are present in many Gram-negative bacteria, including the animal pathogens Yersinia spp., enteropathogenic Escherichia coli, Pseudomonas aeruginosa, Shigella flexneri, Chlamydia spp., and Salmonella enterica, and the plant pathogens or symbionts Pseudomonas syringae, Ralstonia solanacearum, Erwinia spp., Rhizobium spp., and Xanthomonas campestris (29). T3SS allows secretion and translocation of effector proteins from bacteria to eukaryotic cells. The injected proteins are often able to interfere with host signal transduction pathways to enable bacterial invasion and survival in subcellular niches.Salmonella enterica is a facultative intracellular bacterium responsible for gastroenteritis and systemic infections in many animals, including humans (45). S. enterica virulence depends on two distinct T3SS, T3SS1 and T3SS2. T3SS1 translocates proteins through the plasma membrane of the host cell and is necessary for invasion, whereas T3SS2 is induced intracellularly, injects effector through the membrane of the Salmonella-containing vacuole (SCV), and is essential for survival and proliferation inside host cells. Both systems, however, depend on each other for efficient functioning (1). The genes encoding the structural components, many effectors, and some regulators of T3SS1 and T3SS2 are located in two Salmonella pathogenicity islands, SPI1 and SPI2, respectively (16,17,21,41,44,54). Some effector proteins are encoded by genes outside SPI1 and SPI2.SsrA (also named SpiR) and SsrB are the sensor and the response regulator, respectively, of a two-component regulatory system encoded by SPI2 that is necessary for the expression of T3SS2 (9, 28, 44). A genetic screen identified 12 genes (named srfA to srfM, for SsrB-regulated factors) outside SPI2 that were regulated by SsrB (62). These genes were located in horizontally acquired regions and, because of their pattern of expression, were suggested as c...

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Section: Discussionmentioning

confidence: 99%

SrfJ, a Salmonella Type III Secretion System Effector Regulated by PhoP, RcsB, and IolR

2012

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“…Through concerted actions, these effectors are known to modulate host cell functions, thereby contributing to the virulence of S. Typhimurium (82,83). Early in silico analyses of SrgE have provided conflicting predictions as to whether SrgE is a T3SS substrate (68)(69)(70)(71)(72). To test the hypothesis that SrgE is a T3SS, we used two different methods, a ␤-lactamase activity reporter assay and a split GFP system, to demonstrate that SrgE is indeed a T3SS effector delivered into host cells via T3SS2.…”

Section: Discussionmentioning

confidence: 99%

“…Given that SrgE is predicted by some algorithms to encode a T3SS effector (68,69) and that we have demonstrated translocation of SrgE into host cells, we hypothesized that translocation would require either SPI1, SPI2, or the flagellar T3SS. Therefore, we tested mutants lacking any one of these systems and a triple mutant lacking all three for their ability to translocate the SrgE300 -3ϫFlag-Bla fusion protein.…”

Section: Typhimurium Translocates Srge Into Host Cellsmentioning

confidence: 99%

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The SdiA-Regulated Gene srgE Encodes a Type III Secreted Effector

2014

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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 ␤-lactamase activity assay and a split green fluorescent protein (GFP) complementation assay. SrgE with ␤-lactamase fused to residue 40, 100, 150, or 300 was indeed expressed and translocated into host cells, but SrgE with ␤-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.

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“…In contrast to the other methods, Sato et al [14] developed a meta-analytical approach to predict effectors from features derived from two genome sequences through machine learning. The resulting effectors were further enriched by secondary filters such as co-expression analysis.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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

Cited by 28 publications

References 44 publications

SrfJ, a Salmonella Type III Secretion System Effector Regulated by PhoP, RcsB, and IolR

SrfJ, a Salmonella Type III Secretion System Effector Regulated by PhoP, RcsB, and IolR

The SdiA-Regulated Gene srgE Encodes a Type III Secreted Effector

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

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