Effective Identification of Gram-Negative Bacterial Type III Secreted Effectors Using Position-Specific Residue Conservation Profiles

Backgroud

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.     

T3_MM: A Markov Model Effectively Classifies Bacterial Type III Secretion Signals

Motivation

Type III Secretion Systems (T3SSs) play important roles in the interaction between gram-negative bacteria and their hosts. T3SSs function by translocating a group of bacterial effector proteins into the host cytoplasm. The details of specific type III secretion process are yet to be clarified. This research focused on comparing the amino acid composition within the N-terminal 100 amino acids from type III secretion (T3S) signal sequences or non-T3S proteins, specifically whether each residue exerts a constraint on residues found in adjacent positions. We used these comparisons to set up a statistic model to quantitatively model and effectively distinguish T3S effectors.

Results

In this study, the amino acid composition (Aac) probability profiles conditional on its sequentially preceding position and corresponding amino acids were compared between N-terminal sequences of T3S and non-T3S proteins. The profiles are generally different. A Markov model, namely T3_MM, was consequently designed to calculate the total Aac conditional probability difference, i.e., the likelihood ratio of a sequence being a T3S or a non-T3S protein. With T3_MM, known T3S and non-T3S proteins were found to well approximate two distinct normal distributions. The model could distinguish validated T3S and non-T3S proteins with a 5-fold cross-validation sensitivity of 83.9% at a specificity of 90.3%. T3_MM was also shown to be more robust, accurate, simple, and statistically quantitative, when compared with other T3S protein prediction models. The high effectiveness of T3_MM also indicated the overall Aac difference between N-termini of T3S and non-T3S proteins, and the constraint of Aac exerted by its preceding position and corresponding Aac.

Availability

An R package for T3_MM is freely downloadable from: http://biocomputer.bio.cuhk.edu.hk/softwares/T3_MM. T3_MM web server: http://biocomputer.bio.cuhk.edu.hk/T3DB/T3_MM.php.     

Prediction of Type III Secretion Signals in Genomes of Gram-Negative Bacteria

Background

Pathogenic bacteria infecting both animals as well as plants use various mechanisms to transport virulence factors across their cell membranes and channel these proteins into the infected host cell. The type III secretion system represents such a mechanism. Proteins transported via this pathway (“effector proteins”) have to be distinguished from all other proteins that are not exported from the bacterial cell. Although a special targeting signal at the N-terminal end of effector proteins has been proposed in literature its exact characteristics remain unknown.

Methodology/Principal Findings

In this study, we demonstrate that the signals encoded in the sequences of type III secretion system effectors can be consistently recognized and predicted by machine learning techniques. Known protein effectors were compiled from the literature and sequence databases, and served as training data for artificial neural networks and support vector machine classifiers. Common sequence features were most pronounced in the first 30 amino acids of the effector sequences. Classification accuracy yielded a cross-validated Matthews correlation of 0.63 and allowed for genome-wide prediction of potential type III secretion system effectors in 705 proteobacterial genomes (12% predicted candidates protein), their chromosomes (11%) and plasmids (13%), as well as 213 Firmicute genomes (7%).

Conclusions/Significance

We present a signal prediction method together with comprehensive survey of potential type III secretion system effectors extracted from 918 published bacterial genomes. Our study demonstrates that the analyzed signal features are common across a wide range of species, and provides a substantial basis for the identification of exported pathogenic proteins as targets for future therapeutic intervention. The prediction software is publicly accessible from our web server (www.modlab.org).     

An Arabidopsis Secondary Metabolite Directly Targets Expression of the Bacterial Type III Secretion System to Inhibit Bacterial Virulence

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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/.     

Identification of Novel Type III Secretion Chaperone-Substrate Complexes of Chlamydia trachomatis

Chlamydia trachomatis is an obligate intracellular bacterial pathogen of humans that uses a type III secretion (T3S) system to manipulate host cells through the delivery of effector proteins into their cytosol and membranes. The function of T3S systems depends on small bacterial cytosolic chaperone-like proteins, which bind T3S substrates and ensure their appropriate secretion. To find novel T3S chaperone-substrate complexes of C. trachomatis we first searched its genome for genes encoding proteins with features of T3S chaperones. We then systematically tested for interactions between candidate chaperones and chlamydial T3S substrates by bacterial two-hybrid. This revealed interactions between Slc1 (a known T3S chaperone) or CT584 and several T3S substrates. Co-immunoprecipation after protein expression in Yersinia enterocolitica and protein overlay binding assays indicated that Slc1 interacted with the N-terminal region of the known T3S substrates Tarp (a previously described substrate of Slc1), CT694, and CT695, and that CT584 interacted with a central region of CT082, which we identified as a C. trachomatis T3S substrate using Y. enterocolitica as a heterologous system. Further T3S assays in Yersinia indicated that Slc1 or CT584 increased the amount of secreted Tarp, CT694, and CT695, or CT082, respectively. Expression of CT584 increased the intra-bacterial stability of CT082, while Slc1 did not affect the stability of its substrates. Overall, this indicated that in C. trachomatis Slc1 is a chaperone of multiple T3S substrates and that CT584 is a chaperone of the newly identified T3S substrate CT082.     

Function of FlhB,a Membrane Protein Implicated in the Bacterial Flagellar Type III Secretion System

The membrane protein FlhB is a highly conserved component of the flagellar secretion system, and it plays an active role in the regulation of protein export. In this study conserved properties of FlhB that are important for its function were investigated. Replacing the flhB gene (or part of the gene) in Salmonella typhimurium with the flhB gene of the distantly related bacterium Aquifex aeolicus greatly reduces motility. However, motility can be restored to some extent by spontaneous mutations in the part of flhB gene coding for the cytoplasmic domain of Aquifex FlhB. Structural analysis suggests that these mutations destabilize the structure. The secondary structure and stability of the mutated cytoplasmic fragments of FlhB have been studied by circular dichroism spectroscopy. The results suggest that conformational flexibility could be important for FlhB function. An extragenic suppressor mutation in the fliS gene, which decreases the affinity of FliS to FliC, partially restores motility of the FlhB substitution mutants.     

Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals

ObjectivesElectromyography (EMG) is recording of the electrical activity produced by skeletal muscles. The classification of the EMG signals for different physical actions can be useful in restoring some or all of the lost motor functionalities in these individuals. Accuracy in classifying the EMG signal indicates efficient control of prosthesis.Material and methodsThe flexible analytic wavelet transform (FAWT) is used for classification of surface electromyography (sEMG) signals for identification of physical actions. FAWT is an efficient method for decomposition of sEMG signal into eight sub-bands, features namely neg-entropy, mean absolute value (MAV), variance (VAR), modified mean absolute value type 1 (MAV1), waveform length (WL), simple square integral (SSI), Tsallis entropy, integrated EMG (IEMG) are extracted from the sub-bands. Extracted features are fed into an extreme learning machine (ELM) classifier with sigmoid activation function.ResultsComprehensive experiments are conducted on the input sEMG signals and the accuracy, sensitivity and specificity scores are used for performance measurement. Experiments showed that among all sub-bands, the seventh sub-band provided the best performance where the recorded accuracy, sensitivity and specificity values were 99.36%, 99.36% and 99.93%, respectively. The comparison results showed best efficiency of proposed method as compared to other methods on the same dataset.ConclusionThis paper investigates the usage of the FAWT and ELM on sEMG signal classification. The results show that the proposed method is quite efficient in classification of the sEMG signals. It is also observed that the seventh sub-band of the FAWT provides the best discrimination property. In the future works, recent wavelet transform methods will be used for improving the classification performance.     

Identification of a regulatory cascade controlling Type III Secretion System 3 gene expression in Burkholderia pseudomallei

A major and critical virulence determinant of many Gram‐negative bacterial pathogens is the Type III Secretion Systems (T3SS). T3SS3 in Burkholderia pseudomallei is critical for bacterial virulence in mammalian infection models but its regulation is unknown. B. pseudomallei is the causative agent of melioidosis, a potentially fatal disease endemic in Southeast Asia and northern Australia. While screening for bacterial transposon mutants with a defective T3SS function, we discovered a TetR family regulator (bspR) responsible for the control of T3SS3 gene expression. The bspR mutant exhibited significant virulence attenuation in mice. BspR acts through BprP, a novel transmembrane regulator located adjacent to the currently delineated T3SS3 region. BprP in turn regulates the expression of structural and secretion components of T3SS3 and the AraC family regulator bsaN. BsaN and BicA likely form a complex to regulate the expression of T3SS3 effectors and other regulators which in turn affect the expression of Type VI Secretion Systems (T6SS). The complete delineation of the bspR initiated T3SS regulatory cascade not only contributes to the understanding of B. pseudomallei pathogenesis but also provides an important example of how bacterial pathogens could co‐opt and integrate various regulatory motifs to form a new regulatory network adapted for its own purposes.     

Innate Immune Recognition of Yersinia pseudotuberculosis Type III Secretion

Specialized protein translocation systems are used by many bacterial pathogens to deliver effector proteins into host cells that interfere with normal cellular functions. How the host immune system recognizes and responds to this intrusive event is not understood. To address these questions, we determined the mammalian cellular response to the virulence-associated type III secretion system (T3SS) of the human pathogen Yersinia pseudotuberculosis. We found that macrophages devoid of Toll-like receptor (TLR) signaling regulate expression of 266 genes following recognition of the Y. pseudotuberculosis T3SS. This analysis revealed two temporally distinct responses that could be separated into activation of NFκB- and type I IFN-regulated genes. Extracellular bacteria were capable of triggering these signaling events, as inhibition of bacterial uptake had no effect on the ensuing innate immune response. The cytosolic peptidoglycan sensors Nod1 and Nod2 and the inflammasome component caspase-1 were not involved in NFκB activation following recognition of the Y. pseudotuberculosis T3SS. However, caspase-1 was required for secretion of the inflammatory cytokine IL-1β in response to T3SS-positive Y. pseudotuberculosis. In order to characterize the bacterial requirements for induction of this novel TLR-, Nod1/2-, and caspase-1-independent response, we used Y. pseudotuberculosis strains lacking specific components of the T3SS. Formation of a functional T3SS pore was required, as bacteria expressing a secretion needle, but lacking the pore-forming proteins YopB or YopD, did not trigger these signaling events. However, nonspecific membrane disruption could not recapitulate the NFκB signaling triggered by Y. pseudotuberculosis expressing a functional T3SS pore. Although host cell recognition of the T3SS did not require known translocated substrates, the ensuing response could be modulated by effectors such as YopJ and YopT, as YopT amplified the response, while YopJ dampened it. Collectively, these data suggest that combined recognition of the T3SS pore and YopBD-mediated delivery of immune activating ligands into the host cytosol informs the host cell of pathogenic challenge. This leads to a unique, multifactorial response distinct from the canonical immune response to a bacterium lacking a T3SS.