Prediction of bacterial type IV secreted effectors by C-terminal features

T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors

Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp.

The impact of Elaeagnus angustifolia root exudates on Parafrankia soli NRRL B-16219 exoproteome

Root exudates from host plant species are known to play a critical role in the establishment and maintenance of symbiotic relationships with soil bacteria. In this study, we investigated the impact of root exudates from compatible host plant species; Elaeagnus angustifolia on the exoproteome of Parafrankia soli strain NRRL B-16219. A total of 565 proteins were evidenced as differentially abundant, with 32 upregulated and 533 downregulated in presence of the plant exudates. Analysis of the function of these proteins suggests that the bacterial strain is undergoing a complex metabolic reprogramming towards a new developmental phase elicited in presence of host plant root exudates. The upregulation of Type II/IV secretion system proteins among the differentially expressed proteins indicates their possible role in infecting the host plant, as shown for some rhizobia. Additionally, EF-Tu, proteins upregulated in this study, may function as an effector for the T4SSs and trigger plant defense responses. These findings suggest that Parafrankia soli may use EF-Tu to infect the actinorhizal host plant and pave the way for further investigations of the molecular mechanisms underlying the establishment of symbiotic relationships.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global property-based, and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches are described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

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.

Pathogen Profile of Klebsiella variicola, the Causative Agent of Banana Sheath Rot

Banana (Musa spp.) is an important fruit and food crop worldwide. In recent years, banana sheath rot has become a major problem in banana cultivation, causing plant death and substantial economic losses. Nevertheless, the pathogen profile of this disease has not been fully characterized. Klebsiella variicola is a versatile bacterium capable of colonizing different hosts, such as plants, humans, insects, and animals, and is recognized as an emerging pathogen in various hosts. In this study, we obtained 12 bacterial isolates from 12 different banana samples showing banana sheath rot in Guangdong and Guangxi Provinces, China. Phylogenetic analysis based on 16S rRNA sequences confirmed that all 12 isolates were K. variicola strains. We sequenced the genomes of these strains, performed comparative genomic analysis with other sequenced K. variicola strains, and found a lack of consistency in accessory gene content among these K. variicola strains. However, prediction based on the pan-genome of K. variicola revealed 22 unique virulence factors carried by the 12 pathogenic K. variicola isolates. Microbiome and microbial interaction network analysis of endophytes between the healthy tissues of diseased plants and healthy plants of two cultivars showed that Methanobacterium negatively interacts with Klebsiella in banana plants and that Herbaspirillum might indirectly inhibit Methanobacterium to promote Klebsiella growth. These results suggest that banana sheath rot is caused by the imbalance of plant endophytes and opportunistic pathogenic bacteria, providing an important basis for research and control of this disease.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Draft Genome Sequence of Seven Pigmented Strains of Xanthomonas citri pv. anacardii, the Causal Agent of Cashew Angular Spot

Cashew (Anacardium occidentale) angular leaf spot is caused by pigmented and non-pigmented strains of Xanthomonas citri pv. anacardii, which have been isolated from infected plants in Brazil. The disease symptoms can be observed in leaves, stems, and fruits. Given that infection in young fruits results in fruits unsuitable for commercialization, angular leaf spot represents a serious threat to the cashew crop in Brazil. Here, we report the genomic sequencing of seven pigmented strains of X. citri pv. anacardii, obtained from the leaves of cashew trees from São Paulo state, Brazil, in 2009. The construction of the libraries was carried out according to the manufacturer, and whole-genome sequencing was performed using the Illumina HiSeq 2500 platform. Genome size, number of coding sequences, largest contig length, and N50 ranged from 4,996,984 to 5,003,485 bp, 4,621 to 4,643 bp, 212,513 to 362,232 bp, and 113,582 to 141,003 bp, respectively. GC content and RNA numbers were 64.68% and 54, respectively, for all strains. ANIm and dDDH analyses showed values above 99.5 and 92.1% among these strains and the non-pigmented pathotype strain of X. citri pv. anacardii (IBSBF2579PT). A maximum likelihood tree built with 2,708 core genes grouped all X. citri pv. anacardii strains in the same clade, with a 100% bootstrap. These resources will contribute in a relevant way to help understand the ecological, taxonomic, evolutionary, pathogenicity, and virulence aspects of X. citri pv. anacardii, which will be useful for the study and development of techniques for managing cashew angular leaf spot.

Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends

Machine learning (ML) methods are used in cheminformatics processes to predict the activity of an unknown drug and thus discover new potential antibacterial drugs. This article conducts a bibliometric study to analyse the contributions of leading authors, universities/organisations and countries in terms of productivity, citations and bibliographic linkage. A sample of 1596 Scopus documents for the period 2006-2022 is the basis of the study. In order to develop the analysis, bibliometrix R-Tool and VOSviewer software were used. We determined essential topics related to the application of ML in the field of antibacterial development (Computer model in antibacterial drug design, and Learning algorithms and systems for forecasting). We identified obsolete and saturated areas of research. At the same time, we proposed emerging topics according to the various analyses carried out on the corpus of published scientific literature (Title, abstract and keywords). Finally, the applied methodology contributed to building a broader and more specific "big picture" of ML research in antibacterial studies for the focus of future projects.

PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

BACKGROUND

Anti-CRISPR proteins are potent modulators that inhibit the CRISPR-Cas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification.

RESULTS

Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins.

CONCLUSIONS

In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at: https://github.com/Lyn-666/anti_CRISPR.git .

T1SEstacker: A Tri-Layer Stacking Model Effectively Predicts Bacterial Type 1 Secreted Proteins Based on C-Terminal Non-repeats-in-Toxin-Motif Sequence Features

Type 1 secretion systems play important roles in pathogenicity of Gram-negative bacteria. However, the substrate secretion mechanism remains largely unknown. In this research, we observed the sequence features of repeats-in-toxin (RTX) proteins, a major class of type 1 secreted effectors (T1SEs). We found striking non-RTX-motif amino acid composition patterns at the C termini, most typically exemplified by the enriched “[FLI][VAI]” at the most C-terminal two positions. Machine-learning models, including deep-learning ones, were trained using these sequence-based non-RTX-motif features and further combined into a tri-layer stacking model, T1SEstacker, which predicted the RTX proteins accurately, with a fivefold cross-validated sensitivity of ∼0.89 at the specificity of ∼0.94. Besides substrates with RTX motifs, T1SEstacker can also well distinguish non-RTX-motif T1SEs, further suggesting their potential existence of common secretion signals. T1SEstacker was applied to predict T1SEs from the genomes of representative Salmonella strains, and we found that both the number and composition of T1SEs varied among strains. The number of T1SEs is estimated to reach 100 or more in each strain, much larger than what we expected. In summary, we made comprehensive sequence analysis on the type 1 secreted RTX proteins, identified common sequence-based features at the C termini, and developed a stacking model that can predict type 1 secreted proteins accurately.

Recent Advancements in Tracking Bacterial Effector Protein Translocation

Bacteria-host interactions are characterized by the delivery of bacterial virulence factors, i.e., effectors, into host cells where they counteract host immunity and exploit host responses allowing bacterial survival and spreading. These effectors are translocated into host cells by means of dedicated secretion systems such as the type 3 secretion system (T3SS). A comprehensive understanding of effector translocation in a spatio-temporal manner is of critical importance to gain insights into an effector’s mode of action. Various approaches have been developed to understand timing and order of effector translocation, quantities of translocated effectors and their subcellular localization upon translocation into host cells. Recently, the existing toolset has been expanded by newly developed state-of-the art methods to monitor bacterial effector translocation and dynamics. In this review, we elaborate on reported methods and discuss recent advances and shortcomings in this area of tracking bacterial effector translocation.

Modular evolution of secretion systems and virulence plasmids in a bacterial species complex

BACKGROUND

Many named species as defined in current bacterial taxonomy correspond to species complexes. Uncertainties regarding the organization of their genetic diversity challenge research efforts. We utilized the Agrobacterium tumefaciens species complex (a.k.a. Agrobacterium biovar 1), a taxon known for its phytopathogenicity and applications in transformation, as a study system and devised strategies for investigating genome diversity and evolution of species complexes.

RESULTS

We utilized 35 genome assemblies, including 14 newly generated ones, to achieve a phylogenetically balanced sampling of A. tumefaciens. Our genomic analysis suggested that the 10 genomospecies described previously are distinct biological species and supported a quantitative guideline for species delineation. Furthermore, our inference of gene content and core-genome phylogeny allowed for investigations of genes critical in fitness and ecology. For the type VI secretion system (T6SS) involved in interbacterial competition and thought to be conserved, we detected multiple losses and one horizontal gene transfer. For the tumor-inducing plasmids (pTi) and pTi-encoded type IV secretion system (T4SS) that are essential for agrobacterial phytopathogenicity, we uncovered novel diversity and hypothesized their involvement in shaping this species complex. Intriguingly, for both T6SS and T4SS, genes encoding structural components are highly conserved, whereas extensive diversity exists for genes encoding effectors and other proteins.

CONCLUSIONS

We demonstrate that the combination of a phylogeny-guided sampling scheme and an emphasis on high-quality assemblies provides a cost-effective approach for robust analysis in evolutionary genomics. We show that the T6SS VgrG proteins involved in specific effector binding and delivery can be classified into distinct types based on domain organization. The co-occurrence patterns of VgrG-associated domains and the neighboring genes that encode different chaperones/effectors can be used to infer possible interacting partners. Similarly, the associations between plant host preference and the pTi type among these strains can be used to infer phenotype-genotype correspondence. Our strategies for multi-level investigations at scales that range from whole genomes to intragenic domains and phylogenetic depths from between- to within-species are applicable to other bacteria. Furthermore, modularity observed in the molecular evolution of genes and domains is useful for inferring functional constraints and informing experimental works.

T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model

Bacterial type IV secretion systems (T4SSs) are versatile and membrane-spanning apparatuses, which mediate both genetic exchange and delivery of effector proteins to target eukaryotic cells. The secreted effectors (T4SEs) can affect gene expression and signal transduction of the host cells. As such, they often function as virulence factors and play an important role in bacterial pathogenesis. Nowadays, T4SE prediction tools have utilized various machine learning algorithms, but the accuracy and speed of these tools remain to be improved. In this study, we apply a sequence embedding strategy from a pre-trained language model of protein sequences (TAPE) to the classification task of T4SEs. The training dataset is mainly derived from our updated type IV secretion system database SecReT4 with newly experimentally verified T4SEs. An online web server termed T4SEfinder is developed using TAPE and a multi-layer perceptron (MLP) for T4SE prediction after a comprehensive performance comparison with several candidate models, which achieves a slightly higher level of accuracy than the existing prediction tools. It only takes about 3 minutes to make a classification for 5000 protein sequences by T4SEfinder so that the computational speed is qualified for whole genome-scale T4SEs detection in pathogenic bacteria. T4SEfinder might contribute to meet the increasing demands of re-annotating secretion systems and effector proteins in sequenced bacterial genomes. T4SEfinder is freely accessible at https://tool2-mml.sjtu.edu.cn/T4SEfinder_TAPE/.

Effectors of the Stenotrophomonas maltophilia Type IV Secretion System Mediate Killing of Clinical Isolates of Pseudomonas aeruginosa

Previously, we documented that Stenotrophomonas maltophilia encodes a type IV secretion system (T4SS) that allows the organism to kill, in contact-dependent fashion, heterologous bacteria, including wild-type Pseudomonas aeruginosa. Bioinformatic screens based largely on the presence of both a C-terminal consensus sequence and an adjacent gene encoding a cognate immunity protein identified 13 potential antibacterial effectors, most of which were highly conserved among sequenced strains of S. maltophilia. The immunity proteins of two of these proved especially capable of protecting P. aeruginosa and Escherichia coli against attack from the Stenotrophomonas T4SS. In turn, S. maltophilia mutants lacking the putative effectors RS14245 and RS14255 were impaired for killing not only laboratory E. coli but clinical isolates of P. aeruginosa, including ones isolated from the lungs of cystic fibrosis patients. That complemented mutants behaved as wild type did confirmed that RS14245 and RS14255 are required for the bactericidal activity of the S. maltophilia T4SS. Moreover, a mutant lacking both of these proteins was as impaired as a mutant lacking the T4SS apparatus, indicating that RS14245 and RS14255 account for (nearly) all of the bactericidal effects seen. Utilizing an interbacterial protein translocation assay, we determined that RS14245 and RS14255 are bona fide substrates of the T4SS, a result confirmed by examination of mutants lacking both the T4SS and the individual effectors. Delivery of the cloned 14245 protein (alone) into the periplasm resulted in the killing of target bacteria, indicating that this effector, a putative lipase, is both necessary and sufficient for bactericidal activity.

iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles

Many gram-negative bacteria use type IV secretion systems to deliver effector molecules to a wide range of target cells. These substrate proteins, which are called type IV secreted effectors (T4SE), manipulate host cell processes during infection, often resulting in severe diseases or even death of the host. Therefore, identification of putative T4SEs has become a very active research topic in bioinformatics due to its vital roles in understanding host-pathogen interactions. PSI-BLAST profiles have been experimentally validated to provide important and discriminatory evolutionary information for various protein classification tasks. In the present study, an accurate computational predictor termed iT4SE-EP was developed for identifying T4SEs by extracting evolutionary features from the position-specific scoring matrix and the position-specific frequency matrix profiles. First, four types of encoding strategies were designed to transform protein sequences into fixed-length feature vectors based on the two profiles. Then, the feature selection technique based on the random forest algorithm was utilized to reduce redundant or irrelevant features without much loss of information. Finally, the optimal features were input into a support vector machine classifier to carry out the prediction of T4SEs. Our experimental results demonstrated that iT4SE-EP outperformed most of existing methods based on the independent dataset test.

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.

DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors

Gram-negative bacteria can deliver secreted proteins (also known as secreted effectors) directly into host cells through type III secretion system (T3SS), type IV secretion system (T4SS), and type VI secretion system (T6SS) and cause various diseases. These secreted effectors are heavily involved in the interactions between bacteria and host cells, so their identification is crucial for the discovery and development of novel anti-bacterial drugs. It is currently challenging to accurately distinguish type III secreted effectors (T3SEs) and type IV secreted effectors (T4SEs) because neither T3SEs nor T4SEs contain N-terminal signal peptides, and some of these effectors have similar evolutionary conserved profiles and sequence motifs. To address this challenge, we develop a deep learning (DL) approach called DeepT3_4 to correctly classify T3SEs and T4SEs. We generate amino-acid character dictionary and sequence-based features extracted from effector proteins and subsequently implement these features into a hybrid model that integrates recurrent neural networks (RNNs) and deep neural networks (DNNs). After training the model, the hybrid neural network classifies secreted effectors into two different classes with an accuracy, F-value, and recall of over 80.0%. Our approach stands for the first DL approach for the classification of T3SEs and T4SEs, providing a promising supplementary tool for further secretome studies.

Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors

MOTIVATION

Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve only several class(es) of VFs with sufficient samples. However, thousands of VF classes are present in real-world scenarios, and many of them only have a very limited number of samples available.

RESULTS

We first construct a large VF dataset, covering 3446 VF classes with 160 495 sequences, and then propose deep convolutional neural network models for VF classification. We show that (i) for common VF classes with sufficient samples, our models can achieve state-of-the-art performance with an overall accuracy of 0.9831 and an F1-score of 0.9803; (ii) for uncommon VF classes with limited samples, our models can learn transferable features from auxiliary data and achieve good performance with accuracy ranging from 0.9277 to 0.9512 and F1-score ranging from 0.9168 to 0.9446 when combined with different predefined features, outperforming traditional classifiers by 1-13% in accuracy and by 1-16% in F1-score.

AVAILABILITY AND IMPLEMENTATION

All of our datasets are made publicly available at http://www.mgc.ac.cn/VFNet/, and the source code of our models is publicly available at https://github.com/zhengdd0422/VFNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrative and Conjugative Elements of Helicobacter pylori Are Hypothetical Virulence Factors Associated With Gastric Cancer

Helicobacter pylori is a bacteria with high genome plasticity that has been associated with diverse gastric pathologies. The genetic diversity of this bacteria has limited the characterization of virulence factors associated with gastric cancer (GC). To identify potentially helpful disease biomarkers, we compared 38 complete genomes and 108 draft genomes of H. pylori isolated worldwide from patients with diverse gastric pathologies and 53 draft genomes of H. pylori isolated from Mexican patients with GC, intestinal metaplasia, gastritis, peptic ulcer, and dyspepsia. H. pylori strains isolated from GC were 3-11 times more likely to harbor any of seven genes encoded within an integrative and conjugative element (ICE) than H. pylori isolated from subjects with other gastric pathologies. We tested the cytopathic effects on AGS cells of selected H. pylori strains with known cytotoxin-associated gene pathogenicity island (cag-PAI) and ICE status (H. pylori strains 29CaP, 29CaCe, 62A9, 7C, 8822, and 26695) and the histopathological damage of H. pylori 29CaP and 62A9 in a mouse model. H. pylori 29CaP, which harbors a complete ICEHptfs3 but lacks cag-PAI, elicited distinctive morphology changes and higher histopathological scores compared with other H. pylori strains carrying cag-PAI and hybrid ICE with incomplete TFSS. The presence of intact segments of ICE regions might be a risk factor to develop GC that needs to be addressed in future studies.

Machine learning in plant-pathogen interactions: empowering biological predictions from field scale to genome scale

Machine learning (ML) encompasses statistical methods that learn to identify patterns in complex datasets. Here, I review application areas in plant-pathogen interactions that have recently benefited from ML, such as disease monitoring, the discovery of gene regulatory networks, genomic selection for disease resistance and prediction of pathogen effectors. However, achieving robust performance from ML is not trivial and requires knowledge of both the methodology and the biology. I discuss common pitfalls and challenges in using ML approaches. Finally, I highlight future opportunities for ML as a tool for dissecting plant-pathogen interactions using high-throughput data, for example, through integration of diverse data sources and the analysis with higher resolution, such as from individual cells or on elaborate spatial and temporal scales.

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time- and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal features based on protein sequences. After trying 20 different types of features, the best performance was achieved when all features were fed into XGBoost by the 5-fold cross validation in comparison with other machine learning methods. Then, the ReliefF algorithm was adopted to get the optimal feature set on our dataset, which further improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and outperformed other published prediction tools. Furthermore, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework can provide instructive guidance for similar studies to construct prediction methods on related biological problems. The data and source code of this study can be freely accessed at https://github.com/CT001002/T4SE-XGB.

Mapping bacterial effector arsenals: in vivo and in silico approaches to defining the protein features dictating effector secretion by bacteria

Many bacterial pathogens rely on dedicated secretion systems to translocate virulence proteins termed 'effectors' into host cells. These effectors engage and manipulate host cellular functions to support bacterial colonization and propagation. The secretion systems are molecular machines that recognize targeting 'features' in these effector proteins in vivo to selectively and efficiently secrete them. The joint analysis of whole genome sequencing data and computational predictions of amino acid characteristics of effector proteins has made available extensive lists of candidate effectors for many bacterial pathogens, among which Dot/Icm type IVB secretion system in Legionella pneumophila reigns with the largest number of effectors identified to-date. This system is also used by the causative agent of Q fever, Coxiella burnetii, to secrete a large pool of distinct effectors. By comparing these two pathogens, we provide an understanding of the rationale behind effector repertoire expansion. We will also discuss recent bioinformatic advances facilitating high-throughput discovery of secreted effectors through in silico 'feature' recognition, and the current challenge to substantiate the biological relevance and bona fide nature of effectors identified in silico.

Phylogenetic profiling, an untapped resource for the prediction of secreted proteins and its complementation with sequence-based classifiers in bacterial type III, IV and VI secretion systems

In the establishment and maintenance of the interaction between pathogenic or symbiotic bacteria with a eukaryotic organism, protein substrates of specialized bacterial secretion systems called effectors play a critical role once translocated into the host cell. Proteins are also secreted to the extracellular medium by free-living bacteria or directly injected into other competing organisms to hinder or kill. In this work, we explore an approach based on the evolutionary dependence that most of the effectors maintain with their specific secretion system that analyzes the co-occurrence of any orthologous protein group and their corresponding secretion system across multiple genomes. We compared and complemented our methodology with sequence-based machine learning prediction tools for the type III, IV and VI secretion systems. Finally, we provide the predictive results for the three secretion systems in 1606 complete genomes at http://www.iib.unsam.edu.ar/orgsissec/.

EP3: an ensemble predictor that accurately identifies type III secreted effectors

Type III secretion systems (T3SS) can be found in many pathogenic bacteria, such as Dysentery bacillus, Salmonella typhimurium, Vibrio cholera and pathogenic Escherichia coli. The routes of infection of these bacteria include the T3SS transferring a large number of type III secreted effectors (T3SE) into host cells, thereby blocking or adjusting the communication channels of the host cells. Therefore, the accurate identification of T3SEs is the precondition for the further study of pathogenic bacteria. In this article, a new T3SEs ensemble predictor was developed, which can accurately distinguish T3SEs from any unknown protein. In the course of the experiment, methods and models are strictly trained and tested. Compared with other methods, EP3 demonstrates better performance, including the absence of overfitting, strong robustness and powerful predictive ability. EP3 (an ensemble predictor that accurately identifies T3SEs) is designed to simplify the user's (especially nonprofessional users) access to T3SEs for further investigation, which will have a significant impact on understanding the progression of pathogenic bacterial infections. Based on the integrated model that we proposed, a web server had been established to distinguish T3SEs from non-T3SEs, where have EP3_1 and EP3_2. The users can choose the model according to the species of the samples to be tested. Our related tools and data can be accessed through the link http://lab.malab.cn/∼lijing/EP3.html.

Subcellular Location of Piscirickettsia salmonis Heat Shock Protein 60 (Hsp60) Chaperone by Using Immunogold Labeling and Proteomic Analysis

Piscirickettsia salmonis is the causative bacterial agent of piscirickettsiosis, a systemic fish disease that significantly impacts the Chilean salmon industry. This bacterium possesses a type IV secretion system (T4SS), several proteins of the type III secretion system (T3SS), and a single heat shock protein 60 (Hsp60/GroEL). It has been suggested that due to its high antigenicity, the P. salmonis Hsp60 could be surface-exposed, translocated across the membrane, and (or) secreted into the extracellular matrix. This study tests the hypothesis that P. salmonis Hsp60 could be located on the bacterial surface. Immunogold electron microscopy and proteomic analyses suggested that although P. salmonis Hsp60 was predominantly associated with the bacterial cell cytoplasm, Hsp60-positive spots also exist on the bacterial cell envelope. IgY antibodies against P. salmonis Hsp60 protected SHK-1 cells against infection. Several bioinformatics approaches were used to assess Hsp60 translocation by the T4SS, T3SS, and T6SS, with negative results. These data support the hypothesis that small amounts of Hsp60 must reach the bacterial cell surface in a manner probably not mediated by currently characterized secretion systems, and that they remain biologically active during P. salmonis infection, possibly mediating adherence and (or) invasion.

Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery

The type IV bacterial secretion system (SS) is reported to be one of the most ubiquitous SSs in nature and can induce serious conditions by secreting type IV SS effectors (T4SEs) into the host cells. Recent studies mainly focus on annotating new T4SE from the huge amount of sequencing data, and various computational tools are therefore developed to accelerate T4SE annotation. However, these tools are reported as heavily dependent on the selected methods and their annotation performance need to be further enhanced. Herein, a convolution neural network (CNN) technique was used to annotate T4SEs by integrating multiple protein encoding strategies. First, the annotation accuracies of nine encoding strategies integrated with CNN were assessed and compared with that of the popular T4SE annotation tools based on independent benchmark. Second, false discovery rates of various models were systematically evaluated by (1) scanning the genome of Legionella pneumophila subsp. ATCC 33152 and (2) predicting the real-world non-T4SEs validated using published experiments. Based on the above analyses, the encoding strategies, (a) position-specific scoring matrix (PSSM), (b) protein secondary structure & solvent accessibility (PSSSA) and (c) one-hot encoding scheme (Onehot), were identified as well-performing when integrated with CNN. Finally, a novel strategy that collectively considers the three well-performing models (CNN-PSSM, CNN-PSSSA and CNN-Onehot) was proposed, and a new tool (CNN-T4SE, https://idrblab.org/cnnt4se/) was constructed to facilitate T4SE annotation. All in all, this study conducted a comprehensive analysis on the performance of a collection of encoding strategies when integrated with CNN, which could facilitate the suppression of T4SS in infection and limit the spread of antimicrobial resistance.

Stenotrophomonas maltophilia Encodes a VirB/VirD4 Type IV Secretion System That Modulates Apoptosis in Human Cells and Promotes Competition against Heterologous Bacteria, Including Pseudomonas aeruginosa

Stenotrophomonas maltophilia is an emerging opportunistic and nosocomial pathogen. S. maltophilia is also a risk factor for lung exacerbations in cystic fibrosis patients. S. maltophilia attaches to various mammalian cells, and we recently documented that the bacterium encodes a type II secretion system which triggers detachment-induced apoptosis in lung epithelial cells. We have now confirmed that S. maltophilia also encodes a type IVA secretion system (VirB/VirD4 [VirB/D4] T4SS) that is highly conserved among S. maltophilia strains and, looking beyond the Stenotrophomonas genus, is most similar to the T4SS of Xanthomonas To define the role(s) of this T4SS, we constructed a mutant of strain K279a that is devoid of secretion activity due to loss of the VirB10 component. The mutant induced a higher level of apoptosis upon infection of human lung epithelial cells, indicating that a T4SS effector(s) has antiapoptotic activity. However, when we infected human macrophages, the mutant triggered a lower level of apoptosis, implying that the T4SS also elaborates a proapoptotic factor(s). Moreover, when we cocultured K279a with strains of Pseudomonas aeruginosa, the T4SS promoted the growth of S. maltophilia and reduced the numbers of heterologous bacteria, signaling that another effector(s) has antibacterial activity. In all cases, the effect of the T4SS required S. maltophilia contact with its target. Thus, S. maltophilia VirB/D4 T4SS appears to secrete multiple effectors capable of modulating death pathways. That a T4SS can have anti- and prokilling effects on different targets, including both human and bacterial cells, has, to our knowledge, not been seen before.

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.

Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool

Type IV secretion systems (T4SS) are used by a number of bacterial pathogens to attack the host cell. The complex protein structure of the T4SS is used to directly translocate effector proteins into host cells, often causing fatal diseases in humans and animals. Identification of effector proteins is the first step in understanding how they function to cause virulence and pathogenicity. Accurate prediction of effector proteins via a machine learning approach can assist in the process of their identification. The main goal of this study is to predict a set of candidate effectors for the tick-borne pathogen Anaplasma phagocytophilum, the causative agent of anaplasmosis in humans. To our knowledge, we present the first computational study for effector prediction with a focus on A. phagocytophilum. In a previous study, we systematically selected a set of optimal features from more than 1,000 possible protein characteristics for predicting T4SS effector candidates. This was followed by a study of the features using the proteome of Legionella pneumophila strain Philadelphia deduced from its complete genome. In this manuscript we introduce the OPT4e software package for Optimal-features Predictor for T4SS Effector proteins. An earlier version of OPT4e was verified using cross-validation tests, accuracy tests, and comparison with previous results for L. pneumophila. We use OPT4e to predict candidate effectors from the proteomes of A. phagocytophilum strains HZ and HGE-1 and predict 48 and 46 candidates, respectively, with 16 and 18 deemed most probable as effectors. These latter include the three known validated effectors for A. phagocytophilum.

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches

In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion system (T4SS) to translocate effectors directly into the cytosol of host cells. Various type IV secreted effectors (T4SEs) have been experimentally validated to play crucial roles in virulence by manipulating host cell gene expression and other processes. Consequently, the identification of novel effector proteins is an important step in increasing our understanding of host-pathogen interactions and bacterial pathogenesis. Here, we train and compare six machine learning models, namely, Naïve Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector machines (SVMs) and multilayer perceptron (MLP), for the identification of T4SEs using 10 types of selected features and 5-fold cross-validation. Our study shows that: (1) including different but complementary features generally enhance the predictive performance of T4SEs; (2) ensemble models, obtained by integrating individual single-feature models, exhibit a significantly improved predictive performance and (3) the 'majority voting strategy' led to a more stable and accurate classification performance when applied to predicting an ensemble learning model with distinct single features. We further developed a new method to effectively predict T4SEs, Bastion4 (Bacterial secretion effector predictor for T4SS), and we show our ensemble classifier clearly outperforms two recent prediction tools. In summary, we developed a state-of-the-art T4SE predictor by conducting a comprehensive performance evaluation of different machine learning algorithms along with a detailed analysis of single- and multi-feature selections.

Searching algorithm for Type IV effector proteins (S4TE) 2.0: Improved tools for Type IV effector prediction, analysis and comparison in proteobacteria

Bacterial pathogens have evolved numerous strategies to corrupt, hijack or mimic cellular processes in order to survive and proliferate. Among those strategies, Type IV effectors (T4Es) are proteins secreted by pathogenic bacteria to manipulate host cell processes during infection. They are delivered into eukaryotic cells in an ATP-dependent manner via the type IV secretion system, a specialized multiprotein complex. T4Es contain a wide spectrum of features including eukaryotic-like domains, localization signals or a C-terminal translocation signal. A combination of these features enables prediction of T4Es in a given bacterial genome. In this study, we developed a web-based comprehensive suite of tools with a user-friendly graphical interface. This version 2.0 of S4TE (Searching Algorithm for Type IV Effector Proteins; http://sate.cirad.fr) enables accurate prediction and comparison of T4Es. Search parameters and threshold can be customized by the user to work with any genome sequence, whether publicly available or not. Applications range from characterizing effector features and identifying potential T4Es to analyzing the effectors based on the genome G+C composition and local gene density. S4TE 2.0 allows the comparison of putative T4E repertoires of up to four bacterial strains at the same time. The software identifies T4E orthologs among strains and provides a Venn diagram and lists of genes for each intersection. New interactive features offer the best visualization of the location of candidate T4Es and hyperlinks to NCBI and Pfam databases. S4TE 2.0 is designed to evolve rapidly with the publication of new experimentally validated T4Es, which will reinforce the predictive power of the algorithm. The computational methodology can be used to identify a wide spectrum of candidate bacterial effectors that lack sequence conservation but have similar amino acid characteristics. This approach will provide very valuable information about bacterial host-specificity and virulence factors and help identify host targets for the development of new anti-bacterial molecules.

T4SS Effector Protein Prediction with Deep Learning

Extensive research has been carried out on bacterial secretion systems, as they can pass effector proteins directly into the cytoplasm of host cells. The correct prediction of type IV protein effectors secreted by T4SS is important, since they are known to play a noteworthy role in various human pathogens. Studies on predicting T4SS effectors involve traditional machine learning algorithms. In this work we included a deep learning architecture, i.e., a Convolutional Neural Network (CNN), to predict IVA and IVB effectors. Three feature extraction methods were utilized to represent each protein as an image and these images fed the CNN as inputs in our proposed framework. Pseudo proteins were generated using ADASYN algorithm to overcome the imbalanced dataset problem. We demonstrated that our framework predicted all IVA effectors correctly. In addition, the sensitivity performance of 94.2% for IVB effector prediction exhibited our framework’s ability to discern the effectors in unidentified proteins.

Using an optimal set of features with a machine learning-based approach to predict effector proteins for Legionella pneumophila

Type IV secretion systems exist in a number of bacterial pathogens and are used to secrete effector proteins directly into host cells in order to change their environment making the environment hospitable for the bacteria. In recent years, several machine learning algorithms have been developed to predict effector proteins, potentially facilitating experimental verification. However, inconsistencies exist between their results. Previously we analysed the disparate sets of predictive features used in these algorithms to determine an optimal set of 370 features for effector prediction. This study focuses on the best way to use these optimal features by designing three machine learning classifiers, comparing our results with those of others, and obtaining de novo results. We chose the pathogen Legionella pneumophila strain Philadelphia-1, a cause of Legionnaires’ disease, because it has many validated effector proteins and others have developed machine learning prediction tools for it. While all of our models give good results indicating that our optimal features are quite robust, Model 1, which uses all 370 features with a support vector machine, has slightly better accuracy. Moreover, Model 1 predicted 472 effector proteins that are deemed highly probable to be effectors and include 94% of known effectors. Although the results of our three models agree well with those of other researchers, their models only predicted 126 and 311 candidate effectors.

Tiny architects: biogenesis of intracellular replicative niches by bacterial pathogens

Co-evolution of bacterial pathogens with their hosts led to the emergence of a stunning variety of strategies aiming at the evasion of host defences, colonisation of host cells and tissues and, ultimately, the establishment of a successful infection. Pathogenic bacteria are typically classified as extracellular and intracellular; however, intracellular lifestyle comes in many different flavours: some microbes rapidly escape to the cytosol whereas other microbes remain within vacuolar compartments and harness membrane trafficking pathways to generate their host-derived, pathogen-specific replicative niche. Here we review the current knowledge on a variety of vacuolar lifestyles, the effector proteins used by bacteria as tools to take control of the host cell and the main membrane trafficking signalling pathways targeted by vacuolar pathogens as source of membranes and nutrients. Finally, we will also discuss how host cells have developed countermeasures to sense the biogenesis of the aberrant organelles harbouring bacteria. Understanding the dialogue between bacterial and eukaryotic proteins is the key to unravel the molecular mechanisms of infection and in turn, this may lead to the identification of new targets for the development of new antimicrobials.

Genome-wide prediction of bacterial effector candidates across six secretion system types using a feature-based statistical framework

Gram-negative bacteria are responsible for hundreds of millions infections worldwide, including the emerging hospital-acquired infections and neglected tropical diseases in the third-world countries. Finding a fast and cheap way to understand the molecular mechanisms behind the bacterial infections is critical for efficient diagnostics and treatment. An important step towards understanding these mechanisms is the discovery of bacterial effectors, the proteins secreted into the host through one of the six common secretion system types. Unfortunately, current prediction methods are designed to specifically target one of three secretion systems, and no accurate "secretion system-agnostic" method is available. Here, we present PREFFECTOR, a computational feature-based approach to discover effector candidates in Gram-negative bacteria, without prior knowledge on bacterial secretion system(s) or cryptic secretion signals. Our approach was first evaluated using several assessment protocols on a manually curated, balanced dataset of experimentally determined effectors across all six secretion systems, as well as non-effector proteins. The evaluation revealed high accuracy of the top performing classifiers in PREFFECTOR, with the small false positive discovery rate across all six secretion systems. Our method was also applied to six bacteria that had limited knowledge on virulence factors or secreted effectors. PREFFECTOR web-server is freely available at: http://korkinlab.org/preffector .

PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method

Gram-negative bacteria use various secretion systems to deliver their secreted effectors. Among them, type IV secretion system exists widely in a variety of bacterial species, and secretes type IV secreted effectors (T4SEs), which play vital roles in host-pathogen interactions. However, experimental approaches to identify T4SEs are time- and resource-consuming. In the present study, we aim to develop an in silico stacked ensemble method to predict whether a protein is an effector of type IV secretion system or not based on its sequence information. The protein sequences were encoded by the feature of position specific scoring matrix (PSSM)-composition by summing rows that correspond to the same amino acid residues in PSSM profiles. Based on the PSSM-composition features, we develop a stacked ensemble model PredT4SE-Stack to predict T4SEs, which utilized an ensemble of base-classifiers implemented by various machine learning algorithms, such as support vector machine, gradient boosting machine, and extremely randomized trees, to generate outputs for the meta-classifier in the classification system. Our results demonstrated that the framework of PredT4SE-Stack was a feasible and effective way to accurately identify T4SEs based on protein sequence information. The datasets and source code of PredT4SE-Stack are freely available at http://xbioinfo.sjtu.edu.cn/PredT4SE_Stack/index.php.

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.

EBT: a statistic test identifying moderate size of significant features with balanced power and precision for genome-wide rate comparisons

Motivation

In genome-wide rate comparison studies, there is a big challenge for effective identification of an appropriate number of significant features objectively, since traditional statistical comparisons without multi-testing correction can generate a large number of false positives while multi-testing correction tremendously decreases the statistic power.

Results

In this study, we proposed a new exact test based on the translation of rate comparison to two binomial distributions. With modeling and real datasets, the exact binomial test (EBT) showed an advantage in balancing the statistical precision and power, by providing an appropriate size of significant features for further studies. Both correlation analysis and bootstrapping tests demonstrated that EBT is as robust as the typical rate-comparison methods, e.g. χ 2 test, Fisher's exact test and Binomial test. Performance comparison among machine learning models with features identified by different statistical tests further demonstrated the advantage of EBT. The new test was also applied to analyze the genome-wide somatic gene mutation rate difference between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), two main lung cancer subtypes and a list of new markers were identified that could be lineage-specifically associated with carcinogenesis of LUAD and LUSC, respectively. Interestingly, three cilia genes were found selectively with high mutation rates in LUSC, possibly implying the importance of cilia dysfunction in the carcinogenesis.

Availability and implementation

An R package implementing EBT could be downloaded from the website freely: http://www.szu-bioinf.org/EBT .

Contact

wangyj@szu.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

An optimal set of features for predicting type IV secretion system effector proteins for a subset of species based on a multi-level feature selection approach

Type IV secretion systems (T4SS) are multi-protein complexes in a number of bacterial pathogens that can translocate proteins and DNA to the host. Most T4SSs function in conjugation and translocate DNA; however, approximately 13% function to secrete proteins, delivering effector proteins into the cytosol of eukaryotic host cells. Upon entry, these effectors manipulate the host cell’s machinery for their own benefit, which can result in serious illness or death of the host. For this reason recognition of T4SS effectors has become an important subject. Much previous work has focused on verifying effectors experimentally, a costly endeavor in terms of money, time, and effort. Having good predictions for effectors will help to focus experimental validations and decrease testing costs. In recent years, several scoring and machine learning-based methods have been suggested for the purpose of predicting T4SS effector proteins. These methods have used different sets of features for prediction, and their predictions have been inconsistent. In this paper, an optimal set of features is presented for predicting T4SS effector proteins using a statistical approach. A thorough literature search was performed to find features that have been proposed. Feature values were calculated for datasets of known effectors and non-effectors for T4SS-containing pathogens for four genera with a sufficient number of known effectors, Legionella pneumophila, Coxiella burnetii, Brucella spp, and Bartonella spp. The features were ranked, and less important features were filtered out. Correlations between remaining features were removed, and dimensional reduction was accomplished using principal component analysis and factor analysis. Finally, the optimal features for each pathogen were chosen by building logistic regression models and evaluating each model. The results based on evaluation of our logistic regression models confirm the effectiveness of our four optimal sets of features, and based on these an optimal set of features is proposed for all T4SS effector proteins.

A Legionella pneumophila amylase is essential for intracellular replication in human macrophages and amoebae

Legionella pneumophila invades protozoa with an "accidental" ability to cause pneumonia upon transmission to humans. To support its nutrition during intracellular residence, L. pneumophila relies on host amino acids as the main source of carbon and energy to feed the TCA cycle. Despite the apparent lack of a requirement for glucose for L. pneumophila growth in vitro and intracellularly, the organism contains multiple amylases, which hydrolyze polysaccharides into glucose monomers. Here we describe one predicted putative amylase, LamB, which is uniquely present only in L. pneumophila and L. steigerwaltii among the ~60 species of Legionella. Our data show that LamB has a strong amylase activity, which is abolished upon substitutions of amino acids that are conserved in the catalytic pocket of amylases. Loss of LamB or expression of catalytically-inactive variants of LamB results in a severe growth defect of L. pneumophila in Acanthamoeba polyphaga and human monocytes-derived macrophages. Importantly, the lamB null mutant is severely attenuated in intra-pulmonary proliferation in the mouse model and is defective in dissemination to the liver and spleen. Our data show an essential role for LamB in intracellular replication of L. pneumophila in amoeba and human macrophages and in virulence in vivo.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini

Various bacterial pathogens can deliver their secreted substrates also called as effectors through type IV secretion systems (T4SSs) into host cells and cause diseases. Since T4SS secreted effectors (T4SEs) play important roles in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T4SSs. A few computational methods using machine learning algorithms for T4SEs prediction have been developed by using features of C-terminal residues. However, recent studies have shown that targeting information can also be encoded in the N-terminal region of at least some T4SEs. In this study, we present an effective method for T4SEs prediction by novelly integrating both N-terminal and C-terminal sequence information. First, we collected a comprehensive dataset across multiple bacterial species of known T4SEs and non-T4SEs from literatures. Then, three types of distinctive features, namely amino acid composition, composition, transition and distribution and position-specific scoring matrices were calculated for 50 N-terminal and 100 C-terminal residues. After that, we employed information gain represent to rank the importance score of the 150 different position residues for T4SE secretion signaling. At last, 125 distinctive position residues were singled out for the prediction model to classify T4SEs and non-T4SEs. The support vector machine model yields a high receiver operating curve of 0.916 in the fivefold cross-validation and an accuracy of 85.29% for the independent test set.

Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins

BACKGROUND

Fusobacterium nucleatum is a gram-negative anaerobic species residing in the oral cavity and implicated in several inflammatory processes in the human body. Although F. nucleatum abundance is increased in inflammatory bowel disease subjects and is prevalent in colorectal cancer patients, the causal role of the bacterium in gastrointestinal disorders and the mechanistic details of host cell functions subversion are not fully understood.

RESULTS

We devised a computational strategy to identify putative secreted F. nucleatum proteins (FusoSecretome) and to infer their interactions with human proteins based on the presence of host molecular mimicry elements. FusoSecretome proteins share similar features with known bacterial virulence factors thereby highlighting their pathogenic potential. We show that they interact with human proteins that participate in infection-related cellular processes and localize in established cellular districts of the host-pathogen interface. Our network-based analysis identified 31 functional modules in the human interactome preferentially targeted by 138 FusoSecretome proteins, among which we selected 26 as main candidate virulence proteins, representing both putative and known virulence proteins. Finally, six of the preferentially targeted functional modules are implicated in the onset and progression of inflammatory bowel diseases and colorectal cancer.

CONCLUSIONS

Overall, our computational analysis identified candidate virulence proteins potentially involved in the F. nucleatum-human cross-talk in the context of gastrointestinal diseases.

SecretEPDB: a comprehensive web-based resource for secreted effector proteins of the bacterial types III, IV and VI secretion systems

Bacteria translocate effector molecules to host cells through highly evolved secretion systems. By definition, the function of these effector proteins is to manipulate host cell biology and the sequence, structural and functional annotations of these effector proteins will provide a better understanding of how bacterial secretion systems promote bacterial survival and virulence. Here we developed a knowledgebase, termed SecretEPDB (Bacterial Secreted Effector Protein DataBase), for effector proteins of type III secretion system (T3SS), type IV secretion system (T4SS) and type VI secretion system (T6SS). SecretEPDB provides enriched annotations of the aforementioned three classes of effector proteins by manually extracting and integrating structural and functional information from currently available databases and the literature. The database is conservative and strictly curated to ensure that every effector protein entry is supported by experimental evidence that demonstrates it is secreted by a T3SS, T4SS or T6SS. The annotations of effector proteins documented in SecretEPDB are provided in terms of protein characteristics, protein function, protein secondary structure, Pfam domains, metabolic pathway and evolutionary details. It is our hope that this integrated knowledgebase will serve as a useful resource for biological investigation and the generation of new hypotheses for research efforts aimed at bacterial secretion systems.

Comparative Genomics of Pathogenic and Nonpathogenic Strains of Xanthomonas arboricola Unveil Molecular and Evolutionary Events Linked to Pathoadaptation

The bacterial species Xanthomonas arboricola contains plant pathogenic and nonpathogenic strains. It includes the pathogen X. arboricola pv. juglandis, causing the bacterial blight of Juglans regia. The emergence of a new bacterial disease of J. regia in France called vertical oozing canker (VOC) was previously described and the causal agent was identified as a distinct genetic lineage within the pathovar juglandis. Symptoms on walnut leaves and fruits are similar to those of a bacterial blight but VOC includes also cankers on trunk and branches. In this work, we used comparative genomics and physiological tests to detect differences between four X. arboricola strains isolated from walnut tree: strain CFBP 2528 causing walnut blight (WB), strain CFBP 7179 causing VOC and two nonpathogenic strains, CFBP 7634 and CFBP 7651, isolated from healthy walnut buds. Whole genome sequence comparisons revealed that pathogenic strains possess a larger and wider range of mobile genetic elements than nonpathogenic strains. One pathogenic strain, CFBP 7179, possessed a specific integrative and conjugative element (ICE) of 95 kb encoding genes involved in copper resistance, transport and regulation. The type three effector repertoire was larger in pathogenic strains than in nonpathogenic strains. Moreover, CFBP 7634 strain lacked the type three secretion system encoding genes. The flagellar system appeared incomplete and nonfunctional in the pathogenic strain CFBP 2528. Differential sets of chemoreceptor and different repertoires of genes coding adhesins were identified between pathogenic and nonpathogenic strains. Besides these differences, some strain-specific differences were also observed. Altogether, this study provides valuable insights to highlight the mechanisms involved in ecology, environment perception, plant adhesion and interaction, leading to the emergence of new strains in a dynamic environment.

EffectiveDB--updates and novel features for a better annotation of bacterial secreted proteins and Type III, IV, VI secretion systems

Protein secretion systems play a key role in the interaction of bacteria and hosts. EffectiveDB (http://effectivedb.org) contains pre-calculated predictions of bacterial secreted proteins and of intact secretion systems. Here we describe a major update of the database, which was previously featured in the NAR Database Issue. EffectiveDB bundles various tools to recognize Type III secretion signals, conserved binding sites of Type III chaperones, Type IV secretion peptides, eukaryotic-like domains and subcellular targeting signals in the host. Beyond the analysis of arbitrary protein sequence collections, the new release of EffectiveDB also provides a ‘genome-mode’, in which protein sequences from nearly complete genomes or metagenomic bins can be screened for the presence of three important secretion systems (Type III, IV, VI). EffectiveDB contains pre-calculated predictions for currently 1677 bacterial genomes from the EggNOG 4.0 database and for additional bacterial genomes from NCBI RefSeq. The new, user-friendly and informative web portal offers a submission tool for running the EffectiveDB prediction tools on user-provided data.

Encyclopedia of bacterial gene circuits whose presence or absence correlate with pathogenicity--a large-scale system analysis of decoded bacterial genomes

BACKGROUND

Bacterial infections comprise a global health challenge as the incidences of antibiotic resistance increase. Pathogenic potential of bacteria has been shown to be context dependent, varying in response to environment and even within the strains of the same genus.

RESULTS

We used the KEGG repository and extensive literature searches to identify among the 2527 bacterial genomes in the literature those implicated as pathogenic to the host, including those which show pathogenicity in a context dependent manner. Using data on the gene contents of these genomes, we identified sets of genes highly abundant in pathogenic but relatively absent in commensal strains and vice versa. In addition, we carried out genome comparison within a genus for the seventeen largest genera in our genome collection. We projected the resultant lists of ortholog genes onto KEGG bacterial pathways to identify clusters and circuits, which can be linked to either pathogenicity or synergy. Gene circuits relatively abundant in nonpathogenic bacteria often mediated biosynthesis of antibiotics. Other synergy-linked circuits reduced drug-induced toxicity. Pathogen-abundant gene circuits included modules in one-carbon folate, two-component system, type-3 secretion system, and peptidoglycan biosynthesis. Antibiotics-resistant bacterial strains possessed genes modulating phagocytosis, vesicle trafficking, cytoskeletal reorganization, and regulation of the inflammatory response. Our study also identified bacterial genera containing a circuit, elements of which were previously linked to Alzheimer's disease.

CONCLUSIONS

Present study produces for the first time, a signature, in the form of a robust list of gene circuitry whose presence or absence could potentially define the pathogenicity of a microbiome. Extensive literature search substantiated a bulk majority of the commensal and pathogenic circuitry in our predicted list. Scanning microbiome libraries for these circuitry motifs will provide further insights into the complex and context dependent pathogenicity of bacteria.

Common protein sequence signatures associate with Sclerotinia borealis lifestyle and secretion in fungal pathogens of the Sclerotiniaceae

Fungal plant pathogens produce secreted proteins adapted to function outside fungal cells to facilitate colonization of their hosts. In many cases such as for fungi from the Sclerotiniaceae family the repertoire and function of secreted proteins remains elusive. In the Sclerotiniaceae, whereas Sclerotinia sclerotiorum and Botrytis cinerea are cosmopolitan broad host-range plant pathogens, Sclerotinia borealis has a psychrophilic lifestyle with a low optimal growth temperature, a narrow host range and geographic distribution. To spread successfully, S. borealis must synthesize proteins adapted to function in its specific environment. The search for signatures of adaptation to S. borealis lifestyle may therefore help revealing proteins critical for colonization of the environment by Sclerotiniaceae fungi. Here, we analyzed amino acids usage and intrinsic protein disorder in alignments of groups of orthologous proteins from the three Sclerotiniaceae species. We found that enrichment in Thr, depletion in Glu and Lys, and low disorder frequency in hot loops are significantly associated with S. borealis proteins. We designed an index to report bias in these properties and found that high index proteins were enriched among secreted proteins in the three Sclerotiniaceae fungi. High index proteins were also enriched in function associated with plant colonization in S. borealis, and in in planta-induced genes in S. sclerotiorum. We highlight a novel putative antifreeze protein and a novel putative lytic polysaccharide monooxygenase identified through our pipeline as candidate proteins involved in colonization of the environment. Our findings suggest that similar protein signatures associate with S. borealis lifestyle and with secretion in the Sclerotiniaceae. These signatures may be useful for identifying proteins of interest as targets for the management of plant diseases.

A sequence-based two-level method for the prediction of type I secreted RTX proteins

Many Gram-negative bacteria use the type I secretion system (T1SS) to translocate a wide range of substrates (type I secreted RTX proteins, T1SRPs) from the cytoplasm across the inner and outer membrane in one step to the extracellular space. Since T1SRPs play an important role in pathogen-host interactions, identifying them is crucial for a full understanding of the pathogenic mechanism of T1SS. However, experimental identification is often time-consuming and expensive. In the post-genomic era, it becomes imperative to predict new T1SRPs using information from the amino acid sequence alone when new proteins are being identified in a high-throughput mode. In this study, we report a two-level method for the first attempt to identify T1SRPs using sequence-derived features and the random forest (RF) algorithm. At the full-length sequence level, the results show that the unique feature of T1SRPs is the presence of variable numbers of the calcium-binding RTX repeats. These RTX repeats have a strong predictive power and so T1SRPs can be well distinguished from non-T1SRPs. At another level, different from that of the secretion signal, we find that a sequence segment located at the last 20-30 C-terminal amino acids may contain important signal information for T1SRP secretion because obvious differences were shown between the corresponding positions of T1SRPs and non-T1SRPs in terms of amino acid and secondary structure compositions. Using five-fold cross-validation, overall accuracies of 97% at the full-length sequence level and 89% at the secretion signal level were achieved through feature evaluation and optimization. Benchmarking on an independent dataset, our method could correctly predict 63 and 66 of 74 T1SRPs at the full-length sequence and secretion signal levels, respectively. We believe that this study will be useful in elucidating the secretion mechanism of T1SS and facilitating hypothesis-driven experimental design and validation.

Comparative Genomics of Pathogenic and Nonpathogenic Strains of Xanthomonas arboricola Unveil Molecular and Evolutionary Events Linked to Pathoadaptation

The bacterial species Xanthomonas arboricola contains plant pathogenic and nonpathogenic strains. It includes the pathogen X. arboricola pv. juglandis, causing the bacterial blight of Juglans regia. The emergence of a new bacterial disease of J. regia in France called vertical oozing canker (VOC) was previously described and the causal agent was identified as a distinct genetic lineage within the pathovar juglandis. Symptoms on walnut leaves and fruits are similar to those of a bacterial blight but VOC includes also cankers on trunk and branches. In this work, we used comparative genomics and physiological tests to detect differences between four X. arboricola strains isolated from walnut tree: strain CFBP 2528 causing walnut blight (WB), strain CFBP 7179 causing VOC and two nonpathogenic strains, CFBP 7634 and CFBP 7651, isolated from healthy walnut buds. Whole genome sequence comparisons revealed that pathogenic strains possess a larger and wider range of mobile genetic elements than nonpathogenic strains. One pathogenic strain, CFBP 7179, possessed a specific integrative and conjugative element (ICE) of 95 kb encoding genes involved in copper resistance, transport and regulation. The type three effector repertoire was larger in pathogenic strains than in nonpathogenic strains. Moreover, CFBP 7634 strain lacked the type three secretion system encoding genes. The flagellar system appeared incomplete and nonfunctional in the pathogenic strain CFBP 2528. Differential sets of chemoreceptor and different repertoires of genes coding adhesins were identified between pathogenic and nonpathogenic strains. Besides these differences, some strain-specific differences were also observed. Altogether, this study provides valuable insights to highlight the mechanisms involved in ecology, environment perception, plant adhesion and interaction, leading to the emergence of new strains in a dynamic environment.

Common protein sequence signatures associate with Sclerotinia borealis lifestyle and secretion in fungal pathogens of the Sclerotiniaceae

Fungal plant pathogens produce secreted proteins adapted to function outside fungal cells to facilitate colonization of their hosts. In many cases such as for fungi from the Sclerotiniaceae family the repertoire and function of secreted proteins remains elusive. In the Sclerotiniaceae, whereas Sclerotinia sclerotiorum and Botrytis cinerea are cosmopolitan broad host-range plant pathogens, Sclerotinia borealis has a psychrophilic lifestyle with a low optimal growth temperature, a narrow host range and geographic distribution. To spread successfully, S. borealis must synthesize proteins adapted to function in its specific environment. The search for signatures of adaptation to S. borealis lifestyle may therefore help revealing proteins critical for colonization of the environment by Sclerotiniaceae fungi. Here, we analyzed amino acids usage and intrinsic protein disorder in alignments of groups of orthologous proteins from the three Sclerotiniaceae species. We found that enrichment in Thr, depletion in Glu and Lys, and low disorder frequency in hot loops are significantly associated with S. borealis proteins. We designed an index to report bias in these properties and found that high index proteins were enriched among secreted proteins in the three Sclerotiniaceae fungi. High index proteins were also enriched in function associated with plant colonization in S. borealis, and in in planta-induced genes in S. sclerotiorum. We highlight a novel putative antifreeze protein and a novel putative lytic polysaccharide monooxygenase identified through our pipeline as candidate proteins involved in colonization of the environment. Our findings suggest that similar protein signatures associate with S. borealis lifestyle and with secretion in the Sclerotiniaceae. These signatures may be useful for identifying proteins of interest as targets for the management of plant diseases.