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

TSE-ARF: An adaptive prediction method of effectors across secretion system types

Bacterial effector proteins are secreted by a variety of protein secretion systems and play an important role in the interaction between the host and pathogenic bacteria. Therefore, it is important to find a fast and inexpensive method to discover bacterial effectors. In this study, we propose a multi-type secretion effector adaptive random forest (TSE-ARF) to adaptively identify secretion effectors across T1SE-T4SE and T6SE based only on protein sequences. First, we proposed two new feature descriptors by considering some characteristic protein information and fused them with some universal features to form a 290-dimensional feature vector with good versatility. Then, the TSE-ARF model was used to make classification predictions by parameter adaptation of different secretion effectors integrating Shuffled Frog Leaping Algorithm and random forest. The perfect performance in TSE-ARF under different data sets and settings shows its considerable generalization ability, with which more candidate effectors were screened in the whole genome. Source code is available at https://github.com/AIMOVE/TSE-ARF.

High-throughput functional trait testing for bacterial pathogens

Functional traits are characteristics that affect the fitness and metabolic function of a microorganism. There is growing interest in using high-throughput methods to characterize bacterial pathogens based on functional virulence traits. Traditional methods that phenotype a single organism for a single virulence trait can be time consuming and labor intensive. Alternatively, machine learning of whole-genome sequences (WGS) has shown some success in predicting virulence. However, relying solely on WGS can miss functional traits, particularly for organisms lacking classical virulence factors. We propose that high-throughput assays for functional virulence trait identification should become a prominent method of characterizing bacterial pathogens on a population scale. This work is critical as we move from compiling lists of bacterial species associated with disease to pathogen-agnostic approaches capable of detecting novel microbes. We discuss six key areas of functional trait testing and how advancing high-throughput methods could provide a greater understanding of pathogens.

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

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

Expression and Functional Analysis of the Type III Secretion System Effector Repertoire of the Xylem Pathogen Erwinia tracheiphila on Cucurbits

The predicted repertoire of type III secretion system effectors (T3SEs) in Erwinia tracheiphila, causal agent of cucurbit bacterial wilt, is much larger than in xylem pathogens in the closely related genera Erwinia and Pantoea. The genomes of strains BHKY and SCR3, which represent distinct E. tracheiphila clades, encode at least 6 clade-specific and 12 shared T3SEs. The strains expressed the majority of the T3SE genes examined in planta. Among the shared T3SE genes, eop1 was expressed most highly in both strains in squash (Cucurbita pepo) and muskmelon (Cucumis melo) but the clade-specific gene avrRpm2 was expressed 40- to 900-fold more than eop1 in BHKY. The T3SEs AvrRpm2, Eop1, SrfC, and DspE contributed to BHKY virulence on squash and muskmelon, as shown using combinatorial mutants involving six T3SEs, whereas OspG and AvrB4 contributed to BHKY virulence only on muskmelon, demonstrating host-specific virulence functions. Moreover, Eop1 was functionally redundant with AvrRpm2, SrfC, OspG, and AvrB4 in BHKY, and BHKY mutants lacking up to five effector genes showed similar virulence to mutants lacking only two genes. The T3SEs OspG, AvrB4, and DspE contributed additively to SCR3 virulence on muskmelon and were not functionally redundant with Eop1. Rather, loss of eop1 and avrB4 restored wild-type virulence to the avrB4 mutant, suggesting that Eop1 suppresses a functionally redundant effector in SCR3. These results highlight functional differences in effector inventories between two E. tracheiphila clades, provide the first evidence of OspG as a phytopathogen effector, and suggest that Eop1 may be a metaeffector influencing virulence. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Effectidor: an automated machine-learning-based web server for the prediction of type-III secretion system effectors

MOTIVATION

Type-III secretion systems are utilized by many Gram-negative bacteria to inject type-3 effectors (T3Es) to eukaryotic cells. These effectors manipulate host processes for the benefit of the bacteria and thus promote disease. They can also function as host-specificity determinants through their recognition as avirulence proteins that elicit immune response. Identifying the full effector repertoire within a set of bacterial genomes is of great importance to develop appropriate treatments against the associated pathogens.

RESULTS

We present Effectidor, a user-friendly web server that harnesses several machine-learning techniques to predict T3Es within bacterial genomes. We compared the performance of Effectidor to other available tools for the same task on three pathogenic bacteria. Effectidor outperformed these tools in terms of classification accuracy (area under the precision-recall curve above 0.98 in all cases).

AVAILABILITY AND IMPLEMENTATION

Effectidor is available at: https://effectidor.tau.ac.il, and the source code is available at: https://github.com/naamawagner/Effectidor.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection

Identification of bacterial type III secreted effectors (T3SEs) has become a popular research topic in the field of bioinformatics due to its crucial role in understanding host-pathogen interaction and developing better therapeutic targets against the pathogens. However, the recognition of all effector proteins by using traditional experimental approaches is often time-consuming and laborious. Therefore, development of computational methods to accurately predict putative novel effectors is important in reducing the number of biological experiments for validation. In this study, we proposed a method, called iT3SE-PX, to identify T3SEs solely based on protein sequences. First, three kinds of features were extracted from the position-specific scoring matrix (PSSM) profiles to help train a machine learning (ML) model. Then, the extreme gradient boosting (XGBoost) algorithm was performed to rank these features based on their classification ability. Finally, the optimal features were selected as inputs to a support vector machine (SVM) classifier to predict T3SEs. Based on the two benchmark datasets, we conducted a 100-time randomized 5-fold cross validation (CV) and an independent test, respectively. The experimental results demonstrated that the proposed method achieved superior performance compared to most of the existing methods and could serve as a useful tool for identifying putative T3SEs, given only the sequence information.

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/.

Edwardsiella piscicida: A versatile emerging pathogen of fish

Edwardsiella piscicida is an Enterobacteriaceae that is abundant in water and causes food and waterborne infections in fish, animals, and humans. The bacterium causes Edwardsiellosis in farmed fish and can lead to severe economic losses in aquaculture worldwide. E. piscicida is an intracellular pathogen that can also cause systemic infection. Type III and type VI secretion systems are the bacterium’s most lethal weapons against host defenses. It also possesses multi-antibiotic resistant genes and is selected and enriched in the environment due to the overuse of antibiotics. Therefore, the bacterium has great potential to contribute to the evolution of the resistome. All these properties have made this bacterium a perfect model to study bacteria virulence mechanisms and the spread of antimicrobial genes in the environment. We summarize recent advance in E. piscicida biology and provide insights into future research in virulence mechanisms, vaccine development and novel therapeutics.

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.

Xanthomonas TAL effectors hijack host basal transcription factor IIA α and γ subunits for invasion

The Xanthomonas genus includes Gram-negative plant-pathogenic bacteria, which infect a broad range of crops and wild plant species, cause symptoms with leaf blights, streaks, spots, stripes, necrosis, wilt, cankers and gummosis on leaves, stems and fruits in a wide variety of plants via injecting their effector proteins into the host cell during infection. Among these virulent effectors, transcription activator-like effectors (TALEs) interact with the γ subunit of host transcription factor IIA (TFIIAγ) to activate the transcription of host disease susceptibility genes. Functional TFIIA is a ternary complex comprising α, β and γ subunits. However, whether TALEs recruit TFIIAα, TFIIAβ, or both remains unknown. The underlying molecular mechanisms by which TALEs mediate host susceptibility gene activation require full elucidation. Here, we show that TALEs interact with the α+γ binary subcomplex but not the α+β+γ ternary complex of rice TFIIA (holo-OsTFIIA). The transcription factor binding (TFB) regions of TALEs, which are highly conserved in Xanthomonas species, have a dominant role in these interactions. Furthermore, the interaction between TALEs and the α+γ complex exhibits robust DNA binding activity in vitro. These results collectively demonstrate that TALE-carrying pathogens hijack the host basal transcription factors TFIIAα and TFIIAγ, but not TFIIAβ, to enhance host susceptibility during pathogen infection. The uncovered mechanism widens new insights on host-microbe interaction and provide an applicable strategy to breed high-resistance crop varieties.

Post-translational Mechanisms of Host Subversion by Bacterial Effectors

Bacterial effector proteins are a specialized class of secreted proteins that are translocated directly into the host cytoplasm by bacterial pathogens. Effector proteins have diverse activities and targets, and many mediate post-translational modifications of host proteins. Effector proteins offer potential in novel biotechnological and medical applications as enzymes that may modify human proteins. Here, we discuss the mechanisms used by effectors to subvert the human host through blocking, blunting, or subverting immune mechanisms. This capacity allows bacteria to control host cell function to support pathogen survival, replication and dissemination to other hosts. In addition, we highlight that knowledge of effector protein activity may be used to develop chemical inhibitors as a new approach to treat bacterial infections.