Sequence-based prediction of type III secreted proteins

Definition of the effector landscape across 13 phytoplasma proteomes with LEAPH and EffectorComb

'Candidatus Phytoplasma' genus, a group of fastidious phloem-restricted bacteria, can infect a wide variety of both ornamental and agro-economically important plants. Phytoplasmas secrete effector proteins responsible for the symptoms associated with the disease. Identifying and characterizing these proteins is of prime importance for expanding our knowledge of the molecular bases of the disease. We faced the challenge of identifying phytoplasma's effectors by developing LEAPH, a machine learning ensemble predictor composed of four models. LEAPH was trained on 479 proteins from 53 phytoplasma species, described by 30 features. LEAPH achieved 97.49% accuracy, 95.26% precision and 98.37% recall, ensuring a low false-positive rate and outperforming available state-of-the-art methods. The application of LEAPH to 13 phytoplasma proteomes yields a comprehensive landscape of 2089 putative pathogenicity proteins. We identified three classes according to different secretion models: 'classical', 'classical-like' and 'non-classical'. Importantly, LEAPH identified 15 out of 17 known experimentally validated effectors belonging to the three classes. Furthermore, to help the selection of novel candidates for biological validation, we applied the Self-Organizing Maps algorithm and developed a Shiny app called EffectorComb. LEAPH and the EffectorComb app can be used to boost the characterization of putative effectors at both computational and experimental levels, and can be employed in other phytopathological models.

Characterization and the first complete genome sequence of a novel strain of Bergeyella porcorum isolated from pigs in China

BACKGROUND

Bergeyella porcorum is a newly identified bacterium that has an ambiguous relationship with pneumonia in pigs. However, few studies have adequately characterized this species.

RESULTS

In this study, we analyzed the morphological, physiological, and genomic characteristics of the newly identified B. porcorum sp. nov. strain QD2021 isolated from pigs. The complete genome sequence of the B. porcorum QD2021 strain consists of a single circular chromosome (2,271,736 bp, 38.51% G + C content), which encodes 2,578 genes. One plasmid with a size of 70,040 bp was detected. A total of 121 scattered repeat sequences, 319 tandem repeat sequences, 4 genomic islands, 5 prophages, 3 CRISPR sequences, and 51 ncRNAs were predicted. The coding genes of the B. porcorum genome were successfully annotated across eight databases (NR, GO, KEGG, COG, TCDB, Pfam, Swiss-Prot and CAZy) and four pathogenicity-related databases (PHI, CARD, VFDB and ARDB). In addition, a comparative genome analysis was performed to explore the evolutionary relationships of B. porcorum QD2021.

CONCLUSIONS

To our knowledge, this is the first study to provide fundamental phenotypic and whole-genome sequences for B. porcorum. Our results extensively expand the current knowledge and could serve as a valuable genomic resource for future research on B. porcorum.

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.

Transcription activator-like effectors from endosymbiotic bacteria control the reproduction of their fungal host

IMPORTANCE

Interactions between fungi and bacteria are critically important in ecology, medicine, and biotechnology. In this study, we shed light on factors that promote the persistence of a toxin-producing, phytopathogenic Rhizopus-Mycetohabitans symbiosis that causes severe crop losses in Asia. We present an unprecedented case where bacterially produced transcription activator-like (TAL) effectors are key to maintaining a stable endosymbiosis. In their absence, fungal sporulation is abrogated, leading to collapse of the phytopathogenic alliance. The Mycetohabitans TAL (MTAL)-mediated mechanism of host control illustrates a unique role of bacterial effector molecules that has broader implications, potentially serving as a model to understand how prokaryotic symbionts interact with their eukaryotic hosts.

Bacterial secretion systems contribute to rapid tissue decay in button mushroom soft rot disease

The soft rot pathogen Janthinobacterium agaricidamnosum causes devastating damage to button mushrooms (Agaricus bisporus), one of the most cultivated and commercially relevant mushrooms. We previously discovered that this pathogen releases the membrane-disrupting lipopeptide jagaricin. This bacterial toxin, however, could not solely explain the rapid decay of mushroom fruiting bodies, indicating that J. agaricidamnosum implements a more sophisticated infection strategy. In this study, we show that secretion systems play a crucial role in soft rot disease. By mining the genome of J. agaricidamnosum, we identified gene clusters encoding a type I (T1SS), a type II (T2SS), a type III (T3SS), and two type VI secretion systems (T6SSs). We targeted the T2SS and T3SS for gene inactivation studies, and subsequent bioassays implicated both in soft rot disease. Furthermore, through a combination of comparative secretome analysis and activity-guided fractionation, we identified a number of secreted lytic enzymes responsible for mushroom damage. Our findings regarding the contribution of secretion systems to the disease process expand the current knowledge of bacterial soft rot pathogens and represent a significant stride toward identifying targets for their disarmament with secretion system inhibitors. IMPORTANCE The button mushroom (Agaricus bisporus) is the most popular edible mushroom in the Western world. However, mushroom crops can fall victim to serious bacterial diseases that are a major threat to the mushroom industry, among them being soft rot disease caused by Janthinobacterium agaricidamnosum. Here, we show that the rapid dissolution of mushroom fruiting bodies after bacterial invasion is due to degradative enzymes and putative effector proteins secreted via the type II secretion system (T2SS) and the type III secretion system (T3SS), respectively. The ability to degrade mushroom tissue is significantly attenuated in secretion-deficient mutants, which establishes that secretion systems are key factors in mushroom soft rot disease. This insight is of both ecological and agricultural relevance by shedding light on the disease processes behind a pathogenic bacterial-fungal interaction which, in turn, serves as a starting point for the development of secretion system inhibitors to control disease progression.

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.

Effector Identification in Plant Pathogens

Effectors play a central role in determining the outcome of plant-pathogen interactions. As key virulence proteins, effectors are collectively indispensable for disease development. By understanding the virulence mechanisms of effectors, fundamental knowledge of microbial pathogenesis and disease resistance have been revealed. Effectors are also considered double-edged swords because some of them activate immunity in disease resistant plants after being recognized by specific immune receptors, which evolved to monitor pathogen presence or activity. Characterization of effector recognition by their cognate immune receptors and the downstream immune signaling pathways is instrumental in implementing resistance. Over the past decades, substantial research effort has focused on effector biology, especially concerning their interactions with virulence targets or immune receptors in plant cells. A foundation of this research is robust identification of the effector repertoire from a given pathogen, which depends heavily on bioinformatic prediction. In this review, we summarize methodologies that have been used for effector mining in various microbial pathogens which use different effector delivery mechanisms. We also discuss current limitations and provide perspectives on how recently developed analytic tools and technologies may facilitate effector identification and hence generation of a more complete vision of host-pathogen interactions. [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.

From prediction to function: Current practices and challenges towards the functional characterization of type III effectors

The type III secretion system (T3SS) is a well-studied pathogenicity determinant of many bacteria through which effectors (T3Es) are translocated into the host cell, where they exercise a wide range of functions to deceive the host cell's immunity and to establish a niche. Here we look at the different approaches that are used to functionally characterize a T3E. Such approaches include host localization studies, virulence screenings, biochemical activity assays, and large-scale omics, such as transcriptomics, interactomics, and metabolomics, among others. By means of the phytopathogenic Ralstonia solanacearum species complex (RSSC) as a case study, the current advances of these methods will be explored, alongside the progress made in understanding effector biology. Data obtained by such complementary methods provide crucial information to comprehend the entire function of the effectome and will eventually lead to a better understanding of the phytopathogen, opening opportunities to tackle it.

Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Motivation

The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families.

Results

Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes.

Availability and implementation

Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Recruitment of heterologous substrates by bacterial secretion systems for transkingdom translocation

Bacterial secretion systems mediate the selective exchange of macromolecules between bacteria and their environment, playing a pivotal role in processes such as horizontal gene transfer or virulence. Among the different families of secretion systems, Type III, IV and VI (T3SS, T4SS and T6SS) share the ability to inject their substrates into human cells, opening up the possibility of using them as customized injectors. For this to happen, it is necessary to understand how substrates are recruited and to be able to engineer secretion signals, so that the transmembrane machineries can recognize and translocate the desired substrates in place of their own. Other factors, such as recruiting proteins, chaperones, and the degree of unfolding required to cross through the secretion channel, may also affect transport. Advances in the knowledge of the secretion mechanism have allowed heterologous substrate engineering to accomplish translocation by T3SS, and to a lesser extent, T4SS and T6SS into human cells. In the case of T4SS, transport of nucleoprotein complexes adds a bonus to its biotechnological potential. Here, we review the current knowledge on substrate recognition by these secretion systems, the many examples of heterologous substrate translocation by engineering of secretion signals, and the current and future biotechnological and biomedical applications derived from this approach.

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.

Genomic Analysis of Mycobacterium abscessus Complex Isolates from Patients with Pulmonary Infection in China

Members of the Mycobacterium abscessus complex (MABC) are multidrug-resistant nontuberculous mycobacteria and increasingly cause opportunistic pulmonary infections. However, the genetic typing of MABC isolates remains largely unclear in China. Genomic analyses were conducted for 69 MABC clinical isolates obtained from patients with lower respiratory tract infections in Shanghai Pulmonary Hospital between 2014 and 2016. The draft genomes of the 69 clinical strains were assembled, with a total length of 4.5 to 5.6 Mb, a percent GC content (GC%) ranging from 63.9 to 68.1%, and 4,492 to 5,404 genes per genome. Susceptibility test shows that most isolates are resistant to many antimicrobials, including clarithromycin, but susceptible to tigecycline. Analyses revealed the presence of genes conferring resistance to antibiotics, including macrolides, aminoglycosides, rifampicin, and tetracyclines. Furthermore, 80 to 114 virulence genes were identified per genome, including those related to the invasion of macrophages, iron incorporation, and avoidance of immune clearance. Mobile genetic elements, including insertion sequences, transposons, and genomic islands, were discovered in the genomes. Phylogenetic analyses of all MABC isolates with another 41 complete MABC genomes identified three clades; 46 isolates were clustered in clade I, corresponding to M. abscessus subsp. abscessus, and 25 strains belonged to existing clonal complexes. Overall, this is the first comparative genomic analysis of MABC clinical isolates in China. These results show significant intraspecies variations in genetic determinants encoding antimicrobial resistance, virulence, and mobile elements and controversial subspecies classification using current marker gene combinations. This information will be useful in understanding the evolution, antimicrobial resistance, and pathogenesis of MABC strains and facilitating future vaccine development and drug design. IMPORTANCE Over the past decade, infections by Mycobacterium abscessus complex (MABC) isolates have been increasingly reported worldwide. MABC strains often show a high incidence in cystic fibrosis (CF) patients, whereas in Asia, these strains are frequently recovered from non-CF patients with significant genomic diversity. The present work involves analyses of the antimicrobial resistance, virulence, and phylogeny of 69 selected MABC isolates from non-CF pulmonary patients in Shanghai Pulmonary Hospital by whole-genome sequencing; it represents the first comprehensive investigation of MABC strains in China at the genomic level. These findings highlight the diversity of this group of nontuberculous mycobacteria and provide a mechanistic understanding of evolution and pathogenesis, which is valuable for the development of novel and effective antimicrobial therapies for deadly MABC infections in China.

Lawsonia intracellularis LI0666 is a new EPIYA effector exported by the Yersinia enterocolitica type III secretion system

Lawsonia intracellularis is the causative agent of proliferative enteropathy. While it harbors genes encoding the entire apparatus required for the type III secretion system (T3SS) and the expression of some of these components has been detected during experimental infection, the identification of L. intracellularis T3SS substrates (effector proteins) has been hampered. The Yersinia T3SS and yeast growth inhibition assays are two important heterologous systems used for the characterization of effector proteins. Bacterial EPIYA effectors are a distinct class of bacterial effectors defined by the presence of EPIYA or the EPIYA-related motif. When delivered into host cells via a T3SS or type IV secretion system, these effectors undergo tyrosine phosphorylation of the EPIYA motif, which enables them to manipulate host cell signaling by promiscuously interacting with multiple SH2 domain-containing proteins. A previous study showed that L. intracellularis LI0666 contains two EPIYA motifs and speculated that this protein could be a T3SS effector. In this study, we show that LI0666 is secreted by Yersinia in a T3SS-dependent manner and inhibits yeast growth. LI0666 is phosphorylated at tyrosine residues in porcine intestinal epithelial cells and in human epithelial cells. Like the archetypal EPIYA effector CagA, the EPIYA-containing region is not required for LI0666 association with yeast and mammalian cell membranes. Our results indicate that LI0666 is an authentic bacterial EPIYA effector. Identification of the tyrosine kinases that are responsible for LI0666 phosphorylation and the SH2 domain-containing host proteins that LI0666 interacts with will help to explore the molecular mechanisms of LI0666 in disease development.

Genomic attributes differ between Vibrio parahaemolyticus environmental and clinical isolates including pathotypes

Vibrio parahaemolyticus is a marine bacterium and causes opportunistic gastroenteritis in humans. Clinical strains of V. parahaemolyticus contain haemolysin and type III secretion systems (T3SS) that define their pathotype. A growing number of strains isolated recently from the environment have acquired these virulence genes constituting a pool of potential pathogens. This study used comparative genomics to identify genetic factors that delineate environmental and clinical V. parahaemolyticus population and understand the similarities and differences between the T3SS2 phylotypes. The comparative analysis revealed the presence of a cluster of genes belonging to bacterial cellulose synthesis (bcs) in isolates of environmental origin. This cluster, previously unreported in V. parahaemolyticus, exhibit significant similarity to that of Aliivibrio fischeri, and might dictate a potentially new mechanism of its environmental adaptation and persistence. The study also identified many genes predicted in silico to be T3SS effectors that are unique to T3SS2β of tdh^- trh⁺ and tdh⁺ trh⁺ pathotype and having no identifiable homologue in tdh⁺ trh^- T3SS2α. Overall, these findings highlight the importance of understanding the genes and strategies V. parahaemolyticus utilize for the myriad interactions with its hosts, either marine invertebrates or humans.

Draft Genomes of Six Philippine Erwinia mallotivora Isolates: Comparative Genomics and Genome-Wide Analysis of Candidate Secreted Proteins

Erwinia mallotivora is one of the most important bacterial pathogens of papaya and causes bacterial crown rot disease in the Philippines. In this paper, we present the draft genome sequences of six Philippine E. mallotivora isolates to provide insights into the genes involved in host-pathogen interactions and compare their genomes to other Erwinia species. The genomes were sequenced using Illumina Miseq platform. The draft whole-genome assemblies of the E. mallotivora isolates are composed of 36-64 contigs with N50 value ranging from 285 to 332 kbp and cover 96.2-100% of the estimated genome size. Structural genome annotation of these assemblies has predicted 4489-4749 protein-coding genes. Comparative genomic analysis using orthologous gene sets led to the identification of conserved genes within the genus and species-specific gene orthologous groups, which collectively provide a baseline for functional genomic studies to determine genes affecting virulence and host specificity. Secreted proteins of E. mallotivora were also predicted and characterized to unravel putative genes involved in plant-pathogen interactions. This study provides the first draft whole-genome sequences of Philippine isolates of E. mallotivora, thus expanding the genomic knowledge for this species in comparison with other members of the genus Erwinia.

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.

ProtPlat: an efficient pre-training platform for protein classification based on FastText

Background

For the past decades, benefitting from the rapid growth of protein sequence data in public databases, a lot of machine learning methods have been developed to predict physicochemical properties or functions of proteins using amino acid sequence features. However, the prediction performance often suffers from the lack of labeled data. In recent years, pre-training methods have been widely studied to address the small-sample issue in computer vision and natural language processing fields, while specific pre-training techniques for protein sequences are few.

Results

In this paper, we propose a pre-training platform for representing protein sequences, called ProtPlat, which uses the Pfam database to train a three-layer neural network, and then uses specific training data from downstream tasks to fine-tune the model. ProtPlat can learn good representations for amino acids, and at the same time achieve efficient classification. We conduct experiments on three protein classification tasks, including the identification of type III secreted effectors, the prediction of subcellular localization, and the recognition of signal peptides. The experimental results show that the pre-training can enhance model performance effectively and ProtPlat is competitive to the state-of-the-art predictors, especially for small datasets. We implement the ProtPlat platform as a web service (https://compbio.sjtu.edu.cn/protplat) that is accessible to the public.

Conclusions

To enhance the feature representation of protein amino acid sequences and improve the performance of sequence-based classification tasks, we develop ProtPlat, a general platform for the pre-training of protein sequences, which is featured by a large-scale supervised training based on Pfam database and an efficient learning model, FastText. The experimental results of three downstream classification tasks demonstrate the efficacy of ProtPlat.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04604-2.

Predicting Type III Effector Proteins Using the Effectidor Web Server

Various Gram-negative bacteria use secretion systems to secrete effector proteins that manipulate host biochemical pathways to their benefit. We and others have previously developed machine-learning algorithms to predict novel effectors. Specifically, given a set of known effectors and a set of known non-effectors, the machine-learning algorithm extracts features that distinguish these two protein groups. In the training phase, the machine learning learns how to best combine the features to separate the two groups. The trained machine learning is then applied to open reading frames (ORFs) with unknown functions, resulting in a score for each ORF, which is its likelihood to be an effector. We developed Effectidor, a web server for predicting type III effectors. In this book chapter, we provide a step-by-step introduction to the application of Effectidor, from selecting input data to analyzing the obtained predictions.

Binding specificity of type three secretion system effector NleH2 to multi-cargo chaperone CesT and their phosphorylation

Gram-negative pathogens like Enteropathogenic Escherichia coli (EPEC) utilize the type three secretion system (T3SS) to translocate various effector proteins that are needed to "hijack" the host system for pathogenic survival. Specialized T3SS chaperones inside bacterial cells stabilize these effector proteins and facilitate their translocation. CesT is a unique multi-cargo chaperone that interacts with and translocates ~10 different effector proteins. Here, we report the specific interaction between CesT and its key effector, NleH2, and explore the potential role of NleH2 as a kinase for CesT phosphorylation. First, we identified the chaperone-binding domain (CBD; 19-97aa) of NleH2, and mapped the specific interaction sites for both CesT and NleH2. The N- and C-terminal residues of the CBD interact with the dimeric interface of CesT. Further, we compared the CesT binding to NleH2, to that of another key effector Tir and with the global carbon regulator CsrA. Notably, the effectors have the binding regions at the β-sheet core and dimer interface of CesT, whereas the CsrA regulator interacts predominantly through the C-terminal region, which is found ~17 Å away from the effectors-binding sites. Next, we showed that NleH2 remains an active kinase even as a complex with CesT and is responsible for its autophosphorylation as well as phosphorylation of CesT at Tyr153. Collectively, our findings enhance the understanding of the role of multi-cargo chaperone CesT in orchestrating effector translocation through T3SS.

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

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

Application of a C. trachomatis expression system to identify C. pneumoniae proteins translocated into host cells

Chlamydia pneumoniae is a Gram-negative, obligate intracellular pathogen that causes community-acquired respiratory infections. C. pneumoniae uses a cell contact-dependent type-III secretion (T3S) system to translocate pathogen effector proteins that manipulate host cellular functions. While several C. pneumoniae T3S effectors have been proposed, few have been experimentally confirmed in Chlamydia In this study, we expressed 382 C. pneumoniae genes in C. trachomatis as fusion proteins to an epitope tag derived from glycogen synthase kinase 3β (GSK3β) which is the target of phosphorylation by mammalian kinases. Based on the detection of the tagged C. pneumoniae protein with anti-phospho GSK3β antibodies, we identified 49 novel C. pneumoniae-specific proteins that are translocated by C. trachomatis into the host cytoplasm and thus likely play a role as modifiers of host cellular functions. In this manner, we identified and characterized a new C. pneumoniae effector CPj0678 that recruits the host cell protein PACSIN2 to the plasma membrane. These findings indicate that C. trachomatis provides a powerful screening system to detect candidate effector proteins encoded by other pathogenic and endosymbiotic Chlamydia species.Importance Chlamydia injects numerous effector proteins into host cells to manipulate cellular functions important for bacterial survival. Based on findings in C. trachomatis, it has been proposed that between 5-10% of the C. pneumoniae genome, a related respiratory pathogen, encodes secreted effectors. However only a few C. pneumoniae effectors have been identified and experimentally validated. With the development of methods for the stable genetic transformation of C. trachomatis, it is now possible to use C. trachomatis shuttle plasmids to express and explore the function of proteins from other Chlamydia in a more relevant bacterial system. In this study, we experimentally determined that 49 C. pneumoniae-specific proteins are translocated into the host cytoplasm by Chlamydia secretion systems, and identify a novel effector required to recruit the host factor PACSIN2 to the plasma membrane during infection.

CwlQ Is Required for Swarming Motility but Not Flagellar Assembly in Bacillus subtilis

Lytic enzymes play an essential role in the remodeling of bacterial peptidoglycan (PG), an extracellular mesh-like structure that retains the membrane in the context of high internal osmotic pressure. Peptidoglycan must be unfailingly stable to preserve cell integrity, but must also be dynamically remodeled for the cell to grow, divide, and insert macromolecular machines. The flagellum is one such macromolecular machine that transits the PG, and flagellar insertion is aided by localized activity of a dedicated PG lyase in Gram-negative bacteria. To date, there is no known dedicated lyase in Gram-positive bacteria for the insertion of flagella. Here, we take a reverse-genetic candidate-gene approach and find that cells mutated for the lytic transglycosylase CwlQ exhibit a severe defect in flagellum-dependent swarming motility. We further show that CwlQ is expressed by the motility sigma factor SigD and is secreted by the type III secretion system housed inside the flagellum. Nonetheless, cells with mutations of CwlQ remain proficient for flagellar biosynthesis even when mutated in combination with four other lyases related to motility (LytC, LytD, LytF, and CwlO). The PG lyase (or lyases) essential for flagellar synthesis in B. subtilis, if any, remains unknown.IMPORTANCE Bacteria are surrounded by a wall of peptidoglycan and early work in Bacillus subtilis was the first to suggest that bacteria needed to enzymatically remodel the wall to permit insertion of the flagellum. No PG remodeling enzyme alone or in combination, however, has been found to be essential for flagellar assembly in B. subtilis Here, we take a reverse-genetic candidate-gene approach and find that the PG lytic transglycosylase CwlQ is required for swarming motility. Subsequent characterization determined that while CwlQ was coexpressed with motility genes and is secreted by the flagellar secretion apparatus, it was not required for flagellar synthesis. The PG lyase needed for flagellar assembly in B. subtilis remains unknown.

Machine learning for phytopathology: from the molecular scale towards the network scale

With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within the explosion of data, machine learning offers powerful tools to process these complex omics data by various algorithms, such as Bayesian reasoning, support vector machine and random forest. Here, we introduce the basic frameworks of machine learning in dissecting plant-pathogen interactions and discuss the applications and advances of machine learning in plant-pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein-protein networks. The aim of this review is to provide a summary of advances in plant defense and pathogen infection and to indicate the important developments of machine learning in phytopathology.

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.

Transmembrane domains of type III-secreted proteins affect bacterial-host interactions in enteropathogenic E. coli

Many bacterial pathogens utilize a specialized secretion system, termed type III secretion system (T3SS), to translocate effector proteins into host cells and establish bacterial infection. The T3SS is anchored within the bacterial membranes and contains a long needle/filament that extends toward the host-cell and forms, at its distal end, a pore complex within the host membrane. The T3SS pore complex consists of two bacterial proteins, termed SctB and SctE, which have conflicting targeting indications; a signal sequence that targets to secretion to the extracellular environment via the T3SS, and transmembrane domains (TMDs) that target to membrane localization. In this study, we investigate whether the TMD sequences of SctB and SctE have special features that differentiate them from classical TMDs and allow them to escape bacterial membrane integration. For this purpose, we exchanged the SctB and SctE native TMDs for alternative hydrophobic sequences and found that the TMD sequences of SctB and SctE dictate membrane destination (bacterial versus host membrane). Moreover, we examined the role of the SctB TMD sequence in the activity of the full-length protein, post secretion, and found that the TMD does not serve only as a hydrophobic segment, but is also involved in the ability of the protein to translocate itself and other proteins into and across the host cell membrane.

Biodegradation of lignin monomers and bioconversion of ferulic acid to vanillic acid by Paraburkholderia aromaticivorans AR20-38 isolated from Alpine forest soil

Abstract

Lignin bio-valorization is an emerging field of applied biotechnology and has not yet been studied at low temperatures. Paraburkholderia aromaticivorans AR20-38 was examined for its potential to degrade six selected lignin monomers (syringic acid, p-coumaric acid, 4-hydroxybenzoic acid, ferulic acid, vanillic acid, benzoic acid) from different upper funneling aromatic pathways. The strain degraded four of these compounds at 10°C, 20°C, and 30°C; syringic acid and vanillic acid were not utilized as sole carbon source. The degradation of 5 mM and 10 mM ferulic acid was accompanied by the stable accumulation of high amounts of the value-added product vanillic acid (85–89% molar yield; 760 and 1540 mg l⁻¹, respectively) over the whole temperature range tested. The presence of essential genes required for reactions in the upper funneling pathways was confirmed in the genome. This is the first report on biodegradation of lignin monomers and the stable vanillic acid production at low and moderate temperatures by P. aromaticivorans.

Key points

• Paraburkholderia aromaticivorans AR20-38 successfully degrades four lignin monomers.

• Successful degradation study at low (10°C) and moderate temperatures (20–30°C).

• Biotechnological value: high yield of vanillic acid produced from ferulic acid.

AKSF1 Isolated From the Rice-Virulent Strain Acidovorax avenae K1 Is a Novel Effector That Suppresses PAMP-Triggered Immunity in Rice

Microbial pathogens deliver effectors into plant cells to suppress plant immune responses and modulate host metabolism in order to support infection processes. We sought to determine if the Acidovorax avenae rice-virulent K1 strain can suppress pathogen-associated molecular pattern-triggered immunity (PTI) induced by flagellin isolated from the rice-avirulent N1141 strain. The flagellin-triggered PTI, including H₂O₂ generation, callose deposition, and expression of several immune-related genes were strongly suppressed in K1 preinoculated cultured rice cells in a type III secretion system (T3SS)-dependent manner. By screening 4,562 transposon-tagged mutants based on their suppression ability, we found that 156 transposon-tagged K1 mutants lost the ability to suppress PTI induction. Mutant sequence analysis, comprehensive expression analysis using RNA sequencing, and the prediction of secretion through T3SS showed that a protein named A. avenae K1 suppression factor 1 (AKSF1) suppresses flagellin-triggered PTI in rice. Translocation of AKSF1 protein into rice cells is dependent on the T3SS during infection, an AKSF1-disruption mutant lost the ability to suppress PTI responses, and expression of AKSF1 in the AKSF1-disruption mutant complemented the suppression activity. When AKSF1-disruption mutants were inoculated into the host rice plant, reduction of the disease symptoms and suppression of bacterial growth were observed. Taken together, our results demonstrate that AKSF1 is a novel effector that can suppress the PTI in a host rice plant.[Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. 2021.

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.

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.

Keeping in Touch with Type-III Secretion System Effectors: Mass Spectrometry-Based Proteomics to Study Effector-Host Protein-Protein Interactions

Manipulation of host cellular processes by translocated bacterial effectors is key to the success of bacterial pathogens and some symbionts. Therefore, a comprehensive understanding of effectors is of critical importance to understand infection biology. It has become increasingly clear that the identification of host protein targets contributes invaluable knowledge to the characterization of effector function during pathogenesis. Recent advances in mapping protein-protein interaction networks by means of mass spectrometry-based interactomics have enabled the identification of host targets at large-scale. In this review, we highlight mass spectrometry-driven proteomics strategies and recent advances to elucidate type-III secretion system effector-host protein-protein interactions. Furthermore, we highlight approaches for defining spatial and temporal effector-host interactions, and discuss possible avenues for studying natively delivered effectors in the context of infection. Overall, the knowledge gained when unravelling effector complexation with host factors will provide novel opportunities to control infectious disease outcomes.

autoBioSeqpy: A Deep Learning Tool for the Classification of Biological Sequences

Deep learning has proven to be a powerful method with applications in various fields including image, language, and biomedical data. Thanks to the libraries and toolkits such as TensorFlow, PyTorch, and Keras, researchers can use different deep learning architectures and data sets for rapid modeling. However, the available implementations of neural networks using these toolkits are usually designed for a specific research and are difficult to transfer to other work. Here, we present autoBioSeqpy, a tool that uses deep learning for biological sequence classification. The advantage of this tool is its simplicity. Users only need to prepare the input data set and then use a command line interface. Then, autoBioSeqpy automatically executes a series of customizable steps including text reading, parameter initialization, sequence encoding, model loading, training, and evaluation. In addition, the tool provides various ready-to-apply and adapt model templates to improve the usability of these networks. We introduce the application of autoBioSeqpy on three biological sequence problems: the prediction of type III secreted proteins, protein subcellular localization, and CRISPR/Cas9 sgRNA activity. autoBioSeqpy is freely available with examples at https://github.com/jingry/autoBioSeqpy.

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

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

Calcium regulates the mycophagous ability of Burkholderia gladioli strain NGJ1 in a type III secretion system-dependent manner

BACKGROUND

A rice associated bacterium Burkholderia gladioli strain NGJ1 demonstrates mycophagy, a phenomenon wherein bacteria feed on fungi. Previously, we have reported that NGJ1 utilizes type III secretion system (T3SS) to deliver a prophage tail-like protein (Bg_9562) into fungal cells to establish mycophagy.

RESULTS

In this study, we report that calcium ion concentration influences the mycophagous ability of NGJ1 on Rhizoctonia solani, an important fungal pathogen. The calcium limiting condition promotes mycophagy while high calcium environment prevents it. The expression of various T3SS apparatus encoding genes of NGJ1 was induced and secretion of several potential T3SS effector proteins (including Bg_9562) into extracellular milieu was triggered under calcium limiting condition. Using LC-MS/MS proteome analysis, we identified several calcium regulated T3SS effector proteins of NGJ1. The expression of genes encoding some of these effector proteins was upregulated during mycophagous interaction of NGJ1 with R. solani. Further, mutation of one of these genes (endo-β-1, 3- glucanase) rendered the mutant NGJ1 bacterium defective in mycophagy while complementation with full length copy of the gene restored its mycophagous activity.

CONCLUSION

Our study provides evidence that low calcium environment triggers secretion of various T3SS effectors proteins into the extracellular milieu and suggests the importance of cocktail of these proteins in promoting mycophagy.

Draft Genome Sequence of the Bacterium Paraburkholderia aromaticivorans AR20-38, a Gram-Negative, Cold-Adapted Degrader of Aromatic Compounds

Here, we report the draft genome sequence of Paraburkholderia aromaticivorans strain AR20-38, a cold-adapted Gram-negative bacterium. It was isolated from Alpine forest soil and can degrade a range of aromatic compounds.

Here, we report the draft genome sequence of Paraburkholderia aromaticivorans strain AR20-38, a cold-adapted Gram-negative bacterium. It was isolated from Alpine forest soil and can degrade a range of aromatic compounds.

The Putative Type III Secreted Chlamydia abortus Virulence-Associated Protein CAB063 Targets Lamin and Induces Apoptosis

Since intracellular survival of all chlamydiae depends on the manipulation of the host cell through type III secreted effector proteins, their characterization is crucial for the understanding of chlamydial pathogenesis. We functionally characterized the putative type III secreted Chlamydia abortus protein CAB063, describe its intracellular localization and identified pro- and eukaryotic binding partners. Based on an experimental infection model and plasmid transfections, we investigated the subcellular localization of CAB063 by immunofluorescence microscopy, immunoelectron microscopy, and Western blot analysis. Pro- and eukaryotic targets were identified by co-immunofluorescence, co-immunoprecipitation, and mass spectrometry. Transmission electron microscopy and flow cytometry were used for morphological and functional investigations on host cell apoptosis. CAB063 localized in the nuclear membrane of the host cell nucleus and we identified the chaperone HSP70 and lamin A/C as pro- and eukaryotic targets, respectively. CAB063-dependent morphological alterations of the host cell nucleus correlated with increased apoptosis rates of infected and CAB063-transfected cells. We provide evidence that CAB063 is a chaperone-folded type III secreted C. abortus virulence factor that targets lamin thereby altering the host cell nuclear membrane structure. This process may be responsible for an increased apoptosis rate at the end of the chlamydial developmental cycle, at which CAB063 is physiologically expressed.

VFDB 2019: a comparative pathogenomic platform with an interactive web interface

The virulence factor database (VFDB, http://www.mgc.ac.cn/VFs/) is devoted to providing the scientific community with a comprehensive warehouse and online platform for deciphering bacterial pathogenesis. The various combinations, organizations and expressions of virulence factors (VFs) are responsible for the diverse clinical symptoms of pathogen infections. Currently, whole-genome sequencing is widely used to decode potential novel or variant pathogens both in emergent outbreaks and in routine clinical practice. However, the efficient characterization of pathogenomic compositions remains a challenge for microbiologists or physicians with limited bioinformatics skills. Therefore, we introduced to VFDB an integrated and automatic pipeline, VFanalyzer, to systematically identify known/potential VFs in complete/draft bacterial genomes. VFanalyzer first constructs orthologous groups within the query genome and preanalyzed reference genomes from VFDB to avoid potential false positives due to paralogs. Then, it conducts iterative and exhaustive sequence similarity searches among the hierarchical prebuilt datasets of VFDB to accurately identify potential untypical/strain-specific VFs. Finally, via a context-based data refinement process for VFs encoded by gene clusters, VFanalyzer can achieve relatively high specificity and sensitivity without manual curation. In addition, a thoroughly optimized interactive web interface is introduced to present VFanalyzer reports in comparative pathogenomic style for easy online analysis.

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/.

ACNNT3: Attention-CNN Framework for Prediction of Sequence-Based Bacterial Type III Secreted Effectors

The type III secretion system (T3SS) is a special protein delivery system in Gram-negative bacteria which delivers T3SS-secreted effectors (T3SEs) to host cells causing pathological changes. Numerous experiments have verified that T3SEs play important roles in many biological activities and in host-pathogen interactions. Accurate identification of T3SEs is therefore essential to help understand the pathogenic mechanism of bacteria; however, many existing biological experimental methods are time-consuming and expensive. New deep-learning methods have recently been successfully applied to T3SE recognition, but improving the recognition accuracy of T3SEs is still a challenge. In this study, we developed a new deep-learning framework, ACNNT3, based on the attention mechanism. We converted 100 residues of the N-terminal of the protein sequence into a fusion feature vector of protein primary structure information (one-hot encoding) and position-specific scoring matrix (PSSM) which are used as the feature input of the network model. We then embedded the attention layer into CNN to learn the characteristic preferences of type III effector proteins, which can accurately classify any protein directly as either T3SEs or non-T3SEs. We found that the introduction of new protein features can improve the recognition accuracy of the model. Our method combines the advantages of CNN and the attention mechanism and is superior in many indicators when compared to other popular methods. Using the common independent dataset, our method is more accurate than the previous method, showing an improvement of 4.1-20.0%.

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.

A predation assay using amoebae to screen for virulence factors unearthed the first W. chondrophila inclusion membrane protein

Waddlia chondrophila is an intracellular bacterium phylogenetically related to the well-studied human and animal pathogens of the Chlamydiaceae family. In the last decade, W. chondrophila was convincingly demonstrated to be associated with adverse pregnancy outcomes in humans and abortions in animals. All members of the phylum Chlamydiae possess a Type Three Secretion System that they use for delivering virulence proteins into the host cell cytosol to modulate their environment and create optimal conditions to complete their life cycle. To identify W. chondrophila virulence proteins, we used an original screening approach that combines a cosmid library with an assay monitoring resistance to predation by phagocytic amoebae. This technique combined with bioinformatic data allowed the identification of 28 candidate virulence proteins, including Wimp1, the first identified inclusion membrane protein of W. chondrophila.

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.

PyPredT6: A python-based prediction tool for identification of Type VI effector proteins

Prediction of effector proteins is of paramount importance due to their crucial role as first-line invaders while establishing a pathogen-host interaction, often leading to infection of the host. Prediction of T6 effector proteins is a new challenge since the discovery of T6 Secretion System and the unique nature of the particular secretion system. In this paper, we have first designed a Python-based standalone tool, called PyPredT6, to predict T6 effector proteins. A total of 873 unique features has been extracted from the peptide and nucleotide sequences of the experimentally verified effector proteins. Based on these features and using machine learning algorithms, we have performed in silico prediction of T6 effector proteins in Vibrio cholerae and Yersinia pestis to establish the applicability of PyPredT6. PyPredT6 is available at http://projectphd.droppages.com/PyPredT6.html .

Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting

Background

Although pathogenic Gram-negative bacteria lack their own ubiquitination machinery, they have evolved or acquired virulence effectors that can manipulate the host ubiquitination process through structural and/or functional mimicry of host machinery. Many such effectors have been identified in a wide variety of bacterial pathogens that share little sequence similarity amongst themselves or with eukaryotic ubiquitin E3 ligases.

Methods

To allow identification of novel bacterial E3 ubiquitin ligase effectors from protein sequences we have developed a machine learning approach, the SVM-based Identification and Evaluation of Virulence Effector Ubiquitin ligases (SIEVE-Ub). We extend the string kernel approach used previously to sequence classification by introducing reduced amino acid (RED) alphabet encoding for protein sequences.

Results

We found that 14mer peptides with amino acids represented as simply either hydrophobic or hydrophilic provided the best models for discrimination of E3 ligases from other effector proteins with a receiver-operator characteristic area under the curve (AUC) of 0.90. When considering a subset of E3 ubiquitin ligase effectors that do not fall into known sequence based families we found that the AUC was 0.82, demonstrating the effectiveness of our method at identifying novel functional family members. Feature selection was used to identify a parsimonious set of 10 RED peptides that provided good discrimination, and these peptides were found to be located in functionally important regions of the proteins involved in E2 and host target protein binding. Our general approach enables construction of models based on other effector functions. We used SIEVE-Ub to predict nine potential novel E3 ligases from a large set of bacterial genomes. SIEVE-Ub is available for download at https://doi.org/10.6084/m9.figshare.7766984.v1 or https://github.com/biodataganache/SIEVE-Ub for the most current version.

Identification and characterization of putative Aeromonas spp. T3SS effectors

The genetic determinants of bacterial pathogenicity are highly variable between species and strains. However, a factor that is commonly associated with virulent Gram-negative bacteria, including many Aeromonas spp., is the type 3 secretion system (T3SS), which is used to inject effector proteins into target eukaryotic cells. In this study, we developed a bioinformatics pipeline to identify T3SS effector proteins, applied this approach to the genomes of 105 Aeromonas strains isolated from environmental, mutualistic, or pathogenic contexts and evaluated the cytotoxicity of the identified effectors through their heterologous expression in yeast. The developed pipeline uses a two-step approach, where candidate Aeromonas gene families are initially selected using Hidden Markov Model (HMM) profile searches against the Virulence Factors DataBase (VFDB), followed by strict comparisons against positive and negative control datasets, greatly reducing the number of false positives. This approach identified 21 Aeromonas T3SS likely effector families, of which 8 represent known or characterized effectors, while the remaining 13 have not previously been described in Aeromonas. We experimentally validated our in silico findings by assessing the cytotoxicity of representative effectors in Saccharomyces cerevisiae BY4741, with 15 out of 21 assayed proteins eliciting a cytotoxic effect in yeast. The results of this study demonstrate the utility of our approach, combining a novel in silico search method with in vivo experimental validation, and will be useful in future research aimed at identifying and authenticating bacterial effector proteins from other genera.

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.

Modular Cloning of the Type III Secretion Gene Cluster from the Plant-Pathogenic Bacterium Xanthomonas euvesicatoria

Type III secretion (T3S) systems are essential pathogenicity factors of most Gram-negative bacteria and translocate effector proteins into plant or animal cells. T3S systems can, therefore, be used as tools for protein delivery into eukaryotic cells, for instance after transfer of the T3S gene cluster into nonpathogenic recipient strains. Here, we report the modular cloning of the T3S gene cluster from the plant-pathogenic bacterium Xanthomonas euvesicatoria. The resulting multigene construct encoded a functional T3S system and delivered effector proteins into plant cells. The modular design of the T3S gene cluster allowed the efficient replacement and rearrangement of single genes or operons and the insertion of reporter genes for functional studies. In the present study, we used the modular T3S system to analyze the assembly of a fluorescent fusion of the predicted cytoplasmic ring protein HrcQ. Our studies demonstrate the use of the modular T3S gene cluster for functional analyses and mutant approaches in X. euvesicatoria. A potential application of the modular T3S system as protein delivery tool is discussed.