Computational prediction of type III and IV secreted effectors in gram-negative bacteria

Cell-selective proteomics reveal novel effectors secreted by an obligate intracellular bacterial pathogen

Pathogenic bacteria secrete protein effectors to hijack host machinery and remodel their infectious niche. Rickettsia spp. are obligate intracellular bacteria that can cause life-threatening disease, but their absolute dependence on the host cell has impeded discovery of rickettsial effectors and their host targets. We implemented bioorthogonal non-canonical amino acid tagging (BONCAT) during R. parkeri infection to selectively label, isolate, and identify effectors delivered into the host cell. As the first use of BONCAT in an obligate intracellular bacterium, our screen more than doubles the number of experimentally validated effectors for the genus. The seven novel secreted rickettsial factors (Srfs) we identified include Rickettsia-specific proteins of unknown function that localize to the host cytoplasm, mitochondria, and ER. We further show that one such effector, SrfD, interacts with the host Sec61 translocon. Altogether, our work uncovers a diverse set of previously uncharacterized rickettsial effectors and lays the foundation for a deeper exploration of the host-pathogen interface.

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.

Cell-selective proteomics reveal novel effectors secreted by an obligate intracellular bacterial pathogen

Pathogenic bacteria secrete protein effectors to hijack host machinery and remodel their infectious niche. Rickettsia spp. are obligate intracellular bacteria that can cause life-threatening disease, but their absolute dependence on the host cell environment has impeded discovery of rickettsial effectors and their host targets. We implemented bioorthogonal non-canonical amino acid tagging (BONCAT) during R. parkeri infection to selectively label, isolate, and identify secreted effectors. As the first use of BONCAT in an obligate intracellular bacterium, our screen more than doubles the number of experimentally validated effectors for R. parkeri. The novel secreted rickettsial factors (Srfs) we identified include Rickettsia-specific proteins of unknown function that localize to the host cytoplasm, mitochondria, and ER. We further show that one such effector, SrfD, interacts with the host Sec61 translocon. Altogether, our work uncovers a diverse set of previously uncharacterized rickettsial effectors and lays the foundation for a deeper exploration of the host-pathogen interface.

A Novel Effector, FSE1, Regulates the Pathogenicity of Fusarium oxysporum f. sp. cubense Tropical Race 4 to Banana by Targeting the MYB Transcription Factor MaEFM-Like

Phytopathogenic fungi secretes a range of effectors to manipulate plant defenses. Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) is a soil-borne pathogen that causes destructive banana wilt disease. Understanding the molecular mechanisms behind Foc TR4 effectors and their regulation of pathogenicity is helpful for developing disease control strategies. In the present study, we identified a novel effector, Fusarium special effector 1 (FSE1), in Foc TR4. We constructed FSE1 knock-out and overexpression mutants and investigated the functions of this effector. In vitro assays revealed that FSE1 was not required for vegetative growth and conidiation of Foc TR4. However, inoculation analysis of banana plantlets demonstrated that knock-out of FSE1 increased the disease index, while overexpression of FSE1 decreased it. Microscope analysis suggested that FSE1 was distributed in the cytoplasm and nuclei of plant cells. Furthermore, we identified an MYB transcription factor, MaEFM-like, as the target of FSE1, and the two proteins physically interacted in the nuclei of plant cells. In addition, Transient expression of MaEFM-like induced cell death in tobacco leaves. Our findings suggest that FSE1 is involved in the pathogenicity of Foc TR4 by targeting MaEFM-like.

Synthetic biology-inspired cell engineering in diagnosis, treatment, and drug development

The fast-developing synthetic biology (SB) has provided many genetic tools to reprogram and engineer cells for improved performance, novel functions, and diverse applications. Such cell engineering resources can play a critical role in the research and development of novel therapeutics. However, there are certain limitations and challenges in applying genetically engineered cells in clinical practice. This literature review updates the recent advances in biomedical applications, including diagnosis, treatment, and drug development, of SB-inspired cell engineering. It describes technologies and relevant examples in a clinical and experimental setup that may significantly impact the biomedicine field. At last, this review concludes the results with future directions to optimize the performances of synthetic gene circuits to regulate the therapeutic activities of cell-based tools in specific diseases.

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.

The Missing Pieces: The Role of Secretion Systems in Campylobacter jejuni Virulence

Campylobacter jejuni is likely the most common bacterial cause of gastroenteritis worldwide, responsible for millions of cases of inflammatory diarrhea characterized by severe abdominal cramps and blood in the stool. Further, C. jejuni infections are associated with post-infection sequelae in developed countries and malnutrition and growth-stunting in low- and middle-income countries. Despite the increasing prevalence of the disease, campylobacteriosis, and the recognition that this pathogen is a serious health threat, our understanding of C. jejuni pathogenesis remains incomplete. In this review, we focus on the Campylobacter secretion systems proposed to contribute to host-cell interactions and survival in the host. Moreover, we have applied a genomics approach to defining the structural and mechanistic features of C. jejuni type III, IV, and VI secretion systems. Special attention is focused on the flagellar type III secretion system and the prediction of putative effectors, given that the proteins exported via this system are essential for host cell invasion and the inflammatory response. We conclude that C. jejuni does not possess a type IV secretion system and relies on the type III and type VI secretion systems to establish a niche and potentiate disease.

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.

Type III secretion by Yersinia pseudotuberculosis is reliant upon an authentic N-terminal YscX secretor domain

YscX was discovered as an essential part of the Yersinia type III secretion system about 20 years ago. It is required for substrate secretion and is exported itself. Despite this central role, its precise function and mode of action remains unknown. In order to address this knowledge gap, this present study refocused attention on YscX to build on the recent advances in the understanding of YscX function. Our experiments identified a N-terminal secretion domain in YscX promoting its secretion, with the first five codons constituting a minimal signal capable of promoting secretion of the signalless β-lactamase reporter. Replacing the extreme YscX N-terminus with known secretion signals of other Ysc-Yop substrates revealed that the YscX N-terminal segment contains non-redundant information needed for YscX function. Further, both in cis deletion of the YscX N-terminus in the virulence plasmid and ectopic expression of epitope tagged YscX variants again lead to stable YscX production but not type III secretion of Yop effector proteins. Mislocalisation of the needle components, SctI and SctF, accompanied this general defect in Yops secretion. Hence, a coupling exists between YscX secretion permissiveness and the assembly of an operational secretion system.

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.

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.

Substrate-engaged type III secretion system structures reveal gating mechanism for unfolded protein translocation

Many bacterial pathogens rely on virulent type III secretion systems (T3SSs) or injectisomes to translocate effector proteins in order to establish infection. The central component of the injectisome is the needle complex which assembles a continuous conduit crossing the bacterial envelope and the host cell membrane to mediate effector protein translocation. However, the molecular principles underlying type III secretion remain elusive. Here, we report a structure of an active Salmonella enterica serovar Typhimurium needle complex engaged with the effector protein SptP in two functional states, revealing the complete 800Å-long secretion conduit and unraveling the critical role of the export apparatus (EA) subcomplex in type III secretion. Unfolded substrates enter the EA through a hydrophilic constriction formed by SpaQ proteins, which enables side chain-independent substrate transport. Above, a methionine gasket formed by SpaP proteins functions as a gate that dilates to accommodate substrates while preventing leaky pore formation. Following gate penetration, a moveable SpaR loop first folds up to then support substrate transport. Together, these findings establish the molecular basis for substrate translocation through T3SSs and improve our understanding of bacterial pathogenicity and motility.

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.

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.

Type III Secretion 1 Effector Gene Diversity Among Vibrio Isolates From Coastal Areas in China

Vibrios, which include more than 120 valid species, are an abundant and diverse group of bacteria in marine and estuarine environments. Some of these bacteria have been recognized as pathogens of both marine animals and humans, and therefore, their virulence mechanisms have attracted increasing attention. The type III secretion system (T3SS) is an important virulence determinant in many gram-negative bacteria, in which this system directly translocates variable effectors into the host cytosol for the manipulation of the cellular responses. In this study, the distribution of the T3SS gene cluster was first examined in 110 Vibrio strains of 26 different species, including 98 strains isolated from coastal areas in China. Several T3SS1 genes, but not T3SS2 genes (T3SS2α and T3SS2β), were universally detected in all the strains of four species, Vibrio parahaemolyticus, Vibrio alginolyticus, Vibrio harveyi, and Vibrio campbellii. The effector coding regions within the T3SS1 gene clusters from the T3SS1-positive strains were further analyzed, revealing that variations in the effectors of Vibrio T3SS1 were observed among the four Vibrio species, even between different strains in V. harveyi, according to their genetic organization. Importantly, Afp17, a potential novel effector that may exert a similar function as the known effector VopS in T3SS1-induced cell death, based on cytotoxicity assay results, was found in the effector coding region of the T3SS1 in some V. harveyi and V. campbellii strains. Finally, it was revealed that differences in T3SS1-mediated cytotoxicity were dependent not only on the variations in the effectors of Vibrio T3SS1 but also on the initial adhesion ability to host cells, which is another prerequisite condition. Altogether, our results contribute to the clarification of the diversity of T3SS1 effectors and a better understanding of the differences in cytotoxicity among Vibrio species.

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.

The Rich Tapestry of Bacterial Protein Translocation Systems

The translocation of proteins across membranes is a fundamental cellular function. Bacteria have evolved a striking array of pathways for delivering proteins into or across cytoplasmic membranes and, when present, outer membranes. Translocated proteins can form part of the membrane landscape, reside in the periplasmic space situated between the inner and outer membranes of Gram-negative bacteria, deposit on the cell surface, or be released to the extracellular milieu or injected directly into target cells. One protein translocation system, the general secretory pathway, is conserved in all domains of life. A second, the twin-arginine translocation pathway, is also phylogenetically distributed among most bacteria and plant chloroplasts. While all cell types have evolved additional systems dedicated to the translocation of protein cargoes, the number of such systems in bacteria is now known to exceed nine. These dedicated protein translocation systems, which include the types 1 through 9 secretion systems (T1SSs-T9SSs), the chaperone-usher pathway, and type IV pilus system, are the subject of this review. Most of these systems were originally identified and have been extensively characterized in Gram-negative or diderm (two-membrane) species. It is now known that several of these systems also have been adapted to function in Gram-positive or monoderm (single-membrane) species, and at least one pathway is found only in monoderms. This review briefly summarizes the distinctive mechanistic and structural features of each dedicated pathway, as well as the shared properties, that together account for the broad biological diversity of protein translocation in bacteria.

Label free proteomics and systematic analysis of secretome reveals effector candidates regulated by SGE1 and FTF1 in the plant pathogen Fusarium oxysporum f. sp. cubense tropical race 4

BACKGROUND

Phytopathogens secreted effectors during host colonization to suppress or trigger plant immunity. Identification of new effectors is one of the research focuses in recent years. There is only a limited knowledge about effectors of Fusarium oxysporum f. sp. Cubense tropical race 4 (Foc TR4), the causal agent of wilt disease in Cavendish banana.

RESULTS

Two transcription factors, SGE1 and FTF1, were constitutively over-expressed in Foc TR4 to partially mimic the in-planta state. Secreted proteins with high purity were prepared through a two-round extraction method. Then the secretome were analyzed via label free proteomics method. A total of 919 non-redundant proteins were detected, of which 74 proteins were predicted to be effector candidates. Among these candidates, 29 were up-regulated and 13 down-regulated in the strain over-expressing SGE1 and FTF1, 8 were up-regulated and 4 down-regulated in either SGE1 or FTF1 over expression strain.

CONCLUSIONS

Through label free proteomics analysis, a series of effector candidates were identified in secretome of Foc TR4. Our work put a foundation for functional research of these effectors.

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.

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 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.

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.

Temporal and spatial detection of Candidatus Liberibacter asiaticus putative effector transcripts during interaction with Huanglongbing-susceptible, -tolerant, and -resistant citrus hosts

BACKGROUND

Citrus Huanglongbing (HLB) is a bacterial disease with high economic significance. The associated agent Candidatus Liberibacter asiaticus is a fastidious, phloem-limited, intracellular bacterium that is transmitted by an insect vector the Asian citrus psyllid (ACP). The genome of Ca. L. asiaticus contains protein secretion machinery that suggests host cell modulation capacity of this bacterium.

RESULTS

A total of 28 candidate effectors, an important class of secreted proteins, were predicted from the Ca. L. asiaticus genome. Sequence specific primers were designed for reverse transcription (RT) and quantitative PCR (qPCR), and expression was validated for 20 of the effector candidates in infected citrus with multiple genetic background. Using detached leaf inoculation, the mRNA of effectors was detected from 6 h to 7 days post ACP exposure. It was observed that higher bacterial titers were associated with a larger number of effectors showing amplification across all samples. The effectors' expression were compared in citrus hosts with various levels of HLB tolerance, including susceptible Duncan grapefruit and Washington navel orange, tolerant citron and Cleopatra mandarin, and resistant Pomeroy trifoliate and Carrizo citrange. Across all genotypes relatively high expression was observed for CLIBASIA_03695, CLIBASIA_00460, CLIBASIA_00420, CLIBASIA_04580, CLIBASIA_05320, CLIBASIA_04425, CLIBASIA_00525 and CLIBASIA_05315 in either a host-specific or -nonspecific manners. The two genotypes in each HLB-response group also show effector-expression profiles that seem to be different. In a companion study, the expression of effectors was compared between leaves and roots of own-rooted citrus that had been Ca. L. asiaticus-infected for more than a year. Results indicated relatively high expression of CLIBASIA_03875, CLIBASIA_04800 and CLIBASIA_05640 in all leaf and some root tissues of citron, Duncan and Cleopatra.

CONCLUSION

This temporal and spatial expression analysis of Ca. L. asiaticus effectors identified candidates possibly critical for early bacterial colonization, host tolerance suppression and long-term survival which are all worthy of further investigation.

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.

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.

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 .

A conserved motif promotes HpaB-regulated export of type III effectors from Xanthomonas

The type III secretion (T3S) system, an essential pathogenicity factor in most Gram-negative plant-pathogenic bacteria, injects bacterial effector proteins directly into the plant cell cytosol. Here, the type III effectors (T3Es) manipulate host cell processes to suppress defence and establish appropriate conditions for bacterial multiplication in the intercellular spaces of the plant tissue. T3E export depends on a secretion signal which is also present in 'non-effectors'. The latter are secreted extracellular components of the T3S apparatus, but are not translocated into the plant cell. How the T3S system discriminates between T3Es and non-effectors is still enigmatic. Previously, we have identified a putative translocation motif (TrM) in several T3Es from Xanthomonas campestris pv. vesicatoria (Xcv). Here, we analysed the TrM of the Xcv effector XopB in detail. Mutation studies showed that the proline/arginine-rich motif is required for efficient type III-dependent secretion and translocation of XopB and determines the dependence of XopB transport on the general T3S chaperone HpaB. Similar results were obtained for other effectors from Xcv. As the arginine residues of the TrM mediate specific binding of XopB to cardiolipin, one of the major lipid components in Xanthomonas membranes, we assume that the association of T3Es to the bacterial membrane prior to secretion supports type III-dependent export.

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.

A multi-phenotypic imaging screen to identify bacterial effectors by exogenous expression in a HeLa cell line

We present a high-content screen (HCS) for the simultaneous analysis of multiple phenotypes in HeLa cells expressing an autophagy reporter (mcherry-LC3) and one of 224 GFP-fused proteins from the Crohn’s Disease (CD)-associated bacterium, Adherent Invasive E. coli (AIEC) strain LF82. Using automated confocal microscopy and image analysis (CellProfiler), we localised GFP fusions within cells, and monitored their effects upon autophagy (an important innate cellular defence mechanism), cellular and nuclear morphology, and the actin cytoskeleton. This data will provide an atlas for the localisation of 224 AIEC proteins within human cells, as well as a dataset to analyse their effects upon many aspects of host cell morphology. We also describe an open-source, automated, image-analysis workflow to identify bacterial effectors and their roles via the perturbations induced in reporter cell lines when candidate effectors are exogenously expressed.

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.

ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning

The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting whether an effector or plant protein localizes to the apoplast. ApoplastP uncovers features of apoplastic localization common to both effectors and plant proteins, namely depletion in glutamic acid, acidic amino acids and charged amino acids and enrichment in small amino acids. ApoplastP predicts apoplastic localization in effectors with a sensitivity of 75% and a false positive rate of 5%, improving the accuracy of cysteine-rich classifiers by > 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted proteins. The secretomes of fungal saprophytes as well as necrotrophic, hemibiotrophic and extracellular fungal pathogens are enriched for predicted apoplastic proteins. Rust pathogens have low proportions of predicted apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells.

Systematic Identification of Intracellular-Translocated Candidate Effectors in Edwardsiella piscicida

Many bacterial pathogens inject effectors directly into host cells to target a variety of host cellular processes and promote bacterial dissemination and survival. Identifying the bacterial effectors and elucidating their functions are central to understanding the molecular pathogenesis of these pathogens. Edwardsiella piscicida is a pathogen with a wide host range, and very few of its effectors have been identified to date. Here, based on the genes significantly regulated by macrophage infection, we identified 25 intracellular translocation-positive candidate effectors, including all five previously reported effectors, namely EseG, EseJ, EseH, EseK, and EvpP. A subsequent secretion analysis revealed diverse secretion patterns for the 25 effector candidates, suggesting that multiple transport pathways were involved in the internalization of these candidate effectors. Further, we identified two novel type VI secretion system (T6SS) putative effectors and three outer membrane vesicles (OMV)-dependent putative effectors among the candidate effectors described above, and further analyzed their contribution to bacterial virulence in a zebrafish model. This work demonstrates an effective approach for screening bacterial effectors and expands the effectors repertoire in E. piscicida.

The Type III Secretion Chaperone HpaB Controls the Translocation of Effector and Noneffector Proteins From Xanthomonas campestris pv. vesicatoria

Pathogenicity of the gram-negative bacterium Xanthomonas campestris pv. vesicatoria depends on a type III secretion (T3S) system, which translocates effector proteins into plant cells. Effector proteins contain N-terminal T3S and translocation signals and interact with the T3S chaperone HpaB, which presumably escorts effectors to the secretion apparatus. The molecular mechanisms underlying the recognition of effectors by the T3S system are not yet understood. In the present study, we analyzed T3S and translocation signals in the type III effectors XopE2 and XopJ from X. campestris pv. vesicatoria. Both effectors contain minimal translocation signals, which are only recognized in the absence of HpaB. Additional N-terminal signals promote translocation of XopE2 and XopJ in the wild-type strain. The results of translocation and interaction studies revealed that the interaction of XopE2 and XopJ with HpaB and a predicted cytoplasmic substrate docking site of the T3S system is not sufficient for translocation. In agreement with this finding, we show that the presence of an artificial HpaB-binding site does not promote translocation of the noneffector XopA in the wild-type strain. Our data, therefore, suggest that the T3S chaperone HpaB not only acts as an escort protein but also controls the recognition of translocation signals.

Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform

BACKGROUND

Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery.

METHODS

We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner.

RESULTS

We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform.

CONCLUSION

These results have implications for future drug development and repurposing in the context of polypharmacology.

The TAL Effector AvrBs3 from Xanthomonas campestris pv. vesicatoria Contains Multiple Export Signals and Can Enter Plant Cells in the Absence of the Type III Secretion Translocon

Pathogenicity of the Gram-negative plant-pathogenic bacterium Xanthomonas campestris pv. vesicatoria depends on a type III secretion (T3S) system which translocates effector proteins into plant cells. Effector protein delivery is controlled by the T3S chaperone HpaB, which presumably escorts effector proteins to the secretion apparatus. One intensively studied effector is the transcription activator-like (TAL) effector AvrBs3, which binds to promoter sequences of plant target genes and activates plant gene expression. It was previously reported that type III-dependent delivery of AvrBs3 depends on the N-terminal protein region. The signals that control T3S and translocation of AvrBs3, however, have not yet been characterized. In the present study, we show that T3S and translocation of AvrBs3 depend on the N-terminal 10 and 50 amino acids, respectively. Furthermore, we provide experimental evidence that additional signals in the N-terminal 30 amino acids and the region between amino acids 64 and 152 promote translocation of AvrBs3 in the absence of HpaB. Unexpectedly, in vivo translocation assays revealed that AvrBs3 is delivered into plant cells even in the absence of HrpF, which is the predicted channel-forming component of the T3S translocon in the plant plasma membrane. The presence of HpaB- and HrpF-independent transport routes suggests that the delivery of AvrBs3 is initiated during early stages of the infection process, presumably before the activation of HpaB or the insertion of the translocon into the plant plasma membrane.

Ancestral acquisitions, gene flow and multiple evolutionary trajectories of the type three secretion system and effectors in Xanthomonas plant pathogens

Deciphering the evolutionary history and transmission patterns of virulence determinants is necessary to understand the emergence of novel pathogens. The main virulence determinant of most pathogenic proteobacteria is the type three secretion system (T3SS). The Xanthomonas genus includes bacteria responsible for numerous epidemics in agroecosystems worldwide and represents a major threat to plant health. The main virulence factor of Xanthomonas is the Hrp2 family T3SS; however, this system is not conserved in all strains and it has not been previously determined whether the distribution of T3SS in this bacterial genus has resulted from losses or independent acquisitions. Based on comparative genomics of 82 genome sequences representing the diversity of the genus, we have inferred three ancestral acquisitions of the Hrp2 cluster during Xanthomonas evolution followed by subsequent losses in some commensal strains and re‐acquisition in some species. While mutation was the main force driving polymorphism at the gene level, interspecies homologous recombination of large fragments expanding through several genes shaped Hrp2 cluster polymorphism. Horizontal gene transfer of the entire Hrp2 cluster also occurred. A reduced core effectome composed of xopF1, xopM, avrBs2 and xopR was identified that may allow commensal strains overcoming plant basal immunity. In contrast, stepwise accumulation of numerous type 3 effector genes was shown in successful pathogens responsible for epidemics. Our data suggest that capacity to intimately interact with plants through T3SS would be an ancestral trait of xanthomonads. Since its acquisition, T3SS has experienced a highly dynamic evolutionary history characterized by intense gene flux between species that may reflect its role in host adaptation.

Identification of a Novel Salmonella Type III Effector by Quantitative Secretome Profiling

Salmonella enterica serovar Typhimurium is arguably one of the most studied bacterial pathogens and successful infection requires the delivery of its virulence factors (effectors) directly into host cells via the type III secretion systems (T3SSs). Central to Salmonella pathogenesis, these effector proteins have been subjected to extensive studies over the years. Nevertheless, whether additional effectors exist remains unclear. Here we report the identification of a novel Salmonella T3SS effector STM1239 (which we renamed SopF) via quantitative secretome profiling. Immunoblotting and β-lactamase reporter assays confirmed the secretion and translocation of SopF in a T3SS-dependent manner. Moreover, ectopic expression of SopF caused significant toxicity in yeast cells. Importantly, genetic ablation of sopF led to Salmonella strains defective in intracellular replication within macrophages and the mutant were also markedly attenuated in a mouse model of infection. Our study underscores the use of quantitative secretome profiling in identifying novel virulence factors for bacterial pathogens.

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.

Taking the stage: effectors in the spotlight

Plant pathogens are a serious threat to agriculture and to global food security, causing diverse crop diseases which lead to extensive annual yield losses. Production of effector proteins by pathogens, to manipulate host cellular processes, is central to their success. An understanding of fundamental effector biology is key to addressing the threat posed by these pathogens. Recent advances in 'omics' technologies have facilitated high-throughput identification of putative effector proteins, while evolving cellular, structural and biochemical approaches have assisted in characterising their function. Furthermore, structures of effectors in complex with host factors now provide opportunities for applying our knowledge of effector biology to influence disease outcomes. In this review, we highlight recent advances in the field and suggest avenues for future research.

Assembly, structure, function and regulation of type III secretion systems

Type III secretion systems (T3SSs) are protein transport nanomachines that are found in Gram-negative bacterial pathogens and symbionts. Resembling molecular syringes, T3SSs form channels that cross the bacterial envelope and the host cell membrane, which enable bacteria to inject numerous effector proteins into the host cell cytoplasm and establish trans-kingdom interactions with diverse hosts. Recent advances in cryo-electron microscopy and integrative imaging have provided unprecedented views of the architecture and structure of T3SSs. Furthermore, genetic and molecular analyses have elucidated the functions of many effectors and key regulators of T3SS assembly and secretion hierarchy, which is the sequential order by which the protein substrates are secreted. As essential virulence factors, T3SSs are attractive targets for vaccines and therapeutics. This Review summarizes our current knowledge of the structure and function of this important protein secretion machinery. A greater understanding of T3SSs should aid mechanism-based drug design and facilitate their manipulation for biotechnological applications.

Computational Identification and Comparative Analysis of Secreted and Transmembrane Proteins in Six Burkholderia Species

As a step towards discovering novel pathogenesis-related proteins, we performed a genome scale computational identification and characterization of secreted and transmembrane (TM) proteins, which are mainly responsible for bacteria-host interactions and interactions with other bacteria, in the genomes of six representative Burkholderia species. The species comprised plant pathogens (B. glumae BGR1, B. gladioli BSR3), human pathogens (B. pseudomallei K96243, B. cepacia LO6), and plant-growth promoting endophytes (Burkholderia sp. KJ006, B. phytofirmans PsJN). The proportions of putative classically secreted proteins (CSPs) and TM proteins among the species were relatively high, up to approximately 20%. Lower proportions of putative type 3 non-classically secreted proteins (T3NCSPs) (~10%) and unclassified non-classically secreted proteins (NCSPs) (~5%) were observed. The numbers of TM proteins among the three clusters (plant pathogens, human pathogens, and endophytes) were different, while the distribution of these proteins according to the number of TM domains was conserved in which TM proteins possessing 1, 2, 4, or 12 TM domains were the dominant groups in all species. In addition, we observed conservation in the protein size distribution of the secreted protein groups among the species. There were species-specific differences in the functional characteristics of these proteins in the various groups of CSPs, T3NCSPs, and unclassified NCSPs. Furthermore, we assigned the complete sets of the conserved and unique NCSP candidates of the collected Burkholderia species using sequence similarity searching. This study could provide new insights into the relationship among plant-pathogenic, human-pathogenic, and endophytic bacteria.

Secretome analysis of diarrhea-inducing strains of Escherichia coli

Secreted proteins constitute a major part of virulence factors that are responsible for pathogenesis caused by Gram-negative bacteria. Enterohemorrhagic Escherichia coli, O157:H7, is the major pathogen often causing outbreaks. However, studies have reported that the significant outbreaks caused by non-O157:H7 E. coli strains, also known as "Big-Six" serogroup strains, are increasing. There is no systematic study describing differential secreted proteins from these non-O157:H7 E. coli strains. In this study, we carried out MS-based differential secretome analysis using tandem mass tags labeling strategy of non-O157:H7 E. coli strains, O103, O111, O121, O145, O26, and O45. We identified 1241 proteins, of which 565 proteins were predicted to be secreted. We also found that 68 proteins were enriched in type III secretion system and several of them were differentially expressed across the strains. Additionally, we identified several strain-specific secreted proteins that could be used for developing potential markers for the identification and strain-level differentiation. To our knowledge, this study is the first comparative proteomic study on secretome of E. coli Big-Six serogroup and the several of these strain-specific secreted proteins can be further studied to develop potential markers for identification and strain-level differentiation. Moreover, the results of this study can be utilized in several applications, including food safety, diagnostics of E. coli outbreaks, and detection and identification of bio threats in biodefense.

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.

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

BACKGROUND

Many gram-negative bacteria use type III secretion systems (T3SSs) to translocate effector proteins into host cells. T3SS effectors can give some bacteria a competitive edge over others within the same environment and can help bacteria to invade the host cells and allow them to multiply rapidly within the host. Therefore, developing efficient methods to identify effectors scattered in bacterial genomes can lead to a better understanding of host-pathogen interactions and ultimately to important medical and biotechnological applications.

RESULTS

We used 21 genomic and proteomic attributes to create a precise and reliable T3SS effector prediction method called Genome Search for Effectors Tool (GenSET). Five machine learning algorithms were trained on effectors selected from different organisms and a trained (voting) algorithm was then applied to identify other effectors present in the genome testing sets from the same (GenSET Phase 1) or different (GenSET Phase 2) organism. Although a select group of attributes that included the codon adaptation index, probability of expression in inclusion bodies, N-terminal disorder, and G + C content (filtered) were better at discriminating between positive and negative sets, algorithm performance was better when all 21 attributes (unfiltered) were used. Performance scores (sensitivity, specificity and area under the curve) from GenSET Phase 1 were better than those reported for six published methods. More importantly, GenSET Phase 1 ranked more known effectors (70.3%) in the top 40 ranked proteins and predicted 10-80% more effectors than three available programs in three of the four organisms tested. GenSET Phase 2 predicted 43.8% effectors in the top 40 ranked proteins when tested on four related or unrelated organisms. The lower prediction rates from GenSET Phase 2 may be due to the presence of different translocation signals in effectors from different T3SS families.

CONCLUSIONS

The species-specific GenSET Phase 1 method offers an alternative approach to T3SS effector prediction that can be used with other published programs to improve effector predictions. Additionally, our approach can be applied to predict effectors of other secretion systems as long as these effectors have translocation signals embedded in their sequences.

The Structure and Function of Type III Secretion Systems

Type III secretion systems (T3SSs) afford Gram-negative bacteria an intimate means of altering the biology of their eukaryotic hosts--the direct delivery of effector proteins from the bacterial cytoplasm to that of the eukaryote. This incredible biophysical feat is accomplished by nanosyringe "injectisomes," which form a conduit across the three plasma membranes, peptidoglycan layer, and extracellular space that form a barrier to the direct delivery of proteins from bacterium to host. The focus of this chapter is T3SS function at the structural level; we will summarize the core findings that have shaped our understanding of the structure and function of these systems and highlight recent developments in the field. In turn, we describe the T3SS secretory apparatus, consider its engagement with secretion substrates, and discuss the posttranslational regulation of secretory function. Lastly, we close with a discussion of the future prospects for the interrogation of structure-function relationships in the T3SS.

Computational prediction shines light on type III secretion origins

Type III secretion system is a key bacterial symbiosis and pathogenicity mechanism responsible for a variety of infectious diseases, ranging from food-borne illnesses to the bubonic plague. In many Gram-negative bacteria, the type III secretion system transports effector proteins into host cells, converting resources to bacterial advantage. Here we introduce a computational method that identifies type III effectors by combining homology-based inference with de novo predictions, reaching up to 3-fold higher performance than existing tools. Our work reveals that signals for recognition and transport of effectors are distributed over the entire protein sequence instead of being confined to the N-terminus, as was previously thought. Our scan of hundreds of prokaryotic genomes identified previously unknown effectors, suggesting that type III secretion may have evolved prior to the archaea/bacteria split. Crucially, our method performs well for short sequence fragments, facilitating evaluation of microbial communities and rapid identification of bacterial pathogenicity – no genome assembly required. pEffect and its data sets are available at http://services.bromberglab.org/peffect.