Genetic and Functional Diversity Help Explain Pathogenic, Weakly Pathogenic, and Commensal Lifestyles in the Genus Xanthomonas.

The genus Xanthomonas has been primarily studied for pathogenic interactions with plants. However, besides host and tissue-specific pathogenic strains, this genus also comprises nonpathogenic strains isolated from a broad range of hosts, sometimes in association with pathogenic strains, and other environments, including rainwater. Based on their incapacity or limited capacity to cause symptoms on the host of isolation, nonpathogenic xanthomonads can be further characterized as commensal and weakly pathogenic. This study aimed to understand the diversity and evolution of nonpathogenic xanthomonads compared to their pathogenic counterparts based on their cooccurrence and phylogenetic relationship and to identify genomic traits that form the basis of a life history framework that groups xanthomonads by ecological strategies. We sequenced genomes of 83 strains spanning the genus phylogeny and identified eight novel species, indicating unexplored diversity. While some nonpathogenic species have experienced a recent loss of a type III secretion system, specifically the hrp2 cluster, we observed an apparent lack of association of the hrp2 cluster with lifestyles of diverse species. We performed association analysis on a large data set of 337 Xanthomonas strains to explain how xanthomonads may have established association with the plants across the continuum of lifestyles from commensals to weak pathogens to pathogens. Presence of distinct transcriptional regulators, distinct nutrient utilization and assimilation genes, transcriptional regulators, and chemotaxis genes may explain lifestyle-specific adaptations of xanthomonads.

Comparative sequence analysis of pPATH pathogenicity plasmids in Pantoea agglomerans gall-forming bacteria.

Acquisition of the pathogenicity plasmid pPATH that encodes a type III secretion system (T3SS) and effectors (T3Es) has likely led to the transition of a non-pathogenic bacterium into the tumorigenic pathogen Pantoea agglomerans. P. agglomerans pv. gypsophilae (Pag) forms galls on gypsophila (Gypsophila paniculata) and triggers immunity on sugar beet (Beta vulgaris), while P. agglomerans pv. betae (Pab) causes galls on both gypsophila and sugar beet. Draft sequences of the Pag and Pab genomes were previously generated using the MiSeq Illumina technology and used to determine partial T3E inventories of Pab and Pag. Here, we fully assembled the Pab and Pag genomes following sequencing with PacBio technology and carried out a comparative sequence analysis of the Pab and Pag pathogenicity plasmids pPATHpag and pPATHpab. Assembly of Pab and Pag genomes revealed a ~4 Mbp chromosome with a 55% GC content, and three and four plasmids in Pab and Pag, respectively. pPATHpag and pPATHpab share 97% identity within a 74% coverage, and a similar GC content (51%); they are ~156 kb and ~131 kb in size and consist of 198 and 155 coding sequences (CDSs), respectively. In both plasmids, we confirmed the presence of highly similar gene clusters encoding a T3SS, as well as auxin and cytokinins biosynthetic enzymes. Three putative novel T3Es were identified in Pab and one in Pag. Among T3SS-associated proteins encoded by Pag and Pab, we identified two novel chaperons of the ShcV and CesT families that are present in both pathovars with high similarity. We also identified insertion sequences (ISs) and transposons (Tns) that may have contributed to the evolution of the two pathovars. These include seven shared IS elements, and three ISs and two transposons unique to Pab. Finally, comparative sequence analysis revealed plasmid regions and CDSs that are present only in pPATHpab or in pPATHpag. The high similarity and common features of the pPATH plasmids support the hypothesis that the two strains recently evolved into host-specific pathogens.

Complete genome sequence of an Israeli isolate of Xanthomonas hortorum pv. pelargonii strain 305 and novel type III effectors identified in Xanthomonas.

Xanthomonas hortorum pv. pelargonii is the causative agent of bacterial blight in geranium ornamental plants, the most threatening bacterial disease of this plant worldwide. Xanthomonas fragariae is the causative agent of angular leaf spot in strawberries, where it poses a significant threat to the strawberry industry. Both pathogens rely on the type III secretion system and the translocation of effector proteins into the plant cells for their pathogenicity. Effectidor is a freely available web server we have previously developed for the prediction of type III effectors in bacterial genomes. Following a complete genome sequencing and assembly of an Israeli isolate of Xanthomonas hortorum pv. pelargonii - strain 305, we used Effectidor to predict effector encoding genes both in this newly sequenced genome, and in X. fragariae strain Fap21, and validated its predictions experimentally. Four and two genes in X. hortorum and X. fragariae, respectively, contained an active translocation signal that allowed the translocation of the reporter AvrBs2 that induced the hypersensitive response in pepper leaves, and are thus considered validated novel effectors. These newly validated effectors are XopBB, XopBC, XopBD, XopBE, XopBF, and XopBG.

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.

Phylogenetic Distribution and Evolution of Type VI Secretion System in the Genus Xanthomonas.

The type VI secretion system (T6SS) present in many Gram-negative bacteria is a contact-dependent apparatus that can directly deliver secreted effectors or toxins into diverse neighboring cellular targets including both prokaryotic and eukaryotic organisms. Recent reverse genetics studies with T6 core gene loci have indicated the importance of functional T6SS toward overall competitive fitness in various pathogenic Xanthomonas spp. To understand the contribution of T6SS toward ecology and evolution of Xanthomonas spp., we explored the distribution of the three distinguishable T6SS clusters, i3*, i3***, and i4, in approximately 1,740 Xanthomonas genomes, along with their conservation, genetic organization, and their evolutionary patterns in this genus. Screening genomes for core genes of each T6 cluster indicated that 40% of the sequenced strains possess two T6 clusters, with combinations of i3*** and i3* or i3*** and i4. A few strains of Xanthomonas citri, Xanthomonas phaseoli, and Xanthomonas cissicola were the exception, possessing a unique combination of i3* and i4. The findings also indicated clade-specific distribution of T6SS clusters. Phylogenetic analysis demonstrated that T6SS clusters i3* and i3*** were probably acquired by the ancestor of the genus Xanthomonas, followed by gain or loss of individual clusters upon diversification into subsequent clades. T6 i4 cluster has been acquired in recent independent events by group 2 xanthomonads followed by its spread via horizontal dissemination across distinct clades across groups 1 and 2 xanthomonads. We also noted reshuffling of the entire core T6 loci, as well as T6SS spike complex components, hcp and vgrG, among different species. Our findings indicate that gain or loss events of specific T6SS clusters across Xanthomonas phylogeny have not been random.

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.

Machine-learning of complex evolutionary signals improves classification of SNVs.

Conservation is a strong predictor for the pathogenicity of single-nucleotide variants (SNVs). However, some positions that present complex conservation patterns across vertebrates stray from this paradigm. Here, we analyzed the association between complex conservation patterns and the pathogenicity of SNVs in the 115 disease-genes that had sufficient variant data. We show that conservation is not a one-rule-fits-all solution since its accuracy highly depends on the analyzed set of species and genes. For example, pairwise comparisons between the human and 99 vertebrate species showed that species differ in their ability to predict the clinical outcomes of variants among different genes using conservation. Furthermore, certain genes were less amenable for conservation-based variant prediction, while others demonstrated species that optimize prediction. These insights led to developing EvoDiagnostics, which uses the conservation against each species as a feature within a random-forest machine-learning classification algorithm. EvoDiagnostics outperformed traditional conservation algorithms, deep-learning based methods and most ensemble tools in every prediction-task, highlighting the strength of optimizing conservation analysis per-species and per-gene. Overall, we suggest a new and a more biologically relevant approach for analyzing conservation, which improves prediction of variant pathogenicity.

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.

Type III secretion system effectors form robust and flexible intracellular virulence networks.

Infections with many Gram-negative pathogens, including Escherichia coli, Salmonella, Shigella, and Yersinia, rely on type III secretion system (T3SS) effectors. We hypothesized that while hijacking processes within mammalian cells, the effectors operate as a robust network that can tolerate substantial contractions. This was tested in vivo using the mouse pathogen Citrobacter rodentium (encoding 31 effectors). Sequential gene deletions showed that effector essentiality for infection was context dependent and that the network could tolerate 60% contraction while maintaining pathogenicity. Despite inducing very different colonic cytokine profiles (e.g., interleukin-22, interleukin-17, interferon-γ, or granulocyte-macrophage colony-stimulating factor), different networks induced protective immunity. Using data from >100 distinct mutant combinations, we built and trained a machine learning model able to predict colonization outcomes, which were confirmed experimentally. Furthermore, reproducing the human-restricted enteropathogenic E. coli effector repertoire in C. rodentium was not sufficient for efficient colonization, which implicates effector networks in host adaptation. These results unveil the extreme robustness of both T3SS effector networks and host responses.

Domain-Scan: Combinatorial Sero-Diagnosis of Infectious Diseases Using Machine Learning.

The presence of pathogen-specific antibodies in an individual's blood-sample is used as an indication of previous exposure and infection to that specific pathogen (e.g., virus or bacterium). Measurement of the diagnostic antibodies is routinely achieved using solid phase immuno-assays such as ELISA tests and western blots. Here, we describe a sero-diagnostic approach based on phage-display of epitope arrays we term "Domain-Scan". We harness Next-generation sequencing (NGS) to measure the serum binding to dozens of epitopes derived from HIV-1 and HCV simultaneously. The distinction of healthy individuals from those infected with either HIV-1 or HCV, is modeled as a machine-learning classification problem, in which each determinant ("domain") is considered as a feature, and its NGS read-out provides values that correspond to the level of determinant-specific antibodies in the sample. We show that following training of a machine-learning model on labeled examples, we can very accurately classify unlabeled samples and pinpoint the domains that contribute most to the classification. Our experimental/computational Domain-Scan approach is general and can be adapted to other pathogens as long as sufficient training samples are provided.

Show me your secret(ed) weapons: a multifaceted approach reveals a wide arsenal of type III-secreted effectors in the cucurbit pathogenic bacterium Acidovorax citrulli and novel effectors in the Acidovorax genus.

The cucurbit pathogenic bacterium Acidovorax citrulli requires a functional type III secretion system (T3SS) for pathogenicity. In this bacterium, as with Xanthomonas and Ralstonia spp., an AraC-type transcriptional regulator, HrpX, regulates expression of genes encoding T3SS components and type III-secreted effectors (T3Es). The annotation of a sequenced A. citrulli strain revealed 11 T3E genes. Assuming that this could be an underestimation, we aimed to uncover the T3E arsenal of the A. citrulli model strain, M6. Thorough sequence analysis revealed 51 M6 genes whose products are similar to known T3Es. Furthermore, we combined machine learning and transcriptomics to identify novel T3Es. The machine-learning approach ranked all A. citrulli M6 genes according to their propensity to encode T3Es. RNA-Seq revealed differential gene expression between wild-type M6 and a mutant defective in HrpX: 159 and 28 genes showed significantly reduced and increased expression in the mutant relative to wild-type M6, respectively. Data combined from these approaches led to the identification of seven novel T3E candidates that were further validated using a T3SS-dependent translocation assay. These T3E genes encode hypothetical proteins that seem to be restricted to plant pathogenic Acidovorax species. Transient expression in Nicotiana benthamiana revealed that two of these T3Es localize to the cell nucleus and one interacts with the endoplasmic reticulum. This study places A. citrulli among the 'richest' bacterial pathogens in terms of T3E cargo. It also revealed novel T3Es that appear to be involved in the pathoadaptive evolution of plant pathogenic Acidovorax species.