Infection by the human pathogen Legionella pneumophila relies on the translocation of ~300 virulence proteins, termed effectors, which manipulate host-cell processes. However, almost no information exists regarding effectors in other Legionella pathogens. Here we sequenced, assembled and characterized the genomes of 38 Legionella species, and predicted their effector repertoire using a previously validated machine-learning approach. This analysis revealed a treasure trove of 5,885 predicted effectors. The effector repertoire of different Legionella species was found to be largely non-overlapping, and only seven core-effectors were shared among all species studied. Species-specific effectors had atypically low GC content, suggesting exogenous acquisition, possibly from their natural protozoan hosts. Furthermore, we detected numerous novel conserved effector domains, and discovered new domain combinations, which allowed inferring yet undescribed effector functions. The effector collection and network of domain architectures described here can serve as a roadmap for future studies of effector function and evolution.

A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF) was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine learning algorithms for the identification and characterization of bacterial pathogenesis determinants.

SummaryLegionella pneumophila and Coxiella burnetii have been shown to utilize the icm/dot type IV secretion system for pathogenesis and recently a large number of icm/dot-translocated substrates were identified in L. pneumophila. Bioinformatic analysis has revealed that 13 of the genes encoding for L. pneumophila-translocated substrates and five of the C. burnetii icm/dot genes, contain a conserved regulatory element that resembles the target sequence of the PmrA response regulator. Experimental analysis which included the construction of a L. pneumophila pmrA deletion mutant, intracellular growth analysis, comparison of gene expression between L. pneumophila wild type and the pmrA mutant, construction of mutations in the PmrA conserved regulatory element, controlled expression studies as well as mobility shift assays, demonstrated the direct relation between the PmrA regulator and the expression of L. pneumophila icm/dottranslocated substrates and several C. burnetii icm/ dot genes. Furthermore, genomic analysis identified 35 L. pneumophila and 68 C. burnetii unique genes that contain the PmrA regulatory element and few of these genes from L. pneumophila were found to be new icm/dot-translocated substrates. Our results establish the PmrA regulator as a fundamental regulator of the icm/dot type IV secretion system in these two bacteria.

Legionella and Coxiella are intracellular pathogens that use the virulence-related Icm/Dot type-IVB secretion system to translocate effector proteins into host cells during infection. These effectors were previously shown to contain a C-terminal secretion signal required for their translocation. In this research, we implemented a hidden semi-Markov model to characterize the amino acid composition of the signal, thus providing a comprehensive computational model for the secretion signal. This model accounts for dependencies among sites and captures spatial variation in amino acid composition along the secretion signal. To validate our model, we predicted and synthetically constructed an optimal secretion signal whose sequence is different from that of any known effector. We show that this signal efficiently translocates into host cells in an Icm/Dot-dependent manner. Additionally, we predicted in silico and experimentally examined the effects of mutations in the secretion signal, which provided innovative insights into its characteristics. Some effectors were found to lack a strong secretion signal according to our model. We demonstrated that these effectors were highly dependent on the IcmS-IcmW chaperons for their translocation, unlike effectors that harbor a strong secretion signal. Furthermore, our model is innovative because it enables searching ORFs for secretion signals on a genomic scale, which led to the identification and experimental validation of 20 effectors from Legionella pneumophila, Legionella longbeachae, and Coxiella burnetii. Our combined computational and experimental methodology is general and can be applied to the identification of a wide spectrum of protein features that lack sequence conservation but have similar amino acid characteristics.pathogenomics | type-IV secretion | translocated substrates | Legionnaire disease | Q-fever

Type-IV secretion systems are devices present in a wide range of bacteria (including bacterial pathogens) that deliver macromolecules (proteins and single-strand-DNA) across kingdom barriers (as well as between bacteria and into the surroundings). The type-IV secretion systems were divided into two subgroups and Legionella pneumophila and Coxiella burnetii are the only two bacteria known today to utilize a type-IVB secretion system for pathogenesis. In this review we summarized the available information concerning the icm/dot type-IVB secretion systems by comparing the two bacteria that possess this system, the proteins components of their systems as well as the homology of proteins from type-IVB secretion systems to proteins from type-IVA secretion systems. In addition, the phenotypes associated with mutants in the L. pneumophila icm/dot genes, their relations to properties of specific Icm/Dot proteins as well as the protein substrates delivered by this system are described.