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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.

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.

Gil Segal, Howard Shuman and colleagues sequence the genomes of 38 Legionella species and analyze 5,885 predicted effector proteins. Their analysis identifies a core set of seven effectors shared by all 38 species and numerous previously unidentified conserved effector domains. 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 repertoires using a previously validated machine learning approach. This analysis identified 5,885 predicted effectors. The effector repertoires of different Legionella species were found to be largely non-overlapping, and only seven core effectors were shared by all species studied. Species-specific effectors had atypically low GC content, suggesting exogenous acquisition, possibly from the natural protozoan hosts of these species. Furthermore, we detected numerous new conserved effector domains and discovered new domain combinations, which allowed the inference of as 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.

Legionella pneumophila, the causative agent of Legionnaires' disease, and other pathogenic Legionella species multiply inside protozoa and human macrophages by using the intracellular multiplication (Icm)/defect in organelle trafficking (Dot) type-IV secretion system. The IcmQ protein, which possesses pore-forming activity, and IcmR, which regulates the IcmQ activity, are two essential components of this system. Analysis of the region expected to contain these two genes from 29 Legionella species revealed the presence of a conserved icmQ gene and a large hypervariable gene family [functional homologues of icmR (fir) genes], located at the icmR genomic position. Although hypervariable in their sequence, the fir genes from all 29 Legionella species were found, together with their corresponding icmQ genes, to function similarly during infection. In addition, all FIR proteins we examined were found to interact with their corresponding IcmQ proteins. Detailed bioinformatic, biochemical, and genetic analysis of the interaction between the variable FIR proteins and conserved IcmQ proteins revealed that their interaction depends on a variable region located between two conserved domains of IcmQ. This variable region was also found to be critical for IcmQ self-interaction, and the region probably coevolved with the corresponding FIR protein. A FIR-IcmQ pair was also found in Coxiella burnetii, the only known non-Legionella bacterium that contains an Icm/Dot system, indicating the significance of this protein pair for the function of this type-IV secretion system. We hypothesize that this gene variation, which is probably mediated by positive selection, plays an important role in the evolutionary arms race between the protozoan host cell and the pathogen.

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.

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.

Several pathogenic strains of Escherichia coli were isolated from chickens and turkeys with severe colisepticemia. Electron microscopic examination showed that all these strains had thin pili (fimbriae) when grown at 37 C but not at 18 C. These pili facilitated adherence of the bacteria to chick tracheal epithelial cells both in vitro and in vivo. The role of these pili in pathogenicity was examined by comparing chicks infected intratracheally with piliated bacteria and chicks infected with non-piliated bacteria. The presence of adherence pili on the infecting bacteria affected both the number of chicks that developed disease and the severity of the disease. /// La colisepticemia en aves es causada por cepas patógenas de Escherichia coli. Se presume que la puerta de entrada de la bacteria es por las vías respiratorias superiores. Hemos aislado varias cepas patógenas de E. coli a partir de pollos y pavos afectados de colisepticemia severa. El examen al microscopio electrónico demostró que todas estas cepas poseían fimbrias cuando los cultivos habían sido incubados a 37 C pero no cuando se incubaba a 18 C. Las fimbrias facilitaron la adherencia de las bacterias a las células epiteliales de la tráquea de pollos tanto in vivo como in vitro. La función de las fimbrias en la capacidad invasora fué determinada comparando pollos infectados por vía intratraqueal con bacterias que poseían fimbrias y pollos infectados con bacterias sin fimbrias. Los resultados indicaron que la presencia de fimbrias adherentes en las bacterias infectantes afectó tanto el número de pollos que contrajeron la enfermedad así como la severidad de la misma.

Based on the historical Killip Classification, higher Killip class is associated with increased mortality in patients with acute coronary syndrome (ACS), yet data on current prognosis are lacking. We sought to examine temporal trends in the management and outcomes of patients admitted with an ACS by Killip class and to assess its contemporary prognostic value. Time-dependent analysis (early-period 2000 to 2008 vs late-period 2010 to 2016) in patients with lower (=1) and higher (≥2) Killip classes in a national ACS survey. Clinical outcomes included 30d MACE (death, myocardial infarction, stroke, unstable angina, stent thrombosis, urgent revascularization) and 1-year mortality. Included were 9,736 and 5,288 patients in the early and late time-periods of which 18.5% and 11.5% were categorized as higher Killip class, respectively (p <0.001). Baseline co-morbidities (diabetes, hypertension, dyslipidemia) were more prevalent in the late versus early time periods in both study groups (p <0.001). Rates of 30d MACE decreased in both Killip classes (p <0.001), yet 1-year mortality decreased only in patients with lower Killip class (p = 0.02), and remained extremely high (30%) in patients with higher Killip class (p = 0.75). Killip class was a significant independent predictor for 1-year mortality, both in the early (adjusted hazard ratio 3.23, confidence interval 2.8, 3.7) and late (adjusted hazard ratio 4.13, confidence interval 3.21, 5.32) time periods. In conclusion, even in the current era, patients presenting with ACS and higher Killip class have poor 1-year survival. Efforts should focus on improving the adherence to guideline-recommended therapies. The Killip classification system is still a reliable prognostic tool.