Identification of two Legionella pneumophila effectors that manipulate host phospholipids biosynthesis.

The intracellular pathogen Legionella pneumophila translocates a large number of effector proteins into host cells via the Icm/Dot type-IVB secretion system. Some of these effectors were shown to cause lethal effect on yeast growth. Here we characterized one such effector (LecE) and identified yeast suppressors that reduced its lethal effect. The LecE lethal effect was found to be suppressed by the over expression of the yeast protein Dgk1 a diacylglycerol (DAG) kinase enzyme and by a deletion of the gene encoding for Pah1 a phosphatidic acid (PA) phosphatase that counteracts the activity of Dgk1. Genetic analysis using yeast deletion mutants, strains expressing relevant yeast genes and point mutations constructed in the Dgk1 and Pah1 conserved domains indicated that LecE functions similarly to the Nem1-Spo7 phosphatase complex that activates Pah1 in yeast. In addition, by using relevant yeast genetic backgrounds we examined several L. pneumophila effectors expected to be involved in phospholipids biosynthesis and identified an effector (LpdA) that contains a phospholipase-D (PLD) domain which caused lethal effect only in a dgk1 deletion mutant of yeast. Additionally, LpdA was found to enhance the lethal effect of LecE in yeast cells, a phenomenon which was found to be dependent on its PLD activity. Furthermore, to determine whether LecE and LpdA affect the levels or distribution of DAG and PA in-vivo in mammalian cells, we utilized fluorescent DAG and PA biosensors and validated the notion that LecE and LpdA affect the in-vivo levels and distribution of DAG and PA, respectively. Finally, we examined the intracellular localization of both LecE and LpdA in human macrophages during L. pneumophila infection and found that both effectors are localized to the bacterial phagosome. Our results suggest that L. pneumophila utilize at least two effectors to manipulate important steps in phospholipids biosynthesis.

Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.

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.