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.Author Summary: Many pathogenic bacteria exert their function by translocating a set of proteins, termed effectors, into the cytoplasm of their host cell. These effectors subvert various host cell processes for the benefit of the bacteria. Our goal in this study was to identify novel effectors in a genomic scale, towards a better understanding of the molecular mechanisms of bacterial pathogenesis. We developed a computational approach for the detection of new effectors in the intracellular pathogen Legionella pneumophila, the causative agent of the Legionnaires' disease, a severe pneumonia-like disease. The novelty of our approach for detecting effectors is the combination of state-of-the-art machine learning classification algorithms with broad biological knowledge on effector biology in a genomic scale. Applying this method, we detected and experimentally validated dozens of new effectors. Notably, our computational predictions had an exceedingly high accuracy of over 90%. In analyzing these effectors we were able to obtain new insights into the molecular mechanism of the pathogenesis system. Our results suggest, for the first time, that over 10% of the Legionella genome is dedicated to pathogenesis. Finally, our approach is general and can be utilized to study effectors in many other human pathogens.