Genome-Scale Identification of Legionella pneumophila Effectors Using a Machine Learning Approach

Abstract

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.

Legionella oakridgensis ATCC 33761 genome sequence and phenotypic characterization reveals its replication capacity in amoebae

“…oakridgensis seems to have a very limited set of hosts and replicates significantly slower compared to L. pneumophila Corby. This may be in accordance to the low amount of effector proteins and eukaryote-like proteins (total ~80) identified, when compared to what is known for L. pneumophila (>300 effector proteins) (Vogel et al, 1998;Vincent et al, 2006;Burstein et al, 2009;Hubber et al, 2010). A recent in silico analysis of genome sequences revealed a core set of approximately 107 effector proteins present in L. pneumophila strains (Schroeder et al, 2010).…”

Genome Dynamics in Legionella: The Basis of Versatility and Adaptation to Intracellular Replication

“…The main secretion system of Legionella, essential for intracellular growth, is the type IVB Dot/Icm secretion system (Marra and Shuman 1992;Berger and Isberg 1993). Using many dif-ferent methods it has been shown that at least 275 L. pneumophila proteins are secreted by this system (Campodonico et al 2005;de Felipe et al 2005;Shohdy et al 2005;de Felipe et al 2008;Burstein et al 2009;Heidtman et al 2009;Zhu et al 2011), which represents nearly 10% of the protein-coding genes predicted in the L. pneumophila genomes. At least 75 of these are eukaryotic-like proteins or carry eukaryotic domains and, for certain of these, it has also been shown experimentally that they interfere with host-cell signaling pathways ( Table 2).…”

Computational modeling and experimental validation of the Legionella and Coxiella virulence-related type-IVB secretion signal

“…Glutamic acid was found to be enriched at positions −17 to −10 (from the C-terminal end) and depleted from the five C-terminal amino acids, which were enriched with hydrophobic amino acids (e.g., isoleucine, leucine, valine). In addition, serine and threonine were enriched at positions −10 to −5 (18). A more recent study (19) described the importance of the glutamic acid stretch (E-block) for effector translocation and used this feature to identify several novel effectors.…”