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.
A large number of bacterial pathogens utilize secretion systems for pathogenesis. In these systems, a multi-protein complex is used to translocate a repertoire of proteins, termed effectors, into host cells during infection. These effector proteins were found to be critical for the pathogenicity of numerous pathogens, such as Salmonella enterica, Yersinia pestis, Pseudomonas syringae (utilizing a type-III secretion system) [1]–[3], Legionella pneumophila, Coxiella burnetii Helicobacter pylori, Bordetella pertussis, Agrobacterium tumefaciens, Bartonella henselae (utilizing a type-IV secretion system) [4]–[6], Vibrio cholerae, Mycobacterium tuberculosis, and Pseudomonas aeruginosa (utilizing other secretion systems) [7], [8], making them prime targets for research of bacterial virulence systems. L. pneumophila is an intracellular γ-proteobacteria, which is the causative agent of Legionnaires' disease: a severe pneumonia-like disease in which the bacteria infect and replicate in human alveolar macrophages [9]. L. pneumophila also infects a wide range of protozoan hosts, which serve as their environmental reservoir [10], [11]. After internalization by their natural protozoan hosts or by alveolar macrophages, the bacteria are confined to a phagosome and utilize the Icm/Dot type IVb secretion system to subvert host cellular processes [12], [13]. A large number of L. pneumophila encoded proteins were found to be translocated into the host cell in an Icm/Dot dependent manner [6], [14]–[16]. Some of the translocated substrates were shown to manipulate host cellular activities, and it is believed that the vast majority of translocated proteins have a functional role during infection. The first L. pneumophila effector, RalF, was identified based on sequence homology to an eukaryotic Guanine Exchange Factor (GEF) domain [17]. Since then, a total of 105 genes were identified as effectors using various approaches such as sequence homology to eukaryotic domains and markers for horizontal gene transfer [17]–[21], interactions with Icm/Dot components [22], transfer of proteins between bacteria [23], genetic assays in yeast [24]–[26], similar regulatory elements [27]–[29], and a predicted secretion signal [30]. Importantly, the cellular function of most effectors is still unknown, and for most of the effectors examined, their knockout failed to reveal an intracellular growth phenotype [20], [22], [26], [29], [31]. Moreover, in some cases, knocking-out a family of paralogous effector-encoding genes simultaneously produced no significant growth defect [23], [32]. These observations suggest the existence of redundancy in terms of effector functionality, or alternatively, some effectors may function only in specific hosts, showing no intracellular growth defect when absent during infection of other hosts. The L. pneumophila Philadelphia-1 genome harbors 3,005 open reading frames (ORFs) [31]. Identifying ORFs that encode for effector proteins is critical for the understanding of the cellular processes involved in L. pneumophila pathogenesis. In this study, we developed a novel machine learning approach for the identification of effectors. To the best of our knowledge, this is the first attempt to present the task of effector identification as a computational classification problem. Our approach aims to extract features that distinguish effectors from non-effectors. These features are based, in part, on a systematic review of known characteristics of effectors and in part, on the discovery of novel features. These extracted features were then used to train a variety of machine learning algorithms, which produced a list of predicted effectors sorted by their likelihood. We followed up our predictions with experimental validations, using the CyaA reporter system, which led to the discovery of 40 novel L. pneumophila effectors.
We conducted three phases of learning and validation. In each learning phase, we included all the validated effectors known at that time. Specifically, in the second and third phases we added effectors validated in previous phases as well as validated effectors published during the course of this study. Furthermore, the features were updated to reflect the increase in our understanding of effector characteristics and to maximize the information extracted from validated effectors. The effectors discovered in each learning and validation phase are described below. Thus far, analyses of attributes characterizing effectors were based on a limited set of a few dozens of validated effectors [20], [23], [28]–[30]. The availability of 145 validated effectors motivated us to perform an in-depth analysis of effector characteristics. Interesting observations were obtained regarding three such characteristics – distribution of effectors in the genome, G+C content of effector encoding genes, and the C-terminal secretion signal.
In this study we have identified 40 new effectors, bringing the total of known effectors to 145. The high rate of correct predictions suggests that effectors can indeed be clearly distinguished from the remaining ORFs according to the features described in this work. According to available expression array data [38], these newly discovered effectors are expressed during intracellular growth in amoeba. The expression of 18 effectors was elevated post infection, the expression of 7 effectors was decreased, and the remaining 15 did not change substantially (less than 1. 5 fold change). The functional role of these effectors during infection has yet to be determined. Regarding the evolutionary origin of effectors, the low G+C content and spatial clustering (Figure 3) support the hypothesis that effectors are often transferred via HGT. These results are in agreement with a recent publication showing that two L. pneumophila effectors were most likely acquired from Protozoa [39], and with an additional evolutionary study, in which HGT from an amoeba to Legionella was demonstrated [40]. Regarding our 40 newly discovered effectors, the homology to eukaryotes was found to be restricted to specific domains, thus, it is currently impossible to pinpoint the exact evolutionary origins for these genes. The 145 currently validated effectors make L. pneumophila the organism with the highest number of validated effectors. This can provide a lower bound on the percentage of L. pneumophila ORFs that encode for effector proteins – 5% of the total number of ORFs. However, assuming that a large fraction of our predicted effectors are genuine ones, the estimate becomes close to 10% (about 300 effectors), which constitutes an exceedingly large pool of effectors relative to any other known pathogenesis related secretion systems. An important result of our study is the list of additional 126 predicted effectors. Support for the validity of these putative effectors comes from a recent study concerning yeast growth defect of L. pneumophila ORFs [25]. In this study, three new effectors were validated. One of them (ceg19) was independently validated in our study, and an additional ORF (ceg9) is included in our list of putative effectors (Table S2). In the same study, 12 additional ORFs that showed yeast growth defects and were not experimentally tested for translocation were included in our list of putative effectors. Since effectors were shown to often confer a yeast growth defect phenotype [41], [42], this provides additional support for the validity of these putative effectors. Notably, out of our list of 126 predicted effectors, an additional effector (legK1) was also recently validated, see Table S2 [43]. Two previous studies characterized the secretion signal located at the effector C-terminus [30], [37]. We utilized the high number of validated effectors to statistically analyze the abundance of amino-acid groups at the C-terminus of effectors versus non-effectors and suggested a detailed description of this signal (Figure 4). We further computed the secretion signal among the 126 predicted effectors. The resulting signal is essentially similar to the one inferred from the list of validated effectors (Figure S1). This similarity supports both the validity of the putative effectors as well as the biological significance of the secretion signal. It should be noted that the previously suggested secretion signals [30], [44] were not retained by the classification algorithms in the final learning phase and hence, detecting the secretion signal defined in this work among the putative effectors cannot be attributed to the inclusion of these features when training the classifier. Our machine learning approach is general and thus can be applied to other pathogens. However, the applicability of this approach to other bacteria requires a set of validated effectors, adjustment of the features in order to optimally discriminate these effectors from non-effectors, and an experimental system for prediction validation. Nevertheless, we anticipate that the overall scheme of effector identification will be useful for many pathogenesis systems, when their effector research reaches a proper stage. The pathogenesis system most resembling the one of L. pneumophila is that of C. burnetii, an obligate intracellular pathogen and a potential bioterrorism agent. These bacteria utilize an Icm/Dot type-IVb secretion system and translocate effector proteins in a mechanism similar to that of L. pneumophila, as indicated by the ability of C. burnetii effectors to translocate via the L. pneumophila translocation system [21]. Most of our features, the learning algorithms, and the experimental validation experiments used in this study to identify effectors are applicable with relatively minor changes to the study of C. burnetii pathogenesis, while for more distantly related pathogens (e. g. , those using type-IVa secretion systems) further adjustments are required. To summarize, in this work we have developed a combined computational-experimental approach to identify and validate pathogenesis determinants on a genomic scale. We have increased the number of validated effectors by more than 37% and suggested over a hundred putative ones. We have developed a general machine learning scheme for the prediction of effectors, which can be updated when new information becomes available. Finally, this work suggests that our approach is applicable in the identification and characterization of effectors in other bacterial pathogenesis systems.
The following genomes were acquired from the RefSeq database at NCBI (http: //www. ncbi. nlm. nih. gov/RefSeq/): L. pneumophila Philadelphia-1 (NC_002942); L. pneumophila Lens (NC_006366 and NC_006369); L. pneumophila Corby (NC_009494); L. pneumophila Paris (NC_006365 and NC_006368); Escherichia coli strain K-12 DH10B (NC_010473), and Pseudomonas fluorescens Pf-5 (NC_004129). Two datasets of all human proteins were downloaded from ftp: //ftp. ncbi. nih. gov/genomes/H_sapiens/protein/. The first includes all the human annotated proteins and the second is comprised of ab initio protein predictions of “Gnomon”, an NCBI eukaryotic gene prediction tool (http: //www. ncbi. nlm. nih. gov/genome/guide/gnomon. shtml). The genome of Dictyostelium discoideum was downloaded from DictyBase (http: //dictybase. org/). The T. thermophila genome was acquired from the TIGR database (http: //www. tigr. org/tdb/e2k1/ttg/). For each of the three learning phases (see Results), we constructed a dataset of known effectors and a dataset of non-effectors. The size of each non-effector dataset was five folds larger than its corresponding effector dataset. For each learning phase the effector dataset included all effectors known at that time (published effectors and effectors we validated at previous learning phases). Since a dataset of experimentally validated non-effectors is unavailable, we searched for genes that are present in both L. pneumophila and E. coli, under the premise that such genes are most likely not related to the pathogenicity of L. pneumophila and are thus expected to be non-effectors. Specifically, BLAST-P similarity scores were computed for each L. pneumophila protein against the proteins of E. coli. An L. pneumophila protein was defined as a non-effector if it has a hit from E. coli with an E-value lower than 10−20 and sequence similarity higher than 50%. Additionally, the proteins that constitute the Icm/Dot secretion system were included in the non-effector datasets. A full list of all the effectors and non-effectors used in each phase is given in Table S1. Each ORF in the L. pneumophila Philadelphia-1 genome was described using a vector of features. The features used for each learning phase are detailed in Table S2 and are summarized in Table 1. Machine learning algorithms were performed using the WEKA package [47]. The following classification algorithms were tested: Naïve Bayes, Bayesian networks, SVM (SMO), Neural networks (Multilayer perceptron), and a Voting algorithm that is based on these four algorithms. Feature selection was performed using a “Wrapper” to find the best performing features for each one of the algorithms, using hill-climbing search algorithms. The classifiers were trained on datasets in which the ratio of effectors to non-effectors was 1∶5. Specifically, as more effectors were included in the second and third phases, the number of non-effectors was increased accordingly to maintain this ratio. The classification performance was evaluated on the train datasets for each classifier separately. Classification performance for each classifier was evaluated using 10-fold cross validation, i. e. , 90% of the training data were randomly chosen and used to tune the parameters of each classifier, and the remaining 10% were used to evaluate the classifier performance [47]. The performance score is measured in terms of AUC, which accounts for both the fraction of true positives (correctly classified effectors) and false positives (ORFs erroneously classified as effectors). Since the performance depends on the division of the training data, the procedure is repeated 10 times, so that each 10% is used once to evaluate performance. Classifier accuracy is defined as the average over these 10 repeats. The classifier with the highest average AUC was used at each learning phase to predict effectors. When we evaluated the classification performance of each feature group separately, a dataset in which the ratio of 1∶1 between effectors and non-effectors was used. This was done to avoid artificial high performance stemming from the excess of non-effectors in the training data. The computer code used to implement the machine learning scheme described here is available in http: //www. tau. ac. il/~talp/LegionellaMachineLearning. The L. pneumophila strains used in this study were L. pneumophila JR32, a streptomycin-resistant, restriction-negative mutant of L. pneumophila Philadelphia-1, which is a wild-type strain in terms of intracellular growth [48] and GS3011 an icmT deletion mutant [49]. The E. coli strain used was MC1022 [50]. Bacterial media, plates, and antibiotic concentrations were used as described previously [51]. The plasmid pMMB-cyaA-C [28] was used for the cloning of all the cyaA fusions constructed. All the genes examined were amplified by PCR using a pair of primers containing suitable restriction sites at the 5′ end. The PCR products were subsequently digested with the relevant enzymes, and cloned into the pMMB-cyaA-C vector to generate plasmids. Table S5 includes the pair of primers, the enzymes used for digestion, and the generated plasmids. The generated plasmids were sequenced to verify that no mutations were introduced during the PCR. Furthermore, the formation of a fusion protein with a proper size was validated by Western analysis using the CyaA antibody 3D1 (Santa Cruz Biotechnology, Inc.). Others and we have utilized the CyaA translocation assay to validate effector proteins [17], [27], [28], [30], [33]. Specifically, differentiated HL-60-derived human macrophages plated in 24-well tissue culture dishes at a concentration of 2. 5×106 cells/well were used for the assay. Bacteria were grown on ABCYE (ACES buffered charcoal yeast extract) plates containing chloramphenicol (Cm) for 48 h. The bacteria were scraped off the plate, calibrated to OD600 of 0. 2, and 20 µl of these bacteria were spotted on an ABCYE plate containing Cm and 1 mM isopropyl-ß-D-thiogalactopyranoside (IPTG) and grown for 20 h. The bacteria were then scraped off the plate and calibrated in order to result with a multiplicity of infection (MOI) of 5 during infection. Cells were infected with bacteria harboring the appropriate plasmids and the plates were centrifuged at 180 g for 5 minutes followed by incubation at 37°C under CO2 (5%) for 2 h. Cells were then washed twice with ice-cold PBS buffer (1. 4 M NaCl, 27 mM KCl, 100 mM Na2HPO4,18 mM KH2PO4) and lysed with 200 µl of lysis buffer (50 mM HCl and 0. 1% Triton X-100) at 4°C for 30 minutes. Lysed samples were boiled for 5 minutes, centrifuged for 10 minutes, and the supernatants were neutralized with NaOH. The levels of cAMP were determined using the cAMP Biotrak enzyme immunoassay (EIA) system (GE-healthcare) according to the manufacturer' s instructions. The presence of the CyaA fusion proteins was detected by Western blot, using monoclonal antibody anti-CyaA 3D1 (Santa Cruz Biotechnology, Inc.) diluted 1∶500 and goat anti-mouse IgG conjugated to HRP (Jackson Immunoresearch Laboratories, Inc.) diluted 1∶10,000.
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.
infectious diseases/bacterial infections
computational biology/systems biology
computational biology/genomics
infectious diseases/respiratory infections
Genome-Scale Identification of Legionella pneumophila Effectors Using a Machine Learning Approach