Novel Type III Effectors in Pseudomonas aeruginosa

Abstract

Pseudomonas aeruginosa is a Gram-negative, opportunistic pathogen that causes chronic and acute infections in immunocompromised patients. Most P. aeruginosa strains encode an active type III secretion system (T3SS), utilized by the bacteria to deliver effector proteins from the bacterial cell directly into the cytoplasm of the host cell. Four T3SS effectors have been discovered and extensively studied in P. aeruginosa: ExoT, ExoS, ExoU, and ExoY. This is especially intriguing in light of P. aeruginosa’s ability to infect a wide range of hosts. We therefore hypothesized that additional T3SS effectors that have not yet been discovered are encoded in the genome of P. aeruginosa. Here, we applied a machine learning classification algorithm to identify novel P. aeruginosa effectors. In this approach, various types of data are integrated to differentiate effectors from the rest of the open reading frames of the bacterial genome. Due to the lack of a sufficient learning set of positive effectors, our machine learning algorithm integrated genomic information from another Pseudomonas species and utilized dozens of features accounting for various aspects of the effector coding genes and their products. Twelve top-ranking predictions were experimentally tested for T3SS-specific translocation, leading to the discovery of two novel T3SS effectors. We demonstrate that these effectors are not part of the injection structural complex and report initial efforts toward their characterization.

Identification of novel Xanthomonas euvesicatoria type III effector proteins by a machine‐learning approach

“…To identify novel T3S effectors of Xcv strain 85‐10 (Thieme et al ., ), we used a machine‐learning approach similar to that employed previously for the identification of type III secreted proteins from Pseudomonas aeruginosa (Burstein et al ., ) and type IV effectors from Legionella pneumophila and Coxiella burnetii (Burstein et al ., ; Lifshitz et al ., ). The machine‐learning algorithm is based on 79 features that potentially differentiate effectors from non‐effectors (http://onlinelibrary.wiley.com/doi/10.1111/mpp.12288/suppinfo, see Supporting Information).…”

Revealing the inventory of type III effectors in Pantoea agglomerans gall‐forming pathovars using draft genome sequences and a machine‐learning approach

“…To identify novel T3Es of the Pantoea pathovars, we employed a machine-learning approach similar to that used previously to identify T3Es in Xanthomonas euvesicatoria (Teper et al, 2016) and Pseudomonas aeruginosa (Burstein et al, 2015), and type IV effectors (T4Es) in Legionella pneumophila and Coxiella burnetii (Burstein et al, 2009;Lifshitz et al, 2014). The machine-learning algorithm was based on 49 features that potentially differentiate T3Es from non-effectors.…”

“…Effectors have also been predicted based on the presence of conserved cis-acting regulatory motifs in the promoter region of the gene or translocation signals at the protein Nterminus (Jiang et al, 2009;Petnicki-Ocwieja et al, 2002). Recently, a machine-learning approach has been utilized for the prediction of T3Es (Burstein et al, 2009(Burstein et al, , 2015Lifshitz et al, 2014;Teper et al, 2016). The machine-learning approach extracts features that distinguish effector from non-effector proteins and uses them to train a diverse arsenal of machine-learning classification algorithms.…”

Actin activates Pseudomonas aeruginosa ExoY nucleotidyl cyclase toxin and ExoY-like effector domains from MARTX toxins

“…Equipped with a type III secretion system (T3SS), P. aeruginosa can inject effector proteins directly into host cells where they contribute to virulence of the pathogen (for reviews see refs 1, 2). Four different T3SS-delivered effectors have been characterized (exoenzyme T, Y, U and S), but new effectors were recently identified3. Exoenzyme Y (ExoY) is present in 89% of clinical isolates4.…”