ArticlemBio2015
Novel type III effectors in Pseudomonas aeruginosa.
Article in mBio, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
33 citing papers in PubMed, 46 citations in OpenAlex.
- The two-component system ColRS discriminates metal signals to regulate virulence and resistance in Pseudomonas aeruginosa.PLoS pathogens · 2026Article
- Integrated Microfluidic Platform for Longitudinal Imaging, Functional Assays, and Downstream Proteomics in C. elegans.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Beyond pathogenicity: applications of the type III secretion system (T3SS) ofFrontiers in microbiology · 2025Review
- Identification of novel toxins associated with the extracellular contractile injection system using machine learning.Molecular systems biology · 2024Article
- Modeling Host-Pathogen Interactions inInternational journal of molecular sciences · 2024Review
- Article
- Evidence for intracellularJournal of bacteriology · 2024Review
- PemB, a type III secretion effector inHeliyon · 2024Article
- Exploring structural features and potential lipid interactions of Pseudomonas aeruginosa type three secretion effector PemB by spectroscopic and calorimetric experiments.Protein science : a publication of the Protein Society · 2023Article
- Pseudomonas aeruginosa Citrate Synthase GltA Influences Antibiotic Tolerance and the Type III Secretion System through the Stringent Response.Microbiology spectrum · 2023Article
- Review
- Complete genome sequence of an Israeli isolate ofFrontiers in plant science · 2023Article
- Using Structure-guided Fragment-Based Drug Discovery to TargetFrontiers in molecular biosciences · 2022Review
- Natural language processing approach to model the secretion signal of type III effectors.Frontiers in plant science · 2022Article
- Modulation of the immune response by theFrontiers in cellular and infection microbiology · 2022Review
- The Epidemiology and Pathogenesis and Treatment of Pseudomonas aeruginosa Infections: An Update.Drugs · 2021Review
- Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.Trends in microbiology · 2021Review
- Pseudomonas aeruginosa adaptation and evolution in patients with cystic fibrosis.Nature reviews. Microbiology · 2021Review
- Review
- Modulation of phagosome phosphoinositide dynamics by a Legionella phosphoinositide 3-kinase.EMBO reports · 2021Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
unlabelledPseudomonas 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. IMPORTANCE: Pseudomonas aeruginosa uses a type III secretion system (T3SS) to secrete toxic proteins, termed effectors, directly into the cytoplasm of the host cell. The activation of this secretion system is correlated with disease severity and patient death. Compared with many other T3SS-utilizing pathogenic bacteria, P. aeruginosa has a fairly limited arsenal of effectors that have been identified. This is in sharp contrast with the wide range of hosts that this bacterium can infect. The discovery of two novel effectors described here is an important step toward better understanding of the virulence and host evasion mechanisms adopted by this versatile pathogen and may provide novel approaches to treat P. aeruginosa infections.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.