Evidence map›Paper›PMID 25784698›Full record

ArticlemBio2015

Novel type III effectors in Pseudomonas aeruginosa.

David Burstein, Shirley Satanower, Michal Simovitch, Yana Belnik, Meital Zehavi, Gal Yerushalmi, Shay Ben-Aroya, Tal Pupko, Ehud Banin

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 46 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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  5. Modeling Host-Pathogen Interactions inInternational journal of molecular sciences · 2024
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  7. Evidence for intracellularJournal of bacteriology · 2024
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  11. Cells · 2023
    Review
  12. Complete genome sequence of an Israeli isolate ofFrontiers in plant science · 2023
    Article
  13. Using Structure-guided Fragment-Based Drug Discovery to TargetFrontiers in molecular biosciences · 2022
    Review
  14. Article
  15. Modulation of the immune response by theFrontiers in cellular and infection microbiology · 2022
    Review
  16. Review
  17. Review
  18. Review
  19. International journal of molecular sciences · 2021
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

David BursteinDepartment of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel.
Shirley Satanower
Michal Simovitch
Yana Belnik
Meital Zehavi
Gal Yerushalmi
Shay Ben-AroyaThe Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
Tal PupkoDepartment of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel talp@tau.ac.il ehud.banin@biu.ac.il.
Ehud Banintalp@tau.ac.il ehud.banin@biu.ac.il.
Bar-Ilan University · ILTel Aviv University · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Type III Secretion SystemsBacterial ProteinsComputational BiologyHumansMachine LearningPseudomonas aeruginosaVirulence FactorsBacterial ProteinsType III Secretion SystemsVirulence Factors

Identifiers

PMID25784698
PMCPMC4453518
OpenAlexW2120190359

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

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.