Evidence map›Paper›PMID 27194047›Full record

ArticleScientific reports2016

Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa.

Sohyun Hwang, Chan Yeong Kim, Sun-Gou Ji, Junhyeok Go, Hanhae Kim, Sunmo Yang, Hye Jin Kim, Ara Cho, Sang Sun Yoon, Insuk Lee

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  12. A Genome-Scale Co-Functional Network ofMolecules and cells · 2019
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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

10 authors.

Sohyun HwangDepartment of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea.
Chan Yeong KimDepartment of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea.
Sun-Gou JiDepartment of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea.
Junhyeok GoDepartment of Microbiology and Immunology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 120-749, Korea.
Hanhae KimDepartment of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea.
Sunmo YangDepartment of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea.
Hye Jin KimDepartment of Microbiology and Immunology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 120-749, Korea.
Ara ChoDepartment of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea.
Sang Sun YoonDepartment of Microbiology and Immunology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 120-749, Korea.
Insuk LeeDepartment of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pseudomonas aeruginosa is a Gram-negative bacterium of clinical significance. Although the genome of PAO1, a prototype strain of P. aeruginosa, has been extensively studied, approximately one-third of the functional genome remains unknown. With the emergence of antibiotic-resistant strains of P. aeruginosa, there is an urgent need to develop novel antibiotic and anti-virulence strategies, which may be facilitated by an approach that explores P. aeruginosa gene function in systems-level models. Here, we present a genome-wide functional network of P. aeruginosa genes, PseudomonasNet, which covers 98% of the coding genome, and a companion web server to generate functional hypotheses using various network-search algorithms. We demonstrate that PseudomonasNet-assisted predictions can effectively identify novel genes involved in virulence and antibiotic resistance. Moreover, an antibiotic-resistance network based on PseudomonasNet reveals that P. aeruginosa has common modular genetic organisations that confer increased or decreased resistance to diverse antibiotics, which accounts for the pervasiveness of cross-resistance across multiple drugs. The same network also suggests that P. aeruginosa has developed mechanism of trade-off in resistance across drugs by altering genetic interactions. Taken together, these results clearly demonstrate the usefulness of a genome-scale functional network to investigate pathogenic systems in P. aeruginosa.

Indexed as

Drug Resistance, BacterialComputational BiologyGene Regulatory NetworksGenes, BacterialPseudomonas aeruginosaSystems BiologyVirulence FactorsVirulence Factors

Identifiers

PMID27194047
PMCPMC4872156

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LicenceCC BY
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Registered trials

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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.