Evidence map›Paper›PMID 29042652›Full record

ArticleScientific reports2017

Computational Analysis of the Molecular Mechanism of RamR Mutations Contributing to Antimicrobial Resistance in Salmonella enterica.

Yen-Yi Liu, Chih-Chieh Chen

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Combining structure and genomics to understand antimicrobial resistance.Computational and structural biotechnology journal · 2020
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Yen-Yi LiuInstitute of Population Health Sciences, National Health Research Institutes, Miaoli, 35053, Taiwan.
Chih-Chieh ChenInstitute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, 80424, Taiwan. chieh@imst.nsysu.edu.tw.ORCID 0000-0002-5844-8428

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial resistance (AMR) in pathogenic microorganisms with multidrug resistance (MDR) constitutes a severe threat to human health. A major causative mechanism of AMR is mediated through the multidrug efflux pump (MEP). The resistance-nodulation-division superfamily (RND family) of Gram-negative bacteria is usually the major cause of MDR in clinical studies. In Salmonella enterica, the RND pump is translated from the arcAB gene, which is regulated by the activator RamA. Many MEP-caused AMR strains have high ramA gene expression due to mutations in RamR, which has a homodimeric structure comprising the dimerization domain and DNA-binding domain (DBD). Three mutations on the dimerization domain, namely Y59H, M84I, and E160D, are far from the DBD; the molecular mechanism through which they influence RamR's binding affinity to the ramA gene promoter and consequently disrupt RamA remains unclear. The present study conducted molecular dynamics simulations, binding free energy calculations, and normal mode analysis to investigate the mechanism through which Y59H, M84I, and E160D mutations on the dimerization domain influence the binding affinity of RamR to the ramA promoter. The present results suggest that the three mutations alter the RamR structure, resulting in decreased DNA-binding affinity.

Indexed as

Drug Resistance, MicrobialMutationATP-Binding Cassette, Sub-Family C ProteinsBacterial ProteinsMolecular Dynamics SimulationProtein DomainsSalmonella entericaTrans-ActivatorsATP-Binding Cassette, Sub-Family C ProteinsBacterial ProteinsTrans-Activators

Identifiers

PMID29042652
PMCPMC5645378

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.