Evidence map›Paper›PMID 31712565›Full record

ArticleScientific reports2019

Impact of bacteria motility in the encounter rates with bacteriophage in mucus.

Kevin L Joiner, Arlette Baljon, Jeremy Barr, Forest Rohwer, Antoni Luque

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

22 citing papers in PubMed, 44 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
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  7. Article
  8. Multi-strain phage induced clearance of bacterial infections.bioRxiv : the preprint server for biology · 2024
    Article
  9. Metapopulation model of phage therapy of an acutebioRxiv : the preprint server for biology · 2024
    Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Review
  15. Review
  16. Predicting the capsid architecture of phages from metagenomic data.Computational and structural biotechnology journal · 2022
    Article
  17. Review
  18. International journal of environmental research and public health · 2021
    Article
  19. Article
  20. Review
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

5 authors at 3 institutions in 2 countries.

Kevin L JoinerComputational Science Research Center, San Diego State University, San Diego, CA, 92182, USA. kevin.l.joiner1@navy.mil.
Arlette BaljonDepartment of Physics, San Diego State University, San Diego, CA, 92182, USA.
Jeremy BarrSchool of Biological Sciences, Monash University, Clayton, VIC, 3800, Australia.ORCID http://orcid.org/0000-0001-5603-5294
Forest RohwerDepartment of Biology, San Diego State University, San Diego, CA, 92182, USA.
Antoni LuqueComputational Science Research Center, San Diego State University, San Diego, CA, 92182, USA. aluque@sdsu.edu.ORCID http://orcid.org/0000-0002-5817-4914
San Diego State University · USMonash University · AUNaval Information Warfare Center Pacific · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bacteriophages-or phages-are viruses that infect bacteria and are present in large concentrations in the mucosa that cover the internal organs of animals. Immunoglobulin (Ig) domains on the phage surface interact with mucin molecules, and this has been attributed to an increase in the encounter rates of phage with bacteria in mucus. However, the physical mechanism behind this phenomenon remains unclear. A continuous time random walk (CTRW) model simulating the diffusion due to mucin-T4 phage interactions was developed and calibrated to empirical data. A Langevin stochastic method for Escherichia coli (E. coli) run-and-tumble motility was combined with the phage CTRW model to describe phage-bacteria encounter rates in mucus for different mucus concentrations. Contrary to previous theoretical analyses, the emergent subdiffusion of T4 in mucus did not enhance the encounter rate of T4 against bacteria. Instead, for static E. coli, the diffusive T4 mutant lacking Ig domains outperformed the subdiffusive T4 wild type. E. coli's motility dominated the encounter rates with both phage types in mucus. It is proposed, that the local fluid-flow generated by E. coli's motility combined with T4 interacting with mucins may be the mechanism for increasing the encounter rates between the T4 phage and E. coli bacteria.

Indexed as

Bacterial Physiological PhenomenaAlgorithmsBacteriaDiffusionHost-Pathogen InteractionsModels, BiologicalMucusViscosity

Identifiers

PMID31712565
PMCPMC6848219
OpenAlexW2983713820

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.