Evidence map›Paper›PMID 34429748›Full record

ArticleEvolutionary applications2021

Overcoming the growth-infectivity trade-off in a bacteriophage slows bacterial resistance evolution.

Quan-Guo Zhang, Xiao-Lin Chu, Angus Buckling

Open access · goldAbstract read
In one paragraph

Article in Evolutionary applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.4field-weighted citation impact, top 8% 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

15 citing papers in PubMed, 30 citations in OpenAlex.

  1. Article
  2. Article
  3. bioRxiv : the preprint server for biology · 2026
    Article
  4. Review
  5. Article
  6. Review
  7. Bioinformatics advances · 2025
    Article
  8. Phage-based biocontrol ofFrontiers in microbiology · 2025
    Article
  9. Article
  10. Does Phage Therapy Need a Pan-Phage?Pathogens (Basel, Switzerland) · 2024
    Review
  11. Article
  12. No major cost of evolved survivorship inProceedings. Biological sciences · 2022
    Article
  13. Article
  14. Greater Phage Genotypic Diversity Constrains Arms-Race Coevolution.Frontiers in cellular and infection microbiology · 2022
    Article
  15. 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

3 authors at 2 institutions in 2 countries.

Quan-Guo ZhangState Key Laboratory of Earth Surface Processes and Resource Ecology and MOE Key Laboratory for Biodiversity Science and Ecological Engineering College of Life Sciences Beijing Normal University Beijing China.ORCID https://orcid.org/0000-0001-7128-2664
Xiao-Lin ChuState Key Laboratory of Earth Surface Processes and Resource Ecology and MOE Key Laboratory for Biodiversity Science and Ecological Engineering College of Life Sciences Beijing Normal University Beijing China.
Angus BucklingESI & CEC Biosciences University of Exeter Penryn UK.
Beijing Normal University · CNUniversity of Exeter · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of lytic bacteriophages for treating harmful bacteria (phage therapy) is faced with the challenge of bacterial resistance evolution. Phage strains with certain traits, for example, rapid growth and relatively broad infectivity ranges, may enjoy an advantage in slowing bacterial resistance evolution. Here, we show the possibility for laboratory selection programs ("evolutionary training") to yield phage genotypes with both high growth rate and broad infectivity, traits between which a trade-off has been assumed. We worked with a lytic phage that infects the bacterium

Indexed as

bacteriophage therapybiological controlevolutionary trainingexperimental evolutionresistance evolutiontrade‐off

Identifiers

PMID34429748
PMCPMC8372119
OpenAlexW3171311561

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.