Evidence map›Paper›PMID 25853342›Full record

ArticlePLoS biology2015

Using a sequential regimen to eliminate bacteria at sublethal antibiotic dosages.

Ayari Fuentes-Hernandez, Jessica Plucain, Fabio Gori, Rafael Pena-Miller, Carlos Reding, Gunther Jansen, Hinrich Schulenburg, Ivana Gudelj, Robert Beardmore

Open access · goldAbstract read
In one paragraph

Article in PLoS biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
10.7field-weighted citation impact, top 1% 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

55 citing papers in PubMed, 92 citations in OpenAlex.

  1. Article
  2. Clinical microbiology reviews · 2025
    Review
  3. Article
  4. Drug combinations targeting antibiotic resistance.npj antimicrobials and resistance · 2024
    Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Sequential antibiotic therapy in the laboratory and in the patient.Journal of the Royal Society, Interface · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Evolution in alternating environments with tunable interlandscape correlations.Evolution; international journal of organic evolution · 2021
    Article
  20. The Evolution and Ecology of Resistance in Cancer Therapy.Cold Spring Harbor perspectives in medicine · 2020
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 3 countries.

Ayari Fuentes-HernandezCentro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México.
Jessica PlucainBiosciences, Geoffrey Pope Building, University of Exeter, Exeter, United Kingdom.
Fabio GoriBiosciences, Geoffrey Pope Building, University of Exeter, Exeter, United Kingdom.
Rafael Pena-MillerCentro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México.
Carlos RedingBiosciences, Geoffrey Pope Building, University of Exeter, Exeter, United Kingdom.
Gunther JansenEvolutionary Ecology and Genetics, Christian-Albrechts-Universität zu Kiel, Kiel, Germany.
Hinrich SchulenburgEvolutionary Ecology and Genetics, Christian-Albrechts-Universität zu Kiel, Kiel, Germany.
Ivana GudeljBiosciences, Geoffrey Pope Building, University of Exeter, Exeter, United Kingdom.
Robert BeardmoreBiosciences, Geoffrey Pope Building, University of Exeter, Exeter, United Kingdom.
University of Exeter · GBKiel University · DEUniversidad Nacional Autónoma de México · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We need to find ways of enhancing the potency of existing antibiotics, and, with this in mind, we begin with an unusual question: how low can antibiotic dosages be and yet bacterial clearance still be observed? Seeking to optimise the simultaneous use of two antibiotics, we use the minimal dose at which clearance is observed in an in vitro experimental model of antibiotic treatment as a criterion to distinguish the best and worst treatments of a bacterium, Escherichia coli. Our aim is to compare a combination treatment consisting of two synergistic antibiotics to so-called sequential treatments in which the choice of antibiotic to administer can change with each round of treatment. Using mathematical predictions validated by the E. coli treatment model, we show that clearance of the bacterium can be achieved using sequential treatments at antibiotic dosages so low that the equivalent two-drug combination treatments are ineffective. Seeking to treat the bacterium in testing circumstances, we purposefully study an E. coli strain that has a multidrug pump encoded in its chromosome that effluxes both antibiotics. Genomic amplifications that increase the number of pumps expressed per cell can cause the failure of high-dose combination treatments, yet, as we show, sequentially treated populations can still collapse. However, dual resistance due to the pump means that the antibiotics must be carefully deployed and not all sublethal sequential treatments succeed. A screen of 136 96-h-long sequential treatments determined five of these that could clear the bacterium at sublethal dosages in all replicate populations, even though none had done so by 24 h. These successes can be attributed to a collateral sensitivity whereby cross-resistance due to the duplicated pump proves insufficient to stop a reduction in E. coli growth rate following drug exchanges, a reduction that proves large enough for appropriately chosen drug switches to clear the bacterium.

Indexed as

Anti-Bacterial AgentsDose-Response Relationship, DrugDrug Administration ScheduleDrug Resistance, Multiple, BacterialEscherichia coliAnti-Bacterial Agents

Identifiers

PMID25853342
PMCPMC4390231
OpenAlexW2089170761

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.