Evidence map›Paper›PMID 26369961›Full record

ArticleAntimicrobial agents and chemotherapy2015

Antibiotic Selection Pressure Determination through Sequence-Based Metagenomics.

Matthias Willmann, Mohamed El-Hadidi, Daniel H Huson, Monika Schütz, Christopher Weidenmaier, Ingo B Autenrieth, Silke Peter

Abstract read
In one paragraph

Article in Antimicrobial agents and chemotherapy, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

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

  1. Pooled it
  2. Trial
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  4. Article
  5. Article
  6. Article
  7. Serotype Distribution and Antimicrobial Resistance ofAntibiotics (Basel, Switzerland) · 2024
    Article
  8. Antibiotic perturbations to the gut microbiome.Nature reviews. Microbiology · 2023
    Review
  9. Review
  10. Prevalence and Characterization ofPathogens (Basel, Switzerland) · 2023
    Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Quantifying the impact of treatment history on plasmid-mediated resistance evolution in human gut microbiota.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  19. Article
  20. 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

7 authors.

Matthias WillmannInstitute of Medical Microbiology and Hygiene, University of Tübingen, Tübingen, Germany German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany will80@gmx.de.
Mohamed El-HadidiCenter for Bioinformatics, University of Tübingen, Tübingen, Germany.
Daniel H HusonCenter for Bioinformatics, University of Tübingen, Tübingen, Germany.
Monika SchützInstitute of Medical Microbiology and Hygiene, University of Tübingen, Tübingen, Germany German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany.
Christopher WeidenmaierInstitute of Medical Microbiology and Hygiene, University of Tübingen, Tübingen, Germany German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany.ORCID http://orcid.org/0000-0003-4107-739X
Ingo B AutenriethInstitute of Medical Microbiology and Hygiene, University of Tübingen, Tübingen, Germany German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany.
Silke PeterInstitute of Medical Microbiology and Hygiene, University of Tübingen, Tübingen, Germany German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The human gut forms a dynamic reservoir of antibiotic resistance genes (ARGs). Treatment with antimicrobial agents has a significant impact on the intestinal resistome and leads to enhanced horizontal transfer and selection of resistance. We have monitored the development of intestinal ARGs over a 6-day course of ciprofloxacin (Cp) treatment in two healthy individuals by using sequenced-based metagenomics and different ARG quantification methods. Fixed- and random-effect models were applied to determine the change in ARG abundance per defined daily dose of Cp as an expression of the respective selection pressure. Among various shifts in the composition of the intestinal resistome, we found in one individual a strong positive selection for class D beta-lactamases which were partly located on a mobile genetic element. Furthermore, a trend to a negative selection has been observed with class A beta-lactamases (-2.66 hits per million sample reads/defined daily dose; P = 0.06). By 4 weeks after the end of treatment, the composition of ARGs returned toward their initial state but to a different degree in both subjects. We present here a novel analysis algorithm for the determination of antibiotic selection pressure which can be applied in clinical settings to compare therapeutic regimens regarding their effect on the intestinal resistome. This information is of critical importance for clinicians to choose antimicrobial agents with a low selective force on their patients' intestinal ARGs, likely resulting in a diminished spread of resistance and a reduced burden of hospital-acquired infections with multidrug-resistant pathogens.

Indexed as

AdultAlgorithmsAnti-Bacterial Agentsbeta-LactamasesBiodiversityCiprofloxacinDrug Resistance, MicrobialGastrointestinal MicrobiomeHumansMaleMetagenomicsReal-Time Polymerase Chain ReactionSelection, GeneticAnti-Bacterial Agentsbeta-LactamasesCiprofloxacin

Identifiers

PMID26369961
PMCPMC4649198

What OpenQuestion holds

Textmetadata
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.