ArticleMultidisciplinary respiratory medicine2018
Understanding the impact of antibiotic therapies on the respiratory tract resistome: a novel pooled-template metagenomic sequencing strategy.
Article in Multidisciplinary respiratory medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- Multidrug-resistantERJ open research · 2026Article
- Infection and the microbiome in bronchiectasis.European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Lung microbiome: new insights into bronchiectasis' outcome.Frontiers in cellular and infection microbiology · 2024Review
- Rapid Detection of Antimicrobial Resistance Genes in Critically Ill Children Using a Custom TaqMan Array Card.Antibiotics (Basel, Switzerland) · 2023Article
- Antibiotic resistance in chronic respiratory diseases: from susceptibility testing to the resistome.European respiratory review : an official journal of the European Respiratory Society · 2022Review
- Untargeted Metagenomic Investigation of the Airway Microbiome of Cystic Fibrosis Patients with Moderate-Severe Lung Disease.Microorganisms · 2020Article
- Nasal Resistome Development in Infants With Cystic Fibrosis in the First Year of Life.Frontiers in microbiology · 2019Article
- Get together to increase awareness in bronchiectasis: a report of the 2Multidisciplinary respiratory medicine · 2018Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining the effects of antimicrobial therapies on airway microbiology at a population-level is essential. Such analysis allows, for example, surveillance of antibiotic-induced changes in pathogen prevalence, the emergence and spread of antibiotic resistance, and the transmission of multi-resistant organisms. However, current analytical strategies for understanding these processes are limited. Culture- and PCR-based assays for specific microbes require the a priori selection of targets, while antibiotic sensitivity testing typically provides no insight into either the molecular basis of resistance, or the carriage of resistance determinants by the wider commensal microbiota. Shotgun metagenomic sequencing provides an alternative approach that allows the microbial composition of clinical samples to be described in detail, including the prevalence of resistance genes and virulence traits. While highly informative, the application of metagenomics to large patient cohorts can be prohibitively expensive. Using sputum samples from a randomised placebo-controlled trial of erythromycin in adults with bronchiectasis, we describe a novel, cost-effective strategy for screening patient cohorts for changes in resistance gene prevalence. By combining metagenomic screening of pooled DNA extracts with validatory quantitative PCR-based analysis of candidate markers in individual samples, we identify population-level changes in the relative abundance of specific macrolide resistance genes. This approach has the potential to provide an important adjunct to current analytical strategies, particularly within the context of antimicrobial clinical trials.
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Registered trials
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.