Rationale:
Long-term antibiotic use for managing chronic respiratory disease is increasing; however, the role of the airway resistome and its relationship to host microbiomes remains unknown.
Objectives:
To evaluate airway resistomes and relate them to host and environmental microbiomes using ultradeep metagenomic shotgun sequencing.
Methods:
Airway specimens from 85 individuals with and without chronic respiratory disease (severe asthma, chronic obstructive pulmonary disease, and bronchiectasis) were subjected to metagenomic sequencing to an average depth exceeding 20 million reads. Respiratory and device-associated microbiomes were evaluated on the basis of taxonomical classification and functional annotation including the Comprehensive Antibiotic Resistance Database to determine airway resistomes. Co-occurrence networks of gene–microbe association were constructed to determine potential microbial sources of the airway resistome. Paired patient-inhaler metagenomes were compared (
n
= 31) to assess for the presence of airway–environment overlap in microbiomes and/or resistomes.
Measurements and Main Results:
Airway metagenomes exhibit taxonomic and metabolic diversity and distinct antimicrobial resistance patterns. A “core” airway resistome dominated by macrolide but with high prevalence of β-lactam, fluoroquinolone, and tetracycline resistance genes exists and is independent of disease status or antibiotic exposure.
Streptococcus
and
Actinomyces
are key potential microbial reservoirs of macrolide resistance including the
ermX
,
ermF
, and
msrD
genes. Significant patient-inhaler overlap in airway microbiomes and their resistomes is identified where the latter may be a proxy for airway microbiome assessment in chronic respiratory disease.
Conclusions:
Metagenomic analysis of the airway reveals a core macrolide resistome harbored by the host microbiome.
The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication
Early ViewERJ Methods
Author contributions: JKN and SA: performance and design of experiments, data analysis and interpretation, statistical analysis, writing the manuscript. MMA: metagenomic whole genome shotgun and targeted amplicon sequencing analytics, writing manuscript, statistical analysis, and interpretation. TKJ and NABMA: curation of clinical data, sample preparation, DNA extraction, amplification, and sequencing. FXI: metagenomic whole genome shotgun and targeted amplicon sequencing analytics. HSC, YSY, MIGV, ZSL, JXTL: performance of animal experiments. FA, AG, FB: intellectual contributions, patient recruitment and procurement of clinical data and specimens. SW, JS and TNS: oversight of animal experiments and intellectual contributions. KTA: oversight of mathematical methodology and statistics. SHC: conception and design of overall study and experiments, data analysis and interpretation, statistical analysis, writing manuscript and procurement of funding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.