An omics-based framework for assessing the health risk of antimicrobial resistance genes
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s41467-021-25096-3.pdf
Description
Published version
Size
4.77 MB
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Unknown
Checksum (MD5)
900d7528699f26b9e27b89a399606205
Author(s)
Zhang, An-Ni
Gaston, Jeffry M.
Dai, Chengzhen L.
Zhao, Shijie
Poyet, Mathilde
Groussin, Mathieu
Yin, Xiaole
Li, Li-Guan
van Loosdrecht, Mark C. M.
Topp, Edward
Date Issued
August 2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
AbstractAntibiotic resistance genes (ARGs) are widespread among bacteria. However, not all ARGs pose serious threats to public health, highlighting the importance of identifying those that are high-risk. Here, we developed an ‘omics-based’ framework to evaluate ARG risk considering human-associated-enrichment, gene mobility, and host pathogenicity. Our framework classifies human-associated, mobile ARGs (3.6% of all ARGs) as the highest risk, which we further differentiate as ‘current threats’ (Rank I; 3%) - already present among pathogens - and ‘future threats’ (Rank II; 0.6%) - novel resistance emerging from non-pathogens. Our framework identified 73 ‘current threat’ ARG families. Of these, 35 were among the 37 high-risk ARGs proposed by the World Health Organization and other literature; the remaining 38 were significantly enriched in hospital plasmids. By evaluating all pathogen genomes released since framework construction, we confirmed that ARGs that recently transferred into pathogens were significantly enriched in Rank II (‘future threats’). Lastly, we applied the framework to gut microbiome genomes from fecal microbiota transplantation donors. We found that although ARGs were widespread (73% of genomes), only 8.9% of genomes contained high-risk ARGs. Our framework provides an easy-to-implement approach to identify current and future antimicrobial resistance threats, with potential clinical applications including reducing risk of microbiome-based interventions.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-021-25096-3
