4.8 Article

An omics-based framework for assessing the health risk of antimicrobial resistance genes

Journal

NATURE COMMUNICATIONS
Volume 12, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41467-021-25096-3

Keywords

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Funding

  1. University of Hong Kong
  2. MIT
  3. Hong Kong Theme Based Research [T21-705/20-N]
  4. Broad Institute (Broad Next 10 grant) [4000017]
  5. Center for Microbiome Informatics and Therapeutics at MIT

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Antibiotic resistance genes are common among bacteria, but not all pose high risks to human health. Researchers have developed an omics-based framework to rank these genes by risk, taking into account their enrichment in human associated environments, gene mobility, and host pathogenicity.
Antibiotic 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. Antibiotic resistance genes are common but not all are of high risk to human health. Here, the authors develop an omics-based framework for ranking genes by risk that incorporates level of enrichment in human associated environments, gene mobility, and host pathogenicity.

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