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

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

Zhang, An Ni (The University of Hong Kong; Massachusetts Institute of Technology)
Gaston, Jeffry M. (Google LLC)
Dai, Chengzhen L. (Massachusetts Institute of Technology)
Zhao, Shijie (Massachusetts Institute of Technology)
Poyet, Mathilde (Massachusetts Institute of Technology; Broad Institute of MIT and Harvard)
Groussin, Mathieu (Massachusetts Institute of Technology; Broad Institute of MIT and Harvard)
Li, Li Guan (The University of Hong Kong)
van Loosdrecht, Mark C.M. (TU Delft BT/Environmental Biotechnology)
Zhang, Tong (The University of Hong Kong)

2021

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.

http://resolver.tudelft.nl/uuid:0771afc5-2dd8-433f-a72f-d508b963dee6

https://doi.org/10.1038/s41467-021-25096-3

2041-1723

Nature Communications, 12 (1)

Institutional Repository

journal article

© 2021 An Ni Zhang, Jeffry M. Gaston, Chengzhen L. Dai, Shijie Zhao, Mathilde Poyet, Mathieu Groussin, Li Guan Li, Mark C.M. van Loosdrecht, Tong Zhang, More Authors