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The diversity of uncharacterized antibiotic resistance genes can be predicted from known gene variants-but not always

Artikel i vetenskaplig tidskrift
Författare Johan Bengtsson-Palme
Publicerad i Microbiome
Volym 6
ISSN 2049-2618
Publiceringsår 2018
Publicerad vid CARe - Centrum för antibiotikaresistensforskning
Institutionen för biomedicin, avdelningen för infektionssjukdomar
Språk en
Länkar dx.doi.org/10.1186/s40168-018-0508-...
Ämnesord Antibiotic resistance, Diversity measurements, Gene diversity, Risk ranking, Microbial biodiversity, antimicrobial resistance, metagenomics reveals, risk-assessment, soil, bacteria, waste-water, environment, identification, resistomes, reservoir, database, Microbiology, ao a, 1984, scandinavian journal of statistics, v11, p265, iences, v369, ates of america, v106, p1374, ao a, 1992, journal of the american statistical association, v87, p210
Ämneskategorier Mikrobiologi inom det medicinska området

Sammanfattning

Background: Antibiotic resistance is considered one of the most urgent threats to modern healthcare, and the role of the environment in resistance development is increasingly recognized. It is often assumed that the abundance and diversity of known resistance genes are representative also for the non-characterized fraction of the resistome in a given environment, but this assumption has not been verified. In this study, this hypothesis is tested, using the resistance gene profiles of 1109 metagenomes from various environments. Results: This study shows that the diversity and abundance of known antibiotic resistance genes can generally predict the diversity and abundance of undescribed resistance genes. However, the extent of this predictability is dependent on the type of environment investigated. Furthermore, it is shown that carefully selected small sets of resistance genes can describe total resistance gene diversity remarkably well. Conclusions: The results of this study suggest that knowledge gained from large-scale quantifications of known resistance genes can be utilized as a proxy for unknown resistance factors. This is important for current and proposed monitoring efforts for environmental antibiotic resistance and has implications for the design of risk ranking strategies and the choices of measures and methods for describing resistance gene abundance and diversity in the environment.

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