Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-07-08

AUTHORS

Molly K Gibson, Kevin J Forsberg, Gautam Dantas

ABSTRACT

Antibiotic resistance is a dire clinical problem with important ecological dimensions. While antibiotic resistance in human pathogens continues to rise at alarming rates, the impact of environmental resistance on human health is still unclear. To investigate the relationship between human-associated and environmental resistomes, we analyzed functional metagenomic selections for resistance against 18 clinically relevant antibiotics from soil and human gut microbiota as well as a set of multidrug-resistant cultured soil isolates. These analyses were enabled by Resfams, a new curated database of protein families and associated highly precise and accurate profile hidden Markov models, confirmed for antibiotic resistance function and organized by ontology. We demonstrate that the antibiotic resistance functions that give rise to the resistance profiles observed in environmental and human-associated microbial communities significantly differ between ecologies. Antibiotic resistance functions that most discriminate between ecologies provide resistance to β-lactams and tetracyclines, two of the most widely used classes of antibiotics in the clinic and agriculture. We also analyzed the antibiotic resistance gene composition of over 6000 sequenced microbial genomes, revealing significant enrichment of resistance functions by both ecology and phylogeny. Together, our results indicate that environmental and human-associated microbial communities harbor distinct resistance genes, suggesting that antibiotic resistance functions are largely constrained by ecology. More... »

PAGES

207-216

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ismej.2014.106

DOI

http://dx.doi.org/10.1038/ismej.2014.106

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052774714

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25003965


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215 schema:name Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, MO, USA
216 Department of Biomedical Engineering, Washington University, St Louis, MO, USA
217 Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA
218 rdf:type schema:Organization
 




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