Global ocean resistome revealed: Exploring antibiotic resistance gene abundance and distribution in TARA Oceans samples

  1. Rafael R C Cuadrat
  2. Maria Sorokina
  3. Bruno G Andrade
  4. Tobias Goris
  5. Alberto M R Dávila

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Abstract

Background

The rise of antibiotic resistance (AR) in clinical settings is of great concern. Therefore, the understanding of AR mechanisms, evolution, and global distribution is a priority for patient survival. Despite all efforts in the elucidation of AR mechanisms in clinical strains, little is known about its prevalence and evolution in environmental microorganisms. We used 293 metagenomic samples from the TARA Oceans project to detect and quantify environmental antibiotic resistance genes (ARGs) using machine learning tools.

Results

After manual curation of ARGs, their abundance and distribution in the global ocean are presented. Additionally, the potential of horizontal ARG transfer by plasmids and their correlation with environmental and geographical parameters is shown. A total of 99,205 environmental open reading frames (ORFs) were classified as 1 of 560 different ARGs conferring resistance to 26 antibiotic classes. We found 24,567 ORFs in putative plasmid sequences, suggesting the importance of mobile genetic elements in the dynamics of environmental ARG transmission. Moreover, 4,804 contigs with >=2 putative ARGs were found, including 2 plasmid-like contigs with 5 different ARGs, highlighting the potential presence of multi-resistant microorganisms in the natural ocean environment. Finally, we identified ARGs conferring resistance to some of the most relevant clinical antibiotics, revealing the presence of 15 ARGs similar to mobilized colistin resistance genes (mcr) with high abundance on polar biomes. Of these, 5 are assigned to Psychrobacter, a genus including opportunistic human pathogens.

Conclusions

This study uncovers the diversity and abundance of ARGs in the global ocean metagenome. Our results are available on Zenodo in MySQL database dump format, and all the code used for the analyses, including a Jupyter notebook js avaliable on Github. We also developed a dashboard web application (http://www.resistomedb.com) for data visualization.

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  1. GigaScience

    Now published in GigaScience doi: 10.1093/gigascience/giaa046

    Rafael R. C. Cuadrat 1Molecular Epidemiology Department, German Institute of Human Nutrition Potsdam-Rehbruecke - DIfEFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Rafael R. C. CuadratMaria Sorokina 2Friedrich-Schiller University, Lessingstrasse 8, 07743 Jena, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Maria SorokinaBruno G. Andrade 3Embrapa Southeast Livestock - EMBRAPAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteTobias Goris 4Department of Molecular Toxicology, Research Group Intestinal Microbiology, German Institute of Human Nutrition Potsdam-Rehbruecke - DIfEFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteAlberto M. R. Dávila 5Computational and Systems Biology Laboratory, Oswaldo Cruz Institute, FIOCRUZ. Av Brasil 4365, Rio de Janeiro, RJ, Brasil. 21040-900Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Alberto M. R. DávilaFor correspondence: alberto.davila@fiocruz.br

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giaa046 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102215 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102216 Reviewer 3: http://dx.doi.org/10.5524/REVIEW.102217

  2. Version published to 10.1093/gigascience/giaa046
  3. Version published to 10.1101/765446v2 on bioRxiv
  4. Version published to 10.1101/765446v1 on bioRxiv