Abstract
BackgroundThe 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.ResultsAfter 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.ConclusionsThis 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.
Highlights
Antibiotic-resistant bacteria are a global public health issue and an economic burden to the entire world, especially in developing countries
These open reading frame (ORF) were used as input for antibiotic resistance gene (ARG) screening with the deepARG software [20], resulting in the classification of 116,425 TARA ORFs (0.28%) as putative ARGs, related to 594 clinically relevant ARGs that confer resistance to 28 antibiotic classes
It was necessary to conduct an extensive manual curation on the results owing to misannotations and misclassifications of ARGs in the databases used by deepARG
Summary
Antibiotic-resistant bacteria are a global public health issue and an economic burden to the entire world, especially in developing countries. The TARA Oceans project [19] measured several marine environmental conditions across the globe and stored them as structured metadata This rich and unique dataset, together with the metagenome sequences [19], will allow the use of machine and deep learning approaches to search for gene and species distributions and their correlation to environmental parameters. We used 293 metagenomic samples from the TARA Oceans project to detect and quantify environmental antibiotic resistance genes (ARGs) using machine learning tools. 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.
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