Urban metagenomics uncover antibiotic resistance reservoirs in coastal beach and sewage waters View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Pablo Fresia, Verónica Antelo, Cecilia Salazar, Matías Giménez, Bruno D’Alessandro, Ebrahim Afshinnekoo, Christopher Mason, Gastón H. Gonnet, Gregorio Iraola

ABSTRACT

BACKGROUND: Microbial communities present in environmental waters constitute a reservoir for antibiotic-resistant pathogens that impact human health. For this reason, a diverse variety of water environments are being analyzed using metagenomics to uncover public health threats. However, the composition of these communities along the coastal environment of a whole city, where sewage and beach waters are mixed, is poorly understood. RESULTS: We shotgun-sequenced 20 coastal areas from the city of Montevideo (capital of Uruguay) including beach and sewage water samples to characterize bacterial communities and their virulence and antibiotic resistance repertories. As expected, we found that sewage and beach environments present significantly different bacterial communities. This baseline allowed us to detect a higher prevalence and a more diverse repertory of virulence and antibiotic-resistant genes in sewage samples. Many of these genes come from well-known enterobacteria and represent carbapenemases and extended-spectrum betalactamases reported in hospital infections in Montevideo. Additionally, we were able to genotype the presence of both globally disseminated pathogenic clones and emerging antibiotic-resistant bacteria in sewage waters. CONCLUSIONS: Our study represents the first in using metagenomics to jointly analyze beaches and the sewage system from an entire city, allowing us to characterize antibiotic-resistant pathogens circulating in urban waters. The data generated in this initial study represent a baseline metagenomic exploration to guide future longitudinal (time-wise) studies, whose systematic implementation will provide useful epidemiological information to improve public health surveillance. More... »

PAGES

35

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40168-019-0648-z

DOI

http://dx.doi.org/10.1186/s40168-019-0648-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112465711

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30819245


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s40168-019-0648-z'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s40168-019-0648-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40168-019-0648-z'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40168-019-0648-z'


 

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289 Proyecto “Centro de Metagenómica”, Institut Pasteur Montevideo, Montevideo, Uruguay
290 Unidad de Microbiología Molecular, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay
291 rdf:type schema:Organization
292 https://www.grid.ac/institutes/grid.5386.8 schema:alternateName Cornell University
293 schema:name Department of Physiology and Biophysics, Weill Cornell Medicine, New York, USA
294 The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, USA
295 The HRH Prince Alwaleed Bin Talal Bin Abdelaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, USA
296 The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
297 rdf:type schema:Organization
 




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