Characterization of the resistome in manure, soil and wastewater from dairy and beef production systems

Abstract

It has been proposed that livestock production effluents such as wastewater, airborne dust and manure increase the density of antimicrobial resistant bacteria and genes in the environment. The public health risk posed by this proposed outcome has been difficult to quantify using traditional microbiological approaches. We utilized shotgun metagenomics to provide a first description of the resistome of North American dairy and beef production effluents, and identify factors that significantly impact this resistome. We identified 34 mechanisms of antimicrobial drug resistance within 34 soil, manure and wastewater samples from feedlot, ranch and dairy operations. The majority of resistance-associated sequences found in all samples belonged to tetracycline resistance mechanisms. We found that the ranch samples contained significantly fewer resistance mechanisms than dairy and feedlot samples, and that the resistome of dairy operations differed significantly from that of feedlots. The resistome in soil, manure and wastewater differed, suggesting that management of these effluents should be tailored appropriately. By providing a baseline of the cattle production waste resistome, this study represents a solid foundation for future efforts to characterize and quantify the public health risk posed by livestock effluents.

Overexpression of antibiotic resistance genes in hospital effluents over time

“…Our results complement similar findings of ARG carriage by effluents that originate from anthropogenic sources, 28–30 and the work described here also complements the findings of a previous study of the same river catchment by identifying additional ARG-containing effluents and sampling over an extended time period 10 …”

A Review of Potential Public Health Impacts Associated With the Global Dairy Sector

“…Antibiotic-resistant bacteria and their genes can also contaminate the environment (Aitken et al, 2016;Oliver et al, 2011;Tripathi & Tripathi, 2017) and have been isolated from dairy wastewater; soil from dairy farms; dairy manure; and the dairy farm environment (Collis et al, 2019;Noyes et al, 2016;Oliver et al, 2011;Pitta et al, 2016). Antibiotic-resistant bacteria have also been detected in surface water, groundwater, sediments, and wetlands (Aitken et al, 2016;Oliver et al, 2011;Tripathi & Tripathi, 2017).…”

The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies

“…Reference samples of metagenomics experiments are even more complex in this regard, with each sample containing numerous instances of genetic AMR determinants 12, 14, 75 . Metagenomics analyses can detect genes (genotype), which are not necessarily translated into resistance (phenotype); expression of the protein(s), which is not directly revealed by DNA sequencing, is important in this context.…”

“…These are, however, very varied in the approaches and resources they use. Some start with sequencing reads produced by the Illumina 12, 13 , Ion Torrent 14 , PacBio 15 or Nanopore 16 platforms, just to give a few examples. To predict the resistance profile, interesting results were reported with very different strategies, including k-mer analysis of the reads 17 , sequence comparisons of individual reads to databases 12, 16 , first assembling the reads into contigs using various software packages 9, 18 and building and comparing de Bruijn graphs of the sequenced sample reads and the reference database 19 .…”

“…To predict the resistance profile, interesting results were reported with very different strategies, including k-mer analysis of the reads 17 , sequence comparisons of individual reads to databases 12, 16 , first assembling the reads into contigs using various software packages 9, 18 and building and comparing de Bruijn graphs of the sequenced sample reads and the reference database 19 . The reference set of genetic determinants of AMR used by the bioinformatics pipelines also varied, including databases such as ARGANNOT 20 , CARD 16 , ResFinder 9 , Resqu 12 , ARDB 21 , custom-generated from Genbank sequences 18, 22 or combinations of these 14 . Interestingly, the choice of the database was shown to greatly influence the interpretation of risk associated with AMR in public health 23, 24 .…”