Characterization of the Microbial Resistome in Conventional and “Raised Without Antibiotics” Beef and Dairy Production Systems

Abstract

Metagenomic investigations have the potential to provide unprecedented insights into microbial ecologies, such as those relating to antimicrobial resistance (AMR). We characterized the microbial resistome in livestock operations raising cattle conventionally (CONV) or without antibiotic exposures (RWA) using shotgun metagenomics. Samples of feces, wastewater from catchment basins, and soil where wastewater was applied were collected from CONV and RWA feedlot and dairy farms. After DNA extraction and sequencing, shotgun metagenomic reads were aligned to reference databases for identification of bacteria (Kraken) and antibiotic resistance genes (ARGs) accessions (MEGARes). Differences in microbial resistomes were found across farms with different production practices (CONV vs. RWA), types of cattle (beef vs. dairy), and types of sample (feces vs. wastewater vs. soil). Feces had the greatest number of ARGs per sample (mean = 118 and 79 in CONV and RWA, respectively), with tetracycline efflux pumps, macrolide phosphotransferases, and aminoglycoside nucleotidyltransferases mechanisms of resistance more abundant in CONV than in RWA feces. Tetracycline and macrolide–lincosamide–streptogramin classes of resistance were more abundant in feedlot cattle than in dairy cow feces, whereas the β-lactam class was more abundant in dairy cow feces. Lack of congruence between ARGs and microbial communities (procrustes analysis) suggested that other factors (e.g., location of farms, cattle source, management practices, diet, horizontal ARGs transfer, and co-selection of resistance), in addition to antimicrobial use, could have impacted resistome profiles. For that reason, we could not establish a cause–effect relationship between antimicrobial use and AMR, although ARGs in feces and effluents were associated with drug classes used to treat animals according to farms’ records (tetracyclines and macrolides in feedlots, β-lactams in dairies), whereas ARGs in soil were dominated by multidrug resistance. Characterization of the “resistance potential” of animal-derived and environmental samples is the first step toward incorporating metagenomic approaches into AMR surveillance in agricultural systems. Further research is needed to assess the public-health risk associated with different microbial resistomes.

Microbiome-Informed Food Safety and Quality: Longitudinal Consistency and Cross-Sectional Distinctiveness of Retail Chicken Breast Microbiomes

“…Similar levels of antimicrobial resistance were measured between conventional and "Raised Without Antibiotics" ground beef products (36) and beef cattle (37), which led to the doubt on the assumed impact of antimicrobial use in U.S. beef production and whether significant reductions in antimicrobial resistance could be yielded by reducing antimicrobial use. Although a recent study identified differences in resistomes between farms with different practices regarding antibiotic use (conventional versus "Raised Without Antibiotics"), the authors did not conclude a cause-effect relationship between antimicrobial use and the resistome differences because other factors (e.g., location, cattle source, management practices, and diet) could have affected the resistomes (38). It should be noted that our findings regarding the resistomes of ABF versus CONV samples were limited to finished chicken products.…”

Insights Into the Resistome of Bovine Clinical Mastitis Microbiome, a Key Factor in Disease Complication

“…In our study, lack of congruence between resistome and microbial communities in four dairy breeds suggested that other factors, in addition to antimicrobial use, might be associated with changes in resistome, and microbiome composition. It may also reflect the possibility that a small fraction of the total bacterial population was resistant to antibiotics (MacLean and Vogwill, 2015;Rovira et al, 2019), and that horizontal gene transfer disrupted the link between microbiome and resistome composition (Johnson et al, 2015). However, further investigations are necessary to evaluate the real effect of breed specific bacteria on cow mammary gland diseases.…”

Macrolide and Nonmacrolide Resistance with Mass Azithromycin Distribution

“…Non-host reads were then aligned to the MEGARes reference antimicrobial database (version 1.0.1) using the Burrows-Wheeler Aligner (BWA) with default settings 15 . Only antibiotic resistance determinants with gene fraction of >80% were identified as present in the sample and included for further analyses 4 , 5 , 16 . Each identified antibiotic resistance determinant was classified at the class-level using Resistome Analyzer ( https://github.com/cdeanj/resistomeanalyzer ).…”