Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify AMR genes in whole-genome sequences, the National Center for Biotechnology Information (NCBI) has produced AMRFinder, a tool that identifies AMR genes using a high-quality curated AMR gene reference database. The Bacterial Antimicrobial Resistance Reference Gene Database consists of up-to-date gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, it contains 4,579 antimicrobial resistance proteins and more than 560 HMMs. Here, we describe AMRFinder and its associated database. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder and resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica, 770 Campylobacter spp., and 47 Escherichia coli isolates phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene detection system. Most gene calls were identical, but there were 1,229 gene symbol differences (8.8%) between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that ResFinder found, while ResFinder missed 216 loci that AMRFinder identified. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.
We sequenced the genomes of 10 Salmonella enterica serovar Infantis isolates containing bla CTX-M-65 obtained from chicken, cattle, and human sources collected between 2012 and 2015 in the United States through routine National Antimicrobial Resistance Monitoring System (NARMS) surveillance and product sampling programs. We also completely assembled the plasmids from four of the isolates. All isolates had a D87Y mutation in the gyrA gene and harbored between 7 and 10 resistance genes [aph(4)-Ia, aac(3)-IVa, aph(3=)-Ic, bla fosA3, floR, dfrA14, sul1, tetA, aadA1] located in two distinct sites of a megaplasmid (ϳ316 to 323 kb) similar to that described in a bla CTX-M-65 -positive S. Infantis isolate from a patient in Italy. High-quality single nucleotide polymorphism (hqSNP) analysis revealed that all U.S. isolates were closely related, separated by only 1 to 38 pairwise high-quality SNPs, indicating a high likelihood that strains from humans, chickens, and cattle recently evolved from a common ancestor. The U.S. isolates were genetically similar to the bla CTX-M-65 -positive S. Infantis isolate from Italy, with a separation of 34 to 47 SNPs. This is the first report of the bla CTX-M-65 gene and the pESI (plasmid for emerging S. Infantis)-like megaplasmid from S. Infantis in the United States, and it illustrates the importance of applying a global One Health human and animal perspective to combat antimicrobial resistance.
Microbial communities associated with agricultural animals are important for animal health, food safety, and public health. Here we combine high-throughput sequencing (HTS), quantitative-PCR assays, and network analysis to profile the poultry-associated microbiome and important pathogens at various stages of commercial poultry production from the farm to the consumer. Analysis of longitudinal data following two flocks from the farm through processing showed a core microbiome containing multiple sequence types most closely related to genera known to be pathogenic for animals and/or humans, including Campylobacter, Clostridium, and Shigella. After the final stage of commercial poultry processing, taxonomic richness was ca. 2–4 times lower than the richness of fecal samples from the same flocks and Campylobacter abundance was significantly reduced. Interestingly, however, carcasses sampled at 48 hr after processing harboured the greatest proportion of unique taxa (those not encountered in other samples), significantly more than expected by chance. Among these were anaerobes such as Prevotella, Veillonella, Leptrotrichia, and multiple Campylobacter sequence types. Retail products were dominated by Pseudomonas, but also contained 27 other genera, most of which were potentially metabolically active and encountered in on-farm samples. Network analysis was focused on the foodborne pathogen Campylobacter and revealed a majority of sequence types with no significant interactions with other taxa, perhaps explaining the limited efficacy of previous attempts at competitive exclusion of Campylobacter. These data represent the first use of HTS to characterize the poultry microbiome across a series of farm-to-fork samples and demonstrate the utility of HTS in monitoring the food supply chain and identifying sources of potential zoonoses and interactions among taxa in complex communities.
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