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Salmonella infections are a leading cause of bacterial food-borne illness worldwide. Infections are highly associated with the consumption of contaminated food, and in particular, chicken meat. The severity of Salmonella infections depends on the presence of antimicrobial resistance genes and virulence factors. While there are many studies which have investigated Salmonella strains isolated from postharvest chicken samples, there is a gap in our understanding of the genetic properties that influence the persistence of Salmonella in preharvest and in particular their makeup of antimicrobial resistance genes and virulence factors. We used whole genome sequencing and hierarchical clustering to characterize and classify the genetic diversity of Salmonella enterica isolates (n = 55) recovered from the litter of commercial broiler chicken raised in four colocated broiler houses of one integrated farm over three consecutive flocks. The chicken were raised under a newly adopted "No Antibiotics Ever" program, and copper sulfate was administered via drinking water. In-silico serovar prediction identified three S. enterica serovars: Enteritidis (n = 12), Kentucky (n = 40), and Senftenberg (n = 3). Antimicrobial susceptibility testing revealed that only one S. Kentucky isolate was resistant to streptomycin, while the remaining isolates were susceptible to all antibiotics tested. Metal resistance operons, including copper and silver, were identified chromosomally and on plasmids in serovar Senftenberg and Kentucky isolates, respectively, while serovar Enteritidis carried several virulence factors on plasmids. Serovar Kentucky isolates harboring metal resistance operons were the only Salmonella isolates recovered from the litter of third flock cohort. These results suggest that there might be environmental selection for Salmonella strains carrying plasmid-associated metal resistance and virulence genes, which could play a role in their persistence in litter., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Importance: Campylobacter is a leading cause of foodborne illness in the United States due to consumption of contaminated or mishandled food products, often associated with chicken meat. Campylobacter is common in the microbiota of avian and mammalian gut; however, acquisition of antimicrobial resistance genes (ARGs) and virulence factors (VFs) may result in strains that pose significant threat to public health. Although there are studies investigating the genetic diversity of Campylobacter strains isolated from post-harvest chicken samples, there are limited data on the genome characteristics of isolates recovered from preharvest broiler production. Here, we show that Campylobacter jejuni and Campylobacter coli differ in their carriage of antimicrobial resistance and virulence factors may also differ in their ability to persist in litter during consecutive grow-out of broiler flocks. We found that presence/absence of virulence factors needed for evasion of host defense mechanisms and gut colonization played an integral role in differentiating Campylobacter strains., Competing Interests: The authors declare no conflict of interest.

Contamination of food animal products by Escherichia coli is a leading cause of foodborne disease outbreaks, hospitalizations, and deaths in humans. Chicken is the most consumed meat both in the United States and across the globe according to the U.S. Department of Agriculture. Although E. coli is a ubiquitous commensal bacterium of the guts of humans and animals, its ability to acquire antimicrobial resistance (AMR) genes and virulence factors (VFs) can lead to the emergence of pathogenic strains that are resistant to critically important antibiotics. Thus, it is important to identify the genetic factors that contribute to the virulence and AMR of E. coli. In this study, we performed in-depth genomic evaluation of AMR genes and VFs of E. coli genomes available through the National Antimicrobial Resistance Monitoring System GenomeTrackr database. Our objective was to determine the genetic relatedness of chicken production isolates and human clinical isolates. To achieve this aim, we first developed a massively parallel analytical pipeline (Reads2Resistome) to accurately characterize the resistome of each E. coli genome, including the AMR genes and VFs harbored. We used random forests and hierarchical clustering to show that AMR genes and VFs are sufficient to classify isolates into different pathogenic phylogroups and host origin. We found that the presence of key type III secretion system and AMR genes differentiated human clinical isolates from chicken production isolates. These results further improve our understanding of the interconnected role AMR genes and VFs play in shaping the evolution of pathogenic E. coli strains. IMPORTANCE Pathogenic Escherichia coli causes disease in both humans and food-producing animals. E. coli pathogenesis is dependent on a repertoire of virulence factors and antimicrobial resistance genes. Food-borne outbreaks are highly associated with the consumption of undercooked and contaminated food products. This association highlights the need to understand the genetic factors that make E. coli virulent and pathogenic in humans and poultry. This research shows that E. coli isolates originating from human clinical settings and chicken production harbor different antimicrobial resistance genes and virulence factors that can be used to classify them into phylogroups and host origins. In addition, to aid in the repeatability and reproducibility of the results presented in this study, we have made a public repository of the Reads2Resistome pipeline and have provided the accession numbers associated with the E. coli genomes analyzed.

Extended laboratory culture and antimicrobial susceptibility testing timelines hinder rapid species identification and susceptibility profiling of bacterial pathogens associated with bovine respiratory disease, the most prevalent cause of cattle mortality in the United States. Whole-genome sequencing offers a culture-independent alternative to current bacterial identification methods, but requires a library of bacterial reference genomes for comparison. To contribute new bacterial genome assemblies and evaluate genetic diversity and variation in antimicrobial resistance genotypes, whole-genome sequencing was performed on bovine respiratory disease-associated bacterial isolates ( Histophilus somni , Mycoplasma bovis , Mannheimia haemolytica , and Pasteurella multocida ) from dairy and beef cattle. One hundred genomically distinct assemblies were added to the NCBI database, doubling the available genomic sequences for these four species. Computer-based methods identified 11 predicted antimicrobial resistance genes in three species, with none being detected in M. bovis While computer-based analysis can identify antibiotic resistance genes within whole-genome sequences (genotype), it may not predict the actual antimicrobial resistance observed in a living organism (phenotype). Antimicrobial susceptibility testing on 64 H. somni , M. haemolytica , and P. multocida isolates had an overall concordance rate between genotype and phenotypic resistance to the associated class of antimicrobials of 72.7% ( P < 0.001), showing substantial discordance. Concordance rates varied greatly among different antimicrobial, antibiotic resistance gene, and bacterial species combinations. This suggests that antimicrobial susceptibility phenotypes are needed to complement genomically predicted antibiotic resistance gene genotypes to better understand how the presence of antibiotic resistance genes within a given bacterial species could potentially impact optimal bovine respiratory disease treatment and morbidity/mortality outcomes., (Copyright © 2017 Owen et al.)

Extended laboratory culture and antimicrobial susceptibility testing timelines hinder rapid species identification and susceptibility profiling of bacterial pathogens associated with bovine respiratory disease, the most prevalent cause of cattle mortality in the United States. Whole-genome sequencing offers a culture-independent alternative to current bacterial identification methods, but requires a library of bacterial reference genomes for comparison. To contribute new bacterial genome assemblies and evaluate genetic diversity and variation in antimicrobial resistance genotypes, whole-genome sequencing was performed on bovine respiratory disease-associated bacterial isolates (Histophilus somni, Mycoplasma bovis, Mannheimia haemolytica, and Pasteurella multocida) from dairy and beef cattle. One hundred genomically distinct assemblies were added to the NCBI database, doubling the available genomic sequences for these four species. Computer-based methods identified 11 predicted antimicrobial resistance genes in three species, with none being detected in M. bovis While computer-based analysis can identify antibiotic resistance genes within whole-genome sequences (genotype), it may not predict the actual antimicrobial resistance observed in a living organism (phenotype). Antimicrobial susceptibility testing on 64 H. somni, M. haemolytica, and P. multocida isolates had an overall concordance rate between genotype and phenotypic resistance to the associated class of antimicrobials of 72.7% (P < 0.001), showing substantial discordance. Concordance rates varied greatly among different antimicrobial, antibiotic resistance gene, and bacterial species combinations. This suggests that antimicrobial susceptibility phenotypes are needed to complement genomically predicted antibiotic resistance gene genotypes to better understand how the presence of antibiotic resistance genes within a given bacterial species could potentially impact optimal bovine respiratory disease treatment and morbidity/mortality outcomes.

The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to determine the genes present in the sample. Mapping sequence reads to known genes is traditionally accomplished using alignment. Alignment methods have high specificity but are limited in their ability to detect sequences that are divergent from the reference database, which can result in a substantial false negative rate. We address this shortcoming through the creation of Meta-MARC, which enables detection of diverse resistance sequences using hierarchical, DNA-based Hidden Markov Models. We first describe Meta-MARC and then demonstrate its efficacy on simulated and functional metagenomic datasets. Meta-MARC has higher sensitivity relative to competing methods. This sensitivity allows for detection of sequences that are divergent from known antimicrobial resistance genes. This functionality is imperative to expanding existing antimicrobial gene databases. Steven Lakin et al. present Meta-MARC, a computational method for identifying antimicrobial resistance sequences using DNA-based Hidden Markov Models. Because of its increased sensitivity, Meta-MARC is able to detect novel antimicrobial resistance sequences that are divergent from all known sequences. [ABSTRACT FROM AUTHOR]