Overcoming Microbial Inhibition of S. Sonnei Through the Exploitation of Genomically Predicted Antibiotic Resistance Profiles for the Development of Food Enrichment Media.

Linking outbreaks of Shigella spp. to specific foods is challenging due to poor selectivity of current enrichment media. We have previously shown that enrichment media, tailored to the genomically-predicted antimicrobial resistance (AMR) of Shiga toxigenic E. coli strains, enhances their isolation from foods. This study investigates the application of this approach for Shigella isolation. The AMR gene profiles of 21,908 published S. sonnei genomes indicated a high prevalence of genes conferring resistance to streptomycin (aadA, aph(3″)-Ib, aph(6)-Id, 92.8%), sulfonamides (sul1, sul2, 74.8%), and/or trimethoprim (dfrA, 96.2%). Genomic analysis and antibiotic susceptibility testing conducted with a panel of 17 outbreak-associated S. sonnei strains confirmed the correlation of AMR gene detection with resistance phenotypes. Supplementation of Shigella Broth (SB) with up to 400 µg/mL of trimethoprim or sulfadiazine did not suppress the growth of sensitive strains, whereas 100 µg/mL of streptomycin increased the selectivity of this broth. All three antibiotics increased the selectivity of modified Tryptone Soya Broth (mTSB). Based on these results, supplemented media formulations were developed and assessed by measuring the relative growth of S. sonnei in cultures coinoculated with a strain of bacteriocin-producing E. coli that is inhibitory to Shigella growth. S. sonnei was not recovered from cocultures grown in SB or mTSB without antibiotics. In contrast, media supplemented with streptomycin at 50 and 100 µg/mL, trimethoprim at 25 and 50 µg/mL, and sulfadiazine at 100 µg/mL increased the relative proportion of S. sonnei in postenrichment cultures. The enhanced recovery of resistant S. sonnei strains achieved in this study indicates that, in cases where genomic data are available for clinical S. sonnei isolates, customization of selective enrichment media based on AMR gene detection could be a valuable tool for supporting the investigation of foodborne shigellosis outbreaks.

Analysis of Antimicrobial Resistance in Bacterial Pathogens Recovered from Food and Human Sources: Insights from 639,087 Bacterial Whole-Genome Sequences in the NCBI Pathogen Detection Database.

Understanding the role of foods in the emergence and spread of antimicrobial resistance necessitates the initial documentation of antibiotic resistance genes within bacterial species found in foods. Here, the NCBI Pathogen Detection database was used to query antimicrobial resistance gene prevalence in foodborne and human clinical bacterial isolates. Of the 1,843,630 sequence entries, 639,087 (34.7%) were assigned to foodborne or human clinical sources with 147,788 (23.14%) from food and 427,614 (76.88%) from humans. The majority of foodborne isolates were either Salmonella (47.88%), Campylobacter (23.03%), Escherichia (11.79%), or Listeria (11.3%), and the remaining 6% belonged to 20 other genera. Most foodborne isolates were from meat/poultry (95,251 or 64.45%), followed by multi-product mixed food sources (29,892 or 20.23%) and fish/seafood (6503 or 4.4%); however, the most prominent isolation source varied depending on the genus/species. Resistance gene carriage also varied depending on isolation source and genus/species. Of note, Klebsiella pneumoniae and Enterobacter spp. carried larger proportions of the quinolone resistance gene qnrS and some clinically relevant beta-lactam resistance genes in comparison to Salmonella and Escherichia coli. The prevalence of mec in S. aureus did not significantly differ between meat/poultry and multi-product sources relative to clinical sources, whereas this resistance was rare in isolates from dairy sources. The proportion of biocide resistance in Bacillus and Escherichia was significantly higher in clinical isolates compared to many foodborne sources but significantly lower in clinical Listeria compared to foodborne Listeria. This work exposes the gaps in current publicly available sequence data repositories, which are largely composed of clinical isolates and are biased towards specific highly abundant pathogenic species. We also highlight the importance of requiring and curating metadata on sequence submission to not only ensure correct information and data interpretation but also foster efficient analysis, sharing, and collaboration. To effectively monitor resistance carriage in food production, additional work on sequencing and characterizing AMR carriage in common commensal foodborne bacteria is critical.

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools.

BACKGROUND: Although the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches. RESULTS: We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome. CONCLUSIONS: This study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

Genomic Markers for Quaternary Ammonium Compound Resistance as a Persistence Indicator for Listeria monocytogenes Contamination in Food Manufacturing Environments.

ABSTRACT: Persistent contamination of food manufacturing environments by Listeria monocytogenes is an important public health risk, because such contamination events defy standard sanitization protocols, for example, the application of quaternary ammonium compounds such as benzalkonium chloride (BC), providing a source for prolonged dissemination of the bacteria in food products. We performed whole genome sequencing analyses of 1,279 well-characterized L. monocytogenes isolates from various foods and food manufacturing environments and identified the bcrABC gene cassette associated with BC resistance in 531 (41.5%) isolates. The bcrABC cassette was significantly associated with L. monocytogenes isolates belonging to clonal complex (CC) 321, CC155, CC204, and CC199, which are among the 10 most prevalent genotypes recovered from foods and food production environments. All but 1 of the 177 CC321 isolates harbored the bcrABC cassette. In addition, 384 (38.6%) of the 994 isolates recovered from foods representing 67 different CCs and 119 (59.2%) of isolates from food manufacturing environmental samples representing 26 different CCs were found to harbor the intact bcrABC cassette. A representative set of 69 isolates with and without bcrABC was assayed for the ability to grow in the presence of BC, and 34 of 35 isolates harboring the bcrABC cassette exhibited MICs of ≥10 µg/mL BC. Determination of bcrABC in isolates could be achieved using both PCR and whole genome sequencing techniques, providing food testing laboratories with options for the characterization of isolates. The ability to determine markers of quaternary ammonium compound resistance such as bcrABC and epidemiologic lineage may provide risk managers with a tool to assess the potential for persistent contamination of the food manufacturing environment and the need for more targeted surveillance to ensure the efficacy of mitigation actions.

Systematic Evaluation of Whole Genome Sequence-Based Predictions of Salmonella Serotype and Antimicrobial Resistance.

Whole-genome sequencing (WGS) is used increasingly in public-health laboratories for typing and characterizing foodborne pathogens. To evaluate the performance of existing bioinformatic tools for in silico prediction of antimicrobial resistance (AMR) and serotypes of Salmonella enterica, WGS-based genotype predictions were compared with the results of traditional phenotyping assays. A total of 111 S. enterica isolates recovered from a Canadian baseline study on broiler chicken conducted in 2012-2013 were selected based on phenotypic resistance to 15 different antibiotics and isolates were subjected to WGS. Both SeqSero2 and SISTR accurately determined S. enterica serotypes, with full matches to laboratory results for 87.4 and 89.2% of isolates, respectively, and partial matches for the remaining isolates. Antimicrobial resistance genes (ARGs) were identified using several bioinformatics tools including the Comprehensive Antibiotic Resistance Database - Resistance Gene Identifier (CARD-RGI), Center for Genomic Epidemiology (CGE) ResFinder web tool, Short Read Sequence Typing for Bacterial Pathogens (SRST2 v 0.2.0), and k-mer alignment method (KMA v 1.17). All ARG identification tools had ≥ 99% accuracy for predicting resistance to all antibiotics tested except streptomycin (accuracy 94.6%). Evaluation of ARG detection in assembled versus raw-read WGS data found minimal observable differences that were gene- and coverage- dependent. Where initial phenotypic results indicated isolates were sensitive, yet ARGs were detected, repeat AMR testing corrected discrepancies. All tools failed to find resistance-determining genes for one gentamicin- and two streptomycin-resistant isolates. Further investigation found a single nucleotide polymorphism (SNP) in the nuoF coding region of one of the isolates which may be responsible for the observed streptomycin-resistant phenotype. Overall, WGS-based predictions of AMR and serotype were highly concordant with phenotype determination regardless of computational approach used.