Genomic Tools for Customized Recovery and Detection of Foodborne Shiga Toxigenic Escherichia coli

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.

Strain-specific Recovery of S. sonnei from Artificially Contaminated Baby Carrots: Enhancing Food-safety Investigations with a Customized Shigella Detection Method Based on Genomically predicted Antibiotic Resistance Traits

Shigella spp. are Gram-negative gastrointestinal bacterial pathogens that cause bacillary dysentery or shigellosis in humans. Isolation of Shigella from outbreak-associated foods is often problematic due to the lack of selectivity of cultural enrichment broths. To facilitate Shigella recovery from foods, we have developed strain-specific enrichment media based on the genomically-predicted antimicrobial resistance (AMR) features of an outbreak-associated Shigella sonnei strain harboring resistance genes for streptomycin (STR) and trimethoprim (TMP). To assess performance of the method, baby carrots were artificially contaminated with the S. sonnei strain at low (2.4 CFU), medium (23.5 CFU), and high levels (235 CFU) along with 10-fold higher levels of a Shigella-inhibiting Escherichia coli strain. The target S. sonnei strain was successfully recovered from artificially-contaminated baby carrots when enriched in modified Tryptone Soya Broth (mTSB) supplemented with TMP, whereas Shigella was not recovered from Shigella broth (SB) or SB supplemented with STR. Quantitative PCR analysis indicated that supplementation of the enrichment broths with TMP or STR increased the relative proportion of S. sonnei in enrichment cultures, except at the lowest inoculation level for STR. Microbiome profiling of the baby carrot enrichment cultures conducted by 16S rRNA gene sequencing indicated that both SB-STR and mTSB-TMP repressed the growth of competing Enterobacteriaceae in the enrichment cultures, relative to SB without supplementation. Overall, improved Shigella recovery was achieved with the addition of the appropriate custom selective agent during cultural enrichments demonstrating that genomically informed custom selective enrichment of Shigella could be a valuable tool for supporting future foodborne shigellosis outbreak investigations.

Genomically Informed Custom Selective Enrichment of Shiga Toxigenic Escherichia coli (STEC) Outbreak Strains in Foods Using Antibiotics

Shiga toxigenic Escherichia coli (STEC) have been implicated in major foodborne outbreaks worldwide. The STEC family of pathogens is biochemically diverse, and current microbiological methods for detecting STEC are limited by the lack of a universal selective enrichment approach and prone to interference by high levels of background microbiota associated with certain types of foods. A novel approach has been developed for the recovery of foodborne illness outbreak strains during outbreak investigations based on the analysis of whole genome sequence data of implicated clinical isolates to determine antimicrobial resistance (AMR) genes. The presence of certain AMR genes in STEC has been correlated with the ability to grow in the presence of a specific antibiotic, which can be used to supplement enrichment broths to improve the recovery of a target strain. The enhanced recovery of STEC strains with different AMR profiles from various food types (beef, sprouts, leafy greens, and raw milk cheese) containing high levels of background microbiota was demonstrated using AMR predictions for nine different antibiotics. This genomically informed custom selective enrichment approach increases the availability of analytical options and improves the reliability of food microbiological analyses in confirming food vehicles implicated in outbreak events and defining the scope of product contamination to support risk assessment and risk management actions.

Microbial Antagonism in Food-Enrichment Culture: Inhibition of Shiga Toxin-Producing Escherichia coli and Shigella Species

Bacterial pathogens, such as Shiga toxin-producing Escherichia coli (STEC) and Shigella spp., are important causes of foodborne illness internationally. Recovery of these organisms from foods is critical for food safety investigations to support attribution of illnesses to specific food commodities; however, isolation of bacterial cultures can be challenging. Methods for the isolation of STEC and Shigella spp. from foods typically require enrichment to amplify target organisms to detectable levels. Yet, during enrichment, target organisms can be outcompeted by other bacteria in food matrices due to faster growth rates, or through production of antimicrobial agents such as bacteriocins or bacteriophages. The purpose of this study was to evaluate the occurrence of Shigella and STEC inhibitors produced by food microbiota. The production of antimicrobial compounds in cell-free extracts from 200 bacterial strains and 332 food-enrichment broths was assessed. Cell-free extracts produced by 23 (11.5%) of the strains tested inhibited growth of at least one of the five Shigella and seven STEC indicator strains used in this study. Of the 332 enrichment broths tested, cell-free extracts from 25 (7.5%) samples inhibited growth of at least one of the indicator strains tested. Inhibition was most commonly associated with E. coli recovered from meat products. Most of the inhibiting compounds were determined to be proteinaceous (34 of the 48 positive samples, 71%; including 17 strains, 17 foods) based on inactivation by proteolytic enzymes, indicating presence of bacteriocins. The cell-free extracts from 13 samples (27%, eight strains, five foods) were determined to contain bacteriophages based on the observation of plaques in diluted extracts and/or resistance to proteolytic enzymes. These results indicate that the production of inhibitors by food microbiota may be an important challenge for the recovery of foodborne pathogens, particularly for Shigella sonnei. The performance of enrichment media for recovery of Shigella and STEC could be improved by mitigating the impact of inhibitors produced by food microbiota during the enrichment process.

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.

Whole genome sequencing uses for foodborne contamination and compliance: Discovery of an emerging contamination event in an ice cream facility using whole genome sequencing

We review how FDA surveillance identifies several ways that whole genome sequencing (WGS) improves actionable outcomes for public health and compliance in a case involving Listeria monocytogenes contamination in an ice cream facility. In late August 2017 FDA conducted environmental sampling inside an ice cream facility. These isolates were sequenced and deposited into the GenomeTrakr databases. In September 2018 the Centers for Disease Control and Prevention contacted the Florida Department of Health after finding that the pathogen analyses of three clinical cases of listeriosis (two in 2013, one in 2018) were highly related to the aforementioned L. monocytogenes isolates collected from the ice cream facility. in 2017. FDA returned to the ice cream facility in late September 2018 and conducted further environmental sampling and again recovered L. monocytogenes from environmental subsamples that were genetically related to the clinical cases. A voluntary recall was issued to include all ice cream manufactured from August 2017 to October 2018. Subsequently, FDA suspended this food facility's registration. WGS results for L. monocytogenes found in the facility and from clinical samples clustered together by 0-31 single nucleotide polymorphisms (SNPs). The FDA worked together with the Centers for Disease Control and Prevention, as well as the Florida Department of Health, and the Florida Department of Agriculture and Consumer Services to recall all ice cream products produced by this facility. Our data suggests that when available isolates from food facility inspections are subject to whole genome sequencing and the subsequent sequence data point to linkages between these strains and recent clinical isolates (i.e., <20 nucleotide differences), compliance officials should take regulatory actions early to prevent further potential illness. The utility of WGS for applications related to enforcement of FDA compliance programs in the context of foodborne pathogens is reviewed.

Genomically Informed Strain-Specific Recovery of Shiga Toxin-Producing Escherichia coli during Foodborne Illness Outbreak Investigations

Next-generation sequencing plays an important role in the characterization of clinical bacterial isolates for source attribution purposes during investigations of foodborne illness outbreaks. Once an illness cluster and a suspect food vehicle have been identified, food testing is initiated for confirmation and to determine the scope of a contamination event so that the implicated lots may be removed from the marketplace. For biochemically diverse families of pathogens such as Shiga toxin-producing Escherichia coli (STEC), the ability to detect specific strains may be hampered by the lack of a universal selective enrichment approach for their recovery against high levels of background microbiota. The availability of whole genome sequence data for a given outbreak STEC strain prior to commencement of food testing may provide food microbiologists an opportunity to customize selective enrichment techniques favoring the recovery of the outbreak strain. Here we demonstrate the advantages of using the publicly available ResFinder tool in the analysis of STEC model strains belonging to serotypes O111 and O157 to determine antimicrobial resistance traits that can be used in formulating strain-specific enrichment media to enhance recovery of these strains from microbiologically complex food samples. The improved recovery from ground beef of model STEC strains with various antimicrobial resistance profiles was demonstrated using three classes of antibiotics as selective agents, suggesting the universal applicability of this new approach in supporting foodborne illness investigations.

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

Next-Generation Sequencing (NGS) technologies are expected to play a crucial role in the surveillance of infectious diseases, with their unprecedented capabilities for the characterisation of genetic information underlying the virulence and antimicrobial resistance (AMR) properties of microorganisms.  In the implementation of any novel technology for regulatory purposes, important considerations such as harmonisation, validation and quality assurance need to be addressed.  NGS technologies pose unique challenges in these regards, in part due to their reliance on bioinformatics for the processing and proper interpretation of the data produced.  Well-designed benchmark resources are thus needed to evaluate, validate and ensure continued quality control over the bioinformatics component of the process.  This concept was explored as part of a workshop on "Next-generation sequencing technologies and antimicrobial resistance" held October 4-5 2017.   Challenges involved in the development of such a benchmark resource, with a specific focus on identifying the molecular determinants of AMR, were identified. For each of the challenges, sets of unsolved questions that will need to be tackled for them to be properly addressed were compiled. These take into consideration the requirement for monitoring of AMR bacteria in humans, animals, food and the environment, which is aligned with the principles of a “One Health” approach.

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

Next-Generation Sequencing (NGS) technologies are expected to play a crucial role in the surveillance of infectious diseases, with their unprecedented capabilities for the characterisation of genetic information underlying the virulence and antimicrobial resistance (AMR) properties of microorganisms.  In the implementation of any novel technology for regulatory purposes, important considerations such as harmonisation, validation and quality assurance need to be addressed.  NGS technologies pose unique challenges in these regards, in part due to their reliance on bioinformatics for the processing and proper interpretation of the data produced.  Well-designed benchmark resources are thus needed to evaluate, validate and ensure continued quality control over the bioinformatics component of the process.  This concept was explored as part of a workshop on "Next-generation sequencing technologies and antimicrobial resistance" held October 4-5 2017.   Challenges involved in the development of such a benchmark resource, with a specific focus on identifying the molecular determinants of AMR, were identified. For each of the challenges, sets of unsolved questions that will need to be tackled for them to be properly addressed were compiled. These take into consideration the requirement for monitoring of AMR bacteria in humans, animals, food and the environment, which is aligned with the principles of a "One Health" approach.

The Importance of Bacterial Culture to Food Microbiology in the Age of Genomics

Culture-based and genomics methods provide different insights into the nature and behavior of bacteria. Maximizing the usefulness of both approaches requires recognizing their limitations and employing them appropriately. Genomic analysis excels at identifying bacteria and establishing the relatedness of isolates. Culture-based methods remain necessary for detection and enumeration, to determine viability, and to validate phenotype predictions made on the bias of genomic analysis. The purpose of this short paper is to discuss the application of culture-based analysis and genomics to the questions food microbiologists routinely need to ask regarding bacteria to ensure the safety of food and its economic production and distribution. To address these issues appropriate tools are required for the detection and enumeration of specific bacterial populations and the characterization of isolates for, identification, phylogenetics, and phenotype prediction.