GeneSippr: a rapid whole-genome approach for the identification and characterization of foodborne pathogens such as priority Shiga toxigenic Escherichia coli

Prevalence of Shiga Toxin-Producing Escherichia coli (STEC) and Risk Characterization Based on Virulence Genes in Retail Raw Ground Meat of Beef, Veal, and Lamb in Canada

Shiga toxin-producing Escherichia coli (STEC) are potentially pathogenic E. coli that may cause mild to severe gastrointestinal illnesses. STEC-contaminated foods of animal origin have been the most frequently implicated sources of foodborne outbreaks. A multiyear (2016 to 2021) targeted survey was conducted to investigate the prevalence of STEC in retail ground meats (beef, veal, and lamb). Samples were screened for the presence of Shiga toxin genes (stx) to identify presumptive samples, followed by culture and molecular confirmation of isolates to confirm the presence of stx genes and subsequent characterization by whole-genome-sequencing (WGS) for O serogroup and virulence genes (e.g., stx, eae, aggR). A total of 175 STEC strains were isolated from a total of 148 samples where the presence of viable STEC was confirmed out of 2,398 ground meat samples. This represented 1.2% (7/589 positive, 8 unique strains) of the beef samples, 4.7% (58/1,241 positive, 67 unique strains) of the veal samples, and 14.6% (83/568 positive, 100 unique strains) of the lamb samples. The intimin virulence gene, eae, was identified in the STEC strains of veal origin (9/67, 13.4%) only and were classified as belonging to risk level 1 (1/67), level 3 (2/67), and level 4 (6/67) according to the FAO/WHO risk categories. Risk level 2 STEC strains were of beef (2/8, 25.0%), veal (8/67, 11.9%), and lamb (1/100, 1.0%) origin. The majority of the STEC strains, 75.0% (6/8) of the beef, 67.2% (45/67) of the veal, and 94.0% (94/100) of the lamb STEC strains were classified as risk level 5 (lowest level) of the FAO/WHO risk categories. This study's findings indicate that the current food safety control measures implemented for ground meats in Canada are effective at maintaining an acceptable level of possible contamination with STEC strains associated with severe clinical outcomes. Continued application of effective control measures and safe food handling practices by consumers will minimize the potential risk of foodborne infections from raw ground meats.

Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens

Whole-genome sequencing has become the method of choice for bacterial outbreak investigation, with most clinical and public health laboratories currently routinely using short-read Illumina sequencing. Recently, long-read Oxford Nanopore Technologies (ONT) sequencing has gained prominence and may offer advantages over short-read sequencing, particularly with the recent introduction of the R10 chemistry, which promises much lower error rates than the R9 chemistry. However, limited information is available on its performance for bacterial single-nucleotide polymorphism (SNP)-based outbreak investigation. We present an open-source workflow, Prokaryotic Awesome variant Calling Utility (PACU) (https://github.com/BioinformaticsPlatformWIV-ISP/PACU), for constructing SNP phylogenies using Illumina and/or ONT R9/R10 sequencing data. The workflow was evaluated using outbreak data sets of Shiga toxin-producing Escherichia coli and Listeria monocytogenes by comparing ONT R9 and R10 with Illumina data. The performance of each sequencing technology was evaluated not only separately but also by integrating samples sequenced by different technologies/chemistries into the same phylogenomic analysis. Additionally, the minimum sequencing time required to obtain accurate phylogenetic results using nanopore sequencing was evaluated. PACU allowed accurate identification of outbreak clusters for both species using all technologies/chemistries, but ONT R9 results deviated slightly more from the Illumina results. ONT R10 results showed trends very similar to Illumina, and we found that integrating data sets sequenced by either Illumina or ONT R10 for different isolates into the same analysis produced stable and highly accurate phylogenomic results. The resulting phylogenies for these two outbreaks stabilized after ~20 hours of sequencing for ONT R9 and ~8 hours for ONT R10. This study provides a proof of concept for using ONT R10, either in isolation or in combination with Illumina, for rapid and accurate bacterial SNP-based outbreak investigation.

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.

Systematic Evaluation of Whole-Genome Sequencing Based Prediction of Antimicrobial Resistance in Campylobacter jejuni and C. coli

The increasing prevalence of antimicrobial resistance (AMR) in Campylobacter spp. is a global concern. This study evaluated the use of whole-genome sequencing (WGS) to predict AMR in Campylobacter jejuni and C. coli. A panel of 271 isolates recovered from Canadian poultry was used to compare AMR genotype to antimicrobial susceptibility testing (AST) results (azithromycin, ciprofloxacin, erythromycin, gentamicin, tetracycline, florfenicol, nalidixic acid, telithromycin, and clindamycin). The presence of antibiotic resistance genes (ARGs) was determined for each isolate using five computational approaches to evaluate the effect of: ARG screening software, input data (i.e., raw reads, draft genome assemblies), genome coverage and genome assembly software. Overall, concordance between the genotype and phenotype was influenced by the computational pipelines, level of genome coverage and the type of ARG but not by input data. For example, three of the pipelines showed a 99% agreement between detection of a tet(O) gene and tetracycline resistance, whereas agreement between the detection of tet(O) and TET resistance was 98 and 93% for two pipelines. Overall, higher levels of genome coverage were needed to reliably detect some ARGs; for example, at 15X coverage a tet(O) gene was detected in >70% of the genomes, compared to <60% of the genomes for bla(OXA). No genes associated with florfenicol or gentamicin resistance were found in the set of strains included in this study, consistent with AST results. Macrolide and fluoroquinolone resistance was associated 100% with mutations in the 23S rRNA (A2075G) and gyrA (T86I) genes, respectively. A lower association between a A2075G 23S rRNA gene mutation and resistance to clindamycin and telithromycin (92.8 and 78.6%, respectively) was found. While WGS is an effective approach to predicting AMR in Campylobacter, this study demonstrated the impact that computational pipelines, genome coverage and the genes can have on the reliable identification of an AMR genotype.

Whole-Genome Sequence Datasets: A Powerful Resource for the Food Microbiology Laboratory Toolbox

Whole-genome sequencing (WGS) technologies are rapidly being adopted for routine use in food microbiology laboratories worldwide. Examples of how WGS is used to support food safety testing include gene marker discovery (e.g., virulence and anti-microbial resistance gene determination) and high-resolution typing (e.g., cg/wgMLST analysis). This has led to the establishment of large WGS databases representing the genomes of thousands of different types of food pathogenic and commensal bacteria. This information constitutes an invaluable resource that can be leveraged to develop and validate routine test methods used to support regulatory and industry food safety objectives. For example, well-curated raw and assembled genomic datasets of the key food pathogens (Salmonella enterica, Listeria monocytogenes, and Shiga-toxigenic Escherichia coli) have been used in our laboratory in studies to validate bioinformatics pipelines, as well as new molecular methods as a prelude to the laboratory phase of the “wet lab” validation process. The application of genomic information to food microbiology method development will decrease the cost of test development and lead to the generation of more robust methodologies supporting risk assessment and risk management actions.

Shiga Toxin-Producing Escherichia coli (STEC) and STEC-Associated Virulence Genes in Raw Ground Pork in Canada

ABSTRACT

Shiga toxin-producing Escherichia coli (STEC) O157:H7/nonmotile and some non-O157 STEC strains are foodborne pathogens. In response to pork-associated O157 STEC outbreaks in Canada, we investigated the occurrence of STEC in Canadian retail raw ground pork during the period of 1 November 2014 to 31 March 2016. Isolated STEC strains were characterized to determine the Shiga toxin gene (stx) subtype and the presence of virulence genes encoding intimin (eae) and enterohemorrhagic E. coli hemolysin (hlyA). O157 STEC and non-O157 STEC strains were isolated from 1 (0.11%) of 879 and 13 (2.24%) of 580 pork samples, respectively. STEC virulence gene profiles containing both eae and hlyA were found only in the O157 STEC (stx2a, eae, hlyA) isolate. The eae gene was absent from all non-O157 STEC isolates. Of the 13 non-O157 STEC isolates, two virulence genes of stx1a and hlyA were found in four (30.8%) O91:H14 STEC isolates, whereas one virulence gene of stx2e, stx1a, and stx2a was identified in five (38.5%), two (15.4%), and one (7.7%) STEC isolates, respectively, of various serotypes. The remaining non-O157 STEC isolate carried stx2, but the subtype is unknown because this isolate could not be recovered for sequencing. O91:H14 STEC (stx1a, hlyA) was previously reported in association with diarrheal illnesses, whereas the other non-O157 STEC isolates identified in this study are not known to be associated with severe human illnesses. Virulence gene profiles identified in this study indicate that the occurrence of non-O157 STEC capable of causing severe human illness is rare in Canadian retail pork. However, O157 STEC in ground pork can occasionally occur; therefore, education regarding the potential risks associated with STEC contamination of pork would be beneficial for the public and those in the food industry to help reduce foodborne illnesses.

HIGHLIGHTS

Evaluation of WGS performance for bacterial pathogen characterization with the Illumina technology optimized for time-critical situations

Whole genome sequencing (WGS) has become the reference standard for bacterial outbreak investigation and pathogen typing, providing a resolution unattainable with conventional molecular methods. Data generated with Illumina sequencers can however only be analysed after the sequencing run has finished, thereby losing valuable time during emergency situations. We evaluated both the effect of decreasing overall run time, and also a protocol to transfer and convert intermediary files generated by Illumina sequencers enabling real-time data analysis for multiple samples part of the same ongoing sequencing run, as soon as the forward reads have been sequenced. To facilitate implementation for laboratories operating under strict quality systems, extensive validation of several bioinformatics assays (16S rRNA species confirmation, gene detection against virulence factor and antimicrobial resistance databases, SNP-based antimicrobial resistance detection, serotype determination, and core genome multilocus sequence typing) for three bacterial pathogens (Mycobacterium tuberculosis , Neisseria meningitidis , and Shiga-toxin producing Escherichia coli ) was performed by evaluating performance in function of the two most critical sequencing parameters, i.e. read length and coverage. For the majority of evaluated bioinformatics assays, actionable results could be obtained between 14 and 22 h of sequencing, decreasing the overall sequencing-to-results time by more than half. This study aids in reducing the turn-around time of WGS analysis by facilitating a faster response in time-critical scenarios and provides recommendations for time-optimized WGS with respect to required read length and coverage to achieve a minimum level of performance for the considered bioinformatics assay(s), which can also be used to maximize the cost-effectiveness of routine surveillance sequencing when response time is not essential.

Microbiological Survey of Wheat Flour Sold at Retail in Canada, 2018 to 2019

ABSTRACT

Following two O121 Shiga toxin-producing Escherichia coli (STEC) outbreaks linked to wheat flour, this study was conducted to gain baseline information on the occurrence of bacterial pathogens and levels of indicator organisms in wheat flour in Canada. A total of 347 prepackaged wheat flour samples were analyzed for Salmonella species, STEC, Listeria monocytogenes, aerobic colony count (ACC), total coliforms, and Escherichia coli. Salmonella spp. and O157 STEC were not detected in any of the samples. L. monocytogenes was identified in two samples (0.6%) at levels below the limit of detection (<0.7 log CFU/g). Non-O157 STEC were isolated from six samples (1.7%) and were characterized for the presence of STEC virulence genes: stx1, stx2, and their subtypes, eae, hlyA, and aggR. One O103:H25 STEC isolate carried virulence genes (stx1a+eae) that are known to be capable of causing diarrhea and/or bloody diarrhea in humans. Of the five remaining non-O157 STEC isolates, four carried single stx2a or stx2c genes and were considered to have the potential of causing diarrhea. The remaining non-O157 STEC isolate (stx2), while not a priority non-O157 STEC, was not available for sequencing; thus, its potential to cause illness is unknown. ACC, total coliforms, and E. coli were detected (≥0.48 log CFU/g) in 98.8, 72.6, and 0.6% of the flour samples. The mean counts of ACC were greater in whole wheat flour compared with the other flour types tested (P < 0.001). The results of this study suggest that the occurrence of O157 STEC and Salmonella is low but that the occurrence of non-O157 STEC in wheat flour with the potential to cause human illness of diarrhea is relatively common. Therefore, the consumption of raw flour could increase the likelihood of STEC infections. Further research is merited for potential risk mitigation strategies within the food production system and with consumers.

HIGHLIGHTS

Resistance determinants and their genetic context in enterobacteria from a longitudinal study of pigs reared under various husbandry conditions

Pigs are major reservoirs of resistant Enterobacteriaceae that can reach humans through consumption of contaminated meat or vegetables grown in manure-fertilized soil. Samples were collected from sows during lactation and their piglets at five time points spanning the production cycle. Cefotaxime-resistant bacteria were quantified and isolated from feed, feces, manures and carcasses of pigs reared with penicillin-using or antibiotic-free husbandries. The isolates were characterized by antibiotic susceptibility testing, whole genome sequencing and conjugation assays. The extended spectrum β-lactamase (ESBL) phenotype was more frequent in isolates originating from antibiotic-free animals, while the bacteria isolated from penicillin-using animals were on average resistant to a greater number of antibiotics. The ESBL-encoding genes identified were bla _CTX-M-1, bla _CTX-M-15 and bla _CMY-2 and they co-localised on plasmids with various genes encoding resistance to ß-lactams, co-trimoxazole, phenicols and tetracycline, all antibiotics used in pig production. Groups of genes conferring the observed resistance and the mobile elements disseminating multidrug resistance were determined. The observed resistance to ß-lactams was mainly due to the complementary actions of penicillin-binding proteins, an efflux pump and ß-lactamases. Most resistance determinants were shared by animals raised with or without antimicrobials. This suggests a key contribution of indigenous enterobacteria maternally transmitted along the sow lineage, regardless of antimicrobial use. It is unclear if the antimicrobial resistance observed in the enterobacteria populations of the commercial pig herds studied were present before the use of antibiotics, or the extent to which historical antimicrobial use exerted a selective pressure defining the resistant bacterial populations in farms using penicillin prophylaxis.Importance: Antimicrobial resistance is a global threat that needs to be fought on numerous fronts along the One Health continuum. Vast quantities of antimicrobials are used in agriculture to ensure animal welfare and productivity, and are arguably a driving force for the persistence of environmental and food-borne resistant bacteria. This study evaluated the impact of conventional, organic and other antibiotic-free husbandry practices on the frequency and nature of antimicrobial resistance genes and multidrug resistant enterobacteria. It provides knowledge about the relative contribution of specific resistance determinants to observed antibiotic resistance. It also showed the clear co-selection of genes coding for extended-spectrum beta-lactamases and genes coding for the resistance to antibiotics commonly used for prophylaxis or in curative treatments in pig operations.

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.

HIGHLIGHTS

ConFindr: rapid detection of intraspecies and cross-species contamination in bacterial whole-genome sequence data

Whole-genome sequencing (WGS) of bacterial pathogens is currently widely used to support public-health investigations. The ability to assess WGS data quality is critical to underpin the reliability of downstream analyses. Sequence contamination is a quality issue that could potentially impact WGS-based findings; however, existing tools do not readily identify contamination from closely-related organisms. To address this gap, we have developed a computational pipeline, ConFindr, for detection of intraspecies contamination. ConFindr determines the presence of contaminating sequences based on the identification of multiple alleles of core, single-copy, ribosomal-protein genes in raw sequencing reads. The performance of this tool was assessed using simulated and lab-generated Illumina short-read WGS data with varying levels of contamination (0-20% of reads) and varying genetic distance between the designated target and contaminant strains. Intraspecies and cross-species contamination was reliably detected in datasets containing 5% or more reads from a second, unrelated strain. ConFindr detected intraspecies contamination with higher sensitivity than existing tools, while also being able to automatically detect cross-species contamination with similar sensitivity. The implementation of ConFindr in quality-control pipelines will help to improve the reliability of WGS databases as well as the accuracy of downstream analyses. ConFindr is written in Python, and is freely available under the MIT License at github.com/OLC-Bioinformatics/ConFindr.

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.

Identification of Novel Biomarkers for Priority Serotypes of Shiga Toxin-Producing Escherichia coli and the Development of Multiplex PCR for Their Detection

It would be desirable to have an unambiguous scheme for the typing of Shiga toxin-producing Escherichia coli (STEC) isolates to subpopulations. Such a scheme should take the high genomic plasticity of E. coli into account and utilize the stratification of STEC into subgroups, based on serotype or phylogeny. Therefore, our goal was to identify specific marker combinations for improved classification of STEC subtypes. We developed and evaluated two bioinformatic pipelines for genomic marker identification from larger sets of bacterial genome sequences. Pipeline A performed all-against-all BLASTp analyses of gene products predicted in STEC genome test sets against a set of control genomes. Pipeline B identified STEC marker genes by comparing the STEC core proteome and the "pan proteome" of a non-STEC control group. Both pipelines defined an overlapping, but not identical set of discriminative markers for different STEC subgroups. Differential marker prediction resulted from differences in genome assembly, ORF finding and inclusion cut-offs in both workflows. Based on the output of the pipelines, we defined new specific markers for STEC serogroups and phylogenetic groups frequently associated with outbreaks and cases of foodborne illnesses. These included STEC serogroups O157, O26, O45, O103, O111, O121, and O145, Shiga toxin-positive enteroaggregative E. coli O104:H4, and HUS-associated sequence type (ST)306. We evaluated these STEC marker genes for their presence in whole genome sequence data sets. Based on the identified discriminative markers, we developed a multiplex PCR (mPCR) approach for detection and typing of the targeted STEC. The specificity of the mPCR primer pairs was verified using well-defined clinical STEC isolates as well as isolates from the ECOR, DEC, and HUSEC collections. The application of the STEC mPCR for food analysis was tested with inoculated milk. In summary, we evaluated two different strategies to screen large genome sequence data sets for discriminative markers and implemented novel marker genes found in this genome-wide approach into a DNA-based typing tool for STEC that can be used for the characterization of STEC from clinical and food samples.

Genetic Analysis of Virulence Potential of Escherichia coli O104 Serotypes Isolated From Cattle Feces Using Whole Genome Sequencing

Escherichia coli O104:H4, a Shiga toxin-producing hybrid pathotype that was implicated in a major foodborne outbreak in Germany in 2011, has not been detected in cattle. However, serotypes of O104, other than O104:H4, have been isolated from cattle feces, with O104:H7 being the most predominant. In this study, we investigated, based on whole genome sequence analyses, the virulence potential of E. coli O104 strains isolated from cattle feces, since cattle are asymptomatic carriers of E. coli O104. The genomes of ten bovine E. coli O104 strains (six O104:H7, one O104:H8, one O104:H12, and two O104:H23) and five O104:H7 isolated from human clinical cases were sequenced. Of all the bovine O104 serotypes (H7, H8, H12, and H23) that were included in the study, only E. coli O104:H7 serotype possessed Shiga toxins. Four of the six bovine O104:H7 strains and one of the five human strains carried stx1c. Three human O104 strains carried stx2, two were of subtype 2a, and one was 2d. Genomes of stx carrying bovine O104:H7 strains were larger than the stx-negative strains of O104:H7 or other serotypes. The genome sizes were proportional to the number of genes carried on the mobile genetic elements (phages, prophages, transposable elements and plasmids). Both bovine and human strains were negative for intimin and other genes associated with the type III secretory system and non-LEE encoded effectors. Plasmid-encoded virulence genes (ehxA, epeA, espP, katP) were also present in bovine and human strains. All O104 strains were negative for antimicrobial resistance genes, except one human strain. Phylogenetic analysis indicated that bovine E. coli O104 strains carrying the same flagellar antigen clustered together and STEC strains clustered separately from non-STEC strains. One of the human O104:H7 strains was phylogenetically closely related to and belonged to the same sequence type (ST-1817) as the bovine O104:H7 STEC strains. This suggests that the bovine feces could be a source of human illness caused by E. coli O104:H7 serotype. Because bovine O104:H7 strains carried virulence genes similar to human clinical strains and one of the human clinical strains was phylogenetically related to bovine strains, the serotype has the potential to be a diarrheagenic pathogen in humans.

Spfy: an integrated graph database for real-time prediction of bacterial phenotypes and downstream comparative analyses

Public health laboratories are currently moving to whole-genome sequence (WGS)-based analyses, and require the rapid prediction of standard reference laboratory methods based solely on genomic data. Currently, these predictive genomics tasks rely on workflows that chain together multiple programs for the requisite analyses. While useful, these systems do not store the analyses in a genome-centric way, meaning the same analyses are often re-computed for the same genomes. To solve this problem, we created Spfy, a platform that rapidly performs the common reference laboratory tests, uses a graph database to store and retrieve the results from the computational workflows and links data to individual genomes using standardized ontologies. The Spfy platform facilitates rapid phenotype identification, as well as the efficient storage and downstream comparative analysis of tens of thousands of genome sequences. Though generally applicable to bacterial genome sequences, Spfy currently contains 10 243 Escherichia coli genomes, for which in-silico serotype and Shiga-toxin subtype, as well as the presence of known virulence factors and antimicrobial resistance determinants have been computed. Additionally, the presence/absence of the entire E. coli pan-genome was computed and linked to each genome. Owing to its database of diverse pre-computed results, and the ability to easily incorporate user data, Spfy facilitates hypothesis testing in fields ranging from population genomics to epidemiology, while mitigating the re-computation of analyses. The graph approach of Spfy is flexible, and can accommodate new analysis software modules as they are developed, easily linking new results to those already stored. Spfy provides a database and analyses approach for E. coli that is able to match the rapid accumulation of WGS data in public databases.

Draft Genome Sequences of Escherichia coli O104 Strains of Bovine and Human Origin

Cattle harbor and shed in their feces several Escherichia coli O104 serotypes. All O104 strains examined were intimin negative and belonged to the B1 phylogroup, and some were Shiga toxigenic. We report here the genome sequences of bovine O104:H7 (n = 5), O104:H23 (n = 2), O104:H8 (n = 1), and O104:H12 (n = 1) isolates and human clinical isolates of O104:H7 (n = 5).

Metagenomics: The Next Culture-Independent Game Changer

A trend towards the abandonment of obtaining pure culture isolates in frontline laboratories is at a crossroads with the ability of public health agencies to perform their basic mandate of foodborne disease surveillance and response. The implementation of culture-independent diagnostic tests (CIDTs) including nucleic acid and antigen-based assays for acute gastroenteritis is leaving public health agencies without laboratory evidence to link clinical cases to each other and to food or environmental substances. This limits the efficacy of public health epidemiology and surveillance as well as outbreak detection and investigation. Foodborne outbreaks have the potential to remain undetected or have insufficient evidence to support source attribution and may inadvertently increase the incidence of foodborne diseases. Next-generation sequencing of pure culture isolates in clinical microbiology laboratories has the potential to revolutionize the fields of food safety and public health. Metagenomics and other 'omics' disciplines could provide the solution to a cultureless future in clinical microbiology, food safety and public health. Data mining of information obtained from metagenomics assays can be particularly useful for the identification of clinical causative agents or foodborne contamination, detection of AMR and/or virulence factors, in addition to providing high-resolution subtyping data. Thus, metagenomics assays may provide a universal test for clinical diagnostics, foodborne pathogen detection, subtyping and investigation. This information has the potential to reform the field of enteric disease diagnostics and surveillance and also infectious diseases as a whole. The aim of this review will be to present the current state of CIDTs in diagnostic and public health laboratories as they relate to foodborne illness and food safety. Moreover, we will also discuss the diagnostic and subtyping utility and concomitant bias limitations of metagenomics and comparable detection techniques in clinical microbiology, food and public health laboratories. Early advances in the discipline of metagenomics, however, have indicated noteworthy challenges. Through forthcoming improvements in sequencing technology and analytical pipelines among others, we anticipate that within the next decade, detection and characterization of pathogens via metagenomics-based workflows will be implemented in routine usage in diagnostic and public health laboratories.

Multiplexed Single Intact Cell Droplet Digital PCR (MuSIC ddPCR) Method for Specific Detection of Enterohemorrhagic E. coli (EHEC) in Food Enrichment Cultures

Foodborne illness attributed to enterohemorrhagic E. coli (EHEC), a highly pathogenic subset of Shiga toxin-producing E. coli (STEC), is increasingly recognized as a significant public health issue. Current microbiological methods for identification of EHEC in foods often use PCR-based approaches to screen enrichment broth cultures for characteristic gene markers [i.e., Shiga toxin (stx) and intimin (eae)]. However, false positives arise when complex food matrices, such as beef, contain mixtures of eae-negative STEC and eae-positive E. coli, but no EHEC with both markers in a single cell. To reduce false-positive detection of EHEC in food enrichment samples, a Multiplexed, Single Intact Cell droplet digital PCR (MuSIC ddPCR) assay capable of detecting the co-occurrence of the stx and eae genes in a single bacterial cell was developed. This method requires: (1) dispersal of intact bacteria into droplets; (2) release of genomic DNA (gDNA) by heat lysis; and (3) amplification and detection of genetic targets (stx and eae) using standard TaqMan chemistries with ddPCR. Performance of the method was tested with panels of EHEC and non-target E. coli. By determining the linkage (i.e., the proportion of droplets in which stx and eae targets were both amplified), samples containing EHEC (typically greater than 20% linkage) could be distinguished from samples containing mixtures of eae-negative STEC and eae-positive E. coli (0–2% linkage). The use of intact cells was necessary as this linkage was not observed with gDNA extracts. EHEC could be accurately identified in enrichment broth cultures containing excess amounts of background E. coli and in enrichment cultures derived from ground beef/pork and leafy-green produce samples. To our knowledge, this is the first report of dual-target detection in single bacterial cells using ddPCR. The application of MuSIC ddPCR to enrichment-culture screening would reduce false-positives, thereby improving the cost, speed, and accuracy of current methods for EHEC detection in foods.

Comparative Evaluation of Genomic and Laboratory Approaches for Determination of Shiga Toxin Subtypes in Escherichia coli

The determination of Shiga toxin (ST) subtypes can be an important element in the risk characterization of foodborne ST-producing Escherichia coli (STEC) isolates for making risk management decisions. ST subtyping methods include PCR techniques based on electrophoretic or pyrosequencing analysis of amplicons and in silico techniques based on whole genome sequence analysis using algorithms that can be readily incorporated into bioinformatics analysis pipelines for characterization of isolates by their genetic composition. The choice of technique will depend on the performance characteristics of the method and an individual laboratory's access to specialized equipment or personnel. We developed two whole genome sequence-based ST subtyping tools: (i) an in silico PCR algorithm requiring genome assembly to replicate a reference PCR-based method developed by the Statens Serum Institut (SSI) and (ii) an assembly-independent routine in which raw sequencing results are mapped to a database of known ST subtype sequence variants (V-Typer). These tools were evaluated alongside the SSI reference PCR method and a recently described PCR-based pyrosequencing technique. The V-Typer method results corresponded closely with the reference method in the analysis of 67 STEC cultures obtained from a World Health Organization National Reference Laboratory. In contrast, the in silico PCR method failed to detect ST subtypes in several cases, a result which we attribute to assembly-induced errors typically encountered with repetitive gene sequences. The V-Typer can be readily integrated into bioinformatics protocols used in the identification and characterization of foodborne STEC isolates.

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

Genomic antimicrobial resistance (AMR) prediction tools have the potential to support foodborne illness outbreak investigations through their application in the analysis of bacterial genomes from causative strains. The AMR marker profile of a strain of interest, initially identified in outbreak-associated clinical samples, may serve as the basis for customization of selective enrichment media, facilitating its recovery from samples in a food safety investigation. Different possibilities for AMR analyses include the use of comprehensive AMR gene databases such as the Comprehensive Antibiotic Resistance Database, which can be mined with in-house bioinformatics alignment tools (e.g., Antimicrobial Resistance Marker Identifier), or publicly available tools based on clinically relevant acquired AMR gene databases (e.g., ResFinder). In combination with a previously reported pipeline (SigSeekr) designed to identify specific DNA sequences associated with a particular strain for its rapid identification by PCR, it should be possible to deploy custom recovery and identification tools for the efficient detection of priority pathogens such as Shiga toxigenic Escherichia coli (STEC) outbreak strains within the time frame of an active investigation. Using a laboratory STEC strain as a model, trimethoprim resistance identified by both Antimicrobial Resistance Marker Identifier and ResFinder was used as the basis for its selective recovery against a background of commensal E. coli bacteria in ground beef samples. Enrichment in modified tryptic soy broth containing trimethoprim greatly enhanced the recovery of low numbers of model strain cells inoculated in ground beef samples, as verified by the enumeration of colonies on plating media using a strain-specific PCR method to determine the recovery efficiency for the target strain. We discuss the relative merits of different AMR marker prediction tools for this purpose and describe how such tools can be utilized to good effect in a typical outbreak investigation scenario.

Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing

The epidemiological investigation of a foodborne outbreak, including identification of related cases, source attribution, and development of intervention strategies, relies heavily on the ability to subtype the etiological agent at a high enough resolution to differentiate related from nonrelated cases. Historically, several different molecular subtyping methods have been used for this purpose; however, emerging techniques, such as single nucleotide polymorphism (SNP)-based techniques, that use whole-genome sequencing (WGS) offer a resolution that was previously not possible. With WGS, unlike traditional subtyping methods that lack complete information, data can be used to elucidate phylogenetic relationships and disease-causing lineages can be tracked and monitored over time. The subtyping resolution and evolutionary context provided by WGS data allow investigators to connect related illnesses that would be missed by traditional techniques. The added advantage of data generated by WGS is that these data can also be used for secondary analyses, such as virulence gene detection, antibiotic resistance gene profiling, synteny comparisons, mobile genetic element identification, and geographic attribution. In addition, several software packages are now available to generate in silico results for traditional molecular subtyping methods from the whole-genome sequence, allowing for efficient comparison with historical databases. Metagenomic approaches using next-generation sequencing have also been successful in the detection of nonculturable foodborne pathogens. This review addresses state-of-the-art techniques in microbial WGS and analysis and then discusses how this technology can be used to help support food safety investigations. Retrospective outbreak investigations using WGS are presented to provide organism-specific examples of the benefits, and challenges, associated with WGS in comparison to traditional molecular subtyping techniques.

A Primer on Infectious Disease Bacterial Genomics

The number of large-scale genomics projects is increasing due to the availability of affordable high-throughput sequencing (HTS) technologies. The use of HTS for bacterial infectious disease research is attractive because one whole-genome sequencing (WGS) run can replace multiple assays for bacterial typing, molecular epidemiology investigations, and more in-depth pathogenomic studies. The computational resources and bioinformatics expertise required to accommodate and analyze the large amounts of data pose new challenges for researchers embarking on genomics projects for the first time. Here, we present a comprehensive overview of a bacterial genomics projects from beginning to end, with a particular focus on the planning and computational requirements for HTS data, and provide a general understanding of the analytical concepts to develop a workflow that will meet the objectives and goals of HTS projects.

Whole Genome Sequencing for Genomics-Guided Investigations of Escherichia coli O157:H7 Outbreaks

Multi isolate whole genome sequencing (WGS) and typing for outbreak investigations has become a reality in the post-genomics era. We applied this technology to strains from Escherichia coli O157:H7 outbreaks. These include isolates from seven North America outbreaks, as well as multiple isolates from the same patient and from different infected individuals in the same household. Customized high-resolution bioinformatics sequence typing strategies were developed to assess the core genome and mobilome plasticity. Sequence typing was performed using an in-house single nucleotide polymorphism (SNP) discovery and validation pipeline. Discriminatory power becomes of particular importance for the investigation of isolates from outbreaks in which macrogenomic techniques such as pulse-field gel electrophoresis or multiple locus variable number tandem repeat analysis do not differentiate closely related organisms. We also characterized differences in the phage inventory, allowing us to identify plasticity among outbreak strains that is not detectable at the core genome level. Our comprehensive analysis of the mobilome identified multiple plasmids that have not previously been associated with this lineage. Applied phylogenomics approaches provide strong molecular evidence for exceptionally little heterogeneity of strains within outbreaks and demonstrate the value of intra-cluster comparisons, rather than basing the analysis on archetypal reference strains. Next generation sequencing and whole genome typing strategies provide the technological foundation for genomic epidemiology outbreak investigation utilizing its significantly higher sample throughput, cost efficiency, and phylogenetic relatedness accuracy. These phylogenomics approaches have major public health relevance in translating information from the sequence-based survey to support timely and informed countermeasures. Polymorphisms identified in this work offer robust phylogenetic signals that index both short- and long-term evolution and can complement currently employed typing schemes for outbreak ex- and inclusion, diagnostics, surveillance, and forensic studies.

Regulatory bioinformatics for food and drug safety

"Regulatory Bioinformatics" strives to develop and implement a standardized and transparent bioinformatic framework to support the implementation of existing and emerging technologies in regulatory decision-making. It has great potential to improve public health through the development and use of clinically important medical products and tools to manage the safety of the food supply. However, the application of regulatory bioinformatics also poses new challenges and requires new knowledge and skill sets. In the latest Global Coalition on Regulatory Science Research (GCRSR) governed conference, Global Summit on Regulatory Science (GSRS2015), regulatory bioinformatics principles were presented with respect to global trends, initiatives and case studies. The discussion revealed that datasets, analytical tools, skills and expertise are rapidly developing, in many cases via large international collaborative consortia. It also revealed that significant research is still required to realize the potential applications of regulatory bioinformatics. While there is significant excitement in the possibilities offered by precision medicine to enhance treatments of serious and/or complex diseases, there is a clear need for further development of mechanisms to securely store, curate and share data, integrate databases, and standardized quality control and data analysis procedures. A greater understanding of the biological significance of the data is also required to fully exploit vast datasets that are becoming available. The application of bioinformatics in the microbiological risk analysis paradigm is delivering clear benefits both for the investigation of food borne pathogens and for decision making on clinically important treatments. It is recognized that regulatory bioinformatics will have many beneficial applications by ensuring high quality data, validated tools and standardized processes, which will help inform the regulatory science community of the requirements necessary to ensure the safe introduction and effective use of these applications.