Quasimetagenomic source tracking of Listeria monocytogenes from naturally contaminated ice cream

Microbial composition and dynamics in environmental samples from a ready-to-eat food production facility with a long-term colonization of Listeria monocytogenes

Listeria monocytogenes is a foodborne pathogen of significant concern for the food industry due to its remarkable ability to persist through safety control efforts, posing a subsequent health threat to consumers. Understanding the microbial communities coexisting with L. monocytogenes in food processing environments provides insights into its persistence mechanisms. We investigated the microbial communities on non-food contact surfaces in a facility producing ready-to-eat foods, known to harbour a ST121 L. monocytogenes strain over multiple years. A 10-week sampling period was coordinated with the company and public health authorities. Metagenomic analysis revealed a stable microbial composition dominated by Pseudomonas fluorescens. While highly related populations were present in high-care production zones, distinctive taxa characteristic of specific areas were observed (e.g., Sphingomonas aerolata). Although Listeria spp. were not detected in metagenomes, they were detected in cultured samples, suggesting low relative abundance in factory settings. The findings suggest that a stable resident microbiota, with distinct adaptations to different areas within the factory, was selected for by their collective ability to survive control efforts in this environment. Listeria spp. was a member of this microbial community, albeit at low abundance, and may likewise benefit from the mutualism of the overall microbial community.

A metagenomics-based workflow for the detection and genomic characterization of GBS in raw freshwater fish

The unexpected foodborne outbreak in Singapore in 2015 has accentuated Group B Streptococcus (GBS, Streptococcus agalactiae) sequence type 283 as an emerging foodborne pathogen transmitted via the consumption of contaminated raw freshwater fish. Isolation-based workflows utilizing conventional microbiological and whole-genome sequencing methods are commonly used to support biosurveillance efforts critical for the control management of this emerging foodborne pathogen. However, these isolation-based workflows tend to have relatively long turnaround times that hamper a timely response for implementing risk mitigation. To address this gap, we have developed a metagenomics-based workflow for the simultaneous detection and genomic characterization of GBS in raw freshwater fish. Notably, our validation results showed that this metagenomics-based workflow could achieve comparable accuracy and potentially better detection limits while halving the turnaround time (from 2 weeks to 5 days) relative to an isolation-based workflow. The metagenomics-based workflow was also successfully adapted for use on a portable long-read nanopore sequencer, demonstrating its potential applicability for real-time point-of-need testing. Using GBS in freshwater fish as an example, this work represents a proof-of-concept study that supports the feasibility and validity of metagenomics as a rapid and accurate test methodology for the detection and genomic characterization of foodborne pathogens in complex food matrices.

IMPORTANCE

The need for a rapid and accurate food microbiological testing method is apparent for a timely and effective foodborne outbreak response. This is particularly relevant for emerging foodborne pathogens such as Group B Streptococcus (GBS) whose associated food safety risk might be undercharacterized. By using GBS in raw freshwater fish as a case example, this study describes the development of a metagenomics-based workflow for rapid food microbiological safety testing and surveillance. This study can inform as a working model for various foodborne pathogens in other complex food matrices, paving the way for future methodological development of metagenomics for food microbiological safety testing.

Paired metagenomic and chemical evaluation of aflatoxin-contaminated dog kibble

Introduction

Identification of chemical toxins from complex or highly processed foods can present 'needle in the haystack' challenges for chemists. Metagenomic data can be used to guide chemical toxicity evaluations by providing DNA-based description of the wholistic composition (eukaryotic, bacterial, protozoal, viral, and antimicrobial resistance) of foods suspected to harbor toxins, allergens, or pathogens. This type of information can focus chemistry-based diagnostics, improve hazard characterization and risk assessment, and address data gaps. Additionally, there is increasing recognition that simultaneously co-occurring mycotoxins, either from single or multiple species, can impact dietary toxicity exposure. Metagenomic data provides a way to address data gaps related to co-occurrence of multiple fungal species.

Methods

Paired metagenomic and chemical data were used to evaluate aflatoxin-contaminated kibble with known levels of specific mycotoxins. Kibble was ground to a fine powder for both chemical and molecular analyses. Chemical analyses were performed with Liquid Chromatography Mass Spectrometry (LCMS) and according to the AOAC Official method 2005.08: Aflatoxins in Corn, Raw Peanuts, and Peanut Butter using Liquid Chromatography with Post-Column Photochemical Derivatization. Metagenomes were created from DNA extracted from ground kibble and sequenced on an Illumina NextSeq 2000 with an average sequence depth of 180 million reads per replicate.

Results and discussion

Metagenomic data demonstrated that the abundance of DNA from putative aflatoxigenic Aspergillus spp. correlated with the levels of aflatoxin quantified by LCMS. Metagenomic data also identified an expansive range of co-occurring fungal taxa which may produce additional mycotoxins. DNA data paired with chemical data provides a novel modality to address current data gaps surrounding dietary mycotoxin exposure, toxigenic fungal taxonomy, and mycotoxins of emerging concern.

Taxonomy, ecology, and relevance to food safety of the genus Listeria with a particular consideration of new Listeria species described between 2010 and 2022

Since 2010, the genus Listeria has had the addition of 22 new species that more than tripled the number of species identified until 2010. Sixteen of these 22 new species are distantly related to the type species, Listeria monocytogenes, and several of these present phenotypes that distinguish them from classical Listeria species (L. monocytogenes, Listeria innocua, Listeria ivanovii, Listeria seeligeri, Listeria welshimeri, and Listeria grayi). These 22 newly described species also show that Listeria is more genetically diverse than previously estimated. While future studies and surveys are needed to clarify the distribution of these species, at least some of these species may not be widely spread, while other species may be frequently found spread to human-related settings (e.g., farms and processing facilities), and others may be adapted to specific environmental habitats. Here, we review the taxonomic, phylogenetic, and ecological characteristics of these new Listeria species identified since 2010 and re-iterate the suggestion of re-classification of some species into three new genera: Murraya, Mesolisteria, and Paenilisteria. We also provide a review of current detection issues and the relevance to food safety related to the identification of these new species. For example, several new non-pathogenic species could be misidentified as the pathogen L. monocytogenes, based on methods that do not target L. monocytogenes-specific virulence genes/factors, leading to unnecessary product recalls. Moreover, eight species in the proposed new genus Mesolisteria are not good indicators of environmental conditions that could allow L. monocytogenes to grow since Mesolisteria species are unable to grow at low temperatures.

Evaluation of universal preenrichment broth and comparison of rapid molecular methods for the detection of Salmonella from spent sprout irrigation water (SSIW)

Sprouts and spent sprout irrigation water (SSIW) present unique challenges for the development of a Salmonella detection method in food matrices. This study aimed to compare universal preenrichment broth (UPB) and lactose broth (LB) as preenrichment media for cultural and rapid screening methods and to compare their abilities to recover Salmonella in SSIW samples from different sprout varieties (i.e., alfalfa, broccoli, and mung bean sprouts). The associated co-enriched microbiota with different sprout varieties using different preenrichment media were also examined using a quasi-metagenomic approach. The performance of media and detection methods was compared using the relative level of detection (RLOD) value, as recommended by ISO 16140-2:2016. The level of detection (LOD) for Salmonella culture method with UPB was similar to that with LB in low aerobic plate count (APC) background samples (the relative LOD, i.e., RLOD, was nearly 1 after adjusting for the effects of SSIW variety and serovar), but significantly lower than that with LB in high APC background samples (RLOD = 0.32). The LOD for Salmonella with selected rapid methods was comparable to each other (RLOD from 0.97 to 1.50) and to the culture method (RLOD from 0.69 to 1.03), and no significant difference was detected between preenrichment broths in low APC background samples with RLOD values between 0.76 and 1.04. In samples with a high APC background, however, a drastic difference in LOD was observed between methods and between preenrichment broths for each method. The RLOD ranged from 0.03 to 0.32 when UPB was compared to LB as preenrichment broth. The composition and relative abundance (RA) of co-enriched microbiota was affected by multiple factors including food matrices, preenrichment media and Salmonella contamination. Altogether, this study validated UPB as a better preenrichment broth than LB for the detection of Salmonella enterica from SSIW. This study also suggested UPB may also be an optimal preenrichment medium for rapid screening methods when APC level is high. The observation of potential exclusion of Salmonella in preenrichment through the overgrowth of competitive microflora from the quasi-metagenomic study provided novel information that may be used to further optimize preenrichment formulations.

An Alternative Rapid Confirmation Method for Identifying Listeria monocytogenes from a Variety of 125 g Food Samples Within Two Days of a PCR Presumptive Positive

Cultural confirmation following detection of a Listeria monocytogenespresumptive positive can take 3-7 days to finalize; this uncertainty is a point of frustration for food producers needing to make time-sensitive disposition decisions. To address the demand for shortened time-to-results, an alternative L. monocytogenes confirmation method consisting of two components, (i) a secondary screen using a different rapid method, and (ii) concurrent cultural isolation followed by next-day colony identification was evaluated. For the study, four food matrices (hot dogs, peanut butter, frozen vegetables, and multicomponent frozen meals) were inoculated with low levels (0.36-1.39 MPN/125 g) of L. monocytogenes per the AOAC guidelines for a matrix study. Analyses were performed on 125 g test portions and started with a PCR primary screen (Bio-Rad iQ-Check Listeria monocytogenes II). Next, all enriched food samples underwent a secondary screen by bioMérieux's GENE-UP LMO2 Real-Time PCR and VIDAS LMX ELFA along with streaking onto RAPID'L.mono Agar. Presumptive positive L. monocytogenes colonies were identified utilizing a high throughput rapid identification method (Hygiena's BAX System L. monocytogenes Real-Time PCR assay, Neogen's ANSR isothermal nucleic acid amplification assay, and Bruker's MALDI Biotyper). Importantly, this study evaluated multiple commercially available options for the secondary screen (n = 2) and rapid identification (n = 3) to allow for easy adoption by testing laboratories. Overall, there was no statistically significant difference (p ≤ 0.05) between the number of L. monocytogenes-positive 125 g samples obtained by the cultural reference method and the alternative confirmation methods (regardless of which method combinations were evaluated). Additionally, this study supports that, when both the primary and secondary screen methods yield a positive result, the sample could be considered a confirmed positive for L. monocytogenes.

Application of quasimetagenomics methods to define microbial diversity and subtype Listeria monocytogenes in dairy and seafood production facilities

IMPORTANCE

In developed countries, the human diet is predominated by food commodities, which have been manufactured, processed, and stored in a food production facility. Little is known about the application of metagenomic sequencing approaches for detecting foodborne pathogens, such as L. monocytogenes, and characterizing microbial diversity in food production ecosystems. In this work, we investigated the utility of 16S rRNA amplicon and quasimetagenomic sequencing for the taxonomic and phylogenetic classification of Listeria culture enrichments of environmental swabs collected from dairy and seafood production facilities. We demonstrated that single-nucleotide polymorphism (SNP) analyses of L. monocytogenes metagenome-assembled genomes (MAGs) from quasimetagenomic data sets can achieve similar resolution as culture isolate whole-genome sequencing. To further understand the impact of genome coverage on MAG SNP cluster resolution, an in silico downsampling approach was employed to reduce the percentage of target pathogen sequence reads, providing an initial estimate of required MAG coverage for subtyping resolution of L. monocytogenes.

Precision metagenomics sequencing for food safety: hybrid assembly of Shiga toxin-producing Escherichia coli in enriched agricultural water

Culture-independent metagenomic sequencing of enriched agricultural water could expedite the detection and virulotyping of Shiga toxin-producing Escherichia coli (STEC). We previously determined the limits of a complete, closed metagenome-assembled genome (MAG) assembly and of a complete, fragmented MAG assembly for O157:H7 in enriched agricultural water using long reads (Oxford Nanopore Technologies, Oxford), which were 107 and 105 CFU/ml, respectively. However, the nanopore assemblies did not have enough accuracy to be used in Single Nucleotide Polymorphism (SNP) phylogenies and cannot be used for the precise identification of an outbreak STEC strain. The present study aimed to determine the limits of detection and assembly for STECs in enriched agricultural water by Illumina MiSeq sequencing technology alone, followed by establishing the limit of hybrid assembly with nanopore long-read sequencing using three different hybrid assemblers (SPAdes, Unicycler, and OPERA-MS). We also aimed to generate a genome with enough accuracy to be used in a SNP phylogeny. The classification of MiSeq and nanopore sequencing identified the same highly abundant species. Using the totality of the MiSeq output and a precision metagenomics approach in which the E. coli reads are binned before assembly, the limit of detection and assembly of STECs by MiSeq were determined to be 105 and 107 CFU/ml, respectively. While a complete, closed MAG could not be generated at any concentration, a complete, fragmented MAG was produced using the SPAdes assembler with an STEC concentration of at least 107 CFU/ml. At this concentration, hybrid assembled contigs aligned to the nanopore-assembled genome could be accurately placed in a neighbor-joining tree. The MiSeq limit of detection and assembly was less sensitive than nanopore sequencing, which was likely due to factors including the small starting material (50 vs. 1 μg) and the dilution of the library loaded on the cartridge. This pilot study demonstrates that MiSeq sequencing requires higher coverage in precision metagenomic samples; however, with sufficient concentration, STECs can be characterized and phylogeny can be accurately determined.

bettercallsal: better calling of Salmonella serotypes from enrichment cultures using shotgun metagenomic profiling and its application in an outbreak setting

Most current Salmonella subtyping analyses rely on whole genome sequencing (WGS), which focuses on the high-resolution analysis of single genomes or multiple single genomes from the isolated colonies on microbiological agar plates. In this study, we introduce bioinformatics innovations for a metagenomic outbreak response workflow that accurately identifies multiple Salmonella serovars at the same time. bettercallsal is one of the first analysis tools to identify multiple Salmonella enterica serotypes from metagenomic or quasi-metagenomic datasets with high accuracy, allowing these isolate-independent methods to be incorporated into surveillance and root cause investigations. It was tested on an in silico benchmark dataset comprising 29 unique Salmonella serovars, 46 non-Salmonella bacterial genomes, and 10 viral genomes at varying read depths and on previously well-characterized and sequenced non-selective primary and selective enrichments of papaya and peach samples from separate outbreak investigations that resulted in the identification of multiple Salmonella serovars using traditional isolate culturing and WGS as well as nucleic acid assays. Analyses were also conducted on these datasets using a custom-built k-mer tool, SeqSero2, and Kallisto to compare serotype calling to bettercallsal. The in silico dataset analyzed with bettercallsal achieved the maximum precision, recall, and accuracy of 100, 83, and 94%, respectively. In the papaya outbreak samples, bettercallsal identified the presence of multiple serovars in agreement with the Luminex® xMAP assay results and also identified more serovars per sample, as evidenced by NCBI SNP clustering. In peach outbreak samples, bettercallsal identified two serovars in concordance with k-mer analysis and the Luminex xMAP assay. The genome hit reported by bettercallsal clustered with the chicken isolate genome, as reported by the FDA peach outbreak investigation from sequenced isolates (WGS). Overall, bettercallsal outperformed k-mer, Seqsero2, and Kallisto in identifying multiple serovars from enrichment cultures using shotgun metagenomic sequencing.

Use of a taxon-specific reference database for accurate metagenomics-based pathogen detection of Listeria monocytogenes in turkey deli meat and spinach

BACKGROUND

The reliability of culture-independent pathogen detection in foods using metagenomics is contingent on the quality and composition of the reference database. The inclusion of microbial sequences from a diverse representation of taxonomies in universal reference databases is recommended to maximize classification precision for pathogen detection. However, these sizable databases have high memory requirements that may be out of reach for some users. In this study, we aimed to assess the performance of a foodborne pathogen (FBP)-specific reference database (taxon-specific) relative to a universal reference database (taxon-agnostic). We tested our FBP-specific reference database's performance for detecting Listeria monocytogenes in two complex food matrices-ready-to-eat (RTE) turkey deli meat and prepackaged spinach-using three popular read-based DNA-to-DNA metagenomic classifiers: Centrifuge, Kraken 2 and KrakenUniq.

RESULTS

In silico host sequence removal led to substantially fewer false positive (FP) classifications and higher classification precision in RTE turkey deli meat datasets using the FBP-specific reference database. No considerable improvement in classification precision was observed following host filtering for prepackaged spinach datasets and was likely a consequence of a higher microbe-to-host sequence ratio. All datasets classified with Centrifuge using the FBP-specific reference database had the lowest classification precision compared to Kraken 2 or KrakenUniq. When a confidence-scoring threshold was applied, a nearly equivalent precision to the universal reference database was achieved for Kraken 2 and KrakenUniq. Recall was high for both reference databases across all datasets and classifiers. Substantially fewer computational resources were required for metagenomics-based detection of L. monocytogenes using the FBP-specific reference database, especially when combined with Kraken 2.

CONCLUSIONS

A universal (taxon-agnostic) reference database is not essential for accurate and reliable metagenomics-based pathogen detection of L. monocytogenes in complex food matrices. Equivalent classification performance can be achieved using a taxon-specific reference database when the appropriate quality control measures, classification software, and analysis parameters are applied. This approach is less computationally demanding and more attainable for the broader scientific and food safety communities.

Tunable Magneto-Plasmonic Nanosensor for Sensitive Detection of Foodborne Pathogens

Frequent outbreaks of food-borne pathogens, particularly E. coli O157:H7, continue to impact human health and the agricultural economy tremendously. The required cell count for this pathogenic strain of E. coli O157:H7 is relatively low and hence it is vital to detect at low colony forming unit (CFU) counts. Available detection methods, though sensitive, fall short in terms of timeliness and often require extensive sample processing. To overcome these limitations, we propose a novel magneto-plasmonic nanosensor (MPnS) by integrating surface plasmon resonance (SPR) properties with spin-spin magnetic relaxation (T2 MR) technology. We engineered MPnS by encapsulating several gold nanoparticles (GNPs) within the polymer-coating of iron oxide nanoparticles (IONPs). First, the polyacrylic acid (PAA)-coated IONPs were synthesized using a solvent precipitation method, then gold chloride solution was used to synthesize GNPs and encapsulate them within the PAA-coatings of IONPs in one step. A magnetic separation technique was used to purify the MPnS and the presence of GNPs within IONPs was characterized using transmission electron microscopy (TEM), energy dispersive x-ray spectroscopy (EDS), and other spectroscopic methods. The synthesized MPnS exhibits MR relaxation properties while possessing amplified optical properties than conventional GNPs. This allows for rapid and ultrasensitive detection of E. coli O157:H7 by SPR, T2 MR, and colorimetric readout. Experiments conducted in simple buffer and in milk as a complex media demonstrated that our MPnS-based assay could detect as low as 10 CFUs of this pathogenic strain of E. coli O157:H7 in minutes with no cross-reactivity. Overall, the formulated MPnS is robust and holds great potential for the ultrasensitive detection of E. coli O157:H7 in a simple and timely fashion. Moreover, this platform is highly customizable and can be used for the detection of other foodborne pathogens.

Using Culture-Enriched Phenotypic Metagenomics for Targeted High-Throughput Monitoring of the Clinically Important Fraction of the β-Lactam Resistome

High bacterial community diversity and complexity greatly challenge the cost-efficient monitoring of clinically prevalent antibiotic-resistant bacteria, which are usually present as rare and important populations involved in the environmental dissemination of clinical resistance. Here, we introduce culture-enriched phenotypic metagenomics that integrates culture enrichment, phenotypic screening, and metagenomic analyses as an emerging standardized methodology for targeted resistome monitoring and apply it to decipher the extended-spectrum β-lactam resistome in a municipal wastewater treatment plant (WWTP) and its receiving river. The results showed that clinically prevalent carbapenemase genes (e.g., the NDM and KPC families) and extended-spectrum β-lactamase genes (e.g., the CTX-M, TEM, and OXA families) were prevalent in the WWTP and showed prominent potential in horizontal dissemination. Strikingly, carbapenem and polymyxin resistance genes co-occurred in the highly virulent nosocomial pathogens Enterobacter kobei and Citrobacter freundii. Overall, this study exemplifies phenotypic metagenomics for high-throughput surveillance of a targeted clinically important fraction of antibiotic resistomes and substantially expands current knowledge on extended-spectrum β-lactam resistance in WWTPs.

Application of MinION sequencing as a tool for the rapid detection and characterization of Listeria monocytogenes in smoked salmon

Microbial pathogens may be present in different types of foods, and hence the development of novel methods to assure consumers' safeness is of great interest. Molecular methods are known to provide sensitive and rapid results; however, they are typically targeted approaches. In recent years, the advent of non-targeted approaches based on next-generation sequencing (NGS) has emerged as a rational way to proceed. This technology allows for the detection of several pathogens simultaneously. Furthermore, with the same set of data, it is possible to characterize the microorganisms in terms of serotype, virulence, and/ or resistance genes, among other molecular features. In the current study, a novel method for the detection of Listeria monocytogenes based on the "quasimetagenomics" approach was developed. Different enrichment media and immunomagnetic separation (IMS) strategies were compared to determine the best approach in terms of L. monocytogenes sequences generated from smoked salmon samples. Finally, the data generated were analyzed with a user-friendly workflow that simultaneously provided the species identification, serotype, and antimicrobial resistance genes. The new method was thoroughly evaluated against a culture-based approach, using smoked salmon inoculated with L. monocytogenes as the matrix of choice. The sequencing method reached a very low limit of detection (LOD50, 1.2 CFU/ 25 g) along with high diagnostic sensitivity and specificity (100%), and a perfect correlation with the culture-based method (Cohen's k = 1.00). Overall, the proposed method overcomes all the major limitations reported for the implementation of NGS as a routine food testing technology and paves the way for future developments taking its advantage into consideration.

The Saprophytic Lifestyle of Listeria monocytogenes and Entry Into the Food-Processing Environment

Listeria monocytogenes is an environmentally adapted saprophyte that can change into a human and animal bacterial pathogen with zoonotic potential through several regulatory systems. In this review, the focus is on the occurrence of Listeria sensu stricto and sensu lato in different ecological niches, the detection methods, and their analytical limitations. It also highlights the occurrence of L. monocytogenes genotypes in the environment (soil, water, and wildlife), reflects on the molecular determinants of L. monocytogenes for the saprophytic lifestyle and the potential for antibiotic resistance. In particular, the strain-specific properties with which some genotypes circulate in wastewater, surface water, soil, wildlife, and agricultural environments are of particular interest for the continuously updating risk analysis.

Surveillance of Listeria monocytogenes: Early Detection, Population Dynamics, and Quasimetagenomic Sequencing during Selective Enrichment

In this study, we addressed different aspects regarding the implementation of quasimetagenomic sequencing as a hybrid surveillance method in combination with enrichment for early detection of Listeria monocytogenes in the food industry. Different experimental enrichment cultures were used, comprising seven L. monocytogenes strains of different sequence types (STs), with and without a background microbiota community. To assess whether the proportions of the different STs changed over time during enrichment, the growth and population dynamics were assessed using dapE colony sequencing and dapE and 16S rRNA amplicon sequencing. There was a tendency of some STs to have a higher relative abundance during the late stage of enrichment when L. monocytogenes was enriched without background microbiota. When coenriched with background microbiota, the population dynamics of the different STs was more consistent over time. To evaluate the earliest possible time point during enrichment that allows the detection of L. monocytogenes and at the same time the generation of genetic information that enables an estimation regarding the strain diversity in a sample, quasimetagenomic sequencing was performed early during enrichment in the presence of the background microbiota using Oxford Nanopore Technologies Flongle and Illumina MiSeq sequencing. The application of multiple displacement amplification (MDA) enabled detection of L. monocytogenes (and the background microbiota) after only 4 h of enrichment using both applied sequencing approaches. The MiSeq sequencing data additionally enabled the prediction of cooccurring L. monocytogenes strains in the samples.

IMPORTANCE We showed that a combination of a short primary enrichment combined with MDA and Nanopore sequencing can accelerate the traditional process of cultivation and identification of L. monocytogenes. The use of Illumina MiSeq sequencing additionally allowed us to predict the presence of cooccurring L. monocytogenes strains. Our results suggest quasimetagenomic sequencing is a valuable and promising hybrid surveillance tool for the food industry that enables faster identification of L. monocytogenes during early enrichment. Routine application of this approach could lead to more efficient and proactive actions in the food industry that prevent contamination and subsequent product recalls and food destruction, economic and reputational losses, and human listeriosis cases.

Evaluating the accuracy of Listeria monocytogenes assemblies from quasimetagenomic samples using long and short reads

BACKGROUND

Whole genome sequencing of cultured pathogens is the state of the art public health response for the bioinformatic source tracking of illness outbreaks. Quasimetagenomics can substantially reduce the amount of culturing needed before a high quality genome can be recovered. Highly accurate short read data is analyzed for single nucleotide polymorphisms and multi-locus sequence types to differentiate strains but cannot span many genomic repeats, resulting in highly fragmented assemblies. Long reads can span repeats, resulting in much more contiguous assemblies, but have lower accuracy than short reads.

RESULTS

We evaluated the accuracy of Listeria monocytogenes assemblies from enrichments (quasimetagenomes) of naturally-contaminated ice cream using long read (Oxford Nanopore) and short read (Illumina) sequencing data. Accuracy of ten assembly approaches, over a range of sequencing depths, was evaluated by comparing sequence similarity of genes in assemblies to a complete reference genome. Long read assemblies reconstructed a circularized genome as well as a 71 kbp plasmid after 24 h of enrichment; however, high error rates prevented high fidelity gene assembly, even at 150X depth of coverage. Short read assemblies accurately reconstructed the core genes after 28 h of enrichment but produced highly fragmented genomes. Hybrid approaches demonstrated promising results but had biases based upon the initial assembly strategy. Short read assemblies scaffolded with long reads accurately assembled the core genes after just 24 h of enrichment, but were highly fragmented. Long read assemblies polished with short reads reconstructed a circularized genome and plasmid and assembled all the genes after 24 h enrichment but with less fidelity for the core genes than the short read assemblies.

CONCLUSION

The integration of long and short read sequencing of quasimetagenomes expedited the reconstruction of a high quality pathogen genome compared to either platform alone. A new and more complete level of information about genome structure, gene order and mobile elements can be added to the public health response by incorporating long read analyses with the standard short read WGS outbreak response.

Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain

Foodborne pathogens are a major contributor to foodborne illness worldwide. The adaptation of a more quantitative risk-based approach, with metrics such as Food safety Objectives (FSO) and Performance Objectives (PO) necessitates quantitative inputs from all stages of the food value chain. The potential exists for utilization of big data, generated through digital transformational technologies, as inputs to a dynamic risk management concept for food safety microbiology. The industrial revolution in Internet of Things (IoT) will leverage data inputs from precision agriculture, connected factories/logistics, precision healthcare, and precision food safety, to improve the dynamism of microbial risk management. Furthermore, interconnectivity of public health databases, social media, and e-commerce tools as well as technologies such as blockchain will enhance traceability for retrospective and real-time management of foodborne cases. Despite the enormous potential of data volume and velocity, some challenges remain, including data ownership, interoperability, and accessibility. This paper gives insight to the prospective use of big data for dynamic risk management from a microbiological safety perspective in the context of the International Commission on Microbiological Specifications for Foods (ICMSF) conceptual equation, and describes examples of how a dynamic risk management system (DRMS) could be used in real-time to identify hazards and control Shiga toxin-producing Escherichia coli risks related to leafy greens.

Precision long-read metagenomics sequencing for food safety by detection and assembly of Shiga toxin-producing Escherichia coli in irrigation water

Shiga toxin-producing Escherichia coli (STEC) contamination of agricultural water might be an important factor to recent foodborne illness and outbreaks involving leafy greens. Closed bacterial genomes from whole genome sequencing play an important role in source tracking. We aimed to determine the limits of detection and classification of STECs by qPCR and nanopore sequencing using 24 hour enriched irrigation water artificially contaminated with E. coli O157:H7 (EDL933). We determined the limit of STEC detection by qPCR to be 30 CFU/reaction, which is equivalent to 105 CFU/ml in the enrichment. By using Oxford Nanopore's EPI2ME WIMP workflow and de novo assembly with Flye followed by taxon classification with a k-mer analysis software (Kraken2), E. coli O157:H7 could be detected at 103 CFU/ml (68 reads) and a complete fragmented E. coli O157:H7 metagenome-assembled genome (MAG) was obtained at 105-108 CFU/ml. Using a custom script to extract the E. coli reads, a completely closed MAG was obtained at 107-108 CFU/ml and a complete, fragmented MAG was obtained at 105-106 CFU/ml. In silico virulence detection for E. coli MAGs for 105-108 CFU/ml showed that the virulotype was indistinguishable from the spiked E. coli O157:H7 strain. We further identified the bacterial species in the un-spiked enrichment, including antimicrobial resistance genes, which could have important implications to food safety. We propose this workflow provides proof of concept for faster detection and complete genomic characterization of STECs from a complex microbial sample compared to current reporting protocols and could be applied to determine the limit of detection and assembly of other foodborne bacterial pathogens.