Insights from genome-wide approaches to identify variants associated to phenotypes at pan-genome scale: Application to L. monocytogenes' ability to grow in cold conditions

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Methods for studying microbial acid stress responses: from molecules to populations

The study of how micro-organisms detect and respond to different stresses has a long history of producing fundamental biological insights while being simultaneously of significance in many applied microbiological fields including infection, food and drink manufacture, and industrial and environmental biotechnology. This is well-illustrated by the large body of work on acid stress. Numerous different methods have been used to understand the impacts of low pH on growth and survival of micro-organisms, ranging from studies of single cells to large and heterogeneous populations, from the molecular or biophysical to the computational, and from well-understood model organisms to poorly defined and complex microbial consortia. Much is to be gained from an increased general awareness of these methods, and so the present review looks at examples of the different methods that have been used to study acid resistance, acid tolerance, and acid stress responses, and the insights they can lead to, as well as some of the problems involved in using them. We hope this will be of interest both within and well beyond the acid stress research community.

Genes associated with fitness and disease severity in the pan-genome of mastitis-associated Escherichia coli

Introduction

Bovine mastitis caused by Escherichia coli compromises animal health and inflicts substantial product losses in dairy farming. It may manifest as subclinical through severe acute disease and can be transient or persistent in nature. Little is known about bacterial factors that impact clinical outcomes or allow some strains to outcompete others in the mammary gland (MG) environment. Mastitis-associated E. coli (MAEC) may have distinctive characteristics which may contribute to the varied nature of the disease. Given their high levels of intraspecies genetic variability, virulence factors of commonly used MAEC model strains may not be relevant to all members of this group.

Methods

In this study, we sequenced the genomes of 96 MAEC strains isolated from cattle with clinical mastitis (CM). We utilized clinical severity data to perform genome-wide association studies to identify accessory genes associated with strains isolated from mild or severe CM, or with high or low competitive fitness during in vivo competition assays. Genes associated with mastitis pathogens or commensal strains isolated from bovine sources were also identified.

Results

A type-2 secretion system (T2SS) and a chitinase (ChiA) exported by this system were strongly associated with pathogenic isolates compared with commensal strains. Deletion of chiA from MAEC isolates decreased their adherence to cultured bovine mammary epithelial cells.

Discussion

The increased fitness associated with strains possessing this gene may be due to better attachment in the MG. Overall, these results provide a much richer understanding of MAEC and suggest bacterial processes that may underlie the clinical diversity associated with mastitis and their adaptation to this unique environment.

Listeria monocytogenes prevalence and genomic diversity along the pig and pork production chain

The facultative intracellular bacterium Listeria monocytogenes (L. monocytogenes) is the causative agent of listeriosis, a severe invasive illness. This ubiquitous species is widely distributed in the environment, but infection occurs almost exclusively through ingestion of contaminated food. The pork production sector has been heavily affected by a series of L. monocytogenes-related foodborne outbreaks in the past around the world. Ready-to-eat (RTE) pork products represent one of the main food sources for strong-evidence listeriosis outbreaks. This pathogen is known to be present throughout the entire pig and pork production chain. Some studies hypothesized that the main source of contamination in final pork products was either living pigs or the food-processing environment. A detailed genomic picture of L. monocytogenes can provide a renewed understanding of the routes of contamination from pig farms to the final products. This review provides an overview of the prevalence, the genomic diversity and the genetic background linked to virulence of L. monocytogenes along the entire pig and pork production chain, from farm to fork.

Growth, biofilm formation, and motility of Listeria monocytogenes strains isolated from food and clinical samples located in Shanghai (China)

Listeria monocytogenes is a common foodborne pathogen that frequently causes global outbreaks. In this study, the growth characteristics, biofilm formation ability, motility ability and whole genome of 26 L. monocytogenes strains isolated from food and clinical samples in Shanghai (China) from 2020 to 2022 were analyzed. There are significant differences among isolates in terms of growth, biofilm formation, motility, and gene expression. Compared with other sequence type (ST) types, ST1930 type exhibited a significantly higher maximum growth rate, the ST8 type demonstrated a stronger biofilm formation ability, and the ST121 type displayed greater motility ability. Furthermore, ST121 exhibited significantly high mRNA expression levels compared with other ST types in virulence genes mpl, fbpA and fbpB, the quorum sensing gene luxS, starvation response regulation gene relA, and biofilm adhesion related gene bapL. Whole-genome sequencing (WGS) analyses indicated the isolates of lineage I were mostly derived from clinical, and the isolates of lineage II were mostly derived from food. The motility ability, along with the expression of genes associated with motility (motA and motB), exhibited a significantly higher level in lineage II compared with lineage I. The isolates from food exhibited significantly higher motility ability compared with isolates from clinical. By integrating growth, biofilm formation, motility phenotype with molecular and genotyping information, it is possible to enhance comprehension of the association between genes associated with these characteristics in L. monocytogenes.

Improvement of quantitative microbiological risk assessment (QMRA) methodology through integration with gaenetic data

Quantitative microbiological risk assessment (QMRA) methodology aims to estimate and describe the transmission of pathogenic microorganisms from animals and food to humans. In microbiological literature, the availability of whole genome sequencing (WGS) data is rapidly increasing, and incorporating this data into QMRA has the potential to enhance the reliability of risk estimates. This study provides insight into which are the key pathogen properties for incorporating WGS data to enhance risk estimation, through examination of example risk assessments for important foodborne pathogens: Listeria monocytogenes (Lm), Salmonella, Campylobacter and Shiga toxin-producing Escherichia coli. By investigating the relationship between phenotypic pathogen properties and genetic traits, a better understanding was gained regarding their impact on risk assessment. Virulence of Lm was identified as a promising property for associating different symptoms observed in humans with specific genotypes. Data from a genome-wide association study were used to correlate lineages, serotypes, sequence types, clonal complexes and the presence or absence of virulence genes of each strain with patient's symptoms. We also investigated the effect of incorporating WGS data into a QMRA model including relevant genomic traits of Lm, focusing on the dose-response phase of the risk assessment model, as described with the case/exposure ratio. The results highlighted that WGS studies which include phenotypic information must be encouraged, so as to enhance the accuracy of QMRA models. This study also underscores the importance of executing more risk assessments that consider the ongoing advancements in OMICS technologies, thus allowing for a closer investigation of different bacterial subtypes relevant to human health.

Assessment of the relationship between the MLST genetic diversity of Listeria monocytogenes and growth under selective and non-selective conditions

Listeria monocytogenes can grow under stressful conditions and contaminate various food categories. Progresss in DNA sequencing-based identification methods, such as multi-locus sequence typing (MLST) now allow for more accurate characterization of pathogens. L. monocytogenes MLST genetic diversity is reflected by the different prevalence of the "clonal complexes" (CCs) in foods or infections. Better understanding of the growth potentials of L. monocytogenes is essential for quantitative risk assessment and efficient detection across CCs genetic diversity. Using optical density measurements taken with an automated spectrophotometer, we compared the maximal growth rate and lag phase of 39 strains from 13 different CCs and various food origins, in 3 broths mimicking stresful food conditions (8 °C, aw 0.95 and pH5) and in ISO Standard enrichment broths (Half Fraser and Fraser). This is important as growth could influence risk through pathogen multiplication in food. Besides, enrichment problems could lead to a lack of detection of some CCs. Despite small differences highlighting natural intraspecific variability, our results show that growth performances of L. monocytogenes strains under the conditions tested in selective and non-selective broth do not appear to be strongly correlated to CCs and cannot explain higher CC "virulence" or prevalence.

Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends

Several studies have shown a correlation between outbreaks of Salmonella enterica and meteorological trends, especially related to temperature and precipitation. Additionally, current studies based on outbreaks are performed on data for the species Salmonella enterica, without considering its intra-species and genetic heterogeneity. In this study, we analyzed the effect of differential gene expression and a suite of meteorological factors on salmonellosis outbreak scale (typified by case numbers) using a combination of machine learning and count-based modeling methods. Elastic Net regularization model was used to identify significant genes from a Salmonella pan-genome, and a multi-variable Poisson regression developed to fit the individual and mixed effects data. The best-fit Elastic Net model (α = 0.50; λ = 2.18) identified 53 significant gene features. The final multi-variable Poisson regression model (χ² = 5748.22; pseudo R² = 0.669; probability > χ² = 0) identified 127 significant predictor terms (p < 0.10), comprising 45 gene-only predictors, average temperature, average precipitation, and average snowfall, and 79 gene-meteorological interaction terms. The significant genes ranged in functionality from cellular signaling and transport, virulence, metabolism, and stress response, and included gene variables not considered as significant by the baseline model. This study presents a holistic approach towards evaluating multiple data sources (such as genomic and environmental data) to predict outbreak scale, which could help in revising the estimates for human health risk.

Wholegenome sequencing as the gold standard approach for control of Listeria monocytogenes in the food chain

Listeria monocytogenes has been implicated in numerous outbreaks and related deaths of listeriosis. In food production, L. monocytogenes occurs in raw food material and above all, through postprocessing contamination. The use of next-generation sequencing technologies such as whole-genome sequencing (WGS) facilitates foodborne outbreak investigations, pathogen source tracking and tracing geographic distributions of different clonal complexes, routine microbiological/epidemiological surveillance of listeriosis, and quantitative microbial risk assessment. WGS can also be used to predict various genetic traits related to virulence, stress, or antimicrobial resistance, which can be of great benefit for improving food safety management as well as public health.

Variability in Cold Tolerance of Food and Clinical Listeria monocytogenes Isolates

The aim of this study was to investigate the level of strain variability amongst food and clinical Listeria monocytogenes isolates growing at low temperatures (4 and 7 °C) in both laboratory media and real food matrices. Isolates (n = 150) grown in laboratory media demonstrated a large variation in growth profiles measured using optical density. Overall, it was noted that clinical isolates exhibited a significantly higher growth rate (p ≤ 0.05) at 7 °C than the other isolates. Analysis of variance (ANOVA) tests of isolates grouped using Multi Locus Sequence Typing (MLST) revealed that clonal complex 18 (CC18) isolates were significantly (p ≤ 0.05) faster growing at 4 °C than other CC-type isolates while CC101, CC18, CC8, CC37 and CC14 were faster growing than other CC types at 7 °C. Euclidean distance and Ward method-based hierarchical clustering of mean growth rates classified 33.33% of isolates as faster growing. Fast and slow growing representative isolates were selected from the cluster analysis and growth rates were determined using plate count data in laboratory media and model food matrices. In agreement with the optical density experiments, CC18 isolates were faster and CC121 isolates were slower than other CC types in laboratory media, UHT milk and fish pie. The same trend was observed in chocolate milk but the differences were not statistically significant. Moreover, pan-genome analysis (Scoary) of isolate genome sequences only identified six genes of unknown function associated with increased cold tolerance while failing to identify any known cold tolerance genes. Overall, an association that was consistent in laboratory media and real food matrices was demonstrated between isolate CC type and increased cold tolerance.

Identification of Genetic Markers for the Detection of Bacillus thuringiensis Strains of Interest for Food Safety

Bacillus thuringiensis (Bt), belonging to the Bacillus cereus (Bc) group, is commonly used as a biopesticide worldwide due to its ability to produce insecticidal crystals during sporulation. The use of Bt, especially subspecies aizawai and kurstaki, to control pests such as Lepidoptera, generally involves spraying mixtures containing spores and crystals on crops intended for human consumption. Recent studies have suggested that the consumption of commercial Bt strains may be responsible for foodborne outbreaks (FBOs). However, its genetic proximity to Bc strains has hindered the development of routine tests to discriminate Bt from other Bc, especially Bacillus cereus sensu stricto (Bc ss), well known for its involvement in FBOs. Here, to develop tools for the detection and the discrimination of Bt in food, we carried out a genome-wide association study (GWAS) on 286 complete genomes of Bc group strains to identify and validate in silico new molecular markers specific to different Bt subtypes. The analyses led to the determination and the in silico validation of 128 molecular markers specific to Bt, its subspecies aizawai, kurstaki and four previously described proximity clusters associated with these subspecies. We developed a command line tool based on a 14-marker workflow, to carry out a computational search for Bt-related markers from a putative Bc genome, thereby facilitating the detection of Bt of interest for food safety, especially in the context of FBOs.

Control of Listeria monocytogenes in chicken dry-fermented sausages with bioprotective starter culture and high-pressure processing

Listeria monocytogenes is one of the most relevant pathogens for ready-to-eat food, being a challenge for the food industry to comply with microbiological criteria. The aim of the work was to assess the behavior of L. monocytogenes in two types of chicken-based dry-fermented sausages during the fermentation and ripening, with or without a bioprotective starter culture (Latilactobacillus sakei CTC494). To complement the challenge testing approach, simulations with different predictive models were performed to better understand the role of contributing factors. The impact of post-processing strategies, such as high-pressure processing and/or corrective storage was assessed. The chicken meat was inoculated with a cocktail of three L. monocytogenes strains, mixed with other ingredients/additives and stuffed into small (snack-type) or medium (fuet-type) casings. Snack-type was fermented (22°C/3 days) and ripened (14°C/7 days), while fuet-type was ripened (13°C/16 days). At the end of ripening, HPP (600 MPa/5 min) and/or corrective storage (4 or 15°C/7 days) were applied. The suitability of HPP after fermentation was evaluated in the snack-type sausages. Pathogen growth (>3 Log₁₀) was observed only during the fermentation of the snack type without a starter. The bioprotective starter prevented the growth of L. monocytogenes in the snack-type sausages and enhanced the inactivation (1.55 Log₁₀) in fuet-type sausages, which could be related to the higher lactic acid production and consequent decrease of pH, but also the production of the antilisterial bacteriocin sakacin k. The gamma concept model allowed us to identify the main factors controlling the L. monocytogenes’ growth, i.e., the temperature during the early stages and a_w at the end of the production process. The earlier acidification linked with the addition of starter culture made the interaction with the other factors (undissociated lactic acid, a_w and temperature) to be the growth-preventing determinants. High-pressure processing only caused a significant reduction of L. monocytogenes in snack-type, which showed higher a_w. The application of HPP after fermentation did not offer a relevant advantage in terms of efficacy. Corrective storage did not promote further pathogen inactivation. The findings of the work will guide the food industry to apply effective strategies (e.g., fermentation temperature and bioprotective starter cultures) to control L. monocytogenes in chicken dry-fermented sausages.

Genome-Wide Association Study of Listeria monocytogenes Isolates Causing Three Different Clinical Outcomes

Heterogeneity in virulence potential of L. monocytogenes subgroups have been associated with genetic elements that could provide advantages in certain environments to invade, multiply, and survive within a host. The presence of gene mutations has been found to be related to attenuated phenotypes, while the presence of groups of genes, such as pathogenicity islands (PI), has been associated with hypervirulent or stress-resistant clones. We evaluated 232 whole genome sequences from invasive listeriosis cases in human and ruminants from the US and Europe to identify genomic elements associated with strains causing three clinical outcomes: central nervous system (CNS) infections, maternal-neonatal (MN) infections, and systemic infections (SI). Phylogenetic relationships and virulence-associated genes were evaluated, and a gene-based and single nucleotide polymorphism (SNP)-based genome-wide association study (GWAS) were conducted in order to identify loci associated with the different clinical outcomes. The orthologous results indicated that genes of phage phiX174, transfer RNAs, and type I restriction-modification (RM) system genes along with SNPs in loci involved in environmental adaptation such as rpoB and a phosphotransferase system (PTS) were associated with one or more clinical outcomes. Detection of phenotype-specific candidate loci represents an approach that could narrow the group of genetic elements to be evaluated in future studies.

Genomic elements located in the accessory repertoire drive the adaptation to biocides in Listeria monocytogenes strains from different ecological niches

In response to the massive use of biocides for controlling Listeria monocytogenes (hereafter Lm) contaminations along the food chain, strains showing biocide tolerance emerged. Here, accessory genomic elements were associated with biocide tolerance through pangenome-wide associations performed on 197 Lm strains from different lineages, ecological, geographical and temporal origins. Mobile elements, including prophage-related loci, the Tn6188_qacH transposon and pLMST6_emrC plasmid, were widespread across lineage I and II food strains and associated with tolerance to benzalkonium-chloride (BC), a quaternary ammonium compound (QAC) widely used in food processing. The pLMST6_emrC was also associated with tolerance to another QAC, the didecyldimethylammonium-chloride, displaying a pleiotropic effect. While no associations were detected for chemically reactive biocides (alcohols and chlorines), genes encoding for cell-surface proteins were associated with BC or polymeric biguanide tolerance. The latter was restricted to lineage I strains from animal and the environment. In conclusion, different genetic markers, with polygenic nature or not, appear to have driven the Lm adaptation to biocide, especially in food strains but also from animal and the environment. These markers could aid to monitor and predict the spread of biocide tolerant Lm genotypes across different ecological niches, finally reducing the risk of such strains in food industrial settings.

Genetic, physiological, and cellular heterogeneities of bacterial pathogens in food matrices: Consequences for food safety

In complex food systems, bacteria live in heterogeneous microstructures, and the population displays phenotypic heterogeneities at the single-cell level. This review provides an overview of spatiotemporal drivers of phenotypic heterogeneity of bacterial pathogens in food matrices at three levels. The first level is the genotypic heterogeneity due to the possibility for various strains of a given species to contaminate food, each of them having specific genetic features. Then, physiological heterogeneities are induced within the same strain, due to specific microenvironments and heterogeneous adaptative responses to the food microstructure. The third level of phenotypic heterogeneity is related to cellular heterogeneity of the same strain in a specific microenvironment. Finally, we consider how these phenotypic heterogeneities at the single-cell level could be implemented in mathematical models to predict bacterial behavior and help ensure microbiological food safety.

Listeria monocytogenes: Investigation of Fitness in Soil Does Not Support the Relevance of Ecotypes

Listeria monocytogenes (Lm) is a ubiquitous bacterium that causes the serious foodborne illness listeriosis. Although soil is a primary reservoir and a central habitat for Lm, little information is available on the genetic features underlying the fitness of Lm strains in this complex habitat. The aim of this study was to identify (i) correlations between the strains fitness in soil, their origin and their phylogenetic position (ii) identify genetic markers allowing Lm to survive in the soil. To this end, we assembled a balanced panel of 216 Lm strains isolated from three major ecological compartments (outdoor environment, animal hosts, and food) and from 33 clonal complexes occurring worldwide. The ability of the 216 strains to survive in soil was tested phenotypically. Hierarchical clustering identified three phenotypic groups according to the survival rate (SR): phenotype 1 “poor survivors” (SR &lt; 2%), phenotype 2 “moderate survivors” (2% &lt; SR &lt; 5%) and phenotype 3 “good survivors” (SR &gt; 5%). Survival in soil depended neither on strains’ origin nor on their phylogenetic position. Genome-wide-association studies demonstrated that a greater number of genes specifically associated with a good survival in soil was found in lineage II strains (57 genes) than in lineage I strains (28 genes). Soil fitness was mainly associated with variations in genes (i) coding membrane proteins, transcription regulators, and stress resistance genes in both lineages (ii) coding proteins related to motility and (iii) of the category “phage-related genes.” The cumulative effect of these small genomic variations resulted in significant increase of soil fitness.

Advances in multi-omics based quantitative microbial risk assessment in the dairy sector: A semi-systematic review

With the increasing consumption of packaged and ready-to-eat food products, the risk of foodborne illness has drastically increased and so has the dire need for proper management. The conventional Microbial Risk Assessment (MRA) investigations require prior knowledge of process flow, exposure, and hazard assessment throughout the supply chain. These data are often generated using conventional microbiological approaches based either on shelf-life studies or specific spoilage organisms (SSOs), frequently overlooking crucial information such as antimicrobial resistance (AMR), biofilm formation, virulence factors and other physiological variations coupled with bio-chemical characteristics of food matrix. Additionally, the microbial risks in food are diverse and heterogenous, that might be an outcome of growth and activity of multiple microbial populations rather than a single species contamination. The uncertainty on the microbial source, time as well as point of entry into the food supply chain poses a constraint to the efficiency of preventive approaches and conventional MRA. In the last few decades, significant breakthroughs in molecular methods and continuously progressing bioinformatics tools have opened up a new horizon for risk analysis-based approaches in food safety. Real time polymerase chain reaction (qPCR) and kit-based assays provide better accuracy and precision with shorter processing time. Despite these improvements, the effect of complex food matrix on growth environment and recovery of pathogen is a persistent problem for risk assessors. The dairy industry is highly impacted by spoilage and pathogenic microorganisms. Therefore, this review discusses the evolution and recent advances in MRAmethodologies equipped with predictive interventions and "multi-omics" approach for robust MRA specifically targeting dairy products. It also highlights the limiting gap area and the opportunity for improvement in this field to ensure precision food safety.

A European-wide dataset to uncover adaptive traits of Listeria monocytogenes to diverse ecological niches

Listeria monocytogenes (Lm) is a ubiquitous bacterium that causes listeriosis, a serious foodborne illness. In the nature-to-human transmission route, Lm can prosper in various ecological niches. Soil and decaying organic matter are its primary reservoirs. Certain clonal complexes (CCs) are over-represented in food production and represent a challenge to food safety. To gain new understanding of Lm adaptation mechanisms in food, the genetic background of strains found in animals and environment should be investigated in comparison to that of food strains. Twenty-one partners, including food, environment, veterinary and public health laboratories, constructed a dataset of 1484 genomes originating from Lm strains collected in 19 European countries. This dataset encompasses a large number of CCs occurring worldwide, covers many diverse habitats and is balanced between ecological compartments and geographic regions. The dataset presented here will contribute to improve our understanding of Lm ecology and should aid in the surveillance of Lm. This dataset provides a basis for the discovery of the genetic traits underlying Lm adaptation to different ecological niches.

Measurement(s)	whole genome sequencing
Technology Type(s)	Illumina Sequencing
Factor Type(s)	Multi-locus sequence types • Geographic location • Animal associated environment isolates • Food product and food production environment isolates
Sample Characteristic - Organism	Listeria monocytogenes
Sample Characteristic - Environment	Farm • Ruminant • Agricultural soil • Wild animals • food processing building • dairy food product • meat or meat product (from mammal) (us cfr) • chicken meat food product • fish food product • vegetable or vegetable product (us cfr)
Sample Characteristic - Location	Europe

Landscape of Stress Response and Virulence Genes Among Listeria monocytogenes Strains

The pathogenic microorganism Listeria monocytogenes is ubiquitous and responsible for listeriosis, a disease with a high mortality rate in susceptible people. It can persist in different habitats, including the farm environment, the food production environments, and in foods. This pathogen can grow under challenging conditions, such as low pH, low temperatures, and high salt concentrations. However, L. monocytogenes has a high degree of strain divergence regarding virulence potential, environmental adaption, and stress response. This review seeks to provide the reader with an up-to-date overview of clonal and serotype-specific differences among L. monocytogenes strains. Emphasis on the genes and genomic islands responsible for virulence and resistance to environmental stresses is given to explain the complex adaptation among L. monocytogenes strains. Moreover, we highlight the use of advanced diagnostic technologies, such as whole-genome sequencing, to fine-tune quantitative microbiological risk assessment for better control of listeriosis.

Exploring the predictive capability of advanced machine learning in identifying severe disease phenotype in Salmonella enterica

The past few years have seen a significant increase in availability of whole genome sequencing information, allowing for its incorporation in predictive modeling for foodborne pathogens to account for inter- and intra-species differences in their virulence. However, this is hindered by the inability of traditional statistical methods to analyze such large amounts of data compared to the number of observations/isolates. In this study, we have explored the applicability of machine learning (ML) models to predict the disease outcome, while identifying features that exert a significant effect on the prediction. This study was conducted on Salmonella enterica, a major foodborne pathogen with considerable inter- and intra-serovar variation. WGS of isolates obtained from various sources (i.e., human, chicken, and swine) were used as input in four machine learning models (logistic regression with ridge, random forest, support vector machine, and AdaBoost) to classify isolates based on disease severity (extraintestinal vs. gastrointestinal) in the host. The predictive performances of all models were tested with and without Elastic Net regularization to combat dimensionality issues. Elastic Net-regularized logistic regression model showed the best area under the receiver operating characteristic curve (AUC-ROC; 0.86) and outcome prediction accuracy (0.76). Additionally, genes coding for transcriptional regulation, acidic, oxidative, and anaerobic stress response, and antibiotic resistance were found to be significant predictors of disease severity. These genes, which were significantly associated with each outcome, could possibly be input in amended, gene-expression-specific predictive models to estimate virulence pattern-specific effect of Salmonella and other foodborne pathogens on human health.

Phylogenetic Analysis and Genome-Wide Association Study Applied to an Italian Listeria monocytogenes Outbreak

From May 2015 to March 2016, a severe outbreak due to Listeria monocytogenes ST7 strain occurred in Central Italy and caused 24 confirmed clinical cases. The epidemic strain was deeply investigated using whole-genome sequencing (WGS) analysis. In the interested area, the foodborne outbreak investigation identified a meat food-producing plant contaminated by the outbreak strain, carried by pork-ready-to-eat products. In the same region, in March 2018, the epidemic strain reemerged causing one listeriosis case in a 10-month-old child. The aim of this study was to investigate the phylogeny of the epidemic and reemergent strains over time and to compare them with a closer ST7 clone, detected during the outbreak and with different pulsed-field gel electrophoresis (PFGE) profiles, in order to identify genomic features linked to the persistence and the reemergence of the outbreak. An approach combining phylogenetic analysis and genome-wide association study (GWAS) revealed that the epidemic and reemergent clones were genetically closer to the ST7 clone with different PFGE profiles and strictly associated with the pork production chain. The repeated detection of both clones was probably correlated with (i) the presence of truly persistent clones and the repeated introduction of new ones and (ii) the contribution of prophage genes in promoting the persistence of the epidemic clones. Despite that no significant genomic differences were detected between the outbreak and the reemergent strain, the two related clones detected during the outbreak can be differentiated by transcriptional factor and phage genes associated with the phage LP-114.

Application of Whole Genome Sequencing to Aid in Deciphering the Persistence Potential of Listeria monocytogenes in Food Production Environments

Listeria monocytogenes is the etiological agent of listeriosis, a foodborne illness associated with high hospitalizations and mortality rates. This bacterium can persist in food associated environments for years with isolates being increasingly linked to outbreaks. This review presents a discussion of genomes of Listeria monocytogenes which are commonly regarded as persisters within food production environments, as well as genes which are involved in mechanisms aiding this phenotype. Although criteria for the detection of persistence remain undefined, the advent of whole genome sequencing (WGS) and the development of bioinformatic tools have revolutionized the ability to find closely related strains. These advancements will facilitate the identification of mechanisms responsible for persistence among indistinguishable genomes. In turn, this will lead to improved assessments of the importance of biofilm formation, adaptation to stressful conditions and tolerance to sterilizers in relation to the persistence of this bacterium, all of which have been previously associated with this phenotype. Despite much research being published around the topic of persistence, more insights are required to further elucidate the nature of true persistence and its implications for public health.

First Report on the Finding of Listeria mnocytogenes ST121 Strain in a Dolphin Brain

Listeria monocytogenes (Lm) is a ubiquitous bacterium that causes the foodborne illness, listeriosis. Clonal complexes (CC), such as CC121, are overrepresented in the food production industry, and are rarely reported in animals and the environment. Working within a European-wide project, we investigated the routes by which strains are transmitted from environments and animals to food and the food production environment (FPE). In this context, we report, for the first time, the occurrence of a ST121 (CC121) strain isolated from a dolphin brain. The genome was compared with the genomes of 376 CC121 strains. Genomic comparisons showed that 16 strains isolated from food were the closest to the dolphin strain. Like most of the food strains analyzed here, the dolphin strain included genomic features (transposon Tn6188, plasmid pLM6179), both described as being associated with the strain’s adaptation to the FPE. Like all 376 strains, the dolphin strain contained a truncated actA gene and inlA gene, both described as being associated with attenuated virulence. Despite this fact, the strain was able to cross blood-brain barrier in immunosuppressed dolphin exposed polychlorinated biphenyl and invaded by parasites. Our data suggest that the dolphin was infected by a food-related strain released into the Mediterranean Sea.

High-Throughput Characterization of Listeria monocytogenes Using the OmniLog Phenotypic Microarray

High-throughput biochemical screening techniques are an important tool in phenotypic analysis of bacteria. New methods, simultaneously measuring many phenotype responses, increase the output of such investigations and allow a more complete overview of the bacterial phenotype, facilitating large-scale correlation to related genotypes. This chapter describes the application of OmniLog phenotype microarray analysis, a high-throughput assay for the phenotypic characterization of bacterial strains across a variety of different traits such as nutrient utilization and antimicrobial sensitivity, to Listeria species.

Dynamics of mobile genetic elements of Listeria monocytogenes persisting in ready-to-eat seafood processing plants in France

BACKGROUND

Listeria monocytogenes Clonal Complexes (CCs) have been epidemiologically associated with foods, especially ready-to-eat (RTE) products for which the most likely source of contamination depends on the occurrence of persisting clones in food-processing environments (FPEs). As the ability of L. monocytogenes to adapt to environmental stressors met in the food chain challenges the efforts to its eradication from FPEs, the threat of persistent strains to the food industry and public health authorities continues to rise. In this study, 94 food and FPEs L. monocytogenes isolates, representing persistent subtypes contaminating three French seafood facilities over 2-6 years, were whole-genome sequenced to characterize their genetic diversity and determine the biomarkers associated with long-term survival in FPEs.

RESULTS

Food and FPEs isolates belonged to five CCs, comprising long-term intra- and inter-plant persisting clones. Mobile genetic elements (MGEs) such as plasmids, prophages and transposons were highly conserved within CCs, some of which harboured genes for resistance to chemical compounds and biocides used in the processing plants. Some of these genes were found in a 90.8 kbp plasmid, predicted to be" mobilizable", identical in isolates from CC204 and CC155, and highly similar to an 81.6 kbp plasmid from isolates belonging to CC7. These similarities suggest horizontal transfer between isolates, accompanied by deletion and homologous recombination in isolates from CC7. Prophage profiles characterized persistent clonal strains and several prophage-loci were plant-associated. Notably, a persistent clone from CC101 harboured a novel 31.5 kbp genomic island that we named Listeria genomic island 3 (LGI3), composed by plant-associated loci and chromosomally integrating cadmium-resistance determinants cadA1C.

CONCLUSIONS

Genome-wide analysis indicated that inter- and intra-plant persisting clones harbour conserved MGEs, likely acquired in FPEs and maintained by selective pressures. The presence of closely related plasmids in L. monocytogenes CCs supports the hypothesis of horizontal gene transfer conferring enhanced survival to FPE-associated stressors, especially in hard-to-clean harbourage sites. Investigating the MGEs evolutionary and transmission dynamics provides additional resolution to trace-back potentially persistent clones. The biomarkers herein discovered provide new tools for better designing effective strategies for the removal or reduction of resident L. monocytogenes in FPEs to prevent contamination of RTE seafood.

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms

This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.

Biofilm Formation of Listeria monocytogenes Strains Under Food Processing Environments and Pan-Genome-Wide Association Study

Concerns about food contamination by Listeria monocytogenes are on the rise with increasing consumption of ready-to-eat foods. Biofilm production of L. monocytogenes is presumed to be one of the ways that confer its increased resistance and persistence in the food chain. In this study, a collection of isolates from foods and food processing environments (FPEs) representing persistent, prevalent, and rarely detected genotypes was evaluated for biofilm forming capacities including adhesion and sessile biomass production under diverse environmental conditions. The quantity of sessile biomass varied according to growth conditions, lineage, serotype as well as genotype but association of clonal complex (CC) 26 genotype with biofilm production was evidenced under cold temperature. In general, relative biofilm productivity of each strain varied inconsistently across growth conditions. Under our experimental conditions, there were no clear associations between biofilm formation efficiency and persistent or prevalent genotypes. Distinct extrinsic factors affected specific steps of biofilm formation. Sudden nutrient deprivation enhanced cellular adhesion while a prolonged nutrient deficiency impeded biofilm maturation. Salt addition increased biofilm production, moreover, nutrient limitation supplemented by salt significantly stimulated biofilm formation. Pan-genome-wide association study (Pan-GWAS) assessed genetic composition with regard to biofilm phenotypes for the first time. The number of reported genes differed depending on the growth conditions and the number of common genes was low. However, a broad overview of the ontology contents revealed similar patterns regardless of the conditions. Functional analysis showed that functions related to transformation/competence and surface proteins including Internalins were highly enriched.

Genetic and metabolic signatures of Salmonella enterica subsp. enterica associated with animal sources at the pangenomic scale

BACKGROUND

Salmonella enterica subsp. enterica is a public health issue related to food safety, and its adaptation to animal sources remains poorly described at the pangenome scale. Firstly, serovars presenting potential mono- and multi-animal sources were selected from a curated and synthetized subset of Enterobase. The corresponding sequencing reads were downloaded from the European Nucleotide Archive (ENA) providing a balanced dataset of 440 Salmonella genomes in terms of serovars and sources (i). Secondly, the coregenome variants and accessory genes were detected (ii). Thirdly, single nucleotide polymorphisms and small insertions/deletions from the coregenome, as well as the accessory genes were associated to animal sources based on a microbial Genome Wide Association Study (GWAS) integrating an advanced correction of the population structure (iii). Lastly, a Gene Ontology Enrichment Analysis (GOEA) was applied to emphasize metabolic pathways mainly impacted by the pangenomic mutations associated to animal sources (iv).

RESULTS

Based on a genome dataset including Salmonella serovars from mono- and multi-animal sources (i), 19,130 accessory genes and 178,351 coregenome variants were identified (ii). Among these pangenomic mutations, 52 genomic signatures (iii) and 9 over-enriched metabolic signatures (iv) were associated to avian, bovine, swine and fish sources by GWAS and GOEA, respectively.

CONCLUSIONS

Our results suggest that the genetic and metabolic determinants of Salmonella adaptation to animal sources may have been driven by the natural feeding environment of the animal, distinct livestock diets modified by human, environmental stimuli, physiological properties of the animal itself, and work habits for health protection of livestock.

Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges

Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of 'omics' techniques (e.g., transcriptomics, proteomics and metabolomics). We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR.