Caractérisation de la variabilité intra-spécifique et cellulaire ...

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Caractérisation de la variabilité intra-spécifique etcellulaire de Listeria monocytogenes à des températures

bassesLena Fritsch

To cite this version:Lena Fritsch. Caractérisation de la variabilité intra-spécifique et cellulaire de Listeria monocytogenesà des températures basses. Médecine humaine et pathologie. Université Paris-Est, 2019. Français.�NNT : 2019PESC0055�. �tel-03485734�

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THESE DE DOCTORAT

de l’Université Paris-Est

Spécialité : Microbiologie

École doctorale n°581 Agriculture, alimentation, biologie, environnement et santé (ABIES)

par

Lena FRITSCH

Characterization of the intra-specific and cellular variability

of Listeria monocytogenes at low temperatures

Directeur de thèse : Jean-Christophe AUGUSTIN

Co-encadrement de la thèse : Laurent GUILLIER

Thèse présentée et soutenue à Maisons-Alfort, le 18.12.2019:

Composition du jury :

Mme. Annemarie PIELAAT, Responsable de recherche, Unilever, Pays-Bas Rapporteur

M. Louis COROLLER, Professeur, Université de Brest, France Rapporteur

M. Romain BRIANDET, Directeur de recherches, INRA, Jouy-en-Josas, France Examinateur

M. Taurai TASARA, Responsable de recherche, Université Zurich, Suisse Examinateur

M. Jean-Christophe AUGUSTIN, Professeur, Ecole Nationale Vétérinaire d’Alfort, France Directeur de thèse

M. Laurent GUILLIER, Chargé de projets scientifiques, ANSES, Maisons Alfort, France Encadrement de thèse

Unité Salmonella et Listeria, LSAL, Maisons Alfort 14 Rue Pierre et Marie Curie 94701 Maisons Alfort

Summary

During the last years, the cases of the foodborne disease listeriosis are steadily increasing in Europe. Listeriosis is mainly caused by the ingestion of food contaminated with the bacterium Listeria monocytogenes. Especially, ready-to-eat food products represent an increased risk to consumers and have been often causally linked to foodborne outbreaks of listeriosis. Since low temperatures have the potential to inhibit the bacterial pathogens’ growth, the cold chain is of high importance to ensure the safety of the products. Contrary to other pathogens, L. monocytogenes is hard to control by the cold chain because of its ability to grow at low temperatures even around freezing point. Quantitative microbial risk assessment (QMRA) models are used to better understand the risk for human health and therefore helps to find appropriate interventions for public health benefit. Improving risk assessment for L. monocytogenes implies a better understanding of the pathogen behavior near growth limits and description of the effect of the environment on lag time (i.e. delay in growth when bacteria adapt to new environments) and growth probability. Moreover, the characterization of L. monocytogenes subtypes’ phenotypic variability during cold exposure also helps to improve the precision of the predictions. Recent developments in whole genome sequencing (WGS) opened new opportunities for explaining the intraspecific variability of phenotypes. Successful association between WGS-data and specific phenotypes potentially contribute to better predicting microbial behaviors through the discovery of genomic markers. The successful application of combined bioinformatics approaches associating WGS-genotypes and specific phenotypes is a major step in the refinement of QMRA models. In this perspective, the first aim of this thesis was the exploration and application of different tools to conduct Genome Wide Association Studies (GWAS). Three different approaches, covering the pan-genome as also a phylogenomic-based method, were applied to a set of 51 L. monocytogenes strains to gain insights on the genomic features explaining their ability to grow at low temperatures (2 °C). Several genomic features (i.e. genes and SNPs), as well as specific phylogenetic sublineages, were identified as associated with L. monocytogenes' growth at low temperature. These biomarkers can be used in future QMRA studies to perform risk assessments targeted to the L. monocytogenes subpopulations that pose the greatest risk linked to their higher ability to survive and/or grow in the food chain. Therefore, the 2nd study of this thesis focused on the refinements of a QMRA model for listeriosis from the consumption of cold-smoked salmon chain in France considering pheno-genotype associations (e.g. growth ability at low temperature and virulence). Even if QRMA model refinement is still using some strong hypotheses (i.e. Clonal Complex association with virulence), this work highlighted the potential impact of implementing genomic data in QMRA. Large genomic data sets are necessary to obtain robust genetic markers to use in QRMA. Genomic DNA extraction and sequencing are already fully-automated with high throughput. However, when performing pheno-genotype associations studies, experiments for phenotyping are often time-consuming and labor-intensive with a low degree of automation. Therefore, the aim of the third study of this thesis was to develop an automated microscopy method to determine the single-cell growth probability and lag time in cold conditions. Agar-based medium covering slides were inoculated with L. monocytogenes cells and were observed over time by phase-contrast microscopy. Image recordings of the single-cells and micro-colonies were analyzed with an automatic image analysis procedure. This method was successfully used to generate single-cell lag time distributions and to estimate growth probability of the selected L. monocytogenes strains. This method provides a rapid and convenient strategy for the study of single cell behavior determination that would require months with indirect methods. The characterization and the implementation of these variables into predictive models and thus exposure assessments have the potential to increase their certainty.

Résumé

Depuis plusieurs années, les cas de listériose d'origine alimentaire augmentent régulièrement en Europe. Les produits alimentaires prêts-à-consommer sont généralement incriminés car potentiellement contaminés par Listeria monocytogenes (Lm) et consommés sans cuisson préalable, d’où un risque accru pour les consommateurs. Les basses températures permettent de freiner la croissance de la majorité des bactéries pathogènes, et la maitrise de la chaîne du froid est donc très importante. Cependant, Lm est capable de croitre à basse température ce qui rend la maitrise du danger plus difficile. Les modèles d'appréciation quantitative des risques microbiens (AQRM) aident à caractériser le risque et à définir les mesures de lutte pour préserver la santé publique. Avoir une meilleure compréhension du comportement de Lm proche de sa limite de croissance permettrait d’améliorer ces modèles. La validité d'un modèle pour prédire les conditions qui mènent à des niveaux critiques dans les aliments dépend fortement de sa capacité à décrire l'effet de l'environnement sur la latence et la probabilité de croissance. La caractérisation de la variabilité phénotypique lors de l'exposition au froid permet également d'améliorer la précision des résultats prédits. Les développements récents dans le séquençage du génome entier (WGS) ont ouvert de nouvelles opportunités pour expliquer la variabilité intraspécifique des phénotypes. Les données du WGS associées à des phénotypes spécifiques peuvent permettre de mieux prédire les comportements microbiens grâce à la découverte de marqueurs génomiques. L'application d'approches bioinformatiques combinées associant des génotypes WGS et des phénotypes spécifiques est une étape majeure dans le perfectionnement des modèles d'AQRM. Dans cette perspective, le premier objectif de cette thèse était l'exploration et l'application de différents outils pour mener des études d'association à l'échelle du génome (GWAS). Trois approches différentes couvrant le pan-génome ainsi qu'une méthode basée sur la phylogénomique ont été appliquées à un ensemble de 51 souches de Lm pour identifier les caractéristiques génomiques expliquant leur capacité de croissance à basse température (2 °C). Plusieurs caractéristiques génomiques (gènes et SNP), ainsi que des sous-lignées phylogénétiques spécifiques ont été identifiées comme étant associées à la croissance de Lm à basse température. Ces biomarqueurs pourront être utilisés dans des AQRM pour évaluer les risques liés aux sous-populations de Lm qui posent le plus grand risque dû à leur forte capacité à survivre et/ou à croître dans les aliments. Les résultats de la deuxième étude de cette thèse indiquent que la mise en œuvre des données de sous-typage WGS dans un modèle d'AQRM améliore l'estimation de la probabilité de listériose. Même si l'amélioration du modèle d’AQRM repose encore sur des hypothèses fortes (association du complexe clonal avec la virulence), ces travaux ont mis en évidence l'impact potentiel de la mise en œuvre des données génomiques dans l'AQRM. De grandes quantités de données génomiques sont nécessaires pour obtenir des marqueurs génétiques robustes. L'extraction et le séquençage de l'ADN génomique sont déjà entièrement automatisés et à haut débit. Toutefois ces souches doivent être caractérisées phénotypiquement, mais ces expériences sont couteuses en temps car faiblement automatisées. Par conséquent, la troisième étude de ce projet de thèse avait pour but de mettre au point une méthode de microscopie automatisée pour déterminer la probabilité de croissance et le temps de latence cellulaires. Des lames de milieu à base de gélose ont été inoculées avec des cellules de Lm et observées au fil du temps par microscopie à contraste de phase. Les enregistrements d'images de cellules individuelles et de micro-colonies ont été analysés à l'aide d'une procédure automatique d'analyse d'images. Cette méthode a été utilisée avec succès pour générer des distributions de temps de latence et les probabilités de croissance unicellulaires de quatre souches de Lm. Cette méthode permet d’obtenir rapidement et facilement des données de comportement cellulaires dont l’acquisition prendraient des mois avec des méthodes indirectes. La caractérisation de la probabilité de croissance d'une seule cellule et du temps de latence dans les modèles prévisionnels et donc lors d’évaluations de l'exposition augmente leur certitude.

Acknowlegement

First, I want to thank the French National Research Agency for funding the project OptiCold (ANR-15-CE21-0011) and my PhD. Further, I want to thank ANSES for giving me the opportunity to perform my thesis in the food safety laboratory.

A special and huge thank goes to my supervisors Jean-Christophe Augustin and Laurent Guillier. They accompanied me through to the thesis with their patience, continuous support, immense knowledge and passion. I learned incredibly much from them and I feel so thankful for the possibility to work close to such great scientists as they are.

Besides my supervisors, I would like to thank my thesis committee for their insightful comments, fruitful advices, encouragement and the time they took to revise my work: Hélène Bergis, Hélène Bierne, Florence Dubois-Brissonnet, Alexandre Leclercq and Mariem Ellouze.

For sure, a huge thank goes to all my colleagues especially to the members of SeL and GAMeR. I am thankful for all the advices, help and the time we spent together. Especially, I enjoyed the stimulating discussions that opened me to different perspectives and enhanced my knowledge in different areas.

During my PhD, I had the pleasure to supervise Abirami Baleswaran through to her Master internship. She has done a great job and helped me a lot to finish the experiments in the lab. Thank you very much.

I will address also my sincere thanks to Sabine Delannoy for her generous aid in reviewing my draft version.

Besides colleagues of my team, I want to thank SJA and Sabine Herbin. They have always been there for me, pushed me when necessary and lifted my mood with a “carambar joke”. Merci!

Hélène Bergis and Véronique Noel, without them would have been completely lost and I cannot even thank enough for the support, cheering up, advices, open ears, help and and and. I will really miss coming around in their office.

Besides these two young girls, also Federica Palma was such an important person for me, at and besides work. She joined our office last year and it took just a short time to recognize that we were quite similar characters: always a smile on the lips, positive, a little bit freaky, how to say the Mediterranean type ;) Thank you from the bottom of my heart for lightening up every single day by being simply you. And, I cannot thank you enough for the great help at work, especially in the last PhD months. Grazie mille!!! Without my former super supervisors Véronique Zuliani and Mariem Ellouze, that became friends to me, my entire journey would not have been possible. They formed me, opened my horizons, impressed me and made me willing to grow. Thank you for accompanying me during that journey with all your advices and friendship!

Shirin Sygor und Sarah Zitzer gilt auch mein Dank, die mich nun schon einige Jahre begleiten und ohne deren Hilfe ich wohl nicht durchs Studium gekommen wäre ;). Danke, Ihr Master of Disaster

Alina Glitsch, danke für deine Unterstützung, Freundschaft und natürlich für unsere Ausflüge im «Herzen von Europa».

Vielen Dank an meine Sandkastenfreundin Hanna Lüdde, die mich trotz der Entfernung jeden Tag auf meiner Reise begleitet hat, mit mir virtuell den ein oder anderen Abend verbracht hat und auf die ich mich seit 28 Jahren jeden Tag verlassen kann.

Papa, Mama und Julia, vielen Dank für alles. Euch gilt der gröβte Dank! Danke für all die Möglichkeiten, die Unterstützung und all das was Ihr mir mit auf den Weg gegeben habt.

Diese Doktorarbeit möchte ich meiner Oma Elsbeth widmen.

I would like to dedicate this thesis to my grandma Elsbeth.

Table of contents

List of abbreviations ................................................................................................................... I

List of figures ........................................................................................................................... III

List of tables ............................................................................................................................ V

Introduction ............................................................................................................................. VI

Theoretical background ............................................................................................................ 1

1 Listeria monocytogenes .............................................................................................. 1

1.1 Overview.............................................................................................................. 1

1.2 Genomics of Listeria monocytogenes .................................................................. 2

1.3 Monitoring of Listeria monocytogenes in the European Union.............................. 7

1.4 Food sources and outbreaks................................................................................ 8

1.5 Surveillance ......................................................................................................... 9

1.5 Regulations .........................................................................................................11

2 Cold adaptation ..........................................................................................................12

2.1 Importance of cold growth/adaptation .................................................................12

2.2 Mechanisms .......................................................................................................13

2.3 Cold growth and virulence ..................................................................................19

3 Association studies in the genomic era ......................................................................22

3.1 Whole Genome Sequencing ...............................................................................22

3.2 Genomic events driving phenotypic diversity ......................................................24

3.3 Genome Wide Association Studies .....................................................................27

4 Quantitative microbial risk assessment ......................................................................31

4.1 Hazard identification ...............................................................................................32

4.2 Exposure assessment .............................................................................................32

4.3 Hazard characterization ..........................................................................................40

4.4 Risk characterization...............................................................................................41

Objectives ............................................................................................................................... 42

Chapter 1 ................................................................................................................................ 45

Insights from genome-wide approaches to identify variants associated to phenotypes at pan-

genome scale: Application to Listeria monocytogenes’ ability to grow in cold conditions ..45

Résumé ............................................................................................................................46

Abstract ............................................................................................................................48

1 Introduction ................................................................................................................50

1.1 French introduction .............................................................................................50

1.2 English introduction ............................................................................................53

2 Materials and methods ...............................................................................................56

2.1 Strains ................................................................................................................56

2.2 Whole genome sequencing ................................................................................56

2.3 Phenotyping in cold conditions ............................................................................56

2.4 Genomic approaches ..........................................................................................57

3 Results and discussion ..............................................................................................60

3.1 GWAS methods to explore the genetic basis of growth capacity at 2 °C .............60

3.2 Analysis of the growth ability at 2 °C through a Bayesian inference method .......67

4 Conclusion .................................................................................................................70

Acknowledgements ..........................................................................................................70

Chapter 2 ................................................................................................................................ 71

Next generation quantitative microbiological risk assessment: Refinement of the cold

smoked salmon-related listeriosis risk model by integrating genomic data .......................71

Résumé ............................................................................................................................72

Abstract ............................................................................................................................73

1 Introduction ................................................................................................................74



2 Material and methods ................................................................................................79

2.2 Genomic differentiation of L. monocytogenes for hazard identification ................82

2.3 Virulence properties of L. monocytogenes genotypes subtypes ..........................82

2.4 Phenotypic (growth) characteristics of L. monocytogenes subgroups .................83

2.5 Prevalence of L. monocytogenes genotypes in cold-smoked salmon ..................84

2.6 Model parameters and simulation .......................................................................84

3 Results and discussion ..............................................................................................86

3.1 Distributions of phenotypic and virulence properties among L. monocytogenes

subtypes .......................................................................................................................86

3.2 Exposure and risk estimates according to the L. monocytogenes subtypes ........89

Acknowledgement ............................................................................................................92

Chapter 3 ................................................................................................................................ 93

A novel microscopy method for determining individual lag times and growth probability of

individual bacterial cells ....................................................................................................93

Résumé ............................................................................................................................94

Abstract ............................................................................................................................95

1. Introduction ............................................................................................................96



2 Material and methods .............................................................................................. 102

2.1 Strains .............................................................................................................. 102

2.2 Preparation of inocula ....................................................................................... 102

2.3 Preparation and storage of the microscope slides ............................................. 102

2.4 Microscopic observation of cells ....................................................................... 103

2.5 Image analysis procedure ................................................................................. 103

2.6 Growth curve at population level ....................................................................... 106

2.7 Determination of growth probability ................................................................... 106

2.8 Individual lag time determination ....................................................................... 106

3 Results .................................................................................................................... 108

3.1 Growth rates ..................................................................................................... 108

3.2 Determination of cell numbers in the captured images ...................................... 108

3.3 Growth probability ............................................................................................. 111

3.4 Individual lag times of single-cells ..................................................................... 113

4 Discussion ............................................................................................................... 115

5 Conclusion ............................................................................................................... 117

Acknowledgement .......................................................................................................... 117

General discussion ............................................................................................................... 118

Conclusion and future perspectives ...................................................................................... 130

1 Conclusion ............................................................................................................... 130

2 Perspectives ............................................................................................................ 131

Valorization ........................................................................................................................... 132

References ........................................................................................................................... 135

Supplementary material ........................................................................................................ 159

List of abbreviations

I


ABC ATP-Binding Cassette

aw Water activity

BC Benzalkonium Chloride

BetL Betaine porter I

BHI Brain Heart Infusion Broth

bp base pair

CC Clonal Complex

CDS Coding DNA Sequences

CFU Colony Forming Unit

cgMLST Core Genome Multi Locus Sequence Typing

Csp cold shock protein

CSS Cold-Smoked Salmon

DNA Deoxyribonucleic acid

dsRNA Double Stranded Ribonucleic Acid

ECDC European Centre for Disease Prevention and Control

EFSA European Food Safety Authority

EPIS-FWD

Epidemic Intelligence Information System for Food- and Waterborne diseases

EU European union

EWRS Early Warning and Response System

FAO Food and Agriculture Organization

FBO Food Business Operator

GbuABC Betaine porter II

GTR General Time-Reversible

GWAS Genome Wide Association Studies

ILSI Life Science Institute

InDels Insertions or Deletions

LD Linkage Disequilibrium

LD50 Median Lethal Doses

LLO Cytolysin Listeriolysin

LMM Linear Mixed Model

LOC Lab-On-a-Chip

Mbp Mega base pairs

MGE Mobile Genetic Elements


II

ML Maximum Likelihood

MLST Multi Locus Sequence Typing

MPD maximum population density

MPN Most Probable Number

MVLST Multi Virulence Locus Sequence Typing

NCBI National Center for Biotechnology Information

OppA Oligopeptide binding protein

PFGE Pulsed-Field Gel Electrophoresis

PMSC Premature Stop Codon

QAC Quaternary Ammonium Compound

QMRA Quantitative Microbial Risk Assessment

qPCR Quantitative Polymerase Chain Reaction

RASFF Rapid Alert System for Food and Feed

rMLST Ribosomial Multi Locus Sequence Typing

RNA Ribonucleic Acid

RNAP RNA polymerase

RTE Ready-To-Eat

SNP Single Nucleotide Polymorphism

ST Sequence Type

Tmin Minimal Growth Temperature

TSA Tryptone Soya Agar

TSB-YE Tryptone Soya Broth with Yeast Extract

wgMLST Whole Genome Multi Locus Sequence Typing

WGS Whole Genome Sequencing

WHO World Health Organization

µmax Maximal Growth Rate

List of figures

III

List of figures

Figure 1 : Schema of typing methods with different discriminatory power. ............................. 4

Figure 2 : L. monocytogenes lineages and clonal complexes ................................................ 5

Figure 3 : The European Union summary report on trends and sources of zoonoses, zoonotic

agents and food‐borne outbreaks in 2017 ............................................................................. 7

Figure 4 : Trend in reported confirmed human cases of listeriosis in the EU/EEA .................. 8

Figure 5 : Flow diagram for communication following of a cross-border outbreak in EU .......10

Figure 6 : Schematic outline of the cold stress adaptation process in L. monocytogenes .....14

Figure 7 : Role of the Csp proteins in adaptation to low temperature. ...................................15

Figure 8 : The risk analysis process and the positioning of QMRA modelling process ..........31

Figure 9 : Factors and parameters that should be considered in QMRA. ..............................33

Figure 10 : Prediction of Salmonella enterica ser. Typhimurium ...........................................36

Figure 11 : Growth simulation of stressed L. monocytogenes cells in cold-smoked salmon. .37

Figure 12 : The comfort zone. ...............................................................................................37

Figure 13 : Comparison of the stochastic growth of two microbial populations......................39

Figure 14 : Overview of QMRA and the different objectives and chapters of the thesis. .......43

Figure 15 : Phylogenetic tree of 51 L. monocytogenes isolates. ...........................................61

Figure 16 : Manhattan plot of core genome SNPs. ...............................................................62

Figure 17 : Evolution of a discrete phenotype distribution. ....................................................68

Figure 18 : Evolution of a discrete phenotype distribution of inlA gene. ................................69

Figure 19 : Model for assessing exposure of L. monocytogenes by consumption of cold-

smoked salmon ....................................................................................................................81

Figure 20 : Dendrogram based on log10 r-values of 26 different L. monocytogenes strains .86

Figure 21 : Dendrogram created by clinical frequencies. ......................................................87

Figure 22 : Dendrogram based on 166 different strains with corresponding Tmin values .....88

Figure 23 : Schema of a prepared slide with agar layer and coverslip. ............................... 103

Figure 24 : Different process steps of image analysis. ........................................................ 105

Figure 25 : Schema of the principle to determine single-cell lag time. ................................. 107

Figure 26 : Images obtained with phase-contrast microscope. ........................................... 109

Figure 27 : Relationship between the number of cells per colony and the number of pixels for

strain O228.. ....................................................................................................................... 110

Figure 28 : Cell growth probability at 4 °C of the four L. monocytogenes strains tested

according to the different repetitions. .................................................................................. 111

Figure 29 : Uncertainty distribution of individual probability of the four strains of

L. monocytogenes at 4°C. ................................................................................................. 112

List of figures

IV

Figure 30 : Median of the individual cell lag times of the four strains tested according to the

different repetitions of the experiment. ................................................................................ 113

Figure 31 : A) Random individual lag times drawn from fitted lognormal distributions for the

four strains. B) Pairwise comparisons of deciles. ................................................................ 114

Figure 32 : Schema to reduce variants in associations’ studies. ......................................... 123

List of tables

V

List of tables

Table 1 : Species of the Listeria genus. ................................................................................. 1

Table 2 : Summary of L. monocytogenes lineages adapted from (Orsi et al., 2011). ............. 3

Table 3 : MLST schema for L. monocytogenes...................................................................... 6

Table 4 : Summary of the first Genome Wide Association Studies adapted from (Chen and

Shapiro, 2015) ......................................................................................................................28

Table 5 : Bioinformatics tools for GWAS. ..............................................................................30

Table 6 : Methods to monitor the evolution of bacterial density. ............................................35

Table 7 : Extract of SNPs with strong evidences (p-value<0.01) for genome-wide association

to the tested phenotypic trait.................................................................................................63

Table 8 : Distributions of parameters used to simulate the growth of L. monocytogenes in cold-

smoked salmon and to estimate the corresponding listeriosis risk. .......................................85

Table 9 : Relative frequencies of the subgroups of virulence and growth ability at low

temperature observed in L. monocytogenes strains present in cold-smoked salmon. ...........89

Table 10 : Predicted cases of listeriosis of the appropriate virulence and Tmin groups. ..........90

Table 11 : Metadata of L. monocytogenes stains ................................................................ 102

Table 12 : Population growth parameters of the four tested strains with their 95th confidence

interval [0.025 0.0975]. ....................................................................................................... 108

Introduction

VI

Introduction

The role of the cold chain in the food sector is essential for mitigating the risk of

bacterial pathogens’ growth, which can reach such a level in specific food matrices to

cause human illnesses, threatening the public health safety. Among these bacterial

pathogens, those able to grow at cold temperatures are of greater concern due to the

difficulties of hampering their development through the cold chain (Wei et al., 2019).

Especially, Listeria monocytogenes (L. monocytogenes) is a challenging pathogen due

to its low minimal growth temperature allowing its growth at cold temperatures (Coroller

et al., 2012). L. monocytogenes is a ubiquitous foodborne pathogen causative of a

severe human disease called listeriosis. In the last decade, this pathogen has been

used as a model in a large number of studies aiming to address the impact of strain

diversity (Aryani et al., 2015) and the role of L. monocytogenes population

heterogeneity in adaptive stress response and survival capacity (Abee et al., 2016).

Several studies focusing on predictive microbiology have shown that lag time and the

probability of growth of L. monocytogenes are much more uncertain and difficult to

predict compared to the growth rate (Aguirre and Koutsoumanis, 2016; Augustin and

Czarnecka-Kwasiborski, 2012). However, the validity of a mathematical model in

predicting the conditions that lead to critical levels in foods highly depends on its ability

to describe the effect of the environmental conditions on lag time and the growth

probability. This implies that a better understanding of the pathogen’s behavior near

growth limits is of great importance for the effective application of microbial predictive

models in food safety risk assessment.

Distribution of individual cell lag phases and growth probabilities are generally obtained

in laboratory culture media. The classical method for individual cell lag times

acquisition is based on turbidity measurement on microplate wells inoculated with

approximately one bacterial cell per well (Guillier and Augustin, 2006). The growth

probability can be determined either by visually monitoring 96-microwell plates and

deduced from concentrations estimated with the most probable number (MPN)

calculation (Augustin and Czarnecka-Kwasiborski, 2012) or by comparing the ratio of

colony forming units (CFU) formed on agar plates incubated in the studied conditions

to CFU obtained in the optimal conditions (Aguirre and Koutsoumanis, 2016). Yet,

these methods are labor-intensive and would benefit from higher throughput

approaches.

Introduction

VII

Besides the classical molecular methods to obtain microbial data for predictive models,

nowadays the progress in whole genome sequencing (WGS) opens new possibilities

to characterize intra-species variability of L. monocytogenes strains. High-throughput

WGS data allow the applications of advanced statistical approaches through Genome

Wide Association Studies (GWAS) aiming at investigating differences in the genomic

elements linked to a specific phenotype (Chen and Shapiro, 2015). These elements

constitute important biomarkers that could be used to describe the behavior of

L. monocytogenes subpopulations improving the performance of Quantitative

Microbial Risk Assessment (QMRA) (Franz et al., 2016).

Theoretical background

1


1 Listeria monocytogenes

1.1 Overview

Listeria monocytogenes (Phylum: Firmicutes, Class: Bacilli, Order: Bacilliales, Family:

Listeriaceae) is a Gram-positive, rod-shaped, non-spore forming, facultative anaerobic

bacterium with a size of 2 µm x 0.5 µm (ANSES, 2011; Carpentier and Cerf, 2011). It

is part of the Listeria genus, which is currently subdivided into 18 species (Table 1).

Table 1 : Species of the Listeria genus.

Species Publication

L. monocytogenes (Pirie, 1940)

L. welshimeri (Rocourt and Grimont, 1983)

L. seeligeri

L. ivanovii (Seelinger et al., 1984)

L. innocua (Seelinger et al., 1984)

L. marthii (Graves et al., 2010)

L. grayi (Larsen and Seeliger, 1966)

L. rocourtiae (Leclercq et al., 2010)

L. fleischmannii (Bertsch et al., 2013)

L. weihenstephanensis (Halter et al., 2013)

L. floridensis

(den Bakker et al., 2014)

L. aquatica

L. cornellensis

L. riparia

L. grandensis

L. booriae (Weller et al., 2015)

L. newyorkensis

L. costaricensis (Núñez-Montero et al., 2018)

So far, only two species are considered as foodborne pathogens: L. monocytogenes

and L. ivanovii (Núñez-Montero et al., 2018; Orsi and Wiedmann, 2016). L. ivanovii

mainly affects animals and only a few numbers of cases have been linked to human

diseases (Cummins et al., 1994; Guillet et al., 2010; Snapir et al., 2006). In contrast,

L. monocytogenes largely affects both animals and humans. L. monocytogenes,

formerly called Bacterium monocytogenes, was first isolated in 1924 and described in

1926. It was isolated from the livers of laboratory rabbits with bacterial sepsis in


2

Cambridge (Murray et al., 1926). The disease caused by L. monocytogenes is called

listeriosis. It is a zoonosis, which can result in bacteremia, meningitis, septicemia,

miscarriage, and stillbirths. It is particularly dangerous for susceptible persons

including pregnant women, foetuses, elderly and immunosuppressed individuals

(Charlier et al., 2017; Donnelly, 2001; Sheehan et al., 1994). Due to its capacity to

survive in various environments and grow under a wide range of temperatures (-2 –

45C), pH (4.0 - 9.6) and aw (0.92- /), L. monocytogenes can be found in several

ecological niches (e.g. soils, water, and food processing plants) (ANSES, 2011). Of

particular concern is the ability of L. monocytogenes to survive in the harsh conditions

encountered in food processing plants since it increases the risk of spreading along

the food chain. The contamination of the natural environment results mainly from the

release of the bacteria via excreta of animals, whether healthy or sick carriers (ANSES,

2011).

1.2 Genomics of Listeria monocytogenes

The complete genome of the L. monocytogenes reference strain EGDe has been firstly

published in 2001 by Glaser et al. (Glaser et al., 2001). The chromosome of

L. monocytogenes is about 2,7-3 Mbp with a low G+C content of 37-38 % (Buchrieser

et al., 2003; Wiedmann and Zhang, 2011). The genome composition can be divided

between the “core” genome and “accessory” genome. The core corresponds to the

genome sequence shared by all of the analyzed strains, while the accessory genome

corresponds to genes present only in some strains of the observed strains. In a

pangenomic study performed on a panel of 207 strains of L. monocytogenes, Henri et

al. observed a core genome consisting of 2017 genes and an accessory genome of

3101 genes (Henri et al., 2017). The accessory genome is often characterized by

different mobile genetic elements (MGE) such as prophages, plasmids, mobile islands

and transposons (Kuenne et al., 2013). The presence of MGEs in L. monocytogenes

strains is highly variable. For instance, the plasmid detection rate can range from

absence (non-detection) up to 79 % based on different studies (Kolstad et al., 1992;

Lebrun et al., 1992; McLauchlin et al., 1997; Perez-Diaz et al., 1982; Peterkin et al.,

1992).

Since the L. monocytogenes EGDe complete genome was published, the databank of

the National Center for Biotechnology Information (NCBI) is counting today (October

2019) 23210 genome assemblies of L. monocytogenes isolates collected worldwide.


3

1.2.1 Subgrouping of Listeria monocytogenes

L. monocytogenes population has been classified into four different evolutionary

lineages (from I to IV). Lineage I and II have been earlier described in 1989 by Piffaretti

et al. (Piffaretti et al., 1989). Two further lineages were later identified as lineage III,

first published in 1995 (Rasmussen et al., 1995), and lineage IV, first mentioned as IIIB

(Roberts et al., 2006) and then reported as lineage IV two years later (Ward et al.,

2008). The majority of the L. monocytogenes isolates responsible for more than 90 %

of human listeriosis cases belong to lineage I and II (McLauchlin et al., 2004). It is

necessary to discriminate with a high resolution between closely related strains likely

involved in human diseases from those that are unrelated in order to enhance outbreak

investigations, epidemiological surveillance and source attribution studies.

Different techniques have been applied to detect and characterized L. monocytogenes

strains. The first L. monocytogenes subtyping was performed with the serotyping

method, based on the somatic and flagellar antigens (Paterson, 1940; Seeliger and

Höhne, 1979). However, this approach is expensive and time-consuming and only

allows differentiating 13 serotypes (Table 2).

Table 2 : Summary of L. monocytogenes lineages adapted from (Orsi et al., 2011).

Lineage Serotypes Genetic characteristics

Distribution Reference

I 1/2b, 3b, 3c, 4b

Lowest diversity among the lineages; lowest levels of recombination among lineages

Commonly isolated from various sources; overrepresented among human-related isolates

(Piffaretti et al., 1989)

II 1/2a, 1/2c, 3a

Most diverse, highest recombination levels

Commonly isolated from various sources; overrepresented among food and food-related as well as natural environments

(Piffaretti et al., 1989)

III 4a, 4b, 4c Very diverse; recombination levels between those for lineage I and II

Most isolates obtained from ruminants

(Rasmussen et al.,

1995)

IV 4a, 4b, 4c Few isolates analyzed Most isolates obtained from ruminants

(Roberts et al., 2006;

Ward et al., 2008)

It is important to notice, that four serotypes (i.e. 1/2a, 1/2b, 1/2c and 4b) are causing

nearly all reported human listeriosis cases. The phenotypic variability observed within


4

each serotype required the development of subtyping methods with higher

discriminatory power such as the molecular typing techniques (Figure 1).

Before the explosion of WGS technologies, the pulsed-field gel electrophoresis (PFGE)

has been the gold standard method for microbial subtyping of foodborne pathogens

(especially L. monocytogenes and Salmonella enterica) during epidemiological

studies, surveillance and outbreak control (Brosch et al., 1994; Graves and

Swaminathan, 2001; Kerouanton et al., 1998). This method is based on the random

fragmentation of DNA using two restriction enzymes for L. monocytogenes (i.e.

ApaI/AscII). However, the protocol is laborious and difficult to harmonize between

laboratories limiting the inter-laboratory comparisons of the obtained PFGE-profiles.

An additional disadvantage of PFGE is the limited information on the genetic

relationships provided by the distance-based phylogeny inferred on PFGE band

profiles.

A further developed typing method relying on the allelic profiling of seven house-

keeping loci is the multi-locus sequence typing (MLST). It is based on the sequence

analysis of the housekeeping genes: acbZ (ABC transporter), bglA (beta-glucosidase),

cat (catalase), dapE (Succinyl diaminopimelate desuccinylase), dat (D-amino acid

aminotransferase), ldh (lactate dehydrogenase) and lhkA (histidine kinase). This MLST

scheme was first proposed by Salcedo et al. (2003) and later adapted by Ragon and

colleagues to conduct a large study with 360 L. monocytogenes isolates (Ragon et al.,

2008; Salcedo et al., 2003). Compared to serotypes and PFGE profiles, the (ML) STs

allelic variation allowed grouping of L. monocytogenes isolates into clonal

complexes (CCs). The CCs are defined as groups of STs sharing six out of seven

Figure 1 : Schema of typing methods with different discriminatory power. Adapted from (Rossen et al.,

2018).


5

alleles with at least one other profile of the group (Feil et al., 2004). Consequently,

singletons were defined as STs having at least two allelic mismatches with all other

STs (Ragon et al., 2008). In this study, 126 different STs were observed and allocated

into 23 CCs, where most of the isolates were associated with a small number of major

clones (Figure 2).

Figure 2 : L. monocytogenes lineages and clonal complexes (a) Core genome SNP maximum-likelihood phylogeny of L. monocytogenes genome sequences with the clades annotated by 7 loci MLST CC. (b) Minimum spanning tree of the isolates included as described by 7 locus MLST. Each circle represents a single ST that is numbered on the tree (Painset et al., 2019).

MLST has been used as the gold standard typing method for long-term epidemiological

surveillance of L. monocytogenes, however, for outbreak investigations, PFGE often

showed a better discriminatory power (Carrico et al., 2013). Nowadays, WGS

technology is rapidly revolutionizing the way the investigation of bacterial pathogens is

performed along the food chain. The sequencing of the complete bacterial genome

allows to identify different types of genomic variants including Single Nucleotide

Polymorphism (SNPs) and differentiate strains based on their genotypes (i.e. SNPs

differences in number and position across the genome dataset). This WGS-based


6

subtyping is particularly useful for discriminating strains from bacterial species which

tend to maintain a homogeneous genetic background (e.g. clonal reproduction). Other

WGS-based subtyping approaches with high discriminatory power consist in the

application of gene-by-gene profiling (Table 3) at the core genome (core genome

MLST: cgMLST) and whole genome (whole genome MLST: wgMLST) level. These

approaches are an extension of the 7-loci MLST. For instance, the cgMLST schema

available for L. monocytogenes is based on the comparison of several thousand of

genes. The increasing availability of public genomic sequences as well as of WGS-

based subtyping tools allows today to perform comprehensive comparative analysis

(e.g. phylogenomic reconstruction) of food-borne pathogens for surveillance and

outbreak investigations with a discriminatory power never reached before.

Table 3 : MLST schema for L. monocytogenes.

MLST Type Number of loci Reference

MVLST* 6 (Zhang et al., 2004)

MLST 7 (Ragon et al., 2008)

rMLST** 53 (Jolley et al., 2012)

cgMLST 1701 (Ruppitsch et al., 2015)

cgMLST 1748 (Moura et al., 2017)

cgMLST 1827 (Chen et al., 2016)

wgMLST 4797 defined by Applied Maths, Sint-Martens-Latem, Belgium

*Multi Virulence Locus Sequence Typing **Ribosomial Multi Locus Sequence Typing


7

1.3 Monitoring of Listeria monocytogenes in the European Union

In 2017, 2480 cases of human listeriosis were reported in the European Union (EU)

(Figure 3). Compared to the high number of campylobacteriosis or salmonellosis

cases, the relatively low number of listeriosis might appear less relevant.

However, the severe symptoms along with high hospitalization and mortality rates (up

to 30 %) of listeriosis is still a great concern for the public health (Buchanan et al.,

2004). In industrialized countries, L. monocytogenes is among the most frequent

causes of death from food-borne related infections (EFSA and ECDC, 2018). Although

the effort to reduce the risk of listeriosis in Europe, an increasing trend of human cases

has been observed since 2008 (Figure 4) (EFSA and ECDC, 2018).

Figure 3 : The European Union summary report on trends and sources of zoonoses, zoonotic agents and food‐borne outbreaks in 2017 (EFSA and ECDC, 2018)


8

Figure 4 : Trend in reported confirmed human cases of listeriosis in the EU/EEA , by month, 2008-2017 (EFSA and ECDC, 2018).

1.4 Food sources and outbreaks

Listeriosis is a foodborne disease associated with a wide range of food (e.g. raw meat,

seafood, and vegetables) and processed (e.g. sausage, dairy, and ready-to-eat food)

products. In particular, ready-to-eat (RTE) products (e.g. meat and fish tartars, smoked

fish, deli meats, composite salads and raw vegetables) present a high microbiological

risk (Okada et al., 2013; Rocourt et al., 2003; Tompkin, 2002) since directly consumed

without any cooking or other processing after a period of refrigeration. Thanks to

improved detection methods along the food chain, more outbreaks with fewer cases

have been linked to foods previously not considered as a potential risk of

L. monocytogenes contamination as caramel apples (Angelo et al., 2017; Buchanan,

Gorris, et al., 2017). Because of the ability of L. monocytogenes to adapt to different

unfavourable conditions (e.g. acid and oxidative stress), to grow at low temperatures

(e.g. 2 ºC) and to form biofilms, cells can persist in food processing plants and retail

markets over time (Overney et al., 2017). Thus, food contamination can occur at any

stage of the food chain (ANSES, 2011; EFSA BIOHAZ Panel, 2018).

Foodborne diseases can be sporadic or epidemic. A sporadic case is an isolated case

with no epidemiological link to other cases of the same disease. An epidemic is an

excess of illnesses cases, compared to what is usually observed, in a delimited area

and time frame. An epidemic of foodborne disease can be expressed in two forms:


9

- outbreaks linked to a common "circumscribed" source (e.g. among people from

the same family or those sharing the same collective catering);

- diffuse epidemics due to widely distributed and affecting products (e.g. mostly,

people with no apparent connection to each other).

In 2017, ten foodborne outbreaks caused by L. monocytogenes were reported in the

EU. Four of them were classified as strong-evidence outbreaks in Austria (2), Denmark

and Sweden. The food vehicle in these cases were cheese, fish products, meat

products, vegetables and juices and other products thereof (EFSA and ECDC, 2018).

These ten outbreaks were responsible for 39 human cases (illnesses) over 2480

confirmed cases of listeriosis in the EU. These data emphasize that most of the

listeriosis cases reported in the EU are sporadic (EFSA and ECDC, 2018). The largest

worldwide known outbreak of listeriosis was recently reported in South Africa, with a

total of 1060 cases reported in 18 months (from January 2017 to July 2018). Here, the

origin of the outbreak was RTE processed meat products (polony) (Smith et al., 2019).

1.5 Surveillance

For public health safety, the early detection of contamination events in the production

systems is essential to prevent foodborne pathogen outbreaks and to rapidly find the

source of contamination for avoiding the distribution of contaminated food. Multiple

factors like the market globalization, the associated geographical dispersion and the

long period of disease incubation, hinder the control and detection of linked listeriosis

cases as well as their association with the contaminated food products. Thus, the EU

has implemented three different platforms to promptly act and exchange information

between the member states in order to mitigate food-borne diseases (Figure 5).


10

Figure 5 : Flow diagram for communication following of a cross-border outbreak in EU adapted from (Yeni et al., 2017).

The cross-border communication platforms developed in the EU include:

- The Rapid Alert System for Food and Feed (RASFF), a platform to share

information between the member states related to hazards found in food and

feed. With this system, it has been possible to trace back contaminated batches

(Leuschner et al., 2013).

- The Epidemic Intelligence Information System for Food- and Waterborne

diseases (EPIS-FWD), for fast information exchange on an unusually high

increase of human cases and emerging outbreaks detected on the national level

(Gossner, 2016).

- The Early Warning and Response System (EWRS), aiming to link public health

authorities of EU member states and the European Commission to find a rapid

response in case of human listeriosis (Guglielmetti et al., 2006).

Molecular typing data from clinical strains are uploaded into the European Centre for

Disease Prevention and Control (ECDC) database TESSy by the EU member states

since 2012. Food, feed, animal and environmental isolates of L. monocytogenes are

submitted to the European Food Safety Authority (EFSA). Joint analysis between

ECDC and EFSA are requested by the European Commission. For this purpose, a joint

database hosted by the ECDC has been established. This tool increases the

possibilities of surveillance and monitoring of foodborne pathogens by linking clinical


11

isolates with non-human isolates, reducing the response time (Rizzi et al., 2017). A

recently solved L. monocytogenes outbreak (March 2018) well shows how important

the networks between EU member states and the functionality of the implemented

systems are. The EU recorded 32 listeriosis cases and six death due to the infection.

Gathering information from the member states through the communication platforms,

this outbreak was confirmed to be multi-country (Austria, Denmark, Finland, Sweden,

and the UK) and already ongoing since 2015. The outbreak was finally associated with

frozen corn (Authority et al., 2018).

1.5 Regulations

The EU has established microbiological food safety criteria for L. monocytogenes in

RTE foods in Regulation (EC) No 2073/20053 (15/11/2005) on microbiological criteria

for foodstuffs. Annex I of the Regulation specifies the food category, sampling plan,

microbiological limits, analytical methods and stage where the criterion applies.

Specific food safety criteria for L. monocytogenes in RTE foods are described below

(category 1.1 to 1.3 of Annex I of this Regulation).

For RTE foods, other than those intended for infants and for medical purposes

(category 1.1), which are able to support the growth of Listeria monocytogenes

(category 1.2), two microbiological criteria are laid down: either a qualitative criterion,

i.e. absence in 25 g before the food has left the immediate control of the food business

operator (FBO) who has produced it, or a quantitative criterion, i.e. 100 CFU/g for

products placed on the market during their shelf-life. This quantitative criterion applies

if the FBO is able to demonstrate, to the satisfaction of the competent authority, that

its product will not exceed the limit of 100 CFU/g throughout the shelf-life. For this

purpose and according to Article 3.2, the FBO shall conduct studies that are mentioned

in Annex II of this Regulation: predictive microbiology, challenge tests or durability

studies. For products that do not support the growth of L. monocytogenes

(Category1.3), only the quantitative criteria are applied.

Good manufacturing practices, appropriate hygiene and control of the storage

temperature of products are necessary to limit the product contamination and/or the

potential growth of L. monocytogenes in food in order to maintain levels below

100 CFU/g product (EFSA, 2013). In this perspective, the temperature control of high-

risk foods is an essential element from farm-to-fork (Duret, Hoang, et al., 2014).


12

2 Cold adaptation

2.1 Importance of cold growth/adaptation

A specific characteristic of L. monocytogenes is its ability to grow at refrigeration

temperature. Before selective media was used to isolate L. monocytogenes strains, its

capacity to grow at low temperatures gave the advantage of isolating strains by cold

enrichment (Donnelly and Nyachuba, 2007). Gray and colleagues discovered that

L. monocytogenes is able to grow at refrigeration temperature back in 1948. They

observed that strains isolated from bovine brain suspensions had the capacity to grow

at 4 °C after they had been stored for three days up to three months (Gray and

McWade, 1954). Later, it has been also shown that L. monocytogenes is able to grow

at temperatures as low as -2 °C (Bajard et al., 1996; Junttila et al., 1988).

As mentioned before, RTE products are high-risk vehicles of L. monocytogenes

infection (Rodrigues et al., 2017). Even if the initial contamination is low at the end of

the food processing process, there are some critical points along the distribution that

highly increase the risk of L. monocytogenes growing (e.g. non-compliance of the cold

chain). The storage at low temperature is often the only preservation of RTE, due to

an often-limited bacterial inhibition by other environmental factors (i.e. low pH, low aw,

preservatives, etc). The physico-chemical characteristics of many refrigerated RTE

allow L. monocytogenes growth, as also the lack of cold-tolerant competitive microflora

(Cacace et al., 2010; Chan and Wiedmann, 2008). In the case of refrigerated RTE

products, the shelf-life is a critical issue to avoid that L. monocytogenes cells multiply

to a sufficient number for causing listeriosis (Duret, Guillier, et al., 2014). For instance

in France, the shelf-life of rillettes and jellied pork tongue has been reduced from 48 to

28 days after the notification of several outbreaks (De Valk et al., 2001).

An important factor in the assessment of cold adaptation ability of L. monocytogenes

is the lag time. It is inversely proportional to the maximum growth rate. Below four

degrees Celsius, the lag times are highly increased as the growth rate is much slower.

However, slight changes in storage temperature can enhance the growth of

L. monocytogenes in RTE foods and consequently increase the risk of listeriosis (Lu

et al., 2005). In their work, Lu and colleagues (2005) determined the lag time and

growth parameters of L. monocytogenes at 4.4 °C and at 10 °C in frankfurters

products. Increasing the temperature, the lag time was reduced from18 to 6.5 days,


13

whereby the doubling time was four times lower at 10 °C (Lu et al., 2005). In addition,

Okada et al. (2013) showed that the exposition to L. monocytogenes increased when

the RTE food (i.e. cooked sardines) was stored at the wrong temperature (i.e. 10 °C

rather than 4 °C), with cell concentrations up to 3.5 log10 (CFU/g) higher after 7 days

of storage (Okada et al., 2013).

All these observations emphasize the importance to understand the mechanisms of

cold adaptation and the behavior of L. monocytogenes at refrigeration temperature for

enhancing food safety.

2.2 Mechanisms

To adapt to cold conditions, the bacteria have to overcome different obstacles.

According to scientific literature, different temperatures and mechanisms are

associated with cold adaptation (see below). First, the nutrient uptake is reduced due

to decreased membrane fluidity. Additionally, the superhelical coiling of the DNA is

increased, which represents an issue to replicate or transcribe DNA. The translation

can be also affected due to modifications in the secondary structures of the RNA.

Generally, biochemical mechanisms like protein folding or enzymatic reactions

become slow or inefficient at low temperatures. To permit an efficient functioning of the

bacteria, the ribosomes need to adapt to the cold environment (Graumann and

Marahiel, 1999). L. monocytogenes implemented different response mechanisms to

overcome these problems and thus grow at low temperatures (Figure 6). Some of

these response mechanisms are nowadays well studied, although there are still gaps

in knowledge.


14

Figure 6 : Schematic outline of the cold stress adaptation process in L. monocytogenes adapted from (Tasara and Stephan, 2006).

In the following part, the known key response strategies of L. monocytogenes,

including maintenance of the cellular membrane fluidity, the stabilization of ribosome

and nucleic acid structures, the uptake/synthesis of cryoprotective osmolytes and

peptids as well as the production of cold shock proteins (Csps), are described.

Cold shock proteins

Cold shock proteins (Csps) are small and highly conserved polymers, which play a

major role in the cold response (Ermolenko and Makhatadze, 2002). Their expression

is enhanced when temperature decreases (Hébraud and Potier, 1999). The genome

of L. monocytogenes harbors three genes coding for cold shock proteins: cspA, cspB,

and cspD (Schmid et al., 2009). Csps have different functions, among which facilitating

the uptake of compatible solutes (see below) and oligopeptide permease (see below)

as well as acting as RNA chaperones (Beumer et al., 1994; Borezee et al., 2000; Chan

and Wiedmann, 2008). At low temperatures, the RNA structures become more stable,

hurdling an efficient transcription and translation. Thus, the Csps destabilize secondary

structures by melting the double stranded-ribonucleic acid (dsRNA) to further stabilize

the single-stranded state of the target RNA (Figure 7a) (Barria et al., 2013; Ermolenko


15

and Makhatadze, 2002; Schmid et al., 2009). In RNA metabolism, the activity of Csps

can avoid redundant effects such as the affected work of the nucleases, the hindered

formation of the translation initiation complex or the transcription concerned by

premature terminations of RNA polymerase (RNAP) (Figure 7) (Barria et al., 2013).

Figure 7 : Role of the Csp proteins in adaptation to low temperature. (a) Csps (spheres) can melt double-stranded RNA, and stabilize single-stranded RNA conformation. (b) New secondary structures can impair RNA metabolism (Barria et al., 2013).

Schmid and co-workers found out that CspA, CspB, and CspD are not necessary for

the growth of L. monocytogenes at 37 °C. They were able to classify these three

proteins by importance for cold growth (4 °C). The mutant without cspA gene was not

able to grow at 4 °C, the mutant without cspD showed reduced growth while the cspB

mutant showed no significant difference compared to the wildtype (Schmid et al.,

2009). Arguedas-Villa and colleagues also showed the importance of cspA gene for

the growth of L. monocytogenes in cold conditions. They separated 20 strains in two

different groups according to their lag time at 4 °C. The first group with lag times

between 7-30 h showed higher transcriptional activation of cspA than the second group

with lag times of 257-420 h (Arguedas-Villa et al., 2010).

Membrane fluidity

In order to maintain the membrane in the optimal liquid (liquid-crystal) state in cold

conditions, some modifications of the fatty acid composition are necessary for the

transport of nutrients. Three major fatty acids constitute the membrane of

L. monocytogenes: anteiso C15:0, anteiso C17:0 and iso C15:0 (Annous et al., 1997).


16

These three types represent about 95 % of all membrane fatty acids (Annous et al.,

1997). In response to cold conditions, the ratio of the fatty acids is changing in the

membrane. The content of anteiso C15:0 increases whereas the amount of anteiso

C17:0 and iso C15:0 decreases (Annous et al., 1997; Jones et al., 1997; Juneja et al.,

1998; Püttmann et al., 1993). On one hand, there is a reduction of the fatty acid chain,

and on the other, a change from iso to anteiso C15:0 can be noticed. The shorter length

of the chain leads to a lower melting point, which increases the membrane fluidity. The

transformation from iso to anteiso disrupts the close packing of the chains due to the

changed type of branching at the methyl end and thus enhances the membrane fluidity.

ATP-Binding Cassette (ABC) Transporters and uptake of compounds

Different ABC transporters permit the uptake of specific substrates into

L. monocytogenes cells. These substrates are compatible solutes or osmolytes. They

are soluble, low-molecular-weight organic compounds that have no negative effect on

the normal function of the bacteria. Studies showed that a higher concentration of the

cryoprotective osmolytes, as glycine betaine and carnitine, characterized

L. monocytogenes cells during cold exposure (Angelidis and Smith, 2003;

Wemekamp-Kamphuis et al., 2004). Sleator and colleagues (2003) demonstrated that

L. monocytogenes is not able to synthesize these main cryoprotective osmolytes

(Sleator et al., 2003). Glycine betaine is mainly found in plants, while carnitine is

commonly from meat or dairy origin (Mitchell, 1978; Zeisel et al., 2003). Different

studies showed that glycine betaine and carnitine addition in growth media increases

the growth of L. monocytogenes in cold conditions (Bayles and Wilkinson, 2000;

Beumer et al., 1994; Ko et al., 1994; Smith, 1996). Even though L. monocytogenes

cannot synthesize these solutes, its genome codes for different betaine transporters

and transporters that permit the uptake of carnitine (Angelidis and Smith, 2003;

Gerhardt et al., 1996; Gerhardt et al., 2000). During cold growth, the uptake of

cryoprotective osmolytes from the environment into the cell through the upregulated

ABC transporter systems is increased (Angelidis and Smith, 2003; Beumer et al., 1994;

Gerhardt et al., 2000; Wemekamp-Kamphuis et al., 2004). The main transporters for

glycine betaine uptake are betaine porter I (BetL) and betaine porter II (GbuABC), while

for carnitine the major system is OpuC (Fraser and O'Byrne, 2002; Ko and Smith, 1999;

Sleator et al., 1999; Sleator et al., 2000; Verheul et al., 1995). During cold stress the

transporter system GbuABC plays the main role for glycine betaine uptake.


17

Wemekamp-Kamphuis and colleagues showed that the key role between the different

transporter systems can change depending on the accessible solutes in the media

(Wemekamp-Kamphuis et al., 2004). Similar results were also presented by Sleator at

al. (2003), who showed that the importance of each transporter was associated with

the environment (Sleator et al., 2003). Miladi et al. (2017) also observed a higher

expression of opuCA and betL (encoding for carnitine and betaine transporters,

respectively) after cold exposure of L. monocytogenes (Miladi et al., 2017). The

accumulated cryoprotective osmolytes have different functions, which have not been

fully discovered, yet. On one hand, they sustain the turgor and prevent the dehydration

of the cells, but one the other, they contribute to increasing growth in cold conditions

probably through the stabilization of folded proteins and hydrogen bonds due to a

higher volume of cytoplasmic water (Arakawa and Timasheff, 1985; Record Jr et al.,

1998).

Besides the uptake of cryoprotective osmolytes, the accumulation of short peptides

from the environment affects L. monocytogenes’ growth under cold stress positively.

Borzee and colleagues (2000) found an oligopermease operon harboring five genes,

including oppA, oppB, oppC, oppD and oppF. By creating an oppA deletion mutant,

they studied the role of this gene, which encodes a 62 kDa oligopeptide binding protein

(OppA). They discovered that OppA mediates the transport of oligopeptides and is

essential for the growth of LO28 in cold conditions (5 °C). The transcription rate was

higher at 5 °C, and curiously, the transcripts at low temperature were 1.2 kb shorter

than those at 37 °C (Borezee et al., 2000). A reason for this observation could be the

activation of a second promoter. Similarly, Durack and co-workers noted a significant

up-regulation of oppA in all strains adapted to cold stress (Durack et al., 2013). The

role of the short peptides in L. monocytogenes’ cold adaption is not completely

explored. One theory is that they are implicated in transduction pathways enhancing

other mechanisms for a better adaptation and growth in cold environments. Another

theory suggests that they were used as substrate donors, like amino acids or peptide

derivate (Maria-Rosario et al., 1995).

DEAD-box RNA helicase

DEAD-box proteins are RNA helicases, whose best-known functions are the

separation of short duplex RNA regions on one side and a chaperone activity on the


18

other (Jarmoskaite and Russell, 2011). Based on that functionality, these proteins play

an important role in a cold environment. The genome of the reference strain EGD-e

harbors four predicted DEAD-box RNA helicase genes, including lmo0866, lmo1246,

lmo1450 and lmo1722 (Glaser et al., 2001). Chan et al. and Markkula et al. studied the

role of these genes in L. monocytogenes at low temperature (4 °C and 3 °C,

respectively) (Chan et al., 2007; Markkula et al., 2012). Chan and co-workers

demonstrated that the transcript levels of genes encoding three DEAD-box helicases

were enhanced in cold conditions compared to 37 °C. Similar results were published

by Markkula and colleagues that found higher transcript levels of all four DEAD-box

RNA helicase genes grown at cold temperatures than at 37 °C. Furthermore, they

noticed a higher minimum temperature of the gene deletion mutants compared to the

EGD-e wildtype strain. They also reported impaired motility of the mutants. Thus, they

demonstrated that these genes are required for cold tolerance and motility, although

the gene lmo1450 seems to have a universal role not only related to cold (Markkula et

al., 2012).

Transcriptional regulation

Cold response of cells can also occur at the transcription level through molecular

transduction networks. Different studies showed enhanced transcription of particular

genes necessary for cold adaption and growth (Arguedas-Villa et al., 2010; Cacace et

al., 2010; Kaan et al., 2002). The alternative sigma factors, for instance, are key

regulatory mechanisms of prokaryotes for a rapid adaptation to changing

environmental conditions. These bacterial transcription initiators enable specific

binding of RNA polymerase subunits to gene promoters under particular conditions

(Kazmierczak et al., 2003; Kazmierczak et al., 2005; Liu et al., 2017).

L. monocytogenes harbors five different sigma factors including; A, B, C, H, and L.

Whereas B, C, H, and L are alternative factors, A is a housekeeping sigma factor

(Chaturongakul et al., 2011; Liu et al., 2017). These four alternative sigma factors

regulate the transcription of genes that are affecting the virulence and the response to

various stresses of L. monocytogenes (Chaturongakul et al., 2011; Liu et al., 2017).

The largest and the most-studied regulon is the B. A recent study shows that

201 genes are preceded by B -dependent promotors (Liu et al., 2017). However,

several previous studies investigated the role of this sigma factor (Chaturongakul et


19

al., 2011; Oliver et al., 2010; Ribeiro et al., 2014; Toledo-Arana et al., 2009). These

studies highlight that B plays an important part, especially, in virulence and response

to different types of stress (i.e. acid, osmotic, arsenate, oxidative and cold). Moreover,

a study of Becker and colleagues showed that B is required for the transition to

stationary phase, but does not affect on log-phase cells (Becker et al., 2000; Mujahid

et al., 2013; O’Byrne and Karatzas, 2008). As mentioned before, 201 B -dependent

genes are currently known. Part of these genes code for proteins with putative roles in

cold growth of L. monocytogenes, such as the genes coding for compatible solute

transporters opuCA and gbu (Becker et al., 2000; Cetin et al., 2004; Kazmierczak et

al., 2003). Other genes appear to be involved in transcriptional regulation, metabolism

of energy, carbon, and nucleotides as also for other stresses (Liu et al., 2017).

2.3 Cold growth and virulence

The infection path normally starts with the ingestion of contaminated food. This

requires a rapid adaptation of L. monocytogenes in the new environment in order to

invade the host duodenum and then access the blood system. From here they can

expand and circulate to reach other tissues, e.g., the central nervous system or the

placenta (Allerberger and Wagner, 2010; Guldimann et al., 2017). To resist the

infection-related stresses, the cells need to modify their gene expression in order to

rapidly adapt to the changing environmental conditions (Guldimann et al., 2017). The

B activation of the transcription not only of some virulence genes, but also some genes

known to be implicated in cold adaption (as opuCA and gbu), might be a link between

cold growth and pathogenicity (Cetin et al., 2004; Guldimann et al., 2017; Kazmierczak

et al., 2003). Previous studies (Durst, 1975; Wood and Woodbine, 1979) reported that

L. monocytogenes strains were able to develop an increased virulence when grown at

4 °C and then exposed to mice and chick embryos, respectively. Likewise, more recent

studies showed possible associations regarding virulence and cold adaptation

(Arguedas-Villa et al., 2010; Cordero et al., 2016). For instance, Arguedas-Villa and

co-workers observed that the majority of strains able to grow at 4 °C in defined minimal

media, were also those linked to human listeriosis (Arguedas-Villa et al., 2010).

As mentioned before, B plays a role in both virulence and cold adaptation. Becker and

colleagues demonstrated that the absence of B impaired the adaptation of stationary

phase cells to grow at 8 °C, contrary to log-phase cells. This factor is needed for the


20

accumulation of betaine and carnitine (see above) (Becker et al., 2000). Regarding

pathogenicity, B is regulating several virulence genes, such as the inlAB operon. The

inlA gene is coding for a surface protein internalin, which is necessary for the invasion

of the epithelial cells (Kim et al., 2004; Kim et al., 2005). In addition, the expression

and activity of PrfA, a protein that plays an important role after the intestinal barrier as

a primary transcriptional regulator of virulence genes, is also supposed to be related

to B (reviewed by (NicAogáin and O’Byrne, 2016).

Other studies focus on the role of Csps in virulence of L. monocytogenes. Findings

from a recent study of Eshwar and co-workers suggest that Csps are involved in the

expression/regulation of genes related to host pathogenicity, cell aggregation and

flagella-based motility (Eshwar et al., 2017). They demonstrated an affected

expression of those genes in Csp deletion mutants. Furthermore, they suggest a Csp-

dependent transcriptional regulation of prfA, hly, mpl and plcA. It was also observed

that Csp deletion mutants showed reduced or absent production of the cell surface

protein ActA and cell surface flagella. However, in this study, the CspB plays a major

role concerning virulence. A similar conclusion was published by Schärer and

colleagues but focusing on cytolysin listeriolysin (LLO) production. LLO is a pore-

forming protein encoded by the previously mentioned gene hly and necessary for

intracellular survival. The CspB deletion mutant showed decreased levels of hly gene

transcripts, while the mutant with CspB presented no difference compared to the

wildtype (Schärer et al., 2013).

In conclusion of these studies, Csps seems to be involved in the modulation of

L. monocytogenes’ virulence. But to date, studies demonstrated only an association

between virulence and CspB. As mentioned before its role regarding cold stress is the

less important among the Csps (Eshwar et al., 2017; Schmid et al., 2009).

The flagellum-mediated motility of L. monocytogenes was reported to be relevant for

the tissue culture invasion and for raising the infectivity after bacterial ingestion in vivo

(O'Neil and Marquis, 2006). Furthermore, it seems to be an important ability for

L. monocytogenes strains to form biofilms that can later contaminate food in the

processing environments and ultimately reach the consumer with harmful

consequences (Lemon et al., 2007). Mattila and colleagues found that the expression

levels of the motility genes flhA and motA are higher at 3 °C than at 37 °C.


21

Furthermore, they demonstrated that the gene deletion mutants were non-motile at all

tested temperatures. Besides, the mutants showed lower growth rates in cold

compared to the wildtype while no significant differences were observed at 25 and

37 °C (Mattila et al., 2011). This suggests that the motility might be a common point in

virulence and cold growth.


22

3 Association studies in the genomic era

The field of genomics encompasses the study not only of the whole sequence but also

of the structure and the function of genetic information, offering unparalleled

possibilities to better understand and characterize bacterial pathogens and their

phenotypic behavior (Gill, 2017). Several bacterial mechanisms, which are translated

in specific phenotypic response, are the result of multiple genes and their regulatory

systems. Most of these are far from being fully understood, yet. The example of cold

adaption mechanisms in the previous chapter describes the complexity of the network

of genes and regulators that play a role in L. monocytogenes adaptation.

Many of the mentioned examples used knock-out mutants to understand the role of

genes in cold adaptation, which is a highly labor-intensive work focusing only on a few

genes and combination possibilities. With declining sequencing costs and by

combining multiple analysis in a single pipeline, the recent progress in WGS represents

a promising opportunity to decode phenotypic behaviors from the genomic sequence

of the bacteria of interest. Until now, the use of WGS is mainly focusing on outbreak

investigation, source tracking and source attribution in public health (Franz et al.,

2016). For the implication of WGS data into risk assessment, reliable biomarkers for

the prediction of phenotypes are necessary (Franz et al., 2016; Gill, 2017; Pielaat et

al., 2015)

3.1 Whole Genome Sequencing

In the last decade, WGS sequencing has become a method of choice for bacterial

pathogens typing in the field of food safety due to its improvement in performance,

quality and cost-effectiveness. The further development of this powerful tool is still

ongoing (Jagadeesan et al., 2018). Since the Illumina Genome Analyzer was able to

produce Gigabases of sequence data in 2006, numerous studies were published,

demonstrating the potential of WGS-based tools for characterization, functional

annotation and prediction of phenotypes (Bentley et al., 2008; Brynildsrud et al., 2016;

Kleinheinz et al., 2014; Nadon et al., 2017; Seemann, 2014).

Due to the increased demand for WGS, advanced bioinformatics approaches have

been developed to storage and process the high amount of genomic data. In the

second-generation sequencing era, WGS data are mainly generated using shotgun

(i.e. copies of randomly fragmented DNA segments) Illumina technology. Based on the


23

adopted library preparation technology (e.g. NexteraXT), DNA fragments are

sequenced with a specifically targeted length (e.g. 150 bp), and called “short-reads”.

Although Illumina short-read technology is dominating the current bacterial sequencing

market, the single-molecule sequencing technology for the generation of the so-called

“long-reads” is taking off as third-generation sequencing due to improved genome

assembly. However, the short-read Illumina technique is still cheaper than those

generating long-reads (e.g. PacBio or MinION). In addition to the lower costs, it also

generates sequences with a lower error rate in comparison to long-read technology

and is thus nowadays competing as the gold standard method for microbial WGS.

Since the technology of short-reads was earlier accessible for the scientific community,

a number of bioinformatics approaches are available today to process short-reads.

Reference mapping and SNP calling

The reads mapping approach is based on the mapping pre-processed reads against

the selected high-quality reference genome by finding the optimal placement for each

read across that reference (Davis et al., 2015). The process behind read-mapping

against a reference genome relies on algorithms that index the reference genome,

align sequence reads to the reference and index the differences between them,

assigning confidence levels to each variant position (Deng et al., 2016; Taboada et al.,

2017). The choice of the reference genome is an essential step to ensure a good read

mapping performance. It is recommended to use a completely sequenced reference

genome that is closely related to the studied strains. Otherwise, the mapping process

can lead to misalignments and/or several regions where the reads cannot be mapped,

producing unreliable results (Carriço et al., 2018; Schürch et al., 2018). Important

quality check parameters for the read mapping process are the breadth coverage (i.e.

the time each nucleotide position in the reference genome is covered by reads) and

the depth coverage (i.e. the number of reads of a given nucleotide in an experiment).

After reference mapping, the variation between the reference genome and the

analyzed strain can be extracted. If a single nucleotide is substituted in the analyzed

genome the variant is an SNP, else the variation is an insertion or deletion of few (i.e.

less than 50 bp) bases called InDel. This whole process is referred to as a variant

calling. Relative to the reference genome, every variant of an isolate is recorded, thus

the genetic relatedness can be quantified between strains by pairwise variant


24

comparisons (Jagadeesan et al., 2018). The drawback of this approach is that only the

shared regions can be aligned between the reference and the analyzed isolates. Also,

the presence of repetitive regions can lead to an increased pileup. In addition, MGEs

such as plasmids, transposons, and prophages, will not be detected if they are absent

in the chosen reference genome and if they are not shared by all strains in the analysis

(Lynch et al., 2016).

De Novo assembly

The process that assembles short nucleotide sequences into longer ones without the

use of a reference genome is referred to as de novo assembly. Here, contrary to the

read mapping against a reference, the reads are overlapped by algorithms to create

long contiguous consensus sequences called “contigs” (Zerbino and Birney, 2008).

Following, by re-mapping the reads to the contigs even longer sequences are created

and called “scaffolds”. These longer sequences result in high-quality draft genomes.

Different tools have been developed to conduct de novo assembly, as for example

Velvet, Skesa and SPAdes to cite the most commonly used (Bankevich et al., 2012;

Souvorov et al., 2018; Zerbino and Birney, 2008). A major point in the draft genome

generation is the quality check performed at each step of the process. The “Quality

Assessment Tool for Genome Assemblies” QUAST has been proven to be a reliable

tool to assess the quality of the assemblies (Gurevich et al., 2013). This software

evaluates different parameters, as the number of contigs and their lengths as well as

the median contig size (N50) and the size of the assembled genome. The de novo

approach is particularly used for pan genome investigations to allow the extraction of

assembled coding DNA sequences (CDS).

3.2 Genomic events driving phenotypic diversity

Thanks to the possibility to reconstruct complete (or nearly complete) bacterial

genomes through the above described approaches, comparative genomics can be

performed to discover the relations between genetic variants or phylogenomic profiling

and phenotypic traits (Dutilh et al., 2013; Kensche et al., 2007). Genetic variations can

be caused by SNPs, InDels and horizontal gene transfers of longer sequences (i.e.

plasmids, phages, and transposable elements).


25

Plasmids

Plasmids are generally small, circular, double-stranded DNA molecule distinct from the

chromosomal DNA. Plasmids have been often found in L. monocytogenes at

frequency ranging between 28 and 81 %, with a sequence size varying from 14 to

106 kbp (Fagerlund et al., 2016; Harvey and Gilmour, 2001; Hingston et al., 2017;

Kolstad et al., 1992; Kuenne et al., 2010; Lebrun et al., 1992; McLauchlin et al., 1997;

Rychli et al., 2017; Schmitz-Esser et al., 2015). Furthermore, some plasmids show a

high level of conservation within and between L. monocytogenes CCs and can be

found identical over years and in different countries across the globe (Fox et al., 2016;

Hingston et al., 2019; Muhterem-Uyar et al., 2018; Rychli et al., 2017; Schmitz-Esser

et al., 2015). Plasmids can be lost or acquired by L. monocytogenes to maintain their

fitness or to enhance their survival in harsh conditions (Ferreira et al., 2014; Knudsen

et al., 2017; Kuenne et al., 2010; Martín et al., 2014). The frequency of plasmids is

often higher in strains isolated from food or food processing environments than from

clinical isolates (Lebrun et al., 1992; McLauchlin et al., 1997). This led to the hypothesis

that strains harboring a plasmid have an advantage during the exposure to stresses

related food and food processing (Naditz et al., 2019). More precisely, the presence of

plasmids can have a positive effect for enhancing tolerance to stressful conditions

including heat and acid, heavy metal or the presence of antibiotics and benzalkonium

chloride (Elhanafi et al., 2010; Hingston et al., 2019; Kremer et al., 2017; Lebrun et al.,

1994; Li et al., 2016; Pöntinen et al., 2017).

A recent study of Naditz and colleagues (2019) described that wildtype strains of ST5,

ST8, and ST121 (harboring putative stress response genes in plasmids) had higher

counts of survivors than the plasmid-cured strains after exposure to food environment-

associated stresses (i.e. elevated temperature, salinity, acidic environments, oxidative

stress, and disinfectants) (Naditz et al., 2019).

Prophages

Further mobile elements responsible for the most variable fraction of the microbial

genomes are the phages or prophages (Dutilh et al., 2013). Prophages (i.e. the

bacteriophage genomes that have integrated into the chromosome or plasmid of

bacteria) are often found in L. monocytogenes’ genomes. Schmitz-Lesser and


26

colleagues as also Hain et al. reported the presence of one to four prophages per

L. monocytogenes strain (Hain et al., 2012; Schmitz-Esser et al., 2015). Prophages

often encode functions associated with the response to changing conditions as well as

genes related to pathogenicity (Brüssow et al., 2004; Nogueira et al., 2009). Some

genes harbored in prophages are reported to have an effect on antibiotic resistance,

stress resistance (osmotic, oxidative and acid), growth and biofilm formation (Fortier

and Sekulovic, 2013; Verghese et al., 2011; Wang et al., 2010). Besides that, genes

encoding for phage cell cycle (entry, DNA integration and replication, packaging, cell

lysis and the related regulation of expression), cell cycle inhibition of other phages and

viral replication, as well as a number of genes with unknown function are contained

into prophages (Dutilh et al., 2013). Due to their ubiquity, the prophages influence the

evolution of most bacterial species and thus they might be a key feature for explaining

phenotypic variations between closely related strains.

Small mutations

Point mutations (i.e. SNPs) or small insertions or deletions (i.e. InDels) can also affect

the phenotypic behavior of bacteria. These spontaneous mutations can appear during

DNA replication or repair by DNA polymerases, recombination, movement of genetic

elements or resulting from selective pressure (Foster, 2006; Martinez and Baquero,

2000). Drake and colleagues reported mutations rates in microbes nearly up to 1/300

per genome per replication (Drake et al., 1998). A single mutation can lead to a change

of the encoded amino acid (non-synonymous mutation) as also to a premature stop

codon (PMSC), affecting the protein conformation. However, in the case of

synonymous mutation, the same amino acid will be encoded (Syvänen, 2001) without

any effect on the protein conformation. Furthermore, the location of the SNP (e.g. in

coding, non-coding or regulatory regions) has to be taken into account for phenotype

variations. For instance, SNPs might affect the operon expression, leading to a

decreased or increased expression, as impacting RNAPs or transcription factors. A

study by Hoffmann and co-workers (2013) shows the possible impact of a single

mutation. They created a single point mutation in the A-like promotor region of the

secondary transporter BetL for carnitine uptake. Increased stress tolerance and a

significant increase in betL transcript levels under osmo- and chill stress have been

reported (Hoffmann et al., 2013). Moreover, the research of Kovacevic et al. (2013)

highlighted the potential effect of PMSC in inlA to the cold adaptation. Strains that


27

showed quick adaptation to cold were more likely to harbor an inlA gene lacking

PMSCs (i.e. full-length inlA) (Kovacevic et al., 2013).

3.3 Genome Wide Association Studies

In the pre-genomic era, conventional molecular microbiology methods were used to

find genetic events associated with phenotypic traits, often struggling with natural

variations (Falush, 2016). Due to the increasing amount of WGS data, new possibilities

are nowadays accessible to study the association between geno- and phenotypes at

the genomic scale. These possibilities rely upon advanced statistical approaches

called Genome Wide Association Studies (GWAS). This approach is based on a simple

principle: a genome-wide set of genetic variants in different individuals is statistically

observed to see if any variant is associated with a trait. The phenotypic traits and the

genomic sequences of a strain collection are therefore necessary to assess genetic

variant candidates explaining the phenotypic trait of interest. Therefore, it is statistically

tested if some genetic elements are more common in strains presenting the specific

trait than in those that do not (Falush, 2016). GWAS can be made at different genomic

levels. Usually, genomes are broken down into core and accessory genome. Then,

variants such as SNPs, small indels and also k-mers (i.e. a word of DNA that is k long)

are explored in the core genome are (Earle et al., 2016; Jaillard et al., 2018; Sheppard

et al., 2013), whereas, the differences in presence/absence of genes are searched in

the accessory genome (e.g. through the tool Scoary developed by (Brynildsrud et al.,

2016)).

The first successfully application of GWAS on human populations has been published

in 2005. Since then, thousands of human genetic GWAS have been performed, but

only a few studies focused on the bacterial population. Until 2015, only about a dozen

of microbial GWAS have been published, primarily based on clinical linked phenotypes

as antibiotic resistance and virulence (Chen and Shapiro, 2015). Table 4 shows an

overview of the first applied GWAS in microbiology and their strategy.


28

Table 4 : Summary of the first Genome Wide Association Studies adapted from (Chen and Shapiro, 2015)

Study Taxa

Relative

recombination

rate

# of genomes Phenotype Association

method

Addresses

accessory

genome ?

Unit of genetic

variant # of variants

Correction for

population

stratification

(van Hemert et al.,

2010)

L. plantarum Low 42 Host immune

response

Allele counting yes Gene

presence/absence

? (comparative

genomic

hybridization

none

(Sheppard et al.,

2013)

C .jejuni Moderate 29

(+ validation in

161)

Host

specificity

Allele counting Yes 30-bp DNA

sequences

>10,000 Simulation for

population

stratification

(Farhat et al., 2013) M. tuberculosis Low 123 Antibiotic

resistance

Homoplasy

counting

No SNPs ~25,000 Implicit in

phylogenetic

convergence criterion

(Laabei et al., 2014) S. aureus Low 90 Virulence Allele counting No SNPs and small

indels

~3000 Genomic control

(Salipante et al.,

2015)

E. coli Low-moderate 312 Antibiotic

resistance

Allele counting Yes Gene

presence/absence

~15,000 genes Inferred ancestry

clusters

(Alam et al., 2014) S. aureus Low 75 Antibiotic

resistance

Allele counting

and homoplasy

counting

No SNPs ~55,000 Inferred ancestry

clusters

(Chewapreecha et

al., 2014)

S. pneumoniae High 3085

(+validation in

616)

Antibiotic

resistance

Allele counting No SNPs ~400,000 Inferred ancestry

clusters

(Chaston et al.,

2014)

41 strains N/A 41 Host

development

time and

triglyceride

content

Allele counting Yes Gene

presence/absence

~12,000 genes Consideraration of

genes with unique

phylogenetic

distributions

(Chen and Shapiro,

2015)

M. tuberculosis Low 123 Antibiotic

resistance

Allele counting No SNPs ~3000 Inferred ancestry

clusters


29

Different factors influence bacterial GWAS in comparison to the human one. The main

issues rely on the differences in bacterial DNA reproduction and recombination. The

bacterial recombination is not linked to their reproduction and can appear several times

during cell life or not at all. Without recombination events, bacteria’s populations are

highly clonal and exhibit a strong linkage disequilibrium (LD). The landscape of LD in

eukaryotic genomes is quite different than those in bacteria. In bacteria, gene

conversions often occur leaving a reshuffle of recombined tracts on top of a genomic

background of linked regions that erode the linkage of clonal frames. This limits the

performance of common mapping methods because it can hide true causal variants

from the rest of the linked sites in the clonal frame (Chen and Shapiro, 2015; Earle et

al., 2016; Milkman and Bridges, 1990). Another factor that can affect GWAS is the

population stratification (Chen and Shapiro, 2015; Earle et al., 2016). Within a bacterial

population, subpopulations can be shaped including individuals that are on average

more closely related to each other than to individuals of the wide population (Balding,

2006). In this case, the association of variants can arise more from the highly related

genetic background rather than the causality of the phenotype of interest (Falush,

2016).

Besides the differences in LD and population stratification, the phenotypes also

present disparity to those of eukaryotes. While human disease traits evolve mainly by

genetic drift (neutral diversification), the bacterial phenotypes of interest are mostly

driven by strong natural selection (e.g. positive selection for antibiotic resistance)

instead of genetic drift (Sheppard et al., 2018). Regarding these issues, association

studies, that do not consider these factors risk to find a high amount of false-positive

associations. Therefore, prior association studies focused mainly on finding clonal

lineages with linked phenotypic traits rather than studying specific genetic elements

(Lees and Bentley, 2016). For example, Maury et al (2016) associated

L. monocytogenes CCs to epidemiological data (food and clinical isolates) and

identified a gene cluster that causes increased virulence (Maury et al., 2016).

Due to recent developments in microbial GWAS, the above-mentioned factors can now

be taken into account. Earle and co-workers proposed in 2016 a new method based

on linear mixed models (LMMs) that has been referred to as an “important milestone”

to realize the potential of bacterial GWAS (Falush, 2016). The approach suggested by

Earle et al. allows controlling the bacterial population structure taking into account the


30

signal of associations at lineage level in case of strong LD, lack of homoplasy and

strong population structure. This LMM-based approach can manage samples that have

a close relatedness by gathering precisely the fine structure of populations. This work

successfully proved the ability to find causal variants by controlling divergent ancestry

or a close relatedness without losing statistical power (Earle et al., 2016).

Since then, a number of tools have been developed that permit the user to find variants

on different genetic levels associated with either discrete (e.g. resistant/sensitive) or

continuous phenotypes (e.g. maximal growth rate). Table 5 is giving an overview of

bioinformatics tools available to conduct GWAS.

Table 5 : Bioinformatics tools for GWAS.

Names Genome level Location Phenotype Approach Publication

Roary/Scoary Genes Accessory Discrete Accounting for population stratification

(Brynildsrud et al., 2016; Page et al., 2015)

Piggy/Scoary Intergenic regions Pan-genome Discrete (Brynildsrud et al., 2016; Thorpe, Bayliss, Sheppard, et al., 2017)

DBGWAS Unitigs Pan-Genome Discrete LMM (Bugwas from Earle et al., 2016)

(Jaillard et al., 2018)

treeWAS SNPs and Genes Pan-Genome Discrete and continuous

Phylogenomic tree based

(Collins and Didelot, 2018)

Neptune k-mers Pan-Genome Discrete (Marinier et al., 2017)

SEER/pySEER k-mers/unitigs Pan-Genome Discrete Correction for clonal population structure

(Lees et al., 2018)

Kover k-mers Pan-Genome Discrete No correction for population structure

(Drouin et al., 2016)

Earle pipeline SNP, k-mers, Core-Genome Pan-genome

Discrete LMM (Earle et al., 2016)

Bacterial GWAS have the potential to expand our knowledge and help in explaining

complex bacterial responses to environmental factors as well as finding biomarkers to

predict phenotypic behaviors. This opens new doors for applications related to public

health and food safety management. Biomarkers could refine the hazard identification

(virulence or persistence markers), predictive microbiology when thermal processes

are modeled (heat resistance markers), source attribution and quantitative microbial

risk assessment (QMRA) (Franz et al., 2016; Jagadeesan et al., 2018). Franz et

al. (2016) mention that in the next future risk assessment models could be fed with

genomic biomarkers to conduct studies more targeted to high-risk subpopulations

(Franz et al., 2016).


31

4 Quantitative microbial risk assessment

Quantitative microbial risk assessment (QMRA) is defined as “a systematic process for

identifying adverse consequences and their associated probabilities arising from

consumption of foods that may be contaminated with microbial pathogens and/or

microbial toxin“ (Lammerding and Fazil, 2000). QMRA largely used by public health

authorities to manage food safety risks and for decision-making regarding public

health. Usually, the outcome of the models are the probabilities of illness per serving.

However, it is often of interest to gain information along the different steps in the food

supply chain (from farm to fork) to identify the individual stages where risk might be

increased. At the same time, the model can be used to predict the effects of proposed

intervention scenarios (Nauta, 2002). The process of QMRA modelling is illustrated in

Figure 8.

Figure 8 : The risk analysis process and the positioning of QMRA modelling process (Haberbeck et al., 2018).

Before the setting up of a QMRA, it is necessary to have a well-defined question. The

QMRA process is often quite complex and requires a high amount of knowledge and

data for the mathematical analyses.


32

QMRA models are based on four cornerstones: Hazard identification, exposure

assessment, hazard characterization and risk characterization (Lammerding and Fazil,

2000).

4.1 Hazard identification

Hazard identification is defined as “the identification of biological, chemical and

physical agents capable of causing adverse health effects and which may be present

in a particular food or group of foods” (FAO/WHO, 2016) It represents the first step in

microbial risk assessment. In the case of microbial risk assessment, the hazard is

usually known i.e. the pathogen or toxin causing human illness (Lammerding and Fazil,

2000; Nauta, 2002).

4.2 Exposure assessment

According to the Food and Agriculture Organization (FAO) and the World Health

Organization (WHO), exposure assessment is “the qualitative and/or quantitative

evaluation of the likely intake of biological, chemical, and physical agents via food as

well as exposures from other sources if relevant” (FAO/WHO, 2016).

In microbial exposure assessment, the aim is to estimate how likely is the exposure to

a microbial hazard and about the number of microorganisms that will likely be ingested

(Lammerding and Fazil, 2000). In this step, all factors and parameters affecting the

number of pathogens during the analyzed food chain should be identified and

considered (Figure 9).


33

Quantitative data of contamination levels in food at the time of consumption are rarely

available. Models and assumptions based on available data are used to translate

qualitative into quantitative estimates. A particular concern about microbial exposure

assessment is the dynamic character of the hazard due to the ability of microorganisms

to grow or to be inactivated during the food chain (from farm-to-fork). Sources of

information to feed the model can be published articles, expert opinions, survey

outputs for consumers’ behavior and preferences as well as every information on the

processing steps, preparation practices and environmental conditions (e.g.

temperature, time, pH, etc.).

4.2.1 Predictive microbiology

Predictive microbiology is the area of food microbiology aiming to assess the impact of

different environmental conditions (e.g. temperature, pH, substrate concentrations,

inhibitors, etc.) and their possible interactions related to growth, inactivation or survival

of microorganisms (Buchanan, 1993). Providing that the response of the

microorganisms is reproducible in a defined environment, it is possible, after the

characterization of the environment and based on prior observations, to predict the

behavior of these microorganisms. This behavior cannot only be simulated in food, but

also along the food chain from farm to fork.

In predictive microbiology, as in any field related to modelling, the collection of good

quality data is an essential element both for the selection of a model and for the

Figure 9 : Factors and parameters that should be considered in QMRA.


34

estimation of its parameters. The experimental design of a predictive model will depend

mainly on its final application.

The microbial behavior can be studied directly in food or in media. Traditionally, when

comparing the model's predictions based on culture media data with food data, the

growth observed in culture media is much faster than that observed in food (Pin et al.,

1999). Several factors are attributable: the matrix of the food, the presence of flora

indigenous to the food or additional environmental factors present in the food and not

included in the culture medium. Nutrients such as metabolites can broadly diffuse in

the culture medium, and thus the environment can be considered globally uniform. On

the other hand, food is generally not homogeneous (Wilson et al., 2002). The structure

of food creates local physical or chemical conditions that can affect the growth of

microorganisms. The mobility of bacteria in food is also severely restricted, and growth

is mainly expressed in microcolonies (Skandamis and Jeanson, 2015). Several studies

have shown that bacteria forced to develop as colonies have a slower rate of growth,

different metabolic activity or modified maximum growth levels compared to planktonic

cells (Koutsoumanis et al., 2004; Malakar et al., 2003; Stecchini et al., 1998). However,

experiments based on food matrices are more expensive and more difficult to realize.

This explains why most models are culture media-based. Methods used to monitor the

evolution of microbial density are presented in Table 6.


35

Table 6 : Methods to monitor the evolution of bacterial density.

Method Principle Pros Cons

CFU Counting cells capable of forming colonies on

agar media

-Liquid and solid media

(agar or food media)

-A cluster of cells deposited on the culture medium will result in

a single colony, preventing the estimation of the actual number

of individual cells actually present in the medium

-Long waiting times to allow bacterial growth in culture media

(24 to 48 hours).

-The uncertainty of bacterial enumerations is about

0.5 log10 CFU

Optical

density

Absorbance measurement

(or turbidity or optical density)

-Automated (Bioscreen)

-Conditions

-The detection limit (greater than or equal to 106 ml – 1 cells)

-Planktonic cells

-Indirect

Microscopy Surface inoculated with the microorganism of

interest deposited on a thin layer of agar or on a

microscope slide placed in a measuring chamber

-Solid media

-Single cell

-Direct

-relatively heavy to implement

-Low throughput

-Agar

Bio-

chemical

Based on the measurement of impedance, or

conductance, CO2, etc.

The time when a significant change in properties

in the growth medium is detected is used.

-Automated

-Conditions

-LOD

-Planktonic cells

-Indirect

Flow

cytometry

Based on the marking of cells in suspension with

fluorochromes and on the passage of these cells

through a microcapillary equipped with an

electronic detection device.

-Speed -Only broth

qPCR* Estimation of absolute initial copy number of the

DNA template

-Speed and sensitivity -LOD

-Difficult in solid

*quantitative polymerase chain reaction


36

4.2.2 Single cell heterogeneity

Most of the deterministic models rely on describing the behavior of microbial

populations as a whole, without taking account the single-cell heterogeneity. Several

studies have shown that the inoculation size is affecting the variability of growth

(Baranyi, 1998; Métris et al., 2003; Robinson et al., 2001) and inactivation (Aspridou

and Koutsoumanis, 2015). Since most of the initial bacterial contaminations have a low

level in food, taking into account the single-cell behavior is essential for realistic

estimations of food safety risks (Ross and McMeekin, 2003).

The work of Koutsoumanis and Lianou highlights the high variability in division and

growth of single-cells compared to bacterial populations of Salmonella enterica ser.

Typhimurium. Figure 10 shows the Monte Carlo simulation by considering single-cell

behavior (a) or simulating population growth (Koutsoumanis and Lianou, 2013). The

authors pointed out that for bacterial populations with N0 > 100 cells the variability is

almost eliminated and that the variability is increasing with decreasing the initial level

of cells.

Guillier also demonstrated similar results for L. monocytogenes (Guillier, 2005). He

simulated the growth of L. monocytogenes in cold-smoked salmon at 8°C with different

inoculation level. An increased variability of lag time was observed in the case of the

low contaminated cold-smoked salmon packages (Figure 11a) compared to the higher

contaminated ones (Figure 11b).

Figure 10 : Prediction of Salmonella enterica ser. Typhimurium growth based on the stochastic model for (a) initial level of 1 cell, and (b) initial level of 100 cells. Adapted from (Koutsoumanis and Lianou, 2013) (Copyright © American Society for Microbiology).


37

Figure 11 : Growth simulation of stressed L. monocytogenes cells in cold-smoked salmon at 8 °C with an inoculum size of 3 cells (a) and 90 cells (b) (Guillier, 2005).

4.2.3 Physiological state

Bacterial adaption is the product of the gene expression and/or activation of enzyme

systems as a response to changes in their environment. Therefore, environmental

conditions impact on the microbial physiological state. Figure 12 illustrates the

physiological states of cells during stress impact.

The innermost zone presents the stress range where cells will grow at a finite rate. The

central or “survival” zone indicates no cells’ growth and slow death while the outer zone

is characterized by high cells’ death rates. Noteworthy, all three zones represent

numerous cells’ states and their sizes depend on the success of adaption and the

bacterial intrinsic sensitivity (Booth, 2002).

Figure 12 : The comfort zone. For any stress, the bacterial cell has a defined range within which the rate of increase of CFU is positive (growth), zero (survival) or negative (death) (Booth, 2002).


38

The validity of a mathematical model in predicting the conditions that lead

L. monocytogenes physiology to critical levels in foods highly depends on its ability to

describe the effect of the environment on lag time and growth probability. These

parameters are depending on stresses the bacteria were exposed to. In the food

industry environment and during food processing (including heating, freezing,

exposure to acids and high osmotic pressures), cells L. monocytogenes are highly

exposed to stresses. Predictive models should thus be revised by considering injuries

encountered by cells before they contaminate food (Guillier and Augustin, 2006). The

physiological state is an important parameter to quantify the impact of stress on the

bacterial cells. Indeed, the physiological state of the cells, induced by their history,

plays a role in the maintenance or survival of the cells in a stressful environment. The

physiological state can be determined using several parameters depending upon the

ability of cells to multiply. Two important parameters for L. monocytogenes physiology

are the distribution of cellular lag times and the probabilities of growth.

When the L. monocytogenes cells are exposed to environmental stress, they can be

directly adapted to the food or need to do important work (e.g. metabolic adaptations,

repair of cellular damage) before they start to divide (lag time) or are no longer able to

divide (growth probability). Different studies focused on the exposure of bacterial

populations including L. monocytogenes to sub-lethal injuries (Elfwing et al., 2004;

Guillier et al., 2005; Kutalik et al., 2005). They demonstrated that the individual cell lag

times were extended with increasing prior stress, and also the variability of individual

lag times increased when cells were damaged. Nevertheless, the injury of cells can

also inhibit the initiation of the growth of some cells within a population (Wu et al.,

2000). This fraction can be characterized by the growth probability, as the probability

for a single cell to initiate growth in given conditions shown by Dupont and Augustin

(Dupont and Augustin, 2009). Other studies also indicated the possibility that the

minimal values of environmental factors to promote multiplication can vary from one

bacterial cell to another. For instance, Pascual et al. (2001) focused on the effect of

NaCl concentration in the growth medium (Pascual et al., 2001). Increasing the NaCl

concentration, an increased inoculum size needed to initiate growth. Similar

observations were also reported by Koutsoumanis and Sofos (2005) in their growth

boundaries experiments testing temperature, pH, and water activity (aw)

(Koutsoumanis et al., 2004). Some years later, Augustin and Czarnecka-Kwasiborski


39

(2012) characterized the single-cell growth probability of L. monocytogenes regarding

temperature, pH and aw. This work delivers models on the possibility to predict grow

probability in low contaminated chilled food (Augustin and Czarnecka-Kwasiborski,

2012).

Figure 13 clearly shows the impact of the growth probability to the outcome of

predictive models and thus exposure assessment. Koutsoumanis and Lianou included

the growth probability in their stochastic model (Koutsoumanis and Lianou, 2013). The

presence of a non-growing fraction within the bacterial population is therefore taken

into account. When only 10 cells out of 100 (red points) were able to grow, it led to an

apparent longer lag time and an enlarged variability in the population growth.

Figure 13 : Comparison of the stochastic growth of two microbial populations with N0 of 100 cells and probability of growth of the individual cells (Pg) equal to 1.0 (all 100 cells are able to grow) and 0.1 (about 10 of 100 cells are able to grow). Growth is predicted by the stochastic model using Monte Carlo simulation with 10,000 iterations and a uniform distribution for time (Koutsoumanis and Lianou, 2013)


40

4.3 Hazard characterization

To estimate the adverse health effects of foodborne pathogen infections (e.g.

listeriosis), dose-response models are used in QMRA. These dose-response

relationships are highly important for hazard characterization. Obtaining data to

establish these models is pretty hard, so they are built on several assumptions that

increase the variability (Chen et al., 2003). Due to the long incubation time of listeriosis,

gaining adequate quantitative data associated with outbreaks or sporadic cases to

improve these models is rare (Pouillot et al., 2015). That is why most of these models

are based on animal models (Buchanan et al., 1997; Haas et al., 1999).

The exponential dose‐response model is a “single‐hit” model. Here, the probability of

a given cell causing an adverse effect is an independent parameter, it is thus sufficient

to cause illness with a probability superior zero. The parameter corresponding to the

probability of a single cell causing illness is called r (Pouillot et al., 2015).

Differences in host susceptibility are another issue of dose-response models. This

variable is considered by using subgroups of populations with associated dose-

responses. Especially, elderly persons (> 65 years old), pregnant women and

immunosuppressed individuals are highly susceptible as hosts.

Besides the variability in the population, strain variability regarding virulence is also

affecting the dose-response estimations. For instance, Chen and colleagues explored

the use of dose-response models in relation to L. monocytogenes genetic lineages to

enable optimized risk attribution of specific subtypes (Chen et al., 2006). They

assumed that L. monocytogenes subtypes of lineage I have a higher probability to

cause human illness than those of lineage II, which are more often found in foods

indeed. In addition, Pouillot and colleagues proposed an adjustment of the dose-

response model by taking into account the variability of strains’ virulence and host

susceptibility (Pouillot et al., 2015). For refinement of the prior model, they introduced

eleven population subgroups. The distribution of r value includes in their model the

individual within-group as also the strain variability.

Today, the progress of the WGS is expected to improve hazard characterization.

Pielaat and co-workers published a first and promising approach based on GWAS to

show the utility of genetic data to refine QMRA, especially hazard characterization.

They associated SNPs in the genome of Shiga toxin-producing Escherichia coli


41

(STEC) O157 strains with the in vitro ability to adhere to epithelial cells as a proxy for

virulence (Pielaat et al., 2015). Similar approaches will help to better describe and

characterize also L. monocytogenes virulence variability. A different approach has

been shown by Maury et al. who used epidemiological data to identify the level of

virulence of L. monocytogenes CCs. Following, they tested several CCs in a

humanized mouse model to investigate further and to confirm the results obtained by

epidemiological data (Maury et al., 2016).

4.4 Risk characterization

The final step of the QMRA is the risk characterization. At this stage, exposure

assessment and hazard characterization are finally combined. More precisely, the

output of the expose model is used as input for the dose-response assessment. In risk

characterization “the likelihood that the population will suffer adverse effects as a result

of the hazard” is calculated (Buchanan et al., 2000). For the interpretation of the results,

the variability and uncertainty of the outcome have to be taken into account as both

are sources of variation. Uncertainty is referred to a lack of knowledge of the used

parameter values and can be reduced by further measurements or by using more

accurate methods (Buchanan et al., 2000; Nauta, 2000). On the other hand, variability

represents a true heterogeneity. Especially, all biological parts of the assessment are

highly variable.

As mentioned before, the development in WGS has the ability to improve QMRA

models and to reduce their variability, not only for hazard characterization. For

instance, the life Science Institute (ILSI) published four articles showing up the future

application possibilities of omics data in risk assessment (Cocolin, Mataragas, et al.,

2018; den Besten et al., 2017; Haddad et al., 2018; Rantsiou et al., 2018).

Objectives

42

Objectives

As described in the scientific literature review, the recent development in WGS opens

new opportunities for QMRA. Nevertheless, classical microbiological methods remain

essential to describe single-cell behavior in exposure assessment.

The global aim of this thesis is therefore to gain further insight on the

L. monocytogenes’ variability associated with cold adaptation using genomic data and

at the single-cell level. As mentioned in paragraph 3.3, bacterial GWAS are mainly

focusing on clinical phenotypes such as virulence or antibiotic resistance. However,

the characterization of the L. monocytogenes strain growth variability in relation to food

environmental stresses such as the low temperatures during the cold chain is also

crucial for the improvement of QMRA models. The discovery of genetic variants

associated with cold adaptation will improve exposure assessment estimates and thus

the risk characterization for L. monocytogenes.

Besides the refinement of QMRA models through genomic data, the description of

single-cell behavior can also be used to improve exposure assessment, as mentioned

in paragraph 4.2. In this perspective, the development of laboratory methods, which

perform even testing bacterial isolates in harsh conditions with high throughput and

short experiment duration, is needed.

Figure 14 shows the classical QMRA approach with its variabilities at different steps.

The lower part of the figure highlights in which parts the objectives achieved in this

thesis work will help to improve QMRA and to overcome different sources of

L. monocytogenes variability.

Objectives

43

Figure 14 : Overview of QMRA and the different objectives and chapters of the thesis.

The thesis is divided into three parts:

i) Evaluation of the GWAS contribution to study the physiology of food

pathogens with particular focus to the cold adaptation of a major pathogen:

L. monocytogenes

A collection of L. monocytogenes strains representing several CCs collected from food

and food processing environments were sequenced for this study. Different

bioinformatics analyses were conducted to assess the association between genetic

variants and L. monocytogenes strains’ ability for cold adaptation. Therefore, the

phenotypic behavior was studied at 2 °C to divide the strains clearly according to their

growth abilities in case and control groups.

ii) Evaluation of the contribution of WGS based characterization of strains in

QMRA models and the impact on their accuracy

In this work, the outcome of association studies is integrated into a QMRA model to

optimize the risk characterization. A specific focus was given on cold growth during the

Objectives

44

food chain and virulence factors. For that purpose, the QMRA model of cold-smoked

salmon and L. monocytogenes published by Pouillot et al. (Pouillot et al., 2009; Pouillot

et al., 2007) was used. This model was chosen because all necessary data (e.g.

representative prevalence data of cold-smoked salmon) are available to conduct that

study with a genomic approach.

iii) Characterization of the variability at intraspecific and cellular level regarding

the growth initiation of L. monocytogenes at low temperature by the

development of a direct microscopy approach

To improve the throughput of data collection at the single-cell level, an innovative

method based on the use of direct cell observation with phase-contrast time-lapse

microscopy (equipped with a 100× objective and a high-resolution device camera) was

developed. This device was herein used to study the growth initiation and the lag phase

of individual L. monocytogenes cells deposited on microscopic slides covered by agar.

Automation of image acquisition on a large surface was used to reach a high-

throughput.

Through the selected method, the single-cell growth probabilities and lag times at low

temperatures (i.e. 4 °C) were characterized.

Chapter 1

45

Chapter 1

Insights from genome-wide approaches to identify variants associated to

phenotypes at pan-genome scale: Application to Listeria monocytogenes’ ability

to grow in cold conditions

Authors:

Lena Fritsch1, Arnaud Felten1, Federica Palma1, Jean-François Mariet1, Nicolas

Radomski1, Michel-Yves Mistou1, Jean-Christophe Augustin1,2, Laurent Guillier1*

Affiliation:

1 French Agency for Food, Environmental and Occupational Health & Safety (Anses), Laboratory for

Food Safety, Université Paris-Est, Maisons-Alfort F-94701, France

2 Ecole Nationale Vétérinaire d'Alfort, Maisons-Alfort F-94704, France.

Keywords: whole genome sequencing, genome wide association study, cold stress

Published in the International Journal of Food Microbiology, Volume 291,

February 2019, Pages 181-188.

Chapter 1

46

Résumé

La variabilité intraspécifique du comportement de la plupart des pathogènes d'origine

alimentaire est bien décrite et prise en compte dans l'appréciation quantitative des

risques microbiens (AQRM), mais la prise en compte des facteurs (origine de la

souche, sérotype, etc.) expliquant ces différences sont rares ou contradictoires entre

les études. De nos jours, le séquençage du génome entier (WGS) offre de nouvelles

possibilités d'expliquer la variabilité intraspécifique des pathogènes alimentaires, à

partir de divers outils bio-informatiques récemment publiés.

L'objectif de cette étude est de décrire les différentes approches bio-informatiques

existantes pour associer phénotype(s) et génotype(s) bactérien(s). Par conséquent,

un ensemble de données de 51 souches de L. monocytogenes, isolées à partir de

sources multiples (de différentes matrices alimentaires et d‘environnements de

production) et appartenant à 17 complexes clonaux (CC), a été sélectionné pour

représenter une grande diversité de population. De plus, la variabilité phénotypique de

la croissance à basse température a été déterminée (phénotype qualitatif : souche

« lente » ou souche « rapide »), et les génomes des souches sélectionnées ont été

séquencés. Le pangenome, ainsi que la reconstruction phylogénétique basée sur les

SNPs du génome de base (ou « core genome »), ont été établis à partir des génomes

séquencés. Une méthode d'inférence bayésienne a d’abord été appliquée pour

identifier les branches sur lesquelles la distribution des phénotypes « lent » et

« rapide » évoluait significativement. Deux études d'association à l'échelle

pangénomique (c.-à-d. GWAS) sur la base de la présence/absence de gènes et sur la

base de SNP) ont été réalisées indépendamment afin de relier les mutations

génétiques au phénotype qui nous intéresse.

Les analyses génomiques présentées dans cette étude ont été appliquées avec

succès à l'ensemble de données sélectionné. L'approche phylogénétique bayésienne

a mis en évidence une association avec une capacité de croissance "lente" à 2 °C de

la lignée I, ainsi qu'avec CC9 de la lignée II. De plus, les deux approches GWAS ont

montré des associations statistiques significatives avec le phénotype testé. Une liste

de 114 gènes du génome accessoire (incluant des gènes déjà connus pour leur

implication dans le mécanisme d'adaptation au froid et des gènes associés à des

éléments génétiques mobiles) ont été identifiés. D'autre part, un groupe de 184 SNP

Chapter 1

47

hautement associés (avec des valeurs de p<0.01) ont été mis en évidence par le SNP-

GWAS (comprenant des SNP dans des gènes qui déjà connus dans l'adaptation au

froid, des protéines hypothétiques et des régions intergéniques où par exemple des

promoteurs et régulateurs peuvent être situés.

L'application réussie d'approches bioinformatiques combinant des génotypes WGS et

des phénotypes spécifiques, pourrait contribuer à améliorer la prédiction des

comportements microbiens dans les aliments. La mise en œuvre de ces informations

dans les processus d'identification des dangers et d'évaluation de l'exposition ouvrira

de nouvelles possibilités pour alimenter les modèles d'AQRM.

Chapter 1

48

Abstract

Intraspecific variability of the behavior of most foodborne pathogens is well described

and taken into account in Quantitative Microbial Risk Assessment (QMRA), but factors

(strain origin, serotype, …) explaining these differences are scarce or contradictory

between studies. Nowadays, Whole Genome Sequencing (WGS) offers new

opportunities to explain intraspecific variability of food pathogens, based on various

recently published bioinformatics tools.

The objective of this study is to get a better insight into different existing bioinformatics

approaches to associate bacterial phenotype(s) and genotype(s). Therefore, a dataset

of 51 L. monocytogenes strains, isolated from multiple sources (i.e. different food

matrices and environments) and belonging to 17 clonal complexes (CC), were selected

to represent large population diversity. Furthermore, the phenotypic variability of

growth at low temperature was determined (i.e. qualitative phenotype), and the whole

genomes of selected strains were sequenced. The almost exhaustive gene content,

as well as the core genome SNPs based phylogenetic reconstruction, were derived

from the whole sequenced genomes. A Bayesian inference method was applied to

identify the branches on which the phenotype distribution evolves within sub-lineages.

Two different Genome Wide Association Studies (i.e. gene- and SNP-based GWAS)

were independently performed in order to link genetic mutations to the phenotype of

interest.

The genomic analyses presented in this study were successfully applied on the

selected dataset. The Bayesian phylogenetic approach emphasized an association

with “slow” growth ability at 2 °C of the lineage I, as well as CC9 of the lineage II.

Moreover, both gene- and SNP-GWAS approaches displayed significant statistical

associations with the tested phenotype. A list of 114 significantly associated genes,

including genes already known to be involved in the cold adaption mechanism of

L. monocytogenes and genes associated to mobile genetic elements (MGE), resulted

from the gene-GWAS. On the other hand, a group of 184 highly associated SNPs were

highlighted by SNP-GWAS, including SNPs detected in genes which were already

likely involved in cold adaption; hypothetical proteins; and intergenic regions where for

example promotors and regulators can be located.

Chapter 1

49

The successful application of combined bioinformatics approaches associating WGS-

genotypes and specific phenotypes, could contribute to improve prediction of microbial

behaviors in food. The implementation of this information in hazard identification and

exposure assessment processes will open new possibilities to feed QMRA-models.

Chapter 1

50

1 Introduction

1.1 French introduction

Dans le domaine de la microbiologie alimentaire, la variabilité des souches

phénotypiques est un phénomène bien connu (Koutsoumanis et Lianou, 2013). Lors

de l'étude du comportement des pathogènes bactériens dans les aliments, il est assez

courant d'inclure de plusieurs à de grandes collections de souches (Haberbeck et al.,

2015 ; Van Der Veen et al., 2008). En ce qui concerne les principaux pathogènes

d'origine alimentaire, certaines divergences significatives ont été mises en évidence

lors de l'observation et de la modélisation du comportement de la souche. Par

exemple, Aryani et ses collaborateurs (2015) ont étudié la variabilité de croissance de

20 souches de Listeria monocytogenes dans différentes conditions de pH, d'activité

dans l'eau (aw), de concentration en NaCl, de concentration en acide lactique non

dissocié et de température (Aryani et al., 2015). La variabilité a été quantifiée à l'aide

des paramètres du modèle comme étant la température de croissance minimale, Tmin

(°C), avec des valeurs allant de -3,3 °C à -1,1 °C. En ce qui concerne Bacillus cereus,

de grandes différences pour les paramètres de croissance cardinaux, y compris les

températures de croissance minimale, optimale et maximale, ont également été

décrites en observant le paramètre Topt (°C) de 31,0 °C à 43,1 °C pour les 12 souches

étudiées (Carlin et al., 2013). Haberbeck et ses collaborateurs (2015) ont étudié la

variation des limites de croissance/absence de croissance de 188 souches

d'Escherichia coli dont le pH minimal de croissance varie entre 3,8 et 4,3 selon les

souches testées (Haberbeck et collaborateurs, 2015). L'inclusion de plusieurs souches

est également fréquente dans l'étude d'autres phénotypes qui ne sont pas liés à la

modélisation prédictive. De nombreux autres exemples concernent l'adhérence

bactérienne ou la formation de biofilms (Lianou et Koutsoumanis, 2012 ; Piercey et al.,

2017), la capacité à survivre à un stress (Zoz et al., 2017) ou un phénotype plus lâche,

comme la capacité à persister en milieu agroalimentaire (Nowak et al., 2017).

La description de la variabilité de la souche peut suffire à elle seule à identifier la

mesure de contrôle appropriée qui devrait être appliquée pour garantir la sécurité

sanitaire des aliments. Dans une telle situation, la souche ayant la plus grande

capacité de survie/croissance est la souche de référence de référence. Cependant, la

caractérisation de la variabilité phénotypique est habituellement suivie d'études

d'associations statistiques sur les données qualitatives caractérisant les souches et

Chapter 1

51

les différences phénotypiques observées. Selon l'agent pathogène, plusieurs facteurs

d'intérêt peuvent expliquer la variabilité observée. Elles reposent généralement sur

l'origine d'isolement de la souche, sur certaines caractéristiques de sous-typage (c'est-

à-dire le sérotype) ou même sur la présence ou l'absence de marqueurs (Carlin et al.,

2013 ; Shabala et al., 2008 ; Van Der Veen et al., 2008). Par exemple, Carlin et ses

collaborateurs (2013) ont démontré une corrélation claire entre les différents groupes

phylogénétiques de B. cereus et l'adaptation à la température, au pH et à aw. Par

contre, d'autres études axées sur la variabilité phénotypique de la souche n'ont pas

réussi à déceler des associations entre des facteurs comme le sérotype ou l'origine de

la souche (Shabala et al., 2008 ; Van Der Veen et al., 2008).

Ces exemples démontrent qu'il existe un besoin de nouvelles approches analytiques

afin d'identifier les marqueurs génétiques prédictifs des comportements bactériens.

Grâce à l'expansion des technologies WGS, ainsi qu'au développement récent d'outils

bioinformatiques open source associant mutations et phénotypes bactériens, il est

désormais possible de détecter des éléments génétiques impliqués dans des

phénotypes binaires ou continus donnés (Lees et Bentley, 2016 ; Power et al., 2017).

Presque toutes les informations sur les comportements phénotypiques sont codées

dans l'ADN microbien (Chen et Shapiro, 2015). Des études d'association entre cette

information génétique et les caractères phénotypiques d'intérêt peuvent être réalisées

à différents niveaux d'éléments génétiques, comme la présence ou l'absence de gènes

spécifiques (Brynildsrud et al., 2016), k-mers (Earle et al., 2016 ; Lees et al., 2016 ;

Marinier et al., 2017) ou polymorphismes nucléotides simples (SNPs) (Collins et

Didelot, 2018 ; Earle et al., 2016).

Des études d'association à l'échelle du génome (GWAS) ont été récemment mises au

point et appliquées sur les génomes bactériens, principalement axées sur des

phénotypes cliniquement pertinents comme la virulence (Buchanan et al., 2017 ;

Laabei et al., 2014 ; Pielaat et al., 2015) et la résistance aux antibiotiques (Earle et al.,

2016 ; Salipante et al., 2015). notre connaissance, un nombre limité de GWAS

(Hingston, Chen, Dhillon et al., 2017 ; Pielaat et al., 2015) ont évalué de nouvelles

associations de phénotypes pertinentes pour l'industrie et/ou utiles pour les études

d'évaluation des risques.

Chapter 1

52

Même si l'association bactérienne GWAS vise à atténuer les effets préjudiciables de

la structure des populations, les méthodes explorant la relation entre génotypes et

phénotypes peuvent reposer sur la reconstruction phylogénétique. La relation entre un

génotype et un phénotype peut en effet être étudiée en observant comment le

phénotype est distribué en fonction des valeurs génotypiques (Ansari et Didelot, 2016).

L'application réussie des méthodes basées sur les GWAS et la phylogénie a été

largement prouvée pour les phénotypes d'importance clinique (c'est-à-dire la

résistance aux antibiotiques ou la virulence). Jusqu'à présent, l'application de ces

méthodes pour étudier les phénotypes présentant un intérêt pour la microbiologie

alimentaire est encore limitée. L'objectif de cet article est donc de présenter et

d'évaluer la capacité des différentes approches à identifier les marqueurs génétiques

associés à un phénotype d'intérêt afin de prédire le comportement bactérien des

aliments. Ces méthodes ont été appliquées à un ensemble de données sélectionnées

de souches de L. monocytogenes nouvellement séquencées avec des phénotypes

bien caractérisés à basse température.

Chapter 1

53

1.2 English introduction

In the field of food microbiology, phenotypic strain variability is a well-known

phenomenon (Koutsoumanis and Lianou, 2013). While studying the behavior of

bacterial pathogens in food, it is quite common to include from several to large

collection of strains (Haberbeck et al., 2015; Van Der Veen et al., 2008). Concerning

the main foodborne pathogens, some significant discrepancies have been enlightened

while observing and modelling the strain behaviour. For instance, Aryani et al. (2015)

studied the growth variability of 20 Listeria monocytogenes strains in different

conditions of pH, water activity (aw), NaCl concentration, undissociated lactic acid

concentration and temperature (Aryani et al., 2015). The variability was quantified

through model parameters as minimal growth temperature, Tmin (°C), with values

ranging from −3.3 °C to −1.1 °C. With regard to Bacillus cereus, large differences for

cardinal growth parameters, including minimal, optimal and maximal growth

temperatures, were also described observing the parameter Topt (°C) from 31.0 °C to

43.1 °C for the 12 studied strains (Carlin et al., 2013). Haberbeck et al. (2015)

investigated the variation in growth/no growth boundaries of 188 Escherichia coli

strains with a range of minimal pH for growth varying between 3.8 and 4.3 according

to the tested strains (Haberbeck et al., 2015). The inclusion of several strains is also

common when studying other phenotypes which are not related to predictive

modelling. Numerous other examples can be found concerning the bacterial adhesion

or biofilm formation (Lianou and Koutsoumanis, 2012; Piercey et al., 2017), ability to

survive to a stress (Zoz et al., 2017) or more loosely defined phenotype, such as the

ability to persist in food processing environment (Nowak et al., 2017).

The description of strain variability can by itself be sufficient to identify the appropriate

control measure that should be applied to guarantee food safety. In such a situation,

the strain with higher ability to survive/grow is the benchmark reference strain.

However, characterization of phenotypic variability is usually followed by statistical

association studies on qualitative data characterizing the strains and the observed

phenotypic differences. According to the pathogen, several factors of interest may

explain the observed variability. They usually rely on the isolation origin of the strain,

on some sub-typing characteristics (i.e. serotype) or even on any relevant

presence/absence of markers (Carlin et al., 2013; Shabala et al., 2008; Van Der Veen

et al., 2008). For example, Carlin et al. (2013) demonstrated a clear correlation

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between different phylogenetic groups of B. cereus and the adaptation to temperature,

pH and aw. In contrast, other studies focusing on phenotypic strain variability failed to

detect associations between factors as the strain serotype or origin (Shabala et al.,

2008; Van Der Veen et al., 2008). These examples demonstrate that there is a need

of new analytical approaches in order to identify genetic markers predicting bacterial

behaviors. Based on the expansion of WGS technologies, as well as the recent

development of open source bioinformatics tools associating mutations and bacterial

phenotypes, it is now possible to detect genetic elements involved in given binary or

continuous phenotypes (Lees and Bentley, 2016; Power et al., 2017).

Almost all information about the phenotypic behaviors is encoded in the microbial DNA

(Chen and Shapiro, 2015). Association studies between this genetic information and

phenotypic traits of interest can be performed at different levels of genetic elements,

such as the presence/absence of specific genes (Brynildsrud et al., 2016), k-mers

(Earle et al., 2016; Lees et al., 2016; Marinier et al., 2017) or single nucleotide

polymorphisms (SNPs) (Collins and Didelot, 2018; Earle et al., 2016). Genome Wide

Association Studies (GWAS) have been recently developed and applied on bacterial

genomes, mainly focusing on clinically relevant phenotypes such as virulence

(Buchanan, Webb, et al., 2017; Laabei et al., 2014; Pielaat et al., 2015) and antibiotic

resistance (Earle et al., 2016; Salipante et al., 2015). Up to our knowledge, a limited

number of GWAS (Hingston et al., 2017; Pielaat et al., 2015) assessed de novo

associations of phenotypes relevant to industry and/or useful for risk assessment

studies. Even if bacterial GWAS aims to mitigate the prejudicial effects of population

structure, methods exploring the relationship between genotypes and phenotypes may

rely on phylogenetic reconstruction. The relationship between a genotype and a

phenotype can indeed be investigated by observing how the phenotype is distributed

according to genotypic values (Ansari and Didelot, 2016).

The successful application of GWAS and phylogeny-based methods has been

extensively proven for phenotypes with clinical importance (i.e. antibiotic resistance or

virulence). So far, the application of these methods for investigating phenotypes of

interest for food microbiology is still limited. Therefore, the objective of this article is to

present and to assess the ability of different approaches to identify genetic markers

associated to a phenotype of interest in order to predict the bacterial behavior in foods.

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These methods were applied to a selected dataset of newly sequenced

L. monocytogenes strains with well-characterized phenotypes at low temperature.

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2 Materials and methods

2.1 Strains

From the Anses collection stored at −80 °C, a set of 51 L. monocytogenes strains was

constituted in order to achieve large population diversity in terms of genotypes (i.e. CC-

types) and food-related origins (i.e. meat products, dairy, seafood and environment).

The lineages, clonal complexes (CC) based on 7 loci MLST (Ragon et al., 2008), as

well as the strain origin are detailed in Supplementary material 1.

2.2 Whole genome sequencing

Firstly, cryo bead-based strains were inoculated onto tryptone soya agar (TSA, AES

Chemunex, Bruz, France) and incubated for 24h at 37 °C to check isolates purity. A

single colony of each strain isolated on TSA was picked to inoculate different brain

heart infusion broth (BHI, Oxoid, Dardilly, France) tubes (10 ml) incubated overnight

(12-18 h) at 37 °C. The Wizard® Genomic DNA Purification Kit (Promega, France) was

used in order to isolate genomic DNA. Nanodrop® Spectrophotometer and Qbit®

fluorimeter were used to assess the quantity of the extracted DNA. Global integrity of

DNA (200 ng) was assessed via horizontal agarose electrophoresis (Seakem GTG

Agarose gel at 0.8 % migration 3 h with an electrical field at 120V/105mA in TBE 1X

gel) as a quality control measure. Paired-end sequencing (i.e. 2 x 150 bp) were

performed by the ‘Institut du Cerveau et de la Moëlle’ (ICM, France) using TruSeq

automated library preparation (Illumina) and Nextseq500 sequencing system

(Illumina). The paired-end reads, isolates information and genome assemblies are

available under the PRJEB24673 (ERP106517) ENA bio-project.

2.3 Phenotyping in cold conditions

Qualitative phenotypes based on the ability to reach turbidity in a given time (i.e. two

months) at 2 °C were established for each strain and a minimum of three replicates

were performed for all experiments.

One cryo bead of each strain was put in 10ml TSB-YE and incubated for 7 h at 37 °C.

Then, a 1/10 dilution was prepared and further incubated at 37 °C for 17 h. In order to

achieve a density of 5 log cfu.ml-1, overnight cultures were diluted in pre-chilled

tryptone salt (TS, Oxoid, Hampshire, UK) and TSB-YE (2 °C), successively.

Enumerations ensuring the concentration of the obtained suspensions were performed

onto TSA. Microtiter plates were inoculated with 200 µl per well (Overney et al., 2016)

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of the diluted suspensions and incubated at 2 °C. The microplates were covered with

parafilm in order to avoid dehydration. The turbidity of wells was visually inspected

each day for each strain, so that the strains can then be divided into two groups based

on time to observe turbidity. The two groups include strains that showed visible turbidity

before (hereafter called “fast”) and later (hereafter called “slow”) than 21 days,

respectively.

2.4 Genomic approaches

In order to link mutations to qualitative phenotypes, two different SNP-based

phylogenetic reconstructions (i.e. including and excluding recombination events; §

2.4.1), two different GWAS methods (i.e. gene- and SNP-based GWAS; § 2.4.2), as

well as a Bayesian inference method (§ 2.4.3), were performed.

2.4.1 Phylogenetic reconstructions

SNPs calling

A variant calling analysis was performed using iVARCall2, a recently published

workflow based on the GATK HaplotypeCaller algorithm (Felten, Nova, et al., 2017).

Briefly, this pipeline aims to align paired-end reads against a reference genome to

identify SNPs and InDels by local de novo assembly. The complete sequence of

L. monocytogenes strain EGD-e (accession number: NC_003210) was used as

reference genome. The pipeline produces also a multi-fasta file, called

“pseudogenomes”, including pseudo reference genomes obtained by substituting into

the reference all the genotypes of SNPs for each strain included in the analysis.

SNP-based phylogenetic inference

Phylogenetic reconstruction based on core genome SNPs was carried out with RAxML

(Randomized Axelerated Maximum Likelihood) (Stamatakis, 2014). The

pseudogenome fasta file generated by iVARCall2 was used as input for RAxML to build

a phylogenetic tree in newick format. The phylogenetic inference was performed with

bootstrap analysis and searching for the best-scoring Maximum Likelihood (ML) tree

with General Time-Reversible (GTR) model of substitution and the secondary structure

16-state model. One thousand bootstraps were applied and convergence was

checked. The resulting newick trees were further used in the following analyses.

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In order to detect recombination events, ClonalFrameML software package (Didelot

and Wilson, 2015) was applied using as input the phylogenetic tree from RaxML and

the pseudogenome fasta file from iVARCall2. Then, the SNPs corresponding to

recombination events were filtered out from the pseudogenomes using the

Clonal_VCFilter script (Felten, Nova, et al., 2017) in order to obtain a phylogenetic tree

where variants linked to recombination events were excluded.

2.4.2 GWAS methods

Gene-based GWAS

A GWAS based on the accessory gene content of the 51 L. monocytogenes de novo

assemblies was performed. First, draft genomes were obtained based on the SPAdes

algorithm (Bankevich et al., 2012) after quality check of Illumina reads using FASTQC

(Andrews, 2010) and trimming low quality reads with Trimmomatic (Bolger et al., 2014).

Finally, the qualitative evaluation of assemblies was performed by computing various

metrics with QUAST (Gurevich et al., 2013). The whole genome annotation was carried

out using Prokka (Seemann, 2014) with default parameters. Prokka uses the

assemblies as input and produces GFF3-files, including sequences and annotations,

which were used to extract the pan-genome of the 51 L. monocytogenes isolates with

the software Roary (Page et al., 2015). Finally, gene-based GWAS was performed

using Scoary (Brynildsrud et al., 2016). A file including phenotypic traits, the

phylogenetic tree based on SNPs and the gene presence/absence matrix from Roary

were thus used as input-data to Scoary.

SNP-based GWAS

A SNP-based GWAS was performed based on the workflow published by Earle et al.

2016 (Earle et al., 2016). The R scripts developed by Earle et al. and the related

external programs, such as GEMMA (Zhou and Stephens, 2012), were applied to

create SNPs-GWAS outputs. The pseudogenome fasta sequences produced by

iVARCall2 (Felten et al., 2017) and the associated binary phenotypic trait were

supplied as input of the workflow developed by Earle et al. (Earle et al., 2016). In the

SNPs-GWAS, correction for multiple testing was accounted for by applying Bonferroni

adjustments. The consideration of population structure effect provides estimates of the

proportion of variance in phenotypes explained by "SNP heritability". For associated

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SNPs, the related information are provided including annotation, associated p-values,

positions, types and bases of SNPs, as well as reference and non-reference codons

with the corresponding amino acid.

2.4.3 Bayesian inference method

With a view to assessing the evolution of qualitative phenotypes distribution along the

branches of the phylogenetic tree, a recently developed Bayesian inference method

(Ansari and Didelot, 2016) was applied. The R package called TreeBreaker was used

in order to infer the evolution of a discrete phenotype distribution across a phylogenetic

tree and divide the tree into segments where their distributions are constant (Ansari

and Didelot, 2016). Therefore, SNP-based newick trees obtained at core genome and

gene (inlA) level excluding recombination events and the discrete phenotypic traits

(“fast”=0, “slow”=1) were supplied as input to this program.

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3 Results and discussion

Out of the 51 L. monocytogenes strains included in the study, 43 were classified as

“fast” growing strains and 8 as “slow” growing strains at 2 °C (Supplementary

material 1). All results presented below are supported by high-quality reads-mapping

and draft genomes based on depth (median of 328 X) and breadth (median of 87.39 %)

coverages, as well as total contigs length (i.e. cumulated length of 2 984 694 kb with

N50 of 2 950 739), respectively (Supplementary material 2).

3.1 GWAS methods to explore the genetic basis of growth capacity at 2 °C

3.1.1 GWAS based on presence-absence of genes

A gene-based GWAS was carried out exploring the different analytical steps and

corresponding statistical parameters computed by Roary (Page et al., 2015) and

Scoary (Brynildsrud et al., 2016). The pan-genome of the 51 Listeria monocytogenes

strains (Figure 15) is composed of a total of 6 612 genes, including 2 014 core genes

(present in the 99 % of isolates) and 4 598 accessory genes (Supplementary material

3). A similar number of core genes has been already reported in previous studies on

L. monocytogenes: 2 032 (den Bakker et al., 2010) and 2 354 (Kuenne et al., 2013).

In order to select a curated set of genes associated to the “fast” or “slow” growth

phenotype, the 4 598 accessory genes detected by Roary were therefore considered

for the GWAS analysis performed with Scoary. A detailed description of the different

steps applied in the GWAS analysis is reported in Supplementary material 3. It is worth

mentioning that in the results obtained with this analysis, the population structure

correction is taken into account based on the produced SNP-based ML-tree

(Supplementary material 4).

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Figure 15 : Phylogenetic tree of 51 L. monocytogenes isolates compared to a matrix of presence and absence of core and accessory genes in dark blue.

A list of a total of 114 genes with strong statistical associations with the growth ability

at 2 °C (Supplementary material 4) was finally listed. The output table includes a couple

of genes already reported to be involved in the cold adaption mechanism of

L. monocytogenes such as the genes coding for RNA helicase (Markkula et al., 2012)

and for the precursor of internalin A (Kovacevic et al., 2013). Interestingly, 15 out of

114 genes (~13 %) of reported genes, corresponding to MGE (such as phage capsid

family protein or transposase from transposon Tn916), were found to be associated

with the measured phenotype. Despite, 70 out of 114 genes (~61 %) reported in the

output table correspond to hypothetical proteins.

3.1.2 SNP-based GWAS

The objective of the SNP-based GWAS analysis was to detect the phenotype-

associated genetic variants in the core genome. When performing microbial GWAS,

confounding factors (i.e. bacterial population structure and recombination events) may

affect the data analysis and lead to spurious associations. The approach by Earle et

al. (2016) was applied including mixed models which take into account for phylogenetic

relatedness and signals of lineage associations. Therefore, all SNPs located in the

2014 core genome genes and in the intergenic regions which can also be involved in

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phenotype associations, especially the 5’-untranscribed regions which may be

regulated by small RNAs (Cerutti et al., 2017), were included in the analysis (i.e.

104 160 core genome SNPs). Figure 16 presents the association for all these SNPs to

the studied phenotype. In this Figure, the positions of the SNPs in the genome are on

the x-axis while the corresponding –log10 (p-values) quantifying the association are

displayed on the y-axis. The majority of the SNPs (n=101 685) shows no significant

association (p-value>0.05), whereas slight (p-value<0.05) and strong significance

associations (p-value<0.01) were observed for 2 291 and 184 SNPs, respectively.

Figure 16 : Manhattan plot of core genome SNPs which show associations to the given phenotypic traits of the 51 L. monocytogenes strains. Each point presents a SNP with its position and corresponding –log10(p-value), which shows the significance of the geno-phenotype association. The color represents the extent of this significance, red (p-value< 0.01), orange (p-value< 0.05) and grey (p-value>0.05).

Table 7 shows the first 40 SNPs with the highest p-values of association and the

complete table of SNPs with p-value<0.01 is reported in Supplementary material 5.

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Table 7 : Extract of SNPs with strong evidences (p-value<0.01) for genome-wide association to the tested phenotypic trait of L. monocytogenes at 2°C. All associated SNPs at 0.05 significance p-value levels are listed in Supplementary material 5.

Refposition p-value

Allele0 Allele1 A C G T Type Refcodon Non-refcodon

Refaa Non-refaa

Name Start End Product

2335431 0.0067 A T 42 0 0 9 Synonymous CCA CCT Pro Pro lmo2244 2334586 2335455 ribosomal large subunit pseudouridine synthase

67812 0.0068 G A 9 0 42 0 Synonymous GTG GTA Val Val lmo0061 66160 70656 hypothetical protein

326545 0.0068 A G 42 0 9 0 Synonymous AGT AGC Ser Ser lmo0300 325729 327120 phospho-beta-galactosidase

361961 0.0068 G A 9 0 42 0 Synonymous GGG GGA Gly Gly lmo0333 360936 366272 internalin

361964 0.0068 T A 9 0 0 42 Synonymous CTT CTA Leu Leu lmo0333 360936 366272 internalin

361966 0.0068 C T 0 42 0 9 Non-synonymous

TCG TTG Ser Leu lmo0333 360936 366272 internalin

522745 0.0068 G A 9 0 42 0 Synonymous AAC AAT Asn Asn lmo0488 522628 523521 LysR family transcriptional regulator

632556 0.0068 T C 0 9 0 42 Synonymous GAT GAC Asp Asp lmo0590 631045 632811 hypothetical protein

659121 0.0068 T C 0 9 0 42 Synonymous AGT AGC Ser Ser lmo0620 659083 659475 hypothetical protein

659133 0.0068 C T 0 42 0 9 Synonymous GCC GCT Ala Ala lmo0620 659083 659475 hypothetical protein

659139 0.0068 T C 0 9 0 42 Synonymous GAT GAC Asp Asp lmo0620 659083 659475 hypothetical protein

659158 0.0068 A G 42 0 9 0 Non-synonymous

AGT GGT Ser Gly lmo0620 659083 659475 hypothetical protein

821163 0.0068 T G 0 0 9 42 Synonymous CTA CTC Leu Leu lmo0793 821070 821771 hypothetical protein

924279 0.0068 A T 42 0 0 9 Synonymous TCA TCT Ser Ser lmo0884 923161 924540 protoporphyrinogen oxidase

924290 0.0068 G C 0 9 42 0 Non-synonymous

AGA ACA Arg Thr lmo0884 923161 924540 protoporphyrinogen oxidase

924366 0.0068 T A 9 0 0 42 Synonymous CCT CCA Pro Pro lmo0884 923161 924540 protoporphyrinogen oxidase

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Refposition p-value


Refaa Non-refaa


924378 0.0068 T C 0 9 0 42 Synonymous GTT GTC Val Val lmo0884 923161 924540 protoporphyrinogen oxidase

924387 0.0068 G A 9 0 42 0 Synonymous CAG CAA Gln Gln lmo0884 923161 924540 protoporphyrinogen oxidase

924393 0.0068 A G 42 0 9 0 Synonymous CGA CGG Arg Arg lmo0884 923161 924540 protoporphyrinogen oxidase


ATC GTC Ile Val lmo0884 923161 924540 protoporphyrinogen oxidase

924420 0.0068 A G 42 0 9 0 Synonymous AAA AAG Lys Lys lmo0884 923161 924540 protoporphyrinogen oxidase

924438 0.0068 T C 0 9 0 42 Synonymous GTT GTC Val Val lmo0884 923161 924540 protoporphyrinogen oxidase

924456 0.0068 C T 0 42 0 9 Synonymous AGC AGT Ser Ser lmo0884 923161 924540 protoporphyrinogen oxidase

1400214 0.0068 A T 42 0 0 9 Synonymous GCA GCT Ala Ala lmo1375 1399699 1400796 aminotripeptidase

1448590 0.0068 C T 0 42 0 9 Non-synonymous

GCT ACT Ala Thr lmo1418 1448315 1449583 hypothetical protein

1855515 0.0068 G A 9 0 42 0 Synonymous CCG CCA Pro Pro lmo1778 1854748 1855521 ABC transporter ATP-binding protein


ATA ATG Ile Met lmo2763 2844249 2845601 PTS cellbiose transporter subunit IIC

1163023 0.0068 T A 9 0 0 42 Synonymous CTA CTT Leu Leu lmo1129 1162666 1163280 hypothetical protein

1163026 0.0068 C T 0 42 0 9 Synonymous AAG AAA Lys Lys lmo1129 1162666 1163280 hypothetical protein

1163043 0.0068 T C 0 9 0 42 Non-synonymous

AGT GGT Ser Gly lmo1129 1162666 1163280 hypothetical protein

1163044 0.0068 C T 0 42 0 9 Synonymous AAG AAA Lys Lys lmo1129 1162666 1163280 hypothetical protein

1163055 0.0068 C A 9 42 0 0 Non-synonymous

GTA TTA Val Leu lmo1129 1162666 1163280 hypothetical protein

1163059 0.0068 C T 0 42 0 9 Synonymous TTG TTA Leu Leu lmo1129 1162666 1163280 hypothetical protein

66276 0.0097 T C 0 3 0 48 Synonymous CCT CCC Pro Pro lmo0061 66160 70656 hypothetical protein

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Refposition p-value


Refaa Non-refaa


137562 0.0097 G T 0 0 48 3 Synonymous GGG GGT Gly Gly lmo0135 137323 138897 peptide ABC transporter substrate-binding protein

139157 0.0097 A G 48 0 3 0 Synonymous CAA CAG Gln Gln lmo0136 138999 139949 peptide ABC transporter permease

139178 0.0097 A G 48 0 3 0 Synonymous CCA CCG Pro Pro lmo0136 138999 139949 peptide ABC transporter permease

139247 0.0097 T C 0 3 0 48 Synonymous AAT AAC Asn Asn lmo0136 138999 139949 peptide ABC transporter permease

150185 0.0097 T C 0 3 0 48 Intergenic - -

lmo0152:lmo0153 148354:150232 150009:151173 peptide ABC transporter substrate-binding protein: zinc ABC transporter substrate-binding protein

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This group of highly associated SNPs are harbored by genes which were previously

described as likely involved in cold adaption such as lmo0135 ABC-transporter

(Cabrita et al., 2013), lmo033 internalin (Hingston et al., 2017; Kovacevic et al., 2013)

and lmo0316 hydroxyethylthiazole (Mattila et al., 2012). The other highly associated

SNPs are harbored by hypothetical proteins and in intergenic regions, where for

example promotors and regulators can be located (Thorpe, Bayliss, Hurst, et al., 2017).

Although SNP-based GWAS has been recently applied in bacterial genomics, it is

worth to notice that the SNPs associated to cold growth phenotype were supported by

lower p-values in comparison to those reported by already published GWAS-based

studies investigating antibiotic resistance (Chen and Shapiro, 2015; Earle et al., 2016).

At least two reasons can be advanced to explain this discrepancy. The first is related

to the limited number of genomes related to “slow” growing strains. A more balanced

dataset might provide lower p-values supporting stronger phenotypic association. The

other reason is related to the selected phenotype of interest. Contrary to other studies,

the analyzed phenotype in this study is related to multiple compartments of the

bacterial cell (Chan and Wiedmann, 2008). As previously reported, cold adaptation of

L. monocytogenes may involve a variety of mechanisms, like the uptake of compatible

solutes and oligopeptides (Chan and Wiedmann, 2008), the membrane fluidity

(Saunders et al., 2016) and/or the general stress response factors (Cacace et al., 2010;

Chan et al., 2008).

Other methods to perform bacterial GWAS were published these last years. Some of

them rely on k-mers presence/absence (Earle et al., 2016; Lees et al., 2016; Marinier

et al., 2017), these approaches aim to associate traits with substitutions in the core

genome, and the accessory genome (Earle et al., 2016). Another tool was recently

proposed to specifically analyses presence/absence of intergenic regions (Thorpe,

Bayliss, Sheppard, et al., 2017). Herein, SNPs in genes, intergenic regions, as well as

presence/absence of genes can be explored. A novel SNP-based GWAS method was

also recently proposed for continuous phenotype (Collins and Didelot, 2018). An

additional k-mer-based GWAS approach dealing with phenotype-associated genetic

variants (i.e. SNPs, InDels, intergenic regions and recombination events) without prior

knowledge is still under evaluation (Jaillard et al., 2018).

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Notwithstanding, a promising alternative to GWAS which rely on the combination of

high-throughput gene function assays with mechanistic based models prioritizing

genetic variants (i.e. synonymous variants, variants in non-coding regions and

accessory genome) has been recently refined (Galardini et al., 2017). Such method

has been successfully applied on a large collection of E. coli strains with known

genotype and growth phenotype to deliver growth predictions, considering the variant’s

probability of affecting gene functionality, and genetic intervention strategies (Galardini

et al., 2017).

3.2 Analysis of the growth ability at 2 °C through a Bayesian inference method

The distribution of the observed qualitative phenotype was evaluated with phylogenetic

trees based on core genome (Figure 17) and inlA gene (Figure 18). In general, if all

individuals included in a phylogenetic analysis have the same phenotype distribution,

measured phenotypes would be randomly distributed on the tree leaves. Treebreaker

can assess if the phenotype distribution evolves over time. The model is based on

Poisson process of evolving phenotype distribution on the branches of a phylogeny. It

allows checking if that distribution is different from ancestral state reconstruction.

Hence, this Bayesian inference method aims at identifying the steadiness distribution

of phenotype within a tree segment. When a segment has a significant distribution of

a specific phenotype compared to other segments, the corresponding branch is

flagged. The thickness of the branches is proportional to the posterior probability of

phenotype change on the given branch. To perform this analysis a phylogenetic tree

was built based on core genome SNPs detected on the 51 L. monocytogenes strains

excluding recombination events because high rates of recombination events can affect

reconstruction of L. monocytogenes lineages (Moura et al., 2017). Recombination

events are distinct from evolutionary mechanisms related single substitutions and can

consequently bias ancestral sequence reconstruction from where phylogenetic

reconstructions are inferred with regards to evolutionary models of substitutions (Yang

and Rannala, 2012). The variants from recombination events were thus removed in

order to improve the accuracy of phylogeny reconstruction. The two Bayesian

inference branches presenting the highest associations with “slow” growth ability at

2 °C correspond to the lineage I and CC9 of the lineage II (Figure 17).

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These findings are in accordance with the study of Hingston et al. (2017), where the

CC1, CC2 and CC6 belonging to lineage I as also CC9 (lineage II) presented the

slowest growth rates at 4 °C in broth (Hingston et al., 2017).

A similar approach to that herein presented, has been already applied to find

association between antimicrobial resistance along branches of a phylogenetic tree

(Suzuki et al., 2016). Nevertheless, in a study on Streptococcus pyogenes, it has been

shown that phylogenetic inferences based on the alignment of specific SNP loci

associated to specific phenotype may exhibit topological deviation compared to core

genome-based phylogeny (Bao et al., 2016). Moreover, the categorical inferences

established at the accessory gene level (i.e. wgMLST) can also exhibit topology

distortion in comparison to core genome phylogenetic inferences based on SNPs

(Henri et al., 2017). Accordingly, focusing the analysis on specific genes could be more

relevant for investigating the evolution of the phenotypic trait of interest. For example,

Møretrø et al. (2017) succeeded in finding relevant association between Benzalkonium

Chloride (BC) susceptibility differences and a specific variant (i.e. cysteine/serine

difference at amino acid 42) detected in qacH gene in L. monocytognes strains carrying

or not the Quaternary Ammonium Compound (QAC) resistance genes (i.e. bcrABC or

qacH) (Møretrø et al., 2017).

Figure 17 : Evolution of a discrete phenotype distribution (red dot=”slow”, orange dot=”fast”) on a phylogenetic tree including 51 analyzed genomes. The thickness of the branches is proportional to the posterior probability of having a change point. The branch “▲” corresponds to lineage I and the branch “∆” to the CC9.

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Therefore, a candidate gene approach focusing on a pre-specified gene of interest (i.e.

inlA) already associated to cold adaptation (Hingston et al., 2017; Kovacevic et al.,

2013) was applied. A phylogenetic tree (Figure 18) was thus established based on the

sequences alignment of inlA gene and used as input for Treebreaker analysis.

In Figure 18, a pheno-genotype association (thicker branch) is shown, although the

null-hypothesis model (i.e. no association of binary phenotype with the branch) is not

rejected at 0.05 p-value level. Even though, the thicker branch reported in Figure 18 is

the same than that obtained based on core genome SNPs excluding recombination

events and corresponding to the strains of lineage I.

Figure 18 : Evolution of a discrete phenotype distribution (red dot=”slow”, orange dot=”fast”) on a phylogenetic tree based on DNA sequences of inlA gene.

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4 Conclusion

A number of genes and SNPs, as well as specific phylogenetic sub-lineages were

identified as associated to L. monocytogenes’ growth at low temperature (2 °C). The

test data set used to present the different approaches in this study contains 51 strains.

The application on a larger genome data set of accurately selected strains with

contrasting phenotypes would likely increase the power to detect the associations.

The development of various innovative methods, as well as the increasing availability

of bacterial genomes, opens a new area for food microbiology studies. The

identification of genetic markers associated to phenotype of interest along the food

chain should provide sensitive and accurate methods to predict and then control the

behavior of foodborne pathogens (Cocolin, Membré, et al., 2018; den Besten et al.,

2017). The use of GWAS could help to perform risk assessments especially targeting

the subpopulations that pose the greatest risk linked to their higher ability to survive

and/or grow in the food chain (Franz et al., 2016).

A rapid detection of strains harboring genetic markers related to the high ability to

persist in the processing environment or to high virulence potential could guide

reinforced control measures and optimize interventions strategies. The identification of

such biomarkers at the genomic level is only one aspect. The expansion of alternative

omics approaches based on transcriptomic or proteomics is opening new relevant

perspectives for a full understanding of pathogen behavior in foods as well as in the

whole food chain (Cocolin, Mataragas, et al., 2018; Hingston et al., 2017; Renier et al.,

2012).

Acknowledgements

This work received funds from The French National Research Agency under the project

ANR-15-CE21-0011 OPTICOLD.

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Chapter 2

Next generation quantitative microbiological risk assessment: Refinement of the

cold smoked salmon-related listeriosis risk model by integrating genomic data

Authors:

Lena Fritsch1, Laurent Guillier1, Jean-Christophe Augustin1,2*

Affiliation:



2 Ecole Nationale Vétérinaire d'Alfort, Maisons-Alfort F-94704, France.

Keywords: Listeria monocytogenes, Quantitative Microbial Risk Assessment, genome wide

association study, Clonal Complex

Published in Microbial Risk Analysis, Volume 10, December 2018, Pages 20-27.

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Résumé

Les développements récents dans le séquençage du génome ouvrent de nouvelles

opportunités pour expliquer la variabilité intraspécifique des phénotypes (p. ex.

virulence, comportement de croissance). L’association entre les données WGS et des

phénotypes spécifiques ouvre la voie à mieux prédire les comportements microbiens.

L’introduction de ces nouvelles données dans les processus d'identification des

dangers, d'évaluation de l'exposition et de caractérisation des dangers permettra

d'affiner les modèles d’appréciation quantitative des risques microbiens (AQRM). Le

but de cette étude était d'explorer cette voie d’amélioration des études d'AQRM en

considérant les associations phénotype/génomique- en ce qui concerne la capacité de

croissance à basse température (température minimale de croissance, Tmin) et la

virulence. Le modèle d'AQRM utilisé a déjà été développé pour l'évaluation du nombre

de cas de listériose associés au saumon fumé à froid en France. La prévalence globale

dans le modèle existant a été remplacée par la prévalence spécifique pour chaque

sous-groupe génotypique (complexe clonal - CC) en Europe. Afin de décrire plus

précisément la variabilité des caractéristiques de croissance de L. monocytogenes,

deux distributions différentes de Tmin ont été appliquées. Pour la caractérisation des

risques, trois groupes différents de virulence ont été considérés selon les CC. Chaque

groupe a été associé à un modèle dose-réponse spécifique. Le nouveau modèle

d'AQRM a montré que les CC qui contribuent le plus à l'exposition des consommateurs

ne sont pas ceux qui contribuent le plus aux cas de listériose. Les CC les plus répandus

ont entraîné peu de cas de listériose, alors que des souches virulentes bien que

représentant moins de 10 % de l’exposition étaient responsables de la majorité des

cas prévus. De même, le groupe de souches ayant des fortes capacités de croissance

au froid (Tmin faibles) sont deux fois plus impliqués que les souches à faible capacité

de croissance au froid. La prise en compte des données génotypiques dans l'AQRM

ouvre la voie à l'établissement de mesures de maitrise propres à des sous-groupes

distincts de L. monocytogenes.

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Abstract

Recent developments in genome sequencing open new opportunities for explaining

the intraspecific variability of phenotypes (e.g. virulence, growth behavior). Successful

association between WGS-data and specific phenotypes is thought to contribute to

better predicting microbial behaviors. Implementing this information in hazard

identification, exposure assessment, and hazard characterization processes will refine

quantitative microbial risk assessments (QMRA) models. The aim of this study was to

explore the refinements in QMRA studies when considering phenogenotype

associations for the hazard properties, particularly related to the growth ability at low

temperature (minimal growth temperature, Tmin) and the virulence. The used QMRA-

model was previously developed for the assessment of the number of listeriosis cases

associated to cold-smoked salmon in France. The global prevalence in the existing

model was replaced by the specific prevalence for each genotypic subgroup (clonal

complex - CC) in Europe. In order to describe the variability of Listeria monocytogenes’

growth characteristics more accurately, two different distributions of Tmin were

implemented. For risk characterization, three different groups of virulence were

considered according to the CCs. Each group was associated with a specific dose-

response model. The new QMRA model showed that CCs contributing the most in

consumer exposure were not those that contributed the most to listeriosis cases. The

most prevailing CCs led to few listeriosis cases, whereas uncommon high virulent

strains were responsible for the majority of predicted cases. Similarly, the less

prevailing group of strains with high Tmin was approximately two times less implicated

when considering human listeriosis in comparison to food contamination. Considering

genotypic data in QMRA opens the way for the establishment of risk based measures

specific to distinct sub-groups of L. monocytogenes.

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1 Introduction


Dans la gestion des risques pour la santé publique, l'appréciation quantitative des

risques microbiens (AQRM) joue un rôle crucial dans la justification de la mise en

œuvre de mesures de maitrise (den Besten et al., 2017 ; Membré et Guillou, 2016).

L'estimation des risques auxquels les consommateurs d'aliments sont exposés,

fondée sur un modèle, est appliquée depuis près de deux décennies (Rantsiou et al.,

2018). Bien que ces modèles soient bien établis, ils présentent encore des lacunes qui

pourraient avoir une forte incidence sur l'évaluation de l'exposition, p. ex. entre la

variabilité des caractéristiques de croissance des souches (Delignette-Muller et al.,

2006). De plus, le manque d'information précise sur la variabilité de la relation dose-

réponse pour les pathogènes d'origine alimentaire induit une incertitude importante

dans les estimations des risques (Haddad et al., 2018; McLauchlin et al., 2004).

Les modèles d'AQRM tiennent généralement compte de la variabilité observée de

l'agent pathogène d'origine alimentaire à l'étude en mettant en œuvre des distributions

décrivant la variabilité des paramètres biologiques au niveau des espèces. Toutefois,

la corrélation entre les souches contaminant l'aliment et les propriétés de croissance

ou de virulence associées n'est pas prise en compte. De nouvelles approches dans le

séquençage du génome entier (WGS), et les études d'association à l'échelle du

génome (GWAS) pourraient annoncer une nouvelle ère de l'AQRM (Cocolin et al, 2018

; den Besten et al, 2017 ; Rantsiou et al, 2018). La diminution des coûts du WGS et le

nombre croissant d'outils accessibles pour le traitement des données génomiques

ouvrent la voie à de nouvelles approches pour étudier la variabilité phénotypique entre

souches.

La description précise des phénotypes des pathogènes d'origine alimentaire (p. ex.

virulence, capacité de survivre à un stress et caractéristiques de croissance) est

fondamentale pour l'EQRM. Il sera possible dans un proche avenir, à l'aide de données

génomiques/omiques, de cibler les évaluations des risques sur les sous-populations

bactériennes qui présentent le plus grand risque (Franz et al., 2016 ; Pielaat et al.,

2015). Des articles récemment publiés ont éclairé le lien entre le comportement des

pathogènes d'origine alimentaire et leurs génotypes (den Besten et al., 2010 ; Hingston

et al., 2017 ; Maury et al, 2016 ; Pielaat et al., 2015).

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L. monocytogenes est l'une des principales préoccupations des gestionnaires de

risques et l'un des pathogènes les plus considérés dans l'AQRM. Bien que la listériose

ne soit pas la zoonose d'origine alimentaire la plus courante, le taux élevé de létalité

(jusqu'à 30 %) (Ross et al., 2009), combiné à la fréquence relativement élevée de

l'isolement dans les aliments (Buchanan et al., 2017) et sa capacité de croissance

dans des conditions difficiles, ont conduit à l'établissement de nombreux modèles pour

prévoir sa croissance pour évaluer son exposition. Les aliments prêts à consommer

sont la principale source d'exposition (Buchanan et al., 2017 ; EFSA BIOHAZ Panel et

al., 2018), en raison de l'absence de traitement thermique avant consommation

(Lindqvist et Westööö, 2000). Les produits emballés de saumon fumé à froid ont été

décrits comme des sources potentielles de listériose humaine (Ericsson et al., 1997 ;

Loncarevic et al., 1998). Par conséquent, des modèles d'AQRM pour

L. monocytogenes ont été développés pour ce produit (Lindqvist et Westöö, 2000 ;

Pouillot et al., 2007).

Certains travaux récents ont porté sur le lien entre les génotypes et les phénotypes de

L. monocytogenes, ce qui permet une meilleure compréhension du comportement des

différentes souches spécifiques. Maury et ses collaborateurs (2016) se sont

concentrés sur les niveaux de virulence associés aux complexes clonaux (CC). Ils ont

couplé les données génomiques aux données cliniques et épidémiologiques. Bien que

la plupart des études d'association portent sur les phénotypes cliniques, l'étude

d'association à l'échelle du génome portant sur les phénotypes liés aux aliments a

rarement été menée (Hingston et al., 2017). Récemment, Hingston et ses

collaborateurs (2017) ont étudié les génotypes de L. monocytogenes associés au

stress du froid, du sel, de l'acide et de la dessiccation.

Même s'il est maintenant possible de produire les données nécessaires et de réaliser

des études d'association, il reste encore plusieurs défis à relever. Cocolin et ses

collaborateurs (2018) considèrent que le plus grand défi pour les microbiologistes est

maintenant de montrer comment intégrer les données omics dans l'AQRM.

L'objectif de cet article est de présenter comment les données génomiques récemment

publiées peuvent être utilisées pour affiner un modèle évaluant le risque de listériose

lié à la consommation de saumon fumé. Premièrement, l'information sur les

biomarqueurs potentiels peut être utilisée pour décrire la capacité des souches de

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L. monocytogenes à se développer dans des conditions froides, ce qui affecte ensuite

l'exposition des consommateurs à l'agent pathogène d'origine alimentaire.

Deuxièmement, un lien hypothétique entre les types génomiques (CC) et les propriétés

de virulence (relation dose-réponse) est proposé pour évaluer le risque d'infection pour

chaque souche contaminante en fonction de sa classe de virulence.

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In public health risk management, Quantitative Microbial Risk Assessment (QMRA)

plays a crucial role when assessing the risk associated with the consumption of food

and justifying the implementation of control measures (den Besten et al., 2017;

Membré and Guillou, 2016). The model-based estimation of the risk food consumers

are exposed to has been implemented for almost two decades (Rantsiou et al., 2018).

Although these models are well-established for risk assessment, they still have

deficiencies that could strongly impact exposure assessment, e.g. between strains

variability of the growth characteristics (Delignette-Muller et al., 2006). Moreover, the

lack of accurate information on the variability of the dose-response for foodborne

pathogens induces a significant uncertainty in risk estimates (Haddad et al., 2018;

McLauchlin et al., 2004).

QMRA models generally take into account the observed variability for the foodborne

pathogen under consideration by implementing distributions describing the variability

of biological parameters at species level. However, correlation between the strains

contaminating the food and the related growth or virulence properties are not

considered. New approaches and achievements in whole genome sequencing (WGS),

omics and genome wide association studies (GWAS) might herald a new era of QMRA

(Cocolin, Mataragas, et al., 2018; den Besten et al., 2017; Rantsiou et al., 2018). The

decreasing costs of WGS and the increasing number of accessible tools for genomic

data treatment open the way to new approaches to study phenotypic variability

between strains.

The accurate description of foodborne pathogens’ phenotypes (e.g. virulence, ability

to survive to a stress and growth characteristics) is fundamental for QMRA. It will be

possible in the near future, using genomic/omic data, to target risk assessments on

bacterial subpopulations that pose the greatest risk (Franz et al., 2016; Pielaat et al.,

2015). Recently published papers enlightened the link between foodborne pathogens’

behavior and their genotypes (den Besten et al., 2010; Hingston et al., 2017; Maury et

al., 2016; Pielaat et al., 2015).

Listeria monocytogenes is one of major concern of risk managers and is one of the

most considered pathogens in QMRA. Although listeriosis is not the most common

foodborne zoonosis, the high rate of fatal cases up to 30 % (Ross et al., 2009)

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combined with relatively high frequency of isolation in foods (Buchanan, Gorris, et al.,

2017) and its capacity to grow under harsh conditions, lead to the establishment of a

high numbers of models to predict its growth for exposure assessment. Ready-to-eat

(RTE) foods are the major source of exposure (Buchanan, Gorris, et al., 2017; EFSA

BIOHAZ Panel et al., 2018), due to the absence of heat treatment before consumption

(Lindqvist and Westöö, 2000). Packaged products of cold-smoked salmon (CSS) have

been described as potential sources of human listeriosis (Ericsson et al., 1997;

Loncarevic et al., 1998). Hence, CSS QMRA models for L. monocytogenes have been

developed (Lindqvist and Westöö, 2000; Pouillot et al., 2007).

Some of the recent works were focused on the link between genotypes and

phenotypes of L. monocytogenes, which permits a better understanding of the different

specific strains’ behavior. Maury et al. (2016) focused on the virulence levels

associated to Clonal Complexes (CCs). They coupled genomic data to clinical and

epidemiological data. While most association studies are investigating clinical

phenotypes, GWAS dealing with food related phenotypes were rarely conducted

(Hingston et al., 2017). Recently, Hingston et al. (2017) studied the genotypes of

L. monocytogenes associated to cold, salt, acid and desiccation stress.

Even if it is possible now to produce the necessary data and to perform association

studies, there are still several challenges to overcome. Cocolin et al. (2018) considered

that the greatest challenge for microbiologist is now to show how to integrate omics

data in QMRA (Cocolin, Mataragas, et al., 2018).

The objective of this paper is to present how recently published genomic data can be

used to refine a model assessing the listeriosis risk linked to the consumption of CSS.

Firstly, potential biomarker information can be used to describe the ability of

L. monocytogenes strains to grow in cold conditions affecting then the exposure of

consumers to the foodborne pathogen. Secondly, a hypothetical link between the

genomic types (CCs) and the virulence properties (dose-response relationship) is

proposed to assess the risk of infection for each contaminating strain according to its

virulence class.

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2 Material and methods

2.1 Overview of the model: Quantitative risk assessment of

L. monocytogenes in cold-smoked salmon

The quantitative risk assessment model was published by Pouillot et al., (2007, 2009).

This model was developed to estimate the probability of invasive listeriosis associated

to the consumption of CSS in France.

The exposure of consumers to L. monocytogenes was assessed by simulating the

behavior of bacteria contaminating CSS packs from the processing plant to the

consumption taking into account the time-temperature profiles encountered during the

distribution chain (Pouillot et al., 2007). The L. monocytogenes density in CSS at

consumption depends on the prevalence and the initial concentration (end of the

production line) of L. monocytogenes, X0, the growth parameters of L. monocytogenes

in CSS (minimal temperature for growth, Tmin, and reference growth rate in CSS, ref,

maximum population density, MPD), and on the characteristics (duration and

temperature) of the different logistic stages (storage at warehouse, platform, retail cold

room, refrigerated transport, retail display cabinet, transport after purchase, household

conservation at ambient temperature and in refrigerator). Since the background

competitive flora of CSS can inhibit L. monocytogenes growth, a predictive model

simulating the “Jameson effect” (growth of L. monocytogenes and food flora

independent until one of them reaches its maximum population density and stops both

growths) was used. This model requires supplementary data related to the background

CSS flora (X0, Tmin, ref, MPD). The final L. monocytogenes density (at consumption)

was combined with consumption data (serving size) to evaluate the exposure of the

population to L. monocytogenes per CSS serving.

The probability of invasive listeriosis was then assessed with a dose-response model

reflecting the pathogenicity of the microorganism and the sensitivity of the consumers.

An exponential dose-response equation with a parameter r (probability of illness for

the ingestion of one bacterial cell) was used for this risk characterization (Pouillot et

al., 2009). The annual number of listeriosis cases could then be predicted by combining

the distribution of individual risks with the total number of servings per year.

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Figure 19 illustrates the used model and the factors influencing the amount of

L. monocytogenes the consumers are exposed to. These factors are related to: (i) the

product (prevalence, level of contamination, presence of annex flora), (ii) the cold chain

(time-temperature profiles), (iii) microbiological characteristics (minimum temperature

for growth, growth rate, and maximum population level), (iv) consumption data (portion

size and frequency).

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Figure 19 : Model for assessing exposure of L. monocytogenes by consumption of cold-smoked salmon (adapted from Pouillot et al., 2007)

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2.2 Genomic differentiation of L. monocytogenes for hazard identification

The clonal framework of Listeria species can accurately be defined by Multilocus

sequence typing (MLST). MLST profiles are based on the sequence-analysis of seven

housekeeping genes including; acbZ (ABC transporter), bglA (beta-glucosidase), cat

(catalase), dapE (Succinyl diaminopimelate desuccinylase), dat (D-amino acid

aminotransferase), ldh (lactate deshydrogenase), and lhkA (histidine kinase). By

means of these profiles, CCs are categorized. A CC is defined by allelic profiles sharing

6 out of 7 genes, whereby at least one gene is shared with only one of another group

(Ragon et al., 2008).

2.3 Virulence properties of L. monocytogenes genotypes subtypes

For risk characterization, virulence levels were identified from virulence studies

performed with different L. monocytogenes strains in mice (Pine et al., 1991; Pine et

al., 1990; Stelma et al., 1987). The median lethal doses (LD50) obtained by

intraperitoneal infection route for 26 strains (FDA/FSIS, 2003) were firstly translated in

r-values, that is in the probability of a given bacterial cell to cause the adverse effect.

The LD50 is inversely proportional to r (Pouillot et al., 2015), then we can transform

log10 LD50 into log10 r with a scaling factor. This scaling factor was estimated by

assuming that the median log10 LD50 of the 26 strains (i.e., 5.02 log10 CFU) corresponds

to the median log10 r for the whole population published by Pouillot et al. (2015) (i.e., -

13.81 that is equal to log10 of 1.5610-14). The scaling factor to translate log10 LD50 into

log10 r is then equal to -8.79 (log10 r = -log10 LD50 -8.79). To determine a number of

groups relating to virulence-level, a dendrogram based on these values was made. R

packages phytools (Revell, 2012b), “ape” (Paradis et al., 2004) and “dendextend”

(Galili, 2015) were used to build the dendrogram using the function “hclust”

(method="complete").

In a second step, the work of Maury et al. (2016) related to the virulence of

L. monocytogenes clones was used to associate r-values to the diverse CCs of the

pathogen. They characterized 6,633 L. monocytogenes strains collected in France

between 2005 and 2013, including 2,584 clinical and 4,049 food isolates (strains from

own samples of food by industries in France and from passive surveillance in RTE

foods). The CC1, CC2, CC4 and CC6 are the most frequent clones found among

clinical isolates; in addition Maury et al. (2016) demonstrated that CC1, CC4 and CC6

are hypervirulent clones in a humanized mouse model of listeriosis. On the contrary,

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CC9 and CC121 showed hypovirulence in the mouse model and were strongly

associated with food but not in clinical infection. Thus, in this study, strains

corresponding to CC9 and CC121 were attributed to the group of low virulence while

strains corresponding to CC1, CC2, CC4 and CC6 were attributed to the group of high

virulence. Since the virulence assessment in the mouse model was not performed for

all the CCs studied by Maury et al. (2016) and that it is not possible to identify at this

step all the genomic traits associated with the level of virulence, we used the clinical

frequency of CCs as a parameter to characterize their virulence. The clinical frequency

takes into account the exposure and the virulence. It was calculated by dividing the

number of clinical isolates of a particular CC by the total number of clinical and food

isolates of that CC (Maury et al., 2016).

As the frequency of clinical isolates among the total number of isolates was a good

indicator for the hypervirulent clones CC1, CC2, CC4 and CC6 as well as for the

hypovirulent CC9 and CC121, the same assumption was made for the other clones. A

dendrogram (function “hclust”, method="complete") based on these frequencies was

built to classify clones according to their virulence.

It was finally assumed that the virulence groups could be related to the r-values groups,

and for instance, that the CC1, CC2, CC4 or CC6 belonging to the hypervirulent group

are characterized by r-values belonging to the group of the smallest r-values. The 294

strains of L. monocytogenes prevalent in smoked salmon in Europe (Møller-Nielsen et

al., 2017) were then associated to virulence and r-values groups according to their CC

(Supplementary material 6).

2.4 Phenotypic (growth) characteristics of L. monocytogenes subgroups

In order to describe the variability of L. monocytogenes’ growth characteristics more

accurately, different distributions of Tmin were implemented. Data published by

Hingston et al. (2017) describing the ability of 166 L. monocytogenes to grow in cold

(4 °C) stress conditions were used. The standardized values for growth rate at 4 °C,

std-max, were translated into Tmin values assuming a square root model to describe

the effect of temperature on the growth rate and a Tmin value equal to -2.86 °C for the

median isolate (Pouillot et al., 2007). Tmin values for each isolate were then calculated

as follows:

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�� = 4 − (4 + 2.86)��−��

A dendrogram based on the Tmin values was made to classify isolates according to

their growth ability. We then assumed that a potential genomic marker could be

identified to clearly discriminate “low-growing” and “high-growing” strains with high and

low Tmin values, respectively. Then, the respective proportions of these different strains

among the L. monocytogenes isolates contaminating salmon were assumed similar to

those observed by Hingston et al. (2017) in their collection.

2.5 Prevalence of L. monocytogenes genotypes in cold-smoked salmon

A global prevalence for L. monocytogenes of 10.4 % in Europe (EFSA, 2013) was used

to predict listeriosis cases caused by CSS consumption in France. Then, the global

prevalence was split into specific prevalence for each genotypic subgroup. The relative

frequencies for the L. monocytogenes genotypes contaminating CSS were derived

from the recent distribution observed in Europe (Møller-Nielsen et al., 2017).

2.6 Model parameters and simulation

The L. monocytogenes growth in CSS and the individual risk linked to the consumption

of contaminated servings were estimated with Matlab R2017a software (The

Mathworks Inc., Natick, MA, USA). Monte Carlo simulations were performed to take

into account the variability of input parameters (Table 8). Simulation runs of 100,000

iterations were replicated 10 times in order to obtain stable predictions. For each

simulation run, the mean “individual” risk (due to consumption of a serving) was

multiplied by the mean annual number of servings per inhabitant and by the population

size to predict the number of annual cases of listeriosis. The simulations were

performed for each group of L. monocytogenes strains to estimate the expected

number of cases according to the virulence and the growth characteristics (minimal

growth temperature) of the strains. A generic simulation for L. monocytogenes was

also performed by randomly sampling r-values and Tmin value in the entire

distributions (Table 8). This generic simulation corresponds to a non-informative

approach about the virulence and growth genotypes distribution in the population of

the L. monocytogenes strains contaminating CSS in Europe.

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Table 8 : Distributions of parameters used to simulate the growth of L. monocytogenes in cold-smoked salmon and to estimate the corresponding listeriosis risk.

Parameter Symbol (unit)

Distribution/valuea Source

L. monocytogenes characteristics

Initial concentration X0Lm (log10 CFU.g-1)

N(-1.05,0.74) (Pouillot et al., 2007)

Minimal temperature for growth for group LG

TminLG (°C) ED({1/5,1/5,1/5,1/5,1/5},{-2.09,-2.02,-1.97,-1.93,-1.84}) (Hingston et al., 2017)

Minimal temperature for growth for group HG

TminHG (°C) ED({1/161, … ,1/161},{-3.28, … ,-2.44}) (Hingston et al., 2017)

Reference growth rate at 25°C µrefLm (day-

1) NT(6.2,0.36,0,) (Pouillot et al., 2007)

Log10 r-values for hypervirulent strains rHV ED({1/5,1/5,1/5,1/5,1/5},{-12.46,-12.41,-11.68,-11.48,-11.36}) (Pouillot et al., 2015) (FDA/FSIS, 2003)

Log10 r-values for medium virulence strains

rMV ED({1/15,...,1/15},{-15.02,-14.83,-14.79,-14.7,-14.54,-14.26,-13.87,-13.75,-13.74,-13.52,-13.45,13.4, -13.36,-13.35,-13.28})

(Pouillot et al., 2015) (FDA/FSIS, 2003)

Log10 r-values for hypovirulent strains rLV ED({1/6,1/6,1/6,1/6,1/6,1/6},{-18.49,-17.67,-16.33,-16.09,-16.07,-15.59}) (Pouillot et al., 2015) (FDA/FSIS, 2003)

Maximum population density MPD (log10 CFU.g-1)

N(7.27,0.84) (Pouillot et al., 2007)

Background food flora characteristics

Initial concentration X0ff (log10 CFU.g-1)

N(2.78,1.12) (Pouillot et al., 2007)

Minimal temperature for growth Tminff (°C) N(-4.52,7.39) (Pouillot et al., 2007) Reference growth rate at 25°C µreff (day-1) NT(4.1,0.52,0,) (Pouillot et al., 2007)

Cold-smoked salmon characteristics

Serving size serv (g) ED({11/111,1/111,1/111,29/111,12/111,1/111,41/111,4/111,4/111,1/111,4/111,1/111,1/111}, {10,12,19,20,30,34,40,50,60,67.5,80,100,250})

(Pouillot et al., 2007)

Shelf-life printed on the packaging SLp (days) ED({0.99,0.01},{28,21}) (Pouillot et al., 2007) Shelf-life applied by the consumer SLa (days) Triang(SLp,SLp,SLp+5) (Pouillot et al., 2007) Logisitc chain (succession of process stages)

_ ED({1/35, … ,1/35},) (Pouillot et al., 2007)

Time-temperature profiles of process stages

_ _ (Pouillot et al., 2007)

Consumers characteristics

Population size Npop 61624000 (Pouillot et al., 2009) Mean annual number of servings per inhabitant

S 6.4 (Pouillot et al., 2009)

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3 Results and discussion

3.1 Distributions of phenotypic and virulence properties among

L. monocytogenes subtypes

Figure 20 shows the clustering of r-values of 26 strains of L monocytogenes. This

dendrogram defines the number of virulence groups. Three main groups were

observed; hypovirulence (blue), medium virulence (yellow) and hypervirulence (red).

The average dose response log10 r-values were -11.88 for the hypervirulent group, -

13.99 for medium virulence and -16.71 for the hypovirulent strains.

Figure 21 presents in a dendrogram the CCs of L. monocytogenes according to their

level of virulence (based on clinical frequency). Maury et al. (2016) showed that the

hypervirulent strains in humanized mice-model were also characterized by a high

proportion of clinical isolates in comparison to food isolates. Similarly, the CCs not

tested in mice could also be associated to one of the groups based on their relative

clinical frequency.

Figure 20 : Dendrogram based on log10 r-values of 26 different L. monocytogenes strains (FDA/FSIS, 2003) ranging from −18.49 to −11.36 (Supplementary material 7); blue group=hypovirulence, yellow group=medium virulence and red group=hypervirulence. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Dealing with distance analyzes, it is relevant to cluster as many groups as it is

necessary to obtain real differences (sufficiently large distances) between the groups.

Without that contrast between the categories, the growth ability at low temperature or

the virulence of the strains can be incorrectly assessed and influence the final results.

For example, if a CC is incorrectly attributed to the hypervirulent group and its

prevalence is high in the food product under consideration, the risk will be

overestimated. An error in the attribution of a CC in a virulence group can occur using

the approach based on clinical frequency. Indeed, this approach can lead to a false

interpretation of a CCs’ virulence through to the fact that large outbreaks involving non-

hypervirulent strains could artificially increase their clinical frequency and change the

importance of CCs and their virulence group. To minimize that error source it would be

better to use data only from sporadic cases. In near future the use of virulence

biomarkers (e.g. genes/SNPs) can replace the present grouping (Njage et al., 2017).

Such an approach will be also more precise than the MLST approach as the

determination of CCs depends only on seven housekeeping genes.

Figure 21 : Dendrogram created by clinical frequencies (Maury et al., 2016) of different Clonal-Complexes to associate the level of virulence to CCs; blue group=hypovirulence, yellow group=medium virulence and red group=hypervirulence. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Figure 22 represents the dendrogram based on Tmin values. A huge group of “high

growth” (97 % of strains) with an associated average Tmin of -2.86 °C and a small group

of “low growth” (3 % of strains) with an assigned average Tmin of -1.97 °C were defined.

It is worth to notice that Fritsch et al. (2018) also observed a similar proportion of “high

growth” compared to “low growth” strains.

No markers of these two phenotype subgroups were explored in CSS strains isolated

in Europe. In their GWAS, Hingston et al. (2017) and Fritsch et al. (2018) identified

some potential markers of the L. monocytogenes cold adaptation. For example,

Hingston et al. (2017) showed that isolates with full length inlA exhibited enhanced cold

tolerance relative to those harboring a premature stop codon in this gene. Fritsch et al.

(2018) found a significant association between the ability to grow at low temperature

(2 °C) and a single nucleotide polymorphism (SNP) in the ribosomal large subunit

pseudouridine synthase (lmo2244). When markers will be fully validated, the relative

proportion of “high growth” and “low growth” strains in CSS could be adapted in the

QMRA model.

Figure 22 : Dendrogram based on 166 different strains with corresponding Tmin values presenting the participation of low (blue) and high (red) growing strains. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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3.2 Exposure and risk estimates according to the L. monocytogenes subtypes

The proportion of each group is presented in Table 2. The biggest group is represented

by hypovirulent strains (CC9, CC31, CC121, CC193 and CC204) which account for

51.7 % of the total strains. The hypervirulent strains (CC1, CC2, CC6, CC7 and

CC101) show the lowest prevalence and accounts for 12.6 % of the strains isolated in

CSS. Within these categories, 3 % of strains were associated to “low growth” and 97 %

to “high growth” in cold conditions.

Table 9 : Relative frequencies of the subgroups of virulence and growth ability at low temperature observed in L. monocytogenes strains present in cold-smoked salmon.

Low growth High growth Total

Hypervirulent 0.4 % 12.2 % 12.6 %

Medium virulence 1.1 % 34.6 % 35.7 %

Hypovirulent 1.6 % 50.1 % 51.7 %

Total 3.0 % 97.0 % 100.0 %

The generic model in which the population structure in CSS was not considered,

predicted a number of 978 listeriosis cases after consumption of 50 g of CSS with an

initial L. monocytogenes prevalence of 10.4 % while in the model taking into accounts

the prevalence of the subpopulations in CSS, 574 listeriosis cases were predicted. The

number of virulence groups was based on r-values from 26 different strains, wherein

five of them were assessed as hypervirulent. This is a proportion of about 19 %, while

the effective prevalence of strains in CSS with CCs corresponding to hypervirulence

was only 12.6 %. Similarly, for the hypovirulence, where 23 % of strains (6 among 26)

were associated to this group compared to 51.7 % of CCS-prevalent hypovirulent

strains. Omitting the specific prevalence leads, in this case, to the over-estimation of

the predicted listeriosis cases. In consequences, it is necessary to track and identify

all food and clinical isolates by sequencing for obtaining the needed information and

data on prevalence (Buchanan, Gorris, et al., 2017).

It is noticeable that the highest amounts of listeriosis cases (97.0 %) were caused by

the hypervirulent group despite their low proportion (12.6 %) in contaminated CSS.

Inversely, the most prevalent group (51.7 %) was responsible for only 0.02 % of the

cases of listeriosis in a total of 574 predicted cases. These results show how important

the specific prevalence and the level of virulence are.

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Table 10 : Predicted cases of listeriosis of the appropriate virulence and Tmin groups.

Low growth High growth Total

Hypervirulent 9.2 (1.6 %) 547.2 (95.4 %) 556.4 (97.0 %)

Medium virulence 0.3 (0.1 %) 16.9 (3.0 %) 17.2 (3.0 %)

Hypovirulent 0.0 (0.0003 %) 0.1 (0.02 %) 0.1 (0.02 %)

Total 9.5 (1.7 %) 564.2 (98.3 %) 573.7

The predicted cases of listeriosis are highly influenced by the prevalence of CCs and

the associated dose-response. In general the dose-response is estimated by in vitro

and/or in vivo animal tests. However, it is difficult to get accurate dose information after

an outbreak due to the time that passes between the exposure to L. monocytogenes

and the identification of the outbreak (Buchanan, Gorris, et al., 2017). Moreover, the

dose-response relationships in animals are different to those of humans. The

investigation of outbreaks will certainly help to reduce uncertainty of dose-response

model and to improve the knowledge of the virulence potential of CCs (Chen et al.,

2017; Pouillot et al., 2016)

The effect of the low/high growth groups was slighter than the effect of the virulence.

On one hand, this slight influence may be due to the low presence of the low growing

group (3 %). On the other hand, more importance is given to the pathogenicity when

estimating predicted cases. Nevertheless, the impact of the growth ability is visible

considering the discrepancy between the prevalence of low growing strains (3 %)

compared to the predicted cases linked to this group (1.7 %). The mean exposure of

the consumer was two times more important in the high growth groups (1.4 log10

CFU/g, i.e., 25 CFU/g) compared to the low growth groups (1.1 log10 CFU/g, i.e., 13

CFU/g). This tendency is even larger for the extreme percentiles with a 0.5 log10

difference between the two groups of strains for the 99th percentile, i.e., 1 % of portions

contain more than 6.9 log10 CFU/g when they are contaminated with a high growth

strain against 6.4 log10 CFU/g when the contamination occurs with a low growth strain.

As illnesses most frequently come from exposure to the high doses (Buchanan, Gorris,

et al., 2017; EFSA BIOHAZ Panel et al., 2018), this observed factor between the

“growth” groups can be an issue for risk assessment.

This refinement, although using some strong hypotheses, highlighted the effect and

potential impact of implementing genomic data in QMRA. In this work two examples of

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different phenotypic behaviors were included. Additional phenotypic traits of foodborne

pathogens could be included that are highly informative in QMRA (Franz et al., 2016),

such as persistence/biofilm forming abilities, stress resistance (Hingston et al., 2017),

and also in the case, for example, of Bacillus, the robustness to lethal treatments

associated to genotypes (den Besten et al., 2010).

Nonetheless, if biomarkers are used to describe a phenotypic behavior, an uncertainty

should be considered (den Besten et al., 2017). In this paper, groups were created and

the corresponding values were taken into account with an appropriate variability. In

other words, the present proposal does not consider the presence of a genetic marker

leading to an exact behavior/value, furthermore the genetic differences were used as

a hint to precise the predicted behavior more in detail compared to the current

approaches without geno-/phenotype associations.

The use of biomarkers is a statistical approach that provides the possibility to perform

risk assessments more targeted on subpopulations presenting the greatest risk (Franz

et al., 2016).

The research field of GWAS to find biomarkers for bacteria genomes is still young

(Sheppard et al., 2013). First associations to food safety relevant phenotypes were

published recently but their phenotypes were not applied in food matrices (Hingston et

al., 2017; Pielaat et al., 2015). Nowadays, it is possible to create the data which is

necessary to start next generation of QMRA that is already well discussed in recent

papers (Cocolin, Mataragas, et al., 2018; Cocolin, Membré, et al., 2018; den Besten et

al., 2017; Rantsiou et al., 2018).

This new era needs still a couple of time to generate accurate models with

implementation of genomic/ omic data and to learn how this data can be used the best.

In near future this evolution could result in a better risk estimation and thus improve

risk management measures. Potentially, it might improve the hazard identification step,

i.e. hypervirulence linked to CCs and thus the risk analysis could be adapted to

particular groups. Such a new approach could lead to changes in risk management by

food authorities and companies e.g. by adapting or by refining the intervention

strategies according the hazards’ properties (Haddad et al., 2018).

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Acknowledgement

This work received funds from The French National Research Agency under the 411

project ANR-15-CE21-0011 OPTICOLD. Special thanks go to Dr. Steven Duret for his

kindly aid.

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Chapter 3

A novel microscopy method for determining individual lag times and growth

probability of individual bacterial cells

Authors:

Lena Fritsch1, Hélène Bergis1, Abirami Baleswaran1, Jean-Christophe Augustin1,2*, Laurent

Guillier1

Affiliations:



2 Ecole Nationale Vétérinaire d'Alfort, Maisons-Alfort F-94704, France

Keywords: single cell, microscopy, Listeria monocytogenes, lag time, growth probability, cold.

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Résumé

La détermination des temps de latence unicellulaire et des probabilités de croissance

à basse température est un processus long. Les expériences en laboratoire peuvent

généralement durer plus de trois mois pour obtenir suffisamment de données pour

produire des résultats robustes. Le but de cette étude était de mettre au point une

méthode de microscopie rapide à haut débit pour la détermination de la probabilité de

croissance et du temps de latence des cellules individuelles lors d’une croissance au

froid. Par conséquent, quatre souches de L. monocytogenes ont été sélectionnées.

Les lames de microscope ont été recouvertes d'agar, inoculées avec des cellules de

L. monocytogenes et incubées à 4 °C. Les lames ont été analysées à l'aide d'un

microscope à contraste de phase à différents moments d'observation (c.-à-d. Tobs et

Tprob). Pour chaque observation, une nouvelle lame a été utilisée. Les images des

cellules individuelles et des microcolonies ont été capturées puis examinées à l'aide

d'une procédure d'analyse d'images automatique. La taille des micro-colonies et le

nombre de cellules incapables de se multiplier ont donc été déterminés. Par la suite,

des distributions de temps de latence ont été générées avec succès pour les quatre

souches de L. monocytogenes testées. Cette approche innovante offre une méthode

pratique et rapide pour définir la probabilité de croissance et la distribution de latence

des cellules individuelles avec une application possible dans diverses conditions.

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Abstract

The determination of single-cell lag times and growth probabilities in cold conditions is

a long process. Laboratory experiments can commonly run over three months for

obtaining enough data to yield robust results. The aim of this study was to develop a

fast microscopy method with high throughput for the determination of the growth

probability and lag times of bacterial single-cells in cold condition. Therefore, four

unrelated L. monocytogenes strains were selected. Microscope slides were covered

with agar, inoculated with L. monocytogenes cells and incubated at 4 °C. The slides

were analyzed with a phase-contrast microscope at different observation times (i.e.

Tobs and Tprob). For each observation, a new slide was used. Images of the single-cells

and microcolonies were captured and then examined with an automatic image analysis

procedure. The microcolonies size and the number of cells unable to multiply were

therefore determined. Following, lag time distributions were successfully generated for

all four tested L. monocytogenes strains. This innovative approach provides a time-

saving and convenient high throughput method to define growth probability and lag

time distribution of bacterial single-cells with possible application at various conditions.

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1. Introduction


Dans le domaine de la microbiologie prévisionnelle, des modèles mathématiques sont

développés depuis des décennies pour décrire le comportement bactérien des

aliments. De multiples facteurs (expérimentaux et biologiques) peuvent affecter le

comportement bactérien empêchant l'évaluation précise de la réponse bactérienne,

surtout dans des conditions environnementales difficiles (Aryani et al., 2015 ; den

Besten et al., 2016). Outre la variabilité entre les différentes souches d'une espèce,

l'hétérogénéité au sein d'une population doit être prise en compte lors des prédictions

phénotypiques (Aspridou et Koutsoumanis, 2015). Deux approches principales sont

généralement adoptées dans une conception expérimentale pour évaluer les

phénotypes bactériens: une approche « populationnelle » et une approche

« cellulaire ». Dans le premier cas, le comportement est modélisé aux conditions

environnementales sans tenir compte de la variabilité des cellules qui constituent la

population. Pourtant, toutes les bactéries d'une population clonale ne réagissent pas

de la même manière aux changements environnementaux (Koutsoumanis et Aspridou,

2017). En effet, certaines cellules individuelles peuvent montrer une tolérance extrême

aux facteurs de stress ou une sensibilité extrême par rapport à la majorité des cellules

de la population. Une attention particulière doit être portée aux cellules bactériennes

présentant une plus grande résistance aux conditions environnementales stressantes

(Aguirre et Koutsoumanis, 2016 ; Aspridou et Koutsoumanis, 2015 ; Margot et al.,

2016). Cette résistance accrue peut favoriser la persistance de pathogènes bactériens

(p. ex. L. monocytogenes) dans les usines de transformation des aliments et la

colonisation de nouveaux environnements, augmentant ainsi le risque de

contamination des aliments (Pascual et al., 2001). De plus, un temps de latence plus

court a été observé pour les cellules bactériennes ayant une résistance accrue aux

stress environnementaux comparativement aux cellules sensibles (Margot et al.,

2016). Les matrices alimentaires sont souvent contaminées par de faibles

concentrations de bactéries (Ross et McMeekin, 2003). Ainsi, l'application de

l'approche cellulaire offre l'avantage de générer des résultats reflétant un scénario plus

réaliste. L'approche "cellules individuelles" considère le comportement individuel de

chaque cellule de manière indépendante. L'acquisition de données au niveau des

cellules individuelles nécessite l'établissement de méthodes capables d'isoler chaque

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cellule individuellement et en même temps de recueillir un grand nombre de données

(Swinnen et al., 2004). Différentes méthodes sont disponibles pour déterminer le

temps de latence d'une cellule. Les plus couramment pratiquées reposent sur la

mesure de la densité optique de la croissance bactérienne en milieu liquide au cours

du temps (Smelt et al., 2002). Cette technique repose sur l’observation du temps de

détection, ou temps que met une cellule pour atteindre le niveau de détection pour la

mesure de turbidité (~107 cellules/ml, soit 24 générations). Néanmoins, cette approche

indirecte présente certaines faiblesses, en particulier dans des conditions

expérimentales limites (par exemple, à des températures proches des limites de

croissance bactérienne, les expériences prendront beaucoup de temps). De plus, le

fait que cette approche soit menée en milieu liquide rend plus difficile le transfert des

résultats aux matrices alimentaires solides. Le micro-environnement des cellules

planctoniques est en effet très différent de celui des cellules immobilisées en colonies,

donc la capacité de croissance bactérienne peut être très différente (Verheyen et al.,

2019). L'amélioration des connaissances sur la croissance immobilisée est primordiale

pour éviter les sur- ou sous-estimations des modèles prévisionnels (Skandamis et

Jeanson, 2015). D'autres méthodes indirectes d'estimation du temps de latence

individuel de la croissance bactérienne en milieu solide ont été publiées (Guillier et al.,

2006 ; Levin-Reisman et al., 2014). Ces auteurs ont proposé une méthode basée sur

l'analyse d'images de la croissance de colonies bactériennes à la surface de gélose,

où les temps de latence sont estimés par le temps de détection requis pour former des

colonies macroscopiquement visibles. Mertens et al (2012) ont publié une méthode

basée sur une approche similaire, mais la croissance a été suivie par la mesure de la

DO des colonies (Mertens et al., 2012). Dans ce cas, le débit a été amélioré grâce à

l'utilisation des plaques de 48 puits. Cependant, toutes ces approches présentent le

même inconvénient que la méthode indirecte en bouillon quant au temps nécessaire

pour atteindre un niveau d'observation, surtout dans des conditions proches des limites

de croissance. Pour surmonter cette limite, les approches basées sur l'utilisation d'une

cassette de gel en combinaison avec l'analyse d'images pour étudier les paramètres

de croissance des colonies bactériennes individuelles sont un point de départ

intéressant (Brocklehurst et al., 1997 ; Skandamis et al., 2007). En outre, certaines

méthodes directes basées sur la microscopie ont été proposées, telles que la

technique de la chambre d'écoulement et la microscopie en temps réel (Elfwing et al.,

2004 ; Koutsoumanis et Lianou, 2013). La technique de chambre d'écoulement

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d'Elfwing et de ses collègues est basée sur la surveillance des divisions consécutives

d'une même cellule. Les cellules individuelles sont cultivées dans un environnement

liquide, tandis que les cellules mères sont fixées à une surface solide (Elfwing et al.,

2004 ; Niven et al., 2006). D'autre part, Koutsoumanis et Lianou (2013) ont présenté

un système moins complexe pour étudier le temps de latence cellulaire par

microscopie de contraste de phase. Cette méthode permet un suivi direct d'une cellule

à une microcolonie en temps réel, mais une seule cellule est suivie à chaque fois. De

plus, les systèmes liquides ont généralement un débit plus élevé et une installation

expérimentale moins complexe (Mertens et al., 2012).

Outre le temps de latence individuel, il est essentiel d'explorer l'impact de la probabilité

de croissance sur les résultats des modèles prédictifs et donc sur l'évaluation de

l'exposition. Augustin et Czarnecka-Kwasiborski ont étudié la probabilité de croissance

de L. monocytogenes dans différentes conditions (c.-à-d. température, pH et activité

de l'eau) en bouillon en utilisant la méthode du nombre le plus probable (NPP)

(Augustin et Czarnecka-Kwasiborski, 2012). Les inconvénients de cette méthode sont,

d'une part, la longue durée de l'expérience (c'est-à-dire trois mois) et, d'autre part, la

grande incertitude des concentrations estimées par la méthode NPP (da Silva et al.,

2019). Dans cette optique, l'objectif de cette étude était de mettre au point une étude

expérimentale quantitative pour déterminer la probabilité de croissance d'une seule

cellule et le temps de latence de différentes souches de L. monocytogenes, en utilisant

une méthode de microscopie automatisée et permettant de gagner du temps.

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In the field of predictive microbiology, mathematical models are developed since

decades to describe the bacterial behavior in foods. Multiple (experimental and

biological) factors may affect the bacterial behavior hampering the precise assessment

of bacterial response, especially in harsh environmental conditions (Aryani et al., 2015;

den Besten et al., 2016). Besides the variability between different strains of a species,

the heterogeneity within a population should be taken into account during growth or

inactivation predictions (Aspridou and Koutsoumanis, 2015). Two main approaches

are usually adopted in an experimental design to assess bacterial phenotypes: the

“populational” and the “individual cells” ones. In the first case, the behavior is modeled

without taking into account the variability of the cells that constitute the population. Yet,

not all bacteria in a clonal population react in the same way to environmental changes

(Koutsoumanis and Aspridou, 2017). Indeed, some individual cells can show extreme

tolerance to stressor or extreme sensitivity compared to the majority of the cells within

the population. A particular attention should be paid to the bacterial cells showing

higher resistance to stressful environmental conditions (Aguirre and Koutsoumanis,

2016; Aspridou and Koutsoumanis, 2015; Margot et al., 2016). This enhanced

resistance can promote the persistence of bacterial pathogens (e.g.

L. monocytogenes) in food processing plants and the colonization of new

environments, increasing the risk of food contamination (Pascual et al., 2001). In

addition, a shorter lag time has been reported for bacterial cells with enhanced

resistance to environmental stresses in comparison to sensitive ones (Margot et al.,

2016).

Food matrices are often contaminated with low levels of bacteria (Ross and McMeekin,

2003). Thus, the application of single-cell approach offers the advantage of generating

more realistic scenario. The “individual cells” approach considers the individual

behavior of each cell in an independent manner. The acquisition of data at individual

cell level requires the establishment of methods capable of isolating each cell

individually and at the same time to gather a large number of data (Swinnen et al.,

2004). For the determination of single-cell lag time, different methods are available.

The most commonly practiced rely on the measurement of the optical density in broth

media over time (Smelt et al., 2002). This technique requires that one cell develops a

high number of generations to reach the detection level for turbidity

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measurement (~107 cells/ml). Nevertheless, there are certain weaknesses in this

indirect approach especially in particular experimental conditions (e.g. at temperatures

close to bacterial growth limits the experiments will take a long time). In addition, the

fact that this approach is conducted in broth medium makes it harder to transfer results

into solid food matrices. The micro-environment of planktonic cells is indeed quite

different from that of immobilized cells in colonies, thus the bacterial growth capacity

might differ greatly (Verheyen et al., 2019). In case of pathogens’ growth prediction,

the improvement of knowledge on immobilized growth is of greater concern to avoid

over- or under-estimations (Skandamis and Jeanson, 2015). Other indirect methods to

estimate the individual lag time of bacterial growth in solid media were published

(Guillier et al., 2006; Levin-Reisman et al., 2014). These authors proposed a method

based on image analysis of the bacterial colony growth on agar, where the lag times

are estimated by detection times required to form macroscopically visible colonies.

Mertens et al. (2012) published a method based on a similar approach, but the growth

was monitored through the measurement of OD of colonies (Mertens et al., 2012). In

this case, the throughput was improved with the use of 48 well-plates. However, all

these approaches are presenting the same weakness as the indirect method in broth

regarding the time required to reach an observation level, especially in conditions close

to growth limits. To overcome this limit, approaches based on the use of a gel cassette

in combination with image analysis to study the growth parameters of bacterial single

colonies are interesting starting point (Brocklehurst et al., 1997; Skandamis et al.,

2007). In addition, some direct methods have been proposed based on microscopy,

such as the flow chamber technique and the time-lapse microscopy (Elfwing et al.,

2004; Koutsoumanis and Lianou, 2013). Elfwing and colleagues’ flow chamber

technique is based on the monitoring of the consecutive divisions of a single cell

attached to a solid surface (Elfwing et al., 2004; Niven et al., 2006).

Koutsoumanis and Lianou (2013) presented a less complex system to study the single-

cell lag time by contrast phase microscopy. This method allows a direct follow up from

one cell to a microcolony in real-time, but only one cell is recorded at each time. In

addition, the liquid systems generally have a higher throughput and a less complex

experimental setup (Mertens et al., 2012).

Besides the single-cell lag time, it is essential to explore the impact of the growth

probability on the outcome of predictive models and thus exposure assessment.

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Augustin and Czarnecka-Kwasiborski studied the single-cell growth probability of

L. monocytogenes in different conditions (i.e. temperature, pH and water activity) in

broth media by using most probable number (MPN) approach (Augustin and

Czarnecka-Kwasiborski, 2012). The drawbacks of this method are, the long

experiment duration (i.e. three months) and, the great uncertainty of concentrations

estimated with the MPN method (da Silva et al., 2019). In this perspective, the objective

of this study was to develop a quantitative experimental investigation to determine the

single-cell growth probability and lag time of different L. monocytogenes strains, using

a time-saving and automated microscopy method.

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2 Material and methods

2.1 Strains

Four strains of L. monocytogenes have been tested for establishing the herein

developed method (Table 11). They were selected based on their phenotypic and

genetic diversity. Three of them (i.e. SOR100, AER101 and Lm14) were part of

Symp’Previus project with known cardinal temperatures (i.e. Tmin). Before use, the

strains were stored in cryobeads at 80°C.

Table 11 : Metadata of L. monocytogenes stains

Isolate Origin Tmin

SOR100 Sausage -2.52 °C

AER101 Dairy -0.15 °C

Lm14 Meat processing plant environment -0.86 °C

O228 Shrimp unknown

2.2 Preparation of inocula

The preculture of each strain was carried out by inoculating a cryobead in 10 ml

tryptone soy broth with yeast extract (TSB-YE, Oxoid, UK). After seven hours at 37 °C,

0.1 ml was transfered to 9.9 ml fresh TSB-YE. The dilution was then incubated for 17 h

at 37 °C. Following, successive dilutions were made in tryptone salt diluent

(TS, Biomerieux, UK) to obtain a final concentration of 105 CFU/ml.

2.3 Preparation and storage of the microscope slides

Glass slides were prepared by placing 200 µl of melted tryptone soy agar (TSA, Oxoid)

in the middle of the slide. After a cooling time, 10 µl of the diluted inoculum were

pipetted on the solid layer TSA. The samples were dried in a laminar flow hood for

5 minutes and then covered with a glass coverslip. Following, the prepared glass slides

were put in a sealable box and were stored in the incubator at 4 °C. At each point of

measurement, the required number of slides were pulled out for analyses. Before

starting the microscopic observation, coverslips were fixed with silicone.

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2.4 Microscopic observation of cells

For the microscopic observation of the cells, a motorized phase-contrast microscope

(Nikon, Eclipse Ni-E) was used in combination with a 100 x objective and a high-

resolution camera (Nikon, DS-Fi3). Software from Nikon (NIS-Elements, version 4.60)

was applied for automatically capture images in two different areas, each with a surface

of 0.25 mm2, corresponding to 88 images per area (Figure 23).

Figure 23 : Schema of a prepared slide with agar layer and coverslip. The two analyzed areas are highlighted with their dimensions.

2.5 Image analysis procedure

The images were captured in TIFF format. Images were then analyzed with Matlab

(R2018b) including the Image Processing Toolbox™ (Figure 24). The applied image

analysis procedure was previously adapted from Guillier and colleagues (Guillier et al.,

2006). For object recognition, a threshold was estimated to separate pixels associated

to bacterial cells from the TSA background pixels. Because of the variation in thickness

of the TSA, the threshold was adapted for each of the image series. Based on the

threshold, intensities associated to background were displayed in black and those

corresponding to cells in white by using the ind2gray and im2double functions of

Matlab (B). Following, two morphological operations were applied using bwmorph

Matlab function. One to bridge the previously unconnected pixels and another for gap

closing (C). Images with more than three cells or colonies were discarded to avoid bias

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due to interactions. The detected objects were filtered out when smaller than a single

L. monocytogenes cell (e.g. arrow in Figure 24 bottom).

As last step of the procedure, each detected object and the relative number of pixels

were exported into a csv file.

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Figure 24 : Different process steps of image analysis. A) Captured images; B) Black and white contrast; C) Gap closing; D) Detection of objects corresponding

to the given pixel size threshold. The arrow is highlighting an example of a filtered object according to its size.

A B C D

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2.6 Growth curve at population level

For the determination of growth parameters of each tested strain at 4 °C, a total of

20 tubes containing each 50 ml TSA were inoculated with 200 µl of the 105 CFU/ml

suspension to obtain a final concentration of 103 CFU/ml. Kinetics were characterized

with a minimum of 20 data points. Three repetitions were carried out. Growth kinetics

were adjusted with the Baranyi & Roberts primary model (Baranyi and Roberts, 1994)

using the nlsMicrobio package (Baty and Delignette-Muller, 2004) in order to the

population growth parameters (i.e. lag time and growth rate).

2.7 Determination of growth probability

The individual cell growth probability was determined by comparing the number of

single-cells (ns) and number of micro colonies (nc), after a long incubation time at 4°C.

The incubation time was chosen to ensure that the individual cells that did not initiate

growth at that stage have a low probability to still be in lag phase. The relationships

proposed by Guillier and Augustin (2006) were first used to estimate the distribution of

physiological state values (h0i) according to the lag time duration observed at

population level. The 95th percentile of the estimated h0 values (h0i-95) was used to

estimate the incubation time (Tprob) with the following relation h0i-95/µmax. Thus at Tprob,

the probability that an observed single-cell is still a cell in the lag phase is lower than

5 %.

The uncertainty of observed probability of growth at Tobs were calculated by using the

beta distribution (Vose, 1998):

Beta((1-ns)+1, nc+1)

2.8 Individual lag time determination

The estimation of single-cell lag time is based on the vertical distribution method

(d’Arrigo et al., 2006). This method relies on the distribution of numbers of cells

observed in different samples (each sample being inoculated with one cell) after a

given incubation time. The distribution of lag times of the cells is then transposed with

the help of maximum growth rate.

The individual cell lag time �� was thus determined using the time of observation

Tobs, the cell number per micro colony (ln(x)) and the growth rate (µ) of the

Chapter 3

107

corresponding strain based on number of cells in the microcolony and the growth rate

(Figure 25).

�� = �� −ln(�)

�

Figure 25 : Schema of the principle to determine single-cell lag time.

Twenty samples were stored in the incubator (4 °C) during the experiment. Then the

samples were analyzed at three different Tobs, to ensure at least that one point of

analysis corresponds to a time where most of the cells have already left lag-time, but

without starting to grow in the third dimension (double-layer and multi-layer). After

microscopic observation is carried out at room temperature in less than 20 minutes,

the samples were destroyed. For each measurement point, new samples were used.

The number of cells per colony was estimated based on the number of pixels per

colony and a correlation function.

The estimated individual lag time distributions were fitted with the lognormal

distribution and the fitdistcens function from fitdistrplus package (Delignette-Muller and

Dutang, 2015). Parametric bootstrap was used to assess credibility interval on

quantiles characterizing the median of individual lag times. The rogme package

(https://github.com/GRousselet/rogme) was used to compare all deciles.

Chapter 3

108

3 Results

3.1 Growth rates

Table 12 shows the population growth parameters at 4 °C for all tested strains. The

strain SOR100 had the longest lag time (58 h) compared to the others. Contrary, the

strain O228 showed the shortest lag time with 28 h.

Regarding the maximal growth rate, AER101 (0.049 h-1) was the fastest while Lm14

(0.038 h-1) the slowest strain under the tested conditions. It is worth to notice that

SOR100 had the highest h0 value with 2.5 and O228 the lowest one with 1.2.

Table 12 : Population growth parameters of the four tested strains with their 95th confidence interval [0.025 0.0975].

SOR100 AER101 Lm14 O228

Lag time [h] 58 [18 94] 44 [23 55] 42 [8 86] 28 [10 43]

maximal growth

rate μ�� (h-1)

0.044 [0.039 0.052] 0.049 [0.047 0.052] 0.038 [0.035 0.045] 0.043 [0.039 0.045]

Physiological

state h0

2.5 2.2 1.6 1.2

3.2 Determination of cell numbers in the captured images

Figure 26 presents four examples of the captured images. Figure 26a shows a single-

cell of strain AER101 after 3 days at 4 °C. The cell had already initiate cell elongation

(compared to cells at day 0). Figure 26b displays a microcolony of AER101 at Tobs for

lag time determination. Figure 26c and d were both captured at Tprob. The single-cell

(c) was not able to grow in the tested conditions, whereas the Figure 26d presents an

example of a microcolony (with double-layer) developed from a single-cell of the same

strain as in Figure 26 (SOR100).

Chapter 3

109

In order to determine the number of cells in a microcolony from pixel size, a calibration

curve was established for each strain. An example is presented in Figure 27.

Especially, the number of pixels that corresponds to single cells are quite variable,

ranging from 875 to 2624 pixels. In contrast, for the number of pixels corresponding to

microcolonies a lower variability can be observed.

a

c d

b

Figure 26 : Images obtained with phase-contrast microscope (Nikon, Eclipse Ni-E) in combination with a 100 x objective and a high-resolution camera (Nikon, DS-Fi3), the arrow is indicating a double-layer.

Chapter 3

110

Figure 27 : Relationship between the number of cells per colony and the number of pixels for strain O228. The linear regression lines are adjusted with several series of points corresponding to the different repetitions of the experiment; blue=R1, red=R2 and green=R3.

0

0.5

1

1.5

2

2.5

2.5 3 3.5 4 4.5 5 5.5

log 1

0(n

um

ber

of

cells

)

log10(number of pixels)

Chapter 3

111

3.3 Growth probability

Figure 28 shows the difference between the different experiments carried out for the

four strains. The variability observed between experiments is smaller than the

variability between strains. The different experiments were thus merged in a single

dataset for each strain. The comparison of individual cell growth probability according

to the strain was thus carried out on merged dataset. More than 200 observations were

used to determine the growth probability for each strain.

Figure 28 : Cell growth probability at 4 °C of the four L. monocytogenes strains tested according to the different repetitions.

Figure 29 represents the uncertainty distributions of individual cell growth probabilities

for the four tested strains.

The strain SOR100 has the highest growth probability with 81 % [76 %, 85 %]. The

lowest growth probability is represented by strain O228 with 18 % [17 %, 22 %]. For

strain Lm14 a growth probability of 57 % [51 %, 63 %] was determined. It is the second

lowest of the tested strains and significantly different to AER101, SOR100, and O228.

The growth probabilities of the strains SOR100 (81 % [76 %, 85 %]) and AER101

(73 % [67 %, 78 %]) are not significantly different.

Chapter 3

112

Figure 29 : Uncertainty distribution of individual probability of the four strains of L. monocytogenes at 4°C.

Chapter 3

113

3.4 Individual lag times of single-cells

Figure 30 shows the difference between the different experiments carried out for the

four strains. The variability observed between experiments is smaller than the

variability between strains. The different experiments were thus merged in a single

dataset for each strain.

Figure 30 : Median of the individual cell lag times of the four strains tested according to the different repetitions of the experiment.

The range and variability of individual cell lag times are shown in Figure 31A. The

Figure 31B presents the pairwise comparison of deciles of the individual cell lag times.

Regarding Figure 31B1 B3 and B5, O228 strain shows significantly lower deciles.

Individual lag times of strains Lm14 and SOR100 cannot be distinguished (Figure

31B2). The first fifth deciles of strain AER101 are similar to SOR100 and Lm14. The

other deciles for this strain are significantly lower compared to SOR100 and Lm14

(Figure 31B4 and B6).

Chapter 3

114

(A)

(B)

Figure 31 : A) Random individual lag times drawn from fitted lognormal distributions for the four strains. B) Pairwise comparisons of deciles.

B1 B2

B3

B5 B6

B4

Chapter 3

115

4 Discussion

The method presented in this study allows to generate a high amount of observations

for single-cells in harsh conditions in order to estimate lag time and growth probability.

Compared to indirect methods where the duration of the experiment is dependent on

the time needed for a cell to generate enough generations according to the detection

threshold, the proposed microscopy method saves time by reducing the number of

generations to obtain lag time and growth probability results. For instance, Augustin

and Czarnecka-Kwasiborski studied the single-cell growth probability in broth based

on MPN approach (Augustin and Czarnecka-Kwasiborski, 2012). In cold conditions

(5 °C), their experiments took up to three months to determine the growth probability

of L. monocytogenes. The herein developed method was more than six times faster

(less than two weeks) in similar temperature conditions.

Compared to indirect methods, the microscopy appears much faster also regarding the

single-cell lag time determination. Francois and colleagues inoculated microtiter plates

with approximately one cell per well. They determined about 100 individual lag phases

for each set of conditions. Based on a relation between optical density and cell count,

they were able to estimate single-cell lag time. However, the lower limit of linearity for

optical density measurement (around 107 CFU/ml) to estimate the cell concentration

required to overcome 24 generations for one cell to determine the lag time (Francois

et al., 2005). The microscopy method counteracts the drawback of detection limit since

each single-cell can be directly observed. Hence, between these methods a difference

in experimental duration is observed especially in cold conditions, where generations

times are particularly long. In the case of Francois et al (2005), the experiments took

approximately three weeks at 4 °C for lag time determination, whereas the microscopy

method took around one week (depending on the tested strain) at the same

temperature.

Koutsoumanis and Lianou proposed a direct method in agar media based on singe-

cell time-lapse (Koutsoumanis and Lianou, 2013). Compared to optical density

measurement and its correlation to obtain the cell number (Francois et al., 2005), the

advantage of this method is the direct following up of cells. Even though the time to

determine the lag time of one cell is shorter, with this approach it is possible to track

only few cells at the same time. In contrast, the novel approach herein presented

Chapter 3

116

permits to investigate many slides at the same time resulting in a higher amount of

data achievable in comparison to the time-lapse approach. Both these microscopy

approaches permit the following up of cells on agar. Due to the fact that planktonic cell

behave in a different manner compared to immobilized cells, the use of agar better

simulates the food matrix (Skandamis and Jeanson, 2015). Moreover, the colonial

growth dynamics of bacteria in solid foods can be influenced by matrix-specific

interactions and gradients within or around the microcolony (Malakar et al., 2003;

Walker et al., 1997). The above-mentioned method proposed by Elfwing and

colleagues permits to study bacterial mother cells attached to a solid surface and its

daughter cell, however, it is not able to observe colonial growth (Elfwing et al., 2004)

as the case of agar-based methods. Another huge advantage lies in the possibility to

apply dynamic experimental conditions to estimate the growth probability. The slides

can be easily stored in incubator with changing temperature to study the effect of

temperature flows on the bacterial growth ability, for example to reproduce possible

cold chain scenarios.

The presented results for the growth probability are different from those obtained by

Augustin and Czarnecka-Kwasiborski. They used in their study the strain Lm14 and

obtained at 5 °C a growth probability of 14 % [7 %, 20 %), whereas in the present study

the probability of growth for the same strain was 57 % [51 %, 63 %] at 4 °C. One

reason could be explained by the different experimental approach, which is based on

microtiter plates with broth media. In this system, the oxygen transfer to the bottom of

the wells is limited due to missing agitation, likely impacting the growth probability.

Regarding the results for single cell growth probability, a huge variability can be

observed between different L. monocytogenes strains. More precisely, the growth

probability is highly variable between SOR100 and O228 (18 % and 81 %

respectively), highlighting that growth variability could have an impact on exposure

assessment. Interestingly, the strain SOR100 with the lowest minimal growth

temperature (-2.52 °C) showed the highest growth probability at 4 °C and seems thus

more resistant to cold than the other tested strains.

In case of the individual lag time (without prior stress exposure), the work of Francois

and co-workers observed a mean of 40.1 h in broth at 4 °C (Francois et al., 2005). A

similar time was observed for the strain O228 in the present work. However, the

Chapter 3

117

median of individual lag times of O228 was about 70 h shorter compared to the other

here tested strains. The longer lag times compared to those in the study of Francois

and co-workers could be explained by the use of a single strain and/or different culture

media (broth vs agar). Indeed, Koutsoumanis and colleagues showed that growth limits

of L. monocytogenes are affected by the use of solid or liquid media (Koutsoumanis et

al., 2004). They observed a lower growth probability when bacteria are grown on a

solid surface compared to suspensions. They supposed that modifications of the local

environment can lead to a reduced metabolic activity in some regions of the colony.

Although the main purpose of this study was to set up an efficient method for high

throughput phenotypic data generation, between the experimental repetitions of the

four strains unexpected differences were observed. Even when the observed variability

between experiments is smaller than the variability between strains, the variability

could be decreased by the standardization of datasets to correct the sources of

variability between experiments as applied by Guillier et al. (Guillier et al., 2005).

5 Conclusion

This work describes a novel approach to study individual lag times as also single cell

growth probability with a high throughput data generation in a short time. This is a

promising method for the study of bacterial cells’ behaviors in different environmental

conditions (e.g. static or dynamic), during colonial growth starting from a single cell.

Findings from this study demonstrate that the herein presented approach allows to

obtain a high amount of data faster than using indirect or time-lapse methods, even in

extreme experimental conditions. In addition, investigating the growth of microcolonies

on agar gives the advantage of predicting the effects that can influence the colonial

growth dynamics in food matrices.

Acknowledgement

This work received funds from The French National Research Agency under the project

ANR-15-CE21-0011 OPTICOLD. Special thanks go to Dr. Federica Palma for her

kindly aid in reviewing.

General discussion

118

General discussion

When dealing with GWAS and QMRA, there are several questions popping up. In the

following paragraphs, these questions will be addressed and should inspire further

investigations in this field.

Since the beginning of this thesis, which new developments in GWAS happened?

Nowadays, a multiplicity of new methods to conduct microbial GWAS are freely

accessible, well documented and user-friendly. One of these new promising

approaches is DBGWAS (Jaillard et al., 2018). It is an alignment-free end-to-end

bacterial GWAS solution from draft assemblies to associated graph elements. The

offered graphical framework helps to interpret the outcome. DBGWAS does apply the

principle of GEMMA (Zhou and Stephens, 2012), linear mixed model (LMM) proposed

in the “bugwas” method (Earle et al., 2016), to account for population structure. As

reported in a recent benchmarking study on different GWAS approaches (Saber and

Shapiro, 2019), GEMMA is the most efficient method to control for false positive

“errors” caused by population structure. The novelty of DBGWAS consists in the use

of unitigs instead of k-mers. K-mers often lead to redundant descriptions and results

that can be difficult to interpret due to missing information of surrounding sequence of

the k-mers of interest. Unitigs can be defined as high quality contigs. The advantage

of using unitigs is the decrease of computational burden, due to fewer resources

required to count the unitigs, and lower multiple testing burden, as unitigs reduce

redundancy derived by k-mer counting. Additionally, they are easier to interpret than

k-mers because they are usually longer.

What kind of additional analysis could be made based on our genomes?

The GWAS analysis of the accessory genome simply targets genes of interest without

investigating to which compartment they belong. A targeted analysis of the accessory

genome, in particular of mobile genetic elements (MGEs) such as plasmids and

prophages, could thus be made in addition to what has been done in Chapter 1. For

instance, L. monocytogenes plasmids often harbor genes associated to enhanced

survival under acidic, salt and oxidative stress as also contributing to heat and

disinfectant stress survival (Naditz et al., 2019). Plasmid maintenance costs the host

cells energy and imposes metabolic burden, thus it can be also an explanation for

General discussion

119

variability in bacterial growth (Ow et al., 2006). Not only the presence but also the

number of copies of plasmids might affect the growth behavior in the tested conditions.

Besides the plasmids, prophages are important mobile DNA elements (Bushman,

2002). In fact, many virulence factors of bacterial pathogens are phage-coded (Boyd

et al., 2012). Comparing the genomes of closely related bacteria, it comes clear that

prophage sequences have a significant role in shaping differences across the

genomes (Glaser et al., 2001). In this thesis (Chapter 1), the GWAS results show that

several prophages-related accessory genes are statistically associated to the

observed phenotype. This indicates that further analysis focusing on the phagosome

(whole phage content) of L. monocytogenes would be of great interest to find specific

genotypes with a high level of phenotype-association.

By focusing on a gene known to be involved in a particular phenotype (e.g. resistance

to cold, antimicrobials, biocides, etc.), the identification of the surrounding sequence

(often MGEs) from short reads can be challenging. PlasmidTron tries to disentangle

the complex associations between the gene, the linked MGEs (e.g. plasmids and

phages) and the specific phenotypic trait (Page et al., 2018). More precisely, this tool

is specialized on the identification of MGEs that are difficult to assemble from raw reads

due to the numerous repeats which lead to fragmented consensus sequences. This

new promising approach, as well as the above-mentioned unitigs-based GWAS,

should be applied on the present (or expanded) dataset, in order to gain additional

insights on the genetic elements responsible for the phenotypes observed in this

thesis.

How to deal with quantitative phenotypes in GWAS?

The most commonly studied phenotypes in predictive microbiology are quantitative, as

the maximal growth rate, inactivation rate or cardinal values. Dividing strains into well-

defined groups based on a specific trait is often tricky, even in discriminatory conditions

close to growth limits, due to minor differences between strains and uncertainty.

There are different possibilities to use quantitative phenotypic data in association

studies. One simple approach is based on a tree topology comparison. In this

approach, the maximum likelihood phylogenic tree is compared to a neighbor-joining

General discussion

120

dendrogram built on the quantitative phenotypic data and the trees similarity is

statistically analyzed by using Robinson-Folds metric (Supplementary material 8).

A different approach is to study the evolution of the phenotype during time. A

researcher may be interested in whether, for example, a bacterial phenotypic trait (e.g.

lower temperature limit for growth) has evolved in association with environmental

conditions (e.g. temperature) or in association with other traits (e.g. minimal pH limit).

For solving this issue, Phylogenetic Comparative Methods (PCM) are an active field,

that has seen many developments in the last few years (Bastide et al., 2017; Pennell

and Harmon, 2013). Several methods have been specifically developed to study

adaptive evolution (O'Meara, 2012). They rely on different models, such Brownian

motion or OU models that are implemented in R packages (e.g. phytools (Revell,

2012a), l1ou package (https://github.com/khabbazian/l1ou), SURFACE package

(Ingram and Mahler, 2013), PhylogeneticEM (Bastide et al., 2018; Bastide et al.,

2017)). All these modelling approaches have been mainly applied on eukaryotic

organisms, and only few articles described their application to prokaryotes yet (Krause

et al., 2014).

Besides these phylogeny based approaches, a recently developed GWAS approach,

Treewas, can also manage quantitative data as input (Collins and Didelot, 2018).

Is it possible to transform quantitative into qualitative phenotypes?

If a binary input for the phenotype is requested to conduct a GWAS, there is the

possibility to group continuous values into a case and a control group. Therefore, it is

necessary to cluster strains based on the distances between the values of the

quantitative data. Following, the main branches can be associated to case and control

group.

How to deal with the uncertainty of the phenotypes?

As mentioned before, phenotypic data is uncertain (due to measurement uncertainty,

or experimental conditions of reproducibility and repeatability). Uncertainty in

phenotypes can lead to a wrong attribution of a strain to a phenotypic group. In this

work, a temperature of 2 °C was chosen supposing that in this extreme condition, the

differences in strain behaviors would have been easy to observe. It worked out the

experimental set up, and the two groups (e.g. case and control) were separated

General discussion

121

through to a number of days. In contrast, Aryani et al. (2015) showed that the

observable variability is a complex mix of biological patchiness, even when determining

cardinal values as pHmin or [NaCl]max (Aryani et al., 2015).

One possibility to deal with the uncertainty of phenotypes is to use bootstrap in order

to quantify the uncertainty of the outcome. Clustering will be made n-times and at each

outcome a GWAS will be conducted to finally compare the output. For other pathogens

than L. monocytogenes it might be easier to distinguish between uncertainty and

variability as Carlin et al. showed with Bacillus cereus (Carlin et al., 2013).

Another approach applied by Hingston et al. relies on the sole use of the “extreme”

values of the distribution (Hingston et al., 2017). Isolates were classified as sensitive

or tolerant based on their standard deviation from the median of the standardized

maximum growth rate. Within a set of 166 strains, 13 of them were identified as cold

sensitive and 18 as cold tolerant. This kind of approach guarantees that the

phenotypes are well distinguished. On the other hand, the drawback in this case is the

high amount of strains and associated data that is needed to ensure enough strains

on each side of the distribution.

How to validate genetic variants associated to the target phenotype?

As mentioned above, GWAS associate genetic variants to a phenotypic trait. These

associations are evaluated by p-values. These p-values are statistically associations

and not a validation of the outcome. As far as the studied phenotypes are less complex,

the probability to find highly associated variants is enhanced. For example, the

resistance to a specific antibiotic is often related to few genes or single point mutations.

In these cases, a knockout mutant can be created to validate the variant explaining the

phenotype.

In contrast, phenotypes related to food and its processing environment are often linked

to complex networks that involve several genes. The outcome of the genome-wide

analysis showing more than hundreds associated genes or even more SNPs is thus

not surprising. In this context, a validation by knockout would be a heavy workload,

especially testing all the possible combinations. In this case, it would be more

appropriate to perform a validation by testing independent strains that were not

included in the GWAS. For this kind of validation, a number of strains with the

General discussion

122

marker ”x” and the same number of strains without that marker can be phenotyped.

Following, the sensitivity and specificity of the marker can be calculated.

In a risk assessment point of view, less importance is attached to a genomic biomarker

explaining a phenotypic mechanism, as long as sensitivity and specificity of that marker

is high.

How to deal with the outcome of GWAS?

As mentioned before, the outcome of GWAS are often large data frames with genomic

variants associated to the studied phenotype. For assessing the phenotype of interest,

the full list might be not necessary. Several approaches can be envisaged for ranking

the most associated markers among a list, by calculating their sensitivity and specificity

toward a genome dataset (Felten, Guillier, et al., 2017). Different possibilities are

available to solve this problem. A basic approach is presented in Figure 32. In this

example, 150 strains are studied. One hundred of them are associated with the case

group (trait positive) and 50 strains are in the control group (trait negative). Four

different genes have been significantly associated to the studied trait with, for example,

Gene A present in 30 strains of the case group and absent in all strains of the control

group (Figure 32). Associated genes can be mapped to the genomes and according to

the overlapping cases, only the genes necessary to cover all the strains in case group

will be kept. For instance, Gene B is not necessary as it overlaps with Gene A and C.

Thus, all 100 strains can be covered by the presence of three genes (A, C, and D).

General discussion

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Figure 32 : Schema to reduce variants in associations’ studies.

In a more complex situation, modelling approaches can be applied to handle the

outcome. For instance, Njage and co-workers published two studies that focus on

machine learning-based predictive risk modelling using random forest algorithms

(Njage, Henri, et al., 2019; Njage, Leekitcharoenphon, et al., 2019). Machine learning

algorithms improve with experience. In the study focusing on the virulence genes of

L. monocytogenes, they reduced a list of 136 virulence and stress resistance genes to

13 genes that were the ‘best’ predictors for higher frequency of illness (Njage, Henri,

et al., 2019). Their finding suggest that machine learning approaches are a suitable

solution to handle in the next future the high amount of microbial genomic data

produced today. These approaches would also allow implementing the possible

interactions of predictors with other predictors, which were considered less related to

the associated phenotype yet important in combination with other predictors (Okser et

al., 2013).

An alternative modelling approach to machine learning is the stepwise multinomial

logistic model. This method has been proposed to identify within the genes enriched

in different animal sources. Based on accuracy and the Aikaike Information criterion,

the approach permitted to select 8 genes within the 2,802 accessory genes to predict

the animal source (Guillier et al., 2019).

General discussion

124

Do different GWAS tools provide the same results?

There is clearly a need to benchmark GWAS methods. As mentioned before,

nowadays a variety of GWAS tools have been developed and are freely available.

These methods adopt different correction models and association approaches, just to

mention two examples. Thus, it is necessary to define best practices for microbial

GWAS. The methods should be tested regarding their performance dealing with

different recombination rates, realistic effect sizes and sample sizes. Recently, Saber

and Shapiro (2019) proposed a tool called BacGWASim for benchmarking of new

GWAS methods (Saber and Shapiro, 2019). This pipeline simulates bacterial genomic

dataset with different genotypes (e.g. varying rates of mutation, recombination and

other evolutionary parameters) associated to phenotypes of interest in order to assess

the performance of GWAS approaches.

Sources of variability and number of phenotypes are high in QMRA models; do we

need to introduce biomarkers for all of them?

Regarding QMRA models, a high number of variability sources can be found as

mentioned before. Not all of these sources have the same importance concerning the

outcome of QMRA models, thus the implementation of biomarkers is only meaningful

for the most prioritized sources of microbiological variability. It would be more rational

to identify significant factors that mostly affect the exposure assessment. Therefore,

sensitivity analysis can be conducted to identify and rank the most important factors

(Duret, Guillier, et al., 2014). Ellouze and colleagues published a global sensitivity

analysis applied to a contamination assessment model of L. monocytogenes in cold

smoked salmon at consumption (Ellouze et al., 2010). They showed that the duration

and the temperature of the storage at the domestic refrigerator were identified as major

factors as well as the optimum growth rate, μopt, and the physiological state, h0. This

kind of analysis helps to find concrete entry point to introduce genomic data in

exposure assessment along with to the crucial part: the hazard characterization for

strain virulence (main source of variability).

General discussion

125

What is the bottleneck of application of biomarkers in QMRA?

Since several years, scientists discuss about implementing omics data into risk

assessment (Cocolin, Mataragas, et al., 2018; den Besten et al., 2017; Franz et al.,

2016). So far, few publications proposing approaches or presenting some first steps

towards the implementation of omics in risk assessment are available today (Pielaat et

al., 2015). One reason for that is the lack of robust biomarkers. As mentioned before,

not only the complexity of phenotypes is a big challenge but also their uncertainty and

variability. While it is possible to determine some phenotypes with a high reproducibility

and repeatability, thus a low uncertainty, other phenotypes may be difficult to ascertain.

Especially, dose-response relationships are pretty hard to define entailing a much

higher uncertainty. However, dose-response relationships are of key importance in

QMRA. If the exposure assessment is accurate, but dose-response relationship is

poorly performing, the outcome of the model would be greatly affected. As mentioned

before, dose-response models are often based on animal models or Caco-2 models.

Pouillot et al. proposed a dose-response model that uses the variability observed in a

mice model (with different strains) and the dose-response relationships gained through

quantitative epidemiological data (Pouillot et al., 2015). Unfortunately, epidemiological

quantitative data is rarely available.

The progress of WGS data in surveillance might lead to earlier detection and

identification of the contamination source. This augments the probability to obtain more

quantitative data by analyzing the contaminated lot. Therefore, a future goal will be to

replace mice model data by quantitative epidemiological data and link attack rates to

specific CCs of L. monocytogenes.

Once the biomarkers for specific phenotypes are established, genomic sequences are

needed to investigate the presence or absence of the known predictors. In context of

the industrial application, e.g., when a pathogenic strain is isolated from the production

environment, its ability to persist can be “easily” estimated with the application of known

biomarkers. The isolate should be sequenced and the presence or absence of the

marker in silico determined. However, the applicability of biomarkers in QMRA is

dependent on the knowledge of prevalent genotypes. Therefore, representative

prevalence data for specific food products and regions are needed to align biomarkers

with prevalent strains’ genotype and then predicting the behavior of subpopulations.

General discussion

126

This kind of data is nowadays not available on a sufficient scale. The case of cold

smoked salmon was untangled in this thesis because a large dataset of 294 strains

representative of European’s situation were accessible to conduct a scientifically

sound study. Whereas, a lower number of sequences was available for other food

categories and thus not enough representative to conduct QMRA based on the

selected data sets.

The amount of prevalence data will increase since WGS became the standard method

for investigations in many countries. Consequently, the behavior of subpopulations

might be predicted more accurately than at present.

How would it be possible to obtain robust biomarkers for prediction?

The number of sequenced genomes has to be fairly high when performing GWAS

studies to obtain robust biomarkers. Automated platforms can be implemented today

for affordable DNA extraction and wide-scale sequencing. These platforms allow a high

throughput with reasonable effort. On the other hand, laboratory experiments to

estimate reliable phenotypes of several hundreds to thousands of strains is more

challenging. For this, specialized platforms enabling fast, automated and high

throughput phenotyping with a wide applicability across laboratories are in demand.

Multiwell format platforms, such as bioscreen© or biolog©, provide numerous features

allowing quantitative measurement on large scale. Through these technologies,

samples are isolated in wells and different conditions can be adjusted per well (adding

compounds) as also different sizes of inoculum. Contents of each well can be

monitored at each time automatically. However, ready-to use systems more efficient

for the end-users are also available. An example of such a platform is the microfluidic

platform from Amselem et al. (2016). It provides all necessary for end-users: a culturing

environment (liquid or gel) to relate phenotype and genotype measurements on clonal

colonies in multiwall plates (Amselem et al., 2016). The optimal goal for phenotyping

are lab-on-a-chip (LOC) models. These systems can handle thousands of

measurements in parallel for phenotypes like antibiotic susceptibility or microbial

physiology by using droplet microfluidics (Kaminski et al., 2016). The development of

such miniaturized and highly automated technologies takes several years. Starting by

simple manual protocols and extending the automation combining complex protocols

is a challenge of LOC, as also the development from few large reactors to many ultra-

General discussion

127

small reactors. These LOC systems are currently not available on the market, but once

accessible and affordable, they will revolutionize the high throughput phenotyping.

What has to be done to make of the microscopy method a platform for phenotyping?

The herein developed microscopy method represents a promising possibility for high

throughput determination of individual lag time and growth probability. Different points

of this method should be optimized in order to create a platform for quantitative

measurements applicable by any end-user. Instead of object slides, multi well plates

with a thin agar layer could be implemented for phase-contrast microscopy. The agar

could then be inoculated by a multichannel pipette. The development of a specific

program for guiding the motorized board would be helpful to automatically scan every

well (including stack in z-direction). This equipment combined with an ad hoc software

to analyze the high amount of data could be a promising platform in the future.

Which are the possible applications of the proposed microscopy method?

The method developed in this thesis opens the door to different possibilities of

application. As already mentioned, temperatures are often fluctuating along the cold

chain. Thus, studying the impact of dynamic changes in temperatures on the bacterial

behavior would be of great interest.

The microscopy approach also enable to monitor the follow up of interactions between

the pathogen of interest and the background flora or any other competitive

contaminations.

An interesting approach is the adaptation of the agar to simulate food matrices. For

instance, Overney and colleagues proposed a modified broth to mimic smoked salmon

(Overney et al., 2016). Moreover, in a recent work, Verheyen et al. (2019) discuss also

the impact of food microstructure and fat content, which appear to affect the growth

dynamics of L. monocytogenes in a fish-based model (Verheyen et al., 2019).

Regarding the observed differences in lag time and growth probability, what could be

additionally investigated?

For a better understanding of the results of Chapter 3 (single cell lag time and growth

probability), more comprehensive experiments should be carried out. Especially the

strain O228, which showed short lag times but a low growth probability, should be in

General discussion

128

the center of interest. For instance, microscopy could be used in combination with

fluorescent reporter genes for a better understanding of the physiological mechanisms

during adaptation. Français et al. (2019) recently published such kind of approach for

Bacillus cereus (Français et al., 2019). Using a microscopy method, they searched

potential lag time molecular markers at low temperature (12 °C) and low pH (5),

identifying the cshA gene as reliable marker for Bacillus cereus’ lag phase. This marker

is systematically expressed during lag phase, before initiation of growth, even during

exposure to cold and acidic conditions.

Differences in lag time and growth probability between the strains could be also

explained by the presence of plasmids. Plasmids can be an advantage for adaptation,

but their maintenance imposes a metabolic burden on the host cells, resulting in

reduced growth rate and cell density (Ow et al., 2006). Depending on the number of

plasmid copies, the growth rate may change (e.g. decrease with the increasing of

copies) (Bentley et al., 1990). In this perspective, one hypothesis could be that strains

harboring large plasmids or a high number of copies of small plasmids have a longer

lag time due to the metabolic burden for the maintenance of plasmids. However, once

they initiated growth, they are better adapted to cold thanks to the genes sheltered in

the plasmid, finally showing a higher growth probability. In contrast, strains without

plasmids will start to grow earlier but with less success (low growth probability).

Regarding single-cell level observed in this study, the differences between cells to

initiate growth or not could be explained by this reason. As MGEs can exchange easily,

the burden to overcome adaptation and starting to growth is variable due to huge

differences in the accessory genomes between single cells. In order to confirm this

presumption, the strains should be sequenced and a plasmid targeted analysis

performed.

QMRA towards future, what will be the challenge?

Besides the already mentioned problems, a careful evaluation of the bioinformatics

infrastructure and data storage possibilities should be considered. As suggested

above, huge amounts of data are needed to improve the quality of biomarkers and

prevalence data across different food production chains. Thus, big data management

will be included in future work. Servers of computing and storage along with competent

human resources are costly. Nowadays, it is possible to easily download genomic

General discussion

129

sequences (e.g., reads or assemblies) from public databases as NCBI, but QMRA

models require phenotypic data too. A databank combining harmonized genotypic and

phenotypic (e.g. persistence in food processing environment) information together with

strains metadata would be the ultimate accomplishment for QMRA scientists and

beyond. However, its realization will be extremely challenging, if not unreachable in

the next future.

Conclusion and future perspectives

130


1 Conclusion

The discovery of genomic markers for phenotype prediction through genome wide

association studies have the potential to refine QMRA models. For this reason, the first

aim of this thesis was the exploration and application of different tools to conduct

GWAS. Three different approaches that cover the pan-genome as also a

phylogenomic-based method were applied to a set of 51 L. monocytogenes strains to

detect the genomic features associated with their ability to grow at low

temperatures (2 °C). Several genes and SNPs, as well as specific phylogenetic

sublineages, were identified as associated to L. monocytogenes' growth at low

temperatures. These biomarkers can feed QMRA models in future studies to perform

risk assessments targeted to the subpopulations that pose the greatest risk, i.e.

isolates with higher ability to survive and/or grow in the food chain.

Results from the second study of this thesis emphasize the asset of implementing

genomic data into a QMRA model. The contribution of WGS-based subtyping data was

evaluated in a view of QMRA model refinement. Even if this model refinement is still

using some strong hypotheses (e.g. virulence and minimal growth temperature), this

work highlighted the potential impact of implementing genomic data in QMRA. The

development of new genomic-based approaches in QMRA, as presented in this work,

is still in its infancy and more time and comprehensive data are required to generate

accurate models with the implementation of genomic data.

To obtain robust genetic markers, large-scale data sets are necessary. DNA extraction

and sequencing are already fully-automated with high throughput. However, for the

selected number of genomes, the same number of strains has to be phenotypically

characterized. Experiments for phenotyping performed on large data sets are often

time-consuming and labor-intensive. The microscopy method presented in this thesis

is the first step towards a high throughput method at the single-cell level. Thanks to

further automatization in sample preparing and image acquisition, the next goals will

be to characterize a high number of strains (or conditions). This method allows applying

various conditions like temperature, pH and aw.


131

2 Perspectives

This work has shown that future QMRA will demand certain interdisciplinary

approaches. Knowledge on WGS-based analytical approaches as well as the

necessary bioinformatics skills to manage Unix-, Python- and R-based open-source

tools were acquired during this thesis. Freely available open-source bioinformatics

tools have been essential to reach the goal of this thesis. Nevertheless, knowledge

about the physiology of the targeted pathogen and expertise in bacterial behavior

under different conditions remained indispensable to carry out QMRA studies.

Soon, the evolution and integration of omics sciences could result in better risk

estimation and thus improve risk management measures. Potentially, it might improve

the hazard identification step (i.e. hypervirulence linked to CCs) and thus the risk

analysis could be adapted to particular bacterial population subgroups. Such a new

approach could lead to changes in risk management activities by food authorities and

companies (e.g. by adapting or by refining the intervention). However, considerable

improvements and agreements are still needed before Food Safety Authorities will

form their opinions based on genomic data. Therefore, it is necessary to reach full

confidence in the robustness of the data and to convince FBO of the reliability and

benefit of the use of the new genomic-based achievements. Especially, the potential

risk might be re-evaluated for the food categories where L. monocytogenes

hypervirulent strains are prevalent.

In that perspective, risk communication will take a central role once genomic data will

be fully implemented. Scientists are charged with responsibility because genomic-

based behavior prediction can be easily misinterpreted. In particular, when speaking

about risk and the associated virulence, great attention should be paid to “how to

communicate the results” to make sure that for example, hypovirulence is not equal to

no risk.

Valorization

132

Valorization

___________________________________________________________________

Publications:

Next generation quantitative microbiological risk assessment: Refinement of the

cold smoked salmon-related listeriosis risk model by integrating genomic data.

Lena Fritsch, Laurent Guillier and Jean-Christophe Augustin.

Microbial Risk Analysis, Volume 10, December 2018, Pages 20-27.

Insights from genome-wide approaches to identify variants associated to

phenotypes at pan-genome scale: Application to Listeria monocytogenes' ability

to grow in cold conditions.

Lena Fritsch, Arnaud Felten, Federica Palma, Jean-François Mariet, Nicolas

Radomski, Michel-Yves Mistou, Jean-Christophe Augustin, Laurent Guillier.

International Journal of Food Microbiology.

___________________________________________________________________

Oral communications:

ICPMF11 Bragança, (Portugal), September 2019.

Determination of growth probability and lag time at single cell level by the use of an

automated microscopy method

Lena Fritsch, Adrienne Lintz, Abirami Baleswaran, Bernard Hézard, Erwann Hamon,

Hélène Bergis, Jean-Christophe Augustin, Laurent Guillier, Valérie Stahl

Colloquium: Experiences in Listeria consulting, Bern, (Switzerland), August 2019.

Opportunities of Whole Genome Sequencing

Valorization

133

EFET/EFSA Workshop L. monocytogenes in RTE meat products, Athens

(Greece), February 2019.

The use of Whole Genome Sequencing in Risk Assessment

IAFP US, Salt Lake City (USA), July 2018.

Core and Accessory Genome-Wide Association Studies to Investigate Genetic

Determinants Involved in L.monocytogenes Cold Adaptation.

Lena Fritsch, Arnaud Felten, Jean-François Mariet, Jean-Christophe Augustin and

Laurent Guillier.

Workshop Listeria NRL, Teramo (Italy), June 2018.

Clonal Clomplex grouping in L.monocytogenes and implication regarding origin and

virulence.

Lena Fritsch, Benjamin Félix and Laurent Guillier.

IAFP Europe, Stockholm (Sweden), April 2018.

Integrating WGS data into Quantitative Microbial Risk Assessment: Refinement of the

L.monocytogenes in cold smoked salmon model.

Lena Fritsch, Laurent Guillier and Jean-Christophe Augustin.

ICPMF 10, Cordoba (Spain), September 2017.

Modelling approaches for Geno-Phenotype Association Studies based on WGS:

Application on L. monocytogenes.

Lena Fritsch, Arnaud Felten, Jean-François Mariet, Jean-Christophe Augustin and

Laurent Guillier.

Valorization

134

___________________________________________________________________

Poster presentations

IAFP Europe, Brussels (Belgium), March 2017.

Can Listeria monocytogenes growth variability be explained by the genotype?

Lena Fritsch, Anne-Laure Lardeux, Damien Michelon, Jean-Christophe Augustin and

Laurent Guillier

(Supplementary material 8)

ICPMF 10, Cordoba (Spain), September 2017.

Including genotypic data into quantitative microbial risk assessment: Application on

Listeria monocytogenes in cold smoked salmon.

Lena Fritsch, Laurent Guillier, Charlotte Lebouleux and Jean-Christophe Augustin.

(Supplementary material 9)

References

135

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Supplementary material

159


Supplementary material 1: Characteristics of 51 Listeria monocytogenes strains

included in the study

Strain ID Lineage Clonal Complex Origin Mean time to

visible growth at

2°C (days)

02CEB369LM I CC1 Dairy products 21

03CEB46LM II CC9 Dairy products 31*

04CEB500LM II CC26 Dairy products 17




05CEB394LM I CC6 Seafood 19









07CEB380LM I CC54 Dairy products 19.5






08CEB397LM II CC121 Seafood 22

08CEB85LM I CC2 sea food 17





10CEB68LM I CC2 Seafood 26.5*

11CEB239LM I CC5 Seafood 22.5





160

11CEB431LM II CC121 Seafood 45.5*

11CEB475LM II CC121 Seafood 45.5*


12CEB366LM I CC2 Meat products 42.5*

12CEB367LM I CC2 Meat products 17

12CEB368LM I CC2 Meat products 17

12CEB383LM I CC6 Meat products 19.5

14SEL860LM II CC9 Meat products 36.5*

14SEL861LM II CC31 Meat products 19

14SEL862LM I CC3 Industrial environment 17

14SEL863LM I CC2 Industrial environment 43*

14SEL872LM I CC2 Meat products 19



14SEL875LM II CC121 Industrial environment 17




14SEL879LM I CC5 Dairy products 30*


* strains classified as “slow” growing strain at 2°C


161

Supplementary material 2: Quality parameters of raw reads and genome assemblies.

Strain ID Reads Deep Coverage Breadth Coverage Total length N50

02CEB369LM 592.6316 86.61% 2916196 2916196

03CEB46LM 413.5260 97.98% 2984694 2983372

04CEB500LM 398.2204 95.13% 2899096 2899096

05CEB256LM 540.1698 86.08% 2880169 2880169

05CEB373LM 253.0781 85.73% 2779662 2772309

05CEB376LM 665.8138 95.16% 2838841 2838841

05CEB394LM 477.2362 85.6% 3138210 3138210

05CEB671LM 328.2228 85.81% 2920488 2920488

05CEB716LM 911.1483 94.74% 2942538 2942301

05CEB795LM 263.7213 85.51% 3152067 3152067

06CEB420LM 111 87.17% 2918278 244902

06CEB587LM 337.6334 85.61% 2897238 2891523

07CEB235LM 679.7624 85.42% 2883553 2883553

07CEB308LM 256.8279 85.88% 3170479 3170479

07CEB374LM 416.8330 86.44% 2936976 2936976

07CEB380LM 470.7155 86.64% 2937399 2937199

07CEB430LM 598.4284 95.06% 2860128 2859814

07CEB768LM 421.1201 86.28% 2874961 2874961

07CEB794LM 510.0839 85.91% 2999656 2999656

08CEB244LM 71.1349 94.62% 2872591 2869407

08CEB287LM 566.5650 95.38% 3063695 2980989

08CEB397LM 318.8124 95.4% 3111502 3044157

09CEB616LM 172.0089 85.71% 2974093 2960992

10CEB366LM 395.0897 94.38% 3095332 3006029

10CEB368LM 519.2391 95.11% 2983553 2982985

10CEB588LM 309.9157 95.43% 3060115 2993100

10CEB68LM 296.1250 85.26% 2958019 2958019

11CEB239LM 302.1884 87.97% 3015910 2925016

11CEB241LM 169.3014 85.25% 2990583 2990583

11CEB426LM 455.6619 94.91% 2975388 2931277

11CEB429LM 90 98.0% 3019676 330138

11CEB431LM 557.9262 95.31% 3087055 3086193

11CEB475LM 330.3424 95.45% 3117270 3111620

11CEB656LM 376.5166 88.29% 2929122 2921504

12CEB366LM 207.4452 85.32% 3109433 3109433

12CEB367LM 40 88.83% 3015827 144616

12CEB368LM 233.1621 85.02% 3035802 3035416

12CEB383LM 278.3006 87.74% 3062612 3019002

14SEL860LM 326.7772 97.81% 2907238 2907238

14SEL861LM 384.1521 94.78% 3099066 3037310


162

Supplementary material 3: Pangenome characteristics of the 51 Listeria

monocytgenes strains.

14SEL862LM 219.3390 87.39% 2978317 2921062

14SEL863LM 264.0851 85.7% 3066248 3065660

14SEL872LM 196.9262 85.16% 3001583 3001583

14SEL873LM 284.0920 86.55% 2951324 2950739

14SEL874LM 255.7525 86.0% 2963497 2963497

14SEL875LM 266.9115 95.24% 3055756 3055756

14SEL876LM 361.2296 87.64% 2987115 2937466

14SEL877LM 420.5283 88.36% 2995303 2995303

14SEL878LM 453.3566 85.95% 3030556 3030556

14SEL879LM 217.2113 88.32% 3001911 2915272

14SEL906LM 206.9351 85.52% 2891388 2891388


163

Supplementary material 4: List of 114 genes with a statistical association with the

growth ability at 2°C obtained with the gene-based GWAS method (Scoary)

https://www.sciencedirect.com/science/article/pii/S0168160518309218

Supplementary material 5: List of SNPs with a statistical association (p-value < 0.01)

with the growth ability at 2 °C.

https://www.sciencedirect.com/science/article/pii/S0168160518309218

Supplementary material 6: Prevalence of the different CCs associated to virulence

levels in cold smoked salmon.

ClonalComplex Frequency in CSS Virulence group

CC1 1 hypervirulent

CC101 7 hypervirulent

CC121 93 hypovirulent

CC14 5 medium virulent




CC2 4 hypervirulent








CC31 5 hypovirulent





CC4 0 hypervirulent






CC7 8 hypervirulent



CC9 37 hypovirulent


164



CC19 3 hypovirulent

ST124 2 hypovirulent

new CC 1 medium virulent


165

Supplementary material 7: Log10 r-values of 26 different L. monocytogenes strains

(FDA/FSIS, 2003).

Strain log10(LD50) log10(r_whole population)

G9599 2.57 -11.36

G1032 2.69 -11.48

G2618 2.89 -11.68

F4244 3.62 -12.41

F5738 3.67 -12.46

F6646 4.49 -13.28

15U 4.56 -13.35

F4246S 4.57 -13.36

F7208 4.61 -13.4

G2228 4.66 -13.45

F2381 4.73 -13.52

G2261 4.95 -13.74

F2380 4.96 -13.75

F2392 5.08 -13.87

NCTC 7973 5.47 -14.26

F7243 5.75 -14.54

F7245 5.91 -14.7

SLCC 5764 6 -14.79

V37 CE 6.04 -14.83

F7191 6.23 -15.02

V7 6.8 -15.59

Brie 1 7.28 -16.07

Murray B 7.3 -16.09

Scott A 7.54 -16.33

G970 8.88 -17.67

NCTC 5101 9.7 -18.49


166

Supplementary material 8: Poster IAFP Europe 2017.


167

Supplementary material 9: Poster ICPMF 2017.

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