DNA Methods in Food Safety E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 819

Veröffentlichungsjahr: 2014

Beschreibung

Molecular typing of foodborne pathogens has become an indispensable tool in epidemiological studies. Thanks to these techniques, we now have a better understanding of the distribution and appearance of bacterial foodborne diseases and have a deeper knowledge of the type of food products associated with the major foodborne pathogens. Within the molecular techniques, DNA-based techniques have prospered for more than 40 years and have been incorporated in the first surveillance systems to monitor bacterial foodborne pathogens in the United States and other countries. However, DNA techniques vary widely and many microbiology laboratory personnel working with food and/or water face the dilemma of which method to incorporate.

DNA Methods in Food Safety: Molecular Typing of Foodborne and Waterborne Bacterial Pathogens succinctly reviews more than 25 years of data on a variety of DNA typing techniques, summarizing the different mathematical models for analysis and interpretation of results, and detailing their efficacy in typing different foodborne and waterborne bacterial pathogens, such as Campylobacter, Clostridium perfringens, Listeria, Salmonella, among others. Section I describes the different DNA techniques used in the typing of bacterial foodborne pathogens, whilst Section II deals with the application of these techniques to type the most important bacterial foodborne pathogens. In Section II the emphasis is placed on the pathogen, and each chapter describes some of the most appropriate techniques for typing each bacterial pathogen.

The techniques presented in this book are the most significant in the study of the molecular epidemiology of bacterial foodborne pathogens to date. It therefore provides a unique reference for students and professionals in the field of microbiology, food and water safety and epidemiology and molecular epidemiology.

Leseprobe

CONTENTS

Cover

Title page

Copyright page

List of Contributors

Preface

Section I: Typing Method, Analysis, and Applications

1 Polymerase Chain Reaction-Based Subtyping Methods

Randomly amplified polymorphic DNA

Amplified fragment length polymorphism (AFLP)

Repetitive-sequence-based PCR

Multiple-locus variable-number tandem repeat analysis

PCR-restriction fragment length polymorphism (PCR-RFLP)

PCR melting profile analysis

References

2 Pulsed-Field Gel Electrophoresis and the Molecular Epidemiology of Foodborne Pathogens

Background

Theory

Parameters critical to PFGE separations

Instrumentation

Epidemiological applications of PFGE

Conclusion

Acknowledgments

References

3 Multilocus Sequence Typing: An Adaptable Tool for Understanding the Global Epidemiology of Bacterial Pathogens

Multilocus sequence typing

MLST databases

Advantages of MLST

Types of MLST schemes

Discriminatory power and epidemiologic concordance

Clonal complexes, epidemic clones, and outbreak clones

Conclusions

Acknowledgments

References

4 High-Throughput Sequencing

Introduction

Existing subtyping methods

WGS: A comprehensive platform for molecular subtyping

MLST versus WGS

SNP analysis versus WGS

Hypervariable markers

Phenotypic markers versus WGS

Technical process of WGS

Computational tools for WGS analysis

WGS in recent foodborne outbreak investigations

Challenges and future prospects of WGS in molecular subtyping

References

5 Analysis of Typing Results

Introduction

Measuring similarity

Creating groupings of related isolates

Comparison of typing methods

References

6 Databases and Internet Applications

Introduction

Existing online networks and resources

Current challenges, possible solutions, and future trends

GeoGenomic identification and an integrated Web-based global infrastructure

References

7 The Transformation of Disease Surveillance, Outbreak Detection, and Regulatory Response by Molecular Epidemiology

Epidemiology and surveillance

Outbreaks

PulseNet

From steps to principles

The end of the culture era?

Summary

Acknowledgments

References

Section II: Pathogens

8 The Genus

Bacillus

Bacillus

: A highly heterogeneous genus challenging food quality and safety

Bacillus

toxins are gaining increasing prominence as causative agents of foodborne diseases

Polymerase chain reaction methods

Multilocus sequence typing (MLST) and amplified fragment length polymorphism (AFLP): The “golden standards” for population analysis of the

B. cereus

group

Pulsed-field gel electrophoresis

Microarrays and high-throughput sequencing: From genotyping to genomotyping

Conclusions and future direction

References

9 Molecular Typing of Campylobacter jejuni

Introduction

Brief history of typing methods to study

C. jejuni

Most common methods to type

C. jejuni

Less commonly used typing methods

Comparative genomic fingerprinting

Combination of techniques

References

10 DNA Typing Methods for Members of the Cronobacter Genus

Introduction

Cronobacter

pathogenicity and virulence

Taxonomy and genomic diversity of the

Cronobacter

genus

Cronobacter

and the food industry

Biotyping of

Cronobacter

strains

DNA-based typing of

Cronobacter

strains

Multilocus sequence typing of

Cronobacter

spp.

Case studies of using DNA sequence–based typing of

Cronobacter

spp.

Current issues in the application of DNA typing methods for

Cronobacter

spp.

Conclusions

Acknowledgments

References

11 Molecular Subtyping Approaches for Pathogenic

Clostridium

spp. Isolated from Foods

Introduction

Concluding remarks

Disclaimer

References

12 Molecular Characterization of Shiga Toxin-Producing

Escherichia coli

Introduction

DNA fingerprinting

Sequence-based genotyping

Virotyping

Conclusions

References

13 Molecular Subtyping Methods for

Listeria monocytogenes

: Tools for Tracking and Control

Introduction

Fragment-based methods

Hybridization-based methods

DNA sequence-based subtyping methods

Acknowledgments

References

14

Salmonella

Introduction

Restriction analysis-based genotyping

PCR-based typing methods

DNA sequencing-based typing methods

Comparison of molecular subtyping methods for

Salmonella

Conclusions

Disclaimer

References

15

Vibrio cholerae

Introduction

CTX Phage

CTX

cla

(classical type CTX) and CTX-1 (El Tor type CTX or CTX

El

Tor

)

CTX-2

CTX-3

ctxB

Typing

TLC element

Genotyping of

V. cholerae

MLVA analysis of

V. cholerae

O1 strains

Conclusions

References

Index

End User License Agreement

List of Tables

Chapter 01

Table 1.1 Primers and PCR conditions for common repetitive elements

Table 1.2 Select VNTR loci in

L. monocytogenes

identified in the literature

Table 1.3 Comparison of major strain typing methods in terms of performance on various criteria

Chapter 03

Table 3.1 Terms used in population genetics and molecular epidemiology

Table 3.2 The seven known epidemic clones of

L. monocytogenes

and the foodborne outbreaks associated with them

Chapter 04

Table 4.1 Technical specifications of major WGS platforms

Table 4.2 Examples of

de novo

assemblers

Chapter 05

Table 5.1 Band-based similarity metrics

Table 5.2 MLST and MLVA online databases

Table 5.3 Hypothetical MLST data set

Table 5.4 Diversity indexes

Table 5.5 Pairwise agreement coefficients

Table 5.6 Confidence intervals for Simpson's index of diversity, Wallace, and adjusted Wallace

Chapter 06

Table 6.1 Databases and Internet resources

Chapter 08

Table 8.1 Examples of molecular systems for

B. cereus

toxin gene profiling

Table 8.2

B. cereu

s

sensu lato

population structure as defined by MLST and AFLP

Table 8.3 Overview on economically important

Bacillus

species for which multiple genomes are publically available from http://www.ncbi.nlm.nih.gov/genome

Chapter 09

Table 9.1 Differential characteristics of

Campylobacter

,

Salmonella

, and

Listeria

Table 9.2 Typing methods, advantages, disadvantages, and key publications related to these methods

Chapter 10

Table 10.1 Summary of phenotyping and genotyping methods for

Cronobacter

spp.

Table 10.2 MLST of

Cronobacter

isolates received by the CDC in 2011

Chapter 11

Table 11.1 DI of selected

C. botulinum

subtyping methods

Table 11.2 Summary of molecular subtyping methods for various clostridia

Chapter 12

Table 12.1 Comparison of genotyping techniques initially described by Karama and Gyles (2010)

Table 12.2 Seven multilocus sequence typing (MLST) housekeeping genes

Chapter 13

Table 13.1 Description of the targeted TRs and primer sets used in MLVA schemes developed for the subtyping of

L. monocytogenes

Chapter 14

Table 14.1

Salmonella

MLST primers and conditions

Chapter 15

Table 15.1 Number of SNPs on the genes of CTX

cla

and El Tor CTX (CTX-1) phages

Table 15.2 Variable amino acids (nucleotides) of

ctxB

on different CTX phages

Table 15.3 SNPs of

rstA

gene among CTX

cla

, CTX-1, CTX-2, and CTX-3

Table 15.4 Primers used for determining the CTX and RS1 array on each chromosome of

V. cholerae

Table 15.5 CTX array, MLST profile, and MLVA profile of representative strains of classical biotype (O395), El Tor biotype (N16961), and atypical El Tor (B33, 01.07, and IB4548) strains of

V. cholerae

O1

Table 15.6 MLVA loci characteristics and primer sequences

Table 15.7 MLVA profiles of

V. cholerae

O1 strains

List of Illustrations

Chapter 01

Figure 1.1 Randomly amplified polymorphic DNA analysis using arbitrary primers. Arbitrarily designed short primers (8–12 nucleotides) anneal to a large template of genomic DNA. When two primers anneal in the opposite direction to two genomic locations that are reasonably distant from each other, a fragment is amplified. These randomly amplified fragments are then analyzed by gel electrophoresis, resulting in a different pattern of amplified DNA fragments on the gel. To enhance priming with short primers, many primers are designed with a GC content between 10 and 70% and low annealing temperatures are used.

Figure 1.2 Amplified fragment length polymorphism analysis. A DNA template is first digested with two restriction enzymes, preferably a hexa-cutter and a tetra-cutter; and then the restriction fragments are ligated to the adaptors. Primers are designed to be complementary to the adapter and restriction site sequences, and their 3′ ends were added by a random nucleotide for selective amplification. Amplicons of selective amplification are visualized by gel electrophoresis.

Figure 1.3 Repetitive-sequence-based PCR. Primers are designed to bind to the repetitive elements and regions between these repeats are amplified. These fragments are then analyzed by gel electrophoresis.

Figure 1.4 PCR-RFLP. Primers are designed to amplify a specific genomic region and PCR amplicons are then digested with select restriction enzymes to generate fragments of various lengths. These fragments are then analyzed by gel electrophoresis.

Chapter 02

Figure 2.1 G bacteriophage DNA molecules, initially electrophoresed for 600 s using the pulsing regime indicated in I, are then allowed to relax. Fluorescence micrographs, taken at 12 s intervals (A–E), show that the DNA molecule relaxes via the same staircase path that was adopted during electrophoresis (indicated by white arrow) in II.

Figure 2.2 Schematic of pulse-oriented electrophoresis (POE) instrument. The white arrow indicates the net direction of DNA movement. The black arrows in parentheses indicate the polarities of the electrodes and diodes during electrophoresis.

Figure 2.3 Overview of sample preparation for PFGE. Bacterial cells from a pure culture of the pathogen are washed, counted, and mixed with agarose, then poured into a custom acrylic mold for making rectangular inserts. The cells embedded in the insert are then gently lysed and digested with a restriction endonuclease. The agarose insert protects the genomic DNA from shear-induced breakage during sample preparation, while enabling free diffusion of restriction enzyme into the insert. The insert with the digested DNA can be loaded directly on to the pulsed-field gel or stored for future use.

Chapter 03

Figure 3.1 Model for evolution of clonal complexes based on multilocus sequence typing data (MLST) using housekeeping genes. Transient clones within a recombining population. Such a population is composed of two parts. The “background” population consists of a large number of relatively rare and unrelated genotypes (small circles). Because the evolution of these genotypes has been dominated by recombinational replacements from different donors, the relationships between them are most accurately represented as a network rather than a bifurcating tree. A few common genotypes, or clusters of closely related genotypes (clonal complexes), illustrated as cones, are superimposed on this diverse background population. Each clonal complex arises from the emergence of a single adaptive genotype (MLST-defined ST) that increases in frequency under selection to reach an observable frequency within the population. Clones (MLST-defined STs) in such a population typically exist for decades, but during this time they diversify to result in clonal complexes, predominantly by the accumulation of recombinational replacements, but also by point mutation. Smith

et al

. 2000. .

Figure 3.2 Model of the evolution of epidemic clones based on multi-virulence-locus sequence typing (MVLST) data. In contrast to the Model in Figure 3.1, which is based on MLST data, in the earlier model based on MVLST data a single highly adapted MVLST-defined ST or virulence type (VT) emerges as an epidemic clone (EC), which then disseminates widely in time and space (cone) with no divergence in VT, because virulence factors are under strong negative selective pressure. Modified from Smith

et al

. 2000. Reproduced with permission of John Wiley & Sons.

Figure 3.3 Model of the dissemination of an epidemic clone using a combination of MVLST and MLST data. The earlier hypothetical model suggests that MVLST-defined virulence type I/MLST-defined ST1 (VT1/ST1) may represent the origin of epidemic clone I (ECI) in country A. VT1/ST2 of ECI appears to have evolved from VT1/ST1 and then disseminated from country A on continent 1 to country B on continent 2, where it subsequently diverged into additional MLST-defined STs. The date below each VT/ST indicates the first isolation in that country. The size of the circles is proportional to the number of isolates in each ST within each country.

Chapter 04

Figure 4.1 Technical process of WGS and secondary subtyping analyses

a

.

a

Major stages of WGS are highlighted.

b

For example, CRISPR.

c 

For example, serotype determinants.

Chapter 05

Figure 5.1 Hypothetical gel electrophoresis patterns and the resulting similarity matrixes for Jaccard, Dice, and simple matching coefficients.

Figure 5.2 First iteration results of single linkage, average linkage, and complete linkage algorithms for the Dice matrix calculated in Figure 5.1.

Figure 5.3 Second iteration results of single linkage, average linkage, and complete linkage algorithms for the Dice matrix calculated in Figure 5.1.

Figure 5.4 Dendrograms for single linkage, average linkage, and complete linkage algorithms for the hypothetical data set of Figure 5.1.

Figure 5.5 goeBURST algorithms steps for the hypothetical data set of Table 5.3.

Figure 5.6 Adjusted Rand and 95% confidence intervals for a data set of 325 Group A

Streptococcus

isolates for four typing methodologies: T typing,

emm

type, and PFGE with two different endonucleases, SmaI and SfiI, and two different cutoff values.

Figure 5.7 Adjusted Wallace and 95% confidence intervals for a data set of the 325 Group A

Streptococcus

isolates for four typing methodologies: T typing,

emm

type, and PFGE with two different endonucleases, SmaI and SfiI, and two different cutoff values.

Chapter 07

Figure 7.1 Sample

Salmonella

case reporting timeline: Pathogen-specific surveillance provides highly accurate information about the pathogens recovered from patient samples. The information does not provide signals about emerging outbreaks quickly however due to the lag times inherent in the way people seek healthcare and isolates move from clinical laboratories through reference laboratories and into the public health system for molecular analysis. These delays mean that outbreaks may be ongoing for several weeks before epidemiologists are aware that an outbreak is occurring. .

Figure 7.2 Burden of illness pyramid—reported cases versus all cases: Passive surveillance systems can only detect cases that are diagnosed by healthcare providers and have laboratory evidence for the source of infection. Not everyone who is exposed to a pathogen becomes ill, and only a small percentage of ill persons typically seek medical care. The majority of people who present to a healthcare provider with symptoms are not tested. Even when samples are submitted, the correct laboratory test may not be ordered or the sample may test negative, even when the person is infected. Only a fraction of true cases are ever reported to public health authorities. .

Figure 7.3 Sources of information for public health surveillance: Epidemiologists monitoring surveillance systems for information about disease trends or looking for signals about emerging outbreaks draw on multiple data sources. Systems have been established to gather information about diseases in people and animals, environmental information about air and water quality, healthcare-seeking behavior and access to care, and the population characteristics from census data or other sources. All these data streams are considered sources of public health information.

Figure 7.4 Confirmed reported

Salmonella

cases, NC 2000–2010: One advantage of passive surveillance systems is that after they are established, they collect comparable data over time. The consistency of data collection allows for meaningful observation of trends. Systematic changes in the way data are gathered need to be noted when significant events happen that may increase or decrease the number of reported cases. Electronic disease surveillance systems that receive automated data feeds from commercial laboratories are generally more accurate and more complete and may result in more cases being reported for surveillance purposes. .

Figure 7.5 Formula for calculating RR: RR is the appropriate measure of association to use in a cohort study, where all the people at risk are known and their shared exposures can be identified. RR calculates the illness attack rate among people who were exposed and were not exposed to particular variables in the study. Researchers often populate 2 × 2 data tables with the numbers of people who did and did not report a particular exposure, stratified by whether or not they experienced the illness being studied. If the RR = 1, there is no difference in attack rates between exposed and nonexposed people. If the RR > 1, the people exposed may have a higher risk for developing the disease. If the RR < 1, people who were exposed would be considered less likely to develop the disease, demonstrating a potential protective effect.

Figure 7.6 Example of an RR calculation: In this example, 70 of 75 people who ate prime rib at a wedding reception became ill, and 5 of 43 people who did not eat prime rib at the reception also became ill. Applying the RR calculation formula, the RR associated with eating prime rib is 8. This would mean that people who ate prime rib at the wedding reception were eight times more likely to get sick than people who did not eat prime rib at the reception.

Figure 7.7 Formula for calculating an OR: Often, the population at risk is unknown, or the number of people at risk is too large to enroll into a cohort study. In those instances, it may be better to conduct a case–control study where a number of people known to have the disease and a group of similar people without the disease are enrolled in a study, and the measure of association calculated is an OR. ORs approximate RR but are calculated slightly differently. They are similar however in that if the OR = 1, there is no association between the exposure and people who are ill. If the OR > 1, ill people report the exposure more frequently. If the OR < 1, ill people report the exposure less frequently, suggesting a potential protective effect.

Figure 7.8 Example of an OR calculation: In this example, 36 of 51 ill patrons reported eating prime rib in a particular restaurant before their symptoms began. There were also 15 of 65 people who did not get sick reporting that they ate the prime rib in the restaurant on the same days that ill patrons were there. Applying the OR calculation formula, we see that the OR for prime rib is 8, meaning that ill people reported eating prime rib in the restaurant eight times more frequently than nonill patrons did.

Figure 7.9 Map of PulseNet US regions and laboratories: The national molecular subtyping network for foodborne disease surveillance, more commonly known as PulseNet, is the most successful passive surveillance system ever developed to track bacterial enteric disease pathogens and detect foodborne illness outbreaks. Laboratories in all US states and territories participate in the network, which routinely monitors molecular data about particular genetic patterns within strains of common pathogens. Approximately 60 000 bacterial DNA PFGE patterns are uploaded into the network each year.

Chapter 08

Figure 8.1 Schematic workflow of “Gegenees,” a suitable bioinformatic tool for using fragmented whole-genome sequence date for epidemiology and diagnostics of

B. cereus

group.

Chapter 10

Figure 10.1 A Maximum-likelihood phylogenetic tree of the 16S rDNA sequences (528 nt) of the

Cronobacter

spp., generated using MEGA5 (Tamura

et al

., 2011). The tree has been drawn to scale using 1000 bootstrap replicates. The 16S rDNA sequence of

C. koseri

has been used as an outlier.

Figure 10.2 A Maximum-likelihood phylogenetic tree of the

fusA

gene sequences (438 nt) of the

Cronobacter

spp., generated using MEGA5 (Tamura

et al

., 2011). The

fusA

sequence of

C. koseri

has been used as outliers.

Figure 10.3 Maximum-likelihood phylogenetic tree based on the concatenated sequences (3036 bp) of the seven loci of the

Cronobacter

MLST scheme (

atpD

,

fusA

,

glnS

,

gltB

,

gyrB

,

infB

,

ppsA

). The tree is drawn to scale, with 1000 bootstrap replicates.

Figure 10.4 Dendrogram analysis generated for the PFGE profiles of

C. sakazakii

clinical isolates (

n

 = 30) by BioNumerics software, version 3.5. Clustering was done with UPGMA by using the Dice coefficient. Pulse types are identified on the left. The tolerance in the band was 1.5%, with an optimization of 1.5%.

Figure 10.5 Dendrogram analysis of pulsed-field gel electrophoresis using

XbaI

enzyme of

Cronobacter

spp. strains isolated from milk powder processing factory environments. The arrow on the top line indicates the 85% similarity cutoff used to differentiate the profiles.

Figure 10.6 Screen grab for Locus Explorer analysis of

gyrB

online at www.pubMLST.org/cronobacter.

Figure 10.7 Population snapshot of the

C. sakazakii

MLST dataset generated by the goeBURST algorithm using PHYLOViZ (Francisco

et al

., 2012), indicating the diversity of the sources of the strains. The threshold for the output was set to triple locus variation. The dominant STs are represented by the circles with larger diameters. Clusters of isolates linked by the black lines correspond to clonal complexes.

Figure 10.8 Maximum likelihood tree based on the concatenated sequences (3036 bp) of the seven MLST loci for

Cronobacter

isolates submitted to CDC in 2011. The tree is drawn to scale using MEGA5, with 1000 bootstrap replicates.

Chapter 12

Figure 12.1 Differences in the type of interactions between diarrheagenic

E. coli

representing the six pathotypes and eukaryotic epithelial cells.

Chapter 13

Figure 13.1 The figure shows isolates retrieved from different types of sausages sharing the same

Asc

I pattern and also the presence of one pulsotypes retrieved on different sampling times. The dendrogram was produced with UPGMA by calculating Dice similarity coefficient.

Figure 13.2 Schematic representation of the rRNA subunits and the IGS region targeted by RISA PCR.

Figure 13.3 ERIC-PCR (a) and REP-PCR (b) results when applied to the subtyping of

L. monocytogenes

isolates retrieved from the Gorgonzola PDO production chain. These isolates have been previously typed by PFGE (Lomonaco

et al

., 2009), and comparison among the methods confirmed that the REP-PCR provides the same epidemiological information than PFGE.

Figure 13.4 Minisequencing profile obtained with multiplex SNP typing (Lomonaco

et al

., 2011a; Lomonaco and Knabel, 2012) for of

L. monocytogenes

. Profiles were obtained with Peak Scanner Software v1.0 from Applied Biosystems. Orange peaks correspond to the internal size standard (GS LIZ120) and blue, green, red, and black peaks correspond to G, A, T, and C, respectively.

Chapter 15

Figure 15.1 Genome structure of different CTX phages. Block arrows indicate transcriptional direction of genes. Classical type

rstR

and genes containing SNPs of classical CTX phage are shaded and genes of El Tor CTX phage are shown in white.

ctxB

of CTX-3b is shown in a darker shade.

Figure 15.2 An example of determining the CTX and RS1 array on

V. cholerae

chromosomes. The absence of CTX phage or RS1 element on each chromosome can be confirmed by PCR. (a) The array of chromosome 1 needs to be confirmed if the CTX phage integration site is preceded by TLC element. (b) A single PCR reaction can confirm the lack of CTX phage on chromosome 2. The DNA sequences of the PCR primers are shown in Table 15.4. By applying a number of combinations of PCR primers and sequencing the PCR products as described in the text, the presence of CTX phage and RS1 element and the tandem array of each element can be determined. A black triangle on each chromosome indicates CTX phage integration site.

Figure 15.3 Determination of the number of tandem repeats in the first and the second loci employed for the MLVA of

V. cholerae

. (a) The repeat unit sequence of the first locus (VC0147) is AACAGA, which in this example repeats nine times. There is a short stretch of DNA sequence next to the last unit, AACAGC, which should not be counted as a repeat unit. (b) Three repeat units (GACCCTA) of the second locus are identified in this example. The second locus is an intergenic region; therefore, the repeat unit is not a multiple of 3, while other loci are on open reading frames (ORFs) and the repeat units are in multiples of 3.

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DNA Methods in Food Safety

Molecular Typing of Foodborne and Waterborne Bacterial Pathogens

Edited by

Omar A. Oyarzabal

Vice President of Technical Services at IEH Laboratories and Consulting Group, Seattle, WA, USA

Sophia Kathariou

Professor of Bioprocessing and Nutrition Sciences at the Department of Food, North Carolina State University, Raleigh, NC, USA

This edition first published 2014 © 2014 by John Wiley & Sons, Ltd

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Library of Congress Cataloging-in-Publication Data

DNA methods in food safety : molecular typing of foodborne and waterborne bacterial pathogens / edited by Omar Oyarzabal, Sophia Kathariou.    p. ; cm.    Includes bibliographical references and index.

    ISBN 978-1-118-27867-3 (cloth)I. Oyarzabal, Omar A., editor. II. Kathariou, Sophia, editor.[DNLM: 1. Food Safety–methods. 2. DNA Fingerprinting–methods. 3. Food Microbiology–methods. 4. Molecular Typing–methods. 5. Water Microbiology. WA 695]    RA601.5  363.19′26–dc23

2014013804

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List of Contributors

João André CarriçoInstituto de Microbiologia, Instituto de Medicina MolecularFaculty of Medicine, University of LisbonLisboa, Portugal

Catherine D. CarrilloCanadian Food Inspection AgencyOttawa Laboratory (Carling)Ottawa, Ontario, Canada

Yi ChenCenter for Food Safety and Applied NutritionFood and Drug AdministrationCollege Park, MD, USA

A. R. DattaOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied NutritionU.S. Food and Drug AdministrationLaurel, MD, USA

Xiangyu DengCenter for Food SafetyUniversity of GeorgiaGriffin, GA, USA

Monika Ehling-SchulzInstitute of Functional MicrobiologyDepartment of Pathobiology University of Veterinary Medicine, VeterinaerplatzVienna, Austria

Patricia I. FieldsEnteric Diseases Laboratory Branch, Division of Foodborne, Waterborne and Environmental DiseasesNational Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and PreventionAtlanta, GA, USA

Steven L. FoleyDivision of MicrobiologyFDA-National Center for Toxicological ResearchJefferson, AR, USA

Stephen ForsytheSchool of Science and TechnologyNottingham Trent UniversityNottingham, UK

A. A. FrancoOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

G. GopinathOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

C. J. GrimOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

Jing HanDivision of MicrobiologyFDA-National Center for Toxicological ResearchJefferson, AR, USA

K. HaricBio, IncFremont, CA, USA

R. JaincBio, IncFremont, CA, USA

K. G. JarvisOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

Susan JosephSchool of Science and TechnologyNottingham Trent UniversityNottingham, UK

Lee S. KatzEnteric Diseases Laboratory Branch, Division of Foodborne, Waterborne and Environmental DiseasesNational Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and PreventionAtlanta, GA, USA

Dong Wook KimDepartment of Pharmacy, College of PharmacyHanyang UniversityKyeonggi-do, Korea

Stephen J. KnabelDepartment of Food ScienceThe Pennsylvania State UniversityUniversity Park, PA, USA

M. H. KotharyOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

Sara LomonacoDepartment of Veterinary SciencesUniversità degli Studi di TorinoGrugliasco, Italy

Carolina LúquezEnteric Diseases Laboratory Branch, Division of FoodborneWaterborne and Environmental DiseasesNational Center for Emerging and Zoonotic Infectious DiseasesCenters for Disease Control and PreventionAtlanta, GA, USA

Aaron M. LynneDepartment of Biological SciencesSam Houston State UniversityHuntsville, TX, USA

M. K. MammelOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

Shannon D. ManningDepartment of Microbiology and Molecular GeneticsMichigan State UniversityEast Lansing, MI, USA

Susan E. MaslankaEnteric Diseases Laboratory Branch, Division of FoodborneWaterborne and Environmental DiseasesNational Center for Emerging and Zoonotic Infectious DiseasesCenters for Disease Control and PreventionAtlanta, GA, USA

B. A. McCardellOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

Ute MesselhäusserBavarian Health and Food Safety AuthorityOberschleißheim, Germany

Daniele NuceraDepartment of Agricultural, Forest and Food SciencesUniversità degli Studi di TorinoGrugliasco, Italy

Omar A. OyarzabalIEH Laboratories and Consulting Group,Seattle, WA, USA

Mário RamirezInstituto de Microbiologia, Instituto de Medicina MolecularFaculty of Medicine, University of LisbonLisboa, Portugal

Brian H. RaphaelEnteric Diseases Laboratory Branch Division of FoodborneWaterborne and Environmental DiseasesNational Center for Emerging and Zoonotic Infectious DiseasesCenters for Disease Control and PreventionAtlanta, GA, USA

Mohana RayDepartment of ChemistryUniversity of Wisconsin-MadisonMadison, WI, USA

V. SathyamoorthyOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

David C. SchwartzLaboratory for Molecular and Computational GenomicsDepartment of ChemistryLaboratory of GeneticsUniversity of Wisconsin-MadisonMadison, WI, USA

Pallavi SinghDepartment of Microbiology and Molecular GeneticsMichigan State UniversityEast Lansing, MI, USA

M. D. SolomotisOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

Insook SonCenter for Food Safety and Applied NutritionFood and Drug AdministrationCollege Park, MD, USA

David A. SweatShelby County Health DepartmentMemphis, TN, USA

Deborah F. TalkingtonEnteric Diseases Laboratory Branch, Division of FoodborneWaterborne and Environmental DiseasesNational Center for Emerging and Zoonotic Infectious DiseasesCenters for Disease Control and PreventionAtlanta, GA, USA

Ben D. TallOffice of Applied Research and Safety AssessmentCenter for Food Safety and Applied Nutrition U.S. Food and Drug AdministrationLaurel, MD, USA

Wei ZhangInstitute for Food Safety and HealthIllinois Institute of TechnologyBedford Park, IL, USA

Preface

Molecular typing of foodborne pathogens has become an indispensable tool in epidemiological studies. Thanks to these techniques, we can have a better understanding of the distribution and appearance of bacterial foodborne diseases and have a deeper knowledge of the type of food products associated with the major foodborne pathogens. Within the molecular techniques, DNA-based techniques have prospered for more than 40 years and have been incorporated in the first surveillance systems to monitor bacterial foodborne pathogens in the United States and other countries. However, DNA techniques vary widely, from techniques based on amplification of selected segments of the DNA to the latest whole genome sequencing analysis. Because of the wide array of available techniques and the different results they generate, we have compiled in Section I the different DNA techniques in use for the typing of bacterial foodborne pathogens. This section covers the following techniques: (i) pulsed-field gel electrophoresis, the main typing technique at the molecular subtyping network for foodborne bacterial disease surveillance (PulseNet) by the Centers for Disease Control and Prevention (CDC); (ii) multilocus sequence typing, a very powerful technique to study bacterial population structures and changes; and (iii) high-throughput sequencing techniques that are poised to be the predominant techniques in the near future. In Section I, we have also included chapters on the analysis of results obtained with band-migration techniques, the databases and internet applications available as repository of data produced by these techniques, and the application of these molecular techniques to outbreak detection and public heath surveillance.

Section II deals with the application of techniques to type the most important bacterial foodborne pathogens. Here the emphasis is placed on the pathogen, and each chapter describes some of the most appropriate techniques for typing each bacterial pathogen. As techniques progress and as we have better access to automated and robust techniques to study proteins, it is expected that DNA techniques will be used in association with other protein-based techniques or as first screening techniques. Until then, the techniques presented in this book are the most powerful techniques to study the molecular epidemiology of bacterial foodborne pathogens.

Omar A. OyarzabalSeattle, WA, USASophia KathariouRaleigh, NC, USA