The Pennsylvania State UniversitySCHEME FOR SUBTYPING THE MAJOR SEROVARS
OF SALMONELLA ENTERICA SUBSPECIES ENTERICA
A Thesis in
ii
The thesis of Fenyun Liu was reviewed and approved* by the following:
Stephen J. Knabel
Thesis Co-Advisor
John D. Floros
*Signatures are on file in the Graduate School
iii
ABSTRACT
Salmonella enterica subsp. enterica is the leading cause of bacterial foodborne disease in the
United States. Molecular subtyping methods are powerful tools for tracking the farm-to-fork
spread of foodborne pathogens during outbreaks. In order to develop a novel multilocus
sequence typing (MLST) scheme for subtyping the most prevalent serovars of Salmonella, the
virulence genes fimH and sseL and Clustered Regularly Interspaced Short Palindromic Repeat
(CRISPR) regions were sequenced from 171 clinical isolates from serovars Typhimurium,
Enteritidis, Newport, Heidelberg, Javiana, I 4, [5], 12; i: -, Montevideo, Muenchen and Saintpaul.
Another 63 environmental isolates and 70 poultry isolates of S. Enteritidis from poultry industries
in PA were also analyzed. The MLST scheme using only virulence genes was insufficient to
separate all unrelated outbreak clones. However, the addition of CRISPR sequences dramatically
improved discriminatory power of this MLST method. Moreover, the present MLST scheme
provided better discrimination of S. Enteritidis strains than PFGE. Cluster analyses revealed the
current MLST scheme is highly congruent with serotyping and epidemiological data. For the
analyses with S. Enteritidis isolates, the current MLST scheme identified three persistent and
predominant sequence types circulating among humans in the U.S. and poultry and hen house
environments in PA. It also identified an environment-specific sequence type. Moreover, cluster
analysis based on fimH and sseL identified three epidemic clones and one outbreak clone of S.
Enteritidis. In conclusion, the novel MLST scheme described in the present study accurately
differentiated outbreak clones of the major serovars of Salmonella, and therefore may be an
excellent tool for subtyping this important foodborne pathogen during outbreak investigations.
Furthermore, the MLST scheme may provide information about the ecological origin of S.
Enteritidis isolates, potentially identifying strains that differ in virulence capacity.
iv
ACKNOWLEDGEMENTS…………………………………………………………………….x
Chapter 2 Literature review ..................................................................................................... 3
2.1 Salmonellosis ............................................................................................................. 3 2.1.1 Salmonella ....................................................................................................... 4 2.1.2 Salmonella taxonomy and serotyping ............................................................. 4 2.1.3 Evolution of pathogenicity .............................................................................. 5 2.1.4 Salmonella reservoirs ...................................................................................... 6 2.1.5 Salmonella association with foods .................................................................. 8 2.1.6 Most common Salmonella serovars associated with human illnesses ............. 9
2.2 Subtyping of Salmonella ............................................................................................ 15 2.2.1 Important definitions and performance criteria of subtyping methods ........... 16 2.2.2 Salmonella subtyping methods during epidemiologic investigations ............. 17 2.3.2.1 Phenotypic methods ..................................................................................... 18 2.2.2.1.1 Serotyping ................................................................................................. 18 2.2.2.1.2 Phage typing .............................................................................................. 18 2.2.2.1.3 Multilocus enzyme electrophoresis (MLEE)............................................. 19 2.2.2.2 Genotypic methods ....................................................................................... 19 2.2.2.2.1 DNA-fragment-pattern-based methods ..................................................... 20 2.2.2.2.1.1 Pulsed-Field Gel Electrophoresis (PFGE) .............................................. 20 2.2.2.2.1.2 Amplified Fragment Length Polymorphism (AFLP) ............................. 22 2.2.2.2.1.3 Multiple Loci Variable number tandem repeat Analysis (MLVA) ........ 23 2.2.2.2.2 DNA-sequence-based methods ................................................................. 24 2.2.2.2.2.1 Multilocus Sequence Typing (MLST) ................................................... 24 2.2.2.2.2.2 Multi-Virulence-Locus Sequence Typing (MVLST) ............................. 26 2.2.2.2.2.3 Single Nucleotide Polymorphism (SNP) analysis .................................. 27
2.3 Clustered Regularly Interspaced Palindromic Repeat (CRISPR) .............................. 28 2.3.1 CRISPR in Salmonella .................................................................................... 30
2.4 Conclusions ................................................................................................................ 30 2.5 References .................................................................................................................. 31
Chapter 3 Novel virulence gene and CRISPR multilocus sequence typing scheme for
subtyping the major serovars of Salmonella enterica subspecies enterica ...................... 46
3.1 Abstract ...................................................................................................................... 47
v
3.2 Introduction ................................................................................................................ 48 3.3 Materials and methods ............................................................................................... 52 3.4 Results ........................................................................................................................ 55 3.5 Discussion .................................................................................................................. 74 3.6 Acknowledgements .................................................................................................... 78 3.7 References .................................................................................................................. 79
Chapter 4 Characterization of clinical, poultry and environmental Salmonella Enteritidis
isolates using multilocus sequence typing based on virulence genes and CRISPRs ....... 86
4.1 Abstract ...................................................................................................................... 87 4.2 Introduction ................................................................................................................ 88 4.3 Materials and methods ............................................................................................... 91 4.4 Results ........................................................................................................................ 93 4.5 Discussion .................................................................................................................. 104 4.6 Acknowledgements .................................................................................................... 108 4.7 References .................................................................................................................. 109
Chapter 5 Conclusions and future research .............................................................................. 115
5.1 Conclusions ................................................................................................................ 115 5.2 Future research ........................................................................................................... 117
APPENDIX Supplemental materials………………………………………………………... 121
LIST OF FIGURES
Figure 2.1 Model for the three-phase evolution of pathogenicity in Salmonella enterica
subspecies enterica. The phylogenetic tree is not drawn to scale (7). ............................ 6
Figure 2.2 Schematic view of the two CRISPR systems in Salmonella Typhimurium LT2.
.......................................................................................................................................... 29
Figure 3.1. Schematic view of the two CRISPR systems in Salmonella Typhimurium
LT2. .................................................................................................................................. 72
Figure 3.2. (a) Cluster diagram based on only fimH and sseL. (b) Cluster diagram based
on fimH, sseL and CRISPRs (combined allele of CRISPR1 and CRISPR2). .................. 73
Figure 4.1. Potential routes of transmission of S. Enteritidis contamination throughout
the egg food system. ......................................................................................................... 98
Figure 4.2. Schematic view of the two CRISPR systems in Salmonella Enteritidis strain
P125109. .......................................................................................................................... 99
Figure 4.3. Frequency of the five predominant sequence types (E ST1, 3, 4, 8 and 10) in
clinical, poultry and environmental isolates. .................................................................... 100
Figure 4.4. Cluster diagram based on only fimH and sseL for all 27 sequence types. ............ 101
Figure 4.5. Cluster diagram based on virulence genes and CRISPRs for all 27 sequence
types. ................................................................................................................................ 102
Figure 4.6. Graphic representation of spacer arrangements in CRISPR1 and CRISPR2 of
the 27 S. Enteritidis sequence types. ................................................................................ 103
Figure S1. Graphic representation of spacer arrangements in CRISPR1 and CRISPR2. ....... 124
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LIST OF TABLES
Table 2.1 Top ten most frequently reported serovars from human sources in 2005 ................ 10
Table 2.2 Top ten most frequently reported serovars from human sources in 2006 ................ 10
Table 3.1. Top nine most frequently reported serovars from human sources in 2005
which were analyzed in the present study ........................................................................ 60
Table 3.2. Outbreak information, PFGE profile and MLST results for the 171 isolates
analyzed in the present study ........................................................................................... 61
Table 3.3. Size, function and nucleotide location of the four markers targeted in the
present study .................................................................................................................... 65
Table 3.4. Primers used to amplify and sequence the four MLST markers ............................ 66
Table 3.5. Number of isolates, allelic types and sequence types in each serovar ................... 67
Table 3.6. Allelic polymorphisms and nucleotide substitutions in the nucleotide
sequences of fimH and sseL ............................................................................................. 68
Table 3.7. Analysis of CRISPR repeat sequences .................................................................. 69
Table 3.8. Analysis of CRISPR spacers in different serovars................................................. 70
Table 3.9. Comparison of epidemiologic concordance 1 between PFGE and MLST based
on virulence genes and CRISPRs for the selected strains analyzed in the present
study ................................................................................................................................. 71
Table 4.1. Sources, sample types and isolation information for the 167 S. Enteritidis
isolates analyzed in the present study .............................................................................. 96
Table 4.2. Primers used to amplify and sequence the four MLST markers ............................ 97
Table S1. Primers used to amplify and sequence other virulence genes ................................ 121
Table S2. Source, isolate information and MLST results for the 167 isolates analyzed in
the present study ............................................................................................................... 125
ADL Animal Diagnostic Lab
bp Base Pair
°C Degree Celsius
Clone† A group of isolates deriving from a common ancestor as part of a direct
chain of replication and transmission from host to host or from the
environment to host.
D Discriminatory Power
DNA Deoxyribonucleic Acid
dNTP Deoxyribonucleotide Triphosphate
DR Direct Repeat
E Epidemiological Concordance
EC Epidemic Clone
MVLST Multi-Virulence-Locus Sequence Typing
ix
RNA Ribonucleic Acid
from other isolates of the same species
ST Sequence Type
μl microliter
WGS Whole Genome Shotgun
† Clone and strain were defined previously by Struelens et al. (101).
x
ACKNOWLEDGEMENTS
I thank my parents, Zijian Liu and Guixiang Liu, who support and encourage me to study
in the US. I am also grateful for the support of my sister, Fenni Liu.
I would like to give my sincere thanks my advisors, Dr. Stephen J. Knabel and Dr.
Edward G. Dudley. I learned from them not only how to do research but also how to lead my life.
I feel so grateful for the working experience with them. I also thank my committee members, Dr.
Rodolphe Barrangou, and Dr. Bhushan M. Jayarao for their guidance and encouragement.
Additionally, I thank Dr. Kariyawasam, Dr. Gerner-Smidt and Dr. Ribot for their help with the
research.
I thank my labmates, Jia Wen, Mei Lok, Gabari, Michelle, Carrie, and Mat for their help
and encouragement. I also want to give special thanks to Dr. Bindhu Verghese for her guidance
and help with my research. Furthermore, I want to thank all the faculty, graduate students and
staff in the Department of Food Science for their support.
At last, I thank USDA and the Department of Food Science for supporting my research.
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Statement of the problem
Salmonella is one of the most common foodborne bacteria worldwide. In the United
States alone, there were approximately 1.4 million cases of salmonellosis each year since 1996,
which resulted in a heavy burden on public health and the economy. In order to develop effective
intervention strategies to control salmonellosis during outbreaks, it is critical to rapidly and
accurately track the farm-to-fork spread of Salmonella. Molecular subtyping methods are
powerful tools for investigating the transmission of Salmonella by characterizing specific
outbreak clones. Serotyping has been one of the major subtyping methods employed during
outbreaks to provide base line information about the serovar involved. There are approximately
2,500 different serovars of Salmonella; however, the top ten serovars caused approximately 60%
of all outbreak cases. Each of those top serovars is known to cause numerous outbreaks, each of
which is typically caused by a specific outbreak clone. Therefore, molecular subtyping methods,
which are generally more discriminatory than serotyping, are needed to further distinguish
different strains of a particular serovar. Pulsed-field gel electrophoresis (PFGE) is currently
CDC’s gold standard approach for subtyping Salmonella. However, PFGE sometimes lacks
discriminatory power and epidemiologic concordance for typing clonal serovars, such as S.
Enteritidis and S. Montevideo. Many studies have been conducted to develop alternative
subtyping methods, one of which is multi-locus sequence typing (MLST). Previous MLST
schemes for Salmonella focused mainly on discriminatory power; however, none of the previous
MLST studies examined the epidemiologic concordance of the MLST schemes or attempted to
distinguish strains within highly clonal Salmonella serovars, such as S. Enteritidis and S.
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Montevideo. Moreover, for S. Enteritidis, our knowledge of their epidemiology is hindered due
to its clonal nature. Therefore, the main purpose of the present study was to enhance the
molecular epidemiology of Salmonella by developing an MLST scheme that has both high
discriminatory power and high epidemiologic concordance for subtyping the major serovars of
Salmonella.
3
bacteremia and typhoid fever. After ingestion of Salmonella into the gastrointestinal system,
gastroenteritis can develop, which is characterized by symptoms such as abdominal pain, nausea,
vomiting and diarrhea. More severe manifestations of salmonellosis, such as bacteremia and
typhoid fever can develop after the invasion of Salmonella into the bloodstream. Common
symptoms of bacteremia are fever, focal infections, sepsis and meningitis. Typhoid fever is a
deadly systemic infection for humans caused by S. Typhi.
The incidence of typhoid fever has declined in the U.S. with approximately 400 cases
annually (33). On the other hand, infections due to nontyphoidal Salmonella (mainly
gastroenteritis) have increased dramatically during the last 3 to 4 decades (29, 53). The increased
number of infections from nontyphoidal Salmonella may result from modern intensified farming
and food production methods and global trade. Increased spread of Salmonella may also be
promoted by the acquisition of genes for antibiotic resistance (102), and in the case of S.
Enteritidis, genes permitting colonization of chicken ovaries (49).
Globally, it is estimated that there are 93.8 million cases of gastroenteritis due to
Salmonella annually, out of which 80.3 million (86%) cases are foodborne (76). In the United
States, salmonellosis is the leading cause of foodborne bacterial disease, with approximately 1.4
million human cases each year, resulting in 17,000 hospitalizations, 585 deaths (28,116) and a
cost of 2.6 billion dollars due to loss of work, medical care and loss of life (112). Therefore, it is
4
imperative to study the origins, transmission and epidemiology of this pathogen in order to
control and prevent diseases in the future.
2.1.1 Salmonella
Salmonella is one of the most well-known and frequent foodborne bacterial pathogens throughout
the world (76). Salmonella is a genus of rod-shaped, gram negative, non-spore forming,
facultative anaerobic and motile bacteria belonging to the family Enterobacteriaceae.
2.1.2 Salmonella taxonomy and serotyping
The genus Salmonella is comprised of two species: S. enterica and S. bongori. The
species S. bongori is rarely associated with human disease. The species S. enterica has six
subspecies: enterica, salamae, arizonae, diarizonae, houtenae and indica (63, 107). S. enterica
subspecies enterica is responsible for 99% of the human cases of salmonellosis, so it is of greatest
clinical importance (2).
distinguishes Salmonella immunologically based upon O antigens (lipopolysaccharide) and H
antigens (peritrichous flagella). There are more than 2,500 recognized S. enterica serovars, each
with a unique combination of O and H antigens (54). Prior to 2000, serovars were sometimes
used as species names (16). For example, the original S. typhimurium is now referred to as S.
enterica subspecies enterica serovar Typhimurium or simply S. Typhimurium. The latter
nomenclature is used more commonly in publications and public health surveillance programs
such as those administrated by the Centers for Disease Control and Prevention (CDC).
5
2.1.3 Evolution of pathogenicity
S. enterica subspecies enterica was proposed to evolve in 3 main steps (Fig. 2.1) (7). The
first step involved acquisition of Salmonella pathogenicity island 1 (SPI1) which contributed to
the divergence of Salmonella from E. coli and other related organisms. SPI1 is a 40 kb DNA
region present in both S. enterica and S. bongori (78). It encodes a type III secretion system
(T3SS) required for the intestinal phase of infection and promotes inflammation, the invasion of
intestinal epithelial cells, and secretion of intestinal fluid (117).
The second step of evolution was hypothesized to be the acquisition of a second
pathogenicity island SPI2 in the species S. enterica but not in S. bongori (Fig. 2.1) (7). SPI2
encodes another T3SS and various effector proteins that are required for survival and replication
inside host cells during systemic infection (86, 97). For example, one of the many SPI2 effector
proteins, SseL, is involved in macrophage killing, thus promoting survival inside the host (95).
Due to the presence of SPI2, S. enterica has increased capacity for systemic spread and is thus
more virulent than S. bongori, which do not contain SPI2.
Finally, the host range of S. enterica subspecies enterica expanded to warm-blooded
animals, including humans (Fig. 2.1) (7). In contrast, the other five S. enterica subspecies and S.
bongori are mainly associated with cold-blooded animals. The expansion of host range to warm-
blooded animals requires that bacteria recognize the new hosts for the first step of infection.
Recognition and attachment to the host involves adherence and colonization factors called
adhesins. For example, fimbrial adhesin encoded by the gene fimH allows Salmonella to
recognize and adhere to different receptors on host cells (66, 99). Genetic changes of this gene
by point mutation or recombination might allow the subspecies enterica to recognize new
receptors in new hosts, thus helping to expand its host range. After recognition and attachment,
other processes allowing the subspecies enterica to infect warm blooded animals may include the
ability to survive the immune system and proliferating inside host cells (7). It is not clear which
6
genetic changes accounted for these processes during adaptation to new hosts because adaptation
to a new animal host is a complex process that probably involves a large number of genes.
In summary, acquisition of SPI1 separated the genus Salmonella from other related
organisms like E. coli. Then, acquisition of SPI2 separated the genus Salmonella into two distinct
lineages, S. bongori and S. enterica. Finally, the lineage of S. enterica branched into several
distinct phylogenetic groups. This latter phase of evolution was characterized by host range
expansion of the subspecies enterica to warm-blooded animals, including humans. Through all
these evolutionary steps, Salmonella enterica subspecies enterica (hereafter referred to as
Salmonella) became a highly successful human and animal pathogen.
Figure 2.1 Model for the three-phase evolution of pathogenicity in Salmonella enterica
subspecies enterica. The phylogenetic tree is not drawn to scale (7).
2.1.4 Salmonella reservoirs
Salmonella is mostly transmitted through the fecal-oral route. Salmonellosis occurs when
humans consume foods or water contaminated by animal and human feces containing Salmonella
during food-handling or harvesting. Therefore, foods serve as the main transmission vector for
7
Salmonella, which include animal foods that are not thoroughly cooked and contaminated
uncooked vegetables and fruits (116).
Generally speaking, transmission of Salmonella starts from its reservoirs, which are
defined as any person, animal, plant, soil or substance (or combination of these) in which a
microorganism normally lives and grows (67). Salmonella serovars have adapted to live in a
variety of hosts. Many wild animals, such as gorillas (10), rhinoceros (68), lizards (88), reptiles
and snakes (9) harbor Salmonella. More importantly, food animals including chickens, turkeys,
cattle, swine and sheep have also been found to frequently carry Salmonella.
Different serovars have different reservoirs and modes of pathogenesis. For example, S.
Typhi, which causes the deadly disease typhoid fever, is a strict human pathogen. Some other
serovars, such as S. Gallinarum in chickens, S. Choleraesuis in swine and S. Dublin in cattle, are
known to be associated mainly with one animal, but rarely cause disease in humans. In contrast,
other serovars like S. Typhimurium have adapted to a broad host range, including wild and
domestic animals and humans. Moreover, different animals have different predominant serovars
associated with them. Predominant serovars associated with poultry, cattle and swine will be
reviewed here in brief because those animals are the primary vectors for transmitting Salmonella
to humans and are the main focus of this study.
The most prevalent and important reservoirs for Salmonella are poultry (23). The most
common poultry-associated serovars, Enteritidis in eggs and Typhimurium in poultry, accounted
for 33.3 % of the total human foodborne diseases in the U.S. (20). The top 5 most common
serovars associated with broilers are Kentucky, Heidelberg, Enteritidis, Typhimurium and I 4, [5],
12: i: - (113). They represent 81% of all Salmonella isolates from broilers. Similarly, serovars
Hadar, Heidelberg, Reading, Schwarzengrund, and Saintpaul account for 68% of all Salmonella
isolates from turkeys (113).
8
Cattle are also frequently found to harbor Salmonella. They can carry many different
serovars of Salmonella, with Montevideo, Anatum, Muenster, Newport, Mbandanka the most
common serovars that account for 47 % of Salmonella isolates from cattle (114).
As for swine, another important reservoir for Salmonella, the 5 most frequent serovars
are Derby, Typhimurium, Infantis, Anatum and Saintpaul. These 5 serovars comprise 60% of all
isolates from swine (114).
It is noteworthy that most of these serovars found predominantly in food animals are the
same serovars that are frequently associated with human diseases. Given this fact, it is of great
importance to control and monitor levels of the most common serovars in animals and
subsequently prevent their transmission to humans.
2.1.5 Salmonella association with foods
Another important vehicle for transmitting Salmonella to humans is produce. Salmonella
can cycle through the food chain and the environment in soil, water, manure, and insects.
Therefore, contamination of produce can occur by various ways throughout the food system.
Like predominant serovars in animals, there are also predominant produce-associated serovars,
which include Enteritidis, Newport, Poona, Typhimurium, Braenderup, Javiana, Montevideo and
Muenchen (60). The overlap between serovars most commonly associated with animals and
those associated with produce suggests contamination of produce during growing or harvesting
processes directly or indirectly by animals containing Salmonella. Moreover, evidence is
accumulating that enteric bacteria have the ability to grow and persist on and in plants, such as
tomatoes, radish sprouts, bean sprouts, barley, and lettuce (15, 47, 62).
Contamination and persistence of Salmonella on produce promote the transmission of
this pathogen to humans. Salmonella outbreaks associated with fresh produce have increased in
the U.S in recent years (98). Many kinds of produce have been linked to Salmonella outbreaks,
9
such as tomatoes, sprouts, melons, cantaloupe, lettuce, peppers and mangos (98). Produce causes
the highest number of human diseases and second highest number of outbreaks among various
food vehicles in the U.S. (3). For example, the largest Salmonella outbreak to date occurred in
2008 and was caused by consumption of Jalapeño and Serrano peppers that were contaminated
with S. Saintpaul (22).
Besides foods of animal origin and produce, there has been an increase in Salmonella
outbreaks caused by new food vehicles, such as salami, peanut butter, veggie booty, pot pies, and
dry cereals. For instance, in 2010, Italian-style salami and its ingredients (red and black peppers
containing S. Montevideo) caused a multistate outbreak which infected 252 people from 44 states
(27). As a result, approximately 1,378,754 pounds of Italian sausage products were recalled by
Daniele International, Inc. (27). Another recent outbreak caused by a new food vehicle is the
2008-2009 peanut butter outbreak, which infected 714 people from 46 states and caused 6 deaths
(24). As a result, more than 2,100 peanut-containing products were recalled by over 200
companies.
Outbreaks due to those new food vehicles were not expected because they are more or
less processed foods which do not possess conditions that permit the growth of Salmonella. For
example, peanut butter is a dry food with an aw below the minimum level for growth (0.94).
Moreover, Salmonella can be inhibited or killed by heat, acid, high salt concentration, etc. during
food manufacturing processes (38). Persistence of Salmonella in processed foods might be due to
1) high levels of Salmonella in food ingredients; 2) inadequate sanitary practices; 3) and the
ubiquity of Salmonella in animals, produce and the environment.
2.1.6 Most common Salmonella serovars associated with human illnesses
Although there are over 2,500 Salmonella serovars, only a handful of Salmonella
serovars caused most human illnesses (Tables 2.1 and 2.2) (20, 21).
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Table 2.1 Top ten most frequently reported serovars from human sources in 2005
Rank Serovar No. of laboratory-confirmed cases % of total cases
1 Typhimurium 6982 19.3
2 Enteritidis 6730 18.6
3 Newport 3295 9.1
4 Heidelberg 1903 5.3
5 Javiana 1324 3.7
7 Montevideo 809 2.2
8 Muenchen 733 2
9 Saintpaul 683 1.9
10 Braenderup 603 1.7
Source: 2005 Salmonella annual review (20).
Table 2.2 Top ten most frequently reported serovars from human sources in 2006
Rank Serovar No. of laboratory-confirmed cases % of total cases
1 Typhimurium 6872 16.9
2 Enteritidis 6740 16.6
3 Newport 3373 8.3
4 Heidelberg 1495 3.7
5 Javiana 1433 3.5
7 Montevideo 1061 2.6
8 Muenchen 753 1.9
9 Oranienburg 719 1.8
10 Mississippi 604 1.5
Source: 2006 Salmonella annual review (21).
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Compared to all the other serovars of Salmonella, S. Typhimurium caused the highest
number of human illnesses and was associated with a broad range of foods (Table 2.3). As
mentioned before, S. Typhimurium has adapted to various hosts, including birds, amphibians, and
all food animals, especially poultry, cattle and swine. Not only can S. Typhimurium reside in so
many animals, but it can also be found in them at high frequency (114). The ubiquity and
relatively high numbers of S. Typhimurium might explain why it has caused so many outbreaks
via so many kinds of foods (Table 2.3).
The second most common serovar is S. Enteritidis, which caused nearly as many human
cases as S. Typhimurium (Tables 2.1 and 2.2). The major food vehicles for S. Enteritidis are shell
eggs, as 80% of the S. Enteritidis outbreaks were egg-associated (89). S. Enteritidis contaminates
eggs either through horizontal transmission, by which eggs are externally contaminated by feces
containing S. Enteritidis (36), or by vertical transmission, where the inside of the eggs is
contaminated by infected ovaries before the laying of the egg (50, 87). Vertical transmission is
believed to be the more important route because eggs contaminated by vertical transmission
produce a new generation of infected broilers or layers after hatching (50, 57, 79). In order to
control S. Enteritidis in poultry, one of the interventions employed in the U.S. is egg quality
assurance programs on farms. These voluntary programs involve acquisition of S. Enteritidis free
chicks, control of pests (including rodents and flies), use of S. Enteritidis-free feeds, and routine
microbiologic testing for S. Enteritidis in the farm environment (14).
The third most commonly reported serovar causing salmonellosis is S. Newport (Tables
2.1 and 2.2). S. Newport can be detected in many food animals, but is most frequently isolated
from cattle (113). S. Newport has been implicated in many outbreaks via a variety of food
vehicles, such as beef, chicken, pork, tomatoes, cantaloupes, melons, avocadoes and guacamole
12
(23). In 2010, S. Newport caused a multistate outbreak due to contaminated alfalfa sprouts, in
which 35 people became ill (26). Cases of illness caused by S. Newport have increased in recent
years, which might be due to the emerging multidrug-resistant S. Newport isolates (19).
The fourth most common serovar is S. Heidelberg (Tables 2.1 and 2.2). It is often
isolated from commercial broilers and ground chicken (113). As a result, poultry and eggs have
been identified as the major food vehicles for this serovar (32). The largest outbreak caused by S.
Heidelberg occurred in 2007, when 802 people became infected via contaminated hummus (Table
2.3).
Following S. Heidelberg, S. Javiana caused the fifth most human infections (Tables 2.1
and 2.2). Unlike other serovars, S. Javiana is rarely isolated from poultry, cattle or swine (113).
The major reservoirs for S. Javiana were considered to be amphibians, as direct contact with
amphibians has been associated with outbreaks. Amphibian feces-contaminated tomatoes were
identified to be the main food vehicles for S. Javiana (34). For example, tomatoes were identified
to be the food source of S. Javiana for a multistate outbreak in 2002, which resulted in 159 cases
(Table 2.3).
The sixth most common serovar I 4, [5], 12: i :- , a variant of serovar S. Typhimurium, is
antigenically similar to S. Typhimurium, but lacks the second-phase
flagella antigens (39). It is
also one of the most commonly identified serovar in broilers and ground chicken (113). I 4, [5],
12: i :- contaminated pot pies caused a multistate outbreak in 2007 (Table 2.3).
S. Montevideo is the next most commonly reported serovar. S. Montevideo is frequently
isolated from cattle and ground beef (113). Food vehicles of S. Montevideo include beef, turkey,
pork and sprouts (22). The most recent outbreak caused by S. Montevideo occurred in 2010 due
to contaminated Italian-style meats (27).
The eighth most common serovar is S. Muenchen. S. Muenchen can be detected in swine,
cattle, chicken etc. It has been associated with outbreaks due to multiple food vehicles, such as
13
chicken, sprouts, tomato, and cantaloupe (22). In 1999, a multistate outbreak was caused by S.
Muenchen in orange juice, which infected 398 people.
S. Saintpaul ranks as the ninth most common serovar in 2005, but dropped to eleventh in
2006 (20, 21). However, its ranking might have risen higher since then, because it caused the
largest Salmonella outbreak in 2008 due to contaminated peppers. S. Saintpaul is frequently
isolated from swine and has caused outbreaks due to foods like sprouts, tomatoes, mangoes,
orange juice, turkey etc.
The importance of the above top serovars is reflected by the high number of
salmonellosis cases they cause. Their success as human pathogens might be largely due to
adaptation to food animals. For example, 4 of the top 8 serovars are frequently found in poultry,
namely Typhimurium, Enteritidis, Heidelberg and I 4, [5], 12: i :-. Two other serovars, Newport
and Montevideo, are mainly found in cattle.
14
Table 2.3 Salmonella outbreaks caused by the top 8 serovars in the United States from 1998- 2010
Year Serovar Ill Hospitalizations Deaths Food vehicle
2008 Typhimurium 530 116 8 peanut butter
2001 Typhimurium 404 0 4 unidentified
2006 Typhimurium 199 39 0 deli meat
2006 Typhimurium 192 24 0 tomato
2005 Typhimurium 162 0 sauces; fajita
2006 Typhimurium 161 7 0 chicken
1998 Typhimurium 134 10 0 multiple foods
2002 Typhimurium 132 0 0 unidentified
2002 Typhimurium 116 4 0 milk
1999 Typhimurium 112 3 0 clover sprouts
2002 Typhimurium 107 6 0 milk
2007 Typhimurium 87 8 0 Veggie Booty
2007 Typhimurium 76 4 0 lettuce; spinach
2003 Typhimurium 67 2 0 eggs
2007 Typhimurium 66 3 0 pork
2003 Typhimurium 59 2 0 beef
2005 Typhimurium 57 8 0 cake
2003 Typhimurium 56 11 0 ground beef
1998 Typhimurium 50 1 0 smoked fish
2003 Typhimurium 50 7 0 queso fresco
2002 Enteritidis 700 3 0 salsa
2005 Enteritidis 304 56 1 turkey
1999 Enteritidis 256 0 0 ice cream
2001 Enteritidis 231 34 0 egg-based sauce
2002 Enteritidis 196 24 0 cake
2005 Enteritidis 126 15 0 cantaloupe
2006 Enteritidis 113 23 0 oil; chicken
2001 Enteritidis 113 0 0 eggs
2000 Enteritidis 106 14 0 macaroni cheese
2007 Enteritidis 106 14 0 chicken
2003 Enteritidis 104 12 0 crab cakes
2001 Enteritidis 92 7 0 eggs
2002 Enteritidis 90 2 0 beef; pork
2000 Enteritidis 88 orange juice
1999 Enteritidis 82 3 0 honeydew melon
2002 Newport 510 tomato
2004 Newport 97 lettuce
1999 Newport 79 mango
2003 Newport 68 13 2 honeydew melon
2007 Newport 67 5 0 pork
2007 Newport 65 11 0 tomato
2004 Newport 49 8 0 turkey and gravy
15
2007 Newport 46 tomato; avocado
2007 Heidelberg 802 29 0 hummus
2003 Heidelberg 517 chicken
2007 Heidelberg 79 mashed potato
2004 Heidelberg 78 2 0 turkey
2005 Heidelberg 75 5 0 sandwich; vanilla cake
2003 Heidelberg 65 14 0 Swiss cheese
2003 Heidelberg 57 7 0 eggs; pancakes
2000 Heidelberg 56 3 0 macaroni salad
1999 Heidelberg 41 chicken
2002 Javiana 159 3 0 tomato
2004 Javiana 60 1 0 beans
2000 Javiana 44 8 0 bread; chicken
2007 I 4,[5],12:i :- 401 108 3 pot pie
2010 Montevideo 252 Italian-style meats
2006 Montevideo 72 19 0 sandwich, beef
2002 Montevideo 55 6 0 beef
1999 Muenchen 398 orange juice
1999 Muenchen 61 6 0 alfalfa sprouts
2003 Muenchen 58 15 cantaloupe
2002 Muenchen 57 3 0 pasta salad
2005 Saintpaul ;
2009 Saintpaul 235 alfalfa sprouts
Source: CDC foodborne outbreak database (23).
2.2 Subtyping of Salmonella
In order to control Salmonella outbreaks, it is important to trace back the sources and
identify the routes by which Salmonella are transmitted to foods. However, trace-back
investigation of outbreaks can be hindered due to the complexity of the food chain and the
limitations of traditional epidemiologic investigations. The limitations of traditional
epidemiologic investigations include 1) Only a limited number of cases are reported; 2) People
tend not to recall the foods that were eaten before disease onset; 3) Cases are often spread out in
16
time and space; and 4) Investigations can be hindered if the food source is not listed on the
investigation questionnaire (60).
Based on the reasons above, another trace-back method called subtyping is carried out
along with traditional epidemiologic investigations. Subtyping characterizes bacteria at the strain
level (101). By characterizing the outbreak-related strains and separating them from non-related
strains, subtyping can play an essential role in investigating Salmonella outbreaks.
Besides tracking pathogens in epidemiologic investigations, the other use of subtyping
methods is to study the population structure, evolution and diversity of bacteria on a long-term
scale. For example, one subtyping method called multilocus enzyme electrophoresis (MLEE) has
been used to study the genetic diversity of Salmonella populations (8). Studies like this can
provide insight into the evolutionary history and emergence of Salmonella serovars. However,
the focus of this review is on the short-term epidemiologic applications of subtyping methods.
2.2.1 Important definitions and performance criteria of subtyping methods
Before considering the epidemiology of Salmonella, it is important to first clarify the
definitions for outbreak, epidemic, strain, epidemic clone (EC), and outbreak clone (OC) used
frequently in epidemiologic studies. These definitions were previously compiled by Chen and
Knabel (30). Outbreak is an acute appearance of a cluster of an illness that occurs in numbers in
excess of what is expected for that time and place. Epidemic is defined as one or more outbreaks
that spread widely over a long period of time. Strain is defined as isolates that have distinct
phenotypic and genotypic characteristics from other isolates from the same species. Epidemic
clone is a strain or group of strains descended asexually from a single ancestral cell (source strain)
that is involved in one epidemic, and can often include several outbreaks. Outbreak clone is a
strain or group of strains descended asexually from a single ancestral cell (source strain) that is
involved in one outbreak (30).
17
To evaluate and compare different subtyping schemes, there are several performance
criteria, which include typeability, reproducibility, discriminatory power and epidemiologic
concordance. Typeability is the capability of a method to generate an interpretable result for each
strain typed. For example, strains that do not have plasmids cannot be typed by plasmid profiles.
Reproducibility is the ability of a subtyping method to generate the same result each time the
sample is tested. Discriminatory power is the ability of a subtyping method to differentiate
between unrelated epidemic or outbreak clones. Epidemiologic concordance is the capacity of a
typing method to correctly cluster epidemic and outbreak clones, and separate them from clones
that are not epidemiologically related (101). Many studies of subtyping methods focused on the
discriminatory power of the subtyping system. On the other hand, few studies have examined the
epidemiologic concordance of a particular subtyping method. The reason for the lack of studies
examining epidemiologic concordance might be that most studies did not utilize well-defined
strains from multiple outbreaks.
The choice of strain collection is critical when developing and evaluating a new
subtyping system for outbreak investigations. As mentioned before, an ideal strain collection
should include well-defined strains from multiple common-source outbreaks in order to access
both discriminatory power and epidemiologic concordance. A good subtyping system should
separate strains from different outbreaks, but not separate strains within the same
outbreak/outbreak clone.
Subtyping methods can be either phenotypic or genotypic approaches. Phenotypic
methods include screening for antibiotic resistance, bacteriophage susceptibility and surface
antigens, such as the H and O antigens. Genotypic methods differentiate strains based on
differences in genome sequence and/or structure. Major phenotypic and genotypic subtyping
18
methods available for Salmonella will be briefly discussed here with the primary focus on
genotypic methods.
2.3.2.1 Phenotypic methods
Before the advent of genotypic methods, many phenotypic methods were widely used for
typing Salmonella strains. Common phenotypic methods for Salmonella include serotyping,
phage typing and MLEE. In general, although phenotypic methods provide useful information
about the strains, they often lack enough discriminatory power.
2.2.2.1.1 Serotyping
As mentioned in the taxonomy section, serotyping distinguishes Salmonella based on
immunological classification of the H and O antigens (54). Serotyping is one of the most
important phenotypic methods for Salmonella, which provides baseline information before other
typing methods can be carried out to further separate strains in a particular serovar. Serotyping is
very useful because the serovar name often points to the specific reservoir and mode of
pathogenesis. However, serotyping alone is not suit for molecular epidemiology, because
individual serovars are responsible for multiple outbreaks (20, 21). As a result, other subtyping
methods with more resolution need to be carried out after serotyping.
2.2.2.1.2 Phage typing
Phage typing utilizes the selective capacity of individual bacteriophage to infect bacterial
cells. During phage typing, a panel of bacteriophages is used to infect bacteria and phage types
are assigned according to the patterns of lysis. Phage typing has been shown to be a good
19
indicator for pandemic clones of Salmonella. For instance, S. Enteritidis phage type (PT) 4 is the
most common PT in Europe, while PT8 is the most common PT in the U.S. Another example is
S. Typhimurium definitive type 104 (DT104), which is typically resistant to a number of
antibiotics and has had a major impact on global health (106). However, phage typing sometimes
suffers from low typeability in that many strains are resistant to all typing phages (1). Moreover,
it requires maintenance of the typing phage stocks and specially trained personnel (45).
2.2.2.1.3 Multilocus enzyme electrophoresis (MLEE)
MLEE differentiates strains based on the relative electrophoretic mobility of cellular
enzymes. The variation in amino acid sequences of the enzymes from different strains results in
differences in electrostatic charges. This leads to different migrations of the enzymes in an
electric field. By comparing the electrophoretic profiles, genetic relatedness of strains can then
be determined. MLEE has been carried out to analyze the population structure of Salmonella
serovars and the relatedness of strains within a serovar (8). Population studies by MLEE
subtyping revealed that while many serovars have similar electrophoretic types (ETs) that form a
single cluster, other serovars like S. Newport have divergent ETs clustered distantly in MLEE
trees. Using MLEE to determine phylogenetic relationships of bacteria is generally accepted.
However, MLEE has been replaced by a more reproducible and portable method called
multilocus sequence typing (MLST), which looks directly at DNA sequences of several genes
(75). MLST will be introduced later as one of the genotypic methods.
2.2.2.2 Genotypic methods
20
power than phenotypic methods. Because of these advantages, genotypic methods are often
carried out after serotyping during Salmonella outbreak investigations. Two categories of
genotypic methods, DNA-fragment-pattern-based methods and DNA-sequence-based methods,
will be discussed.
2.2.2.2.1 DNA-fragment-pattern-based methods
length polymorphism (AFLP) and multiple loci variable number tandem repeat analysis (MLVA).
2.2.2.2.1.1 Pulsed-Field Gel Electrophoresis (PFGE)
PFGE is currently the gold standard method for subtyping Salmonella and is used by
public health surveillance systems such as the PulseNet program of CDC. During PFGE
procedures, bacterial cells are first immobilized in agarose plugs to avoid mechanical shearing of
the long genomic DNA. Cells in agarose plugs are then lysed and genomic DNA is digested by a
rare-cutting restriction endonuclease. Next, agarose plugs containing digested genomic DNA are
put into wells of an agarose gel. The agarose gel is then subjected to an electric field whose
orientation is periodically changing. This pulsed electrical field can resolve large DNA fragments
that could not be separated by a constant unidirectional electrical field. The standardized PFGE
protocol of Salmonella uses two restriction endonucleases XbaI and BlnI in separate reactions
(40).
PFGE has been used in detection, investigation and control of numerous outbreaks and is
generally very successful (51). The main advantage of PFGE is its comparatively high
discriminatory power for subtyping most serovars of Salmonella. However, PFGE lacks
21
discriminatory power for clonal serovars like Enteritidis (25, 120) and Montevideo (27), or clonal
phage types like S. Typhimurium DT104 (51). This is reflected by low PFGE pattern diversity
for those serovars and clonal phage types in the PulseNet database (51). In the cases of such low
discriminatory power, outbreak clones cannot be separated from sporadic isolates and other non-
outbreak related isolates, which can hinder epidemiologic detection and investigation. For
example, during the recent Italian-style meat outbreak, the outbreak clone of S. Montevideo had
the most common PFGE pattern in PulseNet database, which made it difficult to detect the
outbreak (27).
Besides low discriminatory power for clonal serovars, another limitation of PFGE is the
ambiguous interpretation of banding patterns. Banding patterns can change due to insertions,
deletions and point mutations. For instance, a single nucleotide mutation might cause up to 3-
fragment changes in the PFGE banding pattern. Because of this difficulty, interpretation of PFGE
banding patterns has been proposed to follow several guidelines: 1) strains showing no fragment
differences with the outbreak strain are part of the outbreak; 2) strains showing 1 fragment
difference with the outbreak strain are probably part of the outbreak; 3) strains showing 2-3
fragment differences with the outbreak strain are possibly part of the outbreak; 4) strains showing
more than 3-fragment differences with the outbreak strain are not part of the outbreak (105).
More recommendations for interpretation of PFGE patterns have been published recently. The
recommendations include taking into account the quality of the PFGE gel, the diversity of the
organism and the temporal and geographical information during analysis of PFGE patterns (40).
Although those suggestions helped standardize the interpretation of PFGE patterns, these
recommendations are still not completely objective.
Another drawback of PFGE is low reproducibility if the standardized protocol is not
strictly followed. As a result, subsequent comparison of PFGE banding patterns cannot be carried
out, especially when comparing PFGE patterns between different laboratories. To overcome this
limitation, PulseNet implemented an extensive quality assurance system (51). This system
22
requires laboratories to obtain PFGE gel preparation and gel analysis certification and participate
in the annual proficiency testing program. All these steps help ensure comparability and
reproducibility, but at the same time it requires personnel specially trained by the quality
assurance system.
To sum up, although it is the current gold standard subtyping method, PFGE suffers
from several drawbacks which limit its performance for subtyping Salmonella.
2.2.2.2.1.2 Amplified Fragment Length Polymorphism (AFLP)
AFLP is a method that employs both restriction digestion and polymerase chain reaction
(PCR) techniques. In AFLP, genomic DNA is digested with one or more restriction enzymes.
The ends of the digested DNA fragments are then ligated to adaptors that are complementary to
the restriction sites. The digested and ligated DNA fragments are then selectively amplified using
PCR primers targeting the adaptor sequences. PCR primers typically contain one to three
additional nucleotides on their 3’-end to reduce the number of amplified fragments to a
manageable number. PCR products are then subjected to electrophoresis and characteristic
banding patterns are then produced.
AFLP is a relatively simple and fast approach. The discriminatory power of AFLP is
equal to that of PFGE for subtyping S. Typhimurium (73, 103), but higher than that of PFGE for
subtyping S. Enteritidis (52) and other serovars (109). However, its discriminatory power has
been reported to be insufficient to separate all epidemiologically unrelated S. Typhimurium
strains (92).
Like PFGE, the reproducibility of AFLP among different laboratories is problematic
since comparing AFLP results among different laboratories is difficult (48). Variability in the
AFLP profile can be generated by minor changes in the amplification conditions. Therefore,
replicates of the sample could be identified as different strains (45). To enhance reproducibility,
23
PCR should be performed under highly stringent conditions (84) and gel electrophoresis should
be standardized.
2.2.2.2.1.3 Multiple Loci Variable number tandem repeat Analysis (MLVA)
MLVA targets tandem repeats of short DNA sequences in bacterial genomes. The
difference in the number of repeated DNA motifs is employed to differentiate strains. In a
MLVA assay, a number of well-selected and characterized loci are amplified by PCR using
primers targeting the flanking regions of the repeated loci. PCR products are then separated and
the number of repeat units at each locus can be measured according to the size of the PCR
products. Differences in the number of repeats in each locus are used to distinguish different
strains.
Since this method is based on PCR, MLVA has the advantage of being easy to perform
and rapid. Moreover, MLVA yields discreet and unambiguous data, reported as the number of
repeat units at each locus. Comparison of MLVA profiles between laboratories can be made with
a simple nomenclature recently proposed (70). The discriminatory power of MLVA was reported
to be higher than PFGE and AFLP for subtyping S. Typhimurium (72, 108) and higher than
PFGE for S. Enteritidis (11, 93). However, in some circumstances, strains that have the same
MLVA type were separated by PFGE profiles (13). This indicates that strains of same MLVA
type might not be closely related.
However, the reproducibility of MLVA is a potential problem. The instability of MLVA
alleles has been observed for subtyping S. Newport and S. Typhimurium (18, 35). Replicates of
the same strains have been shown to have different number of repeat units at a specific locus (35).
The instability of the MLVA loci is probably due to DNA polymerase slippage during genome
replication (110). This instability might make interpretation difficult when strains have slightly
different MLVA types.
24
To conclude, by providing improved discriminatory power and having a short turnaround
time, MLVA can be used as a complementary method to PFGE in epidemiologic investigations of
Salmonella. MLVA has been used successfully along with other subtyping methods in outbreak
investigations to track Salmonella (12, 83, 85). However, MLVA also suffered from some
drawbacks and thus it has not been widely used for this purpose.
2.2.2.2.2 DNA-sequence-based methods
DNA-sequence-based methods differentiate strains by the detection of polymorphic DNA
sequences. Multilocus sequence typing (MLST) and single nucleotide polymorphism (SNP)
analysis are both DNA-sequence-based methods and will be briefly reviewed here.
2.2.2.2.2.1 Multilocus Sequence Typing (MLST)
MLST discriminates among bacterial strains by comparing nucleotide sequences of
several DNA loci in bacteria chromosomes. For each locus in the MLST scheme, every new
allele is assigned a unique number in order of discovery and is designated an allelic type. The
collective allelic types make up the allelic profile or sequence type, which may also be assigned a
unique and arbitrary number. For example, in the MLST database (www.mlst.net) based on the
seven loci: aroC, dnaN, hemD, hisD, purE, sucA, and thrA, one of the strains in the database has
an allelic profile of (1, 1, 2, 1, 1, 1, 9) for each of the seven genes, and was assigned sequence
type 3 (80). The collective allelic types and sequence types are compared among bacterial strains
and then cluster analysis can be carried out.
Compared to PFGE, MLST is a less labor-intensive method and involves common
techniques including primer design, PCR amplification and DNA sequencing. Furthermore,
DNA sequence represents discreet, unambiguous, highly informative, highly portable and
reproducible data. Many MLST data sets are available over the internet (www.mlst.net) so that a
uniform nomenclature is ensured and comparison of results among laboratories can be conducted
rapidly. The application of MLST is promoted due to the increased speed and reduced cost of
nucleotide sequencing and improved internet database and tools (74). These advantages make
MLST an attractive subtyping approach.
MLST schemes originally target housekeeping genes, which are genes required for
fundamental metabolic functions and are found within all members of a given species (75). For
example, 7 housekeeping genes were targeted in the first MLST scheme for Neisseria
meningitidis (75). Housekeeping genes are excellent genetic markers for studying the population
structure, long-term evolution and diversity of bacteria. A good overview of Salmonella diversity
and evolution is provided by the internet-based MLST data. Based upon MLST data, Salmonella,
especially S. enterica subspecies enterica, is highly clonal (69). Moreover, the data suggest that
many serovars including Typhimurium, Enteritidis, Newport and Saintpaul may have more than
one origin (69). However, MLST schemes based on housekeeping genes for typing Salmonella
usually have much lower discriminatory power than that of PFGE (43, 61, 109). The results of
those studies suggested that housekeeping genes do not provide sufficient resolution to
distinguish closely related strains. Therefore, MLST schemes based on housekeeping genes are
not suitable for outbreak investigations.
To conclude, MLST possesses many attractive advantages. It is an excellent tool for
global phylogenetic studies. However, housekeeping genes selected in previous MLST studies
lacked sequence variation and thus were ineffective for subtyping Salmonella for epidemiologic
purposes. To track strains of this important pathogen during outbreaks, genetic markers that give
sufficient DNA sequence variations need to be identified.
26
Besides housekeeping genes, virulence genes which are responsible for pathogenesis,
have been selected as genetic markers for MLST schemes. MLST schemes that only target
virulence genes have been referred to as multi-virulence-locus sequence typing (MVLST) (31,
119). Unlike housekeeping genes, virulence genes are commonly under positive selection (41).
As a result, DNA sequences of virulence genes tend to be more variable than housekeeping genes
and thus are able to provide increased discrimination. It is also speculated that virulence genes
can provide high epidemiologic concordance because they are responsible for causing diseases
and thus outbreaks. For example, six virulence genes were targeted in an MVLST scheme for
subtyping Listeria monocytogenes, which showed very high discriminatory power (0.99) and
perfect epidemiologic concordance (1.0) (31).
No MVLST scheme has yet been developed for subtyping Salmonella. However, MLST
based on both virulence genes and housekeeping genes has been published for typing Salmonella
enterica subspecies enterica serovars, which targeted flagellin genes fliC and fljB along with two
housekeeping genes, gyrB and atpD (104). This study included several strains from all
subspecies and 22 of the more prevalent Salmonella enterica subspecies enterica serovars
attempting to develop a DNA-based assay for serotype identification. However, the use of this
MLST scheme to further characterize strains under serovar level was not tested. Another MLST
based on both virulence genes and housekeeping genes has been developed for subtyping S.
Typhimurium and showed high discriminatory power (0.98), which was slightly higher than that
of PFGE (0.96) (46). In that MLST scheme, three virulence genes were included together with
the 16S rRNA gene and three housekeeping genes. One of the virulence genes in that MLST
scheme is hilA which regulates transcription of invasion proteins (4). The other two virulence
genes, pefB and fimH, encode different fimbriae and both mediate adherence to host cells (6, 66).
Although this MLST scheme seems to have adequate discriminatory power for subtyping S.
27
Typhimurium, its capacity to discriminate strains from more clonal serovars such as S. Enteritidis
has not yet been tested. Currently, there is no published MLST study for differentiating strains
within S. Enteritidis. In the SNP database of NCBI (National Center for Biotechnology
Information), two strains of S. Enteritidis were compared side by side to examine their SNPs (56).
Nearly all virulence genes were identical between the two, suggesting that MVLST might not be
discriminatory enough for differentiating strains of S. Enteritidis.
In summary, although MVLST has higher discriminatory power than MLST using
housekeeping genes, it may not provide enough discrimination for clonal serovars like Enteritidis.
In order to develop an MLST scheme for outbreak investigations, additional genetic markers with
even higher sequence variability need to be identified.
2.2.2.2.2.3 Single Nucleotide Polymorphism (SNP) analysis
SNP analysis differentiates strains by nucleotide substitutions at specific sites in the
bacterial genome. SNP analysis often involves three steps: 1) Select SNP sites that are variable to
provide discrimination among strains; 2) Determine the nucleotide bases at the selected sites of
different strains; and 3) Compare the SNPs among strains. Selection of the SNP sites is often
based on previous knowledge of specific polymorphic genes (42, 71) or comparative genomic
studies (118). To determine the nucleotide base (adenine, guanine, cytosine, and thymine) at a
defined SNP site, multiple methods can be used, such as pyrosequencing or realtime PCR (82, 91,
111).
Because SNP analysis targets SNPs in the bacterial genome, it has the potential to be
more rapid and cost efficient than MLST. However, there are very few SNP analysis studies for
subtyping Salmonella. SNP analysis targeting genes associated with quinolone resistance has
been used to study the antibiotic resistance of Salmonella (42, 71). Another SNP analysis study
targeted SNPs in flagella antigens in order to develop a SNP typing method to replace serotyping
28
(82). No SNP typing methods have been developed for differentiating Salmonella strains for
outbreak investigations. The reason might be that the SNP loci of Salmonella that could provide
the desirable discrimination have not been identified.
In conclusion, although SNP analysis has the potential to be rapid, cost efficient and
high-throughput, the lack of information about SNP sites suitable for subtyping Salmonella make
it difficult to develop a SNP typing protocol for epidemiologic purposes.
2.3 Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)
Since virulence genes alone might not provide enough discrimination for subtyping
clonal Salmonella serovars, additional genetic elements that are evolving faster than virulence
genes are needed. One of the fastest evolving genetic elements in bacteria genomes are CRISPRs
(Clustered Regularly Interspaced Palindromic Repeat) (100). CRISPRs are regions of direct
repeats (DRs) and spacers in the chromosomes of archaea and bacteria, including S. enterica (Fig.
2.2) (65, 100). DRs are 21-47 bp long, separated by spacers of similar size (Fig. 2.2). Sequences
of DRs are generally conserved, except the repeat at one end of the CRISPR is not totally
conserved, and is thus called a degenerate direct repeat (Fig. 2.2). On the other hand, sequences
of spacers are quite variable from each other. It was recently demonstrated that CRISPR spacers
are derived from phages or plasmids, which when inserted into the CRISPR of a bacterial cell
help protect that cell from subsequent infection by those same phages and plasmids (5). CRISPR
is generally flanked at one end by a common leader sequence of 200-350 bp, which is believed to
act as a promoter to transcribe CRISPR into small RNAs (77). Immediately upstream from the
CRISPR there are CRISPR-associated (Cas) proteins that carry functional domains of nucleases,
helicases, polymerases and polynucleotide-binding proteins (58). Some Cas proteins can
recognize foreign DNA invading the bacteria, and then integrate a new repeat-spacer unit into
CRISPR at the leader end. Therefore, when the same exogenous nucleic acid invades next time,
29
the CRISPR transcribed crRNAs (CRISPR RNAs) can recognize the foreign nucleic acid and
lead the Cas proteins to degrade these invading nucleic acid (17, 59, 65). In this way, CRISPR
along with Cas proteins can block foreign sequences, such as sequences of phages and plasmids.
Figure 2.2 Schematic view of the two CRISPR systems in Salmonella Typhimurium LT2.
Direct repeats and spacers are represented by black diamonds and white rectangles, respectively.
The degenerate direct repeats are represented by white diamonds. Numbers of direct repeats and
spacers are represented by the numbers of diamonds and white rectangles, respectively. L stands
for leader sequence. cas genes are in grey while other core flanking genes (ygcF, iap and ptps)
are in white. The graph is not drawn to scale.
As a bacterial immune system against phages and plasmids, CRISPRs evolve rapidly and
adaptively (115). As mentioned before, new spacers could be added when foreign DNA invades
the bacteria. Besides addition of new spacers, deletion of spacers is also frequently observed (37,
90). However, the mechanism of deletion of spacers is not clear. The addition of new spacers
and deletion of one or several spacers make CRISPR one of the most variable DNA loci in
bacteria and form a high degree of polymorphism among strains (90).
CRISPRs have been used for subtyping Mycobacterium tuberculosis, this subtyping
method is called Spacer oligotyping or spoligotyping (55). In this method, PCR is carried out
using primers designed according to the sequence of the DR so that each spacer can be amplified.
The PCR products are then hybridized to a membrane containing probes for specific spacers. The
hybridization patterns showing the presence or absence of spacers are then compared among
strains. Spoligotyping is now the standard method for subtyping M. tuberculosis for outbreak
CRISPR1
CRISPR2
30
investigations. It has also been used in subtyping Corynebacterium diphtheria (81). Other than
spoligotyping, CRISPR sequence analysis has also been used for other bacteria, such as Yersinia
pestis (90), Streptococcus (64), and Campylobacter jejuni (96). As for Salmonella, although
CRISPRs have the potential to be excellent markers for separating Salmonella strains, they have
not been widely used for subtyping purposes.
2.3.1 CRISPR in Salmonella
CRISPR can be found in multiple numbers in bacteria. Two CRISPR loci are found in all
Salmonella serovars in the CRISPRs database (http://crispr.u-psud.fr/crispr/). CRISPR direct
repeats in Salmonella are 27-31 bp long. Salmonella CRISPRs have great polymorphism even
among strains belonging to the same serovar. Therefore, CRISPRs might serve as good markers
for subtyping Salmonella during epidemiologic investigations.
2.4 Conclusions
Salmonella is the leading cause of foodborne bacterial disease in the U.S. Most human
illnesses are caused by a handful of serovars, such as Typhimurium, Enteritidis, Newport,
Heidelberg, I 4, [5], 12; i: -, Montevideo, Muenchen and Saintpaul. Salmonella can reside in
many wild and domestic animals and can spread from numerous reservoirs to contaminate
numerous kinds of foods, which makes it especially challenging to track this pathogen during
outbreaks. Therefore, to reduce outbreaks caused by the most common serovars of Salmonella, it
is critical to employ a subtyping method that can accurately identify its sources and pathways of
transmission. Many subtyping methods have been developed for differentiating Salmonella
strains, such as PFGE, AFLP and MLVA. Each method has its own advantages and drawbacks.
PFGE is currently the gold standard method for outbreak investigations. However, PFGE
produces ambiguous data that are hard to interpret and more importantly PFGE often lacks
discriminatory power for subtyping clonal serovars such as Enteritidis. In contrast, MLST
generates highly informative and discreet data consisting of nucleotide sequences that can be
easily interpreted and rapidly compared on internet databases. Previous MLST schemes targeting
housekeeping genes were not very successful largely due to low discriminatory power associated
with conserved housekeeping genes. Unlike housekeeping genes, virulence genes can provide
important information about the pathogenesis of strains and improve the discriminatory power of
MLST. However, the discriminatory power of virulence genes may still not be enough for
subtyping clonal serovars of Salmonella. CRISPRs are one of the fastest evolving genetic
elements that could be implemented in an MLST scheme to provide increased discrimination. In
order to develop an MLST scheme for outbreak investigation, virulence genes and CRISPRs were
targeted in the present study to subtype the top 10 serovars of Salmonella. This MLST scheme
was speculated to provide high discriminatory power and epidemiologic concordance for
subtyping Salmonella for epidemiologic purposes.
2.5 References
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entericas subspecies I genome by microarray. Infect. Immun. 73:7894-7905.
3. Anonymous. 2006. Outbreak alert! Closing the gaps in our federal food safety net.
http://www.cspinet.org/new/pdf/outbreakalert2004.pdf
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4. Bajaj, V., R. L. Lucas, C. Hwang, and C. A. Lee. 1996. Co-ordinate regulation of
Salmonella Typhimurium invasion genes by environmental and regulatory factors is mediated
by control of hilA expression. Mol. Microbiol.. 22:703-714.
5. Barrangou, R., C. Fremaux, H. Deveau, M. Richards, P. Boyaval, S. Moineau, D. A.
Romero, and P. Horvath. 2007. CRISPR provides acquired resistance against viruses in
prokaryotes. Science. 315:1709-1712.
6. Baumler, A., R. Tsolis, F. Bowe, J. Kusters, S. Hoffmann, and F. Heffron. 1996. The pef
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