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Appl Environ Microbiol. 2011 July; 77(13): 4520–4526.
PMCID: PMC3127695

Subtyping Salmonella enterica Serovar Enteritidis Isolates from Different Sources by Using Sequence Typing Based on Virulence Genes and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) [down-pointing small open triangle]


Salmonella enterica subsp. enterica serovar Enteritidis is a major cause of food-borne salmonellosis in the United States. Two major food vehicles for S. Enteritidis are contaminated eggs and chicken meat. Improved subtyping methods are needed to accurately track specific strains of S. Enteritidis related to human salmonellosis throughout the chicken and egg food system. A sequence typing scheme based on virulence genes (fimH and sseL) and clustered regularly interspaced short palindromic repeats (CRISPRs)—CRISPR-including multi-virulence-locus sequence typing (designated CRISPR-MVLST)—was used to characterize 35 human clinical isolates, 46 chicken isolates, 24 egg isolates, and 63 hen house environment isolates of S. Enteritidis. A total of 27 sequence types (STs) were identified among the 167 isolates. CRISPR-MVLST identified three persistent and predominate STs circulating among U.S. human clinical isolates and chicken, egg, and hen house environmental isolates in Pennsylvania, and an ST that was found only in eggs and humans. It also identified a potential environment-specific sequence type. Moreover, cluster analysis based on fimH and sseL identified a number of clusters, of which several were found in more than one outbreak, as well as 11 singletons. Further research is needed to determine if CRISPR-MVLST might help identify the ecological origins of S. Enteritidis strains that contaminate chickens and eggs.


In the United States, Salmonella is the leading cause of bacterial food-borne disease, with approximately 1.4 million human cases each year since 1996 (42). The second most-reported serovar of Salmonella causing human disease is Salmonella enterica subspecies enterica serovar Enteritidis, which causes nearly as many cases of salmonellosis as Salmonella enterica serovar Typhimurium, the most prevalent Salmonella serovar (8). The major food vehicle for S. Enteritidis is shell eggs, as 80% of the S. Enteritidis outbreaks and approximately 50,000 to 110,000 cases are egg associated in the United States each year (6, 35). In 2010 a large S. Enteritidis outbreak was associated with consumption of shell eggs, with more than 1,800 reported cases (9). Another common food vehicle for S. Enteritidis is chicken meat (22, 30); however, other food vehicles have also been reported (30, 34, 35).

Shell egg and chicken meat food systems are complex and contain a large number of niches that may be potential sources of S. Enteritidis (21). S. Enteritidis has been isolated from a wide variety of animals, such as rodents, wild birds, and insects, which could serve as potential sources (17). Additional potential sources for S. Enteritidis include chicken manure, sewage, and other moist and organic materials in farm environments (6). Transmission of S. Enteritidis to poultry can also occur via contaminated water and feed (6). In densely populated poultry houses, the transmission of S. Enteritidis among chickens occurs through direct contact with colonized birds or indirectly through contaminated materials (6, 17). This type of external contamination by feces and environments containing S. Enteritidis is referred to as horizontal transmission (14). Eggs can also become contaminated internally via vertical transmission when the ovaries of layers are colonized by S. Enteritidis (17, 32). In the past, most outbreaks of S. Enteritidis traced back to flocks in New England, especially to states in the mid-Atlantic region, which includes the state of Pennsylvania (31, 35). However, while the total number of outbreaks in the northeastern United States decreased after 1996, the number in the western United States has increased since then (35). The reduction in the number of egg-associated outbreaks in the northeastern United States has been attributed to egg quality assurance programs, such as the Pennsylvania Egg Quality Assurance Program (PEQAP), that attempt to reduce or eliminate sources of S. Enteritidis contamination (31). These programs typically involve acquisition of S. Enteritidis-free chicks, control of pests (including rodents and insects), use of S. Enteritidis-free feeds, flock vaccination, and routine microbiologic testing for S. Enteritidis in the production environment (6, 31).

Several molecular subtyping methods have been developed for studying the epidemiology of S. Enteritidis, including amplified fragment length polymorphism (AFLP) (15, 19, 27, 28, 39), multiple-locus variable-number tandem-repeat analysis (MLVA) (3, 5, 12, 37), and pulsed-field gel electrophoresis (PFGE) (18). PFGE is currently the “gold standard” method used by public health surveillance laboratories for tracking food-borne pathogens, including Salmonella (18). The main advantage of PFGE is its normally high discriminatory power (i.e., ability to separate unrelated strains) for subtyping most serovars of Salmonella. However, PFGE lacks discriminatory power for highly clonal serovars like S. Enteritidis (18, 43). For example, the most recent S. Enteritidis outbreak due to shell eggs was caused by the most common PFGE pattern for S. Enteritidis in the PulseNet database (JEGX01.0004); thus, not all isolates with this pulsotype may be related to this outbreak (9). This lack of adequate discriminatory power makes it difficult to track a specific clone of S. Enteritidis in the food system using PFGE. Besides occasional inadequate discriminatory power, PFGE does not provide appropriate information to infer phylogenetic relationships among subtypes (34). Another subtyping method, MLVA, was reported to have higher discriminatory power than PFGE for S. Enteritidis (3, 5, 12, 37). However, in some circumstances, strains that had the same MLVA type were separated by PFGE (5). Moreover, replicates of the same strains of Salmonella have been shown to have different numbers of repeat units at a specific locus (7, 13), which makes accurate interpretation of results difficult.

Compared to PFGE and MLVA, MLST, which targets nucleotide sequence differences in several DNA loci, generates discrete, highly informative, highly portable, and reproducible data. Moreover, MLST is a well-accepted tool for studying the population structure, evolution, and diversity of bacteria (25). Recently, a new sequence typing scheme based on virulence genes (fimH and sseL) and clustered regularly interspaced short palindromic repeats (CRISPRs) (here designated CRISPR-MVLST) was shown to provide better identification and separation of S. Enteritidis outbreak strains/clones than PFGE (29). Virulence genes were congruent with serotyping and appeared to differentiate epidemic clones within different serotypes of Salmonella, while CRISPRs appeared to differentiate outbreak strains/clones within epidemic clones. CRISPRs encode tandem sequences containing 21- to 47-bp direct repeats (DRs) and spacers of similar size (see Fig. S1 in the supplemental material). Spacers are short DNA sequences obtained from foreign nucleic acids, such as phages or plasmids, that are inserted into bacterial chromosomes to protect them from infection by homologous phages and plasmids (2). Therefore, different CRISPRs arise due to diverse phage and plasmid pools in the environment and therefore contain ecologic and geographic information specific to the bacteria present there (24, 41). This might explain the uneven distribution of Salmonella subtypes from different sources, as observed previously (36). Source attribution is especially critical in the case of S. Enteritidis, because as mentioned above, this is currently difficult using current subtyping methods.

Therefore, the purpose of the present study was to utilize CRISPR-MVLST to subtype S. Enteritidis isolates from different sources, including clinical, poultry, egg, and environmental sources.


Bacterial isolates and DNA extraction.

A summary of all S. Enteritidis isolates analyzed in this study are listed in Table 1. A total of 167 isolates were obtained as follows: 34 human clinical isolates from the Centers for Disease Control and Prevention (CDC), 46 chicken clinical isolates from the Animal Diagnostic Lab (ADL) at the Pennsylvania State University, and 24 egg isolates and 63 environmental isolates from the Pennsylvania Egg Quality Assurance Program (PEQAP) in the ADL at the Pennsylvania State University (see Tables S1 and S2 in the supplemental material). All environmental isolates were from egg production sites, such as pullet houses, layer houses, and chick papers. All 34 human clinical isolates were previously analyzed (29); 32 of these isolates were collected from 11 S. Enteritidis outbreaks, and the other 2 isolates were from sporadic cases. The human isolate collection in the present study is relatively small and therefore not representative of all S. Enteritidis isolates causing infection. All isolates were stored at −80°C in 20% glycerol. When needed, isolates were grown overnight in tryptic soy broth (TSB) (Difco Laboratories, Becton Dickinson, Sparks, MD) at 37°C. DNA was extracted using the UltraClean microbial DNA extraction kit (Mo Bio Laboratories, Solana Beach, CA) and stored at −20°C before use.

Table 1.
Sources, sample types, and isolation information for the 167 S. Enteritidis isolates analyzed in the present study

PCR amplification.

Primers for all four markers were designed based upon consensus alignments of the published S. Typhimurium LT2 genome (accession number AE006468) using Primer 3.0 ( (see Table S3 in the supplemental material). PCR amplifications were performed using a Taq PCR master mix kit (Qiagen, Inc., Valencia, CA) in a Mastercycler PCR thermocycler (Eppendorf Scientific, Hamburg, Germany). A 25-μl PCR system contained 12.5 μl Taq PCR master mix, 9.5 μl PCR-grade water, 1.0 μl DNA template, 1.0 μl forward primer (final concentration, 0.4 μM), and 1.0 μl reverse primer (final concentration, 0.4 μM). A single PCR cycling condition was used to amplify all four markers (initial denaturation at 94°C for 10 min; 28 cycles of 94°C for 1 min, 55°C for 1 min, 72°C for 1 min; and final extension at 72°C for 10 min).

DNA sequencing.

PCR products for sequencing were treated by adding 1/20 volume of shrimp alkaline phosphatase (1 U/μl; USB Corp., Cleveland, OH) and 1/20 volume of exonuclease I (10 U/μl; USB Corp). The mixture was then incubated at 37°C for 45 min to degrade the primers and unincorporated dNTPs. After that, the mixture was incubated at 80°C for 15 min to inactivate the enzymes. Purified PCR products were then sent to the Genomics Core Facility at the Pennsylvania State University for sequencing using the ABI data 3730XL DNA analyzer. In order to obtain complete DNA sequences of fimH and sseL, two more primers targeting the internal regions of these two genes were used together with the forward and reverse primers (see Table S3 in the supplemental material). Both DNA strands of the amplicons were sequenced.

Sequence analysis and ST assignment.

For fimH and sseL, sequences were aligned and single nucleotide polymorphisms (SNPs) were identified using MEGA 4.0 (38). For CRISPR1 and CRISPR2, analyses of the spacer arrangements were performed using CRISPRcompar (20), and spacers were visualized as described by Deveau et al. (16). Different allelic types (ATs) (sequences with at least a one-nucleotide difference, or one-spacer difference in the case of CRISPRs) were assigned arbitrary numbers. The combination of 4 alleles (fimH, sseL, CRISPR1, and CRISPR2) determined its allelic profile, and each unique allelic profile was designated a unique sequence type (ST).

Cluster analysis.

Cluster analyses were performed based on allelic profile data by the unweighted pair group method with arithmetic mean (UPGMA), and results were visualized using the tree drawing tool on PubMLST ( CRISPR1 and CRISPR2 were combined into one allele for a more accurate cluster analysis, because CRISPR1 and CRISPR2 are genetically linked (40).

Nucleotide sequence accession numbers.

DNA sequences of the four genetic MLST markers were deposited in GenBank under accession numbers HQ329919 to HQ329971.


Results of MLST and ST distribution.

In order to gain insights into the sources of S. Enteritidis contamination, the 167 isolates were subtyped using CRISPR-MVLST, which was previously developed in the Dudley and Knabel laboratories (29). Sequences of fimH, sseL, CRISPR1, and CRISPR2 identified 12, 13, 14, and 20 allelic types, respectively. In total, 27 sequence types (STs) were identified for all 167 isolates (Fig. 1; see also Tables S1 and S2 in the supplemental material). There were 9, 8, 7, and 15 STs found in human, chicken, egg, and environmental isolates, respectively. For human isolates, the 9 STs were E ST1, -2, -3, -4, -5, -6, -7, -8, and -9. The number of human isolates in each sequence type is listed in Fig. 1. Out of the 9 STs found in human isolates, 5 STs (E ST2, -5, -6, -7, and -9) were not found in chicken, egg, or environmental isolates (Fig. 1). Those 5 STs came from California, Georgia, Maine, Michigan, and Ohio. For chicken isolates, the 8 STs included E ST1, -4, -8, -21, -22, -23, -24, and -25; 5 of them (E ST21, -22, -23, -24, and -25) were found only in chicken isolates. For egg isolates, the 7 STs included E ST1, -3, -4, -8, -12, -15, and -26, and 2 of them (E ST15 and -26) were found only in egg isolates. For the 15 STs found in environmental isolates (E ST1, -3, -4, -8, -10, -11, -12, -13, -14, -16, -17, -18, -19, -20, and -27), 10 STs (E ST10, -11, -13, -14, -16, -17, -18, -19, -20, and -27) were found exclusively in environmental isolates.

Fig. 1.
Graphic representation of spacer arrangements in CRISPR1 and CRISPR2 of the 27 S. Enteritidis sequence types. Human, chicken, egg, and environmental isolates are designated h, c, e, and env, respectively. The number of isolates from each source is indicated ...

Predominant STs.

An uneven distribution of the 27 STs was observed between different sources. Overall, the 5 STs designated E ST1, -3, -4, -8, and -10 accounted for 19%, 17%, 25%, 8%, and 7% of the total isolates, respectively. Out of the 5 predominant STs, 3 of them (E ST1, -4, and -8) were found in human, chicken, egg, and environmental isolates. E ST1 made up 12% of human isolates, 43% of chicken isolates, 13% of egg isolates, and 8% of environmental isolates, respectively. E ST4 accounted for 15% of human isolates, 39% of chicken isolates, 13% of egg isolates, and 3% of environmental isolates. E ST8 comprised 21% of human isolates, 7% of chicken isolates, 29% of egg isolates, and 40% of environmental isolates. E ST3 was found in 15% of human, 26% of egg, and 2% of environmental isolates but was not found in chicken isolates. E ST10 was found only in environmental isolates, in which it comprised 21% of all environmental isolates (Fig. 2).

Fig. 2.
Frequency of the five predominant sequence types (E ST1, -3, -4, -8, and -10) in human, chicken, egg, and environmental isolates. The five predominant sequence types accounted for 76% of all isolates analyzed in the present study. All unlabeled pie slices ...

Cluster analyses.

A cluster diagram based on virulence genes fimH and sseL identified three clusters (CI, CII, and CIII), which included STs from multiple outbreaks (Fig. 3). CI contained 9 STs, E ST3, -4, -5, -8, -10, -12, -14, -18 and -27. In total, 110 (66% of total isolates) belonged to CI, which was the largest cluster and contained 18 human, 22 chicken, 19 egg, and 51 environmental isolates. CII contained 3 STs (E ST1, -9, and -26) and 22% of all isolates (8 human, 20 chicken, 4 egg, and 5 environmental isolates). CIII contained 3 STs (E ST2, -7, and -13), which included 6 human isolates and 1 environmental isolate. E ST6 occupied one single branch and contained 2 human isolates from the same outbreak. Besides the 3 clusters and E ST6, 11 singletons were identified. Among the 11 singletons, 5 STs (E ST11, -16, -17, -19, and -20) were found in isolates from the farm environment, 5 (E ST21, -22, -23, -24, and -25) were found in chicken isolates and 1 (E ST15) was found in an egg isolate. The 5 chicken singletons either were from broiler hatchery eggs which did not hatch or were necropsy isolates from sick broilers.

Fig. 3.
Cluster diagram based only on fimH and sseL for all 27 sequence types. MLST identified three clusters (CI, CII, and CIII). Human, chicken, egg, and environmental isolates are designated h, c, e, and env, respectively. The number of isolates from each ...

Incorporation of CRISPR data into the analysis separated outbreak strains/clones within the 3 clusters (Fig. 4). Among the 15 STs in the 3 clusters, 3 STs (E ST1, -4, and -8) were found in all sources (human, chicken, egg, and environmental). These STs were also the predominant STs among all isolates (Fig. 2). E ST 3 was found in human, egg, and environmental isolates. E ST12 was found in both egg and environment samples. The other 10 STs were from a single source, including 4 STs (E ST1, -2, -5, and -9) found only in human isolates, E ST26 found only in egg isolates, and 5 STs (E ST10, -13, -14, -18, and -27) found only in environmental isolates.

Fig. 4.
Cluster diagram based on virulence genes and CRISPRs for all 27 sequence types. Human, chicken, egg, and environmental isolates are designated h, c, e, and env, respectively. The number of isolates from each source is indicated before the source designation. ...

Spacer arrangements among STs.

Figure 1 shows the differences in spacer arrangements among STs in CRISPR1 and CRISPR2. In CRISPR1, the number of spacers ranged from 2 to 25; for CRISPR2, the number of spacers ranged from 3 to 25. Generally, the spacer arrangements were similar among STs in the 3 clusters. The singleton E ST16 also shared spacers with STs in the 3 clusters; however, many other spacers were deleted. The other 10 singletons contained spacer arrangements that were different from each other, except E ST21 and E ST22, which shared most spacers within CRISPR1, and had identical CRISPR2 loci. Single or multiple spacers were observed in a few other locations. For CRISPR1, these include the following. The 1st spacer of E ST17 matches the first spacer of E ST21 and E ST22; the 5th spacer of E ST17 matches the 3rd spacer of E ST19; the 10th and 11th spacers of E ST17 match the 8th and 9th spacers of E ST25, respectively; the 1st spacer of E ST24 matches the 1st spacer of E ST11; the 2nd, 3rd, 4th, 7th, 8th, 9th, 10th, 13th, and 14th spacers of E ST21 match the 1st, 2nd, 3rd, 4th, 5th, 6th, 7th, 8th, and 9th spacers of E ST15, respectively; and the 4th and 5th spacers of E ST21 match the 5th and 6th spacers of E ST11. For CRISPR2, they include the following. The 1st, 2nd, and 3rd spacers of E ST3 match the same spacers of E ST25; the 1st and 2nd spacers of E ST21/E ST22 match the 2nd and 3rd and the 1st and 2nd spacers of E ST15 and E ST24, respectively; and the 6th spacer of E ST15 matches the 7th spacer of E ST24.


Salmonella serovar Enteritidis outbreaks due to consumption of eggs and chicken meat pose a great public health threat and burden to the economy (6, 22, 30, 35). To prevent future outbreaks, it is important to identify the reservoirs, sources, and routes of transmission of S. Enteritidis throughout the chicken and egg food system. Because S. Enteritidis is a highly clonal serovar, PFGE is often unable to accurately differentiate outbreak strains/clones from epidemiologically unrelated strains/clones (9, 43). Likewise, many other subtyping methods, including ribotyping and plasmid profiling, were not discriminatory enough to differentiate S. Enteritidis outbreak strains/clones and thus were not useful tracking tools (26). Recently a sequence-based method using virulence genes and CRISPRs was shown to provide better discrimination of S. Enteritidis outbreak strains/clones than PFGE and thus may be a potential tool for accurately differentiating outbreak strains/clones of S. Enteritidis (29). To test the potential of the new MLST scheme to identify the sources of S. Enteritidis outbreak strains/clones, a total of 167 S. Enteritidis isolates from various sources were subtyped by this MLST scheme and 27 STs were identified (Fig. 1; see also Tables S1 and S2 in the supplemental material). While 9 STs were observed among human isolates, 5 of these STs (E ST2, -5, -6, -7, and -9) were found only in human isolates (Fig. 1). It is important to note that the human isolates were from various geographical locations within the United States, while the chicken, egg, and environmental isolates were all from a single state, Pennsylvania. Therefore, some of these former STs may represent strains that were not common in Pennsylvania poultry farms. In contrast, 3 STs (E ST1, -4, and -8) were found in chicken, egg, and environmental isolates in Pennsylvania. For example, E ST4 was found in 3 human isolates, 19 chicken, 3 egg isolates, and 2 environmental isolates from Pennsylvania (Fig. 1; see also Fig. S1 in the supplemental material). Interestingly, STs 3, -12, -26, and -27 were found only in egg isolates, not chicken isolates. Taken together, the above results suggest that the MLST scheme based on virulence genes and CRISPRs might be useful for tracking specific human STs back to their geographic origin. Future work will further test this hypothesis and also include isolates from healthy chickens, which are not collected as part of the PEQAP.

The present MLST scheme identified 5 predominant STs among all 167 isolates analyzed in the present study (Fig. 2). Because these STs (E ST1, -4, and -8) were found in all sources (human, chicken, egg, and environmental), they might represent three predominate STs circulating among humans in the United States and poultry farms and hen house environments in Pennsylvania (31). Moreover, these 3 STs were isolated over 10 years (Table 1; see also Table S1 in the supplemental material), so they are also persistent STs among humans in the United States and poultry industries in Pennsylvania. One of the predominant STs, ST3, was seen only in human, egg, and environmental isolates; if this association with eggs can be confirmed on a larger diverse sample of eggs, layers, and broilers, knowledge about the frequency of this ST in egg production and in humans may be used to calculate the fraction of human Salmonella serovar Enteritidis infections that is caused by egg consumption. As we were unable to find E ST10 among chicken and human isolates, this ST might represent an environment-adapted clone of S. Enteritidis. However, due to the limited number of isolates included in the present study, this hypothesis remains to be tested. A previous MLST study observed clustering of environmental versus human clinical strains/clones and suggested that some environmental strains/clones appear to be underrepresented among clinical isolates (23).

In the present study, virulence genes identified 3 distinct clusters of S. Enteritidis (Fig. 3). Virulence genes have previously been used to identify epidemic clones of Listeria monocytogenes (10, 11) and also the epidemiology of Salmonella among cow and human isolates (1). Among the 3 clusters, CI was the largest cluster with 4 of the 5 predominant STs (E ST3, -4, -8, and -10) and 110 isolates (66%) from all 4 sources (human, chicken, egg, and environmental) (Fig. 3). Therefore, CI might represent a predominant cluster that is circulating in the United States. We suggest that CII represents a second major cluster containing the predominant sequence type E ST1. As with CI, CII included human, chicken, egg, and environmental isolates (Fig. 3). The existence of major clonal groups of S. Enteritidis in humans and poultry was also observed previously using PFGE (4, 33).

CRISPRs separated singletons from the 3 clusters and E ST6 due to significant differences in spacer arrangements in both CRISPRs (Fig. 1 and and4).4). A previous study demonstrated that bacteria from distant geographic locations had strikingly different spacer arrangements, possibly due to the existence of unique phage/plasmid pools in these different geographic locations (24). Different phages are known to be unique to specific niches; therefore, spacer arrangements may be a good indicator of bacterial adaptation to different microenvironments/sources. While we speculate that singletons may represent unique ecotypes that are distinct from STs in the 3 clusters and E ST6, it must be noted that acquisition of new spacers in response to phage and/or plasmids has not yet been reported for S. enterica. Nevertheless, the observation that the three clusters grouped by virulence genes were further separated into many STs by CRISPRs (Fig. 1 and and4)4) indicates that CRISPRs evolve much faster than virulence genes. Therefore, the inclusion of CRISPRs into our MLST scheme provided superior discriminatory power over virulence genes alone, as observed previously (29).

In conclusion, the present MLST scheme may be a valuable molecular subtyping method for identifying different sources of S. Enteritidis. Additional research using isolates from poultry from other states in the United States, as well as isolates from breeder flocks and isolates from sporadic human infections, is needed to validate this. Further research is also needed to evaluate the ability of this MLST scheme to identify routes by which S. Enteritidis is transmitted throughout the shell egg and chicken meat food systems.

Supplementary Material

[Supplemental material]


We thank Bindhu Verghese for technical guidance throughout the study, especially for the idea of combining CRISPRs into one allele in the cluster analysis. We also acknowledge the Penn State Genomics Core Facility—University Park, PA, for DNA sequencing, and Philippe Horvath for providing the CRISPR spacer macro. We thank Lester Hiley (Queensland Health Pathology and Scientific Services) for making corrections to the published-ahead-of-print version of the manuscript.

This study was partially supported by a U.S. Department of Agriculture Special Milk Safety grant to the Pennsylvania State University (contract 2009-34163-20132).


Supplemental material for this article may be found at

[down-pointing small open triangle]Published ahead of print on 13 May 2011.


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