caister.com/bacteriology Revealing the Mosaic Nature of ... · closely related bacteria yet the pathogenic propensity of the genus, even within a given serovariant, remains remarkably

9Revealing the Mosaic Nature of Salmonella Genomes Using MicroarraysMuna F. Anjum

AbstractThe accumulation of whole genome se-quences has truly brought forth the strength of microarray technology. There-by, DNA microarray technologies have gained the ability to generate and provide efficient access to vast genetic information suited not only for comparative genomics, but also for the identification and sub-classification of microbes. Furthermore, DNA-microarrays have opened the possi-bility to probe bacterial transcriptomes at a whole genome level. This review describes the application of DNA microarrays to de-fine the genome content within the genus Salmonella. Also, the review will discuss the diagnostic potential of the array tech-nology in terms of use in routine reference laboratory for sero- and genotyping.

IntroductionIn 1880 Eberth observed Salmonella as hu-man pathogen through examination of his-tological sections of spleen and mesenteric lymph nodes obtained from a patient that had died from typhoid fever. Subsequently the “typhoid bacillus” was cultivated in 1884 by Gaffky (Grimont et al, 2000). Since then, the “typhoid bacillus” has been placed into the genus Salmonella, an ap-parently large collection of closely related Gram-negative bacterial species. Yet, more recent biochemical and DNA-hybridiza-

tion-based studies have established that the genus Salmonella comprises two spe-cies, S. enterica and S. bongori (Crosa et al., 1973; Le Minor et al., 1982a; Le Minor et al, 1982b; 1986). S. enterica is further di-vided into 7 subspecies: enterica, salamae, arizonae, diarizonae, indica and houtenae, or respectively into subspecies I, II, IIIa, IIIb, IV, VI, and VII (Le Minor and Popoff, 1987). Furthermore, the White-Kauff-mann-Le Minor (WKL) scheme divides Salmonella into 2449 serovars, of which only 20 occur in S. bongori. Of the remain-ing serovars 1443 are contained in S. en-terica subspecies enterica, 488 in subspecies salamae, 94 in subspecies arizonae, 70 in subspecies houtenae and 11 in subspecies indica (Popoff et al., 1998).

In spite of this wide variety within Salmonella, only serovars included into subspecies I are associated with disease in warm-blooded animals, and furthermore only a small fraction of the serovars within subspecies I are enteric pathogens. In fact, the 12 most prevalent Salmonella serovars are responsible for more than 70% of all human Salmonella infections (Centre for Disease Control and Prevention, 2001; http://www.cdc.gov/ncidod/bdmd/phlis-data/Salmonella.htm).

For the selected serovars of S. enterica subspecies I that inhabits the intestinal tracts of humans and warm-blooded ani-

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mals, and that act as causative agent of food-borne gastroenteritis or typhoid fe-ver, the outcome of infection is dependent upon the host and the infecting serovar. Ubiquitous serovars such as S. Enteritidis or S. Typhimurium are typical zoonotic pathogens. Poultry and many other ani-mals are often unapparent carriers, latently infected or, less frequently, clinically ill. Yet, the asymptomatically infected representa-tives thereof may excrete, even abundant amounts, of Salmonella in their faeces to form a reservoir and source of further con-tamination for other animals, humans and the environment (Poppe, 2000). Indeed, S. Typhimurium and S. Enteritidis are the current most prevalent causes of hu-man inflammatory gastroenteritis, often referred to as food poisoning (Rodrigue et al., 1990), whereas representatives of these serovars can be carried asymptomatically in chronically infected chicken (Bäumler et al., 1997). Host-specific serovars such as S. Typhi and S. Gallinarum are capable of causing severe invasive forms of salmonel-losis (Mandal, 1979; Barrow et al., 1994). S. Typhi causes typhoid fever in man, a con-dition that does not necessarily accompany symptoms of gastroenteritis (Thisyakorn et al., 1987; Nguyen et al., 2004). Thus, the genus Salmonella consists of a number of closely related bacteria yet the pathogenic propensity of the genus, even within a given serovariant, remains remarkably heteroge-neous. The genetics behind this pathogenic diversity is only beginning to be uncovered and understood, and this review intends to shed light on the chromosomal heteroge-neity of Salmonella with a special emphasis on host-adaptation and pathogenicity.

Comparative genomics of SalmonellaPreviously, Multi Locus Enzyme Electro-phoresis (MLEE) was used to study field

isolates from different Salmonella serovars in order to deduce the evolutionary relat-edness of strains and species. The data thus generated indicated a clonal population structure, due to strong linkage disequi-librium among enzyme loci and the global distribution of certain genotypes (Beltran et al., 1988; Li et al., 1993; Boyd et al., 1996). The total genetic diversity of Salmo-nella, calculated by the average number of alleles per enzyme locus is comparable to that of E. coli, with greater variation among serovars than within serovars (Reeves et al., 1989; Selander et al., 1990). Subsequent comparison of genome sequences of S. Ty-phi CT18 and S. Typhimurium LT2, has suggested that regions of variation were interspersed amongst an essentially con-served chromosomal backbone (McLelland et al., 2001; Parkhill et al., 2001). Several studies have also indicated that differential adaptations have occurred in different Sal-monella serovars through discriminate hor-izontal transfer, recombination, genomic rearrangement and inversions of the chro-mosome (Liu and Sanderson, 1998; Smith et al., 1990; Alokam et al., 2002;). Thereby, a major goal of comparative analysis of Sal-monella genomes is to identify the genetic basis for the unique virulence attributes of these closely related bacterial pathogens.

Genome sequence and the application of microarraysWith the advent of genome sequencing and other advances in molecular biology, there is currently a move to include ge-nomic approaches to typing, taxonomy and understanding the evolution of patho-genic isolates (Gurtler and Mayall, 2001; Schoolnik, 2002). These techniques and approaches evaluate genomic differences in bacterial strains using indices of genome organization that vary both between spe-cies and within isolates of a species e.g. the


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gene content (Anjum et al., 2003; Thom-son et al., 2004); G+C content (Sueoka, 1999); the occurrence and location of gene duplications, rearrangements, insertions (Szpirer et al., 1999) and horizontal gene transfer (Boyd and Hartl, 1997; Brown et al., 2003; Bischoff et al., 2004). Differences between pathogens and commensals that point to possible virulence determinants and disclose evolutionary history may be identified by understanding the extent of genetic variability within natural popula-tions at the gene level of resolution.

Comparison of the genome se-quence composition, codon usage and GC content of both distantly related organisms and within species has revealed the mosaic structure of bacterial genomes and acqui-sition of horizontally acquired genes such as Pathogenicity islands (Fritzgerald and Musser, 2001; Ochman, 2001; Ochman and Moran, 2001; Ochman, 2002; Mira and Ochman, 2002; Daubin et al., 2003). DNA-microarray-based comparative anal-yses allow the genome content of a relative-ly large number of bacterial isolates to be compared to a sequenced reference genome or group of genes to assess the extent of genetic variability within bacterial popula-tion, at a single gene resolution. Microarray technology has also been used to detect cru-cial differences between pathogens by iden-tifying pathogen-specific genes. Currently the genome sequences from S. enterica subspecies I serovar Typhi strains CT18 (Parkhill et al., 2001) and TY2 (Deng et al., 2003), S. Typhimurium strain LT2 (McClelland et al., 2001), and S. Paratyphi strain ATCC 9150 (McClelland et al., 2004) have been completed, whereas the genome sequence of six others Salmonella isolates is nearly completed (http://www.sanger.ac.uk). The section below reviews how microarrays have been used to deduce the evolutionary relatedness of Salmonella

strains and species, and provide informa-tion on diversity.

The origin of Salmonella as assayed through microarraysPorwollik and colleges have taken advan-tage of combining the collection of whole genome sequence annotations and micro-array technology to identify the acquisition of genes by S. Typhimurium as it evolved from other members of the Enterobacte-riaceae family (Porwollik et al., 2002). This study was based on an microarray con-structed upon the S. Typhimurium LT2 genome, and included 22 strains repre-senting the Salmonella genus (i.e. all seven S. enterica subspecies and S. bongori), as well as E. coli, K. pneumoniae, and Y. pes-tis (Porwollik et al., 2002) as comparators. This approach showed that 935 of the LT2 genes added to the microarray were consis-tently absent from the genome of two E. coli strains (K12 and O157:H7), K. pneu-moniae, and the Y. pestis CO92 sequence. Of these 935 Salmonella genes, 56 were al-ways detected as present in all 22 Salmonel-la strains tested. The majority of the genes missing in the pool of comparator strains appeared to be of plasmid or phage origin in the S. Typhimurium LT2 sequence. Also, hybridization signals that corresponded to metabolic operons of S. Typhimurium LT2 appeared absent in S. bongori and in sub-species VII. Sequences corresponding to the 12 fimbrial operons and the “classical” five Salmonella Pathogenicity Islands (SPI to SPI5) of S. Typhimurium LT2 were not recovered in their entirety throughout the collection of Salmonella comparator strains. Of the fimbrial operons, fim (type 1 fim-briae) was present in all and bcf in most (excluding subspecies IV and VII) strains of S. enterica. DNA sequences correspond-ing to SPI1 to SPI5 were distributed such that homologues of SPI1 were universally


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present in all Salmonella, while there were alterations regarding the content of the re-maining four SPIs among the comparator S. enterica strains.

Based on hybridization signals, ob-tained with the DNA extracted from the collection of Salmonella strains and probed on the S. Typhimurium LT2-based mi-croarray, a bifurcating phylogenetic tree could be constructed to illustrate their evolutionary relationships within the ge-nus Salmonella (Fig. 9.1). This phyloge-netic tree predicted that homologues were acquired by various clades, including 513 homologues acquired by all ancestors of Salmonella, 111 acquired by S. enterica, 105 by diphasic Salmonella, and 206 by subspe-cies I. Significantly, the topology of this tree was similar to those using data from

MLEE (Boyd et al., 1996) or sequence in-formation of both housekeeping, invasion genes (Li et al., 1995; Boyd et al., 1997) and rRNA sequences (Christensen et al., 1998).

Chan et al. (2003), conducted a simi-lar microarray-based study, but using a microarray based on the genome of S. Typhimurium strain SL1344, to reveal the genetic organization of a collection of 24 strains that represented serovars of S. enterica subspecies I and IIIa and S. bong-ori. This study defined that Typhimurium strain SL1344 shared its genome with 89 to 100% of the subspecies I serovars tested, and more than 99% with all S. Typhimurium strains tested. In contrast, 73.5 to 77.5% of the S. Typhimurium SL1344 genome hy-bridized with isolates of S. Arizona. This

Figure 9.1 Phylogenetic tree of the Salmonella clade. Five crucial stages in Salmonella evolution are indicated: 1, the divergence of Salmonella from E. coli; 2, the separation of S. enterica from S. bongori; 3, the evolution of the diphasic S. enterica strains; 4, the partition of S. enterica subspecies I; 5, the development of S. Typhimurium. (Porwollik et al., 2002; produced with permission from Author).



degree of conserved DNA was lower than the 83% of genetic information found to be shared between SL1344 and S. bongori. Therefore their data indicate a more dis-tant relationship of S. Arizona (of subspe-cies IIIa) from subspecies I than previously measured. Hierarchical clustering of their data set supported the percentage of ge-nome similarity identified. This is con-tradictory to previously published DNA hybridization work (Crosa et al., 1973; Le minor et al., 1982a; Le minor et al., 1982b) and MLEE data (Reeves et al., 1989), and the aforementioned study (Porwollik et al., 2002) in which S. bongori is most distantly related to S. enterica subspecies I.

There is no doubt that the phyloge-netics of the salmonellae will be resolved in greater detail with larger numbers of complete sequences, in particular with the addition of more complete microarrays. Even so, microarrays differ from MLEE in that the latter method uses a number of enzymes to assess genetic diversity, where-as microarrays probe the whole genome, including genes coding for non-enzyme proteins, and thereby can assess regions or genes that are absent or divergent within the whole genome of isolates. For example, from MLEE analysis of several hundred serovar Typhi isolates of worldwide distri-bution, collected over several years, it has been concluded that this serovar is highly clonal with a predominant clone of glob-al distribution and a second, very similar clone confined to West Africa (Selander, 1990). Microarray analysis of nine differ-ent Typhi isolates, which included two of the defined MLEE types, revealed 13 re-gions of absent or divergent gene content in comparison to CT18 (Boyd et al., 2003). In particular, several CT18 prophage re-gions were absent or divergent in Typhi strains STY1048 to STY1077; STY2038 to STY2077; STY4821 to STY4834,

whereas seven Typhi strains lacked most or the entire IS1 element present in CT18. Thereby the microarray-based analysis has added further detail to previous MLEE data and showed that the genomic reser-voir even within a highly clonal population, such as S. Typhi, can be unstable.

Therefore, the use of microarrays in phylogenetics has provided a quantita-tive measure of the genomic diversity and information about how that diversity arose, which is crucial in unraveling the relationship between pathogenic strains of Salmonella. Future studies in this area will help unravel the correlation between ge-nome plasticity, pathogenesis and the rise of S. enterica serovars such as Enteritidis and Typhimurium to relatively epidemic proportions in the human population. Such questions are currently being ad-dressed in our laboratory (Anjum et al, un-published data).

Core and variant component of the Salmonella genomeThe genome of the genus Salmonella, as judged from the previous section, can be considered to consist of a composite gene pool within which distinctive subgroups have acquired degrees of specialization by acquisition (or loss) of specific subsets of genes. In an attempt to determine the com-mon “core” pool of genes shared within S. enterica, Anjum et al. (2005) have looked in detail at the gene content of 40 strains representing 12 serovars within S. enterica subspecies I using a S. Typhi-based spot-ted microarray. The core of invariant genes, that is to say the collection of homologues genes present in all serovars, were separat-ed mathematically from the variant com-ponent using a simple decision tree based on genes ranked by increasing variance. The results of gene separation by rank-ing showed that 3,237 genes, or approxi-


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mately a two-third of the S. enterica sub-species I genome is conserved in all 40 S. enterica strains analyzed (Fig. 9.2). Genes contained within the invariant core por-tion were also found in six other S. enterica genome sequences (S. Typhi strain TY2, S. Typhimurium strains LT2, DT104, SL1344, S. Gallinarum strain 287/91, and S. Enteritidis PT4).

The core S. enterica component also contained several SPI regions (SPI 1 to 5, a portion of SPI 6, and all of SPI 9), as well as 3 fimbrial operons (fim, bcf, stb). Furthermore, approximately half of genes within the core set were hypothetical pro-teins of unknown function, which had been preserved through the vertical evolution of S. enterica subspecies I. Preservation of these genes implicates that there is some as yet unknown, but nevertheless important, function associated with these genes and their conservation. They could be involved with enhancing the fitness and survival of Salmonella strains under different environ-mental conditions, including those found within the host.

More than 85% of the genes within the core set of genes in S. enterica subspecies I had homologues within S. bongori, indi-cating that these genes may be conserved within the Salmonella genus (Anjum et al., unpublished data). Chan et al. (2003) have similarly shown, using a S. Typhimurium-based microarray, that 54% of their entire data set (2,244 genes), were shared by all 24 Salmonella strains included in their study, a

collection that spanned the Salmonella ge-nus. They designated this set as the core Salmonella genes. The majority of genes within the core set of genes defined by Chan et al (2003) were also present within the core set defined by Anjum et al (Anjum et al., 2005).

A large number of genes from the S. en-terica subspecies I core set had homologues in E. coli K12 strain MG1655 (2688), al-though 547 genes present in all Salmonella strains included in our study, were absent or divergent in E. coli MG1655. A subse-quent deeper analysis of genes within the core component showed that the majority of genes involved with central and interme-diary metabolism e.g. the citric acid cycle, fatty acid biosynthesis, the pentose phos-phate pathway, and pyruvate metabolism were conserved.

The variable component of the S. en-terica genome has also been analyzed in some detail. The gene content of 79 strains from 22 different serovars was studied, us-ing a S. Typhimurium-specific microarray, to understand the extensive genetic varia-tion found between isolates (Porwollik et al., 2004). Overall, 867 Typhimurium LT2 chromosomal genes were absent (or did not reveal close homologues) from at least one isolate of the representative set of strains from subspecies I included in this study. The majority of polymorphic genes occurred in clusters, with a total of 85 polymorphic regions or clusters on the LT2 chromosomal backbone. As may be

Figure 9.2 (opposite) Separation of the core from the variable component of genes within 40 strains of S. enterica. (a) Comparative Genomic Indexing of 40 S. enterica field and clinical isolates, using the Typhi chromosome as baseline, was complied in GeneSpring version 5.0 and shows the microarray date before separation. Mathematical filters were used to separate within all 40 strains the core component, representing the conserved S. Typhi CDS (b) and the non-core genes (c). The non-core genes were further divided into Typhi specific (i), and moderately variant genes (ii). Present genes, with high (Cy5/Cy3) log signal intensity, range from red to gray, whilst absent genes, with low (Cy5/Cy3) log signal intensity, range from gray/blue to deep blue. (Anjum et al., 2005).



A

B


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expected, genetic elements that were fre-quently missing or divergent were the en-tire rfb locus, responsible for the lipopoly-saccharide side chain structure, rfc (the O antigen polymerase), and the fimbrial op-erons saf, stc, sti, stj and lpf. Studies on the rfb gene cluster have shown that the first four genes are relatively conserved and en-code enzymes that generate dTDP-rham-nose, whilst the central and distal genes are quite variable, and contribute to major an-tigenic variability to the cell surface (Raetz, 1996).

To determine the putative relatedness between strains, a relationship matrix was constructed to display the number of dif-ferences in gene presence and absence for all strains against each other. This matrix illustrated that overall the gene content for most strains of the same serovar was similar although for some strains there was greater variation within a serovar than be-tween serovars. Indeed, for selected strains the difference could be accounted in the presence or absence of hundreds of genes. It was thereby hypothesized that groups of strains that share the same gene profile could be referred to as “genovars,” and that the term genovars would be useful in future to define previously unrecognized features of Salmonella infection. The exact gene profile that defines each genovar and the boundaries between genovars will require further work, and probably highly mobile genetic elements such as phages, plasmids and transposons will leap genotype bound-aries (Porwollik et al., 2004).

Prophages and genome variabilityProphage-like elements have been shown to contribute significantly to the diversity of S. enterica. The seven prophage-like ele-ments (ST10, ST15, ST18, ST27, ST35, ST44 or SopEst, and ST46) harbored

in the S. Typhi CT18 chromosome share similarity to the lambda, Mu, P2 and P4 bacteriophage families. Collectively they encompass >180 kb and represent ~3.6% of the Typhi CT18 genome (Thomson et al., 2004). An illustration of the relative alignments of the prophage regions within the chromosomes of the S. Typhi Ty2, S. Typhi CT18 and S. Typhimurium LT2 ge-nomes is shown in Fig. 9.3. It reveals that most of the prophage-like regions are com-mon to both Typhi isolates. In comparison to other phages, ST27, ST35 and SopEst are similar to the P2-family Fels-2 phage (Parkhill et al., 2001; Pelludat, et al., 2003), and ST10 and ST18 share regions of sim-ilarity with the lambda-like phage Fels-1, Gifsy-1 and Gifsy-2 of the S. Typhimuri-um LT2 genome (Figuera-Bossi and Bossi, 1999; McClelland et al., 2001).

Microarray analysis probing for the presence of P2-phage family members SopEst, ST27 and ST35 showed that the genes within each element generally were conserved in S. Typhi isolates. All S. Paratyphi isolates contained 36 out of a to-tal of 46 SopEst phage genes. This suggests that the two S. enterica serovars that are as-sociated with invasive disease in humans each harbor a SopEst prophage determi-nant. Interestingly, the SopE prophage has also been isolated from the epidemic S. Typhimurium strain DT204 (Mirold et al., 1999). Prophage-like elements ST35 and ST27 were present in Paratyphi A isolates, although neither element was present in its entirety. Some of the remaining serovars tested hybridized to regions within ST35 but none hybridized to the ST27 genes. Neither of the lambda-like bacteriophages ST10 and ST18 of S. Typhi was detect-able in any Salmonella tested. Similarly ST15 and ST46 genes were only present in S. Typhi. It is known that prophage-like elements can contribute significantly to



the virulence of a bacterial host (Boyd and Brussow, 2002). Therefore, the presence of a unique combination of prophage-like elements in S. Typhi, often carrying non-essential cargo genes, may contributes to the virulence and invasive nature of the ty-phoid bacillus (Thomson et al., 2004).

Horizontally acquired DNA and genome diversityApart from a collection of prophages, lat-eral transfer of genes has contributed to a considerable proportion of the genomic diversity in Salmonella. However, in con-trast to the seven prophage-like elements being present largely in S. Typhi, many of

the Salmonella pathogenicity islands (SPI) are conserved between different serovars S. enterica. The conserved SPIs include SPI1 to SPI5, whereas others, e.g. SPI7, SPI10, show variation in occurrence (Anjum et al., 2005). Pathogenicity islands are frequently associated with mobile genetic elements including transposons and bacteriophages, and are often found adjacent to tRNA genes (Hou, 1999; Marcus et al.,2000; Hanse-Wester and Hensel, 2002). In S. Typhi, the 134 kb pathogenicity island SPI7 is located between partially duplicated copies of the pheU tRNA genes and codes for the Vi-capsule which is used as an antigenic component of human typhoid vaccines. In

Figure 9.3 Illustration showing the relative alignments of the prophage regions within the chromosomes of the sequenced, S. Typhi Ty2, S. Typhi CT18 and S. Typhimurium LT2 genomes. The gray shading links regions that displayed significance sequence homology. The coordinates of the prophage regions are indicated and similar phage are colored accordingly. The positions of stable RNAs which may potentially act as integration sites are also identified. Reprinted from Thomson, N., Baker, S., Pickard, D., Fookes, M., Anjum, M., Hamlin, N., Wain, J., House, D., Bhutta, Z., Chan, K., Falkow, S., Parkhill, J., Woodward, M. Ivens, A. and Dougan, G., The role of prophage-like elements in the diversity of Salmonella enterica serovars. J. Mol. Biol. 339, 279–300. Copyright (2004), with permission from Elsevier.


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addition to being present in S. Typhi, this region has been detected in some isolates of S. Paratyphi C, S. Dublin and Citrobac-ter freundii, but found to be absent in the majority (24 out of 30) of Salmonella se-rovars examined (Pickard et al., 2003).

Vi-positive organisms can be divided into two groups based on sequence homol-ogy within the viaB region in the Salmonella and C. freundii groups (Hahsimoto and Khan, 1997). Usually, the Vi antigen in S. Typhi is expressed stably, whilst in certain strains of Citrobacter the Vi antigen ex-pression is variable (Snellings et al., 1981). However, it has been found that this re-gion can be unstable and lost upon storage also in S. Typhi strains. Indeed, pulse-field gel electrophoresis of 120 wild type strains of S. Typhi showed a large deletion in the fragment that contains SPI7 in several strains. Further analysis by multiplex PCR and microarray showed that seven of the eight strains had precise deletions of SPI7, whilst part of SPI7 had been retained in the eighth strain. In the former strains deletion could be due to recombination between the direct repeats of tRNApheU (Nair et al., 2004). Therefore instability and loss of the Vi antigen can occur in Typhi and will have implications for monitoring and vaccina-tion programs for typhoid outbreaks.

The tRNAleuX-associated region of S. Typhi CT18 is known as SPI10 and en-codes a P4-like phage (ST46), the sef/pef fimbrial islet and IS element remnants, as well as genes of unknown function. Analysis of this region in silico, or using microarrays, Southern blotting and PCR revealed it to be a hotspot for divergence, within both Salmonella and E. coli (Bishop et al., 2005). As previously described (Thomson et al., 2004), the CT18-like P4 phage (ST46), STY4821-34, was recov-ered in all but one of the S. Typhi isolates

tested whereas a related phage was detect-ed in all S. Paratyphi A strains. All other S. enterica serovars tested did not contain any P4 phage determinant, although hybrid-ization with one phage gene, STY4826 a homologue of the P4 subunit, was com-mon to all but S. Senftenberg and S. bong-ori. Homologues of the CT18 sef genes were present in all 6 S. Typhi tested by microarrays. Most of these genes, were also detected in S. Paratyphi A, S. Pullorum, S. Gallinarum, S. Enteritidis and S. Dublin strain 16 using microarray analysis (Nair et al, 2004). This is consistent with in silico analysis and previous literature describ-ing the sef genes using Southern blotting for sefC and sefD (Bäumler et al., 1997) or sefA (Colligan and Woodward, 2001). The pef genes, which are present in the chromosomes of some salmonellae are thought to be of plasmid origin, as full pef fimbrial operons are found on the F-type virulence plasmids of S. Typhimurium (pSLT), S. Choleraesuis (pKDSC50) and S. Enteritidis (pS72) (Haneda et al., 2001). S. bongori SARC11 was not strongly posi-tive in microarray analysis for any of the CT18 plasmid-derived pef genes, consis-tent with in silico analysis of its tRNAleuX-associated island and the lack of any re-ported pef-containing plasmid. Of the oth-er salmonellae tested only S. Montevideo, S. Senftenberg, S. Derby, S. Binza and S. Newport harbored one of the four pef gene homologues STY4846, suggesting that the chromosomal pef genes were not present in these serovars. The presence of integrated plasmid-related genes in se-rovars such as S. Typhi, S. Paratyphi A and S. Enteritidis strongly suggests that these serotypes had a common ancestor that ac-quired these genes from a plasmid. Indeed, the S. Enteritidis tRNAleuX region encodes three more putative plasmid-related genes,



which lie between IS630 and IS1230 ele-ments at the start of the island (Bishop et al., 2005).

The use of microarrays is allowing detailed analysis of the Salmonella ge-nome and assessment of genomic variation within the natural population. Generally these results are in agreement and supple-ment previously published data; however some studies have provided data to the contrary. For example Chan et al. (2003) showed from their microarray data that the S. Typhimurium virulence plasmid pSLT, which is required for systemic dis-ease in the mouse, was present in S. Dublin and S. Paratyphi C, but mostly missing from other serovars not corresponding to S. Typhimurium. This contradicts the studies by Boyd and Hartl (1998), who demonstrated by Southern hybridiza-tion that the spv gene cluster was present throughout subspecies I as part of a viru-lence plasmid, and chromosomally located in subspecies II, IIIa, IV and VIII. One explanation for the difference between the two sets of data could be that in Southern hybridization a probe against the entire spv cluster was used, where as for microar-rays, hybridizations to short, gene-specific amplicons were used. However, a study by Korpela et al. (1989), probing for pres-ence of the virulence plasmid pLT2 in 35 Salmonella isolates, mainly from subspe-cies I, have shown that representatives of serovars S. Choleraesuis, S. Enteritidis, S. Johannesburg, S. Kottbus, S. Pullorum and S. Typhimurium harboured a large propor-tion (85%) of the pLT2 plasmid sequence, whilst S. Dublin and S. Morehead repre-sentatives harboured most of the pLT2 plasmid sequence – except the traT region – but most or all of the pLT2 plasmid se-quence was absent in S. Typhi, S. Paratyphi B and S. Senftenberg strains.. Future work in this area, complemented by sequencing studies, will verify the presence and absence

of genes in a serovar- and genovar-specific manner and add to our understanding of the genetic diversity encompassed within the genome of Salmonella.

Intrachromosomal rearrangementsSalmonella serovars that are host specific, such as S. Typhi and S. Gallinarum, have added genomic variation through large-scale, stable genomic rearrangements at rrn operons (Liu and Sanderson, 1998), or at insertion sequences (Alokam et al., 2002). In contrast, natural isolates of S. Typhimuri-um usually do not exhibit genomic rear-rangements (Liu and Sanderson, 1995). A study that used microarray and PCR, and probing 14 S. Typhimurium LT2 strains archived for over 40 years, recorded that 9 strains had no detectable genomic anoma-lies and that there were relatively small chromosomal amplification and deletions in the remaining strains. However, in one strain, microarray analysis revealed a large stable amplification. PCR analysis of the same strain showed a genomic duplication that underwent a translocation, indicating the possibility of genomic rearrangement even in S. Typhimurium (Porwollik et al., 2004). Indeed, archival mutator strains of S. Typhimurium are known to have under-gone genomic rearrangements at rrn op-erons (Liu et al., 2003). Archival cultures, which are stored on stabs, can have a high rate of chromosomal duplication due to the presence of limited carbon sources, but these duplications typically are not main-tained for long periods once the strains are grown in the abundance of nutrients (Sonti and Roth, 1989). However, the results of the Porwollik study (Porwollik et al., 2004) show that genomic amplifica-tion and translocation can be stable follow-ing growth on rich media, after revival of strains from the archive. Furthermore, they implied that the survival of S. Typhimuri-


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um strains in carbon-restricted conditions can affect genomic organization.

Use of DNA microarrays for diagnosticsIn the preceding sections DNA and oli-gonucleotide microarrays were used for comparative genomics and to understand similarities and differences between the ge-nomes of Salmonella pathogens, and hints implied for their evolution were discussed. However, microarray-based technologies are increasingly being developed for diag-nostic screening, or for the identification of microbial isolates from disease out-breaks and epidemiological studies. Cur-rently, most diagnostic laboratories use the White-Kauffmann-Le Minor scheme to divide salmonellae into serovars based on antigenic differences between O-antigens of the lipopolysaccharide component, and on differences in the antigenicity of the flagella (Popoff et al., 1998). In addition biochemical reactions and susceptibility to bacteriophages are useful to identify and distinguish clinical specimens (Farmer et al., 1985).

The classical identification schemes are often complemented by molecular techniques to determine genetic polymor-phisms, and include a myriad of typing tools. Molecular typing tools such as in-tergenic spacer (16S-23S rDNA) region typing (Garcia-Martinez et al., 1999); pulse field gel electrophoresis (PFGE; Tenover et al., 1995); randomly amplified polymorphic DNA (RAPD; Akopyanz et al., 1002; MacGowan et al., 1993); and amplified fragment length polymorphism analysis (AFLP; Savelkoul et al., 1999) are commonly used. However, development of microarray-based methods for diagnostics or bacterial typing has the potential to pro-vide more information on genetic diversity and higher resolution differentiation be-

tween closely related pathogens, and so can overcome some of the limitation of the gel-based DNA fragment sizing fingerprinting methods, e.g. PFGE, AFLP, RAPD. This section discusses some of the microarray-based methods that are currently in the literature, that have been developed using various bacteria including Salmonella and have the potential to be universally applied to all microbial isolates.

A genome-independent generic mi-crobial fingerprinting method has been de-veloped by Willse et al. (2004) for forensic and epidemiology applications, based on a simple 192 probe nonamer array. The ap-plication does not require a priori knowl-edge of specific nucleic acid signatures, and its nucleic acid probes were generated by random computer selection based on the sequence of the E. coli K12 genome. Twenty-five S. enterica isolates were used as the test case and required 24 microar-ray replication experiments to achieve a statistically reproducible binary array sig-nature for each organism. The Salmonella isolates were tested in pairs and most pairs had a distinct fingerprint, providing high resolution of differentiation between these closely related pathogens. However, future application of this method in a forensic or epidemiological setting will require the expansion of the fingerprint library and the development of statistical algorithms to compare quantitatively new fingerprint profiles of unknown samples with a refer-ence library.

Another method which has been de-veloped for the identification of microbes uses a hierarchical set of 30 oligonucleotide probes for targeting the 16S ribosomal RNA (rRNA) of five closely related bacilli (Bacillus anthracis, Bacillus cereus, Bacillus mycoides, Bacillus medusa, Bacillus subtilis; Liu et al., 2001). The 16S rRNA probes target at multiple levels of specificity: ap-



proximate taxonomic ranks of domain, kingdom, order, genus and species, and compare the non-equilibrium dissociation rates or melting curves of all probe-target duplexes simultaneously. Reproducible melting curves for probes with different levels of specificity were obtained using an optimized salt concentration and pro-vided excellent discrimination between representatives of different Bacillus spe-cies. However, this approach is not appli-cable to Salmonella as comparison of the complete 16S rRNA and 23S rRNA from about 30 Salmonella strains covering 20 se-rovars and all subspecies, has shown little variation between S. enterica and S. bongori (Christensen and Olsen, 1998; Liu et al., 1999). Therefore, to develop a hierarchical microarray for identification of Salmonella, an alternative approach could be used. This approach would be based on the informa-tion gained from some of the comparative genomics work which have already been reviewed in the previous section, and that have defined genes which can be used to differentiate Salmonella at the level of ge-nus, species and even serovar (Frizgerald and Mussner, 2001; Alvarez et al., 2003). This approach is currently being consid-ered in our laboratory for routine diagnos-tic work (Anjum et al., unpublished data).

Another approach that is currently being considered for use in routine diag-nostics is the use of microarray for DNA-based typing of Salmonella isolates accord-ing to the Kaufmann-White scheme. Two separate microarrays have been developed, one for the detection of flagellar H an-tigens, and one to detect the somatic O antigens. Sixty-five probes representing 11 phase 1 H-antigens and 25 probes rep-resenting 8 phase 2 H-antigens have been designed. For detection of H-antigens in Salmonella isolates, phase 1 and phase 2 H antigens were PCR amplified using phase-

specific primers for the fliC and fljB genes, respectively. To date, all probes on the H antigen array have been tested in duplicate. Twenty-one of these probes detected anti-gens b; e,h; f,g,s/t; i; r; z6; and z10 with no false positives for the antigen/probe combinations tested. The second somatic array comprises 22 oligonucleotides rep-resenting seven serogroups (A, B, C1, C2, D1, D2, and E), based on DNA sequences within their respective rfb (O antigen) gene clusters. Preliminary analyses have demon-strated the specificity of serogroup B oli-gonucleotides when tested against known strains (Yoshida et al, http://www.micro-arrays.ca/MGED/Abstracts/Yoshida.pdf )

Microarray technology can also be use-ful in establishing the identity of Salmonella isolates that have an ambiguous or atypical serovar. For example, the genome of four isolates of a multidrug resistant fljB-lack-ing strain of S. enterica serovar 4,5,12:i:- detected in Spain in 1997 was analyzed by microarray (Garaizar et al., 2002). The ge-nome of this atypical serovar (Garaizar et al., 2002) contained almost all genes pres-ent in strain LT2 from S. Typhimurium serovar 4,5,12:i:1,2 . Two non-phage gene clusters were absent in serovar 4,5,12:i:– , which is in contrast to other serovars within subspecies I that differ from S. Typhimurium by at least 10 clusters of genes (Alvarez et al, 2003). Hence, it was deduced from the similarity of the genetic repertoires of serovar 4,5,12:i:– and S. Typhimurium LT2 that the former is most probably a variant of the S. Typhimurium [4,5,12:i:1,2] (Garaizar et al., 2002).

Microarrays using immobilized source DNAThe principles of microarray discussed so far relies on immobilization of DNA so that individual spots represent one of hun-


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dreds thousands of genes from a given ge-nome. In a novel application of microarray-technology, Zhang et al. (2004) described libraries of entire genomes arrayed on glass slides and then probed for the presence or absence of specific genes and/or gene al-leles. The Library on a Slide technique uses highly purified genomic DNA isolated from strains, which is subsequently frag-mented by sonication to about 2 kb frag-ment size, on average. The DNA samples are mixed with commercial printing buf-fer and printed on amine-modified slides using solid and stealth pins to produce low and high density microarrays (~2000 and ~15,000 spots, respectively). As each spot represents the total bacterial genomic DNA and the target is a tiny fraction of the total genome in terms of quantity, maximum detection sensitivity is required. The Tyramine signal amplification system, an enzyme based secondary amplification system, is used for fluorescently labeling the DNA probe, which is subsequently hy-bridized to the genomic Library on a Slide. The E. coli ECOR collection of strains has been used to construct such a reference array (Ochman and Selander, 1984). The 72 ECOR strains plus controls, compris-ing the Library on a Slide, were screened for presence or absence of virulence genes such as the hemolysin gene (hly). The 16S ribosomal RNA, which is present in all E. coli strains in the same copy number was labeled with the Cy5 dye and used as con-trol to quantify the amount of DNA on the printed spots, whilst the hly gene, la-beled with Cy3, evaluated the presence of the gene of interest in all strains. With the development of more fluorescent dyes and capable scanners, multiple gene screening can be achieved rapidly in a single experi-ment in the future using such genomic li-braries. Therefore, such a method could easily be applied to rapidly screen for the

presence of one or more virulence deter-minant within natural populations of Sal-monella found within a routine diagnostic setting. The populations studied thus can provide valuable insight into the impor-tance of the gene of interest in pathogen-esis, transmission and the epidemiological significance of these observations.

Use of antibody-based microarrays for diagnosticsRecently, a miniaturized protein array suitable for Escherichia coli serotyping has also been developed for use in routine di-agnostic laboratories (Anjum et al., 2006). This system provides a cheap, efficient, rapid and sensitive assay for detecting the bound whole cell E. coli antigen with a cor-responding immobilized antibody. In this initial study antibodies against 17 somatic antigens routinely used for serotyping in diagnostic laboratory were spotted in four replicates. The concomitant binding of the E. coli antigen to its corresponding immo-bilized antibody is detected by another antibody which binds to the distal part of the inner core region common to LPS from all E. coli, Salmonella and Shigella (Di Padova et al., 1993; Muller-Loennies et al., 2003). The formation of immunoglobulin sandwich is finally revealed by the addition of biotinylated anti-mouse IgG, HRP-con-jugate and TMB peroxidase substrate in a series of reactions outlined in Fig. 9.4. The method, which can be used to detect all E. coli LPS types, can also be applied for de-tection of Salmonella and Shigella in future.

Functional genomics using microarraysSo far, methods using the binding of DNA have been discussed. Yet, in principle all microarray formats have in common the capacity to bind DNA or RNA species by complementary base pairing, and in-



Figure 9.4 Reactions for detecting E. coli somatic antigens using miniaturized arrays. Antibodies raised to different somatic antigens were immobilized on the array surface. Addition of boiled cultured E. coli was followed by sequential addition of anti-E. coli core LPS antibody, biotinylated anti-mouse IgG and poly horse radish peroxidase (HRP) conjugate. The bound cell sandwich were finally detected by addition of tetramethylbenzidine substrate derivate (TMB derivate) which resulted in a blue precipitate that was detectable using the ArrayTube reader (Anjum et al., 2006).

deed one can use microarrays for probing RNA levels, and thus expand the use of microarrays to functional genomic. Hy-bridization of the labeled RNA (or cor-responding labeled cDNA) to the array provides information of the organisms transcriptional activity under a particular growth or stress condition. The number of microarray expression studies of bacterial pathogens, including Salmonella, published so far are much more diverse with respect to the experimental question and design, than for comparative genomics. Examples of the use of microarrays to study the tran-scriptome of the Salmonella pathogen and its effect on the host are given in Table 9.1. However, the use and interpretation of microarray expression data can be more complex than for comparative genomics work due to instability of bacterial RNA, slight differences in growth conditions, ex-perimental and technical variability. There-fore such studies require greater numbers

of biological and technical repeats than in comparative genomics, and may be up to ten or more if t-test alone is used to assess differential expression (Lee et al., 2000; Long et al., 2001). Nevertheless, both com-parative genomics and transcriptomics use various statistical tools for standardization and interpretation of the vast quantities of resulting microarray data, e.g. Gaussian distribution, significance analysis of mi-croarrays, hierarchical clustering (Eisen et al., 1998; Arfin et al., 2000; Tusher et al., 2001). Most work in this area has concen-trated on the use of S. Typhimurium as the model organism to understand Salmonella pathogenesis, but no doubt studies look-ing at the response of other Salmonella to various environmental and host-specific factors will also increase as performing mi-croarray experiments become more com-mon place and our understanding of the diversity of Salmonella genomes increase. Although the use of microarrays to study


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Table 9.1 Use of microarrays to study Salmonella pathogenicity

Strain/serovarRegulatory gene Experimental condition Author

S. Typhimurium phoP Effect of Typhimurium on human monocytic tissue

Detweiler et al., 2001

S. Typhimurium Low-shear modeled microgravity environment for culture

Wilson et al., 2002

S. Typhimurium PNP Effect of polynucleotide phosphorylase (PNP) on virulence and persistence

Clements et al., 2002; Eriksson Ygberg et al., 2006

S. Typhimurium Changes in model intestinal epithelia induced by Salmonella flagellin

Zeng et al., 2003

S. Typhimurium csrA Regulation of genes in Typhimurium

Lawhon et al., 2003

S. Typhimurium fis Effect of Fis on metabolism and type III secretion

Kelly et al., 2004

S. Typhimurium Effect of bile on global mechanistic pathways

Prouty et al., 2003

S. Typhimurium Gene expression patterns during swarming

Wang et al., 2004

S. Typhimurium Expression profile of intracellular S. Typhimurium following macrophage infection

Erikson et al., 2003

S. Typhi Transcriptome profiling within macrophages

Faucher et al., 2006

gene expression has become increasingly popular in the past few years, the long used operon or promoter fusion single gene as-says to look for transcript abundance is still evident in recent literature (Kim et al., 2003; Bittner et al., 2004; McKelvie et al., 2004). However, the advantage of us-ing microarrays for assessing transcription is that it paints a global picture of all the genes and network of regulatory compo-nents required for survival or adaptation to a particular condition. Such information on the transcriptome and proteome of the Salmonella will not only inform future in-tervention strategy for controlling its entry and propagation through the food chain,

but also treatment regimes for salmonel-lae-associated disease.

Concluding remarksThe advent of genome sequencing has brought about a new era in understanding biological processes and has also driven the development of methods to exploit this in-formation. Microarrays, which are a result of this drive, take advantage of the knowl-edge of knowing the coding sequence of virtually every gene in an organism, thus allowing monitoring for the presence or ab-sence of homologues of genes in genomes of closely related organisms. It also enables the examining of transcript abundance for



all of an organism’s genes simultaneously under a particular condition, and has the potential for development of rapid and ef-ficient detection of pathogens for routine diagnostics and epidemiological studies. A limitation of the microarray technique, especially for comparative genomic studies, is that only genes present on the sequenced strain from which the microarray was con-structed are detected, and any additional genes present in the test strain are over-looked. However, as more strains of closely related Salmonella are sequenced and genes unique to these organisms are identified, the information gleaned from these studies can be added to existing microarrays. The resulting panarrays will then cover the ge-nome of a number of closely related Salmo-nella strains or serovars, enabling a much more detailed picture to be painted espe-cially with regard to genomic diversity and phylogenetics. As a note of caution, it is worth stating that the design of microarray experiments is crucial for interpretation of its biological significance and should in-clude proper validation experiments using controls, sufficient numbers of experimen-tal and biological repeats, exclusion of data with poor signal intensity and interpreta-tion of data using statistical tools. Such information will result in reliable data that will be invaluable in understanding host specificity and virulence in the Salmonella pathogen, and in identifying targets for fu-ture vaccine development.

AcknowledgmentI am very grateful to Dr. G. Wu, at VLA, for comments and suggestions on the man-uscript. I am also grateful to Dr. S. Porwol-lik for permitting the use of Fig. 9.1.

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