# Combining Microarray Technology and Molecular Epidemiology to Identify Genes Associated with Invasive Group BStreptococcus

Authors: Lixin Zhang; Usha Reddi; Usha Srinivasan; Sheng Li; Stephanie M. Borchardt; Parvathy Pillai; Puja Mehta; Anne N. Styka; Joan DeBusscher; Carl F. Marrs; Betsy Foxman
Journal: Interdisciplinary Perspectives on Infectious Diseases
 (2008)

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## Abstract

Many bacterial species function as both commensals and pathogens; we used this dual nature to develop a high-throughput molecular epidemiological approach to identifying bacterial virulence genes.  We applied our approach to Group BStreptococcus  (GBS).  Three representative commensal and one invasive GBS isolates were selected as tester strains from a population-based collection.  We used microarray-based comparative genomic hybridization to identify open reading frames (ORFs) present in two sequenced invasive strains, but absent or divergent in tester strains.  We screened 23 variable ORFs against 949 GBS isolates using a GBS Library on a Slide (LOS) microarray platform. Four ORFs occurred more frequently in invasive than commensal isolates, and one appeared more frequently in commensal isolates.  Comparative hybridization using an oligonucleotide microarray, combined with epidemiologic screening using the LOS microarray platform, enabled rapid identification of bacterial genes potentially associated with pathogenicity.


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## Body

## 1. Introduction

Group BStreptococcus (GBS), or Streptococcus agalactiae, a common bowel
inhabitant,  also frequently colonizes the vagina, urethra, and pharynx
asymptomatically. However, GBS can cause
a variety of invasive diseases that occur primarily in neonates, young infants,
elderly persons, and pregnant women [1–3]. Host factors are clearly important as GBS
disease occurs primarily in vulnerable populations. Nonetheless, bacterial virulence factors must
also play a role: propensity to cause disease varies by serotype [4], and
genetic sequence types that cross serotype, which have a high potential to
cause invasive disease, have been identified [5].Among
the nine known GBS capsular serotypes, serotypes Ia, III, and V cause the
majority of GBS disease in the United
States
[1, 6–8]. Studies of the population structure and the molecular
epidemiology of GBS isolates suggest that GBS populations are clonal, but that
some strains may be more virulent than others [9–13]. By pulsed-field gel electrophoresis (PFGE),
disease-causing isolates have limited heterogeneity within a serotype (reviewed
in Schuchat, 1998 [2]) while colonizing isolates are quite heterogeneous [14]
within a particular serotype, suggesting that invasive isolates have distinctive
features which enhance pathogenesis.
Differences in virulence are likely related to the presence or absence
of virulence genes [15]. Although
advances have been made in the understanding of classic GBS traits, such as
capsular polysaccharide, β-hemolysin, C5a peptidase, and immunogenic surface
proteins [16–19], our understanding of the pathogenesis of GBS
infections is limited: little is known about which bacterial genetic factors
contribute to virulence or transmission of pathogenic strains.The
availability of complete and draft genome sequences of several GBS strains presents
an opportunity to gain insight into the molecular basis of GBS virulence. Analysis of these genome sequences confirmed
a high level of genetic heterogeneity among GBS strains, even of the same
serotype [20]. The GBS genome contains a
large number of genetic islands that often vary among strains and are likely to
be the regions where virulence genes reside [21–23]. With thousands of genes identified in GBS
genomes, the current challenge is to determine which are important in GBS
pathogenesis and transmission. In a
previous study of E. coli, we
presented an approach of bacterial gene identification and evaluation that
relied on epidemiologic information for selecting isolates for genomic
subtraction and screening of epidemiologically defined collections for
evaluation of the significance of genes identified through genomic subtraction
[24]. In this report, we applied the
same principle in a three-step strategy to the study of GBS. Further, we employed novel microarray
platforms that enabled us to systematically identify candidate genes and
evaluate their importance on a large scale.Genome
comparison between pathogenic and nonpathogenic strains within a species is a
powerful strategy for identifying candidate genes important for virulence [25, 26]. Since none of the currently sequenced GBS
genomes are from commensal isolates, we first selected a few representative
colonizing GBS strains from a population-based sample for comparisons with
sequenced pathogenic serotype III strain (NEM316) and serotype V strain
(2306VR), two serotypes representing the most frequently encountered disease-causing
isolates. We then identified sequence differences
and their associated variable genes between selected colonizing and sequenced
invasive GBS strains using comparative genomic hybridization with fine-tiling
oligonucleotide microarrays. Lastly, a
selected set of variable genes was screened against a large panel of colonizing
and invasive strains using Library on a Slide microarray to evaluate their
association with disease. Our main
objective of this report is to use GBS as an example to demonstrate and
evaluate this study approach.


## 2. Materials and Methods

### 2.1. Bacterial Strains and Culture Conditions

Disease-causing
and commensal GBS isolates were selected from various collections obtained from
previous epidemiologic studies.
Collections included isolates from healthy male and nonpregnant female
college students enrolled at the University of Michigan [27–30],
isolates from symptomatic and asymptomatic pregnant women seen at a University
of Michigan Medical Center clinic [31], and isolates collected from patients
through the Wisconsin Invasive Bacterial Laboratory Surveillance System between
1998 and 2002 [32]. Additional isolates
from newborns in Texas with early and late onset disease [8]  as well as
isolates from pregnant women with and without GBS disease were obtained from
Dr. Carol J. Baker (Baylor College of Medicine, Houston, Texas, USA). These
strains were broadly grouped into two categories: invasive isolates from
patients with invasive diseases (n=386) and colonizing isolates from subjects
without any symptomatic diseases (n=563).
GBS strain NEM316 [21], 2603VR [23], and A909 [33] were used as
reference strains. GBS isolates were
cultured overnight in Todd-Hewitt broth (Oxoid) for DNA isolation.


### 2.2. PFGE and Capsular Typing

PFGE was performed as
described previously [30]. Briefly, GBS
DNA was digested with SmaI and
electrophoresed for 18 hours (initial switch time 4 seconds; final switch time 16
seconds) with the CHEF III apparatus (Bio-Rad, Hercules, CA). Gels were strained for 4 h with Vistra Green
(Amersham Biosciences, Piscataway, NJ, USA) at 4 C, and visualized with a Storm
PhosphorImager (Amersham Biosciences, Piscataway, NJ, USA). PFGE patterns were analyzed using BioNumeric
software (Applied Maths, Kortrijk,
Belgium). A dendrogram was constructed using the
unweighted pair group method with arithmetic means, Dice coefficient, optimization
setting of 1.0%, and a position tolerance of 1.0%. GBS isolates were classified into capsular
types Ia, Ib, and II–VIII using DNA dot blot hybridization, as previously
described [34].


### 2.3. GBS Oligonucleotide Microarray Construction

Two
fine-tiling oligonucleotide microarrays were designed using the published DNA
sequences for serotype III strain (NEM 316, GenBank accession no. AL732656) and
serotype V strain (2603VR, GenBank accession no. AE009948). The NEM 316 array consisted of a total of
368,576 32-mer probes (184,288 pairs) tiling its 2.21 Mb genome every 12 bases
for both strands. The 2603VR array
consisted of a total 360,040 32-mer probes (180,020 pairs) tiling its 2.16 Mb
genome every 12 bases for both strands.
Arrays were designed and constructed through custom Array CGH service
from NimbleGen (Madison, Wiss, USA) using its maskless array synthesis (MAS)
technology. A denser tiling array with
shorter oligonucleotides was usually used in NimbleGen’s two-step comparative
genome sequencing (CGS) [35].


### 2.4. Comparative Genome Hybridization and Data Acquisition

Comparative genomic hybridization
and signal processing were performed by NimbleGen custom service (NimbleGen
Systems Inc., Madison, Wiss, USA).
Briefly, GBS DNA from four tester strains and two reference strains were
broken down into separate pools of low molecular weight fragments, labeled
independently with cyanine fluorescent dye and each was hybridized to one
NEM316 and to one 2603VR whole-genome tiling array. Similar to the Affymetrix chips, the short
oligo GBS arrays were produced by in-slide de novo syntheses. Like Affymetrix
chip hybridization, one slide was used for hybridization per labeled sample. A total of 12 microarray hybridizations were
performed. Genomic hybridization of
NEM316 or 2603VR to its own array served as a signal reference for comparisons
with tester strain hybridizations using the same array. Genome hybridization of one sequenced genome
against the other sequenced genome array was used for validation purposes. The signal intensity ratios of tester DNA to
each reference DNA were compared to identify probe sequences absent or
different from the tester genome. The
ratio was generated by normalizing the signal intensity (setting the median
ratio to 1 and the standard deviation to 0.45), dividing the reference by the
tester for each strand, and then averaging the two strands. Signal ratios were plotted as a function of their
genomic positions and visualized using SignalMap software from NimbleGen. A custom algorithm was used to mark the
potential variable probe sequence (absent or different in tester genome) based
on comparison to a local threshold (a 1800 bp window). This analysis was also performed by NimbleGen
(see Supplementary Material available at doi:10.1155/2007/14762 for the analytical
algorithm).


### 2.5. Additional Bioinformatic and Data Analysis Methods

Genome-scale sequence comparisons
between genomes of NEM316 and 2603VR were performed using GenomeComp [36]. All strain-specific genetic islands greater
than 10 bp were identified using run parameters set to 0.01, −3, and 1 for
expectation value (e), penalty for a mismatch, and reward for a match,
respectively. To identify matches of
probe sequences from one fine-tiling oligoarray in the other sequenced genome, a custom Bioperl program
was used for local batch blast analyses. The stand alone BLAST program [37] for Unix
operation system was used and the percent identity of the best hit for each
probe within the query genome was determined.
All other data analyses were performed using SAS v9.0 (SAS Institute,
Inc Cary, NC, USA) and S plus v6.1 (Insightful Corporation, Seattle, Wash, USA).


### 2.6. GBS Library on a Slide Microarray Construction and Hybridization

We
recently developed a new application of microarray technology, called Library
on a Slide (LOS), for bacterial comparative genomics studies [38]. LOS technology combines dot blot
hybridization with the technology of microarrays resulting in glass slides with
thousands of bacterial genomes arrayed.
Thus libraries of entire genomes rather than the sequence of a single
genome or set of genes are printed on the slides. Slides are used to screen large numbers of
strains for the presence of specific genetic elements of interest. A
GBS LOS microarray was created with genomic DNA from 949 GBS isolates sampled
from a variety of GBS collections and various control strains. Genomic DNA was isolated using a high-throughput
sonication-based method described previously [39]. DNA from GBS strains along with controls were
arrayed in duplicate on Vivid Gene Array slides (Pall Life Sciences, Mich, USA)
using a VersArray ChipWriter compact arrayer (Bio-Rad, Calif, USA). Selected GBS ORFs and four house keeping
genes, (alcohol dehydrogenase (adhP),
phenylalanyl tRNA synthetase (pheS),
glutamine synthetase (glnA), and
glucose kinase (glcK)), were PCR-amplified
from either strain NEM316 or 2603VR. Purified
PCR products were fluorescein-labeled using BioPrime DNA labeling kit (Invitrogen,
Calif, USA). Each probe was hybridized
with a different slide overnight at 68°C in PerfectHyb Plus hybridization
buffer (Sigma, Mo, USA). After washing, fluorescein-labeled
probes were detected using antiflourescein alkaline phosphatase (Roche,
Switzerland) and alkaline phosphatase kit (TeleChem, Calif, USA). The intensity of each spot was normalized to
the intensity of the quantification probe (a mixture of four housekeeping
genes) to account for differences in DNA concentrations at different spots, and
compared to the intensity of the positive control (sequence strain known to
contain the gene probe) to determine presence/absence of the gene fragment in
different bacterial strains using previously established methods [38, 40].


## 2.1. Bacterial Strains and Culture Conditions

Disease-causing
and commensal GBS isolates were selected from various collections obtained from
previous epidemiologic studies.
Collections included isolates from healthy male and nonpregnant female
college students enrolled at the University of Michigan [27–30],
isolates from symptomatic and asymptomatic pregnant women seen at a University
of Michigan Medical Center clinic [31], and isolates collected from patients
through the Wisconsin Invasive Bacterial Laboratory Surveillance System between
1998 and 2002 [32]. Additional isolates
from newborns in Texas with early and late onset disease [8]  as well as
isolates from pregnant women with and without GBS disease were obtained from
Dr. Carol J. Baker (Baylor College of Medicine, Houston, Texas, USA). These
strains were broadly grouped into two categories: invasive isolates from
patients with invasive diseases (n=386) and colonizing isolates from subjects
without any symptomatic diseases (n=563).
GBS strain NEM316 [21], 2603VR [23], and A909 [33] were used as
reference strains. GBS isolates were
cultured overnight in Todd-Hewitt broth (Oxoid) for DNA isolation.


## 2.2. PFGE and Capsular Typing

PFGE was performed as
described previously [30]. Briefly, GBS
DNA was digested with SmaI and
electrophoresed for 18 hours (initial switch time 4 seconds; final switch time 16
seconds) with the CHEF III apparatus (Bio-Rad, Hercules, CA). Gels were strained for 4 h with Vistra Green
(Amersham Biosciences, Piscataway, NJ, USA) at 4 C, and visualized with a Storm
PhosphorImager (Amersham Biosciences, Piscataway, NJ, USA). PFGE patterns were analyzed using BioNumeric
software (Applied Maths, Kortrijk,
Belgium). A dendrogram was constructed using the
unweighted pair group method with arithmetic means, Dice coefficient, optimization
setting of 1.0%, and a position tolerance of 1.0%. GBS isolates were classified into capsular
types Ia, Ib, and II–VIII using DNA dot blot hybridization, as previously
described [34].


## 2.3. GBS Oligonucleotide Microarray Construction

Two
fine-tiling oligonucleotide microarrays were designed using the published DNA
sequences for serotype III strain (NEM 316, GenBank accession no. AL732656) and
serotype V strain (2603VR, GenBank accession no. AE009948). The NEM 316 array consisted of a total of
368,576 32-mer probes (184,288 pairs) tiling its 2.21 Mb genome every 12 bases
for both strands. The 2603VR array
consisted of a total 360,040 32-mer probes (180,020 pairs) tiling its 2.16 Mb
genome every 12 bases for both strands.
Arrays were designed and constructed through custom Array CGH service
from NimbleGen (Madison, Wiss, USA) using its maskless array synthesis (MAS)
technology. A denser tiling array with
shorter oligonucleotides was usually used in NimbleGen’s two-step comparative
genome sequencing (CGS) [35].


## 2.4. Comparative Genome Hybridization and Data Acquisition

Comparative genomic hybridization
and signal processing were performed by NimbleGen custom service (NimbleGen
Systems Inc., Madison, Wiss, USA).
Briefly, GBS DNA from four tester strains and two reference strains were
broken down into separate pools of low molecular weight fragments, labeled
independently with cyanine fluorescent dye and each was hybridized to one
NEM316 and to one 2603VR whole-genome tiling array. Similar to the Affymetrix chips, the short
oligo GBS arrays were produced by in-slide de novo syntheses. Like Affymetrix
chip hybridization, one slide was used for hybridization per labeled sample. A total of 12 microarray hybridizations were
performed. Genomic hybridization of
NEM316 or 2603VR to its own array served as a signal reference for comparisons
with tester strain hybridizations using the same array. Genome hybridization of one sequenced genome
against the other sequenced genome array was used for validation purposes. The signal intensity ratios of tester DNA to
each reference DNA were compared to identify probe sequences absent or
different from the tester genome. The
ratio was generated by normalizing the signal intensity (setting the median
ratio to 1 and the standard deviation to 0.45), dividing the reference by the
tester for each strand, and then averaging the two strands. Signal ratios were plotted as a function of their
genomic positions and visualized using SignalMap software from NimbleGen. A custom algorithm was used to mark the
potential variable probe sequence (absent or different in tester genome) based
on comparison to a local threshold (a 1800 bp window). This analysis was also performed by NimbleGen
(see Supplementary Material available at doi:10.1155/2007/14762 for the analytical
algorithm).


## 2.5. Additional Bioinformatic and Data Analysis Methods

Genome-scale sequence comparisons
between genomes of NEM316 and 2603VR were performed using GenomeComp [36]. All strain-specific genetic islands greater
than 10 bp were identified using run parameters set to 0.01, −3, and 1 for
expectation value (e), penalty for a mismatch, and reward for a match,
respectively. To identify matches of
probe sequences from one fine-tiling oligoarray in the other sequenced genome, a custom Bioperl program
was used for local batch blast analyses. The stand alone BLAST program [37] for Unix
operation system was used and the percent identity of the best hit for each
probe within the query genome was determined.
All other data analyses were performed using SAS v9.0 (SAS Institute,
Inc Cary, NC, USA) and S plus v6.1 (Insightful Corporation, Seattle, Wash, USA).


## 2.6. GBS Library on a Slide Microarray Construction and Hybridization

We
recently developed a new application of microarray technology, called Library
on a Slide (LOS), for bacterial comparative genomics studies [38]. LOS technology combines dot blot
hybridization with the technology of microarrays resulting in glass slides with
thousands of bacterial genomes arrayed.
Thus libraries of entire genomes rather than the sequence of a single
genome or set of genes are printed on the slides. Slides are used to screen large numbers of
strains for the presence of specific genetic elements of interest. A
GBS LOS microarray was created with genomic DNA from 949 GBS isolates sampled
from a variety of GBS collections and various control strains. Genomic DNA was isolated using a high-throughput
sonication-based method described previously [39]. DNA from GBS strains along with controls were
arrayed in duplicate on Vivid Gene Array slides (Pall Life Sciences, Mich, USA)
using a VersArray ChipWriter compact arrayer (Bio-Rad, Calif, USA). Selected GBS ORFs and four house keeping
genes, (alcohol dehydrogenase (adhP),
phenylalanyl tRNA synthetase (pheS),
glutamine synthetase (glnA), and
glucose kinase (glcK)), were PCR-amplified
from either strain NEM316 or 2603VR. Purified
PCR products were fluorescein-labeled using BioPrime DNA labeling kit (Invitrogen,
Calif, USA). Each probe was hybridized
with a different slide overnight at 68°C in PerfectHyb Plus hybridization
buffer (Sigma, Mo, USA). After washing, fluorescein-labeled
probes were detected using antiflourescein alkaline phosphatase (Roche,
Switzerland) and alkaline phosphatase kit (TeleChem, Calif, USA). The intensity of each spot was normalized to
the intensity of the quantification probe (a mixture of four housekeeping
genes) to account for differences in DNA concentrations at different spots, and
compared to the intensity of the positive control (sequence strain known to
contain the gene probe) to determine presence/absence of the gene fragment in
different bacterial strains using previously established methods [38, 40].


## 3. Results and Discussion

### 3.1. Selection of Tester Strains for Comparative  Genomic subtraction.

Comparing
pathogenic and nonpathogenic strains within a species can provide critical insights
into bacterial pathogenesis. However,
all sequenced GBS strains are from invasive diseases. We used molecular epidemiological comparisons
to select representative commensal colonizing GBS isolates for the comparative
genomic hybridization with the highest potential to identify potential
pathogenesis-related genes in sequenced invasive genomes (strains NEM316 and
2603VR). We characterized the diversity
of 882 colonizing isolates from a population-based longitudinal study of
healthy male and nonpregnant female college students [28] using PFGE and
serotyping. Clustering analysis by
dendrogram was performed on these isolates along with a sample of 35 invasive
isolates and sequenced pathogenic strains NEM316 and 2603VR. Based on this analysis, we selected three
commensal isolates that are genetically distant from the two sequenced genomes
but representative of isolates from relatively large strain clusters that are
predominantly of commensal origin.
Isolate 657–461 is a serotype V strain representing the largest clonal
group within our commensal collection.
Isolate G617-061 (serotype III) and G293-061 (serotype II) are from two
additional clusters dominated by colonizing strains. In addition to these three commensal
isolates, one invasive isolate, H-19, was chosen for the comparative genomic
subtraction, because it represents the most common clonal type of the serogroup
Ia strain in our analysis. Since serotype classification does not necessarily reflect genetic distance among strains [20], we did not select tester strains based solely on the differences in serotype. The small number of colonizing stains chosen
here could not and was not intended to capture the diversity of commensal
isolates. It was our first attempt at
performing genome-wide comparisons between colonizing strains and sequenced genomes, so we
could pick gene candidates from a list of several thousands for association
study using LOS microarrays where large collections of population-based GBS
isolates can be screened.


### 3.2. Oligonucleotide Array Validation

We used shared probe
sequences within the two genome arrays to assess the reproducibility of the
comparative genomic hybridization and used hybridization of one sequenced
genome against the array of the other sequenced genome to evaluate the accuracy
of the array in assessing sequence variation.Between
184,288 probe pairs on the NEM316 array and 180,020 probe pairs on the 2603VR
array, a total of 16,364 identical probe pairs (32/32 match) were
identified. Hybridization results for
this probe subset from two arrays for each tester genome were treated as replicas for accessing
the reproducibility of CGH.
Hybridization for each probe was classified as identical or variable in
the tester genome when compared to the reference genome. The percent concordance for tester genomes
G293-061, H1-19, G617-061, and G654-461 were 98.75%, 99.50%, 99.88%, and
98.62%, respectively. The
reproducibility was very high even when the raw signal ratios were
examined. The correlation coefficients
were greater than 90% for duplicates.To
evaluate the accuracy of the array in assessing sequence variation, we compared
results from in silicoanalysis with the classification
results from an actual array hybridization swap between NEM316 and 2603VR
genomes. Almost all perfectly matched
probe sequences were correctly identified as identical by hybridization. Only 4 out of 133,520 and 5 out of 133,570
probe sequences were falsely identified as different with NEM316 and 2603VR
arrays, respectively. However, 28,673
out of 50,768 (56%) and 25,435 out of 46,450 (55%) mismatched probe sequences
were falsely identified as identical with NEM316 and 2603VR arrays,
respectively. At the probe level,
hybridization has a high sensitivity but low specificity for detecting
conserved probe sequences. Nonetheless, the
high-density nature of the tiling array still provides overall sequence
variation information at genome and ORF levels.
We visually displayed the CGH results by plotting hybridization signal
ratios of the probes along their genomic positions and compared them with an in silico comparison of NEM316 and
2603VR. The majority of variable probes
(i.e., probes with high reference versus tester signal ratios) are clustered primarily
around strain-specific genetic islands identified by the in silico analysis. To convert probe-level variation to ORF
sequence variation, we calculated the percentage of variable probes for each
ORF (number of variable probes identified within an ORF divided by the total
number of probes tiling the ORF). Using
different percentage cutoff values in classifying variable ORFs (divergent or
absent), the CGH-based data was compared with in silico analysis (Table1).
For the NEM 316 array, a 15% cutoff value gave a 2.9% false-negative
rate (i.e., ORFs known to be present but classified by hybridization as absent
or very divergent) and the best overall sensitivity and specificity (97% and
94%, resp.). For the 2603VR
array, the 20% cutoff point gave the best overall sensitivity and specificity (97%
and 91%, resp.). Thus, these two
cutoff points were chosen to classify variable ORFs for the remaining CGH
analyses.Table 1
Sensitivity (probability open reading frame is detected, given it is truly present) and specificity assessments (probability open reading frame is not detected, given it is not present) of different cutoff values in classifying variable open reading frames using fine-tiling oligonucleotide genome arrays created from the genomic sequence of group BStreptococcus strains NEM316 and 2603VR.
Reference  genomePercentage  cutoff pointSensitivitySpecificityNEM31620%0.980.9115%0.970.9410%0.960.962603VR20%0.970.9115%0.950.9210%0.890.96Comparative
genomic hybridization with fine-tiling 32-mer oligonucleotide microarrays did
not identify all probe sequence variations in the two reference genomes but
reliably identified variable ORFs using combined hybridization results of all
probes within each ORF.


### 3.3. Distribution and Mapping of Variable Probe Sequences

CGH using NEM316 and 2603VR genome
arrays revealed that 3.4–15.4% of probe sequences were absent or divergent in
the four tester strains (Table2). This
range of diversity is similar to the range of sequence differences (5% to 15%)
recently observed in pairwise comparison of all eight available complete or
draft GBS genome sequences [20]. The
tiling arrays allowed a high-resolution view of genome variation among
comparison strains. Figure 1 displays
comparative hybridization results of tester strains against 2603VR by plotting
the reference to tester signal ratios along their genomic positions. Although the four tester strains represent
four different serotypes (Ia, II, III and V), the majority of absent or
divergent probe sequences in these strains are mapped to the same set of
regions on the NEM316 genome, a serotype III strain. To a lesser extent, the same set of regions
on the 2603VR genome covers the majority of the probes that are absent or
divergent across three of the four tester genomes. The strain G617-061, a serotype III strain,
is very similar to the serotype V reference strain 2603VR genome, with only
3.4% probes identified as different compared to >13% of the other tester
genomes. Comparison of all eight
available complete or draft GBS genome sequences also demonstrated that
serotype classification does not reflect the genetic diversity of the GBS [20]. One possible explanation for closely related
strains exhibiting different capsules is genetic exchange of genes determining
the capsular type by horizontal gene transfer.Table 2
Number (percentage) of variable probe sequences among four tester group BStreptococcus genomes using sequenced strains as a reference revealed by comparative genomic hybridization
Reference/sequenced genomes of invasive isolatesH1-19 (Ia) (invasive)G293-061 (II) (commensal)G617-061 (III) (commensal)G654-461 (V) (commensal)2603VR327
(15.4%)320
(15.1%)72 (3.4%)278
(13.1%)NEM316277
(13.0%)305
(14.3%)305
(14.3%)271
(12.7%)Figure 1
Locations of variable probes sequences identified within genomes of group BStreptococcus strains 2603VR in comparative genomic hybridization using each of the four tester strains of group B Streptococcus.


### 3.4. NEM316 and 2603VR ORFs Absent/Divergent in Tester Genomes

Hybridization
results from all the probes within each ORF were used to determine the presence
or absence/divergence in the tester genome using the criteria established
through analyzing control experiments (described above). Among the 2134 ORFs within the NEM316 genome,
484 (22.7%) were identified as variable ORFs because they were classified as
absent/divergent in at least one tester genome. 269 (56%) of them were absent/divergent in
four tester genomes, and 96, 84, and 35 were classified as absent/divergent
in 1, 2, and 3 genomes, respectively. Of
2124 ORFs within the 2603VR genome, 530 (25%) were identified as variable ORFs. Among them, 81, 121, 162, and 166 were
classified as absent/divergent in 4, 3, 2, and 1 tester genomes,
respectively. Pairwise genome alignment
of the two reference genomes identified strain-specific regions with a total length
of 288 kb and 239 kb in NEM316 and 2603VR, respectively. Greater than 95% of the ORFs residing within
these strain-specific regions were identified as variable ORFs in our CGH with
four tester genomes, representing 64% (309/484) of NEM316 variable ORFs and 52%
(275/530) of the 2603VR variable ORFs. About 80% of variable ORFs identified by CGH
are located within fourteen putative pathogenicity islands previously
identified in NEM316 [22].To
investigate which functional groups these variable ORFs belong to, we
classified ORFs into clusters of orthologous genes (COGs) [41]. Figure 2 shows the number of variable ORFs in
each COG category. Approximately half of
the variable ORFs have not been classified into COGs and are of unknown
function. The most common classifiable
variable ORFs belong to the COG category of DNA replication, recombination, and
repair. This is likely attributable to
the presence of integrated phage or plasmids in the reference genomes. A large number of variable ORFs are predicted
to be involved in transport, regulation, intermediate metabolism, and cell wall
metabolism. These genes may be important
in maintaining the pathogenic life style and transmission of the invasive GBS
strains. Genes within these categories
have also been identified through an in
vivo study in which signature-tagged mutagenesis and a neonatal rat
model were used to identify novel GBS genes implicated in virulence [42]. Relatively few variable ORFs were found to be
involved in coenzyme transport and metabolism and lipid transport and
metabolism.Figure 2
Number of variable open reading frames in group BStreptococcus strains NEM316 and 2603VR classified in each of the clusters of orthologous genes (COG) category plus those not classified in COG database.


### 3.5. ORFs Absent/Divergent in at Least Two Tester  Genomes of Commensal Origin

ORFs consistently absent/divergent in commensal tester strains compared to invasive
strains are likely virulence gene candidates.
Six ORFs were absent/divergent in all three commensal tester strains,
and conserved in the two invasive reference genomes and the invasive tester
strain. We identified an additional 29
ORFs from the reference genomes that were absent/divergent in at least two
out of the three commensal strains (Table3).
Fifteen of these 35 ORFs are predicted hypothetical to be proteins of
unknown function. Several ORFs are
predicted to be proteins involved in transport, metabolism, and other metabolic
functions. Also included are two
putative lipoproteins and two surface proteins.
Gene gbs0850 is predicted to encode a fibrinogen binding protein and is
identical to a previously identified fbsB gene [43, 44]. The best match for gbs0850
in 2603VR is ORF sag0832, which encodes a different variant of the fibrinogen
binding protein. ORFs gbs2015 and
gbs2016 are two adjacent genes with highly similar DNA sequences predicted to
encode glycosyl transferases. These two
genes are also found together in the 2603VR genome as sag2060 and sag2061.Table 3
Open reading frames (ORF) present in invasive strains but absent at least in two out of three commensal tester group BStreptococcus strains by comparative genomic hybridization and their presence among 949 GBS isolates and their prevalence ratio between invasive (n = 386) and colonizing ( n = 563) isolates.
ORFProbe-positive strains (%)Prevalence ratio (95% CI)(a)Predicted  proteinsag0004524 (55%)1.1 (0.96–1.22)hypothetical proteinsag0005706 (74%)1.0 (0.93–1.09)hypothetical proteinsag0027941 (99%)1.0 (0.98–1.00)phosphoribosylaminoimidazole synthetasesag0175692 (73%)1.0 (0.93–1.09)hypothetical proteinsag0206590 (62%)0.9 (0.82–1.01)lipoprotein, putativesag0253(b)(b)acetyltransferase, GNAT familysag0414927 (98%)1.0 (0.97–1.01)phosphorylase, Pnp/Udp family, putativesag0426——cupin family proteinsag0427517 (54%)1.0 (0.93–1.17)transcriptional regulator, MerR familysag0700925 (97%)1.0 (0.97–1.02)2-dehydro-3-deoxyphosphogluconate
aldolase/4-hydroxy-2-oxoglutarate aldolasesag0814117 (12%)0.6 (0.39–0.83)hypothetical proteinsag0815364 (38%)0.9 (0.76–1.06)transcriptional regulator, Cro/CI family-related proteinsag0832371 (39%)1.5 (1.29–1.77)fibrinogen binding proteinsag1130367 (39%)1.1 (0.90–1.25)hypothetical proteinsag1140(b)(b)hypothetical proteinsag1207(b)(b)hypothetical proteinsag1781(b)(b)primase-related proteinsag196887 (9%)1.1 (0.71–1.61)hypothetical proteinsag1969907 (96%)1.0 (0.97–1.03)ribosomal protein L11 methyltransferasesag1974(b)(b)MutT/nudix family proteinsag1975(b)(b)hypothetical proteinsag1976290 (31%)0.9 (0.73–1.08)hypothetical proteinsag1994289 (30%)1.0 (0.82–1.22)hypothetical proteinsag1999(b)(b)hypothetical proteinsag2021395 (42%)1.2 (1.00–1.36)cell wall surface anchor family proteinsag2026224 (24%)1.1 (0.88–1.40)membrane protein, putativesag2027(b)(b)ABC transporter, ATP-binding proteinsag2028(b)(b)hypothetical proteinsag2045364 (38%)1.1 (0.90–1.25)DNA topology modulation protein FlaR, putativesag2057(b)(b)leucyl-tRNA synthetasesag2060427 (45%)1.3 (1.13–1.50)glycosyl transferase, family 8sag2061437 (46%)1.2 (1.07–1.41)glycosyl transferase, family 8sag2088(b)(b)hypothetical proteinsag2147687 (72%)1.0 (0.9–1.06)lipoprotein, putativegbs0474(c)270 (28%)1.5 (1.21–1.80)hypothetical protein(a) Prevalence with confidence interval not overlapping 1 are considered statistical significant.(b) These ORFs were not screened using LOS mostly because of small sizes.(c) Exact corresponding gene in strain 2603VR was not found by blast search while it was classified as present in 2603 VR by CGH.While
we identified a large number of variable ORFs, few were missing in all three or
even two commensal tester strains.
Because multiple genetic factors are involved in GBS virulence and
multiple pathogenesis pathways involving different sets of virulence genes likely
exist, we might expect different sets of virulence genes to be identified when
different pairs of invasive and colonizing isolates are compared.


### 3.6. GBS Library on a Slide Hybridization and  Differentially Distributed Variable ORFs

All variable ORFs are potential virulence gene candidates. Using a novel GBS LOS microarray platform
with a large collection of GBS isolates, the importance of these variable ORFs
can be efficiently evaluated. We present
our initial evaluation of 23 of the 35 ORFs identified above using GBS
LOS. We were not able to synthesize good
probes with strong and specific signals in our initial attempts for the other
12 variable ORFs because of their small sizes or poor PCR amplifications. We therefore left them out of this initial
LOS screening. The GBS LOS microarray contained
genomic DNA from 949 GBS isolates printed in duplicate. Among them, 386 were isolates from patients
with invasive diseases and 563 were commensal colonizing isolates. In addition, the LOS array contained DNA from
various control strains. Table3 lists
the prevalence of 23 ORF in the overall GBS collection and their prevalence
ratios in invasive strains compared to colonizing strains. sag2060, sag2061, sag0832, and gbs0474 appeared
more frequently in invasive isolates than in colonizing commensal strains. By contrast, sag0814 was more frequently
found among colonizing isolates than invasive isolates.ORFs
sag2060 and sag2061 are two putative glycosyl transferase genes. Glycosylation plays an important role in many
biological processes in eukaryotes,  and there is increasing evidence for a role
of glycosylation in bacteria. Many
surface expressed bacterial structures such as LPS, LOS, capsule, flagella, and
pili in pathogenic bacteria are glycosylated [45–47]. Glycosylation can also be used by bacteria to
inactivate antibiotics [48, 49].
Interestingly, the glycosyl transferase gene lic2B in Haemophilus
influenzae was found more frequently among isolates causing otitis media
than in throat isolates from children [50].Future mechanistic studies should
shed light on the roles of these GBS glycosyl transferases in
pathogenesis.The sag0832
is predicted to encode a fibrinogen binding protein. This gene has been suggested to be an important
virulence gene for invasive GBS disease.
In a murine model of sepsis, the wild-type strain was more virulent than
the isogenic strain with this gene inactivated [44]. sag0832 was also shown to promote GBS
invasion into epithelial cells in vitro
[43]. In addition to the three ORFs
encoding known proteins, two ORFs (sag0814 and gbs0474) encoding hypothetical
proteins were differentially distributed between invasive and colonizing
strains. Given the large numbers of
ORFs of no known functions that exist in the sequenced genomes, this is not
surprising. Some of these ORFs probably
involve complicated traits that are difficult to observe in laboratory
conditions and will be more readily identified using association studies. Interestingly, sag0814 is found more
frequently in commensal strains than in invasive strains. It is possible that lack of this gene
enhances virulence. During the process
of commensal-to-pathogen evolution, bacteria not only acquire virulence genes
but also shed genes via deletions [51]. Deletions of genes that facilitate a commensal
lifestyle could provide an additional evolutionary pathway towards virulence. For example, deletion of lysine decarboxylase gene
greatly enhanced the enterotoxin activity in Shigella in its evolution [51].While
five out of 23 variable ORFs were differentially distributed between invasive
and commensal isolates, the associations of these ORFs with invasive isolates
were neither exclusive nor strong. Such
outcomes were not unexpected for several reasons. First, we anticipate some degree of random
misclassification to decrease the observed associations because invasive strains
can be also commensal,
and noninvasive strains can become opportunistic pathogens. Second, similar to the existence of several
distinct pathotypes within many bacterial pathogens, there may be many different
pathotypes within GBS. One virulence
gene may be strongly associated with strains within one specific GBS pathotype
but the association is less pronounced when all invasive isolates are included
in the analysis. Third, GBS pathogenesis
is determined by not one but many virulence genes and any one gene may only
contribute. We are in the process of screening
these and more variable ORFs on an additional 2000 isolates in order to perform
a more definitive analysis. In addition,
incorporating the population structure of GBS could potentially enhance our
analysis and help with interpretation.Screening
23 ORFs against 949 isolates also revealed the striking genome content
diversity of GBS. We assigned each
isolate to a genotype based on the presence or absence of all 23 probed
examined. A total of 503 genotypes were
observed among 949 isolates. Using this
classification, strains with the same PFGE patterns have different gene
composition. Profiling GBS with a
limited number of gene probes could therefore provide a highly discriminative
typing method.


## 3.1. Selection of Tester Strains for Comparative  Genomic subtraction.

Comparing
pathogenic and nonpathogenic strains within a species can provide critical insights
into bacterial pathogenesis. However,
all sequenced GBS strains are from invasive diseases. We used molecular epidemiological comparisons
to select representative commensal colonizing GBS isolates for the comparative
genomic hybridization with the highest potential to identify potential
pathogenesis-related genes in sequenced invasive genomes (strains NEM316 and
2603VR). We characterized the diversity
of 882 colonizing isolates from a population-based longitudinal study of
healthy male and nonpregnant female college students [28] using PFGE and
serotyping. Clustering analysis by
dendrogram was performed on these isolates along with a sample of 35 invasive
isolates and sequenced pathogenic strains NEM316 and 2603VR. Based on this analysis, we selected three
commensal isolates that are genetically distant from the two sequenced genomes
but representative of isolates from relatively large strain clusters that are
predominantly of commensal origin.
Isolate 657–461 is a serotype V strain representing the largest clonal
group within our commensal collection.
Isolate G617-061 (serotype III) and G293-061 (serotype II) are from two
additional clusters dominated by colonizing strains. In addition to these three commensal
isolates, one invasive isolate, H-19, was chosen for the comparative genomic
subtraction, because it represents the most common clonal type of the serogroup
Ia strain in our analysis. Since serotype classification does not necessarily reflect genetic distance among strains [20], we did not select tester strains based solely on the differences in serotype. The small number of colonizing stains chosen
here could not and was not intended to capture the diversity of commensal
isolates. It was our first attempt at
performing genome-wide comparisons between colonizing strains and sequenced genomes, so we
could pick gene candidates from a list of several thousands for association
study using LOS microarrays where large collections of population-based GBS
isolates can be screened.


## 3.2. Oligonucleotide Array Validation

We used shared probe
sequences within the two genome arrays to assess the reproducibility of the
comparative genomic hybridization and used hybridization of one sequenced
genome against the array of the other sequenced genome to evaluate the accuracy
of the array in assessing sequence variation.Between
184,288 probe pairs on the NEM316 array and 180,020 probe pairs on the 2603VR
array, a total of 16,364 identical probe pairs (32/32 match) were
identified. Hybridization results for
this probe subset from two arrays for each tester genome were treated as replicas for accessing
the reproducibility of CGH.
Hybridization for each probe was classified as identical or variable in
the tester genome when compared to the reference genome. The percent concordance for tester genomes
G293-061, H1-19, G617-061, and G654-461 were 98.75%, 99.50%, 99.88%, and
98.62%, respectively. The
reproducibility was very high even when the raw signal ratios were
examined. The correlation coefficients
were greater than 90% for duplicates.To
evaluate the accuracy of the array in assessing sequence variation, we compared
results from in silicoanalysis with the classification
results from an actual array hybridization swap between NEM316 and 2603VR
genomes. Almost all perfectly matched
probe sequences were correctly identified as identical by hybridization. Only 4 out of 133,520 and 5 out of 133,570
probe sequences were falsely identified as different with NEM316 and 2603VR
arrays, respectively. However, 28,673
out of 50,768 (56%) and 25,435 out of 46,450 (55%) mismatched probe sequences
were falsely identified as identical with NEM316 and 2603VR arrays,
respectively. At the probe level,
hybridization has a high sensitivity but low specificity for detecting
conserved probe sequences. Nonetheless, the
high-density nature of the tiling array still provides overall sequence
variation information at genome and ORF levels.
We visually displayed the CGH results by plotting hybridization signal
ratios of the probes along their genomic positions and compared them with an in silico comparison of NEM316 and
2603VR. The majority of variable probes
(i.e., probes with high reference versus tester signal ratios) are clustered primarily
around strain-specific genetic islands identified by the in silico analysis. To convert probe-level variation to ORF
sequence variation, we calculated the percentage of variable probes for each
ORF (number of variable probes identified within an ORF divided by the total
number of probes tiling the ORF). Using
different percentage cutoff values in classifying variable ORFs (divergent or
absent), the CGH-based data was compared with in silico analysis (Table1).
For the NEM 316 array, a 15% cutoff value gave a 2.9% false-negative
rate (i.e., ORFs known to be present but classified by hybridization as absent
or very divergent) and the best overall sensitivity and specificity (97% and
94%, resp.). For the 2603VR
array, the 20% cutoff point gave the best overall sensitivity and specificity (97%
and 91%, resp.). Thus, these two
cutoff points were chosen to classify variable ORFs for the remaining CGH
analyses.Table 1
Sensitivity (probability open reading frame is detected, given it is truly present) and specificity assessments (probability open reading frame is not detected, given it is not present) of different cutoff values in classifying variable open reading frames using fine-tiling oligonucleotide genome arrays created from the genomic sequence of group BStreptococcus strains NEM316 and 2603VR.
Reference  genomePercentage  cutoff pointSensitivitySpecificityNEM31620%0.980.9115%0.970.9410%0.960.962603VR20%0.970.9115%0.950.9210%0.890.96Comparative
genomic hybridization with fine-tiling 32-mer oligonucleotide microarrays did
not identify all probe sequence variations in the two reference genomes but
reliably identified variable ORFs using combined hybridization results of all
probes within each ORF.


## 3.3. Distribution and Mapping of Variable Probe Sequences

CGH using NEM316 and 2603VR genome
arrays revealed that 3.4–15.4% of probe sequences were absent or divergent in
the four tester strains (Table2). This
range of diversity is similar to the range of sequence differences (5% to 15%)
recently observed in pairwise comparison of all eight available complete or
draft GBS genome sequences [20]. The
tiling arrays allowed a high-resolution view of genome variation among
comparison strains. Figure 1 displays
comparative hybridization results of tester strains against 2603VR by plotting
the reference to tester signal ratios along their genomic positions. Although the four tester strains represent
four different serotypes (Ia, II, III and V), the majority of absent or
divergent probe sequences in these strains are mapped to the same set of
regions on the NEM316 genome, a serotype III strain. To a lesser extent, the same set of regions
on the 2603VR genome covers the majority of the probes that are absent or
divergent across three of the four tester genomes. The strain G617-061, a serotype III strain,
is very similar to the serotype V reference strain 2603VR genome, with only
3.4% probes identified as different compared to >13% of the other tester
genomes. Comparison of all eight
available complete or draft GBS genome sequences also demonstrated that
serotype classification does not reflect the genetic diversity of the GBS [20]. One possible explanation for closely related
strains exhibiting different capsules is genetic exchange of genes determining
the capsular type by horizontal gene transfer.Table 2
Number (percentage) of variable probe sequences among four tester group BStreptococcus genomes using sequenced strains as a reference revealed by comparative genomic hybridization
Reference/sequenced genomes of invasive isolatesH1-19 (Ia) (invasive)G293-061 (II) (commensal)G617-061 (III) (commensal)G654-461 (V) (commensal)2603VR327
(15.4%)320
(15.1%)72 (3.4%)278
(13.1%)NEM316277
(13.0%)305
(14.3%)305
(14.3%)271
(12.7%)Figure 1
Locations of variable probes sequences identified within genomes of group BStreptococcus strains 2603VR in comparative genomic hybridization using each of the four tester strains of group B Streptococcus.


## 3.4. NEM316 and 2603VR ORFs Absent/Divergent in Tester Genomes

Hybridization
results from all the probes within each ORF were used to determine the presence
or absence/divergence in the tester genome using the criteria established
through analyzing control experiments (described above). Among the 2134 ORFs within the NEM316 genome,
484 (22.7%) were identified as variable ORFs because they were classified as
absent/divergent in at least one tester genome. 269 (56%) of them were absent/divergent in
four tester genomes, and 96, 84, and 35 were classified as absent/divergent
in 1, 2, and 3 genomes, respectively. Of
2124 ORFs within the 2603VR genome, 530 (25%) were identified as variable ORFs. Among them, 81, 121, 162, and 166 were
classified as absent/divergent in 4, 3, 2, and 1 tester genomes,
respectively. Pairwise genome alignment
of the two reference genomes identified strain-specific regions with a total length
of 288 kb and 239 kb in NEM316 and 2603VR, respectively. Greater than 95% of the ORFs residing within
these strain-specific regions were identified as variable ORFs in our CGH with
four tester genomes, representing 64% (309/484) of NEM316 variable ORFs and 52%
(275/530) of the 2603VR variable ORFs. About 80% of variable ORFs identified by CGH
are located within fourteen putative pathogenicity islands previously
identified in NEM316 [22].To
investigate which functional groups these variable ORFs belong to, we
classified ORFs into clusters of orthologous genes (COGs) [41]. Figure 2 shows the number of variable ORFs in
each COG category. Approximately half of
the variable ORFs have not been classified into COGs and are of unknown
function. The most common classifiable
variable ORFs belong to the COG category of DNA replication, recombination, and
repair. This is likely attributable to
the presence of integrated phage or plasmids in the reference genomes. A large number of variable ORFs are predicted
to be involved in transport, regulation, intermediate metabolism, and cell wall
metabolism. These genes may be important
in maintaining the pathogenic life style and transmission of the invasive GBS
strains. Genes within these categories
have also been identified through an in
vivo study in which signature-tagged mutagenesis and a neonatal rat
model were used to identify novel GBS genes implicated in virulence [42]. Relatively few variable ORFs were found to be
involved in coenzyme transport and metabolism and lipid transport and
metabolism.Figure 2
Number of variable open reading frames in group BStreptococcus strains NEM316 and 2603VR classified in each of the clusters of orthologous genes (COG) category plus those not classified in COG database.


## 3.5. ORFs Absent/Divergent in at Least Two Tester  Genomes of Commensal Origin

ORFs consistently absent/divergent in commensal tester strains compared to invasive
strains are likely virulence gene candidates.
Six ORFs were absent/divergent in all three commensal tester strains,
and conserved in the two invasive reference genomes and the invasive tester
strain. We identified an additional 29
ORFs from the reference genomes that were absent/divergent in at least two
out of the three commensal strains (Table3).
Fifteen of these 35 ORFs are predicted hypothetical to be proteins of
unknown function. Several ORFs are
predicted to be proteins involved in transport, metabolism, and other metabolic
functions. Also included are two
putative lipoproteins and two surface proteins.
Gene gbs0850 is predicted to encode a fibrinogen binding protein and is
identical to a previously identified fbsB gene [43, 44]. The best match for gbs0850
in 2603VR is ORF sag0832, which encodes a different variant of the fibrinogen
binding protein. ORFs gbs2015 and
gbs2016 are two adjacent genes with highly similar DNA sequences predicted to
encode glycosyl transferases. These two
genes are also found together in the 2603VR genome as sag2060 and sag2061.Table 3
Open reading frames (ORF) present in invasive strains but absent at least in two out of three commensal tester group BStreptococcus strains by comparative genomic hybridization and their presence among 949 GBS isolates and their prevalence ratio between invasive (n = 386) and colonizing ( n = 563) isolates.
ORFProbe-positive strains (%)Prevalence ratio (95% CI)(a)Predicted  proteinsag0004524 (55%)1.1 (0.96–1.22)hypothetical proteinsag0005706 (74%)1.0 (0.93–1.09)hypothetical proteinsag0027941 (99%)1.0 (0.98–1.00)phosphoribosylaminoimidazole synthetasesag0175692 (73%)1.0 (0.93–1.09)hypothetical proteinsag0206590 (62%)0.9 (0.82–1.01)lipoprotein, putativesag0253(b)(b)acetyltransferase, GNAT familysag0414927 (98%)1.0 (0.97–1.01)phosphorylase, Pnp/Udp family, putativesag0426——cupin family proteinsag0427517 (54%)1.0 (0.93–1.17)transcriptional regulator, MerR familysag0700925 (97%)1.0 (0.97–1.02)2-dehydro-3-deoxyphosphogluconate
aldolase/4-hydroxy-2-oxoglutarate aldolasesag0814117 (12%)0.6 (0.39–0.83)hypothetical proteinsag0815364 (38%)0.9 (0.76–1.06)transcriptional regulator, Cro/CI family-related proteinsag0832371 (39%)1.5 (1.29–1.77)fibrinogen binding proteinsag1130367 (39%)1.1 (0.90–1.25)hypothetical proteinsag1140(b)(b)hypothetical proteinsag1207(b)(b)hypothetical proteinsag1781(b)(b)primase-related proteinsag196887 (9%)1.1 (0.71–1.61)hypothetical proteinsag1969907 (96%)1.0 (0.97–1.03)ribosomal protein L11 methyltransferasesag1974(b)(b)MutT/nudix family proteinsag1975(b)(b)hypothetical proteinsag1976290 (31%)0.9 (0.73–1.08)hypothetical proteinsag1994289 (30%)1.0 (0.82–1.22)hypothetical proteinsag1999(b)(b)hypothetical proteinsag2021395 (42%)1.2 (1.00–1.36)cell wall surface anchor family proteinsag2026224 (24%)1.1 (0.88–1.40)membrane protein, putativesag2027(b)(b)ABC transporter, ATP-binding proteinsag2028(b)(b)hypothetical proteinsag2045364 (38%)1.1 (0.90–1.25)DNA topology modulation protein FlaR, putativesag2057(b)(b)leucyl-tRNA synthetasesag2060427 (45%)1.3 (1.13–1.50)glycosyl transferase, family 8sag2061437 (46%)1.2 (1.07–1.41)glycosyl transferase, family 8sag2088(b)(b)hypothetical proteinsag2147687 (72%)1.0 (0.9–1.06)lipoprotein, putativegbs0474(c)270 (28%)1.5 (1.21–1.80)hypothetical protein(a) Prevalence with confidence interval not overlapping 1 are considered statistical significant.(b) These ORFs were not screened using LOS mostly because of small sizes.(c) Exact corresponding gene in strain 2603VR was not found by blast search while it was classified as present in 2603 VR by CGH.While
we identified a large number of variable ORFs, few were missing in all three or
even two commensal tester strains.
Because multiple genetic factors are involved in GBS virulence and
multiple pathogenesis pathways involving different sets of virulence genes likely
exist, we might expect different sets of virulence genes to be identified when
different pairs of invasive and colonizing isolates are compared.


## 3.6. GBS Library on a Slide Hybridization and  Differentially Distributed Variable ORFs

All variable ORFs are potential virulence gene candidates. Using a novel GBS LOS microarray platform
with a large collection of GBS isolates, the importance of these variable ORFs
can be efficiently evaluated. We present
our initial evaluation of 23 of the 35 ORFs identified above using GBS
LOS. We were not able to synthesize good
probes with strong and specific signals in our initial attempts for the other
12 variable ORFs because of their small sizes or poor PCR amplifications. We therefore left them out of this initial
LOS screening. The GBS LOS microarray contained
genomic DNA from 949 GBS isolates printed in duplicate. Among them, 386 were isolates from patients
with invasive diseases and 563 were commensal colonizing isolates. In addition, the LOS array contained DNA from
various control strains. Table3 lists
the prevalence of 23 ORF in the overall GBS collection and their prevalence
ratios in invasive strains compared to colonizing strains. sag2060, sag2061, sag0832, and gbs0474 appeared
more frequently in invasive isolates than in colonizing commensal strains. By contrast, sag0814 was more frequently
found among colonizing isolates than invasive isolates.ORFs
sag2060 and sag2061 are two putative glycosyl transferase genes. Glycosylation plays an important role in many
biological processes in eukaryotes,  and there is increasing evidence for a role
of glycosylation in bacteria. Many
surface expressed bacterial structures such as LPS, LOS, capsule, flagella, and
pili in pathogenic bacteria are glycosylated [45–47]. Glycosylation can also be used by bacteria to
inactivate antibiotics [48, 49].
Interestingly, the glycosyl transferase gene lic2B in Haemophilus
influenzae was found more frequently among isolates causing otitis media
than in throat isolates from children [50].Future mechanistic studies should
shed light on the roles of these GBS glycosyl transferases in
pathogenesis.The sag0832
is predicted to encode a fibrinogen binding protein. This gene has been suggested to be an important
virulence gene for invasive GBS disease.
In a murine model of sepsis, the wild-type strain was more virulent than
the isogenic strain with this gene inactivated [44]. sag0832 was also shown to promote GBS
invasion into epithelial cells in vitro
[43]. In addition to the three ORFs
encoding known proteins, two ORFs (sag0814 and gbs0474) encoding hypothetical
proteins were differentially distributed between invasive and colonizing
strains. Given the large numbers of
ORFs of no known functions that exist in the sequenced genomes, this is not
surprising. Some of these ORFs probably
involve complicated traits that are difficult to observe in laboratory
conditions and will be more readily identified using association studies. Interestingly, sag0814 is found more
frequently in commensal strains than in invasive strains. It is possible that lack of this gene
enhances virulence. During the process
of commensal-to-pathogen evolution, bacteria not only acquire virulence genes
but also shed genes via deletions [51]. Deletions of genes that facilitate a commensal
lifestyle could provide an additional evolutionary pathway towards virulence. For example, deletion of lysine decarboxylase gene
greatly enhanced the enterotoxin activity in Shigella in its evolution [51].While
five out of 23 variable ORFs were differentially distributed between invasive
and commensal isolates, the associations of these ORFs with invasive isolates
were neither exclusive nor strong. Such
outcomes were not unexpected for several reasons. First, we anticipate some degree of random
misclassification to decrease the observed associations because invasive strains
can be also commensal,
and noninvasive strains can become opportunistic pathogens. Second, similar to the existence of several
distinct pathotypes within many bacterial pathogens, there may be many different
pathotypes within GBS. One virulence
gene may be strongly associated with strains within one specific GBS pathotype
but the association is less pronounced when all invasive isolates are included
in the analysis. Third, GBS pathogenesis
is determined by not one but many virulence genes and any one gene may only
contribute. We are in the process of screening
these and more variable ORFs on an additional 2000 isolates in order to perform
a more definitive analysis. In addition,
incorporating the population structure of GBS could potentially enhance our
analysis and help with interpretation.Screening
23 ORFs against 949 isolates also revealed the striking genome content
diversity of GBS. We assigned each
isolate to a genotype based on the presence or absence of all 23 probed
examined. A total of 503 genotypes were
observed among 949 isolates. Using this
classification, strains with the same PFGE patterns have different gene
composition. Profiling GBS with a
limited number of gene probes could therefore provide a highly discriminative
typing method.


## 4. Conclusions

As
increasing numbers of bacterial genomes are sequenced, postgenome research will
focus on identifying virulence-related genes and the function of these
genes. We used a three-step molecular
epidemiological approach employing two novel microarray platforms, fine-tiling oligonucleotide
microarrays, and Library on a Slide to identify bacterial virulence genes
potentially contributing to GBS disease.
Among hundreds of variable ORFs identified by CGH, 35 were absent/divergent in two of out three commensal test strains but present in two
invasive reference genomes and a tester invasive strain. We screened 23 of these ORFs against 949 GBS
isolates, and found 5 ORFs that were differentially distributed between
invasive and commensal isolates. We
demonstrated that this approach can rapidly identify and evaluate bacterial
genes potentially associated with pathogenicity.In
our approach, we adopted microarray-based CGH instead of the traditional
genomic subtraction method to identify genetic differences between paired
commensal and invasive GBS strains. The
traditional genomic subtraction approach can sample only a fraction of
strain-specific genes. The high-density
tiling oligonucleotide array-based CGH allowed us to identify the complete
array of DNA sequences unique to an invasive compared to a commensal GBS
isolate. A denser and shorter
oligonucleotide array design followed by a verification oligonucleotide array
can be used for identifying even single nucleotide polymorphisms in the genome
[35]. However, CGH-based genome
comparisons depend on the availability of sequenced genomes, and moreover,
strain-specific genes identified are confined to the sequenced genome. We were not able to detect and identify
potential virulence genes that are likely to exist in other unsequenced
pathogenic GBS strains. Given the
pan-genome nature of the GBS species where the pool of variable genes are
extremely large [20], future genome comparisons of any two strains are likely
to rely on a cheap and fast direct sequencing approach such as pyrosequencing [52].
This new approach will eliminate the limitations
of using CGH.Once
a set of candidate genes are identified by a genomic comparison, an even more
critical step is to evaluate the role that these genes play in disease
pathogenesis. This can be done by
large-scale association studies, bioinformatic prediction, or biological
functional analyses. Bioinformatics
prediction requires databases with solid structural and functional information
on biological molecules. Functional
approaches are often limited to the presence of a characterizable virulence
phenotype. Comparing gene frequencies
among bacterial isolates collected from different sources, for example, disease-causing
and commensal isolates, using statistical association, can provide insight into
the relative importance of a gene sequence in pathogenesis and
transmission. The number of isolates and
diversity of the collections are important in determining the significance of
observations made and in ensuring that there is sufficient power to detect
associations. Large population-based
samples are required to minimize the identification of spurious associations
that often arise with small sample comparisons.
Including commensal isolates (i.e., nondisease-causing strains) for study
is an integral part of this approach to understand bacterial pathogenesis. The LOS microarray platform is a robust
system, adaptable to a wide variety of bacterial pathogens, for detecting the
presence or absence of a candidate gene in thousands of isolates efficiently,
thus providing a truly high throughput system to evaluate genes in the postgenome
era.



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*Source: 314762-2008-02-25.xml*