Microevolution of Group A Streptococci In Vivo:
Capturing Regulatory Networks Engaged in
Sociomicrobiology, Niche Adaptation, and
Hypervirulence
Ramy K. Aziz1,2,3,4*, Rita Kansal1,2, Bruce J. Aronow5, William L. Taylor6, Sarah L. Rowe1,2,6, Michael
Kubal4, Gursharan S. Chhatwal7, Mark J. Walker8, Malak Kotb1,2,9*
1 Research Services, Veterans Affairs Medical Center, Memphis, Tennessee, United States of America, 2 Research Services, Veterans Affairs Medical Center, Cincinnati, Ohio,
United States of America, 3Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, Cairo, Egypt, 4Computation Institute, University of
Chicago, Chicago, Illinois, United States of America, 5 Biomedical Informatics, Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America, 6Health
Science Center, University of Tennessee, Memphis, Tennessee, United States of America, 7Helmholtz Centre for Infection Research, Braunschweig, Germany, 8 School of
Biological Sciences, University of Wollongong, Wollongong, New South Wales, Australia, 9College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States of
America
Abstract
The onset of infection and the switch from primary to secondary niches are dramatic environmental changes that not only
alter bacterial transcriptional programs, but also perturb their sociomicrobiology, often driving minor subpopulations with
mutant phenotypes to prevail in specific niches. Having previously reported that M1T1 Streptococcus pyogenes become
hypervirulent in mice due to selection of mutants in the covRS regulatory genes, we set out to dissect the impact of these
mutations in vitro and in vivo from the impact of other adaptive events. Using a murine subcutaneous chamber model to
sample the bacteria prior to selection or expansion of mutants, we compared gene expression dynamics of wild type (WT)
and previously isolated animal-passaged (AP) covS mutant bacteria both in vitro and in vivo, and we found extensive
transcriptional alterations of pathoadaptive and metabolic gene sets associated with invasion, immune evasion, tissue-
dissemination, and metabolic reprogramming. In contrast to the virulence-associated differences between WT and AP
bacteria, Phenotype Microarray analysis showed minor in vitro phenotypic differences between the two isogenic variants.
Additionally, our results reflect that WT bacteria’s rapid host-adaptive transcriptional reprogramming was not sufficient for
their survival, and they were outnumbered by hypervirulent covS mutants with SpeB2/Sdahigh phenotype, which survived
up to 14 days in mice chambers. Our findings demonstrate the engagement of unique regulatory modules in niche
adaptation, implicate a critical role for bacterial genetic heterogeneity that surpasses transcriptional in vivo adaptation, and
portray the dynamics underlying the selection of hypervirulent covS mutants over their parental WT cells.
Citation: Aziz RK, Kansal R, Aronow BJ, Taylor WL, Rowe SL, et al. (2010) Microevolution of Group A Streptococci In Vivo: Capturing Regulatory Networks Engaged
in Sociomicrobiology, Niche Adaptation, and Hypervirulence. PLoS ONE 5(4): e9798. doi:10.1371/journal.pone.0009798
Editor: Niyaz Ahmed, University of Hyderabad, India
Received February 12, 2010; Accepted February 16, 2010; Published April 14, 2010
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public
domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: This work was supported by grants AI40198-06 from National Institutes of Health, National Institute of Allergy and Infectious Diseases (NIAID) to M.K.
and NIH P30DK078392 to B.A.; by the United States Army Medical Research Acquisition Activity (W81XWH-05-1-0227 to M.K); by the Research and Development
Office, Medical Research Service, Department of Veterans Affairs (Merit Award to M.K.); and by the National Health and Medical Research Council of Australia
project grant 459103 (M.W. and M.K.) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: RKA is a one of approximately 1,000 academic editors for PLoS ONE and as such had no access to the peer review or acceptance process
for this manuscript (which was conducted with no regard to his position on the board). PLoS ONE academic editors are volunteers and receive no compensation
for their services. There are no other competing interests to disclose.
* E-mail: ramy.aziz@salmonella.org (RKA); kotbmk@uc.edu (MK)
Introduction
Group A streptococci (GAS) are human pathogens that infect
over 700 million children and adults each year [1]. Whereas the
overall mortality rate of GAS infections is less than 0.1%, the
mortality rate of invasive GAS infections, which have resurged in
the past 30 years, mounts to 25% (out of .650,000 new cases per
year) [1]. Among the various GAS serotypes, the globally
disseminated M1T1 clonal strain remains the most frequently
isolated from cases of invasive and non-invasive infections [2,3],
and although disease severity partially depends on host genetic
factors [4,5,6], M1T1 GAS possesses unique genomic features
that contribute to its evolutionary fitness [7,8,9,10]. Among these
features is the ability of M1T1 bacteria to switch to a
hypervirulent phenotype associated with invasive diseases in
vivo [11,12,13,14], a phenomenon that is not fully understood
and whose specificity to the M1 serotype remains to be
established [15].
We previously reported that virulent representatives of M1T1
GAS, with the phenotype SpeBhi/SpeA2/Sda1low, irreversibly
switch to the hypervirulent SpeB2/SpeA+/Sda1high phenotype
after $3 days in vivo [12,16] and that the parent phenotype
vanishes by day 7 post-infection [16]. Subsequent studies
uncovered that this genetic switch is driven by host innate
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immune pressure that selects for bacteria with pathoadaptive
mutations in the covRS genetic locus [13,14]. CovRS is a two-
component regulatory system, in which CovS transduces external
signals [17,18] to CovR, which in turn represses the transcription
of several group A streptococcal (GAS) virulence gene sets,
including the capsule synthesis operon (hasABC), the streptolysin S
operon (SLS or sagA-H), and the streptokinase gene (ska) [19,20].
CovR regulatory activity is thought to be triggered when the
protein is phosphorylated at D53 [21], possibly by acetyl
phosphate [22,23]. On the other hand, under stress conditions,
CovS dephosphorylates CovR and reverses virulence gene
repression [21,22].
Mutations in covS are thus expected to affect CovR
phosphorylation status differentially in vitro and in vivo (under
stress conditions), and to consequently modulate CovRS
signaling-regulation circuits in a complex manner that remains
largely unexplored. This complexity is further magnified by the
reported strain-specific differences in the impact of CovS on
pathogenesis [24], by the finding that phosphorylated CovR has
different affinities to different streptococcal promoters [21], and
by the possibility that CovR promoter binding may be
modulated by kinases or phosphatases other than CovS [25].
In fact, CovR retains some of its functions in the absence of wild
type CovS [26], and different covS mutations, albeit clustered in
its histidine kinase domain, might have different effects on
expression of CovR-regulated genes [27]. In accordance with
these biochemical findings, we and others have reported that
some covS mutations generate hypervirulent isolates associated
with invasive forms of streptococcal infection [13,14,26,28].
One of the most striking outcomes of these mutations is the
constitutive repression of a gene encoding the key GAS cysteine
protease, SpeB, which remodels the host-pathogen interface
[29] by differentially degrading bacterial surface and secreted
proteins [12,30,31] as well as host proteins [32,33,34].
Consequently, absence of a proteolytically active SpeB preserves
several virulence factors that it normally degrades [12,35]. One
of these preserved factors is the highly potent DNase, Sda1,
which destroys neutrophil extracellular traps, NETs [36],
protecting the bacteria from neutrophil killing, promoting
bacterial invasion, and facilitating human plasminogen-mediat-
ed bacterial dissemination into normally sterile sites, which
results in invasive infections [14].
Despite the association between covS mutations and the
emergence of the hypervirulent phenotype of M1T1 strains, it is
unclear whether this increased virulence can be entirely attributed
to the modulation of the CovR regulon or if other networks are
also perturbed in vivo directly, indirectly, or independently of the
CovRS system. Additionally, the effects of covS mutations on
bacterial niche adaptability are still undetermined because the in
vivo transcriptomes of the wild type (WT) and animal-passaged
(AP) bacteria have not been compared under the same experimental
conditions.
To address these issues and improve our understanding of the
gene regulatory impact of mutational and adaptive events
contributing to the hypervirulent phenotype of M1T1 strains, we
analyzed differences in growth requirements, transcriptome
profiles, and regulatory circuits of the virulent (WT) and
hypervirulent (AP) phenotypes of the M1T1 strain both in vitro
and during initial in vivo infection. Such comprehensive analyses
highlighted the behavior of genomic subsystems that may be
involved directly or indirectly in S. pyogenes niche adaptation and
pathogenesis. In addition, this approach offered a rare transcrip-
tional snapshot of the SpeBhi/SpeA2/Sda1low population prior to
its extinction in vivo.
Results
Phenotype Microarrays show no major nutritional or
metabolic differences between wild type and
animal-passaged, hypervirulent M1T1 GAS
We used BiologH Phenotype Microarrays (PM) to screen 1900
different growth conditions, including a large set of different
carbon and nitrogen sources, pH values and salt concentrations, as
well as different concentrations of various antimicrobial agents
(Fig. S1), and found that both WT and AP GAS have similar
growth requirements in vitro with minor differences. For example,
AP bacteria grew better than WT bacteria in the presence of N-
acetyl-neuraminic acid, and were more sensitive to four antimi-
crobials (out of 373 screened in PM), including the calcium-specific
metal chelator EGTA, the lipophilic chelator 5-chloro-7-iodo-8-
hydroxyquinoline, and the antibiotics tobramycin and cefotaxime
(Table 1). However, when both bacterial variants were grown at
37uC in the enriched Todd Hewitt broth medium, their growth
rates were indistinguishable, and the only detectable difference was
that AP bacteria were more buoyant in liquid culture compared to
WT. This difference in buoyancy can be attributed to differences
in proteolytic activity (WT .. AP) that may degrade many
surface proteins, including pilin, or induce changes in the surface
charge making bacterial aggregates more compact. The difference
in buoyancy can also be attributed to differences in the expression
of hyaluronic acid capsule (AP . WT).
Although the PM results confirm previous experiments [16]
showing a few minor differences between WT and AP bacteria in
vitro, this technology is limited because it uses minimal media and
measures microbial respiration as a growth indicator (http://www.
Table 1. PM array differences.
PM code PM Phenotype AP relative to WT
(Plate: well) PM Score Comment
PM02A: B02 N-acetyl-neuraminic acid utilization as carbon source +88 Gain (Upregulation)
PM14A: H04 Sensitivity to EGTA (Ca++ chelator) 274 Loss (Downregulation)
PM16A: A09, A10 Sensitivity to 5-chloro-7-iodo-8-hydroxyquinoline (lipophilic chelator) 2101 Loss (Downregulation)
PM12B: F04 Sensitivity to tobramycin (an aminoglycoside acting on protein synthesis) 278 Loss (Downregulation)
PM16A: A01 Sensitivity to cefotaxime (a cephalosporin acting on cell wall) 293 Loss (Downregulation)
Phenotypes gained or lost by the animal-passaged (AP) mutant strain as determined by the consensus of two independent runs of Phenotype Microarrays. PM score =
a differential value reflecting the growth rate of AP relative to WT in minimal culture media containing different nutrients, chemicals, or antimicrobials.
doi:10.1371/journal.pone.0009798.t001
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biolog.com). Minimal media are not optimal for the expression of
GAS proteins, which may explain why GAS failed to grow at
many of the PM conditions (Fig. S1) and why fewer phenotypic
differences were observed between WT and AP bacteria than
transcriptomic differences (see below).
In vivo murine chamber infection model allows the
dissection of regulatory vs. mutation-selection events
Several studies, including ours, have examined phenotypic,
proteomic, and transcriptional differences between WT and
animal-passaged M1T1 GAS strains [For example,
11,12,13,16,26,27,37], but none attempted to capture the dynamic
changes in the bacterial population in vivo. Because bacteria
recovered from animals and cultured in the laboratory are likely to
have reprogrammed many regulatory networks to re-adapt to the
in vitro growth conditions, transcriptome profiling of these
bacteria may not reflect their actual in vivo gene expression and
regulation.
In this study, we designed and performed in vivo passage
experiments in which equal loads of in vitro-grown WT and AP
bacteria were separately inoculated into murine subcutaneous
chambers [16] and collected by needle aspiration after 24 h, a
time sufficient for the bacteria to sense the host environment
and transcriptionally respond to it, but not long enough to allow
detectable restructuring of the bacterial community and
selection of mutants. The concentrations of viable WT and
AP bacteria at 24 h post-inoculation remained essentially the
same: 1256109 colony-forming units (CFU)/ml vs.
,26109 CFU/ml inoculum. We extracted RNA from these
bacteria immediately after their recovery from mouse chambers
with no additional culturing and used the extracted RNA for
transcriptome profiling as detailed in Materials and Methods.
For transcriptional profiling, we followed a cyclic, two-color
design and performed 28 oligonucleotide microarray hybridiza-
tion experiments representing technical and biological replicates
of the four cell states under investigation: WT grown in vitro
(WT-vitro), WT grown in vivo (WT-vivo), AP grown in vitro
(AP-vitro), and AP grown in vivo (AP-vivo), Fig. 1.
Multiple approaches to microarray analysis show
statistically significant and biologically relevant
differences between the WT and AP populations in vitro
and in vivo
To gain biologically relevant knowledge from the transcriptome
studies without compromising statistical significance, we interro-
gated the data using multiple strategies that had been developed
for microarray analysis and visualization, taking into consideration
the strengths and limitations of each strategy.
By clustering normalized expression values from different
biological replicates in all data sets, we generated an overall
‘‘pathovivogram’’ that includes 276 genes in ten coexpression
clusters, CCs (Fig. 2, Fig. S2, and Table S1). This pathovivogram
highlights the transcriptional patterns that distinguish WT from
AP bacteria regardless of their growth habitat (pathogram, Fig 2.
CC4-CC6 and CC8-CC10), and the transcriptional patterns that
are shared by WT-vitro and AP-vitro bacteria but that
differentiate them from their corresponding in vivo samples
(vivogram, Fig. 2, CC1-CC3). Moreover, we identified a unique
cluster, CC7, that includes genes upregulated both in vivo and as a
consequence of the covS mutation (e.g., those encoding M protein,
streptolysin O, and nicotinamide adenine dinucleotide glycohy-
drolase (NADGH), Fig. 2, CC7).
The patterns in clusters CC1-CC3 describe those gene sets
whose transcription was turned on (CC1-CC2) or off (CC3) to
drive the bacterial adaptation to the host environment, but are not
primarily related to bacterial pathogenesis. Many of these genes
encode metabolic enzymes (e.g., carbohydrate metabolism,
arginine degradation, and pyrimidine biosynthesis), ribosomal
proteins (mostly downregulated), and sugar or peptide transport
systems, reflecting the transition from a carbohydrate-rich
laboratory medium to a protein-rich, oxygen-poor subcutaneous
tissue. Such transition is bound to have major downstream effects
on the bacterial virulence gene expression [38,39,40].
While the expression patterns in clusters CC4, CC5, CC8, and
CC9 match the previously described in vitro differences between
WT and AP strains [10,12,13,26,27] (e.g., downregulation of
SpeB, CAMP, and EndoS genes; upregulation of SpeA, SIC, and
capsule genes), the combined comparative analysis of in vitro and
in vivo samples revealed novel expression patterns exemplified by
CC6 and CC10. These two clusters include genes whose
expression has been repressed (CC6) or induced (CC10) by the
AP mutation, but the repression or induction becomes observable
only in vivo. For example, the expression of genes in the SLS and
Trx operons is induced in vivo only in WT bacteria, but is mostly
repressed in AP bacteria (Fig. 2, CC6) both in vitro and in vivo.
Likewise, some genes are only induced in AP-vivo, including those
encoding the toxin/enzyme streptokinase and a RofA/Nra-like
transcriptional regulator (Fig. 2, CC10).
Subsequent to the coexpression analysis, we calculated differ-
ential expression ratios between each pair of conditions (Table S2)
at different statistical significance cutoffs, starting by the commonly
used significance threshold of twofold ratio and P value ,0.05,
and moving to thresholds that are more conservative. At all
significance cutoffs, the fewest observed transcriptional changes
were those differentiating WT and AP bacteria in vitro compared
to other pairs of conditions. By contrast, the most dramatic
transcriptional reprogramming was that exhibited by the WT
bacteria in their attempt to adapt to the in vivo environment
(Table 2). Overall, at the least conservative statistical threshold
(P,0.05), the transcription of 557 (23.9%) out of 2,329 genes
probed in the microarrays was significantly perturbed (up or down,
at one or more conditions, Fig. S3) and the only set of contiguous
Figure 1. Hybridization scheme. Diagram showing the cyclic
hybridization scheme followed in the microarray experiments.
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genes that underwent transcriptional changes at all tested
conditions is the SpeB operon gene set (CC4 in Fig. 2). However,
the fold-change in SpeB operon transcription level largely varied
across the four experimental conditions, ranging from a 2.2-fold to
a 30-fold downregulation (Table 3).
Inspired by the recently described neighbor clustering method
for microarray analysis [41], we also mapped the significant
(P,0.05) differential expression ratios of different genes to their
chromosomal loci (Fig. 3 and Fig. S4) to allow the visualization of
coexpressed contiguous genes, including operons. This mapping
highlighted genes that might otherwise have been overlooked by
the CC or expression ratio methods. Examples of neighbor clusters
(NCs) enriched in differentially expressed genes are a locus
involved in L-ascorbate utilization (SPy0175-SPy0179); the citrate
lyase locus (SPy1186-SPy1191); the Trx chromosomal locus
(SPy1582-SPy1596) that includes the recently described, CovR-
repressed two-component response regulator TrxR [42]; and the
well-studied Mga (SPy2010-SPy2025), SpeB (SPy2037-SPy2042),
and capsule synthesis (SPy2200-SPy2202) loci (Fig. 3 and Fig. S4).
NC analysis also allowed us to visualize genomic clusters that are
similarly regulated in vitro and in vivo, and those that are
reciprocally regulated. For example, in both the SLS operon and
the Trx locus, we observed a striking difference between the effects
of the covS mutation and the in vivo adaptation (Fig. 4, CC6).
Genes of these two chromosomal clusters are significantly
downregulated in AP compared to WT bacteria, but significantly
upregulated in WT-vivo relative to WT-vitro conditions (Fig. 4,
CC6).
As a final step of our multifaceted data analysis approach, we
tabulated significant transcriptional changes of individual genes
that encode known regulators and well-studied virulence factors
(Table 3). Focusing on those genes highlights the pathogenesis-
related changes and allows easy comparison of our data with those
in the literature [11,12,13,16,27,37].
Data integration reveals genomic subsystems influenced
by the covS mutation in vitro and in vivo
By combining different strategies for microarray data analysis,
we took advantage of the strengths of each strategy to generate
biologically relevant gene sets rather than gene lists ordered solely
according to statistical parameters. The next stage in our analysis
was to integrate microarray data, moving from the gene/cluster
level to the level of biological subsystems. A subsystem is a part of a
genome that represents a functional module, e.g., an operon, a
cellular pathway, a regulon, or a complex regulatory network [43].
The genes perturbed by the different experimental conditions of
this study correspond to multiple subsystems (Fig. 5). Of interest,
among the genes of known function, 25% of those whose
transcription was modulated as a consequence of the covS mutation
Figure 2. Pathovivogram of expression microarrays. Heat maps
of clustered normalized expression values from biological replicates
showing ten major coexpression clusters (CCs). Shades of red:
upregulation; shades of blue: downregulation; black: expression value
below threshold. CC1-CC3 are clusters that differentiate bacteria grown
in vitro from those grown in vivo (vivogram) and represent the
adaptational transcriptional program. CC4-CC10 are clusters that
differentiate WT from AP bacteria (pathogram), all of which but CC7
represent transcriptional differences driven largely by the AP mutation.
CC7 represents transcriptional differences driven both by mutation and
by in vivo adaptation. The right column displays the subsystems (SS)
and neighbor clusters (NC) to which these genes belong. A higher
resolution version of this figure is provided online as Figure S2. Detailed
annotations are provided in Table S1.
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are virulence-related and another 25% are related to carbohydrate
metabolism pathways, which are connected to virulence in
streptococci [44,45,46].
When we attempted to correlate the array results with known
GAS regulons, some differentially expressed gene sets could be
fitted into previously described regulatory networks while others
did not belong to any such networks. For example, the
upregulation of Ska, SLO/Nga, and the Has operon is a pattern
that reflects the inhibition of CovR repression, whereas the
downregulation of both SpeB and SLS operons is a pattern
associated with the transcriptional regulator RopB/Rgg [47,48]
but that may also reflect augmented CovR repression [20].
Although CovS is expected to modulate many known CovR-
repressed subsystems, certain operons may be differentially
modulated or may be influenced by other regulator(s). Alterna-
tively, the mutation in covS may have different effects on CovR-
mediated regulation of different genes [26]. In fact, our in vivo
data show that covR itself is downregulated in vivo in both WT and
AP strains (Fig. 2, CC2 and Table S1); however, the SLS operon is
upregulated in vivo in WT bacteria, suggesting that its regulation
is not solely modulated by CovRS. It is possible that SLS is
regulated by the catabolite control protein (CcpA), which was
recently shown to be another key transcriptional regulator of the
SLS operon [46] and which may override CovR-mediated
repression of SLS. Another complex regulatory pattern is
exhibited by the genes of the Trx locus, which include a two-
component regulatory system, TrxRS. The TrxRS system
reportedly responds to a yet-to-be-identified extracellular signal
while TrxR itself is directly regulated by CovR [42]. Most genes
within the Trx locus are downregulated in AP GAS relative to
WT, but upregulated in the WT bacteria in vivo (Fig. 2, CC6 and
Fig. 3) suggesting the possibility of CovS-dependent in vivo
signaling.
One last factor that adds to the complexity of data integration
analysis and that may explain unexpected transcriptional patterns
is that regulators are often controlled by sensitive feedback
mechanisms. Inactivation or downregulation of one regulator may
eventually perturb the entire system, and several other regulators
are likely to become engaged in compensatory mechanisms to
maintain cellular homeostasis.
Discussion
Group A streptococcal sociomicrobiology
It is now well established that bacterial populations often consist
of heterogeneous communities rather than genetically identical
cells with synchronized gene expression profiles [49,50,51]. In this
study, we show how gross changes in the bacterial environment,
such as the onset of infection, can profoundly perturb the
sociomicrobiological structure of the bacterial population, driving
a minor subpopulation with a mutant hypervirulent phenotype to
thrive, prevail, and cause severe disease. Although a number of
informative studies have compared gene and protein expression in
virulent and hypervirulent GAS isolates [11,12,13,16,26,27,37],
none have captured the transitional state that reflects the dynamics
of the bacterial struggle to survive in a new host environment.
Normally, capturing such evolutionary events in real time is
difficult, both because multiple sampling of the bacteria in vivo is
often unfeasible due to their dissemination, and because the fittest
members of the community usually overtake the rest of the
population rather rapidly, thereby hampering the ability to
capture dynamic changes associated with the restructuring of the
bacterial community at different host niches. Our subcutaneous
chamber model of infection allowed us to sample the inoculated
WT and AP bacteria at specific times post-infection, during their
adaptation to their new environment, to determine how their
interaction with the host affects their community structure and
their transcriptional reprogramming. In doing so, we were able to
dissect changes in gene expression associated with niche
adaptation from those resulting from restructuring of the bacterial
community, where undetected minority members in vitro became
the new majority in vivo, armed with the necessary tools to survive
in their new niche. These in vivo-selected bacteria with the AP
phenotype could be differentiated from the WT bacteria by
mutations that are clustered in the sensor kinase-encoding covS
gene in AP bacteria [14,27]. In this study, we captured dynamic
events underlying the phenotypic switch resulting from this
population restructuring.
Environmental adaptation vs. covS mutation
In vitro, the growth of these two variants of theM1T1 bacteria was
comparable and, although 137 (5.9%) of their genes were
differentially expressed (Table 2), in vitro phenotypic screening
revealed negligible differences (Table 1). This suggests that the
majority of in vitro vs. in vivo differentially expressed genes between
the WT and AP variants may be involved in niche adaptation. This
finding is in agreement with studies showing that the CovRS system is
mainly linked to the regulation of virulence factors and virulence-
associated pathways [19,20,24,26,27,28,52,53,54,55].
Indeed, several virulence genes were among the 137 in vitro
differentially expressed genes between these two M1T1 variants.
Upregulated gene sets in AP vs. WT bacteria in vitro included
Mga locus genes (e.g., sic, emm, scp); hyaluronic acid capsule-
encoding genes (hasABC); and genes encoding the toxins SpeA,
SclA, SLO, and the streptodornase Sda1 (Fig. 2, Fig. S2, and
Table 3). Other well-studied gene sets were downregulated in AP
bacteria grown in vitro. Besides metabolic gene sets involved in
trehalose and ascorbate utilization, several known virulence factors
were downregulated in AP bacteria, and these included the SpeB
operon, GRAB, EndoS, and CAMP factor. SpeB is a major
virulence factor in GAS pathogenesis and its expression is
regulated by different systems [40,56,57,58], including the CovRS
Table 2. Number and percentage of genes significantly different between each pair of conditions at different significance
thresholds.
Statistical threshold WT vs. AP in vitro WT vs. AP in vivo WT vitro vs. vivo AP vitro vs. vivo
P,0.05 137 (5.9%) 167 (7.2%) 266 (11.4%) 203 (8.7%)
P,0.01 57 (2.45%) 59 (2.53%) 124 (5.32%) 95 (4.08%)
P,0.05 + FDR* 9 (0.39%) 9 (0.39%) 32 (1.37%) 20 (0.86%)
*Benjamini and Hochberg false-discovery rate (FDR) test.
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system [20,53]. Whereas SpeB expression may be important in the
initial stages of skin infections [29,35], its downregulation has been
associated with an invasive and hypervirulent phenotype
[13,14,59]. The downregulation of GRAB makes biological sense
because this protein binds alpha-2-macroglobulin in blood [60] to
protect the bacteria against its own protease, SpeB, and thus the
Table 3. Expression ratios of selected virulence genes and regulators1.
Gene product SF370 genome M1T1 genome AP/WT vitro AP/WT vivo WT vivo/vitro AP vivo/vitro
HasA SPy2200 + 6.24 6.70 * *
NADGH SPy0165 + * * 2.32 *
SLO SPy0167 + 8.92 * 6.78 *
GAPDH SPy0274 + * * * *
SpyA SPy0428 + * * * *
Hypothetical protein SPy0430 + 5.98 6.20 * *
SLS SPy0738 + * 24.35 3.00 *
IdeS/Mac SPy0861 + * * * *
Hyl SPy1032 + * * * *
CAMP SPy1273 + * 22.25 * *
DltA2 SPy1310 + * * * *
DltA1 SPy1312 + * * * *
GRAB SPy1357 + 22.44 22.49 * *
EndoS SPy1813 + 25.92 * * 2.73
Ska SPy1979 + * 2.47 2.51 *
SclA SPy1983 + 4.50 7.33 * *
Fibronectin-binding SPy2009 + 2.90 * * 23.24
C5a peptidase SPy2010 + 3.32 * * 23.15
SIC SPy2016 + 11.28 4.93 * 23.69
M1 protein SPy2018 + 4.08 * 2.15 *
SpeB SPy2039 + 213.16 229.85 22.29 25.18
Spa2 2 2 * 3.27 * *
Superantigens
SmeZ SPy1998 + * * * *
SpeA 2 + 2.37 4.50 * 3.54
SpeC SPy0711 2 * * * *
SpeG SPy0212 + * * * *
SpeH2 SPy1008 2 * * 24.52 *
SpeI SPy1007 2 * * * *
SpeJ SPy0436 + * * * *
Streptodornases
Spd1/MF SPy2043 + 26.21 27.87 * 22.41
Spd2/MF2 SPy0712 2 * * * *
Spd3/MF3 SPy1436 + * 23.01 2.73 *
Sda13 2 + 5.66 * 6.51 *
Regulators
RofA SPy0124 + * * * *
RopA SPy2037 + * * 24.65 24.46
RopB/Rgg SPy2042 + * * * *
RALP3 SPy0735 + * 3.26 23.01 *
Nra (SPyM3_0097)2 ? ? * 3.43 * *
TrxR SPy1587 + * * 5.45 *
Mga SPy2019 + * * * *
*The transcript was either not significantly altered, or its level was below detection threshold.
1Values in the table are positive or negative fold-change ratios.
2Although these genes are absent in M1T1, their probes cross-hybridized with M1T1 RNA.
3There was no sda1-specific probe in the microarrays; the values shown here are qPCR data [14].
doi:10.1371/journal.pone.0009798.t003
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bacteria no longer need to express high levels of GRAB when
SpeB is not expressed. Similarly, EndoS, an IgG protease, and
CAMP, a hemolysin, are likely needed in blood but with no
defined function in subcutaneous or deep tissue. Their downreg-
ulation in AP is thus consistent with the hypothesis that this
hypervirulent variant is adapted for deep infection. In addition,
the downregulation of SpeB, EndoS, and CAMP is in agreement
with our previous proteomic results [12] as well as other published
transcriptional analyses [13,27].
As both bacterial variants were subjected to the host environment,
they underwent additional changes in gene expression. In vivo, both
WT and AP bacteria downregulated a substantial number of their
genes (127 genes in CC1 and CC2, Fig. 2 and Table S1), and
upregulated many fewer (24 genes in CC3, Fig. 2 and Table S1). By
parsing these differentially expressed genes into operons, subsystems,
and functional pathways, we found that many reflect the engagement
of several regulatory networks involved in metabolic adaptation and
immune camouflage or evasion of host defenses. Many of the in vivo
changes that both the WT and AP bacteria underwent are suggestive
of major metabolic reprogramming associated with the transition
from a saprophytic lifestyle in the carbohydrate-rich laboratory
culture medium to a parasitic lifestyle in the vascularized and the
anaerobic subcutaneous environments rich in peptides, amino acids,
nucleotides, and different types of complex carbohydrates. For
example, among the downregulated gene sets are those involved in
citrate metabolism, arginine degradation, and de novo pyrimidine
synthesis; many of those gene sets were previously reported to be
perturbed upon blood inoculation and to be controlled by CovR [61]
and RopB/Rgg [38].
We also found that many ribosomal and cell-division proteins
were downregulated in vivo, which suggests that the cells may be
slowing down protein synthesis to preserve energy, or redirect this
energy to colonizing the host and evading its immune system. The
downregulation of arginine deiminase, a streptococcal immunogen
and a potential vaccine target (Henningham et al., submitted), is
also suggestive of immune evasion. Among the upregulated genes
are dipepetide and sucrose-specific transporters, whose upregula-
tion supports the hypothesis that the bacteria are switching diets
[39] and attempting to scavenge nutrients available in their new
host environment.
Several phage genes are split between the upregulated and
downregulated gene clusters; this may indicate a stress-dependent
reprogramming of prophage induction and gene expression that
needs to be explored in future studies.
Interestingly, what this study revealed is that some of the
downstream regulatory effects of the covS mutation in AP bacteria
are only manifested in vivo. For example, several gene sets (e.g.,
SLS genes, Trx-locus genes, L-ascorbate utilization genes) were
upregulated in WT bacteria in vivo, but were mostly silenced in
AP bacteria in vivo (Fig. 2, CC6). On the other hand, genes
encoding the fibrinolytic enzyme streptokinase, Ska, a RofA-like
transcriptional regulator, several cell division proteins, and some
phage proteins were only upregulated when the AP bacteria
sensed the in vivo environment (AP-vivo, Fig. 2, CC10). Both these
expression patterns suggest CovS-dependent in vivo signaling via
different downstream pathways.
Finally, genes encoding the antiphagocytic M protein, and the
toxins streptolysin O and NADGH were among few genes that were
upregulated in all conditions except WT-vitro (Fig. 2, CC7), which
suggests that their transcription is dependent on multiple signals,
including signals from the host as well as covS-dependent cues. Taken
together, these results demonstrate how the murine model has
allowed us to finely dissect two classes of events affecting GAS
sociomicrobiology: those related to reversible transcriptional adapta-
tion and those irreversibly caused by the covS mutation in AP bacteria.
Microarray validation and analysis
Although our microarray studies were extensive (28 arrays for 4
conditions), we ran qPCR to validate the microarray findings,
focusing on virulence genes that are biomarkers of the bacterial
switch to a hypervirulent phenotype. These include SpeA, SpeB,
Sda1, M protein, and SIC [data published in 14]. Comparing the
microarray data in this study with previously published work
provides further validation of these results. For example, the in
vitro microarray results are consistent with our previous proteomic
studies of the WT and AP secreted bacterial proteomes [12] as
well as with other recently published studies of the in vitro
transcriptome of closely related strains [13,26,27].
Importantly, we did not base our analysis on single measure-
ments of gene expression, nor did we focus on individual genes;
rather, we used gene expression data from biological replicates to
rule out biological variability (especially between bacteria
recovered from different mice) and to look for changes in gene
clusters, operons, and pathways. In doing so, we were able to
assess trends across experiments rather than absolute numerical
values that could vary due to technical rather than biological
factors. We believe that this approach provided more confidence
in the final assessment of which genes/pathways were expressed
Figure 3. Neighbor clustering of significantly differentially expressed genes mapped to M1 SF370 genome. Fold-change ratios of
significantly differentially expressed genes (P,0.05) are mapped to ORFs of the M1 SF370 genome (the M1 strain used as core for the microarray).
SF370 prophages are shown, including those absent in M1T1. The graph shows sets of contiguous genes with similar coexpression patterns. A higher
resolution version of the figure is provided online as Figure S4.
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Figure 4. Examples of clusters of biological interest. The correlation between different methods of analysis of five clusters is shown. The left
panel includes the different clusters detected by the NC method. The middle panels display heat maps of five coexpression clusters representing
different patterns (CC4, SpeB operon; CC6, SLS operon and Trx locus; CC8, Mga locus; CC9, Has operon). The corresponding NC graphs for genes
within these clusters are shown in the right panels.
doi:10.1371/journal.pone.0009798.g004
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similarly or differentially in both WT and AP bacteria in vitro and
in vivo. It also allowed us to dissect changes related to niche
adaptation from those reflecting the selection of the fittest
members of the bacterial community when faced with different
environments and conditions.
The use of multiple microarray analysis strategies and the
integration of their results strengthened this study since each
strategy has advantages and disadvantages. For example, coex-
pression clustering—one of the earliest developed microarray
analysis tools [62,63]—provides an overall view of expression
patterns of different genes, and greatly helps dissect and
demonstrate the effect of each condition on the overall
transcriptome. However, coexpression analysis alone can miss
some biologically relevant genes that could fail the statistical tests
for non-biological reasons (e.g., poorly hybridizing probes, low
signal-to-noise ratio, or high variance) and may instead include
some irrelevant genes in isolation of their biological networks [41].
In bacteria, biochemical pathways, virulence systems, and multi-
meric proteins are often encoded next to each other by
chromosomally contiguous or clustered genes [64,65]. Analyzing
coexpressed genes in the context of chromosomal clusters is thus
most informative. Indeed, neighbor clustering [41] has allowed the
enrichment for contiguous gene sets and the prediction of more
context-related expression patterns. Genes in neighbor clusters
could have been otherwise overlooked either because they were
misannotated but their co-occurrence in known clusters revealed
their importance, or because they did not pass the statistical tests
but, since many bacterial transcripts are polycistronic, the
expression of two or more members of a polycistron strongly
suggests that the whole operon is expressed.
Besides these two clustering methods, expression ratios provided
pairwise comparison, thereby allowing the quantification of the
impact of each individual change of condition on overall gene
expression (Table 2) as well as on specific genes of interest
(Table 3). However, the use of ratios alone may be misleading,
especially when they are calculated between two transcripts with
low expression levels, resulting in spurious ratios of low biological
significance. Similarly, the common use of statistical constraints
with ratio calculation (e.g., two-fold ratios and P values ,0.05)
filters out many biologically relevant genes that are true positives.
Finally, the use of operons, subsystems, and pathways to describe
the array results avoids inappropriately building conclusions on
the transcriptional changes of individual genes and thus better
reflects biologically relevant perturbations in specific pathways.
The bigger picture: niche adaptation and the evolution
of hypervirulence in S. pyogenes
Having used multiple strategies and integrated the PM data
with the in vitro and in vivo microarray data, we propose the
Figure 5. Genomic subsystems represented by the significantly differentially regulated genes. Annotations and subsystem classification
are based on NMPDR [79] annotations as of October 2009. Subsystem classification has been manually verified and amended when necessary.
doi:10.1371/journal.pone.0009798.g005
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following hypotheses about streptococcal niche adaptation and
switch to hypervirulence:
(1) Exposure to the in vivo environment perturbs a substantial
number of GAS genes and regulatory networks. This has been
demonstrated by the larger number of genes affected during in
vivo adaptation of both M1T1 variants compared to those affected
by the covS mutation (Table 2). However, many of the genes
modulated in vivo seem to be involved in metabolic reprogram-
ming and stress responses rather than pathoadaptation and
virulence. This is why we believe that transcriptional reprogram-
ming by environmental adaptation alone was insufficient to
provide WT bacteria with an in vivo survival advantage. In fact,
WT bacteria underwent the most dramatic in vivo transcriptional
reprogramming (Table 2); yet, they failed to survive since after five
to seven days post-infection, they became extinct, and only AP
bacteria could be isolated from the mice chambers [16].
(2) The covS mutation in AP bacteria seems to be preferentially
modulating virulence mechanisms, as about 25% of the known
genes perturbed by the mutation belong to virulence subsystems
(Fig. 5) and many of the other perturbed genes are indirectly
associated with virulence (e.g., carbohydrate metabolism [66] and
arginine utilization proteins [38]). In addition, the pathoadaptive
clusters (CC4-CC10 in Fig. 2) are more enriched in virulence-
related genes than the clusters involved in in vivo adaption (CC1-
CC3 in Fig. 2). This finding suggests that AP bacteria are
somehow ‘‘pre-adapted’’ to invasiveness and, consequently, when
injected into mice, they do not undergo much virulence-related
changes as they already possess a thicker capsule, lack a functional
SpeB, are equipped with surface virulence proteins, and secrete
ready-to-use toxins, including the potent DNase, Sda1. However,
the degree of AP invasiveness might vary depending on the route
of infection and animal model [15].
It is noteworthy that SpeB expression has a dominant effect,
since even if secreted in low amounts by a minor subpopulation,
this broad-spectrum protease would still be able to degrade, fully
or partially, many GAS virulence proteins, including Sda1,
thereby rendering the bacteria vulnerable to different effector
mechanisms of the host’s innate immune system including
neutrophil killing [14,67,68]. Thus, a complete shutdown of
proteolytically active SpeB is essential to preserve effective
extracellular virulence factors [15]. This dominant SpeB effect
may explain why WT bacteria parish in vivo even though they
partially downregulate SpeB transcription (Table 3).
(3) The GAS genome has at least 13 two-component regulatory
systems [69] in addition to several stand-alone transcriptional
regulators [47,70]. However, the CovRS system is a major player,
among these regulators, in driving the bacterial adaptation to the
host’s environment and regulating virulence directly or through
other downstream regulators [20,54]. It is thus counterintuitive
that bacteria lacking the important environmental sensor, CovS,
would prevail in one of the most stressful environments. However,
losing this sensor might be the last resort for these bacteria
stranded away from their primary niche and surrounded by hostile
immune cells and proteins. From an evolutionary point of view, it
is possible to speculate that the bacteria lose their danger sensor to
keep their ‘‘weapons’’ constitutively expressed and fight to survive
when escape is not an option. Such mutation is likely detrimental
to the bacterial long-term survival and dissemination via
colonization of new hosts [71], as they need a WT sensor for
better adaptability [72] (e.g., in less hostile niches like throat or
saliva [26]). An intact CovRS system would offer the bacteria
enough versatility to turn on and off many regulatory networks
through CovS signaling. This flexible mechanism allows the
bacteria to initially hide from the immune system through SpeB-
driven camouflaging, i.e. degradation of most of their immuno-
genic virulence factors [2,3].
(4) We also show that the impact of the covS mutation goes
beyond the defined CovR regulon. This finding is in accordance
with previous observations that CovR and CovS are not
committed to each other, as CovR could be phosphorylated in
the absence of a functional CovS [22] and as different covS
mutations, albeit clustered in the histidine kinase domain, might
have different transcriptional effects [27], including opposite
effects on different members of the CovR regulon [26].
Concluding remarks
In conclusion, we have established a model that allowed us to
resolve two sets of complex transcriptional events: (i) those
occurring in response to the host environment and (ii) those
caused by a mutation in covS sensor kinase. We believe that our
results offer a proof of principle that in vivo-extracted RNA can
provide transcriptional profiles that better reflect the complexity of
heterogeneous bacterial communities, and, as shown in this study,
can provide a transcriptional snapshot of a bacterial population
right before its extinction. The use of in vivo-driven RNA in
understanding virulence has been appreciated in streptococcal
research [73], but the technique has not previously been used to
explain the switch to hypervirulent, invasive phenotypes or to
dissect heterogeneous microbial subpopulations.
This study is a first step towards exploring the sociomicrobiology
of invasive GAS in vivo. Having captured snapshots of different
transcriptional programs within the same bacterial community, we
plan to follow with single cell studies [74,75] of bacteria associated
with immune cells to further dissect the different roles played by
members of the same bacterial community.
Materials and Methods
Ethics statement
All animal experiments were conducted according to the
Guidelines for the Care and Use of Laboratory Animals of the
National Institutes of Health and approved by the institutional
animal care and use committees at the University of Cincinnati,
OH, USA and the VA Medical Center, Memphis, TN and
Cincinnati, OH, USA.
Bacteria and culture media
We used the clinical isolate, GAS 5448 [4,59], representative of
the clonal M1T1 strain [2,10] as well as its animal-passaged (AP)
descendant 5448 AP [12], which was shown to be a natural covS
mutant [14]. Bacteria were grown in vitro in THY medium (Todd
Hewitt broth, DIFCO, Detroit, MI, supplied with 1.5% yeast
extract, DIFCO). Cultures were routinely tested for purity on
blood agar plates (Becton Dickenson, Franklin Lakes, NJ) and for
proteolytic activity on casein-Columbia agar plates as described
previously [14,48].
Biolog Phenotype Microarray experiments and analysis
Both bacterial strains GAS 5448 WT and 5448 AP were
analyzed by the BiologH Phenotype Microarray (PM) technology.
The analysis and data processing were performed by the BiologH
team (Hayward, CA). Duplicate arrays were run, and the average
of the two runs was calculated. Only results that were significantly
different in both runs are reported as significant.
Expression microarrays
Oligomers (70-mers) of the M1-based microarrays were
obtained from Dr. Kevin McIver and Dr. June Scott, and printed
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in the Molecular Resource Center, UTHSC by the use of
MicroGrid II (Genomic Solutions, Ann Arbor, MI). Each array
consists of 2,346 oligomers that represent all open reading frames
(ORFs) in M1 GAS strain SF370 (GenBank accession #
NC_002737 [76]) in addition to ORFs from prophages in strains
MGAS8232 (GenBank accession# NC_003485 [77]) and
MGAS315 (GenBank accession # NC_004070 [78]). These
oligomers were printed in duplicates at different locations of
polyamine-coated glass slides (Telechem International Inc.,
Sunnyvale, CA) together with so-called alien DNA (Stratagene,
La Jolla, CA) negative controls, which are synthetic DNA
sequences with no homology to any DNA in current sequence
databases.
RNA extraction
To obtain high-quality RNA from GAS cells, we followed a
multi-step protocol. First, we mixed the bacteria with a Lysing
Matrix B (QBiogene, Irvine, CA) and used a FastPrep instrument
(QBiogene) to shear the bacterial walls. Then, we extracted total
RNA from the sheared bacteria using RNeasy kits (Qiagen,
Valencia, CA), treated it with DNase Turbo (Ambion, Austin, TX)
for 1 h to remove contaminating genomic DNA, and further
purified the samples using RNeasy columns (Qiagen). Sometimes
we used RNeasy MinElute columns to concentrate RNA when the
yield was low, typically in case of in vivo-recovered RNA. We
confirmed the absence of genomic DNA in the samples by running
40-cycle PCR reactions using GAS speB or gyrase primers as
described elsewhere [12,27].
Animal model
For in vivo experiments, we used the subcutaneous murine
Teflon chamber model developed in our laboratory and described
earlier [16]. Sterile Teflon chambers were surgically inserted
under the skin of age-matched female BALB/c mice. Three weeks
after surgery, mice were screened, and only those with sealed
subcutaneous chambers containing sterile tissue chamber fluid
(TCF) were selected for the experiments. The bacterial inoculum
was prepared as follows: bacteria were grown overnight in THY
medium then subcultured again for 18 h, washed twice in sterile
phosphate-buffered saline (DIFCO).
To recover enough RNA for downstream experiments, we
inoculated the mouse chambers with 26108 CFU/mouse (100 ul
of a 26109 CFU/ml culture). After 24 h, we used the bulk of the
recovered bacteria for immediate RNA extraction (as detailed
above) with no additional culturing, and kept a small aliquot intact
to verify retention of SpeB phenotype using protease screens on
casein-agar plates. RNA from bacteria homogeneously expressing
or not expressing proteolytic activity was used for transcriptome
profiling while RNA from the few populations that exhibited
mixed SpeB+ and SpeB2 phenotypes was excluded.
Preparation of labeled cDNA probes and microarray
hybridization
To convert the bacterial RNA to labeled cDNA ready for
hybridization, we used the 3DNA Array 900TM kits (Genisphere,
Hatfield, PA; http://www.genisphere.com/array_detection_900.
html), which use the dendrimer technology to amplify the
fluorescent signal of cDNA. Following the manufacturer’s
protocol, we tagged each sample with the proprietary capture
reagent, mixed equal amounts from both tagged samples (WT or
AP, grown in vitro or in vivo), and used the mixed samples to
hybridize with the probes on the microarray slides overnight. After
the first hybridization, we washed the slides, incubated them with
equal amounts of the fluorescent dyes (Alexa Fluor 546 and Alexa
Fluor 647, from Genisphere) for 3 h, washed them again, and
immediately scanned them. Alternatively, we used DyeSaver
(Genisphere) to coat the array slides and protect the dyes from
fading when immediate scanning was not possible.
Design of microarray experiments
In planning the microarray experiments, we chose to follow a
cyclic design according to which we compared, for example, WT-
vitro to AP-vitro, then AP-vitro to AP-vivo, then AP-vivo to WT-
vivo, and then WT-vivo to WT-vitro (Fig. 1). This scheme allowed
an all-to-all comparison without the need for duplicate arrays for
each pair of conditions. Because we used a two-color hybridization
approach, this scheme also controlled for non-specific hybridiza-
tion, since each comparison was repeated at least twice with the
fluorescent dyes flipped. At least three biological replicates (i.e.,
samples recovered from three different mice or three different in
vitro cultures) of each condition were tested, and each biological
replicate was run at least twice. In addition, the probes were
already printed in duplicates on the glass slides. This conservative
design minimizes biological variability caused by mouse-to-mouse
or culture-to-culture variations and reveals differences due only to
dynamic changes in population structure or gene regulation. The
possibility that some true positive results may have been missed
because of this design was compensated by studies assessing the
expression of multiple genes in the same operons or chromosomal
clusters as detailed in the Results section.
Analysis and annotation of microarray data
We scanned the arrays using GenePix 4000B scanner (Axon
Instruments/Molecular Devices, Sunnyvale, CA) and we per-
formed the primary analysis using the GenePixPro 4.0 software
(Axon Instruments/Molecular Devices) The primary analysis
included spot finding, alignment and adjustment, fluorescence
normalization, flagging out poorly hybridized spot, and back-
ground subtraction. We performed subsequent analyses using
multiple tools, including Microsoft Excel, GeneSpring (Agilent
Technologies, Santa Clara, CA), as well as custom-written Perl
scripts that are integrated in the NMPDR (http://www.nmpdr.
org) [79] and SEED [43] platforms; these scripts allowed
calculation of mean fluorescence values and ratios, filtration of
low signal-to-background ratios, clustering and sorting results from
different arrays, and finally uploading the results to the SEED
website (http://seed-viewer.theseed.org) to allow the visualization
of array results. Additional clustering, statistical analysis, and
generation of gene lists and Venn diagrams were performed by
multiple tools in the GeneSpring suite (Agilent Technologies).
All genome annotations and subsystems data used in this study
were obtained from the NMPDR database [79]. All raw
microarray data were submitted to the NCBI Gene Expression
Omnibus (GEO) in accordance with MIAME standards (GEO
accession numbers: GEO platform GPL9701 and series
GSE19103: samples GSM473346 through GSM473374). More-
over, all raw data as well as GeneSpring analysis folders are made
available online (http://host-pathogen.net/publications/Aziz_2010_
Arrays/microarrays).
Supporting Information
Figure S1 Biolog PM consensus results. A consensus Phenotype
Microarray (PM) analysis chart generated from two sets of 20
plates that were run twice for each the wild type (WT) and animal-
passaged (AP) bacteria over a 48 h time period. Each well of each
plate represents a different condition (nutrient source, antimicro-
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bial, pH or salt concentration, etc.). The red curves represent AP
and the green ones represent WT growth curves. Yellow color is
the result of superimposition of red and green areas.
Found at: doi:10.1371/journal.pone.0009798.s001 (6.26 MB TIF)
Figure S2 A higher resolution version of Fig. 2. Because Fig. 2
dimensions are hard to fit in the print paper size, this larger online
version may help readers see the details.
Found at: doi:10.1371/journal.pone.0009798.s002 (25.80 MB
TIF)
Figure S3 Microarray results summary statistics.
Found at: doi:10.1371/journal.pone.0009798.s003 (3.60 MB TIF)
Figure S4 A higher resolution version of Fig. 3. Because Fig. 3
dimensions are hard to fit in the print paper size, this larger online
version may help readers see the details.
Found at: doi:10.1371/journal.pone.0009798.s004 (0.70 MB
PNG)
Table S1 All array data used to generate the pathovivogram
(Figs. 2 and S2).
Found at: doi:10.1371/journal.pone.0009798.s005 (0.30 MB
XLS)
Table S2 Gene lists reflecting significant (P,0.05) differential
expression ratios between each pair of conditions.
Found at: doi:10.1371/journal.pone.0009798.s006 (0.28 MB
XLS)
Acknowledgments
We thank the team managing the animal facilities in the VA Medical
Center and William Ohr for assistance in printing the microarrays.
Author Contributions
Conceived and designed the experiments: RKA MJW MK. Performed the
experiments: RKA RK WLT SLR MJW. Analyzed the data: RKA BJA
WLT MK MK. Contributed reagents/materials/analysis tools: WLT MK
GSC MK. Wrote the paper: RKA BJA MJW MK. Supervised the overall
conduct of the work and collaborations: MK.
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