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ORIGINAL RESEARCH ARTICLE
published: 10 February 2015
doi: 10.3389/fmicb.2015.00027
Analysis of the Salmonella regulatory network suggests
involvement of SsrB and H-NS in E
σ-regulated SPI-2 gene
expression
Jie Li1‡, Christopher C. Overall2‡ , Ernesto S. Nakayasu 2† , Afshan S. Kidwai1,MarcusB.Jones
3,
Rudd C. Johnson1, Nhu T. Nguyen 1, Jason E. McDermott2, Charles Ansong2,FredHeffron
1,
Eric D. Cambronne1and Joshua N. Adkins2*
1Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, OR, USA
2Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
3Department of Infectious Diseases, J. Craig Venter Institute, Rockville, MD, USA
Edited by:
Beiyan Nan, University of California,
Berkeley, USA
Reviewed by:
Martin Wiedmann, Cornell
University, USA
Qiaobin Xiao, University of Notre
Dame, USA
Jinlei Zhao, University of
Pennsylvania, USA
*Correspondence:
Joshua N. Adkins, Biological
Sciences Division, Pacific Northwest
National Laboratory, 902 Battelle
Boulevard, PO Box 999, MSIN
K8-98, Richland, WA 99352, USA
e-mail: joshua.adkins@pnnl.gov
†Present address:
Ernesto S. Nakayasu, Bindley
Bioscience Center, Purdue
University, West Lafayette, IN, USA
‡These authors have contributed
equally to this work.
The extracytoplasmic functioning sigma factor E
σis known to play an essential role for
Salmonella enterica serovar Typhimurium to survive and proliferate in macrophages and
mice. However, its regulatory network is not well-characterized, especially during infection.
Here we used microarray to identify genes regulated by E
σin Salmonella grown in three
conditions: a nutrient-rich condition and two others that mimic early and late intracellular
infection. We found that in each condition E
σregulated different sets of genes, and notably,
several global regulators. When comparing nutrient-rich and infection-like conditions, large
changes were observed in the expression of genes involved in Salmonella pathogenesis
island (SPI)-1 type-three secretion system (TTSS), SPI-2 TTSS, protein synthesis, and
stress responses. In total, the expression of 58% of Salmonella genes was affected by
E
σin at least one of the three conditions. An important finding is that E
σup-regulates
SPI-2 genes, which are essential for Salmonella intracellular survival, by up-regulating
SPI-2 activator ssrB expression at the early stage of infection and down-regulating SPI-2
repressor hns expression at a later stage. Moreover, E
σis capable of countering the
silencing of H-NS, releasing the expression of SPI-2 genes. This connection between E
σ
and SPI-2 genes, combined with the global regulatory effect of E
σ, may account for the
lethality of rpoE-deficient Salmonella in murine infection.
Keywords: Salmonella, RpoE, microarray, SPI-2, H-NS, regulation, ChIP-seq
INTRODUCTION
Salmonella enterica enterica serovar Typhimurium (strain 14028s;
referred to as Salmonella hereafter) is an invasive enteric pathogen
with remarkable adaptability to diverse environments. The host
as well as the residential niche of Salmonella varies, requiring the
pathogen to sense its location within the host and to adjust its
gene expression accordingly. Salmonella has evolved a number
of strategies to sense the environment and to modulate its pro-
duction of virulence factors appropriately (Alpuche Aranda et al.,
1992; Raffatellu et al., 2005; Yoon et al., 2009). The bacterial sur-
face is the frontline of the host-pathogen interaction, making it
both a major target of the host immune response and a primary
location for the pathogen to activate its own defensive strategies
(Rowley et al., 2006).
In Gram-negative bacteria, stresses that affect components of
the cell envelope, such as periplasmic and outer-membrane pro-
teins, illicit a variety of responses in the cell which are collectively
known as extracytoplasmic stress responses (ESRs). There are at
least four signal transduction systems in Gram-negative bacteria
that govern the ESRs: the alternative sigma factor σE(encoded
by rpoE), the two-component regulatory systems CpxR/CpxA
andBaeS/BaeR,andthephageshockprotein(Psp)system(4).
Of these systems only the absence of σEcauses a strong vir-
ulence defect, although there is overlap and crosstalk between
the systems when reacting to different extracytoplasmic stresses
(Connolly et al., 1997; Jones et al., 1997; Humphreys et al.,
1999, 2004; Kenyon et al., 2002; Becker et al., 2005; Karlinsey
et al., 2010). The availability of σEis controlled by antisigma fac-
tor, which sequesters σEto the membrane in an inactive state
when membrane stress is absent. However, in the presence of
stresses that lead to accumulation of misfolded proteins in the
periplasm, a proteolytic cascade is initiated, releasing σEfrom
antisigma factor. Free σErecognizes specific promoters and initi-
ates transcription with core RNA polymerase (Rowley et al., 2006;
Osterberg et al., 2011).
Using molecular genetic approaches and DNA microarray
analyses, the σEregulon has been extensively studied in E. coli
(Dartigalongue et al., 2001; Rezuchova et al., 2003; Kabir et al.,
2005; Rhodius et al., 2006). However, the σEregulon obtained
from E. coli is not directly applicable to Salmonella because σE
plays distinct roles in these two bacteria. For instance, σEis
required for viability in E. coli,whileinSalmonella, it is essential
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Li et al. Salmonella σEregulatory network
for resisting reactive oxygen species, antimicrobial peptides, acid
stress, and likely additional virulence related functions (Bang
et al., 2005; Crouch et al., 2005; Muller et al., 2009). One of the
rpoE promoters, rpoEp3, is more strongly induced by cold shock
than by heat shock in Salmonella,butnotinE. coli,reflecting
the functional variation of σEin combating different stresses in
these two organisms (Miticka et al., 2003). The Salmonella σE
regulon has been studied using an E. coli two-plasmid screen-
ingsystemandbymicroarrays,however,thegrowthconditions
exploited by previous studies did not discriminate different stages
of Salmonella infection (Skovierova et al., 2006; Yoon et al., 2009).
Compared to other regulators important for Salmonella viru-
lence (e.g., fruR,ssrAB,slyA,crp,rpoS,etc.),anrpoE mutant is the
most sensitive to the intracellular environment. Specifically, the
LD50 of an rpoE mutant in BALB/c mice by intraperitoneal infec-
tion is greater than 106CFU, whereas that of the parent strain is
1–2 CFU. Only a few, if any, viable rpoE mutants are recovered
from primary macrophages after 30 min of infection (Yoo n e t al . ,
2009). It is unclear why the absence of rpoE in Salmonella has
such an extreme phenotype during intracellular growth. Previous
studies have shown that hundreds of genes were regulated by σE,
including genes in the Salmonella Pathogenicity Island 2 (SPI-
2) (Osborne and Coombes, 2009; Yoon et al., 2009). SPI-2 genes
encode components of a type III secretion system (TTSS) and its
associated effectors, which are required for intracellular survival.
The expression of SPI-2 genes is tightly regulated temporally and
spatially. Without induction (e.g., carbon limitation, low con-
centrations of Mg2+or Ca2+, and acidic pH), SPI-2 is bound
by nucleoid-associated protein H-NS to silence transcription
and avoid detrimental consequences of inappropriate expression.
Inside professional phagocytic cells, SPI-2 expression is induced
by the two-component systems PhoP/PhoQ, OmpR/EnvZ, and
SsrA/SsrB (Lee et al., 2000; Garmendia et al., 2003; Bijlsma and
Groisman, 2005).
To obtain a more complete picture of the σEregulatory net-
work in Salmonella, in this paper we analyzed the transcriptional
profile of σEby microarray on wild-type (WT) and rpoE mutant
cultured under three conditions: in nutrient-rich Luria-Bertani
(LB) broth to log phase, in acidic minimal medium (LPM) for
4 h to mimic early intracellular infection, and in LPM for 20 h
to mimic late intracellular infection. We established that a large
number of Salmonella genes involving various functional cate-
gories are regulated by σE, both directly and indirectly. Notably,
we found that σEup-regulates SPI-2 gene expression through dif-
ferent mechanisms at different stages of infection: by increasing
the transcription of SPI-2 activator ssrB in early stage, and by
decreasing the transcription of SPI-2 repressor hns in late stage.
σEcan also counter the silencing of H-NS on SPI-2 genes.
MATERIALS AND METHODS
BACTERIAL STRAINS AND GROWTH CONDITIONS
Salmonella STM ATCC 14028s was used as the parent strain in
this study. The rpoE-deletion strain (rpoE) was constructed
using λred recombination system as described (Yo o n e t a l . ,
2009). Bacteria were grown under 3 different conditions to cover
expression of a large number of Salmonella genes. They were: in
Luria-Bertani (LB) medium to log phase (OD600 =0.5), in pH
5.8, low phosphate, low magnesium-containing medium (LPM)
for 4 h (OD600 ∼0.5) or 20 h (OD600 ∼1.0) (for the LPM cul-
ture, bacteria were grown in LB to stationary phase, washed
twiceinLPM,andresuspendedinLPMat1:10dilutionforan
additional 4 h or 20 h) (Niemann et al., 2011). The latter two
conditions partially mimic the intracellular environment of the
Salmonella-containing vacuole and represent the early and late
stage Salmonella infection. All the bacterial cultures were grown in
triplicate. For microarray analysis 3 ml of culture was centrifuged,
pellet was collected and treated with RNAlater (Ambion), then
stored at -20◦C prior to processing. The plasmid (pASK- H-NS
-3xFLAG) expressing H-NS was constructed by cloning a DNA
fragment containing coding sequence of hns on pASK-3xFLAG
(constructed on pASK-IBA33plus by Hyunjin Yoon) via EcoRI
and AvrII. Primers used in the PCR amplification of hns are
shown in Table S 1 .
EXPRESSION AND PURIFICATION OF RECOMBINANT RpoE AND H-NS
S. Typhimurium 14028s rpoE and hns genes were cloned into
the plasmid pET200/D-TOPO (Invitrogen) using directional
TOPO cloning method following the vendor’s instructions. The
genes were inserted downstream of the hexahistidine tag coding
sequence under an isopropyl-1-thio-3/4-D- galactopyranoside
(IPTG)-inducible T7 promoter. The bacterial expression con-
structs were confirmed by sequencing and transformed into BL21
(DE3) E. coli strain (Invitrogen). Transformed bacteria were
grown in LB broth containing 60 μg/ml kanamycin to mid-log
phase. IPTG was added to a final concentration of 1 mM and the
incubation was allowed to proceed for 3h at 37◦C. Bacteria were
harvested by centrifugation, stored at -80◦C, and lysed in lysis
buffer (10 mM Tris-HCl pH 7.5, 100 mM NaCl, 1 mM EDTA)
supplemented with fresh protease inhibitor cocktail (Sigma) and
1 mg/ml lysozyme for 30 min on ice. Subsequently, the lysate was
sonicated and clarified by centrifugation at 10,000 ×g for 10 min
at 4◦C. The supernatant was incubated with HisPur™ Cobalt
resin (Pierce) in batch and the beads were washed extensively with
Wash buffer (50 mM sodium phosphate, 300 mM NaCl, 10 mM
imidazole; pH 7.4). Bound proteins were eluted with Elution
buffer (50 mM sodium phosphate, 300 mM NaCl, 150 mM imi-
dazole; pH 7.4). Elution buffer was replaced with exchange buffer
(50 mM Tris-HCl pH 8.0, 1% SDS, 1 mM EDTA, 10% glycerol)
using an Amicon Ultra-4 Centrifugal filter unit (millipore) with a
molecular weight cutoff of 3000 Da. A small portion of the eluted
protein was withdrawn and subjected to immunoblot to con-
firm that RpoE and H-NS were expressed at the correct size. The
remaining eluted protein sample was separated on SDS-PAGE,
and stained with Coomassie blue. The single gel bands at the size
of recombinant RpoE and H-NS (His tagged) were excised and
sent to Pacific Immunology Corp. (Ramona, CA) for polyclonal
antisera production.
PURIFICATION OF RpoE AND H-NS ANTISERA
The RpoE and H-NS antisera generated in rabbits were puri-
fied by affinity chromatography. Briefly, the purified recombi-
nant RpoE or H-NS in coupling buffer (500 mM NaCl, 100 mM
NaHCO3, pH 8.0) was mixed with activated CH Sepharose 4B
(GE Healthcare) at 1:1 ratio and rotated for 4 h at 4◦C. After
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Li et al. Salmonella σEregulatory network
washing away excess antigens, the remaining active groups on
sepharose was blocked with 0.1 M Tris-HCl, then washed with
buffers of alternating pH for 5 cycles. Each cycle consisted of a
wash with 100 mM acetic acid/sodium acetate, pH 4.0 containing
500 mM NaCl followed by a wash with 100 mM Tris-HCl buffer
pH 8.0 containing 500 mM NaCl. The RpoE or H-NS antisera
were loaded onto antigen-coupled CH Sepharose 4B in a chro-
matography column (Bio-Rad). After sequential washes with PBS
and lithium chloride solution (1 M LiCl, 150 mM NaCl, 0.5%
Nonidet P-40, 10 mM Tris-HCl, pH 8) the column was eluted
with 200 mM glycine pH 2 in 10 ml fractions by the addition of
10×PBS and 1% BSA. The pH of the eluates was adjusted to
7 with 5 N NaOH. Fractions containing the purified anti-RpoE
and anti-H-NS antibodies as judged by SDS-PAGE were frozen
at −20◦C. Protein concentration determination was performed
according to modified Lowry method using bovine serum albu-
min (BSA) as reference protein (Sandermann and Strominger,
1972).
MICROARRAY ANALYSIS
For each of the three experimental conditions (LB, LPM 4h and
LPM 20 h), we identified genes that were differentially expressed
between the WT and rpoE strains of Salmonella. The samples
were assayed to the Salmonella Typhimurium/Typhi microar-
ray (version 8), a two-channel spotted array (70-mer probes)
designed by the Pathogen Functional Genomics Resource Center
at the J. Craig Venter Institute (JCVI). The analysis consisted of
quantifying spot intensities, handling low quality probes, correct-
ing for background intensities, imputing missing values, summa-
rizing replicate probes, normalizing the summarized intensities,
and finally, finding differentially expressed genes.
First, we calculated a single, background-corrected inten-
sity for the probes (spots) on each of our arrays. Using the
scanned array image, we quantified the probe intensities using
the Spotfinder tool from the TM4 Microarray Software Suite
(Saeed et al., 2003, 2006), giving us an MEV file for each array.
To load and manipulate the intensity data in the MEV files,
we used Bioconductor’s limma package (Gentleman et al., 2004;
Smyth, 2005) We subtracted the background intensities from the
foreground, giving us a background-corrected intensity for the
probes on each array. If, after this step a probe had a negative
intensity, we ignored it treating it as a missing value. We then
identified and removed any replicate samples that did not have
at least a 0.7 correlation with other replicates. For WT strain
there were 13, 12, and 7 replicates that passed this array-level QC
step in LB, LPM 4 h, and LPM 20 h conditions, respectively. For
the rpoE-deletion strain, the corresponding numbers were 2, 4,
and 2.
Next, we summarized replicate probe intensities into a single,
normalized expression value for each gene. Before summariza-
tion, we imputed missing values using a k- nearest neighbors
approach, as implemented in Bioconductor’s impute package
(Gentleman et al., 2004;Hastieetal.
1). We then summarized the
replicate intensities (there were two identical probes per gene) by
1Hastie, T., Tibshirani, R., Narasimhan, B., and Chu, G. Impute: Impute:
Imputation for Microarray Data. R package version 1.40.40.
calculating their mean. We finally normalized all of the mutant
and WT expression values using quantile normalization, as imple-
mented in the normalize.quantiles function of the preprocessCore
R package (Bolstad et al., 2003;Bolstad
2). We performed a sepa-
rate normalization for each of the three experimental conditions.
Using the normalized expression values, we next identified dif-
ferentially regulated genes between the σEand WT strains. Since
our sample size for the knockouts was small, we decided to use
the methodology described by Smyth et al., which involves using
a moderated t-statistic that is more reliable for a small number
of arrays (Smyth, 2004). The differential expression analysis was
performed using functions available in the limma package. All
microarray data is deposited on the Gene Expression Omnibus
(GEO) at the National Center for Biotechnology Information
(NCBI), accession numbers GSE25441 and GSE26755.
CHROMATIN IMMUNOPRECIPITATION
Salmonellla 14028s grown in LB to log phase, or in LPM for 4 h
or 20 h were cross-linked by 1% formaldehyde at room temper-
ature for 25 min, then quenched using 125 mM glycine for an
additional 5 min of incubation at room temperature. After cell
lysis and sonication, cell debris was removed by centrifugation at
13,000 rpm for 10 min at 4◦C, and the supernatant was collected
for the immunoprecipitation. The supernatant was split into two
samples. One was mixed with affinity purified rabbit anti-RpoE
antibody to immunoprecipitate σE–DNA complex, and the other
sample was mixed with rabbit monoclonal antibody to GFP as the
control (normal rabbit serum contains anti-Salmonella antibody
so that was not used as control). They were next incubated at 4◦C
overnight, and 50 μl of the Dynabead M-280 sheep anti-rabbit
IgG (Invitrogen) was added into the mixture. After 6h of incu-
bation at 4◦C with rotating, the beads were washed with a serials
of stringent buffers (Cho et al., 2008). Beads were resuspended
in 200 μl of elution buffer (50mM Tris-HCl at pH 8.0, 10mM
EDTA, and 1% SDS) and incubated at 65◦C overnight to reverse
the cross-linking. The supernatant was incubated with 1 μlof
RNaseA (QIAGEN) for 2 h at 37◦C to remove RNAs, followed by
incubation with 4 μl of proteinase K solution (Invitrogen) for 2 h
at 55◦C to remove proteins. The sample was then purified with a
PCR purification kit (QIAGEN). Gene-specific quantitative PCR
was carried out using the DNA samples.
QUANTITATIVE RT-PCR ANALYSIS
Total RNA was isolated using RNAlater Solution (Ambion),
RNeasy mini kit (Qiagen), and DNase to remove residual chro-
mosomal DNA (Qiagen) according to manufacturer’s instruc-
tions. RNA concentration was measured by NanoDrop ND-1000
spectrophotometer (NanoDrop Technologies, Inc.). cDNA was
synthesized using the iScript cDNA synthesis kit (Bio Rad)
and cDNA corresponding to 10 ng of input RNA was used as
template for real-time reaction containing Power SYBR green
(Applied Biosystems) and gene-specific primers. The primers
were designed with Primer Express 3.0 software and tested
for their amplification efficiencies (Table S2 ). The gyrB gene,
2Bolstad, B. M. preprocessCore: A Collection of Pre-Processing Functions. R
Package Version 1.28.20.
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Li et al. Salmonella σEregulatory network
encoding for the B subunit of the DNA gyrase, was utilized as
endogenous control. The RT-PCR reactions were carried out at
95◦C for 10 min, 95◦C for 15 s and 60◦C for 1 min for 40 cycles
(ABI 7700, Applied Biosystems). The expression ratio of each
gene was the average from three independent RNA samples and
was normalized to the level of gyrB.
IMMUNOBLOT ANALYSIS
The WT strain containing pASK-H-NS-3FLAG plasmid was
grown in LPM for 20 h in the presence or absence of anhydrote-
tracycline (AHT) for 4 h. The cells were washed and ∼5×107
colony-forming units were pelleted and re-suspended in Laemmli
sample buffer, boiled for 5min, and then separated on SDS-
PAGE. Proteins on the gel were then transferred to polyvinylidene
difluoride (PVDF) membranes (Millipore). After blocking in
Tris-buffered saline (TBS) plus 5% non-fat dry milk for 1 h,
membranes were probed with anti-FLAG monoclonal antibody
(Sigma). Membranes were washed and probed with secondary
antibody (anti-mouse IgG conjugated with peroxidase) (Sigma).
The immune complexes were detected via chemiluminescence
usingWesternLightning™(PerkinElmer),andthenexposedto
XAR Biofilm (Kodak). For immunoblot on H-NS, WT and rpoE
strains were grown in LB to log phase, or in LPM for 4 h or
20 h. Affinity purified rabbit anti-H-NS antibody was used as pri-
mary antibody, and goat anti-rabbit (IgG) antibody conjugated
to horseradish peroxidase (Cell Signaling Technology) was used
as the secondary antibody. In both cases, DnaK was probed at the
same time as loading control.
RESULTS
IDENTIFICATION OF σE-DEPENDENT GENES BY TRANSCRIPTIONAL
PROFILING
To study the global regulatory effect of σEon Salmonella gene
expression, WT (14028s) and rpoE strains were grown in
nutrient-richLBbrothtologphase,orinLPMfor4hand
20 h to mimic early and late intracellular infection, respec-
tively. Salmonella Typhimurium/Typhi microarray designed by
the Pathogen Functional Genomics Resource Center at the J.
Craig Venter Institute (JCVI) was used to analyze the transcrip-
tional changes under each condition. Gene expression values
were calculated as the log2ratio of fold change of rpoE mutant
to WT (Ta b l e S 2 ), and based on positive or negative value of
the ratio, the genes affected by σEare described as “activated
(up-regulated)” vs. “repressed (down-regulated),” which does not
necessarily imply a direct regulatory activity. In LB log phase, a
total of 1334 genes were differentially expressed (p≤0.05 and
fold change =1.5); in the LPM 4 h condition, 1295 genes were
differentially expressed; in the LPM 20 h condition, 911 genes
were differentially expressed. In total, 58% (2533 genes) of all
4355 Salmonella genes measured by the microarray were differ-
entially expressed in at least one of the three conditions (i.e.,
their expression was σE-dependent) (Figure 1A and Table S2).
In each of the three conditions, the number of genes that σE
activated and repressed was very similar, especially within the
LPM 4 h and 20 h conditions (Figures 1B,C). Sigma factors are
generally associated with positive regulation of gene expression,
but because microarrays cannot discriminate between direct and
indirect regulation, we expected and observed both positive and
negative regulation. There were 81 genes, belonging to miscel-
laneous functional categories, which exhibited the same regu-
latory trend across all three conditions. Sixteen of these genes
were up-regulated by σEin all 3 in vitro conditions, while 65
were down-regulated (Figures 1B,C and Table S3). To explain
why σEhas such a widespread effect on gene expression, we
investigated the influence of σEon other Salmonella regulators.
Approximately 40% of the known/predicted regulators (168 out
of 411) were transcriptionally regulated by σEin at least one
condition (Table S4). This group included most of the global
regulators and many were differentially affected under the differ-
ent growth conditions including Crp, Fis, ArcA, SlyA, Fur, and
H-NS (Figures 1D–F). The regulation of these regulators by σE
either directly or indirectly contributes to the complex regulatory
network at different stages of Salmonella infection.
To further validate the microarray results, we conducted qRT-
PCR on 13 genes that the microarrays identified as up- or
down-regulated; in each case, qRT-PCR confirmed the microar-
ray results (Figure 2). Moreover, out of the 62 σE-dependent
Salmonella genes previously identified by Skovierova et al., 44
(71%) of them were in agreement with our findings, including
rseA,rpoH,fusA,htrA,recB,eno,tolA,apl,yggT (Skovierova et al.,
2006).
FUNCTIONAL CATEGORIES AND GROUPS OF GENES REGULATED BY σE
The genes regulated by σEwere involved in a broad spectrum of
cellular functions, and classified into 19 categories according to
JCVI annotation (Table 1 ). Comparing the genome annotation
to the genes measured by microarray, our experiments covered
92–99% of each category. For the LB log phase, when cells are
actively dividing, energy metabolism and protein synthesis were
the two most abundant functional categories associated with up-
regulated genes, whereas transport and binding and cell envelope
functional categories were the most represented among down-
regulated genes. For the LPM 20 h condition, where cells were
exposed to significant stress, we observed the opposite scenario.
Here, genes involved in energy metabolism and protein synthesis
were mostly down-regulated by σE, while genes encoding trans-
port and binding and cell envelope proteins were up-regulated. For
the LPM 4 h condition, the cell envelope category was most rep-
resented for up-regulated genes (the second most representative
category of up-regulated genes at LPM 20 h), while transport and
binding and energy metabolism was most represented for down-
regulated genes. Across the three growth conditions, cell envelope,
energy metabolism,andtransport and binding functions were the
top three categories of σE-regulated genes.
Notably, using the three growth conditions, we observed that
σEdifferentially regulated four important groups of genes: (1)
SPI-1;(2)SPI-2;(3)protein synthesis;and(4)stress response.SPI-
1 genes were up-regulated by σEin LB log phase and in LPM
4 h condition, whereas in LPM 20 h condition, most of the SPI-
1 genes were unaffected (Figure 3A). In contrast, SPI-2 genes
were up-regulated by σEin LPM 4 h and 20 h conditions, but
were unaffected by σEin LB log phase (Figure 3B). Our obser-
vations are consistent with previous report that σEactivates the
expression of most SPI-2 genes only when Salmonella are grown
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Li et al. Salmonella σEregulatory network
FIGURE 1 | Overview of gene expression regulated by σEin Salmonella
Typhimurium cultured under three growth conditions. Salmonella WT and
rpoE-deletion strains were grown in LB medium to log phase or in acidic
minimal medium (LPM) for 4 h or 20 h in triplicate. Total RNA was isolated
and analyzed by the Salmonella Typhimurium/Typhi microarray (version 8).
The Venn diagrams show overlaps of total genes regulated by σE(A), genes
up-regulated by σE(B) and genes down-regulated by σE(C) in the three
growth conditions. The charts show differential gene transcription regulated
by σEin LB log phase (D), LPM 4 h condition (E) and LPM 20 h condition (F).
Each dot represents one gene of the Salmonella 14028s genome with the
x-axis showing gene order in relation to the gene location on chromosome,
and the y-axis showing log2-based fold changes of transcript of WT vs.
rpoE-deletion strains. Genes activated by σEare red, while genes repressed
by σEare green. Major global regulators regulated by σEare labeled.
in minimal acidic media (Yoon et al., 2009). Genes associated
with protein synthesis (e.g., translational initiation factors, pro-
tein translocation, and elongation) were up-regulated by σEin
LB log phase, which contrasted with samples from the LPM
20 h condition, where they were down-regulated (Figure 3C).
Consistent with previous findings that the phage shock protein
(Psp) system and two-component regulatory system CpxR/CpxA
compensate for the loss of σEfunction, we found that genes
in the psp operon, including pspA,pspB,pspD,andpspE,were
up-regulated in rpoE under LPM 20 h condition when com-
paredtoWT(Figure 3D)(Connolly et al., 1997; Becker et al.,
2005). The expression of cpxP, an indicator of the activation sta-
tus of the CpxR/CpxA system, was also found to be elevated
in rpoE whencomparedtoWTinLPM4hand20hsamples
(Figure 3D)(Kato et al., 2012). Moreover, σEup-regulated the
cold-shock protein genes cspA,cspC,andcspE; the universal stress
genes uspA and ynaF; the oxidative stress-response genes sodA,
sodB, and sodC_2; the hyperosmotic stress genes osmC, osmE, and
osmY; and the heat shock protein genes ibpA and ibpB under LB
log phase condition. These results suggest that σEcoordinates
multiple stress response systems to achieve appropriate gene
regulation.
σEUP-REGULATES SPI-2 GENE EXPRESSION BY INCREASING ssrB
TRANSCRIPTION IN THE CONDITION THAT MIMICS EARLY INFECTION
Although σEhas been found to regulate SPI-2 gene expres-
sion during infection-like conditions in previous studies, the
mechanism of how it accomplishes this is still not clear (Osborne
and Coombes, 2009; Yoon et al., 2009). To investigate how σE
manipulates SPI-2 gene expression, we selected four genes within
SPI-2 in different operons (ssrB,ssaE,sscA,andssaJ)andtwo
genes outside of SPI-2 (sseI and pipB) that encode the effectors
secreted by the SPI-2 TSSS, and then compared their transcrip-
tion in WT vs. rpoE strain in LB log phase, LPM 4 h and LPM
20 h conditions (Figure 4A). We observed that σEup-regulated
the expression of all the above genes in the condition that mim-
ics early infection (LPM 4 h) (Figure 4A, patterned bars). Since
ssrB encodes a general activator of SPI-2 genes, we investigated
whether or not σEup-regulated SPI-2 gene expression in LPM
4 h condition by increasing ssrB transcription (Coombes et al.,
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Li et al. Salmonella σEregulatory network
FIGURE 2 | Validation of microarray results by qRT-PCR. Groups of
genes both up- and down-regulated by σEwere selected from three
growth conditions (LB log, LPM 4 h, and LPM 20 h) based on microarray
results, and validated by qRT-PCR using primers designed inside those
genes. The results are plotted on a log2-scale comparing WT strain to
rpoE-deletion strains. Values are normalized with gyrB mRNA levels and
represent the average of RNA prepared from independent biological
triplicates.
Table 1 | JCVI functional categories of microarray results.
Functional Categories Total from Total measured Up-regulated by σEDown-regulated by σE
genome by microarray LB log LPM 4h LPM 20 h LB log LPM 4 h LPM 20 h
Amino acid biosynthesis 130 125 17 13 11 16 20 19
Biosynthesis of cofactors, prosthetic groups, and carriers 167 163 24 5 8 16 38 21
Cell envelope 479 437 45 77 44 90 54 34
Cellular processes 289 263 43 55 35 54 33 18
Central intermediary metabolism 170 160 14 16 11 19 30 18
DNA metabolism 166 150 18 17 13 21 22 19
Energy metabolism 610 563 111 53 34 55 76 59
Fatty acid and phospholipid metabolism 80 77 6 8 6 9 17 7
Hypothetical proteins 78 65 11 11 9 12 7 3
Mobile and extrachromosomal element functions 250 155 8 33 18 36 10 8
Protein fate 191 181 26 38 29 27 24 16
Protein synthesis 375 313 61 37 28 44 54 54
Purines, pyrimidines, nucleosides, and nucleotides 81 80 8 3 2 10 20 7
Regulatory functions 305 268 22 46 29 44 42 22
Signal transduction 26 20 3 3 4 6 4 2
Transcription 57 54 10 4 3 4 7 6
Transport and binding proteins 628 579 51 48 53 100 81 49
Unclassified 332 267 39 56 37 36 37 23
Unknown function 670 614 69 66 73 105 95 58
2007; Yoon et al., 2009). We confirmed that the presence of SsrB
increased the expression of the selected SPI-2 genes studied here
using qRT-PCR, by comparing the expression ratio of these SPI-2
genes in WT vs. ssrB strains (Figure 4B, black bars). To elim-
inate the effects of SsrB on σE–mediated SPI-2 gene expression,
we constructed an rpoE/ssrB double mutant strain and compared
ssaE,sscA,sseI,ssaJ,andpipB expression in ssrB vs. (rpoE,
ssrB). The expression ratios of the above 5 genes showed no
significant difference between these two strains (Figure 4B,white
bars), which means deletion of ssrB abolished the effects of σE
on SPI-2 genes in LPM 4 h condition. Altogether, our results
suggest that σEup-regulates ssrB transcription which results in
increased SPI-2 gene expression in the condition that mimics
early infection.
σEUP-REGULATES SPI-2 GENE EXPRESSION BY REPRESSING hns
TRANSCRIPTION IN THE CONDITION THAT MIMICS LATE INFECTION
In the condition that mimics late infection (LPM 20 h), the
expression of ssrB was unaffected by the presence of σE
(Figure 4A), suggesting that the general activator SsrB was not
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Li et al. Salmonella σEregulatory network
FIGURE 3 | Heat maps of four groups of genes that are differentially
regulated by σEin Salmonella grown in LB to log phase, or in LPM for
4 h or 20 h. Shown are genes involved in SPI-1 apparatus and effectors (A),
SPI-2 apparatus and effectors (B), protein synthesis (C), and stress response
(D). Red represents up-regulation of genes by σEwhile green represents
down-regulation.
causing the up-regulation of SPI-2 genes at this time point. We
next considered whether a change to the transcription of gen-
eral negative regulators might indirectly induce the expression
of SPI-2 genes. We focused on the nucleoid-associated pro-
tein H-NS because it has been shown to be a general repressor
of SPI-2 genes (Navarre et al., 2006). Under LPM 20 h condi-
tion, microarray data showed that σErepressed the expression
of hns (Table S2 ), which was further confirmed with qRT-PCR
(Figure 5A). Although other nucleoid-associated proteins, such
as YdgT, Hha, and StpA, also recognize and selectively silence the
expression of foreign DNA, none of them were down-regulated
by σE(Table S2 ), and therefore were not further investigated.
Western blot analysis indicated that σE–mediated hns down-
regulation was also reflected at the protein level in LPM 20h
condition (Figure 5B). The effects of H-NS on SPI-2 gene expres-
sion in LPM 20 h condition was examined by over-producing
H-NS instead of deleting hns, as it is required for viability in
Salmonella. Construction of a hns strain requires deletion of an
extra gene (rpoS or phoP), which would complicate the analysis
(Navarre et al., 2006). The WT strain was transformed with a
plasmid containing a FLAG-tagged H-NS under a tetracycline-
regulated promoter. As expected, H-NS was strongly induced
by the addition of anhydrotetracycline (AHT) as visualized by
western blot using anti-FLAG antibody (Figure 5C), and was fur-
ther verified using H-NS specific rabbit polyclonal antibodies
(data not shown). We measured the expression ratios of SPI-
2 genes when H-NS was induced vs. non-induced and found
that induction of H-NS resulted in 2–16 fold decrease of SPI-2
gene expression (Figure 5D). Therefore, under LPM 20 h con-
dition, H-NS functions to repress SPI-2 gene expression. Since
σErepresses hns transcription under the same condition, collec-
tively our results suggest that under the condition that mimics
late infection, σEup-regulates SPI-2 expression by repressing hns
transcription.
σECOUNTERS THE SILENCING OF H-NS ON SPI-2 GENE EXPRESSION
Several regulators (e.g., σS, SsrB and SlyA) have been shown to
be capable of relieving H-NS silencing of transcription (Mujacic
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Li et al. Salmonella σEregulatory network
FIGURE 4 | σEup-regulates SPI-2 gene expression by increasing ssrB
transcription under LPM 4h condition. (A) σEregulates SPI-2 gene
expression differently in three growth conditions. The WT and rpoE
strains were grown in LB to log phase, or in LPM for 4h or 20 h. SPI-2
gene expression was measured by qRT-PCR using gyrB as an internal
control. Shown are expression ratios comparing SPI-2 gene level in WT
vs. rpoE strains prepared from independent biological triplicates. (B) The
effects of SsrB and σEon SPI-2 gene expression in LPM 4h condition.
The WT, ssrB,and(ssrB, rpoE) strains were grown in LPM for 4 h.
The transcription of SPI-2 genes was measured by qRT-PCR. Expression
ratio comparing WT to ssrB indicates that SsrB activates SPI-2 gene
expression; Expression ratio comparing ssrB to (ssrB, rpoE) indicates
that the up-regulating effects of σEon SPI-2 gene expression are through
SsrB in LPM 4 h condition.
FIGURE 5 | σEup-regulates SPI-2 gene expression by repressing hns
transcription under LPM 20h condition. (A) σEdown-regulates hns
expression in LPM 20 h condition. The WT and rpoE strains were grown
in LB to log phase, or in LPM for 4 h or 20 h in biological triplicates. The
transcription of hns was measured by qRT-PCR using gyrB as an internal
control. The expression ratio compares the level of hns in WT vs. rpoE
in each condition. (B) The effects of σEon H-NS expression in LB log
phase, LPM 4 h, and LPM 20 h conditions. Western blots of H-NS in
protein lysates from WT and rpoE strains were generated using affinity
purified rabbit anti-H-NS antibody. DnaK was used as loading control. For
quantification, H-NS level in each strain under each condition was
normalized to DnaK level, then relative protein amount (WT/rpoE)for
each condition was calculated. The ratios of protein amount are shown at
the bottom. (C) Overexpression of H-NS through AHT induction.
Salmonella 14028s strain was transformed with a plasmid containing
FLAG-tagged H-NS (pASK-H-NS-3xFLAG) under AHT-inducible promoter.
The bacteria were grown in LPM for 20h in the presence or absence of
AHT. The level of H-NS was detected by Western blot using monoclonal
antibody to FLAG. (D) Overexpression of H-NS represses SPI-2 gene
expression. The WT strain containing pASK-H-NS-3xFLAG plasmid was
grown in LPM for 20 h with or without AHT induction. SPI-2 gene
expression was measured by qRT-PCR. The results are presented as
expression ratio comparing the strain in which H-NS is induced vs.
un-induced on a logarithmic scale.
and Baneyx, 2006; Perez et al., 2008; Walthers et al., 2011; Galagan
et al., 2013). To investigate if σEcan counter the H-NS silencing
of SPI-2 gene expression, we transformed both WT and rpoE
strains with the AHT-inducible plasmid (pASK-H-NS-3xFLAG),
which allowed us to change the expression of both hns and rpoE
independently. The expression of SPI-2 apparatus (ssaE,sscA)
and effector (sseI) genes were measured using qRT-PCR with
samples generated from LPM 20 h conditions with and without
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Li et al. Salmonella σEregulatory network
FIGURE 6 | σEcounters the silencing of H-NS on SPI-2 gene
expression. The WT and rpoE strain were transformed with plasmid
pASK-H-NS-3xFLAG, and grown in LPM for 20 h in the presence or absence
of AHT. SPI2 gene (ssaE,sscA,andsseI ) expression were measured by
qRT-PCR in each condition (triplicate biological samples) and standardized to
gyrB in a logarithmic scale.
AHT induction (Figure 6). Whether hns was induced or not,
the expression of ssaE,sscA,andsseI were higher in WT than
in rpoE strain (comparing “WT hns uninduced” to “rpoE
hns uninduced,” or “WT hns induced” to “rpoE hns induced”),
which is consistent with results in Figure 4A. The overexpression
of H-NS repressed SPI-2 gene expression (comparing “rpoE
hns uninduced” to “rpoE hns induced”), however when σEwas
present, the repression of H-NS on SPI-2 was relieved (compare
“WT hns induced” to “rpoE hns induced”). These results sug-
gest that σEis capable of countering the silencing of H-NS on
SPI-2 genes.
σEREGULATES ssrB AND hns EXPRESSION INDIRECTLY
σEactivates transcription by recognizing a canonical binding
motif at the promoter region (Skovierova et al., 2006; Osterberg
et al., 2011). To investigate if ssrB and hns are directly regulated
by σE, we searched the -35 and -10 elements of ssrB and hns
but failed to identify σE–dependent promoter (data not shown).
Since not all of the σE-binding sites in Salmonella contain the spe-
cific motif (in vivo ChIP-seq, unpublished data), which is also
true for other regulators (Galagan et al., 2013), we performed
a chromatin immunoprecipitation combined with quantitative
PCR assay (ChIP-qPCR) that compared the enrichment of target
regions in pulldowns using anti-σEor control (anti-GFP) anti-
body (Figure 7). Compared to rpoE promoter 3, which is a known
binding site of σE, primers designed around the promoter region
of ssrB and hns yielded no significant enrichment. Another gen-
eral regulator of SPI-2 gene, SlyA, was also studied, and similarly
did not exhibit elevated binding to σEthan control. Our results
suggestthatthereisnoin vivo occupancy of ssrB and hns promot-
ers by σE, therefore, it is likely that σEindirectly regulates ssrB and
hns, which up-regulates SPI-2 gene expression.
FIGURE 7 | σEdoes not bind to the promoter of ssrB,hns,andslyA
in vivo.Salmonellla 14028s was grown in LB to log phase, or in LPM for
4 h or 20 h, crosslinked, sonicated, and immunoprecipitated with affinity
purified rabbit anti-RpoE antibody (sample) or rabbit monoclonal antibody to
GFP (control) to pull down σEinteractors, after removal of proteins and
RNAs, purified DNA was used as template for quantitative PCR with
primers designed around the promoter region of each gene. The promoter
3 region of rpoE (rpoEp3) was used as a positive control. Shown are the
binding ratios comparing σE-specific vs. non-specific binding displayed in a
logarithmic scale. The mean and S.D. values were obtained from
independent biological triplicates.
DISCUSSION
As an alternative sigma factor, σEfunctions to coordinate gene
expression in response to extracytoplasmic stresses. It recog-
nizes a “canonical sequence” at the promoter region of target
genes and initiates transcription with core RNA polymerase
(Skovierova et al., 2006; Osterberg et al., 2011). Compared to the
genes reported to be regulated by σEin Salmonella, our study
expanded the σEregulon enormously, partially because multi-
ple growth conditions that mimic different stages of Salmonella
infection were exploited (Skovierova et al., 2006; Yoon et al.,
2009). A large number of genes regulated by σEare involved in
various biological processes including cell envelope biosynthesis
and degradation, energy metabolism, protein synthesis, trans-
port and binding, as well as functions that are not known. Since
the activation of SPI-2 genes is essential for Salmonella intra-
cellular survival and our current and previous studies indicated
that σEup-regulates SPI-2 gene expression under infection-like
conditions, we further investigated how σEmanipulates SPI-2
gene expression (Yoon et al., 2009). We found that σEindi-
rectly regulates ssrB and hns transcription, also counters the
silencing of H-NS on SPI-2 genes. Together with more than a
100 other Salmonella regulators transcriptionally regulated by
σE, it is likely the strong regulatory effects of σEmay account
for the extremely attenuated phenotype exhibited in rpoE null
mutant.
Many regulators of Salmonella act on SPI-2 by regulating the
two-component regulator SsrB and the MarR-type regulator, SlyA
(Yoon et al., 2009). It is likely the effects of σEon SPI-2 tran-
scription is mediated by SsrB because overexpression of SsrB, but
not SlyA, can complement the decrease of SPI-2 expression as
www.frontiersin.org February 2015 | Volume 6 | Article 27 |9
Li et al. Salmonella σEregulatory network
a result of rpoE deletion (Yoon et al., 2009). Therefore, in this
study, we focused on the coordinated function of ssrB and σE
on SPI-2 regulation without examining the involvement of SlyA
(Figure 4A). We found that σEup-regulated SPI-2 gene expres-
sion through ssrB in early stages of infection, and through hns
during late stages of infection. It is not known why Salmonella
utilizes different mechanisms to meet its need in activating SPI-
2 genes, yet in late stages of infection overall protein synthesis
was down-regulated by σE(Figure 3C), consistent with its effect
on hns.Moreover,neitherssrB nor hns was regulated by σE
directly (Figure 7), on the contrary, both ssrB and hns contain
σD-recognizable promoters, consistent with previous findings
(Kroger et al., 2012). Since σDhas been shown to be directly reg-
ulated by σE, we speculate that σEmight manipulate ssrB and hns
transcription through a σE-σD-ssrB/hns cascade (Skovierova et al.,
2006). Alternatively, the cascade could be represented as σE-σD-
ompR/slyA/phoP-ssrB/hns asOmpR,SlyA,andPhoPhavebeen
shown to directly regulate ssrB and all of their transcriptional
start sites are associated with σD(Lee et al., 2000; Feng et al.,
2003; Bijlsma and Groisman, 2005; Okada et al., 2007; Kroger
et al., 2012). Further investigations are needed to verify the above
hypotheses.
This is the first report that σEis capable of countering the
silence of H-NS on SPI-2 genes (Figure 6). The SPI-2 genes of
Salmonella are normally bound by H-NS, which is a barrier to
transiting RNA polymerase. However, this transcriptional barrier
is relatively weak (∼7pN) and is easily overcome in conditions
that induce SPI-2 expression (Fang and Rimsky, 2008). It has
been reported that SsrB counters the silencing of H-NS on SPI-2
genes by displacing H-NS bound in its polymerization mode, and
subsequently activates SPI-2 transcription (Walthers et al., 2007,
2011). In contrast, SlyA does not displace H-NS from the DNA,
but remodels the H-NS-DNA nucleoprotein complex to recruit
RNA polymerase and promotes PhoP-mediated gene transcrip-
tion (Perez et al., 2008). σScounters the silencing of H-NS on
selective genes that can be transcribed by both σDand σS, since
H-NS assembles nucleoprotein complexes with σDbut not σS
RNA polymerase holoenzyme, σSis able to escape H-NS trap-
ping and the genes can be transcribed through σS–dependent
promoters (Shin et al., 2005; Typas et al., 2007). Whether σE
exploits similar mechanisms as SsrB, SlyA, or σSor uses another
mechanism to relieve H-NS silencing on SPI-2 genes is under
investigation.
While entering into stationary phase is known to induce σE,
the actual signal that activates σEis the accumulation of misfolded
outer membrane proteins within the periplasm, which occurs
under a variety of conditions (Mecsas et al., 1993; Missiakas et al.,
1996; Raivio and Silhavy, 1999). Here, we reported that a large
amount of genes are regulated by σEin LB log phase (Figure 1),
consistent with previous results (Kabir et al., 2005). Although the
house-keeping sigma factor σDis considered to play the major
role to maintain metabolism during exponential phase and is
required for viability as expected, σEis also required for growth in
both normal and stress conditions (De Las Penas et al., 1997). σE
and σDrecognize different binding motifs, however, both of them
can activate multiple general regulators, through which the reg-
ulation effect is magnified (Hook-Barnard et al., 2006; Rhodius
et al., 2006). We also found that alternative sigma factors func-
tion together to regulate stress response genes. For instance, the
heat shock protein genes ibpA and ibpB are directly regulated
by σH, and indirectly, by the action of σEon σH(Figure 3D)
(Nonaka et al., 2006; Skovierova et al., 2006). σSis the master
regulator of stress response genes, and plays a dominant role
in regulating hyperosmotic stress in Salmonella (Hengge-Aronis,
2002; McMeechan et al., 2007). We observed that the osmolar-
ity stress genes osmB,osmC,andosmY are also regulated by σE
(Figure 3D)(Bang et al., 2005). The co-regulation of gene expres-
sion by sigma factors benefits the pathogen by simultaneously
inducing general and specific stress responses to many environ-
mental factors, even if that resistance is not immediately required
(Battesti et al., 2011).
In addition to up-regulating gene expression, σEwas found to
down-regulate a large number of genes in our study (Table S2).
This phenomenon has been observed before in both Salmonella
and E. coli, however at a much smaller scale (Bang et al., 2005;
Kabir et al., 2005). The down-regulation of gene expression may
not be a direct effect of σE, rather an indirect repression through
its downstream transcriptional regulators, or by binding to small
non-coding RNAs (sRNAs), such as RybB and MicA, both of
which have been found to function as global regulators (Papenfort
et al., 2006; Gogol et al., 2011). Some of the genes repressed by
MicA and RybB were also found to be down-regulated by σE
in our study, such as pal and gloA (Table S2). Additionally, we
found that σEregulates hfq expression in both nutrient-rich and
infection-like conditions (Tab le S 2 ). Hfq is a sRNA-binding pro-
tein that bridges sRNA to act on trans-encoded target mRNAs,
therefore modulates the stability and translation of the target
mRNAs (Vogel and Luisi, 2011). Both RybB and MicA are reg-
ulated by Hfq in repressing outer membrane protein expression.
For instance, Hfq significantly enhances RybB binding to ompC
and ompD mRNAs, facilitating the decay of omp mRNAs when
the cell experiences extracytoplamic stress (Udekwu et al., 2005;
Papenfort et al., 2006). Hfq controls the expression of at least 20%
of all Salmonella genes directly or indirectly, and has a pleiotropic
effect on Salmonella virulence through regulation of motility,
outer membrane protein expression, invasion, and intracellular
growth (Sittka et al., 2007, 2008; Ansong et al., 2009). As the list
of sRNAs in Salmonella grows, these newly identified sRNAs may
help to explain the global impact of σEon gene expression (Kroger
et al., 2012).
By comparing the global transcription profiles of σEacross
three growth conditions, we established that σEcoordinated the
gene expression of Salmonella for proliferation and/or survival
in response to various environments. The role of σEin global
gene regulation and SPI-2 gene activation observed in this study
explains why σEis required for systemic mouse infection, and
shows that σEis the conductor of the Salmonella gene regulation
orchestra, whether under stress or not.
ACKNOWLEDGMENTS
This work was supported by the National Institute of Allergy
and Infectious Diseases (5R01 AI022933 and IAA Y1-AI-
8401) and the National Institute of General Medical Sciences
(GM094623).
Frontiers in Microbiology | Food Microbiology February 2015 | Volume 6 | Article 27 |10
Li et al. Salmonella σEregulatory network
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: http://www.frontiersin.org/journal/10.3389/fmicb.
2015.00027/abstract
Table S1 | List of primers used in this study.
Table S2 | List of genes regulated by σEunder three growth conditions.
Table S3 | List of genes up- and down-regulated by σEin all three growth
conditions.
Table S4 | List of regulators regulated by σEin at least one of the three
growth conditions.
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Conflict of Interest Statement: The authors declare that the research was con-
ducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 11 November 2014; paper pending published: 25 November 2014; accepted:
08 January 2015; published online: 10 February 2015.
Citation: Li J, Overall CC, Nakayasu ES, Kidwai AS, Jones MB, Johnson RC, Nguyen
NT, McDermott JE, Ansong C, Heffron F, Cambronne ED and Adkins JN (2015)
Analysis of the Salmonella regulatory network suggests involvement of SsrB and H-
NS in σE-regulated SPI-2 gene expression. Front. Microbiol. 6:27. doi: 10.3389/fmicb.
2015.00027
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