PLoS ONEplosplosonePLoS ONE1932-6203Public Library of ScienceSan Francisco, USAPONE-D-10-0386410.1371/journal.pone.0015321Research ArticleBiologyGenomicsGenome expression analysisMicrobiologyBacterial pathogensHost-pathogen interactionPathogenesisGenetics and GenomicsMicrobiologyCoxiella burnetii Transcriptional Analysis Reveals
Serendipity Clusters of Regulation in Intracellular BacteriaCoxiella burnetii
Transcriptional AnalysisLeroyQuentin1LebrigandKevin2ArmougomFabrice1BarbryPascal2ThiéryRichard3RaoultDidier1*Unité de Recherche en Maladies Infectieuses et Tropicales
Emergentes, CNRS-IRD, UMR 6236, Faculté de Médecine,
Université de la Méditerranée, Marseille,
FranceInstitut de Pharmacologie Moléculaire et Cellulaire (IPMC), UMR
6079 CNRS/UNSA, Sophia Antipolis, FranceUnité de Pathologie des Ruminants, Agence Française de
Sécurité Sanitaire des Aliments (AFSSA) Sophia Antipolis,
FranceBereswillStefanEditorCharité-University Medicine Berlin, Germany* E-mail: didier.raoult@gmail.com
Conceived and designed the experiments: PB RT DR. Performed the experiments:
QL KL FA. Analyzed the data: QL RT DR. Contributed
reagents/materials/analysis tools: PB DR. Wrote the paper: QL RT DR.
The authors have declared that no competing interests exist.
201021122010512e153212110201051120102010Leroy et alThis is an open-access article distributed under the
terms of the Creative Commons Attribution License, which permits unrestricted use,
distribution, and reproduction in any medium, provided the original author and
source are credited.
Coxiella burnetii, the causative agent of the zoonotic disease Q
fever, is mainly transmitted to humans through an aerosol route. A spore-like
form allows C. burnetii to resist different environmental
conditions. Because of this, analysis of the survival strategies used by this
bacterium to adapt to new environmental conditions is critical for our
understanding of C. burnetii pathogenicity. Here, we report the
early transcriptional response of C. burnetii under temperature
stresses. Our data show that C. burnetii exhibited minor
changes in gene regulation under short exposure to heat or cold shock. While
small differences were observed, C. burnetii seemed to respond
similarly to cold and heat shock. The expression profiles obtained using
microarrays produced in-house were confirmed by quantitative RT-PCR. Under
temperature stresses, 190 genes were differentially expressed in at least one
condition, with a fold change of up to 4. Globally, the differentially expressed
genes in C. burnetii were associated with bacterial division,
(p)ppGpp synthesis, wall and membrane biogenesis and, especially,
lipopolysaccharide and peptidoglycan synthesis. These findings could be
associated with growth arrest and witnessed transformation of the bacteria to a
spore-like form. Unexpectedly, clusters of neighboring genes were differentially
expressed. These clusters do not belong to operons or genetic networks; they
have no evident associated functions and are not under the control of the same
promoters. We also found undescribed but comparable clusters of regulation in
previously reported transcriptomic analyses of intracellular bacteria, including
Rickettsia sp. and Listeria monocytogenes.
The transcriptomic patterns of C. burnetii observed under
temperature stresses permits the recognition of unpredicted clusters of
regulation for which the trigger mechanism remains unidentified but which may be
the result of a new mechanism of epigenetic regulation.
This work was supported by a grant from the Agence National pour la Recherche,
France (contract number: ANR-06-PNRA-031; VIACOX project). The funders had no
role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.Introduction
C. burnetii is a Gram-negative intracellular γ-proteobacterium
that causes Q fever, a zoonotic disease with a worldwide distribution [1]. Q fever can
manifest as an acute or chronic illness. Acute Q fever is typically a self-limiting
febrile illness during which pneumonia or hepatitis can occur, whereas chronic Q
fever is a severe illness that may cause patients to present endocarditis, vascular
infection, osteomyelitis and chronic hepatitis [1]. The major route of
contamination with C. burnetii is as an aerosol. C.
burnetii displays antigenic variation in its lipopolysaccharides [2]. In phase I,
the bacterium is highly infectious, and this corresponds to the natural phase found
in animals, humans and arthropods, whereas phase II, which is not very infectious,
presents truncated lipolysaccharides and can be obtained after several passages in
cell culture or embryonated eggs [1]. The C. burnetii genome was sequenced in
2003, and its size is approximately 2 Mbp with a plasmid of approximately 38 kbp
[3].
Recently, five new isolates of C. burnetii were sequenced [4].
C. burnetii displays a complex intracellular cycle, leading to the
formation of spore-like forms [5]. McCaul and Williams have proposed the terms
“small-cell variant” (SCV) and “large-cell variant” (LCV) to
differentiate the two C. burnetii cell forms observed in
persistently infected cells [6]. SCV are metabolically inactive and resistant to osmotic
pressure and correspond to the extracellular form of the bacterium. SCV attach to
the eukaryotic cell membrane to enter phagocytic cells. After phagolysosomal fusion,
acid activation of the metabolism of SCV may lead to the formation of LCVs. Both LCV
and SCV have a typical bacterial Gram-negative cell wall with two layers separated
by the periplasmic space. However, a dense material fills the periplasmic space in
SCV. This material is composed of proteins and peptidoglycans and may explain the
increased resistance of SCV to environmental conditions [7]. The extracellular forms of
C. burnetii resist environmental conditions such as desiccation
and low or high pH, chemical products such as ammonium chloride, disinfectants such
as 0.5% sodium hypochlorite, and UV radiation [1], [8].
Temperature change is the most common stress that all living organisms encounter in
natural habitats. To overcome critical situations that could be generated by extreme
temperatures, bacteria have evolved complex and specific mechanisms that are
referred to as cold shock and heat shock responses [9]. Intracellular bacteria
exhibit small genomes that show an evolutionary tendency toward genomic reduction,
which could be associated with a lower adaptation capacity to environmental changes
[10]–[12]. A number of intracellular bacteria have been observed to
adapt to environmental changes, including T. whipplei and
Rickettsia sp. [13]–[16]. Different obligate
intracellular bacteria have exhibited the expression of specific genes encoding
chaperone proteins and proteases that regulate the misfolding of proteins under
stress conditions and alarmone accumulation. A previous transcriptional microarray
study has been performed to improve an axenic medium for the
C.burnetii culture [17].
Coordinated virulence gene expression is critical for bacteria during the course of
infection. Global transcriptomic approaches have highlighted epigenetic mechanisms
associated with bacterial pathogenicity. Cossart et al. showed that noncoding RNA
(ncRNA) called small RNA (sRNA) was associated with Listeria
monocytogenes pathogenicity through use of tilling microarray
technology [18]. More recently, a sRNA microarray approach allowed
researchers to discover that 6S RNA is implicated in intracellular multiplication
[19]. A
bacterial RNA seq study found that Chlamydia trachomatis exhibits
regulation of ncRNA, including 5′ or 3′ untranslated regions and sRNA,
during its cellular cycle [20]. These ncRNAs are involved in mechanisms that target gene
regulation [21]–[24]. These levels of regulation show that bacterial gene
regulation seems to be much more complicated than suggested by the
promoter-and-transcription-factor paradigm.
Here, the early adaptive responses and the regulation mechanisms of C.
burnetii exposed to various sudden temperature shifts were investigated
using a whole-genome microarray. We also focus on the specific regulation mechanisms
of C. burnetii and other intracellular bacteria to adapt in
response to environmental stress.
ResultsMicroarray experiments
The complete transcriptional profile of C. burnetii exposed to
stress temperatures was determined using a whole-genome microarray. Our
microarray was spotted in quadruplicate and contained 1990 gene probes that
corresponded to ∼98.7% of the coding sequences of this species. Our
microarray was validated by self-comparison with genomic DNA and cDNA
hybridization (data not shown). In our experimental design, the reference group
corresponded to the Nine Mile strain growing at 35°C in normal conditions,
while the test group corresponded to the Nine Mile strain exposed to stress
temperatures for 30 or 60 min. Bacteria were submitted to stress temperatures of
4 or 42°C, which represent the cold shock (CS) or heat shock (HS),
respectively. RNA from bacteria and L929 cells were extracted simultaneously to
avoid changes in transcriptomic profile after the bacterial purification
process. Eukaryotic RNA was depleted using the MicrobEnrich Kit, which is based
on a subtractive hybridization strategy. We found an atypical profile for
C. burnetii RNA (Figure 1). The cDNA was amplified using
random nucleotides and the highly processive phi29 polymerase. The
hybridizations were performed in triplicate with three independent cultures.
Quantification and t-test analyses were applied to determine
the genes that were differently expressed at a significant level of confidence
of above 95% with a 2-fold cut-off (Table S1).
To confirm the global response of the Nine Mile strain, RT-PCR was performed
(Table
S2).
10.1371/journal.pone.0015321.g001
Eukaryotic RNA depletion and the atypical profile of C.
burnetii rRNA.
(A) This figure represents the electrophoregram showing the overlap of
total RNA after RNA extraction and bacterial RNA after eukaryotic RNA
depletion. (B) This figure represents the electrophoregram showing the
overlap of bacterial RNA after eukaryotic RNA depletion RNA and
bacterial mRNA after bacterial rRNA depletion. (C) This figure
represents the gel-like representation of the fractions obtained after
the different RNA depletions.
General overview
The differentially expressed genes and transcriptomic profile of C.
burnetii grown at 35°C and then submitted either to heat shock
(42°C) or to cold shock (4°C) for 30 min or 1 h, respectively, are shown
in Table
S1. Our transcriptomic analysis of the C. burnetii
response to stress temperatures revealed the differential expression of 190
genes, including 140 genes for the CS treatment (85 for 30 min and 62 for 60 min
of exposure) and 96 genes for the HS treatment (49 genes for 30 min and 58 for
60 min of exposure) (Table S1 and Figure S1).
Surprisingly, a clustering analysis of the differentially expressed genes under
the four temperature stress conditions showed that only small differences of
expression were detectable between the four treatments (Figure 2).
10.1371/journal.pone.0015321.g002
Hierarchical clustering of differentially expressed genes.
CS30, CS 60, HS30 and HS60 represent the cold shock stress for 30 min and
60 min and the heat shock stress for 30 min and 60 min, respectively.
Green plots represent genes that are downregulated upon temperature
stress, red plots represent genes that are upregulated upon temperature
stress, and gray plots represent genes with variable regulation observed
in biological replicates (p>0,05).
Functional analysis
We functionally classified the differentially expressed genes according to
Cluster of Orthologous Groups database (COG) [25]. We determined the
proportion of different functional categories for each condition (Figure 3) and for genes that
were differentially expressed in at least one of the stress conditions. The main
category of differentially expressed genes was cell wall, membrane and envelope
biogenesis (M), with up to 15% following 30 min heat shock. The genes
associated with category M encoded outer membrane proteins or proteins involved
in the synthesis of lipopolysaccharides, peptidoglycans and mureins. The second
principal category observed were genes involved in amino acid transport and
metabolism (F), which mostly included genes coding for transport system
components (arginine and dipeptides). Nucleotide transport and metabolism (E)
genes were also highly regulated, especially under cold shock treatment. In
contrast to category E, the intracellular trafficking, secretion, and vesicular
transport functional category (U) appeared to be heat shock–specific.
Genes involved in cellular functions (transcription (K), replication (L) and
translation (J)), representing principal and secondary metabolism, were also
differentially expressed. Genes without an associated COG or with an unknown
function–associated COG represented approximately 45% of the genes
that were differentially expressed.
10.1371/journal.pone.0015321.g003
Functional category classification of genes differentially expressed
upon exposure to stress temperatures.
(A) The figure represents the proportion of genes differentially
expressed according to the COG functional classification for each
condition and genes differentially expressed at least one time in all of
the conditions (All).
Regulon organization
Figure 4 shows that targeted
large zones of the bacterial chromosome were simultaneously regulated under
stress conditions. We analyzed the structure of these clusters of regulation.
Each cluster contained between 5 and 11 genes, at least half of which were
differentially expressed (Figure S2). These clusters contained genes
that are not necessarily organized into operons, and they can be found on both
genomic strands. To check whether this clustering pattern was statistically
significant, we split the genome into windows of 5 to 11 genes and counted the
number of differentially expressed genes in each. We included in the clusters of
regulation all of the windows in which at least the half of genes were found
differentially expressed. We found that these clusters of regulation contained
differentially expressed genes that were significantly associated compared to a
random distribution in the genome (Figure 5 and Table S3). Although the genes mostly occurred
in complete operons, single ORFs and incomplete operons were also present in
some clusters (Figure S2 and Table S4). To elucidate the mechanism of
these regulation clusters, we monitored gene functions within the clusters based
on COG classification, but we did not find an enrichment of any specific
functional category associated with our clusters compared to rest of the genome
(Table
S5). Finally, we focused on a functional protein association network
using the STRING database 8.2 [26]. Based on the number of connections (score >500)
per protein, we determined whether the proteins encoded by the genes included in
our clusters were specifically connected compared other C.
burnetii proteins, but no significant differences were found (Figure S3
and Table
S6). Though these clusters of regulation included a number of genes
that do not have obvious associated functions, we looked for networks that could
link our clusters together and help us to understand this organization of gene
expression regulation. Analysis of the protein association network showed that
the different clusters seemed to be highly connected for the heat and cold shock
conditions, but the connections were mostly spatial connections and not
functional (Figure S4). We also looked for structural genomic organization
homology between C. burnetii and other sequenced
γ-proteobacteria that are phylogenetically close to this species according
to their 16S rRNA sequences (Legionella sp. and
Francisella sp.). The genes implicated in clusters of
regulation in C. burnetii presented no clearly identified
synteny with those of Legionella sp. or
Francisella sp. (Table S7). Finally, we compared the promoter
sequences included in our clusters. We aligned the regions from -1000 bp to the
translation start site (TSS). The phylogenic trees obtained from these
alignments did not show clustering of promoters associated with either up- or
downregulation. We also examined predicted promoters using the Neural Network
Promoter Prediction method [27]. We did not find any clearer clustering of promoters
associated with gene regulation (Figure S5). We also extracted the region from
-10 to the TSS for every transcriptional unit and analyzed the CG% of
these sequences (Table S8) to look for a correlation between GC% and
transcriptional regulation. We observed no correlation between transcriptional
regulation and the GC% of the -10 to translational start site sequences.
Furthermore, we examined data from transcriptomic studies on other obligate
intracellular bacteria. We collected data from the GEO database and Array
Express to look for this kind of spatial regulation in other species, and we
found that this type of regulation was also present in other species, including
Rickettsial species [14]–[16], [28],
Tropheryma whipplei[13] and
Listeria monocytogenes[18] (Table S9).
Figure 6 shows that
large regions of the genomes of R. rickettsii, T.
whipplei and L. monocytogenes can be highly
regulated, comparable to the clusters of regulation found here.
10.1371/journal.pone.0015321.g004
Transcriptional profiles of the early responses to temperature
stress.
(A) The Outer circle represents the ORFing of C.
burnetii genome. The blue, purple and black sections
represent respectively the spotted ORF from the strand +, the
spotted ORF from the strand – and the ORF not spotted. (B) The
diagram represents the level of interactions with the other proteins
based on String software. (C) The inner circles represent the
transcriptomic profiles observed with the four tested conditions. The
green, red and gray sections represent respectively the down-regulated,
the up-regulated and the not regulated genes.
10.1371/journal.pone.0015321.g005
Distribution of differentially expressed genes.
This figure represents the total number of differentially expressed genes
included in different window sizes (between 5 and 11 genes) that contain
differentially expressed genes.
10.1371/journal.pone.0015321.g006
Transcriptional profiles from previously reported analyses of
intracellular bacteria.
The Outer circle represents the ORFing of the different intracellular
bacteria. The blue and purple sections represent respectively the
spotted ORF from the strand + and the spotted ORF from the strand
–. The inner circles represent the transcriptomic profiles
observed in the different studies. The green, red and gray sections
represent respectively the down-regulated, the up-regulated and the not
regulated genes.
Discussion
In this study, we examined the early response of gene expression patterns in
C. burnetii to cold and heat shock using a global
transcriptional approach based on microarray technology. Microarray-based
transcriptional studies for obligate intracellular bacteria have limitations, such
as obtaining RNA of sufficient quality and quantity [29], [30]. Bacterial purification from
infected cells involves several steps at 4°C, and bacteria are highly sensitive
to cold shock [31].
To prevent the treatments from skewing the results, we extracted eukaryotic and
prokaryotic RNA simultaneously, and eukaryotic RNA was then removed by subtractive
hybridization. We observed an atypical rRNA profile (Figure 1) with three peaks. This atypical profile
is due to an insertion sequence in the 23S rRNA gene, as previously described [32], [33]. Considering
the 23S rRNA split, we obtained purified C. burnetii RNA of good
quality. This strategy of eukaryotic RNA depletion coupled with cDNA amplification
was previously successfully devised for the global transcriptomic analysis of
intracellular bacteria [34]. Finally, the results obtained by qRT-PCR validate our
microarray hybridization experiments, which were carried out with bacterial RNA
extracted from three independent experiments.
According to our results, Coxiella burnetii appears not to be highly
sensitive to temperature shifts corresponding to CS and HS. We found few genes that
were differentially expressed (around 3±1% per temperature stress
conditions). The genes regulated upon exposure to stress temperatures showed minor
changes, with up to a 4-fold change in their expression. Thus, we speculate that
host cells provide a stable environment and can partially decrease transcriptional
responses from occurring in obligate intracellular bacteria. Surprisingly, a
clustering analysis of the differentially expressed genes under the four temperature
stress conditions examined (Figure
2) shows that only slight differences of expression were detectable
between the four conditions (as was previously shown with for T.
whipplei) [13]. These similar transcriptomic profiles suggest that
C. burnetii uses identical strategies to protect itself from CS
and HS during its early exposure to these conditions within cells (Figure 2).
Even if bacteria do have the capacity to adapt quickly, our study largely reflects
the very early and early responses of C. burnetii to temperature
shifts. Moreover, we point out these transcriptomic profiles reflect that C.
burnetii could have been under growth arrest. A slowdown in cellular
division in C. burnetii could be supported by the downregulation of
genes coding for the septum placement (ftsZ) [35], [36][37], the
segregation of the plasmid (parB) [38] and genes associated with cell
division (ftsY, gidAB) [39], [40]. The downregulation of genes
implicated in alarmone degradation (rpoZ, spoT and
gmK) [41], [42] indicates a guanosine pentaphosphate or tetraphosphate
((p)ppGpp) accumulation, which is involved in the stringent response and in
bacterial sporulation [42]–[44]. The stringent response is classically followed by growth
arrest. While most of genes coding for chaperone proteins are generally
underexpressed, Hsp90 could be activated via HemE [45]. The cell wall
and the membrane of C. burnetii seem to be modified and are
associated with a spherical shape (merBCD) [43], [46]. The bacteria also undergo
homeostatic maintenance, in which ABC transport and efflux pumps are implicated
(artM, artQ, opp system). The
decrease in bacterial division coupled with the putative morphological aspects, the
changes in the membrane and cell wall, and the homeostatic maintenance could
potentially correlate to a transformation of C. burnetii into a
metabolically inactive sporulation-like form (SCV) [5]. The SCV form seems to be
associated with the stress response of C. burnetii and could confer
on the bacteria strong resistance to environmental changes, such as CS and HS.
Surprisingly, we observed that significantly differentially expressed genes were
mostly spatially clustered following exposure to stress temperatures (Figure 3), and we found that these
genes were highly significantly spatially associated compared to a random
distribution. Then, we hypothesized that this distribution was associated with a
transcriptional regulation mechanism. Different levels of bacterial gene expression
regulation have been previously characterized, such as organization related to
operons and regulons [47]. However, the clustering found in this study was not
obviously associated with operon organization. Some genes were even found on the two
different DNA strands. The second level of regulation could be related to functional
associations and network connections. A study of the Rickettsia
prowazekii transcriptional response to cold shock found that that only
genes associated with posttranscriptional modification, such as protease and
chaperon proteins, were differentially regulated [14]. However, our investigation of
functional associations using COG classifications and network connections did not
allow us to find any obvious associations. Furthermore, a study of genomic
organization showed that our clusters of differentially expressed genes were not
highly syntenic with those of other bacteria, in particular with phylogenetically
closely related bacteria, including L. pneumophila and F.
tularensis. This could indicate a lack of functional selection
pressure. Another transcriptional level of regulation is the regulon. A regulon is a
collection of genes or operons under regulation by the same regulatory protein. The
observed downregulation of the gene coding for the RNA polymerase omega subunit in
all of our experimental conditions directed our research toward the regulon
phenomenon. We analyzed the downstream intergenic sequences of our differentially
expressed genes to look for similarity in promoter patterns. As we mentioned
previously, heat shock appears to be involved (p)ppGpp accumulation within these
bacteria. ppGpp is known as a transcriptional regulator [48], [49], and DksA, which binds to
the RNA polymerase secondary channel, potentiates the effects of (p)ppGpp on
transcription. The direct activation or repression of a gene promoter by (p)ppGpp
and DksA is dictated by specific DNA sequence motifs [48], [49]. Repressed genes are
typically GC rich between the -10 hexamer box and the TSS, whereas activated genes
are typically AT-rich in this position. Our analysis of promoter regions did not
uncover any correlation of GC content and regulation within the regulated genes or
the genes contained in clusters. The observed clustering of differentially expressed
genes could not be attributed to (p)ppGpp or DksA regulation associated with the -10
to TTS region of these genes. The promoter analysis does not highlight a putative
role associated with regulons. Thus, it is easy to speculate that the regulation
observed in this study could be due to epigenetic regulatory factors, or it could be
an artifact from our methods.
To confirm the existence of this clustering of differentially expressed genes around
the genome, we collected data from transcriptional microarray analyses of different
obligate intracellular bacteria that we listed in a recent review [30]. From these
data, we easily observed undescribed but comparable clusters of differentially
expressed genes in different conditions for Rickettsia sp. [15], [16], [28], [50] and T.
whipplei[13] (Figure 6 and Table S9).
These studies have mostly focused on environmental changes. These findings indicate
that regulation that can occur under conditions of stress. However, these studies
were performed with low-density arrays and could be an artifact of the hybridization
or analysis methods used. DNA probes are generally randomly spotted or synthesized
on glass surfaces. In this regard, we can eliminate hybridization artifacts. A
recent transcriptomic analysis of L. monocytogenes was performed
using tilling microarrays [18]. Tilling arrays permit the investigation of whole genomes
and should clearly reflect transcriptomic profiles. This transcriptomic analysis
using tilling arrays for L. monocytogenes highlighted clusters of
regulation (Figure 6 and Table S9). It
is possible that the limited number of RNA-seq studies of intracellular bacteria
could explain why we have not observed these regulatory arrangements in previous
RNA-seq studies. Here, we only focused on obligate intracellular bacteria and one
facultative bacterium, but the observation of these regulatory clusters in different
bacteria allowed us to confirm that these clusters appear to be a real, undescribed
regulation phenomenon. Though we think that the definition of the observed
regulatory clusters depends on the threshold applied to the data, this may be
indicative of hot spots for coregulation, independent of operons or strain
positions.
These hot spots of regulation do not correspond to classical transcriptomic
regulation within the promoter-and-transcription-factor paradigm. We speculate that
an epigenetic regulation mechanism is responsible for the clustering of
differentially expressed genes. Furthermore, recent studies based on bacterial
RNA-seq methods [20], [51] or tilling microarrays [18] have focused on a new
level of gene expression regulation. ncRNA epigenetic regulation, including sRNA and
riboswitches in 5′ untranslated regions, have been highlighted and associated
with bacterial virulence [18]. We can speculate that our differentially expressed genes
could be targets of sRNA, and riboswitches could represent a plausible hypothesis to
explain our observations. ABC transporters and efflux pumps are differentially
expressed by C. burnetii. Riboswitches act as sensors and can
activate or inhibit transcription in the presence of a specific molecule [52]. We can also
hypothesize that there may be other epigenetic factors involved, such as hot spots
of DNA methylation or DNA supercoiling, that could decrease the accessibility of
transcription factors or RNA polymerase to promoter sequences [47]. Such phenomena are well
known in eukaryotic models, such as in ncRNA silencing, and it is easy to speculate
that this could be responsible for the clusters of regulation we have observed.
In conclusion, C. burnetii appears to be able to rapidly adapt
itself to environmental changes such as cold and heat shock by altering the
transcription of adapted genes that could be involved in transformation into a
sporulation-like form. In bacteria, genes are organized into operons to facilitate
the regulation of genes implicated in the same pathway. Here, we found that many of
the genes that are differentially expressed upon exposure to temperature stresses
are organized into clusters of regulation. Although we have not deciphered the
mechanisms underlying these regulation clusters, this phenomenon seems to be
widespread in obligate intracellular bacteria. Clustering related to the regulation
of gene expression involved in bacterial adaptation could be advantageous for these
bacteria. Thus, we will undertake new experiments related to transcriptional
responses with longer exposure to stress conditions using technology that is adapted
to highlight ncRNA or epigenetic factors (which we could not monitor with the
microarray used here) to elucidate the phenomenon of gene regulation by
clusters.
Materials and MethodsStrain, medium and growth conditions
All experiments were performed with mid-log cultures of C.
burnetii grown at 35°C on L929 cells in MEM (GIBCO, Invitrogen,
Cergy-Pontoise, France) supplemented with 4% SVF (GIBCO) and 1%
L-glutamine (GIBCO). For temperature stress experiments, flasks containing
infected L929 cells were incubated at 4°C or 42°C for 30 min or 1 h with
the Nine Mile I Strain. Infected cells were harvested using glass beads and
centrifuged at 7,500 rpm for 10 min. Pellets were frozen using liquid nitrogen
and stored at −80°C.
RNA extraction and purification
Pellets were resuspended in 100 µl of TE supplemented with 10 mg/ml of
lysozyme (Euromedex, Souffelweyersheim, France) and incubated for 10 min at room
temperature. Total RNA was extracted and purified from resuspended pellets using
the RNeasy Mini Kit (Qiagen, Courtaboeuf, France) as recommended by the
manufacturer. DNase treatment was performed using the DNA Turbo Free Kit
(Ambion, Applied Biosystems, Courtaboeuf, France). Total RNA integrity was
checked using the 2100 BioAnalyzer (Agilent Technologies, Palo Alto, CA), and
the concentrations were quantified using the NanoDrop (Thermo, Wilmington, USA).
Eukaryotic RNA and bacterial rRNA were depleted using the MicrobEnrich kit
(Ambion) as previously described [53] and the MicrobExpress kit (Ambion), respectively. The
integrity of bacterial RNA was checked using the 2100 BioAnalyzer, and the
concentrations were quantified using the NanoDrop.
RNA labeling for microarray experiments
RNA was reverse-transcribed using M-MLV (Invitrogen, Cergy-Pontoise, France) and
random hexamer primers (Invitrogen) as previously described. cDNAs were
amplified using the processive polymerase phi29 with the GenomPhi illustrator V2
kit (GE HealthCare Lifescience, Orsay, France). This strategy was previously
described [29].
The amplified cDNAs were labeled with the Bioprime Labeling System (Invitrogen)
using d-CTP Cy3/5 fluorochromes (GE HealthCare Lifescience). Labeled cDNAs were
purified using QIAquick mini kit columns (Qiagen), and the level of
incorporation was quantified using the NanoDrop.
Coxiella burnetii whole-genome microarray
construction
OligoArray 2.0 [54], [55] was used to design probes based on 2016 CDS extracted
from the NC_002971.gb GenBank sequence file. OligoArray 2.0 integrates BLAST
analysis against a nonredundant set of sequences and probe secondary structure
analyses [56].
Oligonucleotide calculation parameters were set as follows: oligo length from
50- to 52-mers; GC percentage from 35 to 55%; melting temperature from 82
to 86°C. OligoArray 2.0 selected probes with the lowest cross-hybridization
and an absence of secondary structure and balanced the set of probes in terms of
melting temperature. Oligonucleotides containing five consecutive A, C, G or T
were discarded. Following probes design, 1990 probes corresponding to 1990
distinct CDS where selected for synthesis. A total of 100 µmol of each
probe were ordered from Sigma–Proligo (Paris, FRANCE) as 5′
amino–modified oligonucleotides. Oligonucleotide stocks were aliquoted for
use in microarray fabrication. Oligonucleotides were diluted to a final
concentration of 35–50 µM in 35% dimethyl sulfoxide, 100 mM
potassium phosphate (pH 8.0). C. burnetii 2k microarrays (GEO
reference GPL6675) were printed with a ChipWriterProarrayer (Bio-Rad, 1000
Alfred Nobel Drive Hercules, CA) on commercial HydroGel slides (Schott,
Hattenbergstr 10 55122 Mainz, Germany) and processed according to the
manufacturer's instructions.
Microarray hybridizations
Hybridization was carried out using two samples of labeled cDNA (75 pmol of each)
that were labeled with Cy3 or Cy5 d-CTP. The pooled samples were hybridized
using the GE Hybridization Kit (Agilent Technologies) as recommended by the
manufacturer. The mixture was applied to a Surhyb 1 array (Agilent Technologies)
and hybridized onto the Coxiella burnetii array using an
Agilent hybridization chamber (Agilent Technologies). Microarrays were
hybridized for 17 h at 62°C in a rotating oven. Microarrays were washed
using the GE washing buffers (Agilent Technologies) for 5 min with wash buffer 1
at room temperature followed by 1 min with wash buffer 2 at 37°C.
Microarrays were dried using a bath of acetonitrile (VWR, Fontenay sous Bois,
France). The microarrays were scanned using the microarray scanner C (Agilent
Technologies) using XDR at 5 µm resolution.
Analysis of microarray data
All microarray results have been deposited in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) under GEO series accession
number GSE21778. The signal intensity and local background were measured for
each spot by analyzing the array pictures with Feature Extractor software
(Agilent Technologies). The data filtering and normalization were performed
using Midas from the TM4 suite (TIGR). Data normalizations were performed using
global normalization and Lowess normalization methods. Normalized data were
processed using Tmev software from the TM4 suite (TIGR) with a
t-test with a p-value of <0.05 and a cut-off of 2 for
the fold change [50]. All experiments were conducted three times, which
yielded 12 measurements per gene (representing four technical replicates in
three biological replicates). The gene expression level was determined by
determining the mean of the 12 values obtained for each probe.
Cluster of regulation analysis
Differentially expressed gene distributions were calculated using windows of 5 to
11 genes. The number of differentially expressed genes was counted in each
window, and the distribution of differentially expressed genes was compared to a
random distribution. For the synteny analyses, we compared C.
burnetii to Legionella sp. and
Francisella sp. using the Geneplot application. Geneplot is
available on the NCBI website (http://www.ncbi.nlm.nih.gov). Functional analyses were performed
using the Cluster of Orthologous Gene classification (COG) [25]. We used the operon
organization algorithm available in MicrobesOnline to define transcriptional
units [57].
Protein network data were extracted from the program STRING version 8.2 [26]. We used
interactions with a score >0.5. We extracted all downstream intergenic
sequences (−1000 to 0 bp) for all of the genes and considered as putative
promoter sequences all intergenic sequences with a length >50 bp. Promoter
prediction has also been performed on putative promoter sequences using the
Neural Network Promoter Prediction method [27]. Promoter predictions and
downstream intergenic sequences corresponding to our differentially regulated
genes were aligned using the Muscle 3.7 program [58]. Phylogenetic trees were
built using MEGA 4 software [59]. We extracted all of the downstream intergenic
sequences (−10 to 0 bp) for all of the transcriptional units and
considered sequences with intergenic sequence lengths >50 bp. We analyzed the
CG% of the extracted sequences in comparison to the GC% of
intergenic sequences. Statistical analyses were performed using GraphPad Prism
version 5 software. All of the collected information on C.
burnetii genes are listed in Table
S4.
Real-Time RT-PCR
RNA was reverse-transcribed using M-MLV (Invitrogen) and random hexamer primers
(Invitrogen) as recommended by the manufacturer. qPCR was performed on cDNAs for
targeted transcripts using the Quantitec Probes Kit (Qiagen) with the 7900 HT
PCR system (Applied Biosystems). The primers and probes used to perform qPCR
were designed based on the five C. burnetii sequenced genomes
available on the NCBI database. The sequences of primers and probes used are
listed in the Table S2. The relative expression ratios of target genes were
determined by comparing housekeeping genes (com1,
16S, rpoB) with differentially transcribed
genes using the software of the 7900 HT qPCR system.
Supporting Information
Venn diagram of differentially expressed genes.
(PPT)
Clusters of regulation.
(PPT)
Coxiella burnetii gene network connections.
(PPT)
Differentially expressed gene networks.
(PPT)
Promoter sequence analysis.
(PPT)
List of differentially expressed genes.
(XLS)
Table of primers and probes used for qRT-PCR and results.
(XLS)
Statistical analysis of the distribution of differentially expressed
genes.
(XLS)
Genes information.
(XLS)
Statistical analysis of functional categories within clusters.
(XLS)
Statistical analysis of network connections within clusters.
(XLS)
Synteny between C. burnetii and Legionella
sp. and Francisella sp.
(XLS)
Promoter -10 to start site analysis.
(XLS)
Clusters of regulation within other obligate intracellular bacteria.
(XLS)
ReferencesMaurinMRaoultD1999Q fever.12518553HackstadtTPeacockMGHitchcockPJColeRL1985Lipopolysaccharide variation in Coxiella burnetti: intrastrain
heterogeneity in structure and antigenicity.48359365SeshadriRPaulsenITEisenJAReadTDNelsonKE2003Complete genome sequence of the Q-fever pathogen Coxiella
burnetii.1005455546010.1073/pnas.0931379100 [doi];0931379100
[pii]BearePAUnsworthNAndohMVothDEOmslandA2009Comparative genomics reveal extensive transposon-mediated genomic
plasticity and diversity among potential effector proteins within the genus
Coxiella.77642656IAI.01141-08 [pii];10.1128/IAI.01141-08
[doi]McCaulTFWilliamsJC1981Developmental cycle of Coxiella burnetii: structure and
morphogenesis of vegetative and sporogenic differentiations.14710631076WiebeMEBurtonPRShankelDM1972Isolation and characterization of two cell types of Coxiella
burneti phase I.110368377AmanoKWilliamsJCMcCaulTFPeacockMG1984Biochemical and immunological properties of Coxiella burnetii
cell wall and peptidoglycan-protein complex fractions.160982988HeinzenRAHackstadtTSamuelJE1999Developmental biology of Coxiella burnettii.7149154S0966842X99014754 [pii]KlinkertBNarberhausF2009Microbial thermosensors.662661267610.1007/s00018-009-0041-3 [doi]AnderssonSGZomorodipourAAnderssonJOSicheritz-PontenTAlsmarkUC1998The genome sequence of Rickettsia prowazekii and
the origin of mitochondria.39613314010.1038/24094 [doi]RaoultDOgataHAudicSRobertCSuhreK2003Tropheryma whipplei Twist: a human pathogenic
Actinobacteria with a reduced genome.131800180910.1101/gr.1474603 [doi];13/8/1800 [pii]OgataHAudicSRenesto-AudiffrenPFournierPEBarbeV2001Mechanisms of evolution in Rickettsia conorii
and R. prowazekii.2932093209810.1126/science.1061471 [doi];293/5537/2093
[pii]CrapouletNBarbryPRaoultDRenestoP2006Global transcriptome analysis of Tropheryma whipplei in response
to temperature stresses.18852285239188/14/5228 [pii];10.1128/JB.00507-06
[doi]AudiaJPPattonMCWinklerHH2008DNA microarray analysis of the heat shock transcriptome of the
obligate intracytoplasmic pathogen Rickettsia
prowazekii.7478097812AEM.00896-08 [pii];10.1128/AEM.00896-08
[doi]Dreher-LesnickSMCeraulSMRahmanMSAzadAF2008Genome-wide screen for temperature-regulated genes of the
obligate intracellular bacterium, Rickettsia
typhi.8611471-2180-8-61 [pii];10.1186/1471-2180-8-61
[doi]EllisonDWClarkTRSturdevantDEVirtanevaKHackstadtT2009Limited transcriptional responses of Rickettsia
rickettsii exposed to environmental stimuli.410.1371/journal.pone.0005612 [doi]e5612OmslandACockrellDCHoweDFischerERVirtanevaK2009Host cell-free growth of the Q fever bacterium Coxiella
burnetii.106443044340812074106 [pii];10.1073/pnas.0812074106
[doi]Toledo-AranaADussurgetONikitasGSestoNGuet-RevilletH2009The Listeria transcriptional landscape from saprophytism to
virulence.459950956nature08080 [pii];10.1038/nature08080
[doi]FaucherSPFriedlanderGLivnyJMargalitHShumanHA2010Legionella pneumophila 6S RNA optimizes intracellular
multiplication.107753375380911764107 [pii];10.1073/pnas.0911764107
[doi]AlbrechtMSharmaCMReinhardtRVogelJRudelT2010Deep sequencing-based discovery of the Chlamydia trachomatis
transcriptome.38868877gkp1032 [pii];10.1093/nar/gkp1032 [doi]ArgamanLHershbergRVogelJBejeranoGWagnerEG2001Novel small RNA-encoding genes in the intergenic regions of
Escherichia coli.11941950S0960-9822(01)00270-6 [pii]BouvierMSharmaCMMikaFNierhausKHVogelJ2008Small RNA binding to 5′ mRNA coding region inhibits
translational initiation.32827837S1097-2765(08)00807-1 [pii];10.1016/j.molcel.2008.10.027
[doi]FrohlichKSVogelJ2009Activation of gene expression by small RNA.12674682S1369-5274(09)00143-X [pii];10.1016/j.mib.2009.09.009
[doi]DambachMDWinklerWC2009Expanding roles for metabolite-sensing regulatory
RNAs.12161169S1369-5274(09)00011-3 [pii];10.1016/j.mib.2009.01.012
[doi]TatusovRLGalperinMYNataleDAKooninEV2000The COG database: a tool for genome-scale analysis of protein
functions and evolution.283336gkd013 [pii]JensenLJKuhnMStarkMChaffronSCreeveyCMullerJ2009STRING 8–a global view on proteins and their functional
interactions in 630 organisms.37D412D416gkn760 [pii];10.1093/nar/gkn760 [doi]ReeseMG2001Application of a time-delay neural network to promoter annotation
in the Drosophila melanogaster genome.265156S0097-8485(01)00099-7 [pii]RenestoPRoveryCSchrenzelJLeroyQHuygheA2008Rickettsia conorii transcriptional response
within inoculation eschar.3e368110.1371/journal.pone.0003681 [doi]LaMVFrancoisPRoveryCRobineauSBarbryP2007Development of a method for recovering rickettsial RNA from
infected cells to analyze gene expression profiling of obligate
intracellular bacteria.71292297S0167-7012(07)00325-9 [pii];10.1016/j.mimet.2007.09.017
[doi]LeroyQRaoultD2010Review of microarray studies for host-intracellular pathogen
interactions.818195S0167-7012(10)00067-9 [pii];10.1016/j.mimet.2010.02.008
[doi]WeberMHMarahielMA2003Bacterial cold shock responses.86975AfsethGMoYYMallaviaLP1995Characterization of the 23S and 5S rRNA genes of Coxiella
burnetii and identification of an intervening sequence within the 23S rRNA
gene.17729462949RaghavanRMillerSRHicksLDMinnickMF2007The unusual 23S rRNA gene of Coxiella burnetii: two self-splicing
group I introns flank a 34-base-pair exon, and one element lacks the
canonical omegaG.18965726579JB.00812-07 [pii];10.1128/JB.00812-07
[doi]LaMVCrapouletNBarbryPRaoultDRenestoP2007Comparative genomic analysis of Tropheryma
whipplei strains reveals that diversity among clinical isolates
is mainly related to the WiSP proteins.83491471-2164-8-349 [pii];10.1186/1471-2164-8-349
[doi]WeissDS2004Bacterial cell division and the septal ring.54588597MMI4283 [pii];10.1111/j.1365-2958.2004.04283.x
[doi]FeuchtAErringtonJ2005ftsZ mutations affecting cell division frequency, placement and
morphology in Bacillus subtilis.15120532064151/6/2053 [pii];10.1099/mic.0.27899-0
[doi]BearePAHoweDCockrellDCOmslandAHansenB2009Characterization of a Coxiella burnetii ftsZ mutant generated by
Himar1 transposon mutagenesis.19113691381JB.01580-08 [pii];10.1128/JB.01580-08
[doi]EdgarRBiekDYarmolinskyM2006P1 plasmid partition: in vivo evidence for the ParA- and
ParB-mediated formation of an anchored parS complex in the absence of a
partner parS.59276287MMI4933 [pii];10.1111/j.1365-2958.2005.04933.x
[doi]OgasawaraNYoshikawaH1992Genes and their organization in the replication origin region of
the bacterial chromosome.6629634YimLMoukadiriIBjorkGRArmengodME2006Further insights into the tRNA modification process controlled by
proteins MnmE and GidA of Escherichia coli.3458925905gkl752 [pii];10.1093/nar/gkl752 [doi]ChatterjiDOgawaYShimadaTIshihamaA2007The role of the omega subunit of RNA polymerase in expression of
the relA gene in Escherichia coli.2675155FML532 [pii];10.1111/j.1574-6968.2006.00532.x
[doi]ChatterjiDOjhaAK2001Revisiting the stringent response, ppGpp and starvation
signaling.4160165S136952740000182X [pii]BendezuFOde BoerPA2008Conditional lethality, division defects, membrane involution, and
endocytosis in mre and mrd shape mutants of Escherichia
coli.19017921811JB.01322-07 [pii];10.1128/JB.01322-07
[doi]TraxlerMFSummersSMNguyenHTZachariaVMHightowerGA2008The global, ppGpp-mediated stringent response to amino acid
starvation in Escherichia coli.6811281148MMI6229 [pii];10.1111/j.1365-2958.2008.06229.x
[doi]SaitoMWatanabeSYoshikawaHNakamotoH2008Interaction of the molecular chaperone HtpG with uroporphyrinogen
decarboxylase in the cyanobacterium Synechococcus elongatus PCC
7942.7213941397JST.JSTAGE/bbb/80093 [pii]VarmaAYoungKD2009In Escherichia coli, MreB and FtsZ direct the synthesis of
lateral cell wall via independent pathways that require PBP
2.19135263533JB.01812-08 [pii];10.1128/JB.01812-08
[doi]Martinez-NunezMAPerez-RuedaEGutierrez-RiosRMMerinoE2010New insights into the regulatory networks of paralogous genes in
bacteria.1561422mic.0.033266-0 [pii];10.1099/mic.0.033266-0
[doi]PotrykusKCashelM2008(p)ppGpp: still magical?62355110.1146/annurev.micro.62.081307.162903 [doi]DalebrouxZDSvenssonSLGaynorECSwansonMS2010ppGpp conjures bacterial virulence.7417119974/2/171 [pii];10.1128/MMBR.00046-09
[doi]BechahYElKKMediannikovOLeroyQPelletierNRobertC2010Genomic, proteomic, and transcriptomic analysis of virulent and
avirulent Rickettsia prowazekii reveals its adaptive mutation
capabilities.gr.103564.109 [pii];10.1101/gr.103564.109
[doi]SharmaCMHoffmannSDarfeuilleFReignierJFindeissS2010The primary transcriptome of the major human pathogen
Helicobacter pylori.464250255nature08756 [pii];10.1038/nature08756
[doi]HenkinTM2008Riboswitch RNAs: using RNA to sense cellular
metabolism.223383339022/24/3383 [pii];10.1101/gad.1747308
[doi]LaMVRaoultDRenestoP2008Regulation of whole bacterial pathogen transcription within
infected hosts.32440460FMR103 [pii];10.1111/j.1574-6976.2008.00103.x
[doi]RouillardJMHerbertCJZukerM2002OligoArray: genome-scale oligonucleotide design for
microarrays.18486487RouillardJMZukerMGulariE2003OligoArray 2.0: design of oligonucleotide probes for DNA
microarrays using a thermodynamic approach.3130573062ZukerM2003Mfold web server for nucleic acid folding and hybridization
prediction.3134063415DehalPSJoachimiakMPPriceMNBatesJTBaumohlJK2010MicrobesOnline: an integrated portal for comparative and
functional genomics.38D396-D400. gkp919 [pii];10.1093/nar/gkp919
[doi]EdgarRC2004MUSCLE: multiple sequence alignment with high accuracy and high
throughput.321792179710.1093/nar/gkh340 [doi];32/5/1792 [pii]TamuraKDudleyJNeiMKumarS2007MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software
version 4.0.2415961599msm092 [pii];10.1093/molbev/msm092 [doi]