Live Staphylococcus aureus and bacterial soluble factors induce different transcriptional responses in human airway cells

Chimène Moreilhon1, Delphine Gras2, Coralie Hologne2, Odile Bajolet2, Françoise Cottrez3, Virginie Magnone1, Marc Merten4, Hervé Groux3, Edith Puchelle2 and Pascal Barbry1

1 Institut de Pharmacologie Moléculaire et Cellulaire UMR 6097 Centre National de la Recherche Scientifique, Université de Nice-Sophia Antipolis, Valbonne
2 Institut National de la Santé et de la Recherche Médicale (INSERM) UMR-S 514, IFR 53, CHU Hôpital Maison Blanche, Reims
3 INSERM U576, Hôpital de l’Archet, Nice
4 INSERM EMI 10014, Vandoeuvre-les-Nancy, France


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
To characterize the response of respiratory epithelium to infection by Staphylococcus aureus (S. aureus), human airway cells were incubated for 1 to 24 h with a supernatant of a S. aureus culture (bacterial supernatant), then profiled with a pangenomic DNA microarray. Because an upregulation of many genes was noticed around 3 h, three independent approaches were then used to characterize the host response to a 3-h contact either with bacterial supernatant or with live bacteria: 1) a DNA microarray containing 4,200 sequence-verified probes, 2) a semiquantitative RT-PCR with a set of 537 pairs of validated primers, or 3) ELISA assay of IL-8, IL-6, TNF{alpha}, and PGE2. Among others, Fos, Jun, and EGR-1 were upregulated by the bacterial supernatant and by live bacteria. Increased expression of bhlhb2 and Mig-6, promoter regions which harbor HIF responding elements, was explained by an increased expression of the HIF-1{alpha} protein. Activation of the inducible form of cyclooxygenase, COX-2, and of the interleukins IL-1, IL-6, and IL-8, as well as of the NF-{kappa}B pathway, was observed preferentially in cells in contact with bacterial supernatant. Early infection was characterized by an upregulation of anti-apoptotic genes and a downregulation of pro-apoptotic genes. This correlated with a necrotic, rather than apoptotic cell death. Overall, this first global description of an airway epithelial infection by S. aureus demonstrates a larger global response to bacterial supernatant (in term of altered genes and variation factors) than to exponentially growing live bacteria.

transcriptome; microarray; inflammation; infection


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
STAPHYLOCOCCUS AUREUS is a gram-positive bacteria and a major cause of infection of skin, bone joints, endovascular, ocular, and respiratory tissues (31a). S. aureus is involved in nosocomial respiratory infections (50a) and is one of the first pathogens to colonize the airways in cystic fibrosis (CF) (21, 48). Along with mucus, which represents a first line of airway antibacterial defense, the airway surface epithelium is a major site of interaction with S. aureus. After contact with the bacteria, specialized cells from the airway surface and glandular epithelium secrete various peptides and proteins such as lysozyme, lactoferrin, secretory IgA, peroxidase, and proteinase inhibitors that play a crucial role in the innate host defense (37, 41). S. aureus surface proteins such as adhesins and protein A are produced during exponential growth phase and then downregulated at a later time, whereas most secreted exoproteins, including toxins, hemolysins, and tissue-degrading enzymes are produced at the end of the exponential growth phase (38). The exposure of host cells to live S. aureus and to their virulence factors may induce diverse injuries including degradation of host tissue and inactivation of host defense mechanisms. Nevertheless, little is known about the responses induced by S. aureus in human airway cells, even at a transcriptional level.

We examined the host transcriptional response to the interaction between human airway epithelial cells, represented here by the human airway glandular cell line MM-39 (13, 34), and a reference S. aureus strain (8325-4 strain). To provide a molecular portrait of airways cells response to infection, a time course experiment corresponding to an interaction between airway epithelial cell and bacterial supernatant was first performed. It was followed by a comparison of the epithelial cells responses to live bacteria or to products secreted by bacteria after 3 h of contact. The aims of the present study were to analyze whether the cells in direct contact with live bacteria or only in presence with S. aureus soluble virulence factors induce different transcriptional responses in human airway cells.


    EXPERIMENTAL PROCEDURES
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Airway Epithelial Cell Culture
Confluent monolayers of MM-39, a transformed human tracheal gland cell line, were cultured in culture dishes coated with type I collagen as described by Merten et al. (34). Two hours before each experiment, cells were washed and cultured in DMEM-F12 without antibiotics and without Ultroser G.

Bacterial Strain and Culture Conditions
S. aureus strain (8325-4), a reference laboratory strain was a gift from T. J. Foster (Department of Microbiology, Dublin, Ireland). Bacteria were grown at 37°C overnight in trypticase soy broth (TSB). Bacteria/cell interactions were analyzed under two distinct experimental conditions. In the first experimental condition, S. aureus bacteria were grown in TSB up to a concentration of 5 x 108 cfu/ml, then washed with phosphate-buffered saline (PBS 0.1 M, pH 7.2), resuspended in DMEM-F12, and added to the apical surface of confluent airway epithelial cells (MM-39). Interaction took place for 3 h at a ratio of 50 bacteria for 1 epithelial cell as previously described (5, 22). This experiment was named "live bacteria." In the second experimental condition, the initial suspension (5 x 108 cfu/ml) was centrifuged (1,500 g, 10 min) and the supernatant was diluted 10-fold in DMEM-F12 (9, 12). The airway epithelial cells were then incubated with the bacterial supernatant (soluble virulence factors) for 1, 3, 6, 9, and 24 h. Additional experiments were performed at 3 h, to compare with live bacteria. This experiment was named "bacterial supernatant." Each of these two experimental conditions included their own control corresponding to either airway cell culture medium for the live bacteria experiment or epithelial cell culture medium supplemented with 1/10 of bacterial growth medium for the bacterial supernatant.

RNA Extraction
At the end of the different incubations, cells were washed with PBS (0.1 M), and RNA was extracted with High Pure RNA Isolation Kit (Roche), according to the manufacturer’s instructions.

Microarray Technique
Array preparation.
Both oligonucleotide microarrays and cDNA microarrays were used for this study, as follows.

oligonucleotide microarrays. The oligonucleotide microarrays contained ~25,000 distinct oligonucleotide probes covering most of the known human transcripts. The list of the 25,279 probes (length ~51 bp) is available online (http://www.microarray.fr:8080/mediante; also see Supplemental Table S1, available at the Physiological Genomics web site).1 Microarrays were printed with a ChipWriter Pro (Bio-Rad) on commercial UltraGAPS II slides (amino-silane-coated slides, Corning 40017) and processed according to the manufacturer’s instructions.

cdna microarrays. The cDNA microarrays contained ~4,200 distinct cDNA probes. Gene selection was based on relevance to inflammation, infection, differentiation, ion transport, cell signaling, cell migration, etc. A large fraction of the probes also corresponded to transcripts encoding membrane proteins. Such a microarray probed a significant portion of all known human transcripts and represented a powerful tool for studying molecular mechanisms of lung physiopathology. The list of the 4,200 probes is available at http://www.microarray.fr/IPMC/cDNA_microarray5k.html. The cDNA probes were PCR-amplified from cDNA derived from Universal Human Reference RNA (Stratagene) by reverse transcription. Probes 1) had a normalized length of 250 ± 19 bp; 2) had a normalized GC content of 52 ± 8%; 3) were specific for a unique human gene; and 4) were controlled by DNA sequencing. PCR products were purified by using QIAquick 96 PCR Purification Kit (Qiagen), resuspended in 3x SSC at a concentration of 200 ng/µl. Microarrays were printed with a SDDC-2 (Bio-Rad) on homemade aldehyde-coated glass microscope slides (11). Valid amplifications were characterized by the presence of a fragment at the correct molecular weight and by the identification of the correct sequence by direct sequencing. Data presented into the present manuscript only refer to sequence-verified probes. Microarray processing was then performed as described in Dayem et al. (11).

RNA labeling and hybridization.
Two different RNA labelings were performed for this study: amplification protocol for oligonucleotide microarrays and direct RT labeling for cDNA microarrays.

amplification protocol (oligo array) We amplified 2 µg total RNA with the Amino Allyl MessageAmp aRNA kit (Ambion ref. 1752) according to the manufacturer’s instructions. Cy3- and Cy5-labeled aRNA were fragmented with the Ambion aRNA Fragmentation Reagents (ref. 8740), then added to 250 µl ChipHybe 80 (Ventana ref. 760-127) for hybridization in a Discovery station (Ventana, Illkirch, France). After a 10-min denaturation at 70°C, hybridization was performed 10 h at 48°C. Arrays were then washed twice in RiboWash solution (Ventana ref. 760-105) for 1 min, twice in 1x SSC solution (Invitrogen, 15557-044) for 1 min, rapidly dipped in water and ethanol, then spun dry.

rt protocol (cdna array). We reverse transcribed 10 µg of total RNA according to Dayem et al. (11), except that 500 µM dATP, 500 µM dCTP, 500 µM dGTP, 100 µM dTTP, and 400 µM amino-allyl-dUTP were used for the reaction. After 2.5 h of incubation at 42°C, RNA degradation, and removal of unincorporated nucleotides using the Nucleotide Removal Kit (Qiagen), cDNA were then labeled with Cy3/5 monofunctional reactive dyes (Amersham). Initial experiments were performed using a direct labeling method and provided similar results. Microarrays were then hybridized at 48°C, according to Dayem et al. (11).

Both oligonucleotide and cDNA microarrays were scanned with a ScanArray Express (version 2.0.19 microarray acquisition system; Packard BioScience, Rungis, France). The two red and green lasers operated at 633 nm and 543 nm to excite Cy5 and Cy3, respectively. The intensity was measured at 670 nm for Cy5 and 570 nm for Cy3. Laser power was set to 100%, and photomultiplier tube (PMT) power was set between 65% and 75% depending on the slides.

Bioinformatics Analysis
All results are available in GEO (http://www.ncbi.nlm.nih.gov/geo/), under the accession numbers GSE1853 (cDNA microarrays) and GSE1704 (oligonucleotide microarrays). For oligonucleotide microarrays, TIF images containing the data from each fluorescence channel were quantified with the GenePix Pro 5.0 program (Axon Instruments) using an "irregular features" quantification method. Lowess normalization was performed using the GeneSpring program (version 6.1). Genes characterized by a 75th percentile intensity of less than 1,000 for all measurements were discarded. For cDNA microarrays, TIF images containing the data from each fluorescence channel were quantified with the QuantArray program (version 3.0.0.0) (PerkinElmer, Rungis, France) using a fixed circle quantification method. For each spot, intensity and background values for Cy3 and Cy5 were obtained as average intensities. Negative controls ("neg") were spotted on each slide. These corresponded to nonmammalian mRNA sequences with no significant identity with any human sequences. Signals derived from these spots were subtracted from the specific signal so that a null gene expression was associated with a null fluorescence signal. The specific signal for spot i under condition j was thus defined as the total spot intensity (totij) minus local background (bgdij) minus the median of negj. Because incorporations of Cy3 and Cy5 dyes into cDNA can differ significantly, data was further normalized using a dye-swap method (8, 25, 55). This method required duplication of experiments, but improved the reproducibility of the quantification (Moreilhon and Barbry, unpublished data). For that purpose, a first microarray was hybridized with experimental and control samples labeled with Cy3 and Cy5, respectively. A second "swap" microarray was hybridized with experimental and control samples labeled with inverted dyes (experimental = Cy5; control = Cy3). The specific signal associated with spot i in sample j was defined by the geometrical average of the specific intensities in direct and swapped experiments:


Each PCR product was spotted four times on each slide (2 independent clusters of 2 spots spatially separated), to reduce positional bias of the fluorescence readout. Up to four experimental and four control values were also collected for each probe.

Lists of genes significantly down- and upregulated under the different experimental conditions were established using two distinct approaches.

In a first statistical approach, we used Significance Analysis of Microarrays (SAM, http://www-stat.stanford.edu/~tibs/SAM/index.html), a software developed by Tusher et al. (49) to compute a statistical value for each probe, which represented the strength of the relationship between gene expression and one of our qualitative response variables (bacteria, factor, or controls in our experiments). In the present study, 12 independent measurements for each condition [four log2 (ratio) measurements for each experiment, times three independent biological experiment] were tested for statistical significance. Only probes with less than five missing values were kept for analysis, since SAM performance was affected by an excess of missing values. A cutoff for significance, called the delta value, was chosen in order to minimize the false-positive rate, defined by the ratio between the number of called genes and the 90th percentile of the number of falsely called genes. An identical cutoff value was used for bacteria and for soluble factors. SAM was first run using a one-class function on normalized log2 (ratio) for each experimental conditions, to identify genes upregulated or downregulated by bacteria or by soluble factors. A two-class function was also selected to highlight genes differentially expressed between bacterial supernatant and live bacteria condition. Additional analyses were performed to compare log2 (ratio) between bacteria and soluble factors.

In a second statistical approach, we took into account the median signal intensity associated with each probe. With this second independent approach, signals derived from one probe were compared with a subgroup of spots having similar fluorescence intensity. Typically, for one experimental condition, a local Student’s t-test was run between the 12 spot ratio values associated with 1 probe and the 240 ratios associated with the 20 nearest neighbors (according to the median spot intensity). The analysis was performed using standard Microsoft Excel functions.

Ontologies attached to each gene were then used to classify altered genes according to main biological themes. For that, we used Expression Analysis Systematic Explorer (EASE) program, available at http://david.niaid.nih.gov/david/ease.htm (20), and MEDIANTE, a local database containing diverse information about our probe sets (http://www.microarray.fr/mediante/index?language=en). Additional statistical analyses, including K-means, principal component analysis (PCA), and unsupervised hierarchical classification, were performed using the XLSTAT program running on Microsoft Excel (Addinsoft, Paris, France) and MeV program (version 3) (44).

Real-Time Semiquantitative RT-PCR
cDNA was synthesized using the SuperScript system (Invitrogen) as previously described (51). Briefly, the reaction was carried out in 25 µl with 5 µg RNA, 100 ng oligo(dT), 100 ng random hexamers (Roche), and 200 U SuperScript II reverse transcriptase during 1 h at 45°C, followed by a 5-min incubation step at 95°C. The cDNA was then adjusted at 50 ng/µl. The real-time semiquantitative PCR (sqRT-PCR) was performed using the SYBR Green technology and a Applied Biosytems model 9600 apparatus. Primers (MWG Biotech, Courtaboeuf, France) were designed to span exon-intron junctions to prevent amplification of possible trace of genomic DNA and to result in amplicons between 100 and 150 bp. All pairs of primers were qualified by restriction enzyme digestion and electrophoresis. A 20-µl PCR reaction contained 50 mM Tris·HCl (pH 8.4, Invitrogen), 200 nM of each primer, 50 ng cDNA, 0.2 mM dNTP (Amersham), 2.5 mM MgCl2, 0.4 U Taq polymerase (Platinum, Invitrogen), and 1/10,000 dilution of SYBR Green (Molecular Probes) in optical PCR plates and caps (Applied Biosystems). Expression of target genes was measured after normalization of RNA with four different housekeeping genes, and values were expressed using the CT method, as fold increased expression above a theoretical negative control (user bulletin no. 2, Applied Biosystems, December 1997).

ELISA
The enzyme-linked immunoabsorbent assays (ELISA) for IL-6 (R&D systems, ELISA Quantikine ref. D6050), IL-8 (R&D Systems, ELISA quantikine ref. D8050), PGE2 (R&D Systems, high-sensitivity ELISA ref. DE2100), and TNF (R&D, Quantkine HS Human TNF{alpha} Immunoassay systems ref. HSTA00C) were performed according to the manufacturer’s instructions. Data were expressed as picograms per 5 x 106 cells for IL-6 and IL-8 experiments and as picograms per milliliter for PGE2 and TNF experiments (sensitivity: ≥0.7 pg/ml for Il-6, ≥10 pg/ml for IL-8, ≥8.2 pg/ml for PGE2, and ≥0.12 pg/ml for TNF).

Western Blot Analysis
MM-39 cells were washed twice in ice-cold PBS and lysed in lysis buffer [20 mM Tris-Cl (pH 7.5), 100 mM NaCl, 5 mM MgCl2, 0.5% Nonidet P-40, 1 mM sodium orthovanadate, 5 mM sodium fluoride] supplemented with protease inhibitors (Roche Applied Science). After 20 min at 4°C under continuous agitation, extracts were centrifuged at 12,000 g for 10 min at 4°C. We electrophoresed 50 µg of protein extracts on 9% SDS-PAGE, and these were electrotransferred onto a polyvinylidene fluoride membrane (Immobilon-P, Millipore). The membrane was incubated overnight at 4°C with a anti-HIF-1{alpha} monoclonal antibody at a 1,000th dilution (Novus Biologicals). Horseradish peroxidase-conjugated rabbit anti-mouse IgG antibody (1:5,000) was then applied for 30 min at room temperature. Immunoreactive bands were revealed by enhanced chemiluminescence (ECL, Amersham).

Quantification of the Cell-Bacteria Interaction
A fluorescence staining method using the LIVE/DEAD Bacterial Viability Kit (BacLight; Molecular Probes, Eugene, OR) was used to study the interaction between airway epithelial cells and S. aureus. BacLight is composed of two nucleic acid binding stains: SYTO 9, which stains cells (bacteria or host cells) with both intact and damaged membranes; and propidium iodide, which only stains damaged cells. The percentage of airway epithelial cells with attached and/or internalized bacteria after the 3-h incubation period was determined from the fluorescence images. Experiments were performed in triplicate.

Evaluation of Airway Epithelial Cell Death by Apoptosis or Necrosis
Three days after confluence, airway epithelial cells were incubated either with S. aureus at 5 x 108 CFU/ml or with bacterial supernatant, together with YO-PRO-1 (10 µg/ml, which stains apoptotic cells) and propidium iodide (1 µl/ml, which stains dead cells) probes. Fluorescent images were recorded every 30 min for 24 h as previously described (10). Variations of the YO-PRO-1 and of the propidium iodide fluorescence intensities were expressed as the ratio of the fluorescence intensity at a given time to the initial fluorescence intensity.

Experimental Design
Figure 1 describes the experimental design of the work presented in the manuscript.



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Fig. 1. Experimental design. MM-39 epithelial airway cells were either not infected ("control" condition), or infected for 3 h with PBS-washed bacteria ("live bacteria" condition) or with supernatant of overnight bacterial culture ("bacterial supernatant" condition). Interaction of MM-39 cells with live bacteria and bacterial supernatant were then analyzed with cDNA microarray and with semiquantitative RT-PCR (sqRT-PCR) experiments. cDNA microarray experiments were performed on 3 independent biological experiments, with 2 slides hybridized per biological experiment (dye swap). On each cDNA array, genes were spotted 4 times. We performed 537 sqRT-PCRs on an independent biological experiment with 1–4 technical replicates. There were 176 genes common between sqRT-PCR and the cDNA microarray.

 

    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
The transcriptional response of the human airway epithelial cell line MM-39 to an interaction with S. aureus supernatant was analyzed after 1, 3, 6, 9, and 24 h with a pangenomic microarray containing 25,279 distinct probes. This first experiment revealed several major alterations at the different time points and highlighted the early alteration of many genes coding proteins involved in transcription, inflammation, and apoptosis (Fig. 2).



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Fig. 2. Hierarchical clustering of a selection of 49 genes identified during a time course experiment describing the interaction of MM-39 cells with S. aureus bacterial supernatant. Genes and experiments were clustered using an average linkage method (MeV program). The time course interaction of MM-39 epithelial airway cells with bacterial supernatant was performed on pangenomic microarrays, as described in the EXPERIMENTAL PROCEDURES. Color scale was representative of log2 (ratio) (bacterial supernatant/control supernatant).

 
To provide a clearer picture of the early steps of the interaction, a second transcriptional study was performed on the same epithelial cells at a single time point (3 h). MM-39 cells were incubated with either live bacteria or bacterial supernatant (prepared as described in EXPERIMENTAL PROCEDURES), and RNA were profiled to evaluate the respective contributions of a direct contact with live bacteria or of soluble components to the transcriptional response. After 3 h of exposure to 108 CFU/ml of live S. aureus (equivalent to a multiplicity of infection of 50 bacteria per cell), 15.0 ± 6.5% of the total number of airway epithelial cells exhibited adherent bacteria. Internalized bacteria were detected in 3.0 ± 2.3% of the cells.

To monitor the effects of live bacteria and bacterial supernatant on airway epithelial cells at 3 h, total RNA was isolated from the four experimental conditions and reverse transcribed into fluorescently labeled cDNAs. The entire microarray procedure (from infection to hybridization) was done independently three times for each experimental condition. Samples were hybridized to human cDNA microarrays comprising 4,200 distinct cDNA probes, then analyzed according to standard protocols (see EXPERIMENTAL PROCEDURES for microarray procedures) (Fig. 1).

Microarray Measurements
Significant changes in the expression profile were first identified with SAM. Table 1 shows that 36 SAM-positive genes were upregulated after a 3-h direct contact with S. aureus, and 7 SAM-positive genes were downregulated (live bacteria condition). Similarly, 72 SAM-positive genes were upregulated after contact with bacterial supernatant, and 42 SAM-positive genes were downregulated under the same experimental condition (bacterial supernatant condition). Thirteen genes were increased in both experimental conditions, whereas only one gene was found significantly decreased in both experimental conditions. A very similar qualitative picture was provided by two other independent analyses, based either on the use of a local Student’s t-test or on sqRT-PCR (sqRT-PCR results are described in the next paragraph). Student’s t-test revealed 58 altered genes in the live bacteria condition (43 repressed, 15 activated), whereas 359 genes were altered in the bacterial supernatant condition (170 repressed, 189 activated). Table 1 summarizes the results obtained with microarrays and sqRT-PCR. Whatever the quantification procedure, there are more altered genes in the bacterial supernatant condition than in the live bacteria condition. The poor overlap existing between the two conditions (only 10% of the genes were altered in both conditions) illustrated the existence of differential and specific responses of airway epithelial cells to a direct contact with live bacteria or to incubation with bacterial supernatant.


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Table 1. Comparison of the results by SAM, local t-test, and sqRT-PCR

 
Additional statistical analyses performed on the 405 best scores after a local Student’s t-test provided consistent observations. A hierarchical classification of the 24 experimental data points clearly discriminated live bacteria experiments from bacterial supernatant experiments, as evidenced by the existence of two distinct branches (Fig. 3). This distinction between live bacteria and bacterial supernatant was confirmed by a PCA performed on the same 405 probes (not shown).



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Fig. 3. Hierarchical clustering of the experimental replicates discriminates live bacteria from bacterial supernatant. Hierarchical clustering using Euclidean distance dissimilarity and Ward’s method of aggregation, performed on the first 405 genes selected by a Student’s t-test method (see EXPERIMENTAL PROCEDURES) to compare the 24 experimental data points (live bacteria 1–12 and bacterial supernatant 1–12).

 
Semiquantitative RT-PCR Measurements
Additional gene expression measurement after 3-h incubation was performed using a sqRT-PCR strategy. We tested 537 distinct genes by sqRT-PCR. These encoded proteins involved in adhesion, cell growth, apoptosis, inflammation, or corresponding to cytokines, receptors, adapters, kinases, phosphatases, and transcription factors. There are 176 genes that are common between sqRT-PCR and the cDNA microarray. Among these genes, 25 were significantly upregulated in sqRT-PCR experiments. Four of them were also SAM positives and t-test positives. One gene was only SAM positive.

A differential expression superior to 5 was observed for 18 genes in the live bacteria condition (6 being downregulated and 12 being upregulated). On the other hand, 42 genes were regulated in the bacterial supernatant condition when using a 5-fold factor (16 being downregulated and 26 being upregulated). Among these genes, 7 genes were commonly upregulated in live bacteria and bacterial supernatant conditions with a ratio superior to 5. Only one gene was downregulated in the two experimental conditions, with a ratio superior to 5 (Table 1). These results confirmed that the transcriptional response to bacterial supernatant was prominent over the response to a 3-h direct contact with bacteria.

EASE analysis
Time course analysis using ratios cutoff was performed. Hierarchical clustering was performed on modulated genes encoding proteins implicated in transcription, inflammation, and apoptosis using the MeV program (Fig. 2). Analysis of the 3-h interaction results showed that SAM-positive genes (see EXPERIMENTAL PROCEDURES) can be clustered in one of the following functional classes: 1) genes coding proteins involved in transcription (i.e., fos, jun, junB, EGR-1, etc.), 2) genes coding proteins related to inflammation (i.e., IL-8, IL-1{alpha} and IL-1ß, COX-2, etc.), and 3) genes coding proteins involved in regulation of cell apoptosis (i.e., Sgk, A20, etc.). These functional classes were also found after a direct analysis of the Gene Ontology terms associated with 405 genes selected with the Student’s t-test (not shown).

1) Regulators of transcription.
Analysis after a 3-h interaction revealed that several genes encoding proteins of the activator protein-1 (AP-1) complex were upregulated in the bacterial supernatant condition: fos, jun, junB. This was further confirmed by sqRT-PCR. In the live bacteria condition, fos, jun, and junB were also found upregulated and SAM positive, although the upregulation was less than in bacterial supernatant condition (Tables 2 and 3).


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Table 2. Selection of significantly altered genes analyzed by cDNA microarray experiments, ordered by functional classes

 

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Table 3. Selection of genes analyzed by sqRT-PCR experiments, ordered by functional classes

 
EGR-1 (confirmed with sqRT-PCR), core promoter element-binding protein (COPEB), and KLF2 were increased in bacterial supernatant condition, and in live bacteria condition. CEBPG was also upregulated in bacterial supernatant condition, but not in live bacteria condition (Tables 2 and 3).

Although our experimental plan could not provide directly information about the initial steps leading to all these modifications, NF-{kappa}B activation appears as one of the key players of the orchestrated gene regulation. It is already known that NF-{kappa}B can be activated at a protein level in many models of bacterial infections (15, 50). Transcriptional upregulation of NF-{kappa}BIA indeed indicates such activation. Moreover, Fig. 2 shows that NF-{kappa}B1 and NF-{kappa}BIE were significantly upregulated at a transcriptional level after 3 and 6 h of contact with bacterial supernatant. Rel was upregulated only at 3 h, whereas Rel A and Rel B were not regulated (Fig. 2). The activation of NF-{kappa}B target genes (39), especially by the bacterial supernatant condition, is also indicative of an early activation of the NF-{kappa}B protein complex. This was typically the case at 3 h for IL-8 and IL-6, Myc, NF-{kappa}B IA, junB, TNFAIP3, and NR4A2, which are known to be regulated by NF-{kappa}B. NR4A2 was also induced by live bacteria (Table 2). Incidentally, time course experiment revealed that TLR2, a potential activator of the NF-{kappa}B pathway, was upregulated when cells were in contact with bacterial supernatant (Fig. 2).

2) Inflammation.
jak/stat pathway. Many genes encoding proteins known to induce the JAK/STAT pathway were upregulated during the time course interaction with bacterial supernatant: LIF, PDGFB, PDGFC, as well as interferon receptor proteins, IFNAR1, IFNGR1, and IFNGR2. Moreover, JAK1, Lyn, STAT1, STAT3, PIAS1, and SOCS-2, involved in the JAK/STAT pathway, were also upregulated (Fig. 2).

chemokines and interleukins. The chemokines CXCL1, CXCL2, CXCL3 (also named GRO1, GRO2, and GRO3), and CCL20, as well as interleukins IL-8, IL-1{alpha} and IL-1ß, IL-20, and IL-24, and also IL-1RAP, were upregulated after interaction with bacterial supernatant. However, IL-20RA was downregulated, mostly after 6-h interaction with bacterial supernatant (Fig. 2). Analysis after a 3-h interaction showed that IL-1{alpha}, IL-1ß, IL-8, IL-6, and the leukemia inhibitory factor (LIF) were upregulated in the bacterial supernatant condition expressions but were marginally altered after a direct contact with live S. aureus. YARS, which can behave as an interleukin-8-like cytokine, was upregulated in both conditions (Tables 2 and 3).

The response of epithelial cells to S. aureus infection was also quantified at the protein level by an ELISA test for IL-6 and IL-8. Figure 4, A and B, showed an increase in IL-8 and IL-6 in the cell culture supernatant after a 3-h incubation with bacterial supernatant. Smaller variations were noticed after a direct contact with the bacteria.



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Fig. 4. ELISA quantification of IL-6 (A), IL-8 (B), PGE2 (C), and TNF{alpha} (D) secretion by MM-39 cells in contact with the bacterial supernatant or with the live bacteria. Results correspond to the means ± SD and are representative of four independent experiments performed in duplicate. TNF secretion induced by bacterial supernatant was performed two times.

 
cox. Prostaglandins are synthesized by two isoforms of cyclooxygenases (COX, also named PTGS for "prostaglandin-endoperoxide synthases"): COX-1 is a constitutive isoform that maintains cellular homeostasis, whereas COX-2 is an inducible isoform regulated by many proinflammatory stimuli (31). COX-1/2 transform arachidonic acid into prostaglandin H2, a precursor of many prostaglandins such as prostaglandin E2 (PGE2), prostacyclin, and thromboxanes. Accordingly, the COX-1 isoform was constitutively expressed. On the other hand, COX-2 expression was induced and maintained during 9 h after interaction after contact with bacterial supernatant (Fig. 2). Three hours interaction with live bacteria did not modulate COX-2 expression (Tables 2 and 3). The concentration of PGE2 was also measured in the culture supernatants by ELISA after a 3-h interaction. PGE2 level in the live bacteria condition was not modulated in comparison with PGE2 level in the control condition, whereas it was 2.9 times more abundant in the bacterial supernatant condition (Fig. 4C).

ALOX5 and LTA4H, two genes related to leukotrienes synthesis were downregulated by bacterial supernatant in a similar time-dependent manner (Fig. 2).

3) Apoptosis.
Time course experiment on bacterial supernatant condition showed that genes encoding proteins that inhibit apoptosis such as BIRC3, SGK, PIM, and BCL2A1 were upregulated, mainly after 3 h. SCYE1, described at sites of apoptosis, and pro-apoptotic genes encoding CASP1 and DAPK1 were downregulated rapidly after interaction with bacterial supernatant. This major anti-apoptotic response was, however, concomitant with a pro-apoptotic response as interleukins IL-1{alpha}, IL-1ß, IL-24, STK17B, INHBA, and TNFRSF10B were upregulated rapidly after interaction with bacterial supernatant (Fig. 2). Analysis on cDNA microarrays after a 3-h interaction rather confirmed an anti-apoptotic transcriptional response of airway cells after contact with live bacteria or bacterial supernatant. Indeed, several genes that encoded products with anti-apoptotic properties such as PIM and TNFAIP3 were upregulated in both conditions after 3-h interaction. At 3 h, most airway cells incubated with live bacteria or bacterial supernatant exhibited a necrotic phenotype. Apoptosis was only transiently observed 8–10 h after interaction with bacterial supernatant and live bacteria, respectively (Fig. 5).



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Fig. 5. Time course analysis of necrosis (A) and apoptosis (B) of MM-39 cells over 24 h of interaction with live bacteria or with bacterial supernatant.

 
In both conditions, TNF{alpha} was only slightly regulated at the transcriptional level after 3-h interaction with live bacteria or after 1, 3, 6, 9, or 24 h with bacterial supernatant. Secreted TNF{alpha} protein was also slightly increased after a 3-h contact with bacterial supernatant (Fig. 4D).

4) Genes regulated by hypoxia or by oxidative stress.
Some genes mainly regulated in the live bacteria condition (bhlhb2, MIG-6, TFRC, and EGR1) have been reported in the literature to be induced by hypoxia, through a HIF-1{alpha}-dependent cascade. HIF-1{alpha} was not regulated at the transcriptional level (neither after 3 h of interaction with live bacteria nor after interaction with bacterial supernatant at 1, 3, 6, 9, or 24 h), suggesting a stabilization of HIF-1{alpha} in presence of hypoxia and oxidative stress (23, 24). In line with this hypothesis, expression of the HIF-1{alpha} protein was found increased in MM-39 cells after 3-h interaction with live bacteria and with bacterial supernatant (Fig. 6).



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Fig. 6. Increase of HIF-1{alpha} protein expression in MM-39 airway cells after a contact with live bacteria and bacterial supernatant. MM-39 cells were incubated in absence (lane 1, live bacteria control) or in presence (lane 2) of live bacteria (50 bacteria/cell) or were incubated in absence (lane 3) or in presence (lane 4) of bacterial supernatant (dilution 1/10) for 3 h. Samples were resolved by SDS/PAGE and immunodetected with an anti-HIF-1{alpha} antibody.

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
The present study is, to our knowledge, the first report describing the transcriptional host response of human airway epithelial cells after an interaction with live S. aureus or with S. aureus soluble factors. Parallel analysis of gene expression provides a powerful tool for studying interplay of signals and transcriptional responses in complex biological systems. Here we have modeled in vitro the complex interaction existing during infection of airway epithelium by S. aureus and provided information about the major pathways involved in the response to infection.

A first experiment analyzed the interaction of bacterial supernatant with epithelial cells during a time course and was performed using a pangenomic microarray (Fig. 2). This first analysis clearly showed an early alteration (i.e., around 3 h) of many genes encoding proteins implicated in transcription, inflammation, and apoptosis.

To compare the response of epithelial cells with bacterial supernatant and the response of cells with live bacteria, another more specific analysis was performed after 3 h of interaction with either live bacteria and bacterial supernatant. Three reasons made us choose this time point. We first observed that transcriptional response to bacterial supernatant was prominent in the early response to interaction (1, 3, and 6 h) compared with late response (9 and 24 h) (Fig. 2). In contrast with the experiments of Boldrick et al. (6), S. aureus bacteria were not heat killed, leading to a potential effect of in vitro cytotoxicity and differential bacterial growth rate. We indeed observed that prolonged interaction between live bacteria and airway epithelial cells led to irreversible airway cell damage. Moreover, PBS-washed bacteria were in log-phase growth after a 3-h incubation with MM-39 cells (not shown). During log-phase growth, bacteria synthesize predominantly cell surface virulence factors such as protein A. On the other hand, genes encoding secreted virulence factors, such as {alpha}-toxin, are transcribed at low levels during log-phase growth and are upregulated during the stationary phase of growth (31a, 38).

We anticipated that a 3-h incubation with washed live bacteria would differ significantly from a 3-h incubation with bacterial supernatant, which was largely enriched in soluble virulence factors. We therefore designed our study to discriminate the responses of host cells after a 3-h contact with live bacteria, or with soluble secreted virulence factors without live bacteria. To analyze the different responses between live bacteria and bacterial supernatant, more specific microarray consisting of ~4,200 sequence-verified probes, representing ~1/6th of the protein coding capacity of the human genome was selected. Although the fraction covered by this microarray may appear small, available probes were associated with 2,487 terms of the Gene Ontology database, representing 49% of all annotations for all known human transcripts. Moreover, this microarray was enriched with genes coding proteins implicated in inflammation, infection, differentiation, ion transport, cell signaling, and cell migration.

sqRT-PCR was also performed on a set on ~537 pairs of validated primers. Larger stimulation factors were found with sqRT-PCR than with microarray, in general agreement with previous reports by Gavin et al. (18).

The distinct profiles observed between the live bacteria and bacterial supernatant conditions (only 10% of all SAM-positive genes were common to both) was indicative of different responses of airway epithelial cells in contact with either soluble S. aureus factors present in the supernatant of stationary phase bacteria or in contact with log-phase growing bacteria.

To identify relevant metabolic pathways participating to the host response at 3 h, several gene lists were established. Two independent statistical approaches were used. The first one used the classic SAM method, developed by Tusher et al. (49). Since this method hardly takes into account the intensity of the signals, we analyzed our results with a second approach, where probes were sorted according to their intensity level. Probes were selected when their ratio measurements differed significantly from the ratio measurements of the 20 nearest probes (as measured by the intensity), using a t-test. Although this test probably captured a few false-positive probes, the corresponding gene list included a majority of genes already identified by the SAM method (64%). The gene list obtained with this method defined a clear-cut distinction between live bacteria and bacterial supernatant (Fig. 3). Overall, the magnitude of alteration of these genes was stronger in the bacterial supernatant than in the live bacteria condition, as well for the number of altered genes as for the amplitude of the stimulation factors.

A second important observation was the upregulation of proinflammatory molecules such as cytokines and chemokines, as previously reported by Boldrick et al. (6).

Analysis of a time course experiment showed that genes associated with the JAK/STAT pathway were upregulated shortly after incubation with bacterial supernatant (Fig. 2). The JAK kinase activity is critical for normal transmission of cytokine and growth factor signals in host cells (42). The chemokines CXCL2, CXCL3, and CCL20 were upregulated, in agreement with a previous report by Kobayashi et al. (26) about the contact of S. aureus with human polymorphoneutrophils. The release of cytokines IL-1{alpha}, IL-1ß, and IL-6, as well as the chemokine IL-8, was significantly upregulated in the bacterial supernatant condition but not or only slightly in the live bacteria condition. IL-6 and IL-8 interleukins have been extensively identified in response to bacterial infection and during the ensuing inflammation (4, 32, 45, 56). Most of the regulated genes were increased both at mRNA and protein levels and can be under a tight control by the NF-{kappa}B complex. Indeed, NF-{kappa}B-responsive elements are present on IL-1, IL-6, and IL-8 promoters (28, 30, 36, 39).

Parallel with the downregulation of the leukotriene pathway, there was an increased expression of PGE2, correlated with an increased expression of cyclooxygenase (COX-2) in the bacterial supernatant condition. This COX-2 upregulation was in agreement with results found by Rose et al. (43) on A549, and by Lin et al. (31) on human pulmonary epithelial cells. Xie et al. (54) have shown that jun, which is upregulated in the bacterial supernatant condition, can mediate v-src-induced COX-2 gene expression, through its participation to the COX-2 promoter CRE site. This suggests that the activation of jun in the bacterial supernatant condition may contribute to the observed stimulation of COX-2. Additional factors may play a role in the regulation of COX genes. For instance, COX-2 expression is known to be induced by several cytokines (35), and its promoter contains NF-{kappa}B binding sites. As for IL-1 {alpha}, IL-1 ß, IL-6, and IL-8, increased transcription of COX-2 was mainly found in the bacterial supernatant condition, consistent with a possible direct regulation by NF-{kappa}B.

NF-{kappa}B effects are indeed not restricted to S. aureus, and many reports indicate similar effects after interaction with gram-negative bacteria such as Escherichia coli (1, 27) or Shigella flexneri (40). These other reports attribute a key role to Toll-like receptors (TLR) to initiate this mechanism, and it was interesting to notice an increased transcription of TLR2 in bacterial supernatant condition (Fig. 2).

Boldrick et al. (6) found that most of genes encoding NF-{kappa}B/Rel proteins (i.e., NF-{kappa}B1, NF-{kappa}B2, relB, cRel, I-{kappa}B{alpha}) can be regulated after infections by diverse bacteria. We identified an effect on the NF-{kappa}B/Rel complex transcription (NF-{kappa}B1/p105, Rel, and NF-{kappa}BIE) in bacterial supernatant condition during the time-course experiment (Fig. 2). Unfortunately, this effect was not detected at 3 h, neither by cDNA microarray (Table 2) nor by sqRT-PCR (Table 3). Since Bottero et al. (7) have shown that activation of the NF-{kappa}B protein complex closely correlates with the transcriptional level of NF-{kappa}B IA, the measurement of its mRNA level appears as a rapid, sensitive, and powerful method to quantify the transcriptional power of NF-{kappa}B. The increased expression of NF-{kappa}B IA observed in our experiments therefore signed the activation of the NF-{kappa}B complex in the bacterial supernatant condition and to a lesser extent in the live bacteria condition. Bacterial activation of NF-{kappa}B is in agreement with results of DiMango et al. (14), who showed that NF-{kappa}B was activated after interaction between Pseudomonas aeruginosa and respiratory epithelial cells. We also noticed the activation of many NF-{kappa}B-responsive genes as a strong indication of the activation of this cascade.

Expression of several "anti-apoptotic" genes was observed in the bacterial supernatant condition, and, to a lesser extent, in the live bacteria condition. The development of an anti-apoptotic response may appear surprising, since many reports rather suggested pro-apoptotic mechanisms consecutive to infection. This apoptotic response was shown in bovine mammary epithelial cells (MAC-T) (3, 53) and in endothelial cells, via adhesion and internalization of S. aureus (33). However, Weinrauch et al. (52) showed that low doses of {alpha}-toxin induce DNA fragmentation and cell death, whereas high doses result in massive necrosis without DNA fragmentation. In agreement with Da Silva et al. (10), who have worked on the same MM-39 cell line, we observed a prominent necrosis phenotype of the MM-39-cells, with a transient apoptotic process 9 h after interaction (Fig. 5).

TNF{alpha} expression was not altered at a RNA level (microarray and sqRT-PCR), but TNF{alpha} secretion was slightly increased in the bacterial supernatant condition. Under these experimental conditions, TNF{alpha} concentration reached 15 pg/ml of MM-39 cell culture medium, whereas it reached almost 1,000 pg/ml in the experiments of Haddad and coworkers (19), when epithelial cells were in contact with E. coli LPS. Our results were in agreement with those found by Stout et al. (47), who showed that addition of staphylococcal glycocalyx induced production of PGE2 and IL-1 by murine peritoneal macrophage, but not of TNF{alpha}. The modest increase in TNF{alpha} release in our experiment might very well be related to the inhibitory effect of PGE2 on TNF{alpha} secretion in macrophages, which does not affect IL-1 release (16, 29, 46). Bacterial supernatant also induced an upregulation of the gene encoding BIRC3 (AIP1), which can act as a repressor of TNF signaling by binding to the tumor necrosis factor receptor-associated factors TRAF1 and TRAF2.

In conclusion, our results show that the transcriptional response of airway epithelial cells to S. aureus differ markedly when cells interact with live bacteria in log-phase growth or with soluble factors from stationary growth phase. The activation of the JAK/STAT pathway, as well as NF-{kappa}B and AP-1 represent major activating pathways after stimulation by soluble virulence factors. It accounts for the induction of proinflammatory cytokines such as IL-8 as well as inflammatory mediators such as PGE2. These "stereotypic" properties are consistent with many other studies performed with other bacteria and other cells. These results therefore appear a posteriori as a good validation of our experimental approach. Besides, our study shows that additional transcription factors, such as HIF-1{alpha}, may play a role in the orchestrated response of airway cells to S. aureus infection. S. aureus infection also lead cells to a necrotic, rather than an apoptotic cell death. One important aspect about S. aureus infection concerns its capacity to favor subsequent infections by gram-negative bacterial infections such as P. aeruginosa in cystic fibrosis patients. We anticipate that upregulation of inflammatory mediators and/or any other modifications observed in the present study may provide interesting new pharmacological targets to improve the clinical status of S. aureus infected patients.


    GRANTS
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
This work was performed thanks to support from the French Association Vaincre la Mucoviscidose, GlaxoSmithKline, the GIP Aventis, CNRS, Inserm, and the French Ministry of Industry (réseau GenHomme). Microarray experiments were carried out using the facilities of the Nice-Sophia Antipolis Transcriptome Platform of the Marseille-Nice Genopole.


    ACKNOWLEDGMENTS
 
We thank Géraldine Rios for excellent technical assistance in microarray experiments, Jean Marie Zahm (INSERM Reims) for excellent assistance in video microscopy imaging, Franck Aguila, and Jacqueline Kervella for editing the manuscript, and Dr. Yves Berthiaume and Dr. Patrick Brest for helpful discussions.


    FOOTNOTES
 
*E. Puchelle and P. Barbry equally contributed to this work.

Address for reprint requests and other correspondence: P. Barbry, Institut de Pharmacologie Moléculaire et Cellulaire UMR 6097 CNRS, Sophia-Antipolis, 06560 Valbonne, France (E-mail: barbry{at}ipmc.cnrs.fr).

10.1152/physiolgenomics.00135.2004.

1 The Supplementary Material for this article (Supplemental Table S1) is available online at http://physiolgenomics.physiology.org/cgi/content/full/00135.2004/DC1. Back


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

  1. Agace W, Hedges S, Andersson U, Andersson J, Ceska M, and Svanborg C. Selective cytokine production by epithelial cells following exposure to Escherichia coli. Infect Immun 61: 602–609, 1993.[Abstract]
  2. Bayles KW, Wesson CA, Liou LE, Fox LK, Bohach GA, and Trumble WR. Intracellular Staphylococcus aureus escapes the endosome and induces apoptosis in epithelial cells. Infect Immun 66: 336–342, 1998.[Abstract/Free Full Text]
  3. Becker MN, Sauer MS, Muhlebach MS, Hirsh AJ, Wu Q, Verghese MW, and Randell SH. Cytokine secretion by cystic fibrosis airway epithelial cells. Am J Respir Crit Care Med 169: 645–653, 2004.[Abstract/Free Full Text]
  4. Belcher CE, Drenkow J, Kehoe B, Gingeras TR, McNamara N, Lemjabbar H, Basbaum C, and Relman DA. From the cover: the transcriptional responses of respiratory epithelial cells to Bordetella pertussis reveal host defensive and pathogen counter-defensive strategies. Proc Natl Acad Sci USA 97: 13847–13852, 2000.[Abstract/Free Full Text]
  5. Boldrick JC, Alizadeh AA, Diehn M, Dudoit S, Liu CL, Belcher CE, Botstein D, Staudt LM, Brown PO, and Relman DA. Stereotyped and specific gene expression programs in human innate immune responses to bacteria. Proc Natl Acad Sci USA 99: 972–977, 2002.[Abstract/Free Full Text]
  6. Bottero V, Imbert V, Frelin C, Formento JL, and Peyron JF. Monitoring NF-{kappa}B transactivation potential via real-time PCR quantification of I{kappa}B-{alpha} gene expression. Mol Diagn 7: 187–194, 2003.[Medline]
  7. Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet 32, Suppl: 490–495, 2002.[CrossRef][ISI][Medline]
  8. Coraux C, Kileztky C, Polette M, Hinnrasky J, Zahm JM, Devillier P, De Bentzmann S, and Puchelle E. Airway epithelial integrity is protected by a long-acting ß2-adrenergic receptor agonist. Am J Respir Cell Mol Biol 30: 605–612, 2004.[Abstract/Free Full Text]
  9. Da Silva MC, Zahm JM, Gras D, Bajolet O, Abely M, Hinnrasky J, Milliot M, De Assis MC, Hologne C, Bonnet N, Merten M, Plotkowski MC, and Puchelle E. Dynamic interaction between airway epithelial cells and Staphylococcus aureus. Am J Physiol Lung Cell Mol Physiol 287: L543–L551, 2004. First published May 14, 2004; doi:10.1152/ajplung.00256.2003.[Abstract/Free Full Text]
  10. Dayem M, Moreilhon C, Magnone V, Christen R, Ponzio G, and Barbry P. Early gene expression in wounded human keratinocytes revealed by DNA microarray analysis. Comp Funct Genom 4: 47–55, 2003.[CrossRef][ISI]
  11. de Bentzmann S, Roger P, and Puchelle E. Pseudomonas aeruginosa adherence to remodelling respiratory epithelium. Eur Respir J 9: 2145–2150, 1996.[Abstract/Free Full Text]
  12. Delmotte P, Degroote S, Merten M, Bernigaud A,Van Seuningen I, Figarella C, Roussel P, and Perini JM. Influence of culture conditions on the alpha 1,2-fucosyltransferase and MUC gene expression of a transformed cell line MM-39 derived from human tracheal gland cells. Biochimie 83: 749–755, 2001.[CrossRef][ISI][Medline]
  13. DiMango E, Ratner AJ, Bryan R, Tabibi S, and Prince A. Activation of NF-{kappa}B by adherent Pseudomonas aeruginosa in normal and cystic fibrosis respiratory epithelial cells. J Clin Invest 101: 2598–2605, 1998.[Abstract/Free Full Text]
  14. Elewaut D, DiDonato JA, Kim JM, Truong F, Eckmann L, and Kagnoff MF. NF-kappa B is a central regulator of the intestinal epithelial cell innate immune response induced by infection with enteroinvasive bacteria. J Immunol 163: 1457–1466, 1999.[Abstract/Free Full Text]
  15. Fieren MW,van den Bemd GJ, Ben-Efraim S, and Bonta IL. Prostaglandin E2 inhibits the release of tumor necrosis factor-alpha, rather than interleukin 1 beta, from human macrophages. Immunol Lett 31: 85–90, 1992.[CrossRef][ISI][Medline]
  16. Gavin MA, Clarke SR, Negrou E, Gallegos A, and Rudensky A. Homeostasis and anergy of CD4(+)CD25(+) suppressor T cells in vivo. Nat Immunol 3: 33–41, 2002.[CrossRef][ISI][Medline]
  17. Haddad JJ and Land SC. Redox/ROS regulation of lipopolysaccharide-induced mitogen-activated protein kinase (MAPK) activation and MAPK-mediated TNF-alpha biosynthesis. Br J Pharmacol 135: 520–536, 2002.[CrossRef][ISI][Medline]
  18. Hosack D, Dennis G, Sherman B, Lane H, and Lempicki R. Identifying biological themes within lists of genes with EASE. Genome Biol 4: R70, 2003.[CrossRef][Medline]
  19. Hutchison ML and Govan JR. Pathogenicity of microbes associated with cystic fibrosis. Microbes Infect 1: 1005–1014, 1999.[CrossRef][ISI][Medline]
  20. Ichikawa JK, Norris A, Bangera MG, Geiss GK,van’t Wout AB, Bumgarner RE, and Lory S. Interaction of Pseudomonas aeruginosa with epithelial cells: identification of differentially regulated genes by expression microarray analysis of human cDNAs. Proc Natl Acad Sci USA 97: 9659–9664, 2000.[Abstract/Free Full Text]
  21. Ivan M, Kondo K, Yang H, Kim W, Valiando J, Ohh M, Salic A, Asara JM, Lane WS, and Kaelin WG Jr. HIF{alpha} targeted for VHL-mediated destruction by proline hydroxylation: implications for O2 sensing. Science 292: 464–468, 2001.[Abstract/Free Full Text]
  22. Jaakkola P, Mole DR, Tian YM, Wilson MI, Gielbert J, Gaskell SJ, Kriegsheim A, Hebestreit HF, Mukherji M, Schofield CJ, Maxwell PH, Pugh CW, and Ratcliffe PJ. Targeting of HIF-alpha to the von Hippel-Lindau ubiquitylation complex by O2-regulated prolyl hydroxylation. Science 292: 468–472, 2001.[Abstract/Free Full Text]
  23. Kerr C. Experimental design for gene expression microarrays. Technical Report (Biostatistics) 2: 183–201, 2001.
  24. Kobayashi SD, Braughton KR, Whitney AR, Voyich JM, Schwan TG, Musser JM, and DeLeo FR. Bacterial pathogens modulate an apoptosis differentiation program in human neutrophils. Proc Natl Acad Sci USA 100: 10948–10953, 2003.[Abstract/Free Full Text]
  25. Kreft B, Bohnet S, Carstensen O, Hacker J, and Marre R. Differential expression of interleukin-6, intracellular adhesion molecule 1, and major histocompatibility complex class II molecules in renal carcinoma cells stimulated with S fimbriae of uropathogenic Escherichia coli. Infect Immun 61: 3060–3063, 1993.[Abstract]
  26. Kunsch C and Rosen CA. NF-kappa B subunit-specific regulation of the interleukin-8 promoter. Mol Cell Biol 13: 6137–6146, 1993.[Abstract]
  27. Lehmmann V, Benninghoff B, and Droge W. Tumor necrosis factor-induced activation of peritoneal macrophages is regulated by prostaglandin E2 and cAMP. J Immunol 141: 587–591, 1988.[Abstract/Free Full Text]
  28. Libermann TA and Baltimore D. Activation of interleukin-6 gene expression through the NF-kappa B transcription factor. Mol Cell Biol 10: 2327–2334, 1990.[ISI][Medline]
  29. Lin CH, Kuan IH, Lee HM, Lee WS, Sheu JR, Ho YS, Wang CH, and Kuo HP. Induction of cyclooxygenase-2 protein by lipoteichoic acid from Staphylococcus aureus in human pulmonary epithelial cells: involvement of a nuclear factor-kappa B-dependent pathway. Br J Pharmacol 134: 543–552, 2001.[CrossRef][ISI][Medline]
  30. Lowy FD. Staphylococcus aureus infections. N Engl J Med 339: 520–532, 1998.[Free Full Text]
  31. Massion PP, Hebert CA, Leong S, Chan B, Inoue H, Grattan K, Sheppard D, and Nadel JA. Staphylococcus aureus stimulates neutrophil recruitment by stimulating interleukin-8 production in dog trachea. Am J Physiol Lung Cell Mol Physiol 268: L85–L94, 1995.[Abstract/Free Full Text]
  32. Menzies BE and Kourteva I. Internalization of Staphylococcus aureus by endothelial cells induces apoptosis. Infect Immun 66: 5994–5998, 1998.[Abstract/Free Full Text]
  33. Merten MD, Kammouni W, Renaud W, Birg F, Mattei MG, and Figarella C. A transformed human tracheal gland cell line, MM-39, that retains serous secretory functions. Am J Respir Cell Mol Biol 15: 520–528, 1996.[Abstract]
  34. Mitchell JA, Belvisi MG, Akarasereenont P, Robbins RA, Kwon OJ, Croxtall J, Barnes PJ, and Vane JR. Induction of cyclooxygenase-2 by cytokines in human pulmonary epithelial cells: regulation by dexamethasone. Br J Pharmacol 113: 1008–1014, 1994.[ISI][Medline]
  35. Mori N and Prager D. Transactivation of the interleukin-1{alpha} promoter by human T-cell leukemia virus type I and type II Tax proteins. Blood 87: 3410–3417, 1996.[Abstract/Free Full Text]
  36. Moser C, Weiner DJ, Lysenko E, Bals R, Weiser JN, and Wilson JM. ß-Defensin 1 contributes to pulmonary innate immunity in mice. Infect Immun 70: 3068–3072, 2002.[Abstract/Free Full Text]
  37. Novick RP and Muir TW. Virulence gene regulation by peptides in staphylococci and other Gram-positive bacteria. Curr Opin Microbiol 2: 40–45, 1999.[CrossRef][ISI][Medline]
  38. Pahl HL. Activators and target genes of Rel/NF-{kappa}B transcription factors. Oncogene 18: 6853–6866, 1999.[CrossRef][ISI][Medline]
  39. Pedron T, Thibault C, and Sansonetti PJ. The invasive phenotype of Shigella flexneri directs a distinct gene expression pattern in the human intestinal epithelial cell line Caco-2. J Biol Chem 278: 33878–33886, 2003.[Abstract/Free Full Text]
  40. Pilewski JM and Frizzell RA. Role of CFTR in airway disease. Physiol Rev 79: S215–255, 1999.[Medline]
  41. Rane SG and Reddy EP. Janus kinases: components of multiple signaling pathways. Oncogene 19: 5662–5679, 2000.[CrossRef][ISI][Medline]
  42. Rose F, Dahlem G, Guthmann B, Grimminger F, Maus U, Hanze J, Duemmer N, Grandel U, Seeger W, and Ghofrani HA. Mediator generation and signaling events in alveolar epithelial cells attacked by S. aureus alpha-toxin. Am J Physiol Lung Cell Mol Physiol 282: L207–L214, 2002; doi:10.1152/ajplung.00156.2001.[Abstract/Free Full Text]
  43. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, and Quackenbush J. TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34: 374–378, 2003.[ISI][Medline]
  44. Soell M, Diab M, Haan-Archipoff G, Beretz A, Herbelin C, Poutrel B, and Klein JP. Capsular polysaccharide types 5 and 8 of Staphylococcus aureus bind specifically to human epithelial (KB) cells, endothelial cells, and monocytes and induce release of cytokines. Infect Immun 63: 1380–1386, 1995.[Abstract]
  45. Spengler RN, Spengler ML, Lincoln P, Remick DG, Strieter RM, and Kunkel SL. Dynamics of dibutyryl cyclic AMP- and prostaglandin E2-mediated suppression of lipopolysaccharide-induced tumor necrosis factor alpha gene expression. Infect Immun 57: 2837–2841, 1989.[ISI][Medline]
  46. Stout RD, Li Y, Miller AR, and Lambe DW Jr. Staphylococcal glycocalyx activates macrophage prostaglandin E2 and interleukin 1 production and modulates tumor necrosis factor alpha and nitric oxide production. Infect Immun 62: 4160–4166, 1994.[Abstract]
  47. Tummler B and Kiewitz C. Cystic fibrosis: an inherited susceptibility to bacterial respiratory infections. Mol Med Today 5: 351–358, 1999.[CrossRef][ISI][Medline]
  48. Tusher VG, Tibshirani R, and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98: 5116–5121, 2001.[Abstract/Free Full Text]
  49. Verma QLIM. NF-{kappa}B regulation in the immune system. Nat Rev Immunol 2: 725–734, 2002.[CrossRef][ISI][Medline]
  50. Voss A and Doebbeling BN. The worldwide prevalence of methicillin-resistant Staphylococcus aureus. Int J Antimicrobial Agents 5: 101–106, 1995.
  51. Wakkach A, Fournier N, Brun V, Breittmayer JP, Cottrez F, and Groux H. Characterization of dendritic cells that induce tolerance and T regulatory 1 cell differentiation in vivo. Immunity 18: 605–617, 2003.[CrossRef][ISI][Medline]
  52. Weinrauch Y and Zychlinsky A. The induction of apoptosis by bacterial pathogens. Annu Rev Microbiol 53: 155–187, 1999.[CrossRef][ISI][Medline]
  53. Wesson CA, Deringer J, Liou LE, Bayles KW, Bohach GA, and Trumble WR. Apoptosis induced by Staphylococcus aureus in epithelial cells utilizes a mechanism involving caspases 8 and 3. Infect Immun 68: 2998–3001, 2000.[Abstract/Free Full Text]
  54. Xie W and Herschman HR. v-src induces prostaglandin synthase 2 gene expression by activation of the c-Jun N-terminal kinase and the c-Jun transcription factor. J Biol Chem 270: 27622–27628, 1995.[Abstract/Free Full Text]
  55. Yang IV, Chen E, Hasseman JP, Liang W, Frank BC, Wang S, Sharov V, Saeed AI, White J, Li J, Lee NH, Yeatman TJ, and Quackenbush J. Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biol 3: research0062, 2002.
  56. Yao L, Lowy FD, and Berman JW. Interleukin-8 gene expression in Staphylococcus aureus-infected endothelial cells. Infect Immun 64: 3407–3409, 1996.[Abstract]