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Physiol. Genomics 21: 324-336, 2005. First published February 8, 2005; doi:10.1152/physiolgenomics.00289.2004
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Received 8 December 2004; accepted in final form 6 February 2005.
Physiological Genomics 21:324-336 (2005)
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Transcriptional response of bronchial epithelial cells to Pseudomonas aeruginosa: identification of early mediators of host defense

Joost B. Vos1, Marianne A. van Sterkenburg1, Klaus F. Rabe1, Joost Schalkwijk2, Pieter S. Hiemstra1 and Nicole A. Datson3

1 Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands
2 Department of Dermatology, University Medical Center St. Radboud, Nijmegen, The Netherlands
3 Department of Medical Pharmacology, Leiden/Amsterdam Center for Drug Research, Leiden University Medical Center, Leiden, The Netherlands


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
The airway epithelium responds to microbial exposure by altering expression of a variety of genes to increase innate host defense. We aimed to delineate the early transcriptional response in human primary bronchial epithelial cells exposed for 6 h to a mixture of IL-1ß and TNF-{alpha} or heat-inactivated Pseudomonas aeruginosa. Because molecular mechanisms of epithelial innate host defense are not fully understood, the open-ended expression-profiling technique SAGE was applied to construct gene expression profiles covering 30,000 genes: 292 genes were found to be differentially expressed. Expression of seven genes was confirmed by real-time qPCR. Among differentially expressed genes, four classes or families were identified: keratins, proteinase inhibitors, S100 calcium-binding proteins, and IL-1 family members. Marked transcriptional changes were observed for keratins that form a key component of the cytoskeleton in epithelial cells. Expression of antimicrobial proteinase inhibitors SLPI and elafin was elevated after microbial or cytokine exposure. Interestingly, expression of numerous S100 family members was observed, and eight members, including S100A8 and S100A9, were among the most differentially expressed genes. Differential expression was also observed for the IL-1 family members IL-1ß, IL-1 receptor antagonist, and IL-1F9, a newly discovered IL-1 family member. Clustering of differentially expressed genes into biological processes revealed that the early inflammatory response in airway epithelial cells to IL-1ß-TNF-{alpha} and P. aeruginosa is characterized by expression of genes involved in epithelial barrier formation and host defense.

serial analysis of gene expression; primary bronchial epithelial cells; airway inflammation; innate immunity; secretory leukocyte proteinase inhibitor


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
EVERY DAY HUMANS BREATHE thousands of liters of ambient air containing numerous potentially harmful pathogens such as bacteria, fungi, parasites, or viruses. Despite the exposure to these pathogens, severe lung infections are rare. The innate immune system plays a pivotal role in safeguarding the airways from inhaled substances. The airway epithelium is continuously exposed to respiratory pathogens and plays a central role in innate immunity (3, 52). Three main mechanisms are utilized by the epithelium to protect the host from infection. First, epithelial cells form a major part of the physical barrier against entry of pathogens. Second, the airway epithelium actively contributes to innate immunity by the secretion of defense substances such as antimicrobial polypeptides and proteinase inhibitors and by mucociliary clearance through coordinated secretion of mucus and ciliary activity. Third, epithelial cells produce mediators including cytokines and chemokines that serve to attract, mature, and activate cells of the innate and adaptive immune system. Through this, epithelial cells relay signals of danger from the outside world to the inner body.

The epithelial tissue of the airways is frequently exposed to inhaled respiratory pathogens. These pathogens may colonize the airways when first-line defense is hampered, a phenomenon observed in a variety of chronic inflammatory lung disorders including cystic fibrosis and chronic bronchitis. The innate immune function of the airway epithelium is known to be increased after exposure to microorganisms. Microorganisms may directly affect epithelial gene expression or may do so indirectly by stimulating macrophages to release proinflammatory cytokines such as IL-1ß and TNF-{alpha} that subsequently activate epithelial cells (32, 53). Direct cell activation by microorganisms and microbial products is mediated, at least in part, by pattern recognition receptors expressed on host cells such as the Toll-like receptors (TLR) (21, 49). It is well recognized that a broad spectrum of effector molecules secreted by epithelial cells is involved in innate immunity (4, 19, 52). Over the past decade, a large number of antimicrobial peptides and proteinase inhibitors with antimicrobial activity have been identified. The expression of many of these effector molecules, including ß-defensins, secretory leukocyte proteinase inhibitor (SLPI) and elafin, is induced on microbial exposure. However, it is largely unknown which molecules are essential in mounting the innate immune response, nor have the kinetics of these processes been studied.

The aim of the present study was to delineate the early response of human bronchial epithelial cells to direct microbial stimulation with heat-killed Pseudomonas aeruginosa or to indirect stimulation via exposure to the macrophage-derived cytokines IL-1ß and TNF-{alpha}. Therefore, the effect of these stimuli on bronchial epithelial cells was assessed at the level of gene transcription, using serial analysis of gene expression (SAGE). SAGE is a highly sensitive and reliable method to generate comprehensive expression profiles. Because SAGE is not limited by a predefined set of genes, both known and unknown genes can be studied (56, 57). The sensitivity and reliability of SAGE have been confirmed by a number of independent techniques such as Northern blot (28), real-time PCR (37), DNA macroarray (38), and DNA microarray technology (27). With the use of SAGE, at least four families of genes were identified to be affected in epithelial cells after exposure to the proinflammatory cytokines IL-1ß and TNF-{alpha} and to P. aeruginosa. Keratins, proteinase inhibitors, members of the IL-1 family, and S100 calcium-binding proteins were prominently present among the most differentially expressed genes. Together with the other differentially expressed genes, these four families of genes may contribute to the onset of the innate immune response.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Bronchial epithelial cells.
Subcultures of human bronchial epithelial cells were derived from bronchial tissue specimens obtained from patients who underwent a thoracotomy with lobectomy for lung cancer at the Leiden University Medical Center (LUMC, Leiden, The Netherlands), as described previously (55). Tissue specimens were found to be normal, as determined macroscopically by a pathologist and microscopically at the time of dissection of the epithelial cells. Cells were grown to near-confluence in 25-cm2 culture flasks precoated with a matrix of vitrogen (30 µg/ml; Celtrix Laboratories, Palo Alto, CA), fibronectin (10 µg/ml; isolated from human plasma), and bovine serum albumin (BSA, 10 µg/ml; Boehringer Mannheim, Mannheim, Germany) in serum-free keratinocyte-SFM medium (KSFM; Gibco-BRL/Life Technologies, Breda, The Netherlands) supplemented with 0.2 ng/ml epidermal growth factor (EGF; Gibco-BRL/Life Technologies), 25 µg/ml bovine pituitary extract (BPE; Gibco-BRL/Life Technologies), 1 mM isoproterenol (Sigma Chemicals, St. Louis, MO), 20 U/ml penicillin (BioWhittaker, Walkersville, MD), and 20 µg/ml streptomycin (BioWhittaker). After the cells had reached near-confluence, cells were incubated for 36 h in high-calcium medium to allow differentiation, as described previously (55). High-calcium medium was composed of KSFM (Gibco-BRL/Life Technologies) supplemented with 1 mM CaCl2, 5 nM retinoic acid (Sigma Chemicals), 0.2 ng/ml EGF, 25 µg/ml BPE, 20 U/ml penicillin, and 20 µg/ml streptomycin.

P. aeruginosa (PAO1).
The nonmucoid P. aeruginosa strain PAO1 (BAA-47; American Type Culture Collection, Rockville, MD) was grown overnight in brain-heart infusion (BHI) medium to stationary phase at an agitation rate of 275 rotations/min at 37°C. After three washes with PBS, bacteria were resuspended in PBS containing 50% glycerol at a concentration of 109 colony-forming units (CFU)/ml, as determined by optical density. Samples of the bacterial suspension were plated onto blood agar plates to verify the measured CFU of the suspension. The bacteria were heat inactivated for 45 min at 95°C in a water bath and stored in aliquots at –80°C until further use.

Stimulation experiments.
For the construction of the SAGE libraries, subcultures of primary bronchial epithelial cells derived from seven different donors were cultured in 25-cm2 flasks and stimulated for 6 h with either a mixture of the proinflammatory cytokines IL-1ß (20 ng/ml; PeproTech, Rocky Hill, NJ) and TNF-{alpha} (20 ng/ml, PeproTech) or heat-inactivated P. aeruginosa (107 CFU/ml), or with high-calcium medium alone. Three 25-cm2 culture flasks containing ~5 x 106 cells were used for each donor and each challenge. After challenge, RNA was isolated for SAGE and subsequent quantitative real-time PCR (qPCR) validation.

RNA isolation.
Total RNA from the cell cultures was extracted using TRIzol reagent (Gibco-BRL/Life technologies) according to the manufacturer’s instructions. Enriched mRNA was obtained using the Oligotex mRNA Mini Kit (Qiagen/Westburg, Leusden, The Netherlands) according to the manufacturer’s instructions. After purification, equal amounts of mRNA derived from the seven donors were pooled to a final amount of 5 µg of mRNA per challenge for the generation of SAGE libraries. The remainder of the purified mRNA samples was stored individually per donor at –80°C until further use.

Construction and analysis of SAGE libraries.
In SAGE, two standard endonuclease reactions are subsequently performed to isolate short nucleotide sequences called tags. These tags are derived from a defined position at the 3'-end of each transcript flanked by the most 3'-end restriction site of NlaIII. The second endonuclease specifically cleaves at a given distance of ~14 bp from the NlaIII restriction site, resulting in the release of the 10- to 14-bp tags. Isolated tags are paired tail to tail, ligated, and cloned for automated sequencing. After sequencing, tags are easily recognized and extracted from the raw sequence data, since each tag is flanked at one side by the NlaIII restriction site.

SAGE was performed as described by Datson et al. (12), based on the original protocol developed by Velculescu et al. (56). For the generation of each library, 5 µg of mRNA were used. Double-stranded biotinylated cDNA was synthesized using Superscript Choice system (Gibco-BRL/Life Technologies) and biotin(dT) primers. In the first endonuclease reaction, double-stranded biotinylated cDNA was cleaved by NlaIII (New England Biolabs, Beverly, MA), divided into two pools, and bound to streptavidin-coated magnetic beads (Dynal Biotech, Oslo, Norway) for the attachment of linkers containing an endonuclease restriction site for BsmF1 and a PCR primer annealing site. After ligation of the linkers, the bound cDNA was cleaved in the second endonuclease reaction using the type IIS endonuclease BsmFI, thereby releasing the 10- to 14-bp tags. Released tags were blunt ended, paired tail to tail, and ligated. After PCR amplification of the ligation product, linker sequences were released by cleavage with NlaIII. The cleavage product containing the ditags was ligated into multimers, cloned into the SphI site of pZero 1.0 (Invitrogen/Life Technologies), and electroporated in ElectroMAX DH10B cells (Gibco-BRL/Life Technologies). Colonies were transferred into 96-well plates containing Luria broth-7.5% glycerol-Zeocin (50 µg/ml) medium, grown overnight at 37°C, and stored at –80°C until further use. PCR products were sequenced on ABI377 and ABI3700 automated sequencers (Applied Biosystems, Foster City, CA) using BigDye terminator sequencing kit v2 (Applied Biosystems).

Sequenced clones were analyzed with the SAGE2000 v4.13 software kindly provided by K. W. Kinzler (John Hopkins Oncology Center, Baltimore, MD). Tags corresponding to linker sequences were discarded. SAGE tags derived from mitochondrial DNA were inferred from the human mitochondrial genome (RefSeq: NC_001807; Ref. 25). Statistical analysis (Monte Carlo simulation) was performed using the SAGE2000 software. Differences in expression levels were determined at statistical significance levels of P < 0.05 and P < 0.01. For tag identification, the libraries were compared with the National Center for Biotechnology Information (NCBI)’s reliable Unigene cluster to SAGE tag map (ftp://ftp.ncbi.nlm.nih.gov/pub/sage) (30) and with the Cancer Genome Anatomy Project (CGAP)’s SAGE Genie (http://cgap.nci.nih.gov/SAGE) (6). Both databases were based on Unigene build no. 161. Combining these approaches decreased the degree of ambiguous mapping of SAGE tags.

To classify groups of genes showing similar changes in expression patterns across the libraries, self-organizing maps (K-means clusters) of differentially expressed genes (P < 0.05) were constructed using Spotfire Decision Site software 7.1 (Spotfire, Göteborg, Sweden). Tag numbers were converted to relative expression values to visualize the change in expression rather than the absolute change in levels of expression.

For annotation of tags to biological processes and molecular function of genes, differentially expressed genes were matched to the Gene Ontology database (February 2004) using GoMiner (59) and AmiGO (http://www.godatabase.org/cgi-bin/amigo/go.cgi).

qPCR.
qPCR was used to validate the SAGE data. For qPCR, samples derived from the same mRNA pool as used in SAGE were analyzed for expression of selected genes. For characterization and validation of normalization genes, mRNA samples from the individual donors were used to verify constant expression ratios of these genes in each sample. Single-stranded cDNA was synthesized using Moloney murine leukemia virus reverse transcriptase primed with oligo(dT) (both from Invitrogen/Life Technologies, Breda, The Netherlands). Gene-specific primers were designed for two calcium-binding proteins (S100A8, S100A9), for two proteinase inhibitors (elafin and SLPI), and for members of the IL-1 family (IL-1ß, IL-1RN, and IL-1F9). Primers were synthesized by Isogen (Maarssen, The Netherlands). To correct for differences in cDNA concentration of different samples, gene-specific primers for normalization genes [keratin 6A (KRT6A), ribosomal protein L5 (RPL5), and lamin A/C (LMNA); see RESULTS for details] were used. Primer sequences and reaction conditions are listed in Table 1.


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  		Table 1. Sequences of primers for qPCR

 
qPCR analysis was performed on an iCycler PCR machine (Bio-Rad, Hercules, CA) using SYBR Green I chemistry (47). Samples were analyzed in triplicate, and threshold cycle (CT) numbers were calculated using the iCycler v3.0a analysis software (Bio-Rad). CT values were used to calculate arbitrary mRNA concentrations using the relative standard curve method. The standard curve was generated in each reaction using a serial dilution of a cDNA sample containing message for the gene of interest. Relative mRNA concentrations for both the selected genes and normalization genes were determined and were used to calculate the expression ratios. Fold changes and standard errors of the mean for the validated genes were derived by averaging the ratios obtained from the independent normalizations for KRT6A, RPL5, and LMNA. Significance levels were determined at P < 0.05 using a two-tailed paired Student’s t-test.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
SAGE expression profiles.
Subcultures of human primary bronchial epithelial cells (PBEC) were stimulated for 6 h with either a mixture of the proinflammatory cytokines IL-1ß and TNF-{alpha} or heat-inactivated P. aeruginosa. Expression profiles of stimulated cultured PBEC were constructed using SAGE. The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (17) (GEO; http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO series accession no. GSE2056. In three libraries, a total number of 86,166 tags corresponding to 29,249 different transcripts were identified. Table 2 shows the number of analyzed and unique tags in each library, including the number of duplicate dimers and linker tags encountered in each library. A relatively large number of tags (~75%) occurred only once in each library (Fig. 1). Tags that were detected at least four times corresponded to 5–8% of the unique tags while contributing to ~50% of the total number of analyzed tags. Unique tags with an abundance of at least 50 copies contributed ~19% to the total number of sequenced tags. The difference in the number of unique tags between the control and challenge groups is explained by an increased number of tags that were expressed with at least 50 copies after exposure to IL-1ß-TNF-{alpha} or P. aeruginosa (Fig. 1 and Table 2).


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  		Table 2. Nos. of analyzed and unique tags

 


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  		Fig. 1. Tag frequency in the 3 libraries. Unique tags were classified into frequency classes based on their abundance in the 3 libraries. Open bars represent the frequency classes found in the control library, the gray bars the IL-1ß-TNF-{alpha} library, and the dark gray bars the Pseudomonas aeruginosa library. Selected genes represented by corresponding tags in the different frequency classes are indicated at top. Results show that transcripts encoding S100 proteins and proteinase inhibitors are abundant and those encoding cytokines are intermediate, whereas ß-defensin transcripts are rare. SLPI, secretory leukocyte proteinase inhibitor.

 
The frequency distribution of tags in the libraries reflected the general pattern of gene expression in mammalian cells. Only a limited number of genes were expressed at high copy numbers (5–10% of all expressed genes), whereas the majority of genes displayed low levels of expression. For comparison, selected tags identified in the libraries are indicated in different frequency classes (Fig. 1).

Tag identity was assigned using CGAP’s SAGE Genie "Hs best gene to tag" and to NCBI’s "UniGene cluster to SAGE tag reliable database" (NCBI SAGEmap). This allowed the assignment of 76.8% unique tags. The remaining 6,803 tags (23.2%) did not match to any genetic database. These unidentified tags might represent novel sequences expressed by PBEC. Most of these unidentified transcripts were expressed at low levels, as illustrated by the fact that 6,705 unidentified tags appeared only once, whereas a limited number of 98 tags showed expression levels of at least 3 tags in a single library.

Differentially expressed genes.
Differential expression of tags between libraries was determined using the Monte Carlo simulation method. Using two cut-off values, a total number of 652 genes (P < 0.05) and 292 genes (P < 0.01) were found to be differentially expressed in one or more library comparisons. The number of genes found to be differentially expressed at a significance level of P < 0.01 is shown in the Venn diagram in Fig. 2. The largest number of differentially expressed genes was found in the comparison of the control group vs. the P. aeruginosa group. The number of genes showing different levels of expression between the IL-1ß-TNF-{alpha} library and the P. aeruginosa library is relatively low, most likely because these stimuli activate similar cellular processes. Table 3 shows the number of differentially expressed genes increased or decreased in expression after challenge.



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  		Fig. 2. Venn diagram of differentially expressed tags. Differentially expressed tags were subdivided into tags that were expressed in both libraries (overlapping areas) in the comparison or in 1 of the libraries (left or right ovals) in the comparison.

 

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  		Table 3. Comparison of the SAGE libraries

 
Among the 50 most differentially expressed genes, 45 were increased in expression, whereas only 5 genes were decreased in expression after P. aeruginosa or cytokine challenge. The 50 most differentially expressed genes sorted on tag abundance in the P. aeruginosa library are listed in Table 4. Among these, several genes were present that encode for proteins that are directly or indirectly involved in epithelial defense, such as the S100 calcium-binding proteins S100A6 and S100A9, the proteinase inhibitors elafin (PI3) and cystatin B (CSTB) and the cytokine IL-1 family member 9 (IL-1F9), a novel member of the IL-1 family of cytokines. Genes encoding structural components of the cytoskeleton that are also involved in differentiation, including keratins, {gamma}-actin (ACTG), and small proline-rich protein 1B (SPRR1B), were also well represented in the top-50 list.


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  		Table 4. Fifty most differentially expressed genes in P. aeruginosa-treated PBEC

 
To structure the SAGE data, self-organizing maps of differentially expressed genes (P < 0.05) were constructed using the K-means clustering method. Nine clusters were created visualizing the change in direction of expression in groups of genes (Fig. 3). The majority of differentially expressed genes was allocated to cluster 1. The pattern of expression shows that genes allocated to cluster 1 increased after IL-1ß-TNF-{alpha} exposure and further increased after exposure to P. aeruginosa. This is in line with the data presented in Table 3. Among the top-50 list, 42 genes were allocated to K-means cluster 1, including S100A6, S100A9, elafin, and FAU (see Table 4). Small proline-rich protein 2A (SPRR2A) and IL-1F9 were allocated to cluster 7, since there was little or no expression in the control group and cytokine group, whereas high levels of expression were found in the P. aeruginosa group, indicating that these genes might be specific for P. aeruginosa-initiated responses in PBEC. Three of the five downregulated genes, ribosomal protein L32 (RPL32), aldo-keto reductase loop ADR (LoopADR), and stratifin (SFN), were allocated to cluster 9. In this cluster, gene expression was decreased to a similar extent after both IL-1ß-TNF-{alpha} and P. aeruginosa exposure. The two remaining downregulated genes, annexin A1 (ANXA1) and fatty acid binding protein-5 (FABP5), were allocated to cluster 2. The general expression pattern of genes allocated to cluster 2 showed high expression in the control group, low levels of expression in the IL-1ß-TNF-{alpha} group, and moderate expression in the P. aeruginosa group.



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  		Fig. 3. K-means clustering of differentially expressed genes (P < 0.05). Eleven genes derived from the top-50 list are indicated in their corresponding cluster. Data indicate the change in direction of expression between control and challenge groups.

 
Classification of differentially expressed genes.
Differentially expressed genes after P. aeruginosa exposure were annotated to their corresponding biological processes using Gene Ontology. This analysis showed that the response of epithelial cells is characterized by the expression of genes encoding for proteins involved in biological processes such as metabolism, cell growth/maintenance, development, cell communication, and response to stimulus (Fig. 4). The cell growth/maintenance category refers to genes that exert molecular functions that are related to cell cycle, cell adhesion, and cytoskeletal architecture.



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  		Fig. 4. Gene Ontology: biological processes. Differentially expressed genes in primary bronchial epithelial cells after P. aeruginosa exposure were subjected to Gene Ontology annotation to identify the corresponding biological processes. The major biological processes, represented by wedges, are shown, including the percentage of differentially expressed genes annotated to the biological process. The no. of genes increased or decreased for each of the processes is indicated outside each wedge. The smallest categories are singled out and summed. Redundancy in Gene Ontology exists due to the fact that genes may be involved in multiple biological processes.

 
Following this functional analysis of the gene expression profiles, we selected four classes/families of genes that were affected (P < 0.01) in stimulated PBEC: keratins, proteinase inhibitors, IL-1 family members, and S100 calcium-binding proteins (Table 5). The best represented family of genes involved in development in our data is the family of keratins. These genes encode for structural component proteins of epithelial cells. With 19 members expressed, representing 3.5% of all sequenced tags, and with 5 members among the most differentially expressed genes (P < 0.01), the family of keratins is likely to play a crucial role in forming and maintaining the physical epithelial barrier. In addition to keratins involved in barrier formation, epithelial cells expressed large amounts of transcripts encoding proteinase inhibitors, including the serine proteinase inhibitors SLPI and elafin (PI3/SKALP) and the cysteine proteinase inhibitor CSTB. Increased release of proteinase inhibitors has been associated with inflammation. These molecules protect the airway epithelium from proteinase activity of endogenous and exogenous proteinases. Roughly 2.5% of all sequenced tags corresponded to various proteinase inhibitors under control conditions, increasing up to 3.6% of all tags after P. aeruginosa exposure. Assignment of biological processes to these molecules using Gene Ontology remained difficult, since only a limited number of proteinase inhibitors have been correlated to a biological process.


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  		Table 5. Classification of differentially expressed genes

 
Among the immune signaling molecules, a number of cytokines and chemokines were expressed. The most abundantly expressed cytokines were the members of the IL-1 family. According to Gene Ontology, these molecules are involved in the biological process "response to stimulus." Six of ten members were found to be expressed by bronchial epithelial cells, of which three were differentially expressed (P < 0.01), including IL-1ß, IL-1RN, and IL-1F9, a novel member of this family.

Finally, S100 calcium-binding proteins were abundantly expressed by PBEC and increased after microbial exposure. These molecules exert diverse functions, including antimicrobial activity and involvement in differentiation and intracellular signaling (16, 39). In total, 13 of 21 members of this family were encountered. SAGE tags for S100A2, S100A6, S100A8, and S100A9 were detected at high frequencies in our SAGE libraries and were also among the 292 most differentially expressed genes (P < 0.01; Table 4). With 668 tags in the SAGE library of P. aeruginosa-challenged PBEC, the tag corresponding to S100A9 was the most frequently encountered tag in the three SAGE libraries. In total, these four families of genes contributed 13% to the total number of sequenced tags in the SAGE library of the P. aeruginosa challenge group.

Generally, genes involved in metabolism were abundantly expressed, and the expression fluctuated upon changes in the environment. Indeed, a large proportion of differentially expressed genes were classified to be involved in metabolism. In total, 17 tags corresponding to transcripts encoding for ribosomal proteins were found among the 50 most differentially expressed genes in PBEC after P. aeruginosa exposure. In addition, the Gene Ontology analysis showed that 26% of the differentially expressed genes were correlated to processes related to metabolism. The general trend in expression was that the number of tags encoding for ribosomal proteins increased after stimulation with IL-1ß-TNF-{alpha} and increased more markedly after P. aeruginosa challenge. The overall increase in expression of ribosomal proteins suggests an increased protein synthesis after challenge with proinflammatory cytokines and P. aeruginosa.

Validation of SAGE results by qPCR.
The SAGE results were validated with qPCR. Primers were designed for selected genes that were found to be differentially expressed by SAGE. For normalization of the qPCR data, we used a panel of three endogenous control genes instead of a single normalization gene. All of the commonly used normalization genes such as ß-actin (ACTB) or glyceraldehyde-3-phosphate dehydrogenase (GAPDH) showed variable levels of expression under the experimental conditions or were not expressed at all. We selected three genes from our SAGE libraries that showed similar levels of expression for use as normalization genes: KRT6A, RPL5, and LMNA (Table 5). The expression of KRT6A, RPL5, and LMNA was assessed by qPCR in 24 cDNA samples. These samples were derived from PBEC of the seven individual donors separately exposed to medium, cytokines, or P. aeruginosa and the corresponding pooled RNA samples from the seven donors (as used for SAGE). The results showed minimal variation in the ratios of the three normalization genes used (KRT6A/LMNA, KRT6A/RPL5, and LMNA/RPL5; Fig. 5), indicating that these genes were suitable for use as normalization genes in PBEC exposed to the stimuli used in this study.



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  		Fig. 5. Ratios of normalization genes. Three genes were used to accurately normalize the quantitative real-time PCR (qPCR) data. Each bar represents the mean expression ratio of a pair of normalization genes in the 24 samples tested. Variation in expression ratios is displayed as SE of the mean. RPL5, ribosomal protein L5; KRT6A, keratin 6A; LMNA, lamin A/C.

 
In general, the change in direction and pattern of expression of the selected genes after exposure to P. aeruginosa or IL-1ß-TNF-{alpha} was observed using both SAGE and qPCR (Fig. 6). The best correlation between SAGE and qPCR data was observed for those genes that were abundantly expressed, such as S100A8, S100A9, SLPI, and elafin. For low-abundance transcripts such as the IL-1 family members, the observed change in direction of expression correlated well between SAGE and qPCR. However, the magnitude of the response showed more variation.



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  		Fig. 6. Validation of 7 target sequences using qPCR. From left to right are depicted the expression levels of control, IL-1ß-TNF-{alpha}, and P. aeruginosa, respectively, as determined by qPCR and SAGE. SAGE, serial analysis of gene expression. Statistical significance was determined at P < 0.05. *Significant difference in expression between control and challenge groups. #Statistical difference between challenge groups.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
The innate immune system of the airways is a complex and dynamic system that continuously adapts itself to the changing environment to prevent infection by respiratory pathogens. Although epithelial innate immunity has gained renewed interest over the past two decades, little is still known about the underlying molecular mechanisms that lead to the onset of the host defense response in epithelial cells upon microbial exposure. In the present study, genes affected by IL-1ß-TNF-{alpha} and P. aeruginosa exposure were identified using SAGE. Differentially expressed genes in PBEC, after both IL-1ß-TNF-{alpha} and P. aeruginosa exposure, were found to be mainly implicated in biological processes such as metabolism, cell growth/maintenance, development, and response to stimulus. In this study, we show that at least four families of genes are involved in the epithelial response to the proinflammatory cytokines IL-1ß and TNF-{alpha} or P. aeruginosa: keratins, proteinase inhibitors, IL-1 family members, and S100 calcium-binding proteins. The expression profiles were validated using qPCR. To our knowledge, this is the first study in which a large-scale expression-profiling technique was used to assess the epithelial response to proinflammatory cytokines or heat-inactivated microorganisms, providing a comprehensive view on the epithelial response to these stimuli at the mRNA level of thousands of transcripts simultaneously.

Previously, two expression-profiling studies have been published using various lung-derived epithelial cell lines that were exposed to microorganisms. Inherent to the use of tumor or transformed (immortalized) cell lines is that these cells may respond differently compared with primary cells such as the cells used in our study. In the study of Ichikawa et al. (24), the lung carcinoma cell line A549 was exposed to P. aeruginosa over a period of 1–3 h, after which changes in expression profiles were determined. This study showed that interferon regulatory factor-1 is essential in the host defense response of A549 cells to the microorganism. A substantial drawback of this study is that a custom-made microarray was used that contained a limited number of 1,506 probesets, of which the identity has not been made publicly available. Furthermore, this microarray was developed in an earlier study to assess transcriptional changes in CD4+ T cells after human immunodeficiency virus infection (20). It is therefore not clear to what extent the probesets present on this chip are relevant for studying epithelial gene expression. In the other published expression-profiling study, Belcher et al. (5) assessed the transcriptional changes in the SV40 virus-transformed bronchial epithelial cell line BEAS-2B upon exposure to Bordetella pertussis using an Affymetrix HU6800 microarray containing ~6,800 probesets. Transcriptional changes were assessed 1 and 3 h after B. pertussis exposure. The response after 3 h was most robust in this model system, whereas after 1 h of exposure minimal changes in the transcriptome of B. pertussis-infected BEAS-2B cells were observed. At 3 h, a modest number of 33 genes showed a relative change of plus or minus threefold in expression, including cytokines such as, among others, IL-1ß and IL-6 and chemokines such as IL-8, CCL2/MCP-1, CXCL1/Gro{alpha}, and CXCL2/Groß. A number of the genes that were identified by Belcher et al. to be important in the defense against B. pertussis were also detected in our analysis. However, it should be noted that the levels of expression of these genes are close to the detection limit that can be reliably measured by large-scale gene expression-profiling methods such as SAGE and microarray. These studies support our conclusion that substantial changes in the transcriptome of epithelial cells occur at least after 3–6 h after microbial exposure. However, our study differs from these studies because of the use of PBEC instead of immortalized or tumor cell lines and the choice of expression-profiling technique.

One of the main advantages of SAGE is its open-ended approach of assessing gene expression in an unbiased fashion. This was demonstrated in a similar in vitro model of epithelial inflammation using TNF-{alpha}-stimulated keratinocytes (28). This study revealed novel insights into cornification and the barrier function of the skin under inflammatory conditions. With the use of SAGE, the proteinase inhibitor cystatin M/E (CST6) was identified to be one of the key genes that was overexpressed in TNF-{alpha}-stimulated keratinocytes. Previously, this gene had not been associated with barrier formation in this cell type. Further investigation unveiled a central role for cystatin M/E in keratinization of the skin (60, 61). Comparison of the SAGE results of the keratinocyte study and the present study showed a marked similarity in the response of airway epithelial cells and keratinocytes regarding barrier function and host defense under inflammatory conditions. For both cell types, highly expressed classes of genes were keratins, proteinase inhibitors, and S100 proteins. The epithelial antimicrobial proteinase inhibitors SLPI and elafin were found at high levels in both keratinocytes and PBEC. Among the S100 calcium-binding proteins, the tag corresponding to S100A9 was the most abundantly expressed S100 protein in both keratinocytes and PBEC.

Also in the present study, SAGE revealed the transcription of numerous genes that were previously not known to be expressed by bronchial epithelial cells after exposure to proinflammatory cytokines or P. aeruginosa. A selection of the 292 differentially expressed genes could be classified into four processes based on their function in innate immunity. First, barrier formation is an important feature of innate immunity. Genes encoding for structural components of the cytoskeleton are abundantly expressed and are involved in assembly and disassembly of the cytoskeleton, controlling cell size and shape, and cell-cell or cell-matrix interactions. Keratin family members that form intermediate filaments in epithelial cells (reviewed in Ref. 29) were frequently encountered among the list of differentially expressed genes. Coexpression of a number of keratins such as KRT5 and KRT14 (36) that were previously reported in skin was also observed in bronchial epithelial cells. In the skin, mutations in KRT5 and KRT14 cause a number of skin disorders (26). Whether these mutations affect the bronchial mucosa is unclear. Coexpression of KRT6 isoforms (except for KRT6A, which is expressed at steady-state levels) and KRT17 was increased after stimulation, whereas the expression of KRT16 was not affected. Coexpression of KRT6 isoforms, KRT16, and KRT17 has been associated with wound healing after cutaneous skin injury (35). These results suggest that keratins are important players in the recovery process of epithelial cells after microbial insult. Furthermore, these data indicate similarities between the regulation of certain keratins in bronchial airway epithelial cells and keratinocytes.

The second class of differentially expressed genes consists of genes encoding proteinase inhibitors. These molecules are produced to protect the epithelium from enzymatic attack by microbial or endogenous host proteinases. The protective role of elafin for epithelial tissues has been described previously (42). Proteinase inhibitors, including SLPI, elafin, and cystatin family members, were expressed at high levels under control conditions and were increased in expression upon stimulation. Interestingly, SLPI and elafin also display antimicrobial activity against a range of microorganisms, including P. aeruginosa (43, 46, 58), and may be involved in proliferation and repair of epithelial tissues (2, 43).

Our finding that several IL-1 gene family members were expressed upon exposure of bronchial epithelial cells to proinflammatory cytokines and a microbial stimulus is interesting. Differential expression was observed for three IL-1 family members (IL-1ß, IL-1RN, and IL-1F9; P < 0.01). Both IL-1ß and IL-1 receptor antagonist (IL-1RN) exert their function through the IL-1 type I receptor (reviewed in Refs. 1 and 15). Recently, a novel IL-1 signaling cascade has been described that employs a novel IL-1 receptor, the IL-1 receptor-related protein-2 (IL-1Rrp2), and two novel IL-1 family members, IL-1F5 and IL-1F9 (14). Binding of IL-1F9 to IL-1Rrp2 leads to activation of NF{kappa}B (51), while IL-1F5 functions as antagonist of the receptor (14). The inducible expression of IL-1F9 in bronchial epithelium, as observed in the present study, is remarkable, since expression of this tag, as determined by SAGE, has only been detected in three other tissue types at very low levels. This finding suggests a role for both IL-1ß and IL-1F9 signaling in bronchial epithelial cell function, which may allow fine tuning of the onset and boost of the inflammatory response.

S100 calcium-binding proteins form the fourth group of differentially expressed genes. These proteins exert a wide range of functions, including signal transduction, cell-cell communication, regulation of cell cycle, cell differentiation, gene transcription, and antimicrobial activities (39). Four members of the S100 family, S100A2, -A6, -A8, and -A9, were found to be differentially expressed (P < 0.01). The SAGE tag corresponding to S100A9 was the most abundant transcript detected in both the IL-1-ß-TNF-{alpha} library and the P. aeruginosa library. This protein forms a heterodimer with S100A8 in a calcium-dependent manner. The complex, also known as cystic fibrosis antigen or calprotectin, displays antimicrobial activity that has been ascribed to its ability to chelate Zn2+ (11, 33), thereby depriving the microenvironment of zinc ions that are essential for microbial growth. A prominent role for S100A8/S100A9 in inflammation has been suggested, since the complex is abundantly detected in a number of inflammatory conditions such as abscesses, cystic fibrosis, chronic bronchitis (48), and Crohn’s disease (50). S100A2 may function as a tumor suppressor gene, although this is a matter of debate, since both a decrease (18, 31, 41) and an increase in expression (23, 34, 44) of this gene have been observed in various types of tumors. Alternatively, Zhang et al. (62) have suggested that the primary function of S100A2 is the participation in the oxidative stress response. For S100A6, it has been demonstrated that this protein is involved in cytoskeletal organization, morphology, and the regulation of proliferation of lung fibroblasts in vitro (7). Additionally, S100A6 may be involved in tumor progression, since the levels of expression of S100A6 are proportional to the increase of malignancy, as was demonstrated in human colon tissue (8).

Remarkably, expression of genes encoding antimicrobial peptides was limited, which was also demonstrated by the absence of expression of these molecules in the studies of Belcher et al. (5) and Ichikawa et al. (24). This suggests that these molecules may not predominate in the early host defense response by bronchial epithelial cells. The most abundant transcript encoding an antimicrobial peptide in our dataset, FAU, was found to be expressed at increased levels by PBEC after stimulation with IL-1ß-TNF-{alpha} or P. aeruginosa, whereas little expression was found under control conditions (Table 4). Posttranslational modification of the FAU gene product yields the antimicrobial peptide ubiquicidin (22). Among the antimicrobial defensins, human ß-defensin-2 (hBD-2; DEFB4) was detected in our SAGE study. However, the levels of expression for the tag corresponding to DEFB4 were at the lower detection limit of SAGE. Using qPCR, we confirmed the absence of DEFB4 expression in PBEC under control conditions and observed an induction of expression after IL-1-ß-TNF-{alpha} or P. aeruginosa exposure (data not shown).

To validate our SAGE results, qPCR was performed on selected target sequences. To optimize the accuracy of validation by qPCR, we carefully selected three genes for normalization to correct for differences in cDNA input. Widely used normalization genes such as GAPDH and ß-actin showed variable expression levels in our SAGE libraries. On the basis of the SAGE data, KRT6A, RPL5, and LMNA were selected for normalization. Accurate normalization of qPCR data is essential for detecting small differences in gene expression or studying differential expression of low-abundance genes and can be achieved by using multiple internal control genes, an approach described by Vandesompele et al. (54). The best correlation between qPCR and SAGE was observed for genes that were represented by large tag counts, such as S100A8 and S100A9. In contrast, whereas the direction of change in expression of low-abundance genes such as IL-1ß and IL-1F9 was the same using SAGE and qPCR data, the magnitude of the effect determined by SAGE and qPCR differed slightly. Because the SAGE technique is based on random sampling of transcripts, the more copies of the same SAGE tag encountered, the more accurately the fold change in expression can be estimated. In addition, the more frequently a SAGE tag is encountered, the better the result can be reproduced using independent detection techniques such as qPCR. Therefore, low copy numbers of SAGE tags can be used to predict the change of direction in gene expression but should not be qualified as quantitative (13).

Experimental data acquired by using large-scale gene expression-profiling methods such as SAGE provide a wealth of information. A drawback of these techniques is that very large quantities of starting material (microgram range of mRNA) are required to perform the gene expression analysis. Acquiring large amounts of RNA is problematic in many model systems, and therefore immortalized or tumor cell lines are often used for large-scale gene expression-profiling studies. Instead of using immortalized or tumor cell lines, we used subcultures of PBEC, because these cells are more likely to reflect the cells lining the airways. Furthermore, to limit donor variation in the expression profiles, cells derived from seven different donors were used. Generated expression profiles were validated on the pooled material as used in SAGE as well as on individual donors.

SAGE was chosen as the large-scale gene expression-profiling method, since this technique provides the ability of gene discovery. This advantage is illustrated by the observation that 6,803 tags could not be annotated. These tags may represent novel sequences including transcripts encoding for antimicrobial peptides, cytokines, and chemokines. Further investigation of these unknown tags may lead to the discovery of previously unidentified genes or single nucleotide polymorphisms (SNP) in genes that are involved in the epithelial defense. Because mRNA of seven donors was used for the generation of our libraries, we cannot exclude the possibility that SNP noise has been introduced in our libraries. SNPs may in part account for a number of no match tags (45). In addition to SNP noise, unmatched tags could also have been generated because of technical limitations inherent to the SAGE technology. For example, internal priming of transcripts in the construction of SAGE libraries may result in contamination of libraries with tags that are not derived from the most 3'-end restriction site of NlaIII. On the basis of the Ludwig Transcript Viewer, provided with SAGE Genie (6), it is estimated that ~10% of unique tags are derived from internally primed transcripts, as determined for the 652 differentially expressed genes (P < 0.05). Furthermore, sequencing errors may have introduced false tags into the SAGE libraries that do not represent the original mRNA sample. However, it has been demonstrated that the number of tags resulting from sequencing errors is limited (9, 10).

From our data and the literature, we hypothesize that the transcriptional response of epithelial cells on stimulation with proinflammatory cytokines or microorganisms is characterized by two events that proceed subsequently. First, epithelial cells respond to proinflammatory cytokines and microbial exposure by affecting the expression of genes that are mainly involved in strengthening the physical epithelial barrier, as demonstrated by the increased expression of keratins, proteinase inhibitors, and the calcium-binding proteins S100A8/A9. Furthermore, epithelial cells are urged into a repair process to limit damage caused by the microbial insult. To provide host defense during the early response, defense strategies such as depriving the microenvironment of essential nutrients, mediated by, e.g., S100A8/A9, provide a simple and elegant mechanism to inhibit microbial growth. The involvement of cell communication, crucial for both the onset and boost of the innate immune response, is illustrated by expression of components of the IL-1 signaling route. The IL-1ß signaling cascade has been previously described to initiate and mediate the inflammatory response in keratinocytes (32). It is hypothesized that, in a later stage of the response of PBEC upon exposure to IL-1ß-TNF-{alpha} or P. aeruginosa, expression of more specialized antimicrobial agents such as ß-defensins occurs. In keratinocytes, it has been previously described that expression of ß-defensin-2 (DEFB4) occurs at a relatively late stage, peaking at 24 h after IL-1ß exposure (32).

In summary, using SAGE we identified a number of genes and pathways that have not previously been implicated in lung inflammation. Gene Ontology analysis indicated that the involvement of a number of biological processes such as metabolism, cell growth, development, and cell communication is crucial in the early response of epithelial cells to microbial exposure. Collectively, the differentially expressed genes may play functional roles in the strengthening of the epithelial barrier, initiation of epithelial repair, and induction and regulation of the inflammatory response. However, extrapolation of the results presented in this study to the in vivo situation remains challenging. Verification of our findings in in vivo studies will not only further our understanding of the molecular mechanisms underlying the epithelial innate immune response but may also provide new treatment targets in inflammatory lung diseases.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This research was supported by the Netherlands Organization of Scientific Research Program Grants 902-11-092, 903-42-085, and 903-68-320.


   ACKNOWLEDGMENTS
 
We thank André Wijfjes and Katerina Pispilli (Leiden Genome Technology Center, LUMC) for providing sequencing facilities and technical support. We also thank Sylvia Lazeroms and Renate Verhoosel (Dept. of Pulmonology, LUMC) for assistance in the generation of SAGE libraries and qPCR validation.


   FOOTNOTES
 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: J. B. Vos, Dept. of Pulmonology, Leiden Univ. Medical Center, PO Box 9600, NL-2300 RC Leiden, The Netherlands (E-mail: j.b.vos{at}lumc.nl).

10.1152/physiolgenomics.00289.2004.


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 INTRODUCTION
 MATERIALS AND METHODS
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 DISCUSSION
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