Physiol. Genomics Fuel your research with LabChart
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Physiol. Genomics 29: 161-168, 2007. First published January 9, 2007; doi:10.1152/physiolgenomics.00134.2006
1094-8341/07 $8.00
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Material
Right arrow All Versions of this Article:
29/2/161    most recent
00134.2006v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (3)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Bossé, Y.
Right arrow Articles by Hudson, T. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bossé, Y.
Right arrow Articles by Hudson, T. J.
Received 26 June 2006; accepted in final form 5 January 2007.
Physiological Genomics 29:161-168 (2007)
1094-8341/07 $8.00 © 2007 American Physiological Society

1{alpha},25-Dihydroxy-vitamin D3 stimulation of bronchial smooth muscle cells induces autocrine, contractility, and remodeling processes

Yohan Bossé1, Karim Maghni2 and Thomas J. Hudson1

1 McGill University and Génome Québec Innovation Centre, Montréal, Québec, Canada
2 University of Montréal, Sacré-Coeur Hospital, Research Centre, Montréal, Québec, Canada


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Genetic variants in the vitamin D receptor (VDR) gene were recently associated with asthma. The biological mechanisms explaining this association are unknown but are likely to involve many cell types given the pleiotropic effect of its ligand, 1{alpha},25-dihydroxy-vitamin D3 [1{alpha},25(OH)2D3]. Considering the prominent role of bronchial smooth muscle cells (BSMCs) in the pathogenesis of asthma, experiments were conducted to explore the gene regulatory effects of 1{alpha},25(OH)2D3 in these cells. Using RT-PCR and Western blot, we showed that VDR is present both at the mRNA transcript and protein levels in human BSMCs. The functionality of the receptor was then demonstrated by showing a >200-fold change in the expression of the 24-hydroxylase (CYP24A1) gene following 1{alpha},25(OH)2D3 stimulation. Microarray experiments were then performed to identify differentially regulated genes and pathways in BMSCs treated or not with 1{alpha},25(OH)2D3. A total of 729 probe sets on the U133 plus 2.0 Affymetrix GeneChip showed fold-change differences above the 1.5 threshold using the Robust Multichip Average intensities. This corresponds to 231 unique genes that were upregulated and 215 unique genes that were down-regulated following 1{alpha},25(OH)2D3 stimulation. A high similarity between microarray and real-time PCR results was observed for 13 random genes, with a concordance correlation coefficient of 0.91. Real-time PCR was also performed to confirm the regulation of asthma candidate genes. To identify the biological relevance of this regulation, biological pathways analyses were performed. The most significant network of upregulated genes included genes involved in morphogenesis, cell growth, and survival as well as genes encoding structural proteins, which are potentially involved in airway remodeling.

microarray; real-time PCR; pathways analyses; candidate genes


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
THE VITAMIN D RECEPTOR (VDR) is a ligand-regulated transcription factor that mediates the effects of the biologically active form of vitamin D {1{alpha},25-dihydroxyvitamin D3 [1{alpha},25(OH)2D3]}. Upon activation, VDR ligand/receptor complex alters the transcription rate of target genes involved in a wide spectrum of biological responses. Recently, two groups co-reported that genetic variants within the VDR gene were associated with asthma (20, 23). The mechanisms explaining this association are unclear, but allele-specific expression at this locus implicates the presence of genetic variants that affect VDR expression (19). The hypothesis that VDR levels play a role in asthma was also reinforced by the resistance of VDR knockout mice to experimentally induced asthma (28). Indeed, these mice fail to develop airway inflammation, eosinophilia, or airway hyperresponsiveness, despite high IgE concentrations and elevated Th2 cytokines. Many mechanisms have been postulated to explain the link between VDR and asthma. In fact, the potential scenarios are numerous, considering the large number of genes regulated by 1{alpha},25(OH)2D3 (27). In addition to having a role in calcium homeostasis and bone metabolism, VDR is also considered an autocrine/paracrine system involved in many physiological and cellular processes including immunoregulation as well as cell proliferation and differentiation (6).

Bronchial smooth muscle cells (BSMCs) play a crucial role in the pathogenesis of asthma. Indeed, remodeling and abnormal contractility of BSMCs are two major characteristics of asthma. Asthmatic patients have an increase in BSMC mass, which is believed to explain the majority of airway luminal narrowing. This increase in airway smooth muscle content is not fully elucidated but appears to be due to an increase in both myocyte size and number (9). The accumulation of BSMCs can occur through increased proliferation (increased rate of division), reduced rate of apoptosis or excessive growth and migration of existing smooth muscle populations or subpopulations. Asthmatic patients are also characterized by exaggerated airway narrowing to bronchoconstrictor agonists and attenuated ß-adrenoceptor-mediated airway relaxation. The BSMCs are recognized to have a central role in the pathological process (7). Indeed, it was shown that BSMCs constitute an autocrine system that when activated in a sensitized state, is capable of producing and responding to cytokines and other proinflammatory molecules.

Considering the prominent role of BSMCs in the pathogenesis of asthma (12), we used an in vitro model of human primary BSMCs to investigate gene expression following 1{alpha},25(OH)2D3 stimulation. Analyses allowed us to elucidate key pathways affected by 1{alpha},25(OH)2D3 and gain insight on one of many processes that may shed light on the genetic association observed between VDR genetic variants and asthma.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Cell culture.
Human primary BSMCs (Cambrex, East Rutherford, NJ) were used for all experiments. These cells were derived from a 31-yr-old Hispanic male. Thawing of cells, initiation of culture process, and subculturing were performed according to manufacturer's instructions. Cells were growth in smooth muscle growth medium (SmGM-2 Bulletkit, Cambrex) which included smooth muscle basal medium (SmBM) supplemented with 5% fetal bovine serum (FBS), hEGF (0.5 ng/ml), hFGF (2 ng/ml), insulin (5 µg/ml), and GA-1000 (100 ng/ml of gentamicin and 0.1 ng/ml of amphotericin-B). Subcultures were done when cells were at 70–90% confluence, and harvesting for downstream applications was performed when cells were at near-confluence. All experiments were performed at the 4th passage, and all incubation steps were performed in an atmosphere of 5% CO2 at 37°C.

1{alpha},25(OH)2D3 stimulation.
BSMCs were grown in 25-cm2 flasks in SmGM and starved for 24 h (SmBM supplemented with 0.1% FBS) before stimulation. Supernatant was removed, and cells were incubated for 24 h in fresh growth factor-free medium with 100 nM of 1{alpha},25(OH)2D3; (Sigma, St. Louis, MO) or with the same concentration of vehicle (ethanol at 0.05%). The experiment was done in triplicate.

Protein extraction.
Medium was removed, and cells were rinsed with HEPES-buffered salt solution (Cambrex). Cells were then lysed with CellLytic MT reagent (Sigma) supplemented by a protease inhibitor cocktail. Cells were incubated on a shaker for 10 min and scraped before collection. Lysed cells were centrifuged to pellet the cellular debris and supernatant was transferred to fresh tubes. Lysates were frozen at –80°C until utilization.

RNA extraction.
Cells were lysed by adding 2 ml of TRIzol reagent (Invitrogen) and homogenized by passing through a syringe with a needle (26G). RNA was picked up in the aqueous phase following phase separation by chloroform. The RNA was then precipitated with isopropanol, washed with ethanol (75%), dissolved in DEPC-treated RNase/DNase-free water, and quantified by spectrophotometry (Ultrospec 2100 pro).

RT-PCR.
Two micrograms of RNA were converted to cDNA in a total volume reaction of 20 µl using the Oligo(dT)12–18 Primer (Invitrogen) and the SuperScript II Reverse Transcriptase enzyme (Invitrogen). PCR was performed in a final volume of 25 µl containing 1 µl of RT reaction product (17). The PCR reaction was carried out on a thermocycler (MJ Research PTC 100) and following a 3-min denaturation step at 94°C, 40 PCR amplification cycles were performed as follows: 30 s denaturation at 94°C, 30 s annealing at 58°C, and 45 s extension at 68°C. The cycling was terminated with a 3-min extension at 68°C. VDR was amplified with the sense primer 5'-CTGACCCTGGAGACTTTGA-3' and the antisense primer 5'-TTCCTCTGCACTTCCTCATC-3'. The final reaction volume included 0.04 U/µl of Taq DNA polymerase (Invitrogen), 1x PCR buffer, 1.5 mM MgCl2, 0.2 mM of each dNTP, and 0.4 µM of each primers.

Western blot.
The samples were separated by 10% SDS-PAGE and blotted to polyvinylidene difluoride membrane. The blot was blocked overnight at 4°C in Tris-buffered saline (TBS) containing 5% nonfat milk and 0.05% Tween 20. The VDR Ab-1 rat monoclonal antibody (Clone 9A7y.E10.E4, purified Ab without BSA and Azide; Lab Vision, Fremont, CA) was then added at 1 µg/ml (1:200) in the same buffer for 2 h at room temperature. The blot was then washed three times in TBS containing 0.05% Tween 20 and incubated in the same buffer with the peroxidase-conjugated AffiniPure donkey anti-rat IgG (Jackson ImmunoResearch Laboratories, West Grove, PA) for 1 h at 0.16 µg/ml (1:5,000). The blot underwent three washing steps and was revealed with the Syngene Image Analysis system after adding the SuperSignal West Femto Maximum Sensitivity Substrate (Pierce, Rockford, IL).

Real-time PCR.
The stimulatory action of 1{alpha},25(OH)2D3 in BSMC was verified by quantifying the relative expression of CYP24A1 by real-time PCR (LightCycler, Roche) using the ribosomal RPS9 gene as housekeeping gene. A total reaction volume of 20 µl was inserted in LightCycler capillaries (Roche) containing 2 µl of LightCycler FastStart DNA Master SYBR Green I (Roche Applied Science), 3 mM of MgCl2, and 0.4 µM of each primer. Cycling conditions for CYP24A1 include a 10-min denaturing step at 95°C and 40 cycles performed as follows: 0 s denaturation at 95°C, 10 s annealing at 60°C, and 8 s extension at 72°C. Data acquisition was performed at the end of the extension. Cycling conditions for RPS9 include a 10-min denaturing step at 95°C and 40 cycles performed as follows: 0 s denaturation at 95°C, 10 s annealing at 55°C, and 10 s extension at 72°C. Data acquisition was performed at 87°C during the increment phase between the extension and the denaturation. The concentrations of the target (CYP24A1) and nonregulated reference (RPS9) genes were calculated from standard curves. The standard curves were generated from three parallel dilution series (1E6 to 1E2) of the CYP24A1 and RPS9 isolated amplicons. One calibrator concentration (1E3) used to construct the standard curve was added to each run to obtain the calibrator-normalized target/reference ratio, which adjusted for sample heterogeneity as well as possible differences in sensitivity between the analyzed samples and those used to construct the standard curve. This ratio was also adjusted for PCR efficiency between the target and the reference by a method implemented in the Relative Quantification Software version 1.0 (Roche). This calibrator-normalized target/reference ratio with efficiency correction was then compared between cells treated or not with 1{alpha},25(OH)2D3.

Real-time PCR was performed on the ABI Prism 7900HT (Applied Biosystems). The total volume reaction was 10 µl containing 5 µl of SYBR Green PCR Master Mix (Applied Biosystems), 0.3 µM of each primer and 5 ng of cDNA. Cycling conditions were as follows: 2 min of activation at 50°C, 10 min denaturation at 95°C, and 40 amplification cycles consisting of 15 s at 95°C and 1 min at 58°C. Each pair of primers was tested on agarose gel electrophoresis to verify the presence of a single band of the predicted size. Primers used are showed in Table 1. A total of 96 real-time PCR reactions were performed for each gene. These consist of six replicates of the following: a calibration curve from a twofold dilution series ranging from 20 to 0.078 ng of nonstimulated BSMC cDNA (9 dilutions), a control containing no cDNA, and each of the six experimental samples. The quantification for a given gene was always run in the same plate. However, the location of the nine dilutions for the standard curve, the six experimental samples, and the control were randomized within the 384-well plates. Results of the experimental samples were calibrated according to the standard curve. The average of replicates for each sample was obtained and normalized to the GAPDH average to obtain the target/reference ratio. This ratio was then average from the control (n = 3) and the 1{alpha},25(OH)2D3-treated (n = 3) samples. Finally, fold-changes in gene expression levels were obtained by dividing the control average ratio from the 1{alpha},25(OH)2D3 average ratio.


View this table:
[in this window]
[in a new window]

  		Table 1. PCR primers used for real-time PCR

 
Microarray.
Expression studies were performed using the human U133 plus 2.0 Affymetrix GeneChip microarrays (Affymetrix). RNA was reverse transcribed into cDNA and in vitro transcription was performed to generate biotin-labeled cRNA for subsequent hybridization. Hybridized target cRNA was then stained with streptavidin phycoerythrin, and arrays were scanned using a GeneArray Scanner at an excitation wavelength of 488 nm. Expression values were extracted using the robust multichip average (RMA) method (11). The complete data set has been deposited in the National Center for Biotechnology Information's Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) repository and are accessible through GEO Series accession number GSE5145. An RMA threshold of 1.5 was used as a threshold to claim significant regulation. This corresponds to a false discovery rate of 2.75% based on the significance analysis of microarrays method (26).

Biological pathway analyses.
The Gene Map Annotator and Pathway Profiler (3, 5) (GenMAPP, www.genmapp.org) and the Ingenuity Pathway Analysis system (Ingenuity Systems, www.ingenuity.com) were used to visualize gene expression data in the context of biological pathways. In both bioinformatics tools, an RMA threshold of 1.5 was used to identify up- or downregulated genes. In GenMAPP, three color-coding criteria were used. The first two conditions tested pathways (MAPPs) with up- and downregulated genes, respectively. The third condition was set to identify pathways having both up- and downregulated genes. The GenMAPP input file as well as the MAPPFinder results can be found in the supplementary materials (http://www.genomequebec.mcgill.ca/downloads/other/supplMaterials.zip). Similarly, three rounds of analyses were performed with the Ingenuity Pathway Analysis system considering 1) upregulated genes, 2) downregulated genes, and 3) both up- and downregulated genes. The Ingenuity input file and the top results including networks and canonical pathways can be found in the supplementary materials.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
VDR is functional in BSMCs.
RT-PCR and Western blot were conducted to demonstrate the presence of VDR at the mRNA transcript and protein levels in BSMCs (data not shown). The functionality of the receptor was evaluated using 1{alpha},25(OH)2D3 stimulation. In many cell types, the CYP24A1 gene is known to be one of the most upregulated genes following 1{alpha},25(OH)2D3 stimulation (21). The relative expression of CYP24A1 was thus tested in the BSMC model by real-time using ribosomal RPS9 gene as a housekeeping gene. After 24 h of BSMC stimulation with 100 nM of 1{alpha},25(OH)2D3, the CYP24A1 mRNA transcript increased by a factor of 241 (Fig. 1). With the same stimulation, the VDR mRNA transcript increased by 1.6-fold (Fig. 1). These preliminary data indicate that 1{alpha},25(OH)2D3 alters gene transcription in BSMC.


Figure 1
View larger version (10K):
[in this window]
[in a new window]

  		Fig. 1. Fold-changes in mRNA transcript levels for CYP24A1 and vitamin D receptor (VDR) in bronchial smooth muscle cells (BMSCs) following 1{alpha},25-dihydroxy-vitamin D3 [1{alpha},25(OH)2D3] stimulation. Notice that bars are illustrated on a different scale. The fold-changes were obtained by dividing the calibrator-normalized target/reference ratio (see MATERIALS AND METHODS) of a sample treated with 1{alpha},25(OH)2D3 and a second sample treated with vehicle. The experiment was conducted in duplicate and triplicate for VDR and CYP24A1, respectively. Error bars are SE.

 
Microarray results.
Expression profiling was conducted with Affymetrix GeneChip microarrays to identify genes regulated by 1{alpha},25(OH)2D3 in BSMCs. All RMA expression values as well as a figure showing fold-change versus intensity of the 54,675 probe sets are available in the supplementary materials (http://www.genomequebec.mcgill.ca/downloads/other/supplMaterials.zip). A total of 729 probe sets exhibit fold-changes above the 1.5 threshold using the RMA intensities. This corresponds to 231 unique genes that are upregulated and 215 unique genes that are downregulated following 1{alpha},25(OH)2D3 stimulation. Figure 2 shows a heat map illustrating the within-group variance for the 231 genes upregulated and the 215 genes downregulated.


Figure 2
View larger version (40K):
[in this window]
[in a new window]

  		Fig. 2. Gene expression profiles for up- and downregulated genes following 1{alpha},25(OH)2D3 stimulation. Genes that are upregulated (n = 231) are shown in red and genes that are downregulated (n = 215) are shown in green. The columns represent triplicate samples of BSMCs treated with 1{alpha},25(OH)2D3 (VD1, VD2, and VD3) or vehicle (Ctl1, Ctl2, and Ctl3). Each row represents a different gene. The gene list is provided with additional information in Supplementary Table S1.

 
Global validation of microarray.
Recently, Miron et al. (18) developed a methodology for validating microarray data. The method consists of validating a subset of randomly selected genes by real-time PCR and extrapolation to the remaining nonvalidated genes. More specifically, the 231 upregulated genes in the experiment were sorted by descending order of fold-change. Gene selection was then performed by a random-stratified sampling method using a bin size of 16 (by randomly selecting one gene per bin). As a result, the following 14 genes were selected and tested by real-time PCR: DKK2, CRIP1, DRCTNNB1A, BTC, TPARL, ZNF533, ATP8A1, CXCL12, USP13, MAN1A1, CL640, ZNF326, RBJ, and TMOD1. The ZNF533 gene was excluded from the subsequent analyses because the standard curve was unreliable. A concordant correlation coefficient (CCC) was then estimated to evaluate the similarity between microarray and real-time PCR results. The CCC is a global quality index combining the accuracy and precision coefficients in one index (18). As shown in Fig. 3, a CCC value of 0.91 was observed for the present experiment, which indicates very good agreement between microarray and real-time PCR results. This result can be visualized by how close the data points are from the regression line (correlation = 0.93) and how close the regression line is from the identity line.


Figure 3
View larger version (10K):
[in this window]
[in a new window]

  		Fig. 3. Validation of the microarray experiment by real-time PCR. Each dot represents a gene that was selected for global microarray validation based on a random-stratified strategy. The concordance correlation coefficient (CCC) is an index quality of the microarray study [see Miron et al. (18) for more details]. The solid line represents the least-squares regression line and the dashed line represents the identity line. The genes included are DKK2, CRIP1, DRCTNNB1A, BTC, TPARL, ATP8A1, CXCL12, USP13, MAN1A1, CL640, ZNF326, RBJ, and TMOD1.

 
Validation of candidate genes.
A total of 15 additional genes were assessed by real-time PCR (Fig. 4). Three were selected to confirm well know genes regulated by 1{alpha},25(OH)2D3 including VDR itself, CYP24A1 and thrombomodulin (THBD). The highest up- and downregulated genes were also included (SULT1C1 and DHRS3, respectively). Ten other genes showing regulation with microarrays were evaluated by real-time PCR based on their potential effect in the pathogenesis of asthma. In all cases, the direction of change was similar between microarray and real-time PCR. In addition, the fold-changes observed between the two techniques were similar except for two highly upregulated genes. Microarray fold-changes for CYP24A1 and SULT1C1 were 3.8 and 11.8, respectively, and the same values for real-time PCR were 308.6 and 35.9.


Figure 4
View larger version (18K):
[in this window]
[in a new window]

  		Fig. 4. Confirmation of selected genes based on known function, using real-time PCR following 1{alpha},25(OH)2D3 stimulation. Expression levels were determined from cDNA extracted from BSMCs treated with (n = 3) or without (n = 3) 1{alpha},25(OH)2D3 (100 nM) for 24 h. Fold-changes were obtained by normalizing on the mean target (candidate genes)/reference (GAPDH) ratio of untreated samples. Error bars are SD on a fold-change scale.

 
Pathway analyses.
GenMAPP and MAPPFinder were used to identify pathways having a significant proportion of up- and down-regulated genes. Pathways were derived from two sources: local MAPPs and Gene Ontology (GO) terms. A total of 97 and 366 genes regulated in the experiment were present in local MAPPs and GO terms, respectively. For each source, three runs of analyses were performed to identify 1) pathways with upregulated genes, 2) pathways with downregulated genes, and 3) pathways having both up- and downregulated genes. All pathways reaching a P value below 0.05 for local MAPPs and 0.001 for GO terms were summarized and can be viewed in the supplementary materials. For local MAPPs, four pathways met the significantly increased criterion, four met the significantly decreased criterion and eight met the third criterion for genes up- and downregulated. The same numbers for the GO terms were 10, 9, and 10, respectively. Some of the GO terms are very general including thousands of genes and some have large degree of interrelatedness, but all have been retained for completeness. The relevance of these pathways is considered in the DISCUSSION.

The Ingenuity Pathway Analysis system was also used to analyze the same expression dataset in the context of biological pathways. A total of 259 genes were eligible for generating networks. Again, three runs of analyses were performed considering 1) only upregulated genes, 2) only downregulated genes, and 3) up- and downregulated genes. The top 10 canonical pathways and the top networks generated by the system can be visualized in the supplementary materials. The top functions associated with the most significant network of upregulated genes were cellular movement, cellular growth, and proliferation, and cell death. This particular network is shown in Fig. 5, and its relevance is considered in DISCUSSION. The top functions associated with the most significant network of downregulated genes were gene expression, cancer, and cell death. Finally, the top functions associated with the most significant network of up- and downregulated genes were cellular growth and proliferation, cancer, and cellular movement.


Figure 5
View larger version (20K):
[in this window]
[in a new window]

  		Fig. 5. Network of upregulated genes in BSMCs following 1{alpha},25(OH)2D3 treatment. This figure was created using the Ingenuity Pathways Analysis system. The microarray dataset was used as an input file with a robust multichip average threshold of 1.5 to specify upregulated genes. Genes upregulated are shown in red. Grey nodes are genes present on the microarray but not regulated by 1{alpha},25(OH)2D3. The node in white represents a gene absent from the microarray.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
Recent genetic association studies have implicated VDR genetic variants in the etiology of asthma (20, 23, 29). However, the mechanistic understanding of this relationship is unclear. To unravel this issue, we explored gene expression in BSMC following 1{alpha},25(OH)2D3 stimulation. The first set of experiments demonstrated that VDR is present and functional in BSMC. Microarray data demonstrated that 1{alpha},25(OH)2D3 regulates a large number of genes in these cells, which is concordant to observations made with other cell lines (27). The microarray data were then validated by a method recently described (18), which confirmed the reliability of the data. A number of candidate genes were also validated and showed great concordance between microarray and real-time PCR results. Finally, bioinformatics tools were used to expose the biological relevance of this process. Based on the analyses, many biological scenarios can be conceived, but the most affected networks of genes suggest functions related to cell movement, growth and survival that are potentially important for airway remodeling.

A quick glance at the top genes regulated by 1{alpha},25(OH)2D3 and the GenMAPP/MAPPFinder results reveals many genes implicated in asthma, either by genetic association methods or functional studies. First, an important transcription factor (CEBPB) as well as a proinflammatory cytokine (IL6) involved in immune and inflammatory responses were upregulated following 1{alpha},25(OH)2D3 stimulation. Second, 1{alpha},25(OH)2D3 also increased the expression of AKR1C3, which is believed to play an important role in the pathogenesis of allergic diseases by regulating the synthesis of prostaglandins (PG), such as PGD2. Indeed, the enzyme encoded by the AKR1C3 gene catalyzes the reduction of PGD2, PGH2, and phenanthrenequinone, and the oxidation of 9{alpha},11ß-PGF2 to PGD2. PGD2 is a lipid mediator that is known to direct the earliest phases of T-cell recruitment to the airways immediately after exposure to allergens (16). Furthermore, the 20-ketosteroid reductase activity of the AKR1C3 enzyme is known to inactivate cortisol (25), which may promote the inflammatory responses in allergic airways. A third potential scenario implicates genes involved in smooth muscle contraction. Smooth muscle contraction is mediated by Ca2+-dependent and Ca2+-independent pathways. The later Ca2+-independent pathway, termed Ca2+ sensitization, is mainly regulated by a monomeric GTP binding protein RhoA and its downstream target Rho-kinase (2). A small Rho GTPase transcript (CDC42EP3) was highly upregulated following 1{alpha},25(OH)2D3 treatment. It is tempting to speculate that this upregulation will increase Rho-kinase activity, leading to an inhibition of MLC phosphatase, and cause the promotion of a contractile state, that is Ca2+ sensitization. In addition, 1{alpha},25(OH)2D3 also upregulated the inositol 1,4,5-triphosphate receptor (ITPR1). Activation of this receptor is known to cause calcium release from the sarcoplasmic reticulum. Together these data suggest that 1{alpha},25(OH)2D3 treatment in BSMC induces genes in both Ca2+-dependent and Ca2+-independent pathways in a manner that could potentially lead to greater bronchial smooth muscle contractility. Finally, activation of glucocorticoids may also be involved. The HSD11B1 gene encodes for the 11ß-hydroxysteroid dehydrogenase type 1 enzyme (11ß-HSD1). 11ß-HSD1 is a bidirectional enzyme but in intact cells displays predominately oxo-reductase activity, a reaction requiring NADPH and leading to activation of glucocorticoids (conversion of inactive cortisone to active cortisol). Thus, there is a close relationship between the 11ß-HSD1 and the NADPH generation system. The pentose phosphate pathway is a major source of NADPH in the cytosol. The rate-limiting enzyme in this system is glucose-6-phosphate dehydrogenase (G6PD). It is possible that G6PD provides NADPH to 11ß-HSD1 (8). Interestingly, these two genes are upregulated following 1{alpha},25(OH)2D3 stimulation. It is thus tempting to speculate that 1{alpha},25(OH)2D3 increases glucocorticoid bioavailability by upregulating G6PD and HSD11B1 expression. Knowing that 1{alpha},25(OH)2D3 enhances the glucocorticoids response in regulatory T cells (31) makes this hypothesis particularly interesting.

Although biologically relevant, these observations are made on the basis of only a few of the many genes regulated by 1{alpha},25(OH)2D3. To gain a more comprehensive understanding, the Ingenuity Pathways Analysis system was used to generate a knowledge-based network of regulated genes. This bioinformatics tool reveals networks of genes with important functions related to airway remodeling. The most significant network of upregulated genes contains nodes directly associated with asthma pathogenesis (Fig. 5). VEGF is a highly connective node in this network and is known to induce remodeling. Indeed, VEGF was shown to cause smooth muscle hyperplasia and subepithelial fibrosis as well as to induce airway hyperresponsiveness in lung-targeted VEGF transgenic mice (15). The IL6 gene is also highly connected in this network. Overexpression of IL-6 in mice is known to increase the thickness of the bronchial wall and to enhance accumulation of subepithelial and adventitial collagen (14). However, despite these changes, the IL6-transgenic mice shows decreased methacholine responsiveness (4). In agreement, the IL6-deficient mice exhibited thinner basement membrane and less subepithelial fibrosis with no change in airway responsiveness (22). Other genes in this network have important implications in airway remodeling. First, fibronectin (FN1), is a major constituent of the extracellular matrix and promotes cytoskeletal organization as well as cell adhesion and migration. FN1 is essential for branching morphogenesis of many organs including the lung (24). Secondly, the insulin-like growth factor binding protein 3 was shown to inhibit cell growth in lung cancer cell lines and tumorigenicity in xenotransplated mice (10). Third, the chemokine ligand 12 (CXCL12) transgene, also named SDF-1 (stromal cell-derived cell factor), demonstrates enhanced survival after growth factors withdrawal in myeloid progenitor cell lines (1). Furthermore, the CXCL12/CXCR4 axis was found to play a pivotal role in vascular remodeling by promoting smooth muscle cell hyperplasia (32) as well as to regulate signaling pathways involved in smooth muscle cell migration (13). Finally, collagen type 1 alpha 1 (COL1A1) is an important structural protein that was also upregulated in this network. Taken together, the most significant network of upregulated genes included genes involved in remodeling, airway hyperresponsiveness, morphogenesis, cell growth, and cell survival, as well as extracellular matrix.

Extracting biological information from microarray data is a major challenge. Here we used bioinformatics tools containing knowledge-based information to generate new networks and test pre-existing pathways. These tools can facilitate the interpretation of the overwhelming amount of data produced by this technology. However, identification of a functional pathway by these tools does not prove its implication and must be confirmed by other studies. For example, confirming whether asthmatic patients manifest perturbations of the network identified here will be important. It should also be noted that the source of data coming from the mRNA transcript level may not reflect the protein and the cellular functions. Another limitation of the present study comes from using only BSMC to understand the implication of VDR in asthma, which is a disease that involves many cell types.

Many microarray studies were conducted in different cell types to unravel the genes regulated by vitamin D (Supplementary Table S2). Numerous features that vary between these studies make comparisons difficult, including cellular origins, species, microarray platforms, statistical methods, thresholds to claim significance, and vitamin D stimulation conditions (concentration and time). In addition, many reports provide only a partial list of modulated genes without access to full data. Recently, Zhang et al. (33) compared vitamin D-regulated genes reported by different researchers using microarray analysis in eight different cancer cell lines. They concluded that only a few genes were commonly identified by two different groups and, except for the enzyme 24-hydroxylase (CYP24A1), which was used to confirm the biologic response of 1{alpha},25(OH)2D3 in the present study, no other gene was identified by more than three groups. Perhaps the best comparison with our study is the one conducted by Wu-Wong et al. (30) using human coronary artery smooth muscle cells. However, from the 176 genes regulated in their study, they only list 21 genes involved in cell proliferation/differentiation and 16 genes linked to cardiovascular functions. Despite this limiting comparison, five genes were upregulated in both datasets: CYP24A1, MAPK13, TGFB3, THBD, and G0S2.

In conclusion, we have provided the first evidence that VDR is present and functional in BSMC. We also demonstrated that the expression of many genes are regulated in these cells following 1{alpha},25(OH)2D3 stimulation, including genes previously implicated in asthma predisposition and pathogenesis. Analyses of the expression dataset permit the elaboration of different biological scenarios by which VDR might be associated with asthma, including smooth muscle cell contraction and inflammation as well as glucocorticoid and prostaglandin regulation. More comprehensive analyses indicate a network of upregulated genes with functional importance for cellular movement, cellular growth and proliferation, and cell death. This network of 1{alpha},25(OH)2D3-upregulated genes in BSMC may play an important role in airway remodeling, and therefore may have important implication in the pathogenesis of asthma. Further studies are required to test these arguments.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
Support for the project was available from Genome Canada and Genome Quebec.


   ACKNOWLEDGMENTS
 
We thank M. Bertrand Lefort and M. Philippe Garneau for valuable technical assistance.

Y. Bossé is recipient of a fellowship award from the Canadian Institutes of Health and Research. K. Maghni is recipient of a scholarship award from the Fonds de Recherche en Santé du Québec. T. J. Hudson is the recipient of a Clinician-Scientist Award in Translational Research by the Burroughs Wellcome Fund and an Investigator Award from the Canadian Institutes of Health Research. Y. Bossé and T. J. Hudson are members of the Allergy, Genes and Environment Network (AllerGen).


   FOOTNOTES
 
Address for reprint requests and other correspondence: Y. Bossé, McGill Univ. and Génome Québec Innovation Centre, 740 Dr. Penfield Ave., Rm. 7500, Montréal, Québec, H3A 1A4, Canada (e-mail: yohan.bosse{at}mail.mcgill.ca).

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


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

  1. Broxmeyer HE, Cooper S, Kohli L, Hangoc G, Lee Y, Mantel C, Clapp DW, Kim CH. Transgenic expression of stromal cell-derived factor-1/CXC chemokine ligand 12 enhances myeloid progenitor cell survival/antiapoptosis in vitro in response to growth factor withdrawal and enhances myelopoiesis in vivo. J Immunol 170: 421–429, 2003.[Abstract/Free Full Text]
  2. Chiba Y, Misawa M. The role of RhoA-mediated Ca2+ sensitization of bronchial smooth muscle contraction in airway hyperresponsiveness. J Smooth Muscle Res 40: 155–167, 2004.[CrossRef][Medline]
  3. Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC, Conklin BR. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet 31: 19–20, 2002.[CrossRef][ISI][Medline]
  4. DiCosmo BF, Geba GP, Picarella D, Elias JA, Rankin JA, Stripp BR, Whitsett JA, Flavell RA. Airway epithelial cell expression of interleukin-6 in transgenic mice. Uncoupling of airway inflammation and bronchial hyperreactivity. J Clin Invest 94: 2028–2035, 1994.[ISI][Medline]
  5. Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, Conklin BR. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 4: R7, 2003.[CrossRef][Medline]
  6. Dusso AS, Brown AJ, Slatopolsky E. Vitamin D. Am J Physiol Renal Physiol 289: F8–F28, 2005.[Abstract/Free Full Text]
  7. Hakonarson H, Grunstein MM. Autocrine regulation of airway smooth muscle responsiveness. Respir Physiol Neurobiol 137: 263–276, 2003.[CrossRef][ISI][Medline]
  8. Hewitt KN, Walker EA, Stewart PM. Minireview: hexose-6-phosphate dehydrogenase and redox control of 11beta-hydroxysteroid dehydrogenase type 1 activity. Endocrinology 146: 2539–2543, 2005.[Abstract/Free Full Text]
  9. Hirst SJ, Martin JG, Bonacci JV, Chan V, Fixman ED, Hamid QA, Herszberg B, Lavoie JP, McVicker CG, Moir LM, Nguyen TT, Peng Q, Ramos-Barbon D, Stewart AG. Proliferative aspects of airway smooth muscle. J Allergy Clin Immunol 114: S2–S17, 2004.[CrossRef][Medline]
  10. Hochscheid R, Jaques G, Wegmann B. Transfection of human insulin-like growth factor-binding protein 3 gene inhibits cell growth and tumorigenicity: a cell culture model for lung cancer. J Endocrinol 166: 553–563, 2000.[Abstract]
  11. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4: 249–264, 2003.[Abstract]
  12. Johnson PR, Burgess JK. Airway smooth muscle and fibroblasts in the pathogenesis of asthma. Curr Allergy Asthma Rep 4: 102–108, 2004.[ISI][Medline]
  13. Kodali R, Hajjou M, Berman AB, Bansal MB, Zhang S, Pan JJ, Schecter AD. Chemokines induce matrix metalloproteinase-2 through activation of epidermal growth factor receptor in arterial smooth muscle cells. Cardiovasc Res 69: 706–715, 2006.[Abstract/Free Full Text]
  14. Kuhn C 3rd, Homer RJ, Zhu Z, Ward N, Flavell RA, Geba GP, Elias JA. Airway hyperresponsiveness and airway obstruction in transgenic mice. Morphologic correlates in mice overexpressing interleukin (IL)-11 and IL-6 in the lung. Am J Respir Cell Mol Biol 22: 289–295, 2000.[Abstract/Free Full Text]
  15. Lee CG, Link H, Baluk P, Homer RJ, Chapoval S, Bhandari V, Kang MJ, Cohn L, Kim YK, McDonald DM, Elias JA. Vascular endothelial growth factor (VEGF) induces remodeling and enhances TH2-mediated sensitization and inflammation in the lung. Nat Med 10: 1095–1103, 2004.[CrossRef][ISI][Medline]
  16. Luster AD, Tager AM. T-cell trafficking in asthma: lipid mediators grease the way. Nat Rev Immunol 4: 711–724, 2004.[CrossRef][ISI][Medline]
  17. Maghni K, Michoud MC, Alles M, Rubin A, Govindaraju V, Meloche C, Martin JG. Airway smooth muscle cells express functional neurokinin-1 receptors and the nerve-derived preprotachykinin-a gene: regulation by passive sensitization. Am J Respir Cell Mol Biol 28: 103–110, 2003.[Abstract/Free Full Text]
  18. Miron M, Woody OZ, Marcil A, Murie C, Sladek R, Nadon R. A methodology for global validation of microarray experiments. BMC Bioinformatics 7: 333, 2006.[CrossRef][Medline]
  19. Pastinen T, Ge B, Gurd S, Gaudin T, Dore C, Lemire M, Lepage P, Harmsen E, Hudson TJ. Mapping common regulatory variants to human haplotypes. Hum Mol Genet 14: 3963–3971, 2005.[Abstract/Free Full Text]
  20. Poon AH, Laprise C, Lemire M, Montpetit A, Sinnett D, Schurr E, Hudson TJ. Association of vitamin D receptor genetic variants with susceptibility to asthma and atopy. Am J Respir Crit Care Med 170: 967–973, 2004.[Abstract/Free Full Text]
  21. Prosser DE, Jones G. Enzymes involved in the activation and inactivation of vitamin D. Trends Biochem Sci 29: 664–673, 2004.[CrossRef][ISI][Medline]
  22. Qiu Z, Fujimura M, Kurashima K, Nakao S, Mukaida N. Enhanced airway inflammation and decreased subepithelial fibrosis in interleukin 6-deficient mice following chronic exposure to aerosolized antigen. Clin Exp Allergy 34: 1321–1328, 2004.[CrossRef][ISI][Medline]
  23. Raby BA, Lazarus R, Silverman EK, Lake S, Lange C, Wjst M, Weiss ST. Association of vitamin D receptor gene polymorphisms with childhood and adult asthma. Am J Respir Crit Care Med 170: 1057–1065, 2004.[Abstract/Free Full Text]
  24. Sakai T, Larsen M, Yamada KM. Fibronectin requirement in branching morphogenesis. Nature 423: 876–881, 2003.[CrossRef][Medline]
  25. Sharma KK, Lindqvist A, Zhou XJ, Auchus RJ, Penning TM, Andersson S. Deoxycorticosterone inactivation by AKR1C3 in human mineralocorticoid target tissues. Mol Cell Endocrinol 248: 79–86, 2006.[CrossRef][ISI][Medline]
  26. Tusher VG, Tibshirani R, 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]
  27. Wang TT, Tavera-Mendoza LE, Laperriere D, Libby E, MacLeod NB, Nagai Y, Bourdeau V, Konstorum A, Lallemant B, Zhang R, Mader S, White JH. Large-scale in silico and microarray-based identification of direct 1,25-dihydroxyvitamin D3 target genes. Mol Endocrinol 19: 2685–2695, 2005.[Abstract/Free Full Text]
  28. Wittke A, Weaver V, Mahon BD, August A, Cantorna MT. Vitamin D receptor-deficient mice fail to develop experimental allergic asthma. J Immunol 173: 3432–3436, 2004.[Abstract/Free Full Text]
  29. Wjst M. Variants in the vitamin D receptor gene and asthma. BMC Genet 6: 2, 2005.[CrossRef][Medline]
  30. Wu-Wong JR, Nakane M, Ma J, Ruan X, Kroeger PE. Effects of Vitamin D analogs on gene expression profiling in human coronary artery smooth muscle cells. Atherosclerosis 186: 20–28, 2006.[CrossRef][ISI][Medline]
  31. Xystrakis E, Kusumakar S, Boswell S, Peek E, Urry Z, Richards DF, Adikibi T, Pridgeon C, Dallman M, Loke TK, Robinson DS, Barrat FJ, O'Garra A, Lavender P, Lee TH, Corrigan C, Hawrylowicz CM. Reversing the defective induction of IL-10-secreting regulatory T cells in glucocorticoid-resistant asthma patients. J Clin Invest 116: 146–155, 2006.[CrossRef][ISI][Medline]
  32. Zernecke A, Schober A, Bot I, von Hundelshausen P, Liehn EA, Mopps B, Mericskay M, Gierschik P, Biessen EA, Weber C. SDF-1alpha/CXCR4 axis is instrumental in neointimal hyperplasia and recruitment of smooth muscle progenitor cells. Circ Res 96: 784–791, 2005.[Abstract/Free Full Text]
  33. Zhang X, Li P, Bao J, Nicosia SV, Wang H, Enkemann SA, Bai W. Suppression of death receptor-mediated apoptosis by 1,25-dihydroxyvitamin D3 revealed by microarray analysis. J Biol Chem 280: 35458–35468, 2005.[Abstract/Free Full Text]



This article has been cited by other articles:


Home page
 Endocr. Rev.Home page
R. Bouillon, G. Carmeliet, L. Verlinden, E. van Etten, A. Verstuyf, H. F. Luderer, L. Lieben, C. Mathieu, and M. Demay
Vitamin D and Human Health: Lessons from Vitamin D Receptor Null Mice
Endocr. Rev., October 1, 2008; 29(6): 726 - 776.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Material
Right arrow All Versions of this Article:
29/2/161    most recent
00134.2006v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (3)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Bossé, Y.
Right arrow Articles by Hudson, T. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bossé, Y.
Right arrow Articles by Hudson, T. J.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Visit Other APS Journals Online
Copyright © 2007 by the American Physiological Society.