This article has Open Peer Review reports available.
Network analysis identifies a putative role for the PPAR and type 1 interferon pathways in glucocorticoid actions in asthmatics
© Diez et al.; licensee BioMed Central Ltd. 2012
Received: 10 February 2012
Accepted: 19 June 2012
Published: 19 June 2012
Asthma is a chronic inflammatory airway disease influenced by genetic and environmental factors that affects ~300 million people worldwide, leading to ~250,000 deaths annually. Glucocorticoids (GCs) are well-known therapeutics that are used extensively to suppress airway inflammation in asthmatics. The airway epithelium plays an important role in the initiation and modulation of the inflammatory response. While the role of GCs in disease management is well understood, few studies have examined the holistic effects on the airway epithelium.
Gene expression data were used to generate a co-transcriptional network, which was interrogated to identify modules of functionally related genes. In parallel, expression data were mapped to the human protein-protein interaction (PPI) network in order to identify modules with differentially expressed genes. A common pathways approach was applied to highlight genes and pathways functionally relevant and significantly altered following GC treatment.
Co-transcriptional network analysis identified pathways involved in inflammatory processes in the epithelium of asthmatics, including the Toll-like receptor (TLR) and PPAR signaling pathways. Analysis of the PPI network identified RXRA, PPARGC1A, STAT1 and IRF9, among others genes, as differentially expressed. Common pathways analysis highlighted TLR and PPAR signaling pathways, providing a link between general inflammatory processes and the actions of GCs. Promoter analysis identified genes regulated by the glucocorticoid receptor (GCR) and PPAR pathways as well as highlighted the interferon pathway as a target of GCs.
Network analyses identified known genes and pathways associated with inflammatory processes in the airway epithelium of asthmatics. This workflow illustrated a hypothesis generating experimental design that integrated multiple analysis methods to produce a weight-of-evidence based approach upon which future focused studies can be designed. In this case, results suggested a mechanism whereby GCs repress TLR-mediated interferon production via upregulation of the PPAR signaling pathway. These results highlight the role of interferons in asthma and their potential as targets of future therapeutic efforts.
Asthma is a chronic inflammatory airway disease influenced by genetic and environmental factors, characterized by variable and recurring symptoms (wheezing, coughing, chest tightness, shortness of breath), airflow obstruction and bronchospasm . Asthma affects ~300 million people worldwide, leading to ~250,000 deaths annually . The majority of the asthmatic population is characterized by the development of an inflammatory response following allergen exposure (allergic asthma). The airway epithelium plays a critical role in allergen sensitization in asthma. Allergens and allergen products activate specific receptors in the surface of the epithelial cells, including Toll-like receptors (TLRs). The activated epithelium releases inflammatory cytokines that enhance dendritic cell migration to lymph nodes, recruitment of innate immune and Th2 cells to the airways, and activation of Th2 cells [3, 4].
While no cure exists to date, glucocorticoids (GCs) are commonly prescribed to control asthma symptoms [5, 6]. Glucocorticoids bind to the glucocorticoid receptor (GCR), which interacts and represses (trans-repression) the activity of transcription factors that regulate the expression of inflammatory genes (e.g., transcription factors AP1 and NFKB1). The activated GCR also binds to glucocorticoid response elements (GRE) in the DNA and promotes the expression of genes with anti-inflammatory actions (e.g., GILZ MKP-1) .
Although the effects of GCs on the airway epithelium are well known, the mechanisms mediating GCR actions are not as well understood. A significant percentage of the asthmatic population requires the maximum doses of inhaled GCs and suffers the associated side effects and a small fraction of asthmatics are resistant to GCs . Accordingly, increased insight into the molecular mechanisms mediating GC anti-inflammatory actions will assist in the development of therapies that minimize resistance and side effects as well as increase our understanding of the disease. In addition, an understanding of individual responses to GC treatment will assist in establishing treatment regimens tailored for personal susceptibility to GCs.
We previously investigated the effects of a synthetic GC, fluticasone propionate (Flovent), on the transcriptional profile of the airway epithelium of asthmatics . In that study the aim was to identify genes regulated by GCs in the airways, and therefore the focus was on the most significantly altered genes. We demonstrated the GC-dependent regulation of a number of genes, including CLCA1, POSTN, SERPINB2 and FKBP5. To gain further insight into the processes governing the actions of GCs in the airways, we performed an unbiased analysis to interrogate the microarray dataset from a systems-based perspective. Our approach was primarily that of a hypothesis generating experiment, where two complementary network approaches were applied to mine different aspects of cellular networks. The first method used co-transcriptional information to characterize the functional pathways activated in the airways. The second method combined protein-protein interaction (PPI) networks with gene expression changes to identify pathways altered following Flovent treatment. Subsequently, combining results from both networks provided a weight of evidence approach to elucidate mechanistic processes in the action of Flovent in asthmatics. These results were further supported by promoter enrichment analysis to highlight a putative role of PPAR in suppressing the TLR-mediated interferon pathway following GC treatment. The combination of these different approaches demonstrated how multiple analysis methods can be combined in an in silico experiment to generate novel hypotheses. Taken together, these efforts provided a concerted investigation into the action of GCs in the airway epithelium of asthmatics.
Analysis of the co-expression network
The remaining modules generally contained less information, with the exception of modules M5, M6 and M9. Module M5 contained the greatest number of differentially expressed genes (Additional file 3: Figure S4, 26 differentially expressed genes at P < 0.05, and 22 genes with q < 0.05), which were both negatively and positively correlated (absolute mean Spearman ρ = 0.665; Additional file 3: Figure S5). Only three KEGG pathways were overrepresented, including ‘Steroid hormone biosynthesis’ (Additional file 2: Table S2). GO functional analysis revealed 40 biological processes; however, 35 of them consisted of only a single gene association. Overrepresented terms included ‘steroid metabolic process’, ‘thiamin transport’, ‘positive regulation of epithelial cell proliferation’ and ‘progesterone receptor signaling pathway’ (Additional file 2: Table S2). Module M6 contained 39 correlated genes (average Spearman ρ = 0.6730; Additional file 3: Figures S5 and S6) and appeared to be functionally related to module M1. Some of the genes in module M6 participate in pathways enriched in module M1, including TLR4 ('Toll-like receptor signaling pathway'), PPARG and NFAM1 ('PPAR signaling pathway'). Module M9 had 7 correlated genes (average Spearman ρ = 0.8055) genes, including JUN, JUNB and FOS, which are part of the AP1 transcription factor that regulates the expression of inflammatory genes, and is a target of the activated GCR. The enriched KEGG pathways that passed the filtering were ‘B-cell receptor signaling pathway’, ‘MAPK signaling pathway’, ‘T cell receptor signaling pathway’ and ‘Toll-like receptor signaling pathway’ (Additional file 3: Figure S7; Additional file 2: Table S2). The top-ten ranked GO terms included ‘response to cytokine stimulus’ and ‘SMAD protein signal transduction’ (Additional file 2: Table S2).
Analysis of the human interactome
Analysis of common pathways
Promoter enrichment analysis
The analysis of common pathways highlighted the TLR and PPAR pathways as targets of GC actions. Consequently, some of the genes regulated by these pathways should be differentially expressed following Flovent treatment. To explore this possibility we analyzed the promoter region of differentially expressed genes (q < 0.05) to look for transcription factor binding sites (TFBS) associated with TLR and PPAR pathways. The method Pscan was used to find enriched TFBS from the JASPAR and TRANSFAC databases . The top enriched motifs in the JASPAR database were IRF2 and IRF1 (P = 4.1E-4 and P = 4.1E-3), whereas the top motif in the TRANSFAC database was V$IRSE_01 (P = 4.1E-3) (Additional file 4: Table S4). Motifs V$IRF1 (P = 0.027), V$IRF2 (P = 0.041) and V$IRF7 (P = 0.07) were also significantly enriched in the TRANSFAC database. ISRE (Interferon Stimulated Response Element) motifs are involved in the regulation of gene expression in response to interferon type 1 (IFNA-alpha/beta) activation . IRF motifs are the target of Interferon Regulatory Factors (IRFs), which function as transcriptional co-activators of interferon-regulated genes [18, 19]. Interestingly, type 1 interferons (alpha and beta) are produced after activation of TLRs (especially TLR4 and TLR7). The TFBS results also showed enrichment of PPARG/RXRA binding motifs from the JASPAR database (PPARG:::RXRA, P = 0.0182) and the TRANSFAC database (V$PPARG_01; P = 0.084).
The enrichment motifs determined by Pscan provided the possibility that a transcription factor could bind to the promoter of genes. Cscan (http://22.214.171.124/cscan/) was used in order to obtain some experimental evidence for binding of transcription factors to the promoter region of the list of differentially expressed genes . Cscan compiles ChIP-seq data from published experiments assessing the binding of several transcription factors and epigenetic markers in different cell lines for human and mice. Results from Cscan identified GCR as the most significantly enriched transcription factor in six different cell lines subjected to variable doses of a glucocorticoid receptor agonist (Additional file 4: Table S4). STAT1 and RXRA binding were detected at the respective ranks of 10 and 12 (Bonferroni corrected P-values: q = 0.0009 and q = 0.0018, respectively). Binding for PPARs or IFR9 could not be assessed due to a lack of available experimental data.
Interferon regulatory network
Promoter analysis suggested the type 1 interferon pathway as the most important target of GC actions. To further validate this finding we queried the database Interferome (http://www.interferome.org), which maintains a curated list of known interferon-regulated genes extracted from studies on which control and interferon-treated samples were analyzed. We found that 20 out of 114 genes in the FDR list (17%) have been previously identified as being regulated by interferons (Additional file 5: Table S5). This results in an associated probability of rejecting the null (no enrichment) hypothesis based on the hypergeometric distribution of P = 0.0505, indicating that the FDR list is enriched in interferon-regulated genes. Additional file 5: Table S5 contains the number of datasets in which regulation by a certain type of interferon was found. Type 1 interferons regulated 17 out of 20 genes in a total of 72 datasets, whereas type 2 interferons were the only source of regulation for three genes in a total of 3 datasets. Taken together, these results suggest that GCs target the type 1 interferon pathway in the epithelium of asthmatics.
Classical statistical and functional enrichment analysis of the Flovent/placebo dataset provided little insight into the biological processes associated with the treated asthmatics. Results from the uncorrected tests contained many genes known to be associated with asthma, and some enriched pathways and GO terms linked these genes to inflammatory responses and metabolism of GCs. However, the high number of potential false positives hampered the results and correction for multiple testing eliminated most of these genes from the final list. Genes selected after multiple testing correction (FDR) identified the SERPINB2 and FKBP5 genes, which were previously validated . However, the FDR approach failed to identify CLCA1, another gene experimentally confirmed , although it was significantly altered based on uncorrected P-values. This effect could partially be attributed to the reduced statistical power associated with the use of multiple testing correction methods compared to the uncorrected analysis . It has been well demonstrated that the excessive penalty imposed on P-values following FDR adjustment increases the number of false negatives . On the other hand, POSTN, which was also experimentally validated, had a P > 0.05 and therefore was not differentially expressed in our analysis. This comparison highlights some of the limitations associated with statistical analyses that rely on a single source of evidence to derive conclusions on biological function, and argues for the need for integrative approaches to data analysis.
Network analysis offers some advantages over classical analysis by being able to incorporate additional information from multiple sources [21, 23, 24]. We applied a weight-of-evidence approach by using multiple methods that mine different aspects of the cellular processes, which converged to ‘Toll-like receptor signaling pathway’ and ‘PPAR signaling pathway’ as the most relevant pathways related to GC treatment (Figure 4). Toll-like receptors (TLRs) regulate inflammatory responses by inducing the expression of inflammatory cytokines upon binding of viral or bacterial proteins and mediate the signaling pathways that regulate innate and Th2 responses in the epithelium [3, 25, 26]. Toll-like receptors, including TLR4 (found in module M6) and TLR7 (found in module M1) mediate the production of interferons alpha and beta (type 1 interferons) . This important link between TLR and the interferon 1 pathway, not evident from the pathway enrichment analysis, was revealed by the promoter enrichment analysis.
STAT1 phosphorylation also mediates the transduction of type 2 interferon (IFN-gamma) signaling by binding to gamma-activated sequence (GAS) elements as an homodimer. Therefore, it is possible that downregulation of STAT1 and inhibition of phosphorylation of the protein represses type 2 interferon signaling. Indeed, GCs are known to inhibit IFN-gamma signaling by downregulating STAT1 mRNA and protein expression in PBMCs . Gene expression for type 2 interferon and its receptor (IFN-gamma receptor, IFNGR) were found in the microarray dataset after filtering non-expressed genes (not shown). However, promoter analysis only identified enrichment of ISRE elements, the binding motif for the ISGF3 transcriptional complex, GAS elements were not detected. In addition, interrogation of the interferome database resulted in a list of interferon-regulated genes that were, in the majority of datasets assayed, regulated by type 1 interferons. Consequently, although a role of type 2 interferons cannot be excluded, our findings suggest that type 1 interferons are the target of GCs in the epithelium of asthmatics.
Peroxisome proliferator-activated receptors (PPAR) are lipid-activated transcription factors that regulate the expression of target genes . PPAR-alpha and -gamma modulate allergic inflammation, and agonists are able to reduce levels of inflammatory cytokines [31, 32]. PPARs exert their activity by forming a heterodimer with the retinoid receptor RXRA, which then binds to co-activator proteins, including PPARGC1A (PGC-1), to regulate gene expression. Transcriptional co-activators amplify the transcription of nuclear receptor regulated target genes . In particular, PPARGC1A can recruit other co-activator proteins with histone acetyltransferase (HAT) activity that open up the chromatin and enhance the expression of target genes . Consequently, an increase in the expression of RXRA and PPARGC1A co-activators can intensify the activity of the PPAR pathway, resulting in an increased repression of STAT1 phosphorylation. Both RXRA and PPARGC1A are present in the BioNet module and are upregulated by Flovent (Figure 3). Promoter analysis also resulted in an enrichment of genes with PPARG:RXRA motifs (P ~ 0.01). This suggests that GCs modulates the activity of the PPAR pathway by upregulating co-transcriptional activators. Supporting this evidence, the expression profiles for RXRA and PPARGC1A are correlated (r = 0.4078, P = 0.022) and show a trend of higher levels in Flovent-treated patients (Additional file 3: Figure S8).
Our results link GCs to type 1 interferon and PPAR pathways. There is previous evidence that connects PPARs to interferons, providing an interesting mechanism integrating GC actions. Activation of the PPAR-alpha (PPARA) pathway was found to suppress STAT1 phosphorylation in rat glia . However, in our study promoter analysis identified PPARG, but not PPARA (P ~ 0.2), motifs enriched in the list of differentially expressed genes. This lack of significance could be explained by the fact that PPARA motifs are missing in the JASPAR database, which retrieved higher significant results for the complex PPARG:RXRA transcription factor motif. In addition, our search strategy looked for motifs in the 1 kb region upstream of the transcription start site, whereas some transcription factors can bind to more distal locations. Alternatively, as our data suggest, GC action on type 1 interferons may be mediated via a PPAR-gamma-dependent process. For example, it has been shown that PPAR-gamma can repress the type 1 interferon pathway by downregulating the production of INF-beta upon TLR4 activation . Treatment with the PPAR-gamma agonist troglitazone and challenge with LPS and poly(I:C) impaired IRF3 binding to the IFN-beta-promoter. Downregulation of IFN-beta prevented activation of the IFN-beta receptor and subsequent STAT1 phosphorylation and ISRE activation . However, in our dataset type 1 interferons were present, but not differentially expressed (average P ~ 0.58 for type 1 interferon genes in the microarray). Interestingly, activation of the PPAR-gamma pathway was previously found to downregulate the expression of IFN-gamma activated genes . These findings highlight the complex nature of PPAR-mediated interferon regulation, which can affect different pathways (type 1 vs. type 2) at multiple regulatory points.
A link between GCs and the interferon pathways has been previously reported [5, 40–42], and interferons are known to affect symptoms in asthmatics [43–46]. Interferon-alpha has been associated with severe exacerbation of asthma symptoms , which provides a simple mechanistic interpretation of the beneficial effects of GC-mediated repression of the interferon pathway. On the other hand, low doses of interferon-alpha have been associated with therapeutic effects in GC-resistant patients [44–46]. It is unclear if these disparate responses are due to differences in interferon dose or different patient phenotype. This clearly highlights the inherent complexity of the underlying regulatory networks and the need of further studies investigating the mechanisms of GCs and their relation with the interferon pathway.
Multifactorial diseases such as asthma challenge our ability to identify distinct mechanisms involved in disease etiology and pathology. The advent of high-throughput approaches has increased the amount of information that can be extracted from a sample set, but linking together disparate datasets remains an elusive goal. Technologies that summarize the principal disease processes affected (e.g., functional enrichment analysis) assist in characterizing the primary functional features, but often fail to suggest detailed information of disease and treatment action. Accordingly, approaches that incorporate information regarding multiple biological aspects from disparate sources can stimulate the generation of hypotheses that will uncover novel mechanisms. Given that asthma is a heterogeneous inflammatory condition, it is of interest to examine for distinct molecular phenotypes. For example, it has been shown that responsiveness to inhaled GCs correlates with the degree of Th2 inflammation. It is possible that distinct subgroups of asthmatics exist with specific shifts in PPAR/TLR pathways that correlate with GC responsiveness or other molecular phenotypes, such as the IL-13 related Th2 sub-phenotypes previously described for the current data set [47, 48]. Further investigations to investigate sub-phenotypical responses in the PPAR/TLR pathways are thus warranted.
With the workflow presented herein, we were able to identify specific pathways as being targets of GCs in the epithelium of asthmatics. Results highlighted the prominent role of interferons in mediating inflammatory processes in asthmatics, and further supported the finding that GCs mediate their activities by repressing the interferon pathway. We found that this repression may be mediated by GC-dependent upregulation of the PPAR pathway. A potential mechanism of direct repression of the interferon pathway by downregulation of key transcription factors is also suggested. If confirmed, these findings will be potentially valuable in the design of new therapies tailored for the GC-resistant asthmatic sub-population.
Materials and methods
Microarray data acquisition and preprocessing
The microarray data set was published previously, and is available in the GEO database (accession number GSE4302) . The dataset consisted of 64 Affymetrix HGU133plus2 arrays measuring expression profiles from airway epithelial brush biopsies obtained from 32 asthmatics in a randomized, blinded parallel group study design with sampling at baseline levels and after 1 week treatment with either placebo or fluticasone propionate (Flovent). Observation 55 was identified as an outlier due to poor technical quality of the microarray data, and was excluded from all analyses. Custom CDFs (Chip Description File) from the BrainArray Project were used to map the array probes to 17,788 probe sets corresponding to individual Entrez Gene entries . Arrays were preprocessed with the RMA method from the Bioconductor package affy[50, 51]. Non-expressed genes were eliminated on the basis of XIST expression, which is not expressed in males . The distribution of intensities for XIST in male and females was computed (Additional file 3: Figure S9), and the background threshold estimated as the median value of the distribution in males (log2 median intensity = 5.32). Genes were eliminated when the intensity in all arrays was below that of the determined threshold value. The resulting microarray data contained 16,015 remaining genes. Baseline correction was performed by computing the log difference of the intensity values from baseline and post-treated arrays prior to statistical analysis.
Statistical analysis was performed with the Bioconductor package limma. A linear model was constructed including drug (i.e., Flovent or placebo), sex and age in the design matrix. P values associated with the comparison Flovent vs. placebo were computed. Multiple testing correction was applied using the method of Benjamini and Hochberg .
A co-transcriptional network was constructed from the microarray data using the ARACNE method, which uses mutual information (MI) to test statistical dependency between expression profiles . Due to concerns about the effects of low sample numbers in combination with the inherent biological variability upon the statistical dependency, all individuals in the dataset (Flovent-treated and placebo-treated) were included in the co-transcriptional network inference. To reduce the inter-individual variance from the gene expression profiles, expression values were normalized to the baseline values for each individual (i.e., samples collected prior to initialization of treatment regimen). Mutual information was computed using a Gaussian Kernel estimate. Two critical parameters for the ARACNE method are the kernel width and the MI threshold, which are normally estimated using precomputed calibration curves. However, for this project, custom calibration curves were constructed using the MATLAB scripts provided in the original publication . A calibration curve for kernel width was generated (Additional file 3: Figure S10A) and the optimal kernel width was determined to be 0.2179931, which is 5.3% lower than that estimated by ARACNE (0.230167). The calibration curve for the MI threshold was computed (Additional file 3: Figure S10B) and used during network reconstruction. Next, the optimal P-value cutoff for network reconstruction was determined by computing networks at different P-values over a sufficiently wide range. For each network, the number of nodes, edges and degree distribution was computed (Additional file 3: Figure S11A), and a P-value of 1.0E-8 was chosen, which resulted in a power law degree distribution for the network (Additional file 3: Figures S3 and S11B). Finally, in order to assess the effect of sample ordering upon correlation estimations (MI), a bootstrapped network was computed by setting 100 cycles and the consensus network was calculated with a Bonferroni corrected q < 0.001.
Protein-protein interaction network
Information about protein-protein interactions (PPI) was obtained from the BioGRID database , which contains experimentally validated data from a range of high-throughput and low-throughput techniques. The database version 3.1.70 contained information on 9,935 genes, which formed a network with 50,531 edges. This network contained multiple edges joining the same pair of genes (i.e., the interaction has several sources of evidence), and therefore for module detection the network was simplified by using the simplify function in the R package igraph (http://igraph.sourceforge.net), to reduce the number of edges per pair of genes to one. The resulting simplified network contained 33,282 edges.
For the co-transcriptional network, detection of clusters of densely connected nodes was performed using the MCODE plugin for the Cytoscape software with default parameters . For the BioGRID PPI network, module detection was performed using the Bioconductor package BioNet , which enables the determination of an exact solution to the problem of finding connected sub-graphs with low P-values. A Binomial Uniform Mixture (BUM) model was fitted to the distribution of P-values, and scores were derived for each network node at a given FDR (q = 0.05). Finally, these scores were used to detect modules using the exact Heinz method .
Functional enrichment analysis
Functional enrichment analysis for KEGG pathways and Gene Ontology (GO) Biological Process (BP) terms (downloaded on 2010/09/07 and 2010/09/04 respectively) was performed using a hypergeometric test as implemented in the Bioconductor package GOstats. For GO, a conditional test that considers the dependence structure of the GO terms was performed . The lists of probe sets from the statistical analysis and modules from the co-transcriptional network were converted into lists of Entrez Gene identifiers prior to enrichment analysis. Enrichment results were considered significant at P < 0.05.
KEGG pathways were restricted to those involved in biological processes. Consequently, disease pathways were discarded (except the KEGG pathway ‘Asthma’). In addition, pathways that were supported with less than two genes in any module or list of differentially expressed genes were discarded. The final list of KEGG pathways was used to filter modules in the co-transcriptional network. Modules that did not contain any genes annotated in the KEGG pathways that passed the filtering were discarded. Pathways were grouped using a hierarchical clustering with Pearson correlation as a distance measure, and the gene counts displayed as a heatmap.
Common pathways analysis
To link the results from the co-transcriptional and PPI network analyses, common pathways were selected when present simultaneously in the BIONET module and the MCODE module M1 from the co-transcriptional network. To assess the possibility that the selected common pathways appeared by chance in our dataset, we performed simulations with random lists of genes. First, background lists of genes were generated by randomly selecting genes from the total number of genes (universe) used for enrichment analysis. The number of genes in each simulated list was the same as the target list (i.e., the same number of genes as found in the BIONET and MCODE module M1). A KEGG enrichment analysis was then performed for each list as described above, and the common pathways were computed. Finally, the common pathways in the simulation were compared to those obtained with the real dataset and the number of positive hits n recorded. This process was repeated N = 1000 times and the probability of a pathway appearing by chance was computed as the number of times it appeared as common in the simulated datasets divided by the number of simulations (P = n / N).
Promoter enrichment analysis
Promoter enrichment analysis was performed to identify transcription factor binding motifs enriched in the promoter regions of sets of genes by using the software Pscan. Entrez Gene identifiers were converted into REFSEQ ids and these were loaded into the Pscan web server. JASPAR and TRANSFAC databases of transcription factor motifs were selected to analyze the region from −950 to +50 bases relative to the transcription start site (TSS). Transcription factor binding assessment was performed to identify experimental evidence (based on published ChIP-seq data) of binding of transcription factors to the promoter region of the differentially expressed genes. Entrez Gene identifiers were converted into REFSEQ ids, which were loaded into the Cscan web server . A region between −1,000 upstream of the TSS and the transcribed region of the gene was selected to identify transcription factor binding events.
Interferon regulated genes
The database Interferome (http://www.interferome.org) was used to identify interferon target genes . This database catalogs genes regulated in microarray and other experiments where comparisons between control and interferon treated samples were performed. Entrez Gene identifiers were converted into EnsEMBL gene identifiers and loaded into the Interferome web server. Enrichment of interferon-regulated genes was computed using the hypergeometric test implemented in the R software. The parameters were set with 20,900 as total number of genes in the human genome (based on data from the EnsEMBL database release 64) and 2,000 as the number of interferon-regulated genes (as stated in the Interferome web site). Since the number of interferon-regulated genes is an estimate, the enrichment calculation is only approximate.
This work was supported in part by the Center for Allergy Research, the Bernard Osher Initiative for Research on Severe Asthma, the Swedish Heart-Lung Foundation, the Swedish Foundation for Strategic Research, a VINNOVA VINN-MER International Grant, and VINNOVA and JSPS under the Sweden-Japan Research Cooperative Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
- Fanta CH: Asthma. N Eng J Med. 2009, 360 (10): 1002-1014. 10.1056/NEJMra0804579.View ArticleGoogle Scholar
- Global Initiative for Asthma (GINA): Global strategy for asthma management and prevention. 2010, Global Initiative for Asthma (GINA), Bethesda (MD)Google Scholar
- Bulek K, Swaidani S, Aronica M, Li X: Epithelium: the interplay between innate and Th2 immunity. Immunol Cell Biol. 2010, 88 (3): 257-268. 10.1038/icb.2009.113.View ArticlePubMedGoogle Scholar
- Hammad H, Lambrecht BN: Dendritic cells and epithelial cells: linking innate and adaptive immunity in asthma. Nat Rev Immunol. 2008, 8 (3): 193-204. 10.1038/nri2275.View ArticlePubMedGoogle Scholar
- Stellato C: Glucocorticoid actions on airway epithelial responses in immunity: functional outcomes and molecular targets. J Allergy Clin Immunol. 2007, 120 (6): 1247-1263. 10.1016/j.jaci.2007.10.041. quiz 1264–1245View ArticlePubMedGoogle Scholar
- Barnes PJ: Inhaled glucocorticoids for asthma. N Eng J Med. 1995, 332 (13): 868-875. 10.1056/NEJM199503303321307.View ArticleGoogle Scholar
- Barnes PJ: Glucocorticosteroids: current and future directions. Br J Pharmacol. 2010, 120 (2-3): 76-85.Google Scholar
- Barnes PJ, Adcock IM: Glucocorticoid resistance in inflammatory diseases. Lancet. 2009, 373 (9678): 1905-1917. 10.1016/S0140-6736(09)60326-3.View ArticlePubMedGoogle Scholar
- Woodruff PG, Boushey HA, Dolganov GM, Barker CS, Yang YH, Donnelly S, Ellwanger A, Sidhu SS, Dao-Pick TP, Pantoja C, et al: Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids. Proc Natl Acad Sci USA. 2007, 104 (40): 15858-15863. 10.1073/pnas.0707413104.View ArticlePubMedPubMed CentralGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological). 1995, 57: 289-300.Google Scholar
- Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006, 7 (1): S7-10.1186/1471-2105-7-S1-S7.View ArticlePubMedPubMed CentralGoogle Scholar
- Shi Z, Derow CK, Zhang B: Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression. BMC Syst Biol. 2010, 4: 74-10.1186/1752-0509-4-74.View ArticlePubMedPubMed CentralGoogle Scholar
- Ideker T, Ozier O, Schwikowski B, Siegel AF: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 2002, 18 (Suppl 1): S233-S240. 10.1093/bioinformatics/18.suppl_1.S233.View ArticlePubMedGoogle Scholar
- Beisser D, Klau GW, Dandekar T, Muller T, Dittrich MT: BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics. 2010, 26 (8): 1129-1130. 10.1093/bioinformatics/btq089.View ArticlePubMedGoogle Scholar
- Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006, 34: D535-D539. 10.1093/nar/gkj109. Database issueView ArticlePubMedGoogle Scholar
- Zambelli F, Pesole G, Pavesi G: Pscan: finding over-represented transcription factor binding site motifs in sequences from co-regulated or co-expressed genes. Nucleic Acids Res. 2009, 37: W247-W252. 10.1093/nar/gkp464. Web Server issueView ArticlePubMedPubMed CentralGoogle Scholar
- Darnell JE, Kerr IM, Stark GR: Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins. Science. 1994, 264 (5164): 1415-1421. 10.1126/science.8197455.View ArticlePubMedGoogle Scholar
- Lawrence T, Natoli G: Transcriptional regulation of macrophage polarization: enabling diversity with identity. Nat Rev Immunol. 2011, 11 (11): 750-761. 10.1038/nri3088.View ArticlePubMedGoogle Scholar
- Hervas-Stubbs S, Perez-Gracia JL, Rouzaut A, Sanmamed MF, Le Bon A, Melero I: Direct effects of type I interferons on cells of the immune system. Clin Cancer Res. 2011, 17 (9): 2619-2627. 10.1158/1078-0432.CCR-10-1114.View ArticlePubMedGoogle Scholar
- Zambelli F, Pavesi G: Cscan: Finding Common Regulators In A Set Of Genes Using Genome-Wide Chip-Seq Data. Next Generation Sequencing Workshop. 2011, Bari, Italy, October 12–14 2011Google Scholar
- Diez D, Wheelock AM, Goto S, Haeggstrom JZ, Paulsson-Berne G, Hansson GK, Hedin U, Gabrielsen A, Wheelock CE: The use of network analyses for elucidating mechanisms in cardiovascular disease. Mol Biosyst. 2010, 6 (2): 289-304. 10.1039/b912078e.View ArticlePubMedGoogle Scholar
- Ioannidis JP, Tarone R, McLaughlin JK: The false-positive to false-negative ratio in epidemiologic studies. Epidemiology. 2011, 22 (4): 450-456. 10.1097/EDE.0b013e31821b506e.View ArticlePubMedGoogle Scholar
- Barabasi AL, Gulbahce N, Loscalzo J: Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011, 12 (1): 56-68. 10.1038/nrg2918.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, Barabasi AL: The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci USA. 2008, 105 (29): 9880-9885. 10.1073/pnas.0802208105.View ArticlePubMedPubMed CentralGoogle Scholar
- Uematsu S, Akira S: Toll-like receptors and innate immunity. J Mol Med (Berl). 2006, 84 (9): 712-725. 10.1007/s00109-006-0084-y.View ArticleGoogle Scholar
- Lambrecht BN, Hammad H: The role of dendritic and epithelial cells as master regulators of allergic airway inflammation. Lancet. 2010, 376 (9743): 835-843. 10.1016/S0140-6736(10)61226-3.View ArticlePubMedGoogle Scholar
- Baccala R, Hoebe K, Kono DH, Beutler B, Theofilopoulos AN: TLR-dependent and TLR-independent pathways of type I interferon induction in systemic autoimmunity. Nat Med. 2007, 13 (5): 543-551. 10.1038/nm1590.View ArticlePubMedGoogle Scholar
- Bhattacharyya S, Zhao Y, Kay TW, Muglia LJ: Glucocorticoids target suppressor of cytokine signaling 1 (SOCS1) and type 1 interferons to regulate Toll-like receptor-induced STAT1 activation. Proc Natl Acad Sci USA. 2011, 108 (23): 9554-9559. 10.1073/pnas.1017296108.View ArticlePubMedPubMed CentralGoogle Scholar
- Hu X, Li WP, Meng C, Ivashkiv LB: Inhibition of IFN-gamma signaling by glucocorticoids. J Immunol. 2003, 170 (9): 4833-4839.View ArticlePubMedGoogle Scholar
- Kelly DP: The pleiotropic nature of the vascular PPAR gene regulatory pathway. Circ Res. 2001, 89 (11): 935-937.PubMedGoogle Scholar
- Lee KS, Park SJ, Hwang PH, Yi HK, Song CH, Chai OH, Kim JS, Lee MK, Lee YC: PPAR-gamma modulates allergic inflammation through up-regulation of PTEN. FASEB J. 2005, 19 (8): 1033-1035.PubMedGoogle Scholar
- Trifilieff A, Bench A, Hanley M, Bayley D, Campbell E, Whittaker P: PPAR-alpha and -gamma but not -delta agonists inhibit airway inflammation in a murine model of asthma: in vitro evidence for an NF-kappaB-independent effect. Br J Pharmacol. 2003, 139 (1): 163-171. 10.1038/sj.bjp.0705232.View ArticlePubMedPubMed CentralGoogle Scholar
- Wolf IM, Heitzer MD, Grubisha M, DeFranco DB: Coactivators and nuclear receptor transactivation. J Cell Biochem. 2008, 104 (5): 1580-1586. 10.1002/jcb.21755.View ArticlePubMedGoogle Scholar
- Liu C, Lin JD: PGC-1 coactivators in the control of energy metabolism. Acta Biochim Biophys Sin. 2011, 43 (4): 248-257. 10.1093/abbs/gmr007.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee JH, Joe EH, Jou I: PPAR-alpha activators suppress STAT1 inflammatory signaling in lipopolysaccharide-activated rat glia. Neuroreport. 2005, 16 (8): 829-833. 10.1097/00001756-200505310-00010.View ArticlePubMedGoogle Scholar
- Zhao W, Wang L, Zhang M, Wang P, Zhang L, Yuan C, Qi J, Qiao Y, Kuo PC, Gao C: Peroxisome proliferator-activated receptor gamma negatively regulates IFN-beta production in Toll-like receptor (TLR) 3- and TLR4-stimulated macrophages by preventing interferon regulatory factor 3 binding to the IFN-beta promoter. J Biol Chem. 2011, 286 (7): 5519-5528. 10.1074/jbc.M110.149823.View ArticlePubMedGoogle Scholar
- Marx N, Mach F, Sauty A, Leung JH, Sarafi MN, Ransohoff RM, Libby P, Plutzky J, Luster AD: Peroxisome proliferator-activated receptor-gamma activators inhibit IFN-gamma-induced expression of the T cell-active CXC chemokines IP-10, Mig, and I-TAC in human endothelial cells. J Immunol. 2000, 164 (12): 6503-6508.View ArticlePubMedPubMed CentralGoogle Scholar
- Spears M, Donnelly I, Jolly L, Brannigan M, Ito K, McSharry C, Lafferty J, Chaudhuri R, Braganza G, Bareille P, et al: Bronchodilatory effect of the PPAR-gamma agonist rosiglitazone in smokers with asthma. Clin Pharmacol Ther. 2009, 86 (1): 49-53. 10.1038/clpt.2009.41.View ArticlePubMedGoogle Scholar
- Ogawa S, Lozach J, Benner C, Pascual G, Tangirala RK, Westin S, Hoffmann A, Subramaniam S, David M, Rosenfeld MG, et al: Molecular determinants of crosstalk between nuclear receptors and toll-like receptors. Cell. 2005, 122 (5): 707-721. 10.1016/j.cell.2005.06.029.View ArticlePubMedPubMed CentralGoogle Scholar
- Stojadinovic O, Lee B, Vouthounis C, Vukelic S, Pastar I, Blumenberg M, Brem H, Tomic-Canic M: Novel genomic effects of glucocorticoids in epidermal keratinocytes: inhibition of apoptosis, interferon-gamma pathway, and wound healing along with promotion of terminal differentiation. J Biol Chem. 2007, 282 (6): 4021-4034.View ArticlePubMedGoogle Scholar
- Tliba O, Cidlowski JA, Amrani Y: CD38 expression is insensitive to steroid action in cells treated with tumor necrosis factor-alpha and interferon-gamma by a mechanism involving the up-regulation of the glucocorticoid receptor beta isoform. Mol Pharmacol. 2006, 69 (2): 588-596.View ArticlePubMedGoogle Scholar
- Tliba O, Damera G, Banerjee A, Gu S, Baidouri H, Keslacy S, Amrani Y: Cytokines induce an early steroid resistance in airway smooth muscle cells: novel role of interferon regulatory factor-1. Am J Respir Cell Mol Biol. 2008, 38 (4): 463-472. 10.1165/rcmb.2007-0226OC.View ArticlePubMedGoogle Scholar
- Bini EJ, Weinshel EH: Severe exacerbation of asthma: a new side effect of interferon-alpha in patients with asthma and chronic hepatitis C. Mayo Clin Proc. 1999, 74 (4): 367-370. 10.4065/74.4.367.View ArticlePubMedGoogle Scholar
- Gratzl S, Palca A, Schmitz M, Simon HU: Treatment with IFN-alpha in corticosteroid-unresponsive asthma. J Allergy Clin Immunol. 2000, 105 (5): 1035-1036. 10.1067/mai.2000.105317.View ArticlePubMedGoogle Scholar
- Mouthon L, Guillevin L: Interferon-alpha in corticosteroid-resistant asthma and Churg-Strauss syndrome. Allergy. 2003, 58 (12): 1244-1246. 10.1046/j.1398-9995.2003.00347.x.View ArticlePubMedGoogle Scholar
- Simon HU, Seelbach H, Ehmann R, Schmitz M: Clinical and immunological effects of low-dose IFN-alpha treatment in patients with corticosteroid-resistant asthma. Allergy. 2003, 58 (12): 1250-1255. 10.1046/j.1398-9995.2003.00424.x.View ArticlePubMedGoogle Scholar
- Woodruff PG, Modrek B, Choy DF, Jia G, Abbas AR, Ellwanger A, Koth LL, Arron JR, Fahy JV: T-helper type 2-driven inflammation defines major subphenotypes of asthma. Am J Respir Crit Care Med. 2009, 180 (5): 388-395. 10.1164/rccm.200903-0392OC.View ArticlePubMedPubMed CentralGoogle Scholar
- Choy DF, Modrek B, Abbas AR, Kummerfeld S, Clark HF, Wu LC, Fedorowicz G, Modrusan Z, Fahy JV, Woodruff PG, et al: Gene expression patterns of Th2 inflammation and intercellular communication in asthmatic airways. J Immunol. 2011, 186 (3): 1861-1869. 10.4049/jimmunol.1002568.View ArticlePubMedGoogle Scholar
- Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, et al: Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 2005, 33 (20): e175-10.1093/nar/gni179.View ArticlePubMedPubMed CentralGoogle Scholar
- Gautier L, Cope L, Bolstad BM, Irizarry RA: affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004, 20 (3): 307-315. 10.1093/bioinformatics/btg405.View ArticlePubMedGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.View ArticlePubMedPubMed CentralGoogle Scholar
- Brown CJ, Hendrich BD, Rupert JL, Lafreniere RG, Xing Y, Lawrence J, Willard HF: The human XIST gene: analysis of a 17 kb inactive X-specific RNA that contains conserved repeats and is highly localized within the nucleus. Cell. 1992, 71 (3): 527-542. 10.1016/0092-8674(92)90520-M.View ArticlePubMedGoogle Scholar
- Smyth GK: Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions using R and Bioconductor. Edited by: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. 2005, Springer, New York, 397-420.View ArticleGoogle Scholar
- Bonferroni CE: Il calcolo delle assicurazioni su gruppi di teste. Studi in Onore del Professore Salvatore Ortu Carboni. 1935, 13-60.Google Scholar
- Bader GD, Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003, 4: 2-10.1186/1471-2105-4-2.View ArticlePubMedPubMed CentralGoogle Scholar
- Dittrich MT, Klau GW, Rosenwald A, Dandekar T, Muller T: Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics. 2008, 24 (13): i223-i231. 10.1093/bioinformatics/btn161.View ArticlePubMedPubMed CentralGoogle Scholar
- Falcon S, Gentleman R: Using GOstats to test gene lists for GO term association. Bioinformatics. 2007, 23 (2): 257-258. 10.1093/bioinformatics/btl567.View ArticlePubMedGoogle Scholar
- Alexa A, Rahnenfuhrer J, Lengauer T: Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006, 22 (13): 1600-1607. 10.1093/bioinformatics/btl140.View ArticlePubMedGoogle Scholar
- Samarajiwa SA, Forster S, Auchettl K, Hertzog PJ: INTERFEROME: the database of interferon regulated genes. Nucleic Acids Res. 2009, 37: D852-D857. 10.1093/nar/gkn732. Database issueView ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/5/27/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.