Abstract
Aims: To investigate the association between single nucleotide polymorphism (SNP) of peroxisome proliferator-activated receptors γ (PPAR γ) and additional gene-gene interactions on asthma risk. Methods: A total of 882 subjects (602 males, 280 females), with a mean age of 61.3±14.8 years old, including 430 asthma patients and 452 normal subjects were selected in this study, including the genotyping of polymorphisms. Logistic regression was performed to investigate association between SNP and asthma. Generalized MDR (GMDR) was used to analysis the interaction among four SNP. Results: Asthma risk was significantly lower in carriers of Ala allele of the rs1805192 polymorphism than those with Pro/Pro (Pro/Ala+ Ala/Ala versus Pro/Pro, adjusted OR (95% CI)=0.70 (0.51-0.94). In addition, we also found a significant association between rs10865710 and asthma, asthma risk was significantly lower in carriers of G allele of the rs10865710 polymorphism than those with CC (CG+ GG versus CC, adjusted OR (95% CI)=0.68 (0.55-0.95). There was a significant three-locus model (P=0.0107) involving rs1805192, rs10865710 and rs709158, indicating a potential gene-gene interaction among rs1805192, rs10865710 and rs709158. Overall, the three-locus models had a cross-validation consistency of 10 of 10, and had the testing accuracy of 60.72% after covariates adjustment. Conclusions: Our results support an important association of rs1805192 and rs10865710 with asthma, and additional interaction among rs1805192, rs10865710 and rs709158.
Keywords: Asthma, PPAR γ, interaction, SNP
Introduction
Asthma is one of common disorders of the airway. The burden of asthma appears to be increasing world-wide and both morbidity and mortality from asthma have increased in many parts of the world, making it a global health concern. Asthma is defined as a chronic inflammatory disease of the airway that is characterized by increased responsiveness to a multiplicity of stimuli [1]. Although genetic and environmental factors may play a role in the development of asthma, the exact pathogenesis of asthma is not clearly understood.
Peroxisome proliferator-activated receptors (PPARs) are members of the nuclear receptor super family containing transcription factors regulating gene expression. More recently, PPARs have been shown to implicate diverse conditions such as atherosclerosis, inflammatory response, obesity, diabetes, immune response, and aging [2,3]. They are ubiquitously expressed through whole body, and three subtypes, encoded by separated genes, have been identified, namely PPAR α, PPAR β/δ, and PPAR γ. PPAR γ has been the most extensively studied receptor among the three PPAR subtypes. PPAR γ was initially identified as a transcription factor involved in fat cell differentiation, and recently, accumulating evidence indicates that PPAR γ affects cell cycle, differentiation, and apoptosis [4]. Activation of PPAR γ could down regulate the synthesis and release of immunomodulatory cytokines from various cell types, and participate in the regulation of inflammatory processes [5,6], so PPAR γ is one of the potential candidates in the treatment of inflammatory diseases, such as asthma. However, less study focused on the association between PPAR γ single nucleotide polymorphism (SNP) and asthma in human study, so the aim of this study was to investigate the association between SNP of PPAR γ and additional gene-gene interactions on asthma risk.
Materials and methods
Subjects
This was a hospital based case-control study. Participants were consecutively recruited from asthma patients who received treatment between January 2011 and November 2013 from the First Affiliated Hospital of Chengdu Medical College. We excluded participants with diabetes, CVD, missing data and participants with BMI <18.5 kg/m2, because PPAR γ was also associated with these diseases. A total of 882 subjects (602 males, 280 females), with a mean age of 61.3±14.8 years old, including 430 asthma patients and 452 normal subjects were selected in this study, including the genotyping of polymorphisms. Informed consent was obtained from all participants.
Body measurements
Data on demographic information, lifestyle risk factors for all participants were obtained using a standard questionnaire administered by trained staffs. Body weight, height, waist circumference were measured, and body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in meters. Waist circumference (WC) was measured two times at 1 cm above the umbilicus at minimal respiration by trained observers; the mean of the two WC measurements was utilized in the analysis. Cigarette smokers were those who self-reported smoking cigarettes at least once a day for 1 year or more. Alcohol consumption was expressed as the sum of milliliters of alcohol per week from wine, beer, and spirits. Blood samples were collected in the morning after at least 8 hours of fasting.
All plasma and serum samples were frozen at -80°C until laboratory testing. Plasma glucose was measured using an oxidase enzymatic method. The concentrations of HDL cholesterol and triglycerides were assessed enzymatically using an automatic biochemistry analyzer (Hitachi Inc., Tokyo, Japan) and commercial reagents. All analysis was performed by the same lab.
Genomic DNA extraction and genotyping
We selected SNPs within the PPAR γ gene, which have been reported associations with asthma and minor allele frequency (MAF) greater than 1%. 5 SNP of PPAR γ were selected for genotyping in the study: rs1805192, rs10865710, rs709158, rs4684847 and rs3856806. Genomic DNA from participants was extracted from EDTA-treated whole blood, using the DNA Blood Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. All SNPs were detected by Taqman fluorescence probe. ABI Prism7000 software and allelic discrimination procedure was used for genotyping of fore-mentioned four SNP. 25 μl reaction mixture including 1.25 ul SNP Genotyping Assays (20×), 20 ng DNA, 12.5 μl Genotying Master Mix (2×), initial denaturation for 10 min and 95°C, denaturation for 15 s and 92°C, annealing and extension for 90 s and 60°C, 50 cycles. Probe sequences of all SNPs were shown in Table 1.
Table 1.
Description for 5 SNPs and Probe sequence used for Taqman fluorescence probe analysis
| SNP ID | SNP | Position | Exon/Intron | Nucleotide substitution | Probe sequence |
|---|---|---|---|---|---|
| rs709158 | Intron A>G | 12403176 | Intron_2 | A>G | 5’-AGATACGGGGGAGGAAATTCACTGG[A/G] |
| TTTTACAATATATTTTTCAAGGCAA-3’ | |||||
| rs10865710 | C681G | 12293198 | Intron | C>G | 5’-TTGGCATTAGATGCTGTTTTGTCTT[C/G] |
| ATGGAAAATACAGCTATTCTAGGAT-3’ | |||||
| rs1805192 | Pro12Ala | 12361238 | Exon_B | C>G | 5’-ACCTCAGACAGATTGTCACGGAACA[C/T] |
| GTGCAGCTACTGCAGGTGATCAAGA-3’ | |||||
| rs4684847 | Intron C>T | 12326337 | Intron_3 | C>G | 5’-ATTTATTTAAATCATCTCTAATTCT[C/T] |
| ACAACTCCGAAAAGATAAGAAAACA-3’ | |||||
| rs3856806 | C161T | 12415557 | Exon-6 | C>T | 5’-GGTTGACACAGAGATGCCATTCTGG[C/G] |
| CCACCAACTTTGGGATCAGCTCCGT-3’ |
Statistical analysis
The mean and SD for normally distributed continuous variables, and percentages for categorical variable, were calculated and compared. The genotype and allele frequencies were obtained by direct count. The categorical data were analyzed using χ2 test. Further, continuous variables were analyzed using Student’s t test or one-way analysis of variance. Hardy-Weinberg equilibrium (HWE) was performed by using SNP Stats (available online at http://bioinfo.iconcologia.net/SNPstats). Logistic regression was performed to investigate association between SNP and EH using gender, age, smoking and alcohol status, WC and BMI as covariates in the model. Generalized MDR (GMDR) [7] was used to analysis the interaction among four SNP, cross-validation consistency, the testing balanced accuracy, and the sign test, to assess each selected interaction were calculated.
Results
A total of 882 subjects (602 males, 280 females), with a mean age of 61.3±14.8 years old, were selected, including 430 asthma patients and 452 normal subjects. Participants characteristics stratified by cases and controls are shown in Table 2. Age, gender, alcohol consumption, high fat diet and low fiber diet were not significantly different between cases and controls. The rate of smoking and the mean of WC and BMI were higher in asthma patients than that in control group subjects.
Table 2.
General characteristics of study participants in case and control group
| Variables | Asthma cases group (n=430) | Control group (n=452) | p-values |
|---|---|---|---|
| Age (years) | 57.2±14.0 | 56.6±14.2 | 0.528 |
| Males N (%) | 202 (47.0) | 222 (49.1) | 0.579 |
| Smoke N (%) | 177 (41.2) | 154 (34.1) | 0.035 |
| Alcohol consumption N (%) | 181 (42.1) | 178 (39.4) | 0.455 |
| High fat diet N (%) | 174 (40.5) | 169 (37.4) | 0.382 |
| Low fiber diet N (%) | 187 (43.5) | 178 (39.4) | 0.243 |
| WC (cm) | 83.7±9.8 | 82.1±9.3 | 0.013 |
| BMI (kg/m2) | 24.4±6.7 | 23.2±6.2 | 0.006 |
Note: means ± standard deviation for age, WC, BMI.
All genotypes were distributed according to Hardy-Weinberg equilibrium (All p values more than 0.05). There were significant differences in alleles and genotypes distributions in rs1805192 and rs10865710 between asthma patients and controls (Table 3). Logistic regression analysis showed a significant association of rs1805192 variants genotypes with decreased asthma risk, after adjustment for gender, age, smoking and alcohol status, WC and BMI. Asthma risk was significantly lower in carriers of Ala allele of the rs1805192 polymorphism than those with Pro/Pro (Pro/Ala+ Ala/Ala versus Pro/Pro, adjusted OR (95% CI)=0.70 (0.51-0.94). In addition, we also found a significant association between rs10865710 and asthma, asthma risk was significantly lower in carriers of G allele of the rs10865710 polymorphism than those with CC (CG+ GG versus CC, adjusted OR (95% CI)=0.68 (0.55-0.95).
Table 3.
Genotype and allele frequencies of 5 SNP between case and control group
| SNPs | Genotypes and Alleles | Frequencies N (%) | OR (95% CI) a | P-values | HWE test | |
|---|---|---|---|---|---|---|
|
| ||||||
| Control (n=430) | Case (n=452) | |||||
| rs1805192 | ProPro | 218 (50.7) | 262 (58.0) | 1.00 | <0.001 | 0.461 |
| ProAla | 174 (40.5) | 173 (38.3) | 0.97 (0.82-1.32) | |||
| AlaAla | 38 (8.8) | 17 (3.7) | 0.60 (0.42-0.90) | |||
| ProAla+AlaAla | 212 (49.3) | 190 (42.0) | 0.70 (0.51-0.94) | 0.018 | ||
| Pro | 610 (70.9) | 697 (77.1) | 0.003 | |||
| Ala | 250 (29.1) | 207 (22.9) | ||||
| rs709158 | AA | 225 (52.3) | 257 (56.8) | 1.00 | 0.372 | 0.436 |
| AG | 176 (40.9) | 170 (37.6) | 0.96 (0.81-1.28) | |||
| GG | 29 (6.7) | 25 (5.5) | 0.91 (0.56-1.11) | |||
| AG+GG | 205 (47.7) | 195 (43.1) | 0.86 (0.53-1.22) | 0.192 | ||
| A | 626 (72.8) | 684 (75.7) | 0.168 | |||
| G | 234 (27.2) | 220 (24.3) | ||||
| rs10865710 | CC | 220 (51.2) | 256 (56.6) | 1.00 | 0.012 | 0.989 |
| CG | 169 (39.3) | 175 (38.7) | 0.98 (0.79-1.22) | |||
| GG | 41 (9.5) | 21 (4.6) | 0.53 (0.42-0.91) | |||
| GG+CG | 210 (48.8) | 196 (43.4) | 0.68 (0.55-0.95) | 0.038 | ||
| C | 609 (70.8) | 687 (76.0) | 0.015 | |||
| G | 251 (29.2) | 217 (24.0) | ||||
| rs4684847 | CC | 222 (51.6) | 252 (55.8) | 1.00 | 0.431 | 0.383 |
| CG | 178 (41.4) | 174 (38.5) | 0.95 (0.73-1.26) | |||
| GG | 30 (7.0) | 26 (5.7) | 0.89 (0.49-1.42) | |||
| GG+CG | 208 (48.4) | 200 (44.2) | 0.93 (0.60-1.25) | 0.236 | ||
| C | 622 (72.3) | 678 (75.0) | 0.202 | |||
| G | 238 (27.7) | 226 (25.0) | ||||
| rs3856806 | CC | 224 (52.1) | 255 (56.4) | 1.00 | 0.436 | 0.191 |
| CT | 180 (41.9) | 172 (38.1) | 0.96 (0.76-1.20) | |||
| TT | 26 (6.0) | 25 (5.5) | 0.97 (0.53-1.35) | |||
| TT+CT | 206 (47.9) | 197 (43.6) | 0.96 (0.58-1.23) | 0.225 | ||
| C | 628 (73.0) | 682 (75.4) | 0.245 | |||
| T | 232 (27.0) | 222 (24.6) | ||||
Adjusted for gender, age, smoke and alcohol status, BMI, WC.
We employed the GMDR analysis to investigate the impact of the interaction among 5 SNP in PPAR γ, after adjustment for covariates including gender, age, smoking and alcohol status, WC and BMI. Table 4 summarizes the results obtained from GMDR analysis for two to five locus models. There was a significant three-locus model (P=0.0107) involving rs1805192, rs10865710 and rs709158, indicating a potential gene-gene interaction among rs1805192, rs10865710 and rs709158. Overall, the three-locus models had a cross-validation consistency of 10 of 10, and had the testing accuracy of 60.72%.
Table 4.
Best gene-gene interaction models, as identified by GMDR
| Locus no. | Best combination | Cross-validation consistency | Testing accuracy | p-valuesa |
|---|---|---|---|---|
| 2 | rs1805192 rs10865710 | 8/10 | 0.5669 | 0.1719 |
| 3 | rs1805192 rs10865710 rs709158 | 10/10 | 0.6072 | 0.0010 |
| 4 | rs1805192 rs10865710 rs709158 rs4684847 | 8/10 | 0.5399 | 0.0547 |
| 5 | rs1805192 rs10865710 rs709158 rs4684847 rs3856806 | 7/10 | 0.5587 | 0.1719 |
Adjusted for gender, age, smoke and alcohol status, BMI, WC.
In order to obtain the odds ratios and 95% CI for the joint effects of rs1805192, rs10865710 and rs709158 on asthma, we conducted interaction analysis between 3 SNP by using logistic regression. We found that subjects with Pro/Ala or Ala/Ala-CG or GG-AG or GG genotype have lowest asthma risk, compared to subjects with Pro/Pro-CC-AA genotype, OR (95% CI) was 0.26 (0.12-0.57), after covariates adjustment (Table 5).
Table 5.
Interaction analysis for 3-locus models by using logistic regression
| rs1805192 | rs10865710 | rs709158 | OR (95% CI)a | P-values |
|---|---|---|---|---|
| ProPro | CC | AA | 1.00 | - |
| ProPro | CC | AG or GG | 0.95 (0.67-1.64) | 0.627 |
| ProPro | CG or GG | AA | 0.92 (0.51-1. 69) | 0.574 |
| ProPro | CG or GG | AG or GG | 0.61 (0.30-1.23) | 0.216 |
| AlaAla or AlaAla | CC | AA | 0.81 (0.50-0.88) | <0.001 |
| AlaAla or AlaAla | CC | AG or GG | 0.45 (0.26-0.86) | <0.001 |
| AlaAla or AlaAla | CG or GG | AA | 0.44 (0.25-0.95) | 0.012 |
| AlaAla or AlaAla | CG or GG | AG or GG | 0.26 (0.12-0.57) | <0.001 |
Adjusted for gender, age, smoke and alcohol status, BMI, WC.
Discussion
In this study, we found that there were significant differences in alleles and genotypes distributions in rs1805192 and rs10865710 between asthma patients and controls, and minor allele of the rs1805192 and rs10865710 polymorphisms were associated with lower asthma risk. To date, PPAR γ has been the most extensively studied receptor among the three PPAR subtypes and in the last few years growing evidence indicates that PPAR γ plays an important role in controlling immune and inflammatory responses. In addition, PPAR γ agonist was also reported that it has a modest effect in the allergen challenge model in asthma [8]. However, to our knowledge, this is the first study to investigate the association between PPAR γ polymorphisms and asthma in Chinese populations. Mueller [9] et al. indicated that ligands for PPAR γ may be efficacious in treating allergic asthma and have recently been implicated as the targets of cellular inflammatory and immune response [10,11]. Given its anti-inflammatory and immunomodulatory properties, PPAR γ has been used in the treatment of inflammatory diseases, including asthma [12]. Benayoun et al. [13] reported that PPAR γ protein is expressed in human subjects who were asthmatic and found elevated expression in the bronchial submucosa, the airway epithelium, and smooth muscle cells compared with control subjects. Similarly, Patel et al. [14] reported that human airway smooth muscle cells expressed PPAR γ, and that exposure of these cells to PPAR γ ligands could inhibit their secretion of granulocyte macrophage colony stimulating factor as well as granulocyte colony stimulating factor. Palmer et al. [15] conducted a study in children and young adults, the results suggested that common genetic variation at the PPARG locus may play an important role in modulating the long-term control of asthma.
Asthma was a complex phenotype, which could be influenced by many types of gene, the most of which were minor SNP. So it is necessary to investigate the synergetic effect among several PPAR γ SNPs on risk of asthma. In this study, we employ the GMDR method to investigate the interaction among 5 SNPs of PPAR γ on risk of asthma, the results indicated that there was a significant interaction among rs1805192, rs10865710 and rs709158, subjects with Pro/Ala or Ala/Ala-CG or GG-AG or GG genotype have lowest asthma risk, compared to subjects with Pro/Pro-CC-AA genotype, the risk of asthma was 0.26 times in the mutation allele of three SNP carriers than that in homozygous carriers. In the correlation analysis, the rs709158 was not associated with asthma, however, in the interaction analysis, the minor allele of this SNP can significantly affect asthma risk when accompanied with minor allele of rs1805192 and rs10865710. These findings indicate that a minor gene (even when its main effects are close to nil) can have a strong effect on obesity, due to the presence of gene-gene interaction.
Several underlying mechanism of minor allele of induced decreased asthma risk was reported in previous studies [16-20]. Kim et al. [16] reported that expression of PPAR γ was increased by ovalbumin inhalationand the increased IL-10 levels in lung tissues after ovalbumin inhalation were further increased by the administration of rosiglitazone, pioglitazone, or AdPPAR γ. Levels of IL-4, IL-5, and ovalbumin-specific IgE were also increased after ovalbumin inhalation, and the increased levels were significantly reduced by the administration of the PPAR γ agonists or AdPPAR γ. These findings suggest that a protective role of PPAR γ in the pathogenesis of the asthma is partly mediated through an IL-10-dependent mechanism, to reduce airway hyper responsiveness and activation of eosinophils that are increased by induction of asthma [17-19]. Lee et al. [17] indicated the administration of PPAR γ agonists or AdPPAR γ up-regulated phosphatase and tensin homologue deleted on chromosome ten (PTEN) expression in allergen-induced asthmatic lungs and they demonstrate a protective role of PPAR γ in the pathogenesis of the asthma phenotype through regulation of PTEN expression. An animal study [20] suggested that the therapeutic effect of rosiglitazone in neutrophilic asthma is partially due to the role of the PPAR γ pathway in regulating T cell proliferation and differentiation.
Till now, to our knowledge, this is the first study involved in the gene-gene interaction of PPAR γ on asthma risk. How, the limitations of this study should be considered. Firstly, although the number of study participants met the requirement for analysis, the present sample size was relatively small. Interaction analysis should be conducted for gene-gene interaction with others gene, including PTEN or IL-10 gene, also interaction with others environmental risk factors should be investigated in the future studies, such as diet behavior or smoking and so on.
In conclusion, we found a significant association between rs1805192 and rs10865710 and decreased asthma risk, and we also obtained a significant interaction among rs1805192, rs10865710 and rs709158, based on this Chinese hospital based case-control study.
Acknowledgements
The writing of this paper was supported by the First Affiliated Hospital of Chengdu Medical College. We thank all the partners and staffs who help us in the process of this study.
Disclosure of conflict of interest
None.
References
- 1.Bousquet J, Jeffery PK, Busse WW, Johnson M, Vignola AM. Asthma. From bronchoconstriction to airways inflammation and remodeling. Am J Respir Crit Care Med. 2000;161:1720–1745. doi: 10.1164/ajrccm.161.5.9903102. [DOI] [PubMed] [Google Scholar]
- 2.Blanquart C, Barbier O, Fruchart JC, Staels B, Glineur C. Peroxisome proliferator-activated receptors: Regulation of transcriptional activities and roles in inflammation. J Steroid Biochem Mol Biol. 2003;85:267–273. doi: 10.1016/s0960-0760(03)00214-0. [DOI] [PubMed] [Google Scholar]
- 3.Na HK, Surh YJ. Peroxisome proliferator-activated receptor gamma (PPARgamma) ligands as bifunctional regulators of cell proliferation. Biochem Pharmacol. 2003;66:1381–1391. doi: 10.1016/s0006-2952(03)00488-x. [DOI] [PubMed] [Google Scholar]
- 4.Chinetti G, Griglio S, Antonucci M, Torra IP, Delerive P, Majd Z, Fruchart JC, Chapman J, Najib J, Staels B. Activation of proliferator-activated receptors alpha and gamma induces apoptosis of human monocyte-derived macrophages. J Biol Chem. 1998;273:25573–25580. doi: 10.1074/jbc.273.40.25573. [DOI] [PubMed] [Google Scholar]
- 5.Gelman L, Fruchart JC, Auwerx J. An update on the mechanisms of action of the peroxisome proliferator-activated receptors (PPARs) and their roles in inflammation and cancer. Cell Mol Life Sci. 1999;55:932–943. doi: 10.1007/s000180050345. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Jiang C, Ting AT, Seed B. PPAR-gamma agonists inhibit production of monocyte inflammatory cytokines. Nature. 1998;391:82–86. doi: 10.1038/34184. [DOI] [PubMed] [Google Scholar]
- 7.Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, Elston RC, Li MD. A generalized combinatorial approach for detecting gene-by gene and gene-by- environment interactions with application to nicotine dependence. Am J Hum Genet. 2007;80:1125–37. doi: 10.1086/518312. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Richards DB, Bareille P, Lindo EL, Quinn D, Farrow SN. Treatment with a peroxisomal proliferator activated receptor gamma agonist has a modest effect in the allergen challenge model in asthma: A randomized controlled trial. Respir Med. 2010;104:668–74. doi: 10.1016/j.rmed.2009.11.006. [DOI] [PubMed] [Google Scholar]
- 9.Mueller C, Weaver V, Vanden Heuvel J, August A, Cantorna M. Peroxisome proliferator-activated receptor gamma ligands attenuate immunological symptoms of experimental allergic asthma. Arch Biochem Biophys. 2003;418:186–96. doi: 10.1016/j.abb.2003.08.006. [DOI] [PubMed] [Google Scholar]
- 10.Glass CK, Saijo K. Nuclear receptor transrepression pathways that regulate inflammation in macrophages and T cells. Nat Rev Immunol. 2010;10:365–76. doi: 10.1038/nri2748. [DOI] [PubMed] [Google Scholar]
- 11.Kim SH, Hong JH, Lee YC. Ursolic acid, a potential PPAR gama agonist, suppresses ovalbumin-induced airway inflammation and Penh by down-regulating IL-5, IL-13, and IL-17 in a mouse model of allergic asthma. Eur J Pharmacol. 2013;701:131–43. doi: 10.1016/j.ejphar.2012.11.033. [DOI] [PubMed] [Google Scholar]
- 12.Park SJ, Lee YC. Peroxisome proliferator-activated receptor gamma as a novel therapeutic target in asthma. J Asthma. 2008;45:1–8. doi: 10.1080/02770900701247319. [DOI] [PubMed] [Google Scholar]
- 13.Benayoun L, Letuve S, Druilhe A, Boczkowski J, Dombret M, Mechighel P, Megret J, Leseche G, Aubier M, Pretolani M. Regulation of peroxisome proliferator-activated receptor gamma expression in human asthmatic airways: Relationship with proliferation, apoptosis, and airway remodeling. Am J Respir Crit Care Med. 2001;164:1487–94. doi: 10.1164/ajrccm.164.8.2101070. [DOI] [PubMed] [Google Scholar]
- 14.Patel H, Belvisi M, Bishop-Bailey D, Yacoub M, Mitchell J. Activation of peroxisome proliferator-activated receptors in human airway smooth muscle cells has a superior anti-inflammatory profile to corticosteroids: Relevance for chronicobstructive pulmonary disease therapy. J Immunol. 2003;170:2663–9. doi: 10.4049/jimmunol.170.5.2663. [DOI] [PubMed] [Google Scholar]
- 15.Palmer CN, Doney AS, Ismail T, Lee SP, Murrie I, Macgregor DF, Mukhopadhyay S. PPARG Locus Haplotype Variation and Exacerbations in Asthma. Clin Pharmacol Ther. 2007;81:713–8. doi: 10.1038/sj.clpt.6100119. [DOI] [PubMed] [Google Scholar]
- 16.Kim SR, Lee KS, Park HS, Park SJ, Min KH, Jin SM, Lee YC. Involvement of IL-10 in Peroxisome Proliferator-Activated Receptor -Mediated Anti-Inflammatory Response in Asthma. Mol Pharmacol. 2005;68:1568–75. doi: 10.1124/mol.105.017160. [DOI] [PubMed] [Google Scholar]
- 17.Lee KS, Park SJ, Hwang PH, Yi HK, Song CH, Chai OH, Kim JS, Lee MK, Lee YC. PPAR-gamma modulates allergic inflammation through upregulation of PTEN. FASEB J. 2005;19:1033–1035. doi: 10.1096/fj.04-3309fje. [DOI] [PubMed] [Google Scholar]
- 18.Honda K, Marquillies P, Capron M, Dombrowicz D. Peroxisome prolifera- tor-activated receptor gamma is expressed in airways and inhibits features of airway remodeling in a mouse asthma model. J Allergy Clin Immunol. 2004;113:882–888. doi: 10.1016/j.jaci.2004.02.036. [DOI] [PubMed] [Google Scholar]
- 19.Woerly G, Honda K, Loyens M, Papin JP, Auwerx J, Staels B, Capron M, Dombrowicz D. Peroxisome proliferator-activated receptors alpha and gamma down-regulate allergic inflammation and eosinophil activation. J Exp Med. 2003;198:411–421. doi: 10.1084/jem.20021384. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Zhao Y, Huang Y, He J, Li C, Deng W, Ran X, Wang D. Rosiglitazone, a peroxisome proliferator-activated receptor-γ agonist, attenuates airway inflammation by inhibiting the proliferation of effector T cells in a murine model of neutrophilic asthma. Immunol Lett. 2014;157:9–15. doi: 10.1016/j.imlet.2013.11.004. [DOI] [PubMed] [Google Scholar]