Association of Genetic Variants of NLRP4 with Exacerbation of Asthma: The Effect of Smoking

Abstract

Asthma exacerbation is induced by the interaction of genes and environmental factors such as cigarette smoke. NLRP4 counteracts the activity of the inflammasome, which is responsible for asthma exacerbation. In this study, we analyzed the association of single-nucleotide polymorphisms of NLRP4 with the annual rate of exacerbation and evaluated the additive effect of smoking in 1454 asthmatics. Asthmatics possessing the minor allele of rs1696718G > A had more frequent exacerbation episodes than those homozygous for the common allele (0.59 vs. 0.36/year) and the association was present only in current and ex-smokers. There was a significant interaction between the amount smoked and rs16986718 genotypes (p = 0.014) and a positive correlation between the number of annual exacerbation episodes and amount smoked only in rs16986718G > A AA homozygotes. The prevalence of frequent exacerbators (≥2 exacerbation episodes/year) was 2.5 times higher in rs16986718G > A minor allele homozygotes than in common allele homozygotes (12.0% vs. 5.9%). Furthermore, the prevalence was 6 times higher in rs16986718G > A minor allele homozygotes who were current and ex-smokers than in nonsmokers (25.6% vs. 4.1%). The minor allele of rs16986718G > A in NLRP4 may be a genetic marker that predicts asthma exacerbation in adult asthmatics who smoke.

Introduction

Although most asthmatics are successfully controlled with conventional asthma medications, some patients suffer from frequent exacerbation (Wenzel, 2005; Chanez et al., 2007; Moore et al., 2007; Kim et al., 2017). Exacerbation is induced by environmental factors under genetic influence (Park and Tantisira, 2017).

Identified genetic variants that cause exacerbation include rs1800925 in IL13 (Hunninghake et al., 2007) and rs7216389 in ORMDL3 in children (Bisgaard et al., 2009), rs4950928 in CHI3L1 in children and young adults (Cunningham et al., 2011), and two nonsynonymous coding single-nucleotide polymorphisms (SNPs) (rs1805011 [E375A] and rs1801275 [Q551R]) in IL4R in adult asthmatics (Wenzel et al., 2007). In addition to the well-known asthma genes, GSDMB, IL33, RAD50, IL1RL1, CTNNA3, and SEMA3D, in childhood asthmatics (Bonnelykke et al., 2014; McGeachie et al., 2015), recent genome-wide association studies (GWAS) have also identified CDHR3 (encoding cadherin-related family member 3) and CMTR1 in children and adults and TRIM24 and MAGI2 in adult asthmatics as susceptible genes (Dahlin et al., 2015).

Environmental and social factors predisposing asthmatics to exacerbation include allergenic or occupational sensitization, exposure to mold, viral infection, heavy exercise, tobacco smoke, and some medications (Saxon and Saxon, 2005; Bloomberg, 2011). Immune responses to these factors induce activation of the inflammasome. Recognition of viral RNA by a set of cytosolic sensors induces production of type I interferons (IFNs) and the assembly of inflammasome complexes that activate caspase-1 (Kanneganti, 2010). The inflammasome consists of nucleotide-binding oligomerization domain-like receptor (NLR) family members containing the pyrin domain (NLRP), the adaptor molecule apoptosis-associated speck-like protein containing a Caspase activation and recruitment domain, and caspase-1 (Schroder and Schroder, 2010; De Nardo et al., 2014).

Similarly, exposure to air pollutants and cigarette smoke elicits immune responses mediated by specific Toll-like receptor- and NLR-dependent mechanisms (Jiang et al., 2006; Bauer et al., 2012). In the latter, NLRP3 binds to procaspase-1 through ASC, subsequently activating caspase-1, which leads to maturation of proinflammatory cytokines such as interleukin (IL)-1β, IL-18, and IL-33 (Dinarello, 2009; De Nardo et al., 2014). By contrast, NLRP4 counteracts inflammasome activity by inhibiting tumor necrosis factor- and IL-1β-induced nuclear factor-κB activation (Fiorentino et al., 2002) and negatively regulates type I IFN signaling by serving as an adapter to promote the ubiquitination of activated TBK1 (Cui et al., 2012).

Consequently, downregulation of the function of NLRP4 might be responsible for the frequent exacerbation in asthmatics in specific environments, such as in smokers, in whom the number of cigarettes smoked is easily assessed. This prompted us to analyze the association of SNPs of NLRP4 with the annual rate of asthma exacerbation and to evaluate relationships of current and previous smoking with the genetic effect.

Materials and Methods

Study subjects

DNA from 1454 asthmatics (995 nonsmokers, 274 ex-smokers, and 225 smokers), who had been followed for more than 1 year since enrollment, was obtained from the biobank of Soonchunhyang University Bucheon Hospital, Korea. Informed consent was obtained from each study subject who donated DNA to the biobank. The subjects were all of Korean descent and met the criteria for asthma: a physician's diagnosis of asthma and the presence of reversible airway obstruction (short-acting bronchodilator-induced increase of forced expiratory volume in the first second [FEV₁] >12% and 200 mL or variation of peak expiratory flow rate >20% over a 4-week period); improvement in FEV₁ >20% after treatment with asthma medication for 2 weeks, including inhaled or systemic corticosteroids (SCSs); or airway hyper-reactivity (methacholine PC20 < 10mg/mL). FEV₁, forced vital capacity (FVC), and the postbronchodilator FEV₁ were measured at baseline and every 3 months. The number of exacerbation episodes in the first year after enrollment was counted. Exacerbation recovery was defined as in the American Thoracic Society/European Respiratory Society statement (Reddel et al., 2009)—as recovery from the exacerbation by increasing the dose of inhaled corticosteroid (ICS) or adding bronchodilators and antileukotrienes, or as recovery from the exacerbation by adding oral corticosteroids (OCSs; >0.5 mg/kg of prednisolone for more than 3 days), while considering hospitalization or an emergency room visit. The respective amounts of ICSs and OCSs are expressed as the equivalent dosage of fluticasone per day or prednisone per year used in the first year. A frequent exacerbator (n = 126) was defined as an asthmatic experiencing two or more exacerbation episodes over the first year and the other subjects were defined as nonfrequent exacerbators (n = 1328). The study protocol was approved by the Ethics Committee of Soonchunhyang Bucheon Hospital (SCHBC_2014_07_028).

Selection of SNPs and genotyping

SNPs in exons of NLRP4 were selected using the Asian population database from the International HapMap Project (http://hapmap.ncbi.nlm.nih.gov) and NCBI (www.ncbi.nlm.nih.gov) databases as follows. First, SNPs with minor allele frequencies (MAFs) >5% were extracted from exons. A representative SNP was selected in the case of absolute linkage disequilibrium (LD; |D′| = 1 and r² > 0.95) between the SNPs. Sixteen SNPs were genotyped using the GoldenGate assay with VeraCode microbeads (Illumina, San Diego, CA) (Busacker et al., 2009). These were scanned using the BeadXpress^® system (Illumina).

Statistics

Fisher's exact test was used to compare the observed numbers of each genotype with those expected for a population in Hardy–Weinberg equilibrium (HWE). Haplotypes were inferred using the expectation maximization algorithm in C (Stephens et al., 2001). A type III univariate general linear model was applied to continuous variables (number of exacerbation episodes) and multiple logistic regression to discrete variables (presence of frequent exacerbation). In the logistic regression analysis, odds ratios (ORs) and 95% confidence intervals were calculated for each genotype and haplotype. Correlation between the amount smoked and the annual number of exacerbation episodes was evaluated using Spearman's rho (ρ). To evaluate the interaction between genotype (or haplotype) and smoking, we performed a multiple regression analysis, including an interaction term (genotype × amount smoked). Data were analyzed using SAS, version 9.1 (SAS, Cary, NC), and SPSS, version 12.0 (SPSS, Chicago, IL). To correct p-values for multiple comparisons, the effective number of independent SNPs in NLRP4 was calculated using SNP spectral decomposition (http://genepi.qimr.edu.au/general/daleN/SNPSpD) (Nyholt, 2004). Corrected p (p _corr) values <0.05 were considered significant.

Results

Characteristics of study subjects

The study enrolled 1454 asthmatics: 126 frequent exacerbators and 1328 nonfrequent exacerbators (Table 1). The frequent exacerbator group was dominated by older asthmatics with a larger amount smoked, less atopy, and lower FEV₁ and FVC compared with the nonfrequent exacerbator group (p < 0.05). The mean daily ICS and annual SCS dosages were significantly higher in the frequent exacerbator group than in the nonfrequent exacerbator group (p < 0.05). Therefore, age, amount smoked, atopy, FEV₁% of the predicted value at baseline, and total ICS and SCS dosages were used as covariates in the analyses of genetic associations.

Table 1.

Clinical Characteristics of Study Subjects

	Nonfrequent exacerbators	Frequent exacerbators	p
Number	1328	126	-
Age (years)	46.27 ± 0.43	51.2 ± 1.16	1.05E-04
Sex (male, %)	38.1%	41.3%	0.503
Smoking status (ES+SM^*, %)	33.7%	41.3%	0.086
Smoking amount (pack/year)	5.88 ± 0.37	8.47 ± 1.46	0.041
Atopy (%)	48.8%	39.7%	0.050
Duration of asthma (years)	3.06 ± 0.2	5.95 ± 0.94	0.003
Serum total IgE (IU/mL)	356.3 ± 18.87	453.17 ± 62.05	0.131
Body–mass index (kg/m²)	23.87 ± 0.12	23.96 ± 0.47	0.859
Baseline FVC%, predicted	83.02 ± 0.48	67.65 ± 1.4	1.45E-20
Baseline FEV1%, predicted	82.58 ± 0.6	58.4 ± 1.59	3.06E-30
Baseline FEV1/FVC	75.64 ± 0.34	64.81 ± 1.15	6.96E-20
PC20, methacholine (mg/mL) (No. of study subjects)	7.6 ± 0.29 (1218)	5.52 ± 0.83 (109)	0.037
Duration of follow-up (years)	6.17 ± 0.12	7.29 ± 0.37	0.007
Number of exacerbation episodes in the first year	0.18 ± 0.01	3.63 ± 0.24	6.08E-29
Total ICS dosage used in the first year (fluticasone eqv./day)	211.36 ± 7.25	571.34 ± 43.82	3.16E-13
Systemic steroid dose in the first year (mg/year)	80.31 ± 9.55	564.14 ± 95.39	1.51E-06

Data are expressed as mean ± standard error.

ES, ex-smoker; SM, current smoker.

FVC, forced vital capacity; ICS, inhaled corticosteroid.

Frequencies, heterozygosity, and HWE of SNPs of the NLRP4 gene

The HWE of the 16 SNPs was >0.05 (Supplementary Table S1; Supplementary Data available online at www.libertpub.com/dna). Eight SNPs with an MAF <0.01, (rs145740276, rs151288574, rs147261455, rs142191212, rs141198839, rs146693875, rs140263319, and rs199922435) were excluded from the analysis. Two haplotype (ht) blocks were generated based on LD among the remaining eight SNPs (Fig. 1A). HapBlock 1 included six haplotypes (frequency >0.01), and HapBlock 2 included four haplotypes (Fig. 1B). ht2, ht5, and ht6 in block 1 and ht2 and ht3 in block 2 were excluded from further statistical analysis because of their equivalence to rs16986718, rs117636433, rs192117499, rs12462372, and rs302456, respectively.

FIG. 1.

(A) Map, SNP location, and linkage disequilibrium of the NLRP4 gene. Among 16 SNPs genotyped, we choose 8 SNPs according to MAF >0.01 for analyzing association with asthma exacerbation (Table 2). Closed and open box represent CDS and UTR, respectively. (B) Haplotypes of each HapBlock in the NLRP4 gene. MAFs, minor allele frequencies; SNP, single-nucleotide polymorphism.

Association of SNPs of the NLRP4 gene with the annual number of exacerbation episodes

The eight SNPs of NLRP4 were analyzed for associations with the number of exacerbation episodes using a univariate general linear model. Minor allele homozygotes and heterozygotes of rs16986718G > A had significantly more annual exacerbation episodes compared with common allele homozygotes (mean ± standard error; 0.59 ± 0.13 vs. 0.59 ± 0.07 vs. 0.36 ± 0.04, p = 0.001, in dominant models, Table 2). The difference remained after correcting for multiple comparisons (p _corr = 0.007). Rs441827 and ht1 in block 1 also showed an association with the number of exacerbation episodes (p = 0.022 in the dominant model), although these differences were not significant after correcting for multiple comparisons (Table 2).

Table 2.

Association of Single-Nucleotide Polymorphisms and Haplotypes on NLRP4 with the Number of Exacerbation Episodes over the First 1 Year of Follow-Up in Total Study Subjects

Locus	No. of exacerbation episodes, mean ± SE (n)			Codominant		Dominant		Recessive
Locus	CC	CR	RR	p^*	pcorr	p^*	pcorr	p^*	pcorr
rs441827	0.38 ± 0.04 (602)	0.54 ± 0.06 (675)	0.56 ± 0.11 (177)	0.074	0.518	0.022	0.157	0.469	1.000
rs16986718	0.36 ± 0.04 (735)	0.59 ± 0.07 (600)	0.59 ± 0.13 (117)	0.004	0.031	0.001	0.007	0.367	1.000
rs192117499	0.48 ± 0.03 (1399)	0.44 ± 0.23 (48)	0	0.771	1.000	0.771	1.000	.	.
rs117636433	0.48 ± 0.04 (1377)	0.42 ± 0.11 (77)	0	0.885	1.000	0.885	1.000	.	.
rs379327	0.47 ± 0.04 (1044)	0.53 ± 0.08 (374)	0.21 ± 0.1 (33)	0.580	1.000	0.595	1.000	0.459	1.000
rs12462372	0.45 ± 0.04 (861)	0.54 ± 0.07 (515)	0.41 ± 0.1 (76)	0.765	1.000	0.521	1.000	0.879	1.000
rs302456	0.52 ± 0.04 (981)	0.41 ± 0.06 (428)	0.16 ± 0.06 (44)	0.208	1.000	0.134	0.937	0.201	1.000
rs12975929	0.46 ± 0.04 (828)	0.5 ± 0.06 (530)	0.52 ± 0.13 (92)	0.883	1.000	0.812	1.000	0.623	1.000
	+/+	−/+	−/−	p^*	pcorr	p^*	pcorr	p^*	pcorr
Block1-ht1	0.39 ± 0.06 (302)	0.46 ± 0.04 (755)	0.59 ± 0.08 (397)	0.067	0.675	0.082	0.822	0.045	0.446
Block1-ht3	0.29 ± 0.14 (24)	0.51 ± 0.08 (346)	0.47 ± 0.04 (1084)	0.541	1.000	0.345	1.000	0.722	1.000
Block1-ht4	0.17 ± 0.17 (6)	0.37 ± 0.07 (182)	0.49 ± 0.04 (1266)	0.382	1.000	0.203	1.000	0.740	1.000
Block2-ht1	0.52 ± 0.06 (479)	0.48 ± 0.05 (717)	0.4 ± 0.07 (258)	0.334	1.000	0.337	1.000	0.168	1.000
Block2-ht4	0	0.38 ± 0.19 (48)	0.48 ± 0.03 (1406)	0.913	1.000	0.913	1.000	.	..

Adjusted for age, smoking amount, atopy, predicted FEV1% at the first visit, and total ICS and SCS doses in the first year.

CC, common allele homozygote; CR, heterozygote; pcorr, corrected p value for multiple comparisons; RR, minor allele homozygote; SCS, systemic corticosteroid; SE, standard error of mean.

Effect of smoking on the genetic association of NLRP4 with exacerbation

Because acute exacerbation is related to smoking (Dougherty and Fahy, 2009; Polosa and Thomson, 2013), we compared the association of the eight SNPs and haplotypes of NLRP4 with exacerbation between the current or ex-smoker (SM) and never-smoker (NS) groups. The association of rs16986718 with the annual number of exacerbation episodes was more robust in the SM group. The minor allele of rs16986718G > A showed susceptibility to developing exacerbation with a good gene–dose effect (1.12 ± 0.31 for minor allele homozygotes, 0.64 ± 0.11 for heterozygotes, and 0.39 ± 0.06 for common allele homozygotes) (p = 0.004, p _corr = 0.030, in the dominant model, Table 3). By contrast, this genetic effect of rs16986718 disappeared in the NS group (Supplementary Table S2).

Table 3.

Association of NLRP4 Single-Nucleotide Polymorphisms with the Number of Exacerbation Episodes Over the First 1 Year of Follow-Up in the Smoker Group of Study Subjects

Locus	No. of exacerbation episodes, mean ± SE (n)			Codominant		Dominant		Recessive
Locus	CC	CR	RR	p^*	pcorr	p^*	pcorr	p^*	pcorr
rs441827	0.43 ± 0.07 (207)	0.58 ± 0.1 (230)	0.92 ± 0.22 (62)	0.069	0.482	0.028	0.193	0.172	1.000
rs16986718	0.39 ± 0.06 (255)	0.64 ± 0.11 (199)	1.12 ± 0.31 (43)	0.005	0.037	0.004	0.030	0.020	0.140
rs192117499	0.57 ± 0.06 (480)	0.38 ± 0.26 (16)	, (0)	0.270	1.000	0.270	1.000
rs117636433	0.58 ± 0.07 (471)	0.32 ± 0.1 (28)	, (0)	0.760	1.000	0.760	1.000
rs379327	0.59 ± 0.07 (356)	0.49 ± 0.12 (132)	0.44 ± 0.34 (9)	0.793	1.000	0.496	1.000	0.863	1.000
rs12462372	0.46 ± 0.07 (284)	0.73 ± 0.12 (193)	0.43 ± 0.2 (21)	0.441	1.000	0.239	1.000	0.840	1.000
rs302456	0.63 ± 0.08 (342)	0.43 ± 0.11 (145)	0.18 ± 0.12 (11)	0.125	0.866	0.053	0.366	0.291	1.000
rs12975929	0.46 ± 0.07 (276)	0.65 ± 0.12 (193)	0.89 ± 0.34 (27)	0.275	1.000	0.268	1.000	0.157	1.000
	+/+	−/+	−/−	p^*	pcorr	p^*	pcorr	p^*	pcorr
Block1-ht1	0.48 ± 0.11 (104)	0.51 ± 0.08 (258)	0.72 ± 0.15 (137)	0.284	1.000	0.324	1.000	0.137	1.000
Block1-ht3	0.57 ± 0.43 (7)	0.49 ± 0.13 (120)	0.59 ± 0.07 (372)	0.201	1.000	0.125	1.000	0.583	1.000
Block1-ht4	0 ± 0 (2)	0.3 ± 0.08 (63)	0.6 ± 0.07 (434)	0.662	1.000	0.507	1.000	0.619	1.000
Block2-ht1	0.57 ± 0.11 (161)	0.54 ± 0.08 (259)	0.63 ± 0.2 (79)	0.396	1.000	0.229	1.000	0.314	1.000
Block2-ht4	0	1 ± 0.55 (15)	0.55 ± 0.06 (484)	0.111	1.000	0.111	1.000		.

Adjusted for age, smoking amount, atopy, predicted FEV1% at the first visit, and total ICS and SCS doses in the first year.

When the interaction term was applied to the association analysis, the interaction between the amount smoked and the rs16986718 genotypes was significant (F = 1.572, p = 0.014). There was also a positive correlation between the number of exacerbation episodes and amount smoked in asthmatics who were rs16986718 AA homozygotes (ρ = 0.326; p = 0.00034), but not in those possessing the G allele (ρ = 0.054; p = 0.141 in GG homozygotes and ρ = 0.066; p = 0.107 in GA heterozygotes, Fig. 2).

FIG. 2.

Correlation between smoking amount and the number of exacerbation episodes for 1 year in asthmatics according to the genotype of rs16986718. The correlation was evaluated using Spearman's Rho (ρ test).

Association of frequent exacerbators with SNP genotypes in the NLRP4 gene

The eight SNPs were analyzed for associations with the risk of frequent exacerbation using logistic regression analysis. The frequency of asthmatics who were rs16986718 G > A AA homozygotes and GA heterozygotes was significantly higher in the frequent exacerbator group compared with nonfrequent exacerbators (65.3% vs. 47.9%, p = 0.000067, OR = 2.56, Supplementary Table S3). The difference remained after correcting for multiple comparisons (p = 0.00047 in the dominant model).

The association of SNP rs16986718 G > A with the risk of frequent exacerbation became more apparent in the SM group: the frequency of minor allele homozygotes of rs16986718 G > A was three times higher in frequent exacerbators than in nonfrequent exacerbators (22% vs. 7.2%; OR = 3.52, p = 0.001, in the dominant model, Supplementary Table S4). However, this genetic effect of rs16986718 on the risk of frequent exacerbation disappeared in the NS group (Supplementary Table S5).

Comparison of the prevalence of frequent exacerbators among genotypes of rs16986718 G > A in the NLRP4 gene

The overall prevalence of frequent exacerbators was two times higher in those carrying the minor allele rs16986718G > A than in those carrying the common allele (12% in rs16986718AA, 11% in rs16986718GA, and 5.9% in rs16986718GG, OR = 1.84 [1.33–2.55], p = 0.000205, p _corr = 0.0014, Fig. 3). The genotype-dependent difference in the prevalence of frequent exacerbators became more robust in the SM group: the prevalence of frequent exacerbators was four times higher in those carrying minor allele homozygotes of rs16986718G > A than in those carrying common allele homozygotes (25.6% in rs16986718AA vs. 6.3% in rs16986718GG, OR = 2.44 [1.53–3.9], p = 0.00018, p _corr = 0.0013, Fig. 3). By contrast, the prevalence of frequent exacerbators did not differ between the rs16986718GA genotypes in the NS group.

FIG. 3.

Proportion of frequent exacerbators (≥2 in 1 year) among indicated genotypes of rs16986718 in total study subjects (n = 1454) (A) and nonsmokers (n = 955) or smokers (n = 499) (B). p values were obtained using χ² test for comparison of the proportion of frequent exacerbators between smokers and nonsmokers and using the univariate general linear model (type III) for between-genotype comparison of frequent exacerbators in the nonsmoker group and smoker group, controlling age, sex, serum total IgE level, predicted FEV1% at the first visit, and total ICS and systemic steroid dose in the first year of visit as confounding variables. *p _corr values represent corrected p-values for multiple comparisons using SNPSpD. ICS, inhaled corticosteroid.

When comparing SM and NS groups, the prevalence of frequent exacerbators was six times higher in the minor allele homozygotes of rs16986718GA in the SM group than in the NS group (25.6% vs. 4.1%, p = 0.00054, Fig. 3). By contrast, there was no difference in the prevalence of frequent exacerbators in the common allele homozygotes and heterozygotes of rs16986718GA between the SM and NS groups.

Discussion

To our knowledge, this is the first study to demonstrate that the minor allele of rs16986718 G > A in NLRP4 was strongly associated with frequent exacerbation in adult asthmatics and that smoking had a decisive effect on the occurrence of asthmatics with the susceptible SNP. The smoking effect seems to be dependent on the amount smoked by subjects carrying the minor allele of rs16986718: the amount smoked was related to the number of acute exacerbation episodes only in minor allele homozygotes of rs16986718 G > A. Furthermore, even in minor allele homozygotes of rs16986718GA, smoking robustly increased the prevalence of frequent exacerbators. Based on these results, we suggest that the genetic effect of the rs16986718 G > A minor allele on asthma exacerbation is exerted under the complete influence of smoking in adult asthmatics.

Although it is still not known how the genetic component and smoking exert their interactive effect on exacerbation of asthma, the smoking-induced attenuation of the defense mechanism against respiratory viral infections is a plausible explanation. Of all asthma exacerbation episodes, 50% in adult asthmatics and ∼80% in childhood asthmatics are induced by respiratory viral infections (Dougherty and Fahy, 2009; Kim et al., 2018). For episodes of exacerbation, rhinovirus is the virus most frequently detected in adults, followed by influenza A, parainfluenza, respiratory syncytial virus, and adenovirus (Seo et al., 2017).

One of the mechanisms underlying the susceptibility to viral infection in asthmatics is the change in epithelial function, including an increase in intracellular adhesion molecule-1, disruption of epithelial barrier function, impaired apoptosis, increased cell lysis, and deficient Th1 response involving IFNs (Wark and Gibson, 2006). The airway epithelial cell responses to type II IFN are critical for regulating the defense against these respiratory viral infections.

Interestingly, cigarette smoke extracts decrease the inhibitory effect of IFN-γ on proliferation of viruses such as respiratory syncytial virus (Modestou et al., 2010). Cigarette smoke extracts also suppress IFN-γ-dependent gene expression by airway epithelial cells (Modestou et al., 2010) and the antiviral effects of type I IFN through phosphorylation-dependent downregulation of the IFNAR1 subunit of type I IFN receptor (HuangFu et al., 2008). As the NLRP4 signalosome is tightly related through negative regulation of type I IFN signaling (Lin et al., 2016), marked attenuation of the type I IFN pathway is expected in smoking asthmatics with the A allele of rs16986718G > A, although this has not been validated because it was not feasible to obtain respiratory epithelium from the study subjects.

The effect of smoking on asthma exacerbation was observed only in about 25% of subjects carrying the minor allele of rs16986718 G > A. This indicates that other genetic and environmental factors contribute to inducing frequent exacerbators. A recent review found that acute exacerbation is associated with SNPs of several candidate genes, including IL13, IL4R, CHI3L1, and ORMDL3 (Park and Tantisira, 2017). Using GWAS, SNPs of CDHR3, CTNNA3, and SEMA3D have been identified as new risk genetic components.

More importantly, gene–environment interactions seem to be pivotal in asthma exacerbation. Rs512625 in ADAM33 located on chromosome 20p13 showed significant interactive effects with environmental tobacco smoke on severe exacerbation (Bukvic et al., 2013). In addition, exposure to house dust mites significantly modifies the relationship of SNPs (rs2486953, rs4950936, and rs1417149) of CHIT1 with severe exacerbation (Wu et al., 2010). Two genetic variants (rs2915863 in CD14 and rs17226566 in LY96) showed different relationships between endotoxin exposure and severe exacerbation (Kljaic-Bukvic et al., 2014). However, most of these studies examined childhood asthmatics, so interactive effects between genetic factors and indoor environment exposure in adult asthmatics, as in the present study, need to be investigated further.

Our previous cluster analysis study demonstrated two types of exacerbation-prone asthma in nonsmoking asthmatics: late-onset nonatopic asthma with impaired lung function and early-onset atopic asthma with severely impaired lung function (Kim et al., 2017). In our study, the prevalence of atopy was comparable between SM and NS groups. Therefore, atopy may not affect the interaction of smoking and rs16986718 G > A with the risk of frequent exacerbation.

This study has several limitations. First, the SNPs evaluated were located in exons. Especially because rs16986718 is a synonymous coding SNP (Thr185Thr), the allelic difference of this SNP is unlikely to affect the primary structure of NLRP4. However, silent SNP codons have been reported to affect gene function such as substrate specificity, which may be caused by changes in the timing of cotranslational folding when frequent codons are replaced by rare codons in a cluster of infrequently used codons (Kimchi-Sarfaty et al., 2007; Komar, 2007). Rs16986718 changes the 185th codon ACG to ACA in the NACHT domain of the NLRP4 protein, which may be important in regulating NLRP4 function. The original codon (ACG) is 2.38 times more frequent than the altered codon (ACA) in the human genome, and there are 10 human tRNA genes for the ACG codon, but only seven for the ACA codon (Lander et al., 2001). However, rs16986718 is not a site for transcription factor binding, splicing, splicing regulation, or microRNA molecular functions, according to a functional evaluation of SNPs in Asian populations (SNPinfo Web Server, https://snpinfo.niehs.nih.gov/), and it is not an expression quantitative trait locus for mRNA expression according to the ENCODE dataset (www.encodeproject.org). Therefore, a future study should evaluate the possibility that the allelic difference in rs16986718 affects NLRP4 function through impaired protein folding, followed by changes in substrate/ligand specificity. We also could not exclude the possibility that other SNPs in LD with rs16986718 could have biological functions related to smoking-induced asthma exacerbation. Therefore, to evaluate this possibility and the biological function of NLRP4 polymorphisms, further investigation of other SNPs in LD with rs16986718 (not included in this study) is necessary. Second, exacerbation of asthma is related to other environmental factors, including viral infection; occupational exposure; hormonal changes; drugs such as aspirin, nonsteroidal anti-inflammatory drugs, and beta-blockers; and air pollutants. Therefore, these factors should be considered when interpreting results of the gene–environment interaction. Third, we had no replication data using other cohort subjects. However, our sample size was relatively large. Other genetic studies of exacerbation in asthma contained fewer than 1000 subjects, which is a limiting factor when interpreting results (Denlinger et al., 2013; Dahlin et al., 2015). Although it is not clear what an adequate sample size is when studying gene–environment interactions in terms of frequent exacerbators, larger sample sizes should produce more reliable results. Fourth, several genetic studies of adult asthma exacerbation have examined the response to asthma medication (Venarske et al., 2006; Turner et al., 2016). Therefore, we considered the doses and kinds of asthma medications to be confounding factors.

Conclusions

The NLRP4 genetic polymorphism was significantly associated with frequent acute exacerbation through an interaction with smoking status in adult asthmatics. The minor (A) allele of rs16986718 may be a risk factor for the occurrence of acute exacerbation in smokers. Our observations suggest that NLRP4 plays a role in the smoking-induced pathogenesis of asthma exacerbation and that genetic polymorphism is a promising marker for predicting frequent asthma exacerbation while considering the smoking behavior of asthmatic individuals.

Footnotes

Acknowledgments

The study was supported by a research grant from Soonchunhyang University to H.S.C. and by a grant from the Korean Health technology Research and Development project, Ministry of Health and Welfare, Republic of Korea (2016-ER7402-00). Clinical data and DNA were provided by a biobank of Soonchunhyang University Bucheon Hospital.

Disclosure Statement

No competing financial interests exist.

References

Bauer

R.N.

, Diaz-Sanchez

, and Jaspers

(2012). Effects of air pollutants on innate immunity: the role of Toll-like receptors and nucleotide-binding oligomerization domain-like receptors. J Allergy Clin Immunol, 129, 14–24; quiz 5–6.

Bisgaard

, Bonnelykke

, Sleiman

P.M.

, Brasholt

, Chawes

, Kreiner-Moller

, et al. (2009). Chromosome 17q21 gene variants are associated with asthma and exacerbations but not atopy in early childhood. Am J Respir Crit Care Med, 179, 179–185.

Bloomberg

G.R.

(2011). The influence of environment, as represented by diet and air pollution, upon incidence and prevalence of wheezing illnesses in young children. Curr Opin Allergy Clin Immunol, 11, 144–149.

Bonnelykke

, Sleiman

, Nielsen

, Kreiner-Moller

, Mercader

J.M.

, Belgrave

, et al. (2014). A genome-wide association study identifies CDHR3 as a susceptibility locus for early childhood asthma with severe exacerbations. Nat Genet, 46, 51–55.

Bukvic

B.K.

, Blekic

, Simpson

, Marinho

, Curtin

J.A.

, Hankinson

, et al. (2013). Asthma severity, polymorphisms in 20p13 and their interaction with tobacco smoke exposure. Pediatr Allergy Immunol, 24, 10–18.

Busacker

, Newell

J.D.

Jr. , Keefe

, Hoffman

E.A.

, Granroth

J.C.

, Castro

, et al. (2009). A multivariate analysis of risk factors for the air-trapping asthmatic phenotype as measured by quantitative CT analysis. Chest, 135, 48–56.

Chanez

, Wenzel

S.E.

, Anderson

G.P.

, Anto

J.M.

, Bel

E.H.

, Boulet

L.P.

, et al. (2007). Severe asthma in adults: what are the important questions?. J Allergy Clin Immunol, 119, 1337–1348.

Cui

, Li

, Zhu

, Liu

, Songyang

, Wang

H.Y.

, et al. (2012). NLRP4 negatively regulates type I interferon signaling by targeting the kinase TBK1 for degradation via the ubiquitin ligase DTX4. Nat Immunol, 13, 387–395.

Cunningham

, Basu

, Tavendale

, Palmer

C.N.

, Smith

, and Mukhopadhyay

(2011). The CHI3L1 rs4950928 polymorphism is associated with asthma-related hospital admissions in children and young adults. Ann Allergy Asthma Immunol, 106, 381–386.

10.

Dahlin

, Denny

, Roden

D.M.

, Brilliant

M.H.

, Ingram

, Kitchner

T.E.

, et al. (2015). CMTR1 is associated with increased asthma exacerbations in patients taking inhaled corticosteroids. Immun Inflamm Dis, 3, 350–359.

11.

De Nardo

, De Nardo

C.M.

, and Latz

(2014). New insights into mechanisms controlling the NLRP3 inflammasome and its role in lung disease. Am J Pathol, 184, 42–54.

12.

Denlinger

L.C.

, Manthei

D.M.

, Seibold

M.A.

, Ahn

, Bleecker

, Boushey

H.A.

, et al. (2013). P2X7-regulated protection from exacerbations and loss of control is independent of asthma maintenance therapy. Am J Respir Crit Care Med, 187, 28–33.

13.

Dinarello

C.A.

(2009). Immunological and inflammatory functions of the interleukin-1 family. Annu Rev Immunol, 27, 519–550.

14.

Dougherty

R.H.

, and Fahy

J.V.

(2009). Acute exacerbations of asthma: epidemiology, biology and the exacerbation-prone phenotype. Clin Exp Allergy, 39, 193–202.

15.

Fiorentino

, Stehlik

, Oliveira

, Ariza

M.E.

, Godzik

, and Reed

J.C.

(2002). A novel PAAD-containing protein that modulates NF-kappa B induction by cytokines tumor necrosis factor-alpha and interleukin-1beta. J Biol Chem, 277, 35333–35340.

16.

HuangFu

W.C.

, Liu

, Harty

R.N.

, and Fuchs

S.Y.

(2008). Cigarette smoking products suppress anti-viral effects of Type I interferon via phosphorylation-dependent downregulation of its receptor. FEBS Lett, 582, 3206–3210.

17.

Hunninghake

G.M.

, Soto-Quirós

M.E.

, Avila

, Su

, Murphy

, Demeo

D.L.

, et al. (2007). Polymorphisms in IL13, total IgE, eosinophilia, and asthma exacerbations in childhood. J Allergy Clin Immunol, 120, 84–90.

18.

Jiang

, Liang

, Li

, and Noble

P.W.

(2006). The role of Toll-like receptors in non-infectious lung injury. Cell Res, 16, 693–701.

19.

Kanneganti

T.D.

(2010). Central roles of NLRs and inflammasomes in viral infection. Nat Rev Immunol, 10, 688–698.

20.

Kim

C.K.

, Callaway

, and Gern

J.E.

(2018). Viral infections and associated factors that promote acute exacerbations of asthma. Allergy Asthma Immunol Res, 10, 12–17.

21.

Kim

M.-A.

, Shin

S.-W.

, Park

J.-S.

, Uh

S.-T.

, Chang

H.S.

, Bae

D.-J.

, et al. (2017). Clinical characteristics of exacerbation-prone adult asthmatics identified by cluster analysis. Allergy Asthma Immunol Res 9, e35.

22.

Kimchi-Sarfaty

, Oh

J.M.

, Kim

I.W.

, Sauna

Z.E.

, Calcagno

A.M.

, Ambudkar

S.V.

, et al. (2007). A “silent”. polymorphism in the MDR1 gene changes substrate specificity. Science, 315, 525–528.

23.

Kljaic-Bukvic

, Blekic

, Aberle

, Curtin

J.A.

, Hankinson

, Semic-Jusufagic

, et al. (2014). Genetic variants in endotoxin signalling pathway, domestic endotoxin exposure and asthma exacerbations. Pediatr Allergy Immunol, 25, 552–557.

24.

Komar

A.A.

(2007). Silent SNPs: impact on gene function and phenotype. Pharmacogenomics, 8, 1075–1080.

25.

Lander

E.S.

, Linton

L.M.

, Birren

, Nusbaum

, Zody

M.C.

, Baldwin

, et al. (2001). Initial sequencing and analysis of the human genome. Nature, 409, 860–921.

26.

Lin

, Zhao

, Yang

, Meng

, Tan

, Xie

, et al. (2016). USP38 inhibits type I interferon signaling by editing TBK1 ubiquitination through NLRP4 signalosome. Mol Cell, 64, 267–281.

27.

McGeachie

M.J.

, Wu

A.C.

, Tse

S.M.

, Clemmer

G.L.

, Sordillo

, Himes

B.E.

, et al. (2015). CTNNA3 and SEMA3D: promising loci for asthma exacerbation identified through multiple genome-wide association studies. J Allergy Clin Immunol, 136, 1503–1510.

28.

Modestou

M.A.

, Manzel

L.J.

, El-Mahdy

, and Look

D.C.

(2010). Inhibition of IFN-gamma-dependent antiviral airway epithelial defense by cigarette smoke. Respir Res 11, 64.

29.

Moore

W.C.

, Bleecker

E.R.

, Curran-Everett

, Erzurum

S.C.

, Ameredes

B.T.

, Bacharier

, et al. (2007). Characterization of the severe asthma phenotype by the National Heart, Lung, and Blood Institute's Severe Asthma Research Program. J Allergy Clin Immunol, 119, 405–413.

30.

Nyholt

D.R.

(2004). A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet, 74, 765–769.

31.

Park

H.W.

, and Tantisira

K.G.

(2017). Genetic signatures of asthma exacerbation. Allergy Asthma Immunol Res, 9, 191–199.

32.

Polosa

, and Thomson

N.C.

(2013). Smoking and asthma: dangerous liaisons. Eur Respir J, 41, 716–726.

33.

Reddel

H.K.

, Taylor

D.R.

, Bateman

E.D.

, Boulet

L.P.

, Boushey

H.A.

, Busse

W.W.

, et al. (2009). An official American Thoracic Society/European Respiratory Society statement: asthma control and exacerbations: standardizing endpoints for clinical asthma trials and clinical practice. Am J Respir Crit Care Med, 180, 59–99.

34.

Saxon

, and Diaz-Sanchez

(2005). Air pollution and allergy: you are what you breathe. Nat Immunol, 6, 223–226.

35.

Schroder

, and Tschopp

(2010). The inflammasomes. Cell, 140, 821–832.

36.

Seo

K.H.

, Bae

D.J.

, Kim

J.N.

, Lee

H.S.

, Kim

Y.H.

, Park

J.S.

, et al. (2017). Prevalence of respiratory viral infections in Korean adult asthmatics with acute exacerbations: comparison with those with stable state. Allergy Asthma Immunol Res, 9, 491–498.

37.

Stephens

J.C.

, Schneider

J.A.

, Tanguay

D.A.

, Choi

, Acharya

, Stanley

S.E.

, et al. (2001). Haplotype variation and linkage disequilibrium in 313 human genes. Science, 293, 489–493.

38.

Turner

, Francis

, Vijverberg

, Pino-Yanes

, Maitland-van der Zee

A.H.

, Basu

, et al. (2016). Childhood asthma exacerbations and the Arg16 beta2-receptor polymorphism: a meta-analysis stratified by treatment. J Allergy Clin Immunol, 138, 107–113. e5.

39.

Venarske

D.L.

, Busse

W.W.

, Griffin

M.R.

, Gebretsadik

, Shintani

A.K.

, Minton

P.A.

, et al. (2006). The relationship of rhinovirus-associated asthma hospitalizations with inhaled corticosteroids and smoking. J Infect Dis, 193, 1536–1543.

40.

Wark

P.A.

, and Gibson

P.G.

(2006). Asthma exacerbations. 3: pathogenesis. Thorax, 61, 909–915.

41.

Wenzel

(2005). Severe asthma in adults. Am J Respir Crit Care Med, 172, 149–160.

42.

Wenzel

S.E.

, Balzar

, Ampleford

, Hawkins

G.A.

, Busse

W.W.

, Calhoun

W.J.

, et al. (2007). IL4R alpha mutations are associated with asthma exacerbations and mast cell/IgE expression. Am J Respir Crit Care Med, 175, 570–576.

43.

A.C.

, Lasky-Su

, Rogers

C.A.

, Klanderman

B.J.

, and Litonjua

A.A.

(2010). Fungal exposure modulates the effect of polymorphisms of chitinases on emergency department visits and hospitalizations. Am J Respir Crit Care Med, 182, 884–889.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB