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Novel genes in Human Asthma Based on a Mouse Model of Allergic Airway Inflammation and Human Investigations

Purpose Based on a previous gene expression study in a mouse model of asthma, we selected 60 candidate genes and investigated their possible roles in human asthma. Methods In these candidate genes, 90 SNPs were genotyped using MassARRAY technology from 311 asthmatic children and 360 healthy controls of the Hungarian (Caucasian) population. Moreover, gene expression levels were measured by RT PCR in the induced sputum of 13 asthmatics and 10 control individuals. t-tests, chi-square tests, and logistic regression were carried out in order to assess associations of SNP frequency and expression level with asthma. Permutation tests were performed to account for multiple hypothesis testing. Results The frequency of 4 SNPs in 2 genes differed significantly between asthmatic and control subjects: SNPs rs2240572, rs2240571, rs3735222 in gene SCIN, and rs32588 in gene PPARGC1B. Carriers of the minor alleles had reduced risk of asthma with an odds ratio of 0.64 (0.51-0.80; P=7×10-5) in SCIN and 0.56 (0.42-0.76; P=1.2×10-4) in PPARGC1B. The expression levels of SCIN, PPARGC1B and ITLN1 genes were significantly lower in the sputum of asthmatics. Conclusions Three potentially novel asthma-associated genes were identified based on mouse experiments and human studies.


INTRODUCTION Subjects for the sputum expression
Thirty-four adults were enrolled in the study to measure their gene expression levels of the selected genes in induced sputum, but 11 of them were excluded from the analysis because of the quality of the sputum samples (the study population was comprised of 13 asthmatic patients and 10 controls). Their detailed characteristics were previously described by Ungvári et al. 12 The asthmatic patients had mild to moderate stable asthma, with neither other lung diseases nor lower respiratory tract infection. Patients were required to have FEV1 of greater than 70% of predicted, baseline methacholine PC20 (the provocative concentration of methacholine causing a 20% fall in FEV1) of less than 16 mg/mL. Healthy volunteers were recruited from the staff and students of the participating Hungarian universities. They gave no history of respiratory diseases, and had a FEV1/FVC>80% and normal methacholine airway responsiveness (PC20>16 mg/mL). The included groups did not differ statistically in age, sex, smoking status, and allergy.
The study was conducted according to the principles expressed in the Declaration of Helsinki and approved by the Ethics Committee of the Hungarian Medical Research Council (ETT TUKEB) (Ethics Committee of the Hungarian Medical Research Council [http://www.ett.hu/tukeb/tukeb.htm]). Written informed consents were obtained from all patients, or the parents or guardians of the minors involved in the study.

Candidate gene and SNP selection, and genotyping
The current study is based on our former gene expression microarray analysis of the ovalbumin-induced mouse model of asthma. The former experiment used female 6-8 weeks old, female, pathogen-free, BALB/c mice kept on an ovalbumin-free diet. Groups of mice were sensitized (on days 0 and 14) and challenged with ovalbumin (on days 28, 29, and 30), and 1 group was sensitized and challenged with placebos. On day 31, 24 hours after the third (last) allergen challenge, mice were anesthetized, bronchoalveolar lavage fluid was isolated, and the lungs were removed for further analysis. RNA was isolated from the lung tissues and gene expression patterns were measured with Agilent Whole Mouse Genome Oligo Microarray 4×44 K chips (record number GSE11911 in the GEO database). Microarray data was analyzed with unpaired t-test and one-way analysis of variance (ANOVA). Over thousand transcripts showed> 2.0-fold statistically significant differential expression in the 3 groups relative to the control group. See Tölgyesi et al. 10 for further details.
The gene expression results of the mouse model were used to identify genes whose orthologues may be candidates for human asthma or atopy. A gene was selected for further consideration if its expression level in the lung changed significantly during the asthmatic process (corrected P value under 0.02, see the paper  13 In the final SNP sorting, we preferred the promoter, missense, and UTR region polymorphisms. To reach the best coverage of a studied gene or genomic region, linkage disequilibrium (LD) data was involved in the selection process (International HapMap Project, 14 Haploview software 15 ).
Genomic DNA was isolated from whole blood samples using iPrep PureLink gDNA Blood Kit, iPrep Purification Instrument (Invitrogen). A total of 90 SNPs were genotyped using the Sequenom iPLEX Gold MassARRAY technology at McGill University and Génome Québec Innovation Centre, Montréal, Canada.

Sputum induction, RNA isolation, and gene-expression measurement
Sputum induction was carried out as previously described by Ungvári et al. 12,16 RNA was isolated with Qiagen Mini Rneasy Kit (Qiagen, MD USA) and transcribed to cDNA with High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Real-time quantitative PCR was performed for the selected genes using the ABI 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The relative mRNA levels in each sample were normalized to its β-actin content, which proved to be a stable control reference.

Statistical analysis and bioinformatics
Hardy-Weinberg equilibrium (HWE) was tested for the control population using the chi-square goodness-of-fit test (results in Supplementary Material 2) implemented in the online DeFinetti HWE application (Helmholtz Zentrum München, Institut für Humangenetik http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). The significance level was set at 0.01. The same software was used to perform Pearson's chi-squared tests of association to asthma and calculate odds ratios (using single marker allele, recessive and dominant models). Confidence intervals (CI) were calculated at the 95 percent level. For each SNP, missing genotypes were imputed by sampling from the univariate distribution. The Haploview application 15 was used to perform single marker and haplotype association tests. To account for multiple hypothesis testing, we performed permutation tests in Haploview with both single marker and haplotype associations with a minimum of 1,000 permutations. The single marker associations and the dominant-recessive models were also verified with IBM SPSS Statistics V20 (Pearson's chi-squared test); to account for multiple hypothesis testing in this case, we applied the more stringent Bonferroni correction.
In all multivariate logistic regression analyses (IBM SPSS Sta-tistics V20), age was included as a covariate to apply sufficient statistical adjustment for the mean age difference between cases and controls (-11 years). Additional statistical verifications were also carried out (IBM SPSS Statistics V20) to avoid false positive associations resulting from population stratification. We used binary logistic regression models with and without age as a covariate and assessed how the polymorphisms' P values and odds ratios differ. If the P values and odds ratios differed considerably, that would be a clear sign of a confounded relation between asthma and the polymorphism. However, the assessed P values and odds ratios were nearly identical in all of the models; the preventive or risk status of the predictor remained the same (the largest odds ratio differences were in the range of 20%-30%), thus no evidence for a population stratification was found.
For the analysis of the RT PCR measurements, we used the delta-delta-CT algorithm to normalize CT values for housekeeping genes and for the control samples. Samples with RNA Integrity Number of poor quality or extreme CT values were excluded from the study. We used Student's t-test on the normalized fold change values to assess statistical significance.

SNP association studies
The basic characteristics of the studied SNPs (containing gene, rs id, functional classification and alleles) are presented in Supplementary Material 1, whereas genotype frequencies in controls and cases, odds ratios, and results for statistical tests of HWE and of population association are available in Supplementary Material 2. The total percentage of missing genotype data was 7.1%. Patients with over 80% missing genotype data were excluded (n=9, ~1%), and polymorphisms with over 20% missing ratio were also excluded (rs2432561, rs3814896, rs1821142, rs17027410). Out of the remaining 86 polymorphisms 2 showed significant deviation from the HWE in the control population, thus they were also excluded (rs11016071, rs2857600).
Between asthma cases and controls, the genotype distributions of 4 SNPs of 2 genes differed significantly in all of the applied statistical tests: SCIN (rs2240572, rs2240571, rs3735222) and PPARGC1B (rs32588). The polymorphisms of 6 other genes (ITLN1, FABP3, MAT1A, OSGIN, LY9, LGMN) showed statistically borderline differences (they were significant with most of the statistical tests but failed multiple testing correction with permutation testing: 0.05<P value <0.5). However, based on its statistical significance in the genotyping study, our results in mice, and its possible role in the asthmatic process, we included one of these genes in our further investigations: ITLN1 (rs4656958). We examined if the P values of the dominant and recessive models show a difference compared to the single marker allele associations tests, but none of the uncorrected P value differences were notable, thus these models were not ex-Allergy Asthma Immunol Res. 2014 November;6(6):496-503. http://dx.doi.org/10.4168/aair.2014.6.6.496 amined further ( Table 2). The single marker results and the dominant-recessive model ones were also verified by IBM SPSS Statistics V20 with Pearson's chi-squared test combined with Bonferroni correction (the corrected significance threshold for 90 polymorphisms was 0,00055). The results are in a complete agreement with our earlier findings, in which the same poly-morphisms proved to be significantly associated with asthma.
To see if the polymorphisms have greater combined effects compared to what was seen in single SNP analyses, we tested for their association to asthma at the level of haplotype blocks. This analysis confirmed the present single SNP findings, but the haplotype-level P values did not differ significantly from the corresponding single marker results: the association of the SCIN haplotypes was very significant, and the weak association of ITLN1 was stronger compared to the single marker results, but the latter was still not significant after correction for multi-   (Table 3). We calculated LD coefficients between the different SNPs ( Table 4) and found that, in both genes, the significantly associated SNPs were in strong linkage with each other. This suggests that, in each gene, there is only 1 causal variant, which mediates the association of all nearby SNPs to asthma. In PPARGC1B, only 1 polymorphism was studied; therefore, no haplotype was investigated. We analyzed the multivariate relevance of the polymorphisms investigated with logistic regression using age and gender as covariates. All the 3 previously selected SNPs (SCIN: rs2240572, PPARGC1B: rs32588, ITLN1: rs4656958) remained significantly related to asthma with protective effects. Similar to our previous results, a strong haplotype level association was not observed (1 SNP from each gene got into the model) (Supplementary Material 3). We also examined the effects of these polymorphisms within each gender group using logistic regression. The effect of SCIN: rs2240572 (P =0.002, OR =0.41) and PPARGC1B: rs32588 (P=0.013, OR=0.48) was highly significant in women, but that of ITLN1: rs4656958 was not significant in this group. In men, ITLN1: rs4656958 (P=0.005, OR=0.59) was highly significant, but not the other 2 polymorphisms. Logistic regression also revealed that ITLN1: rs4656958 had a significant (P =0.007, OR=0.665) protective effect in the severe atopic asthmatic subgroup (Supplementary Material 3).
Taken together, these results indicate association to asthma of 3 genes in our study: SCIN, PPARGC1B, and ITLN1.

Gene expression in induced sputum
Next, we investigated whether gene expression levels in induced sputum samples corroborate the association of SCIN, PPARGC1B, and ITLN1. Indeed, the expression level of all 3 genes was significantly lower in the sputum sample of asthmatics than of controls. We compared our results to the third measurement (24 hours after the third/last allergen challenge) of the original asthmatic mouse model study. 10 The results were somewhat unexpected: the expression of all 3 genes changed in the opposite direction, i.e., the expression levels were significantly higher in the lung of mice with ovalbumin-induced airway inflammation (Figure).

Fusion of the results
We can apply Fisher's method to combine the P values of the statistically independent studies, such as the genotyping study and the gene expression studies in our case. This method quantitatively combines the strength of the evidence from both experiments in order to rank the results (Table 5). According to these, the SCIN gene had the lowest combined P value, indicating the strongest evidence for true association. The PPARGC1B also had a relatively low combined P value. It must be noted, however, that an insignificant combined P value does not mean the absence of association in this respect.

DISCUSSION
In the present study, we selected 60 genes from our previous experiments on a mouse model of asthma based on significantly different gene expressions in the lungs of mice, and according to different considerations mentioned previously in the text. We carried out a genetic association study on our human biobank and detected several differences in genotype distributions between asthmatics and controls. Among these, the distribution of 4 SNPs in 2 genes (SCIN and PPARGC1B) proved to be statistically different with all of the applied statistical tests. SNPs in 6 other genes also showed nominally significant differences. We compared the expression levels of 3 genes in induced sputum samples from asthmatics and controls. The expression levels of SCIN, PPARGC1B, and ITLN1 were significantly lower in the sputum of asthmatics. These latter results are unexpected, since in our previous mouse model experiments, all the differences in the gene expression levels were in the opposite directions. The original mouse study had 3 temporal measurement points and the expression levels for all three of the investigated genes were trend-like and did not oscillate; thus short-term temporal effects probably do not account for the result. An important question is how these seemingly contradictory results can be reconciled? We compared publicly available data at the NCBI GEO from asthmatic microarray studies to our results and calculated the expression fold change statistics. Tsitsiou et al. 17 examined cir-  Figure. Gene expression level changes between cases and controls in human induced sputum samples and ovalbumin-induced mouse model: all the 3 investigated genes (SCIN, PPARGC1B, ITLN1) had significant expression level change, but their expressions were higher in the mouse model (marked with darker columns) and lower in human asthmatics (marked with brighter columns) than in the controls. For statistical significance of the sputum samples, please refer to Table 5.
Allergy Asthma Immunol Res. 2014 November;6(6):496-503. http://dx.doi.org/10.4168/aair.2014.6.6.496 culating T cells, and their microarray measurements (GSE31773) covered SCIN, PPARGC1B, but not ITLN1. Their results are in line with our findings in human induced sputum in case of SCIN, but they did not have significant results for PPARGC1B. Another public asthmatic microarray study available at NCBI GEO GSE473 examined also T cells isolated form peripheral blood. This study also has measurements for SCIN and PPARGC1B, but not for ITLN1. They studied severe and mild asthmatic patients with or without atopy. The study confirmed our results for PPARGC1B and also had a statistically borderline confirming result for SCIN.
Both studies strengthen our results showing unexpected direction of the expression change for the investigated genes, and it is also worth mentioning that even the statistically insignificant results were in line with our findings in humans. We reviewed the literature for other examples of 'discordant' humanmouse expression pairs, and the phenomenon is well known. Tsaparas et al. systematically reviewed gene co-expression networks in human and mouse orthologs, and besides conserved global topological properties they found substantial divergence. 18 Kuhn et al. reviewed human-mouse orthologous gene expression and found that the ratio of concordant-discordant expressions of their significant results were 5:1. 19 Several separate studies also studied the phenomenon and reported similar findings. [20][21][22][23][24][25] There are several possible explanations for this phenomenon in our study. First, in the mouse model we measured the gene expression levels in whole lungs, while in humans in induced sputum. Although both of them are influenced by airway inflammation, their cell compositions differ considerably, thus the measured average gene expression levels can also be significantly different. Furthermore, it is known that mouse allergeninduced airway inflammation is an acute process. In these experiments the dynamic gene expression changes during this process are measured. In contrast, the human asthma is a chronic disease; the individuals participating in the present study had stable, chronic asthma but no acute symptoms during the protocol. It can be hypothesized that genes that over-or underexpressed during acute inflammatory processes are overcompensated in the chronic phase. Naturally, it is also possible that different mechanisms are involved in the 2 species, although this would not easily explain the opposite direction in 3 genes. Nevertheless, differential expression of a gene in a process provides clear evidence for involvement in that process.
However, it must also be added that our induced sputum biobank is relatively small for drawing definitive conclusions, and the asthma patients represent a relatively heterogenous population. In addition, due to the limited number of available study subjects we could not create subpopulations in this study.
Our study is the first to investigate the association of SCIN with asthma in the human population. The minor alleles of SCIN SNPs rs2240572 (H61R) on exon 1 and rs3735222 on pro-moter region had protective effects against asthma, and these associations proved to be more prominent when studying the female cohort separately. Additionally, we found reduced SCIN mRNA level in the sputum samples of asthma patients compared to those of healthy controls.
Functionally, the protein scinderin (also known as adseverin, encoded by the SCIN gene) is an actin filament-severing andcapping enzyme which is able to rearrange the apical actin cap in airway goblet cells. 26,27 Although the SCIN overexpression followed by allergen challenge is well known from the literature of murine asthma models, 10,11 up to the present, there are no human studies reported in which altered expression of scinderin was observed in the airways of asthma patients compared to controls. Among the associated SNPs in the gene, rs2240571 is located approximately 400 bp upstream from the SCIN gene on chromosome 7 and has previously been found associated with sclerosis multiplex. 28 Until now, there have been no functional studies investigating the effect of SCIN polymorphisms on gene regulation or function, Future research is needed on this topic.
Peroxisome proliferator-activated receptor gamma coactivator 1-beta (PPARGC1B) is a nuclear hormone receptor which is involved in cell activation, differentiation, proliferation, and apoptosis. Upregulation of PPARgamma expression has been observed in asthmatic airways, and a growing body of evidence suggests that PPARgamma contributes to airway inflammation and airway hyperresponsiveness in asthma. 29 Furthermore, studies carried out with PPARgamma agonists pose the possibility to consider PPARgamma as a novel therapeutic target in asthma. 30 The activity of PPAR is enhanced by the interaction with the PPARGC1B; therefore, genetic variations in the PPAR-GC1B gene may contribute to the functional changes in this transcription factor, which may lead to airway inflammation and remodeling in asthma.
Recently, Lee et al. 31 have reported a study which found no genetic associations between SNPs in the PPARGC1B gene with the risk of asthma. However, by gene and protein expression measurements they showed that the -427C>T polymorphism on the promoter and+102525G>A (R265Q, rs45520937) on exon 5 of the PPARGC1B gene may affect the development of airway hyperresponsiveness through the modulation of PPARGC1B gene products. Our study is the first to report that the minor allele of rs32588 (L42L) on exon 2 is associated with asthma and has a protective effect, but the exact mechanism by which this coding SNP exerts its effect is still unclear. Besides, considering that this polymorphism is in LD with many other SNPs, we cannot exclude the possibility that other functional polymorphisms are responsible for the observed strong association.
Intelectin-1 (ITLN1) may recognize carbohydrate chains of pathogens and bacterial components and thus play a role in the defense system against microorganisms. It has an anti-inflammatory role by preventing the TNF-α-induced COX-2 expres-Allergy Asthma Immunol Res. 2014 November;6(6):496-503. http://dx.doi.org/10.4168/aair.2014.6.6.496 sion. As both microbial infection and TNF-α can have an important role in asthma, [32][33][34][35] the ITLN1 gene might have a role in the asthmatic process. Although genetic variations in the gene showed associations only in certain tests, the expression levels of the gene differed both in animal lung and human samples.
Theoretically, a possible bias can originate from the slight, but statistically significant age difference between cases and controls. Nevertheless on one hand all of our multivariate analyses were carried out with age as a covariate to apply sufficient statistical adjustment for the mean age difference; on the other hand, both the cases and controls had the same ethnicity and were recruited from the same location, so it is very unlikely that the distributions of the investigated polymorphisms would be influenced by the 11-year age difference which was also confirmed by our analysis.
In summary, based on a gene expression study in a mouse model of asthma we selected 60 candidate genes and investigated their involvement in human asthma. We detected polymorphisms in 2 genes (SCIN and PPARGC1B) which significantly influenced the risk of asthma. The expression level of 3 genes (SCIN, PPARGC1B, and ITLN1) was significantly lower in induced sputum samples of the asthmatics. According to these results, these genes may play a role in human asthma. Future research on the function of these genes may help identify new candidates for therapeutic targeting and promote our understanding of the pathogenesis of asthma.