Chronic mucus hypersecretion (CMH) is associated with an increased frequency of respiratory infections, excess lung function decline, and increased hospitalisation and mortality rates in the general population. It is associated with smoking, but it is unknown why only a minority of smokers develops CMH. A plausible explanation for this phenomenon is a predisposing genetic constitution. Therefore, we performed a genome wide association (GWA) study of CMH in Caucasian populations.
GWA analysis was performed in the NELSON-study using the Illumina 610 array, followed by replication and meta-analysis in 11 additional cohorts. In total 2,704 subjects with, and 7,624 subjects without CMH were included, all current or former heavy smokers (≥20 pack-years). Additional studies were performed to test the functional relevance of the most significant single nucleotide polymorphism (SNP).
A strong association with CMH, consistent across all cohorts, was observed with rs6577641 (p = 4.25×10−6, OR = 1.17), located in intron 9 of the special AT-rich sequence-binding protein 1 locus (SATB1) on chromosome 3. The risk allele (G) was associated with higher mRNA expression of SATB1 (4.3×10−9) in lung tissue. Presence of CMH was associated with increased SATB1 mRNA expression in bronchial biopsies from COPD patients. SATB1 expression was induced during differentiation of primary human bronchial epithelial cells in culture.
Our findings, that SNP rs6577641 is associated with CMH in multiple cohorts and is a cis-eQTL for SATB1, together with our additional observation that SATB1 expression increases during epithelial differentiation provide suggestive evidence that SATB1 is a gene that affects CMH.
Citation: Dijkstra AE, Smolonska J, van den Berge M, Wijmenga C, Zanen P, et al. (2014) Susceptibility to Chronic Mucus Hypersecretion, a Genome Wide Association Study. PLoS ONE 9(4): e91621. doi:10.1371/journal.pone.0091621
Editor: Dominik Hartl, University of Tübingen, Germany
Received: July 24, 2013; Accepted: November 21, 2013; Published: April 8, 2014
Copyright: © 2014 Dijkstra et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The COPACETIC study was funded by EU FP7 grant 201379. The NELSON study was supported by ‘Zorg Onderzoek Nederland-Medische Wetenschappen (ZONMW),’ KWF Kankerbestrijiding,’ and ‘Stichting Centraal Fonds Reserves van Voormalig Vrijwillige Ziekenfondsverzekeringen’ (RvvZ). The GLUCOLD study was supported by the Netherlands Organization for Scientific Research, Netherlands Asthma Foundation, University of Groningen, Leiden University Medical Center, and GlaxoSmithKline. The LifeLines cohort study was sponsored by the Dutch ministry of Health, Welfare and Sport, the ministry of Economic Affairs, Agriculture and Innovation, the province of Groningen, the European Union (regional development fund), the Northern Netherlands Provinces (SNN), the Netherlands Organisation for Scientific Research (NWO), University Medical Center Groningen (UMCG), University of Groningen, de Nierstichting (the Dutch Kidney Foundation), and the Diabetes Fonds (the Diabetic Foundation). The ECLIPSE study was funded by GSK and NIH grants RO I HL089897 and by a grant from GlaxoSmithKline. The COPDGene study was funded by NIH grants RO I HL089856 and by the COPD foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, Novartis, Pfizer, and Sunovion. Data sampling for the Norway study was funded by GlaxoSmithKline. The Dutch Lung Foundation supported the cell culture work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Firms occurring more frequently are coded as follows: A = AstraZeneca; B = Chiesi; C = Merck; D = Boehringer Ingelheim; E = GlaxoSmithKline; F = Nycomed; G = TEVA Pharma; H = Roche; I = Siemens; J = Pfizer. M. Dahlback and K. Tosh are employees of A. The University of Groningen: money for D. S. Postma regarding an unrestricted educational grant for research from A and B, an unrestricted grant for W. Timens from C, a grant from the Dutch Asthma Foundation for W. Timens, and grants for G. H. Koppelman and D. S. Postma from the BBMRI-NL, The Dutch Asthma Foundation and the Stichting Astma Bestrijding, and fees for consultancies of D. S. Postma by A, D, B, E, F, and G, and payment for lectures from A, B, and G and for consultancies of H. J. M. Groen by Eli Lilly, J and H. The University of Leiden: money for P. S. Hiemstra regarding an unrestricted educational grant for research from D, Galapagos. The University of Amsterdam: a public-private grant for P. S. Sterk by Innovative Medicine Initiative by European Union and European Federation of Pharmaceutical Industries and Associations. Boston University Medical Center holds patents. A. Spira: money for board membership, consultancy, and stock options from Allegro Diagnostics, Inc. Money for consultancy and for lectures was paid to J. Vestbo by E, F, B, Syntaxin, Bioxydyn, Novartis, A, and D. University Hospital of Heidelberg: money for expert testimony for H.U.Kauczor. H. U. Kauczor: money for board membership from I, for lectures from Bracco, I, D, and Bayer, and for development of educational presentations from I. The Jagiellonian University Medical College of Krakow: a Grant (Z/7PR/00010,201 379) for E. Nizankowska-Mogilnicka. E. Silverman: a grant for Harvard University from E. E. Silverman: consulting fee and support for travel to meetings for the study or other purposes (COPDGene and ECLIPSE) from E and the COPD foundation and money for consultancy and lectures from E, A and C. M. Cho: NIH/NHLBI grants (K12HL089990 and K08 HL097029) for Harvard University and money for consultancy from C. Erasmus Medical Center Rotterdam: a grant for H. de Koning from ZonMw, KWF, Kankerbestrijding, Stichting Centraal Fonds Reseves van Voormalig Vrijwillige Ziekenfondsverzekeringen; H provided a grant for the performance of proteomics research, and I provided 4-digital workstations for LungCare for the performance of 3Dmeasurements. Johns Hopkins Bloomberg School of Public Health in Baltimore: a NIH grant for J. Crapo. The National Jewish Health Center in Denver: a grant for T. Beaty from the National Institutes of Health and National Institute Heart, Lung & Blood Disease. T. Beaty: money for travel support by the COPD foundation. University of Cambridge: a grant for D. Lomas from E. D. Lomas: money for board membership, consultancy, lectures, and travel accommodations from E, Talecris/Grifols, and D. P. Bakke: money for lectures from E, A, and J. L. Lahousse: a travel award for the ATS (2012) and a short-term Research Fellowship for transport to Rotterdam. Part of the GLUCOLD study was supported by GlaxoSmithKline, the ECLIPSE study was partly funded by GSK, and the COPDGene study was partly funded by the COPD foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, Novartis, Pfizer, and Sunovion. Data sampling for the Norway study was funded by GlaxoSmithKline. There are no further patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors. Patent nr. subjectdate 1US 2013/0023437Diagnostic for lung disorders using class prediction 2012-06-15 2US 2012/0322673Isolation of nucleic acid from mouth epithelial cells 2012-03-23 3US 2012/0190567Diagnostic and prognostic methods for lung disorders using gene expression profiles from nose epithelial cells 2011-12-12 4US 2011/0217717Diagnostic and prognostic methods for lung disorders using gene expression profiles from nose epithelial cells2010-11-05 5US 2011/0190150Isolation of nucleic acid from mouth epithelial cells 2010-09-17 6US 2012/0041686Diagnostic for lung disorders using class prediction 2010-08-26 7US 2010/0055689Multifactorial methods for detecting lung disorders2009-03-30 8US 2009/0311692Isolation of nucleic acid from mouth epithelial cells 2009-01-09 9US 2009/0186951Identification of novel pathways for drug development for lung disease 2008-09-19 10US 2009/0061454Diagnostic and prognostic methods for lung disorders using gene expression profiles from nose epithelial cells 2007-03-08 11 US 2010/0035244Diagnostic for lung disorders using class prediction2006-04-14 12US 2006/0154278Detection methods for disorders of the lung2005-12-06 13US 2007/0148650Isolation of nucleic acid from mouth epithelial cells2004-11-12.
The secretion of mucus is a natural part of the airway defense against inhaled noxious particles and substances. Chronic mucus hypersecretion (CMH) is a condition of overproduction of mucus and defined as the presence of sputum production during at least three months in two consecutive years without any explaining origin whereas airway obstruction is not a prerequisite . Smoking is a risk factor for CMH, i.e. the prevalence of CMH in the general population is reported to be 7.4% in current smokers, 3.7% in ex-smokers and 2.4% in never smokers . CMH is the key presenting symptom in chronic bronchitis, one of the three main sub-groups of chronic obstructive pulmonary disease (COPD), a complex disease characterized by the presence of incompletely reversible and generally progressive airflow limitation . Moreover, CMH is a risk factor for the development of COPD , .
Worldwide, COPD affected 65 million people in 2004 and more than 3 million people died of COPD in 2005, representing 5% of all deaths. It is predicted that COPD will be the third leading cause of death worldwide in 2030 . COPD markedly reduces quality of life and is responsible for high healthcare costs. For instance, the combined (direct and indirect) yearly costs of COPD and asthma in the United States of America were projected at $68 billion in 2008 . CMH is not only associated with COPD but also with an increased duration and frequency of respiratory infections, excess decline in forced expiratory volume in 1 second (FEV1) and increased hospitalization and mortality rates in the general population , , , .
It is not known why only a minority of all smokers develops CMH, yet a plausible explanation is the presence of a genetic predisposition for CMH, as evidenced by familial aggregation of mucus overproduction and higher prevalence of CMH in monozygotic than in dizygotic twins –. Little is known about the identity of the genes that predispose to CMH. One publication suggested that CTLA4 is associated with chronic bronchitis in COPD .
The aim of our study was to identify genetic factors for CMH, thereby obtaining a better insight into the origins of this disorder.
Materials and Methods
The Dutch ministry of health and the Medical Ethics Committee of the hospital approved the study protocol for all Dutch centers. Ethics approval and written informed consent was obtained from all participants in all studies participating. For detailed information, see Supplement S1.
Subjects and genotyping
We performed GWA studies in participants of the NELSON-study (n = 3,729), a male population-based lung cancer screening study investigating heavy smokers (≥20 pack-years) .
Replication of SNPs with p≤10−4 was attempted in six cohorts participating in ‘COPD Pathology: Addressing Critical gaps, Early Treatment & diagnosis and Innovative Concepts’ (COPACETIC) and in five non-COPACETIC cohorts. Caucasian subjects with ≥20 pack-years smoking with genotype-, spirometric- and demographic data were included.
An overview of the CMH definitions used in this study is presented in Table 1. A brief description of the included cohorts and details according to the period of data collection, type of population, genotyping platforms and genetic imputation software are presented in in Table 2.
We searched for SNPs associated with CMH by using a two-stage strategy followed by a replication stage and meta-analysis (Figure 1).
We performed GWA studies in the NELSON cohort and in additional healthy controls. CMH was analyzed using logistic regression with adjustment for center (Groningen and Utrecht). Since current smoking can affect the presence of CMH, we additionally performed the GWAS in the NELSON cohort correcting for center and smoking. SNPs with a p-value<10-4 present in both GWA studies were selected for replication. To test for generalizability of associations with CMH in other populations, we compared our results with data in CMH-cases and controls with a smoking history of ≥20 pack-years with eleven replication populations using logistic regression with adjustment for sex and current smoking. Finally, we performed a meta-analysis on shared SNPs across the NELSON identification population and the 11 replication populations.
General characteristics of CMH-cases and controls were compared using Student's t- and Mann-Whitney-U tests for continuous variables and χ2 tests for dichotomous variables with SPSS 20.0. Sample and SNP quality control (QC), regression- and meta-analysis were performed with PLINK 1.07 . QC criteria are described in Supplement S1.
Logistic regression analysis under an additive model was used to identify SNPs associated with CMH. SNPs with a p-value<10−4 were included for replication. When two SNPs were in strong linkage disequilibrium (r2≥0.8), the SNP with the lowest p-value was further analyzed.
SNPs in COPACETIC cohorts and in LifeLines were analyzed using logistic regression with adjustment for sex and smoking (ex-/current smoking). In LifeLines, imputed SNPs with an info-score <0.3 (imputation quality score) were removed. SNPs in non-COPACETIC cohorts were analyzed by the cohort investigators using the same model.
Meta-analysis was performed on SNPs across NELSON and the 11 replication cohorts. The Cochran's Q test was used to test for heterogeneity in the meta-analysis.
We performed multivariate logistic regression analysis, adjusted for pack-years and lung function, to associate CMH with the risk allele of rs6577641 in the identification cohort.
Functional relevance of SATB1 and rs6577641, our highest ranked-SNP
We performed 4 functional studies with the identified top-SNP. Details on their methods are given in Supplement S1.
- whether rs6577641 is an eQTL, by analyzing the association of SATB1 expression levels with rs6577641 genotypes in lung tissue from three independent cohorts recruited from Laval University, University of British Columbia, and University of Groningen as described previously ;
- CMH-associated mRNA expression in airway wall biopsies from 77 COPD participants in the GLUCOLD-study ;
- the association of homozygous genotypes for rs6577641 with a) immunohistochemical staining (IHC) for SATB1 and b) the fraction of mucus positivity on bronchial tissue explanted from COPD or lung cancer subjects that underwent lung surgery;
- SATB1 expression levels during mucociliary differentiation of primary bronchial epithelial cells cultured at air-liquid interface .
Characteristics of the identification and replication populations are presented in Table 3. Subjects with CMH were more often current smokers and had worse lung function, except for populations including subjects with COPD only.
After QC, 492,700 SNPs and 2,512 individuals (717 CMH cases, 1,795 controls) from the NELSON study remained. Logistic regression analysis was performed including these individuals supplemented with 590 additional healthy controls, adjusting for center. The QQ-plot provided no evidence of population stratification (λ = 1.0185). 77 SNPs were associated with CMH with a p-value<10−4. CMH was associated with current smoking in our identification cohort (p<0.001). Therefore, we performed a second GWA adjusting for center and current/ex-smoking (717 CMH-cases, 1,795 controls). The QQ-plot showed no evidence of population stratification (λ = 1.0056). We observed 64 SNPs with a p-value<10−4. Genome wide association for CMH ordered by chromosome is shown in the Manhattan plot. Figure 2 shows QQ-plots (A, C) and genome wide association signals for CMH ordered by chromosome (Manhattan-plots, B and D) of these sequential analyses. We identified 36 SNPs associated with CMH with a p-value<10−4 in both analyses Table 4. Of these, 32 SNPs were included for replication and 4 SNPs were removed because they were in strong linkage disequilibrium (r2>0.8) with another associated SNP.
Quantile-quantile plot and Manhattan plot of GWA results for association of SNPs with CMH in NELSON, corrected for center and smoking habits (C and D).
Replication of associated SNPs
Genotyping of SNP rs4775569 failed in the COPACETIC populations, and was removed for further analysis. CMH-associated top-SNPs for each cohort are presented in Table 5, with a complete overview in Table 6. When applying Bonferroni correction in the meta-analysis (p = 1.61×10−3 for 31 SNPs), we found a strong association with one SNP:
- rs6577641, a SNP located on chromosome 3 in intron 9 of the special AT-rich sequence-binding protein 1 locus (SATB1) (combined p-value = 4.25×10−6, OR = 1.17; 1.10–1.26).
The SATB1 SNP rs6577641 had the lowest p-value for association with CMH in the meta-analysis. Figure 3 shows the forest plot of rs6577641 in the identification and replication cohorts and meta-analysis.
Vertically left, the identification cohort and the replication cohorts included in the meta-analysis. The boxes represent the precision and the horizontal lines represent the confidence intervals. The squares represent the pooled effect estimate from the meta-analysis of all cohorts. The horizontal axis shows the scale of the effects.
We assessed the percentage of subjects with CMH in each genotyping group for rs6577641 in NELSON-total and stratified for current and ex smokers (Figure 4). Multivariate logistic regression analysis, corrected for pack-years and FEV1%predicted, showed that CMH was significantly associated with the number of G-alleles in the 1,385 current smokers (reference = AA: heterozygous mutant (AG) p = 0.001; OR = 1.50, homozygous mutant (GG) p = 0.001; OR = 1.80) but not in 1,127 ex-smokers (reference = AA: heterozygous mutant (AG) p = 0.380; OR = 1.18, homozygous mutant (GG) p = 0.143; OR = 1.42).
Functional relevance of SATB1 and rs6577641
1) Transcriptional regulation of SATB1 mRNA expression
We analyzed the association of SATB1 expression levels in lung tissue with rs6577641 genotype in 3 independent data sets of the Universities of Groningen, Laval and UBC . A cis-acting effect of rs6577641 on SATB1 expression was identified and present in all three datasets (n = 1,095), with the same direction of effect across all three SATB1 probes on the array. The (susceptibility) G allele increased expression, the (protective) A allele reduced expression (p = 4.3×10−9) in the meta-analysis across the three datasets and across all three SATB1 probes measured (Table 7).
2) SATB1 mRNA expression and CMH
We compared SATB1 expression in baseline airway wall biopsies of COPD patients with (n = 38) and without (n = 39) CMH in GLUCOLD . CMH was significantly associated with SATB1 expression levels (corrected for ex-/current smoking; p = 0.0045; Figure 5). After stratification, the same direction of effect was present in ex- and current smokers. However, this association reached statistical significance in current smokers (p = 0.021) and not in ex- smokers (p = 0.132), probably due to a difference in power as 46 subjects were current smokers versus 33 ex-smokers.
3) Genotype related protein expression and mucus positivity in bronchial epithelium
SATB1 protein expression has previously been observed in IHS analysis of bronchial epithelial cells . Therefore, we stained SATB1 on paraffin embedded lung tissue biopsies of individuals from the Groningen population contributing to the eQTL analysis. We observed clear nuclear staining for SATB1 in bronchial epithelial cells. No significant difference for % of strong positive, positive and weak positive cells was observed between the protective (AA, n = 9) and risk (GG, n = 14) rs6577641 genotypes (11.8%±5.8 versus 12.7%±6.9, p = 0.74).
We determined whether the fraction of mucus positive bronchial epithelium was different in subjects with different homozygous rs6577641 genotypes and performed PAS-staining on tissue biopsies from the same cohort. We observed no significant difference between individuals with the homozygous protective (AA, n = 10) and risk (GG, n = 7) alleles (19.7%±11.9 versus 14.3%±9.6, p = 0.34).
4) SATB1 expression levels during bronchial epithelial cell mucociliary differentiation
We investigated whether SATB1 expression was induced during mucociliary differentiation of primary human bronchial epithelial (HBE) cells in vitro and compared SATB1 mRNA expression levels at different time points of an air-liquid interface (ALI) culture for up to 45 days. ALI culture of HBE cells induced mucociliary differentiation, as confirmed by induction of expression of FOXJ1, a marker for ciliated cells (19) and MUC5AC, a marker of goblet cells. SATB1 expression was induced over time (Figure 6), with an approximately 8-fold increased expression from the start to the end of the 45-day ALI culture period.
Since not every ex- or current heavy smoker suffers from chronic mucus hypersecretion (CMH), we aimed to identify genetic variants conferring susceptibility to CMH. Therefore, we performed the first GWA study on CMH, the key presenting symptom in chronic bronchitis. CMH was associated with 36 SNPs at the p<10−4 significance level in the identification cohort. In the meta-analysis combining our identification and replication cohorts, strong association was observed with rs6577641, a SNP located on chromosome 3 in intron 9 of SATB1. Although the association of rs6577641 with CMH did not reach conventional genome-wide significance, its effect was in the same direction and was significant (4.25×10−6) at nominal levels (1.61×10−3) across eleven study populations, showing the robustness of this finding. The detected odds ratio for this SNP suggests an additional risk of 17% per G allele to develop CMH in a population of ex- and current heavy smokers.
Multivariate regression analysis, stratified for current an ex-smoking, showed essentially the same effect sizes and direction of the association of CMH and the risk allele of rs6577641. It is likely that lack of power is the reason for not reaching the level of significance in ex-smokers.
These data strongly suggest that SATB1 plays a role in the susceptibility to CMH in subjects with a history of heavy smoking (≥20 pack-years) within the general population. Moreover, rs6577641 has a cis-eQTL effect on SATB1 lung tissue expression, the risk allele at rs6577641 (G) increasing and the A-allele reducing expression of SATB1 significantly. Additionally, we found a higher SATB1 expression in bronchial biopsies of COPD-patients with CMH. We found no differences between the GG and AA genotypes for protein expression of SATB1 in airway epithelium by IHC in a small sample from our lung tissue registry. Finally, we demonstrate that SATB1 mRNA expression is induced during mucociliary differentiation in ALI cultures of human bronchial epithelial cells of 2 donors supporting our eQTL findings. Interestingly, expression of the mucin gene MUC5AC was also induced during this culture period, with a slightly delayed kinetics compared to SATB1. Together these data strongly suggest that SATB1 is induced during differentiation of bronchial epithelial cells and affects chronic mucus hypersecretion.
The forest plot clearly shows that the effect of SNP rs6577641 is lower in cohorts including COPD patients only (GLUCOLD, Rucphen, COPDGene, ECLIPSE and Norway) than in the other cohorts. Additional meta-analysis of COPD-cohorts and general population based cohorts separately confirmed this (COPD cohorts, combined p-value = 0.236, OR = 1.07 and general population based cohorts, combined p-value = 5.18×10−7, OR = 1.26). This suggests genetic heterogeneity of CMH in subjects with and without COPD.
The SNP most significantly associated with CMH, rs6577641, is located in an intron of SATB1. SATB1 is a transcription factor and chromatin (re)organizer important for controlling the expression of many genes in a tissue or cell-type specific fashion, for instance in differentiating thymus T-cells  or differentiating skin keratinocytes . Expression of SATB1 has been observed in normal human bronchial epithelial cells by immunohistochemistry and lower levels were observed in non-small lung cancer cells . In our study, we also showed the presence of SATB1 in bronchial epithelial cells by IHC staining of lung tissue. However, no significant differences were found between patients homozygous for the protective and risk alleles, for either specific SATB1 staining or for PAS staining, the latter specifically detecting mucus. This inability to detect a genotype effect on protein staining may be due to lack of power, as we found a large variation in SATB1 and PAS protein expression in the relatively small number of lung tissue samples. Other explanations include possible expression regulation of SATB1 by smoke exposure which could be a dynamic process not readily detected at the protein level by any single-time point analysis such as IHC staining on lung biopsies. Alternatively SATB1 expression levels may vary throughout the lungs or the technique used here is not sensitive enough to detect relatively small differences in protein levels. To further explore the association of SATB1 protein and its underlying regulation, it would be of interest to perform longitudinal investigations on lung tissue samples of subjects with and without CMH, or time series of in vitro cultured epithelial cells from donors with a specific genotype and cigarette smoke exposure. This would also allow further studies on epigenetic regulation with methylation, microRNA or histone modifications.
The lack of association between the SATB1 protein and rs6577641 might additionally be due to the location of mucus positive cells in lung tissue. Mucus is produced both by goblet cells and submucosal glands, which we did not investigate further. Normal mucus consists of 97% water and 3% solids including 30% mucins. In case of dysregulation of mucus production, the concentration of solids in mucus may increase up to 15%. A further step therefore could involve investigating mucins/proteins present in mucus, e.g. MUC5AC is predominantly produced by goblet cells in proximal airways and MUC5B by secretory cells throughout the airways and by submucosal glands.
How does SATB1 expression contribute to CMH? SATB1 is known to be a genome organizer, a tissue specific chromatin remodeling protein with a property to modifying chromatin architecture by formation of loops, allowing contact of condensed genomic DNA to regulatory transcription proteins . Thus SATB1 can control gene expression of a series of target genes located within a single locus at a specific chromosomal location . This has for instance been elegantly shown in case of differentiating keratinocytes , where Satb1 expression regulates genes located in the keratinocyte-specific loci, leading to adaptation of a specific cell fate of the differentiating keratinocytes. Similarly, a mechanism by which SATB1 could contribute to CMH is the induction of a gene expression program during differentiation of bronchial epithelial cells, leading to adaptation of a cell fate specific for mucus producing cells in the submucosal glands or a goblet cell phenotype in the bronchial epithelium. Involvement of Satb1 in pneumocyte differentiation was previously observed by Baguma et al. in mice . We observed induction of SATB1 expression in bronchial epithelial cells differentiating under ALI culture conditions. Further research will need to test whether a specific gene expression profile is induced by SATB1 expression in differentiating bronchial epithelial cells. SATB1 is also highly expressed in thymocytes, but absent in mature non-activated T cells . Moreover, Satb1 has been shown in mice to be essential for expression of Thelper2 (Th2) cells important in the regulation of genes encoding interleukin 4, 5 and 13 . In Satb1-deficient mice, development of thymocytes stopped after the CD4+/CD8+ stage with deregulation of many genes . Conversely, in case of excessive SATB1-production an excess of Th2 cells may be formed which all produce IL-13, which may contribute to increased mucus production. Therefore, a putative role of SATB1 in T-cells for the CMH phenotype should not be disregarded.
Strength of our study is the fact that we were able to replicate our findings in different populations, ranging from cohorts consisting of individuals with severe airflow limitation to cohorts mainly consisting of healthy smokers. There are some limitations, e.g. the presence of CMH was not based on actual measurements of the amount of sputum produced but based on questionnaires that were not completely similar in all study cohorts. Underreporting of CMH occurs since those experiencing CMH become accustomed to these symptoms, believing they are smoking related or because they are embarrassed to admit to cough and sputum. We demonstrated that SATB1 mRNA expression is induced during mucociliary differentiation in ALI cultures of HBE cells in a small dataset (n = 2). However, these data seem reliable as they are supported by eQTL data from lung tissue. Despite this drawback, we consistently found evidence for association of SATB1 with CMH in the populations studied, showing the robustness of our finding. Moreover, we corroborated this finding by functional studies in lung tissue, airway wall biopsies of COPD patients and epithelial cultures. More extensive research is needed to investigate which factors induce SATB1 expression in airway epithelium.
In summary, we performed identification analyses and meta-analyses using data from almost 7,000 participants to identify genes involved in susceptibility for CMH. It is remarkable that we found a genetic association for CMH given this phenotype is partly subjectively determined and not well delineated. Moreover, despite cohort differences to define CMH and severity of airflow limitation, we found consistent effects of SNP rs6577641 on CMH. This confirms that the CMH phenotype, despite the fact that it is self-reported, is a robust phenotype irrespective of the presence or absence of airflow limitation. The association of rs6577641 on chromosome 3 at the SATB1 locus with CMH was supported by functional studies including gene expression findings, demonstrating SATB1 to be associated with CMH.
Chronic mucus hypersecretion is a bothersome symptom for many people, it increases in prevalence with aging and affects quality of life, exacerbations of symptoms due to respiratory infections and ultimately increases mortality. The involvement of SATB1 in CMH offers opportunities to better understand the process leading to CMH, and future development of tailored medicines.
The authors would like to thank the research staff at Respiratory Health Network Tissue Bank of the FRQS for their valuable assistance in the lung eQTL study.
LifeLines Cohort Study: BZ Alizadeh1, RA de Boer2, HM Boezen1, M Bruinenberg3, L Franke4, P van der Harst2, HL Hillege1,2, MM van der Klauw5, G Navis6, J Ormel7, DS Postma8, JGM Rosmalen7, JP Slaets9, H Snieder1, RP Stolk1, BHR Wolffenbuttel5, C Wijmenga4
1University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands;
2University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, the Netherlands;
3University of Groningen, University Medical Center Groningen, the LifeLines Cohort Study, Groningen, the Netherlands;
4University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, the Netherlands;
5University of Groningen, University Medical Center Groningen, Department of Endocrinology, Groningen, the Netherlands;
6University of Groningen, University Medical Center Groningen, Department of Internal Medicine, Division of Nephrology, Groningen, the Netherlands;
7University of Groningen, University Medical Center Groningen, Interdisciplinary Center of Psychopathology of Emotion Regulation (ICPE), Department of Psychiatry, Groningen, the Netherlands;
8University of Groningen, University Medical Center Groningen, Department of Pulmonology, Groningen, the Netherlands
¶ Membership of the LifeLines Cohort study is provided in the Acknowledgments. Conceived and designed the experiments: AED DSP HMB CW JS PZ HG JWL WT MCN MvdB. Performed the experiments: AED JS ML MCN UB MP. Analyzed the data: AED MvdB DL WT MHC MCN MP ML MB FM M. Obeidat. Contributed reagents/materials/analysis tools: ML MP JS M. Obeidat MvdB DL. Wrote the paper: AED DSP HMB CW JMV GHK PJS EKS MHC MCN. Acquisition of replication cohorts: M. Dahlback KT PSH PJS AS JV BGN M. Dahl WMV HSP HAS M. Owsijewitsch HUK HJK EN PN CCvD EKS JDC THB DAL PB AG YB LL FR AGU AH BHS GGB CMvD UB GHK SN.
- 1. Anonymous (1995) Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease. american thoracic society. Am J Respir Crit Care Med 152: S77–121.
- 2. Ferre A, Fuhrman C, Zureik M, Chouaid C, Vergnenegre A, et al. (2012) Chronic bronchitis in the general population: Influence of age, gender and socio-economic conditions. Respir Med 106: 467–471. doi: 10.1016/j.rmed.2011.12.002
- 3. Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, et al. (2007) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 176: 532–555. doi: 10.1164/rccm.200703-456so
- 4. Vestbo J, Rasmussen FV (1989) Respiratory symptoms and FEV1 as predictors of hospitalization and medication in the following 12 years due to respiratory disease. Eur Respir J 2: 710–715.
- 5. Vestbo J, Prescott E, Lange P (1996) Association of chronic mucus hypersecretion with FEV1 decline and chronic obstructive pulmonary disease morbidity. copenhagen city heart study group. Am J Respir Crit Care Med 153: 1530–1535. doi: 10.1164/ajrccm.153.5.8630597
- 6. World Health Organization website. Available: Http://www.who.int/respiratory/copd/burden/en/index.html. Accessed January 2013.
- 7. Shurin S (2012) Morbidity & mortality: 2012 chart book on cardiovascular, lung, and blood diseases. 2013: 107.
- 8. Lange P, Parner J, Prescott E, Vestbo J (2003) Chronic bronchitis in an elderly population. Age Ageing 32: 636–642. doi: 10.1093/ageing/afg108
- 9. Ekberg-Aronsson M, Pehrsson K, Nilsson JA, Nilsson PM, Lofdahl CG (2005) Mortality in GOLD stages of COPD and its dependence on symptoms of chronic bronchitis. Respir Res 6: 98. doi: 10.1186/1465-9921-6-98
- 10. Viegi G, Carrozzi L, Di Pede F, Baldacci S, Pedreschi M, et al. (1994) Risk factors for chronic obstructive pulmonary disease in a north italian rural area. Eur J Epidemiol 10: 725–731. doi: 10.1007/bf01719289
- 11. Silverman EK, Chapman HA, Drazen JM, Weiss ST, Rosner B, et al. (1998) Genetic epidemiology of severe, early-onset chronic obstructive pulmonary disease. risk to relatives for airflow obstruction and chronic bronchitis. Am J Respir Crit Care Med 157: 1770–1778. doi: 10.1164/ajrccm.157.6.9706014
- 12. Hallberg J, Dominicus A, Eriksson UK, Gerhardsson de Verdier M, Pedersen NL, et al. (2008) Interaction between smoking and genetic factors in the development of chronic bronchitis. Am J Respir Crit Care Med 177: 486–490. doi: 10.1164/rccm.200704-565oc
- 13. Zhu G, Agusti A, Gulsvik A, Bakke P, Coxson H, et al. (2009) CTLA4 gene polymorphisms are associated with chronic bronchitis. Eur Respir J doi: 10.1183/09031936.00141808
- 14. Hao K, Bosse Y, Nickle DC, Pare PD, Postma DS, et al. (2012) Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet 8: e1003029. doi: 10.1371/journal.pgen.1003029
- 15. Lapperre TS, Snoeck-Stroband JB, Gosman MM, Jansen DF, van Schadewijk A, et al. (2009) Effect of fluticasone with and without salmeterol on pulmonary outcomes in chronic obstructive pulmonary disease: A randomized trial. Ann Intern Med 151: 517–527. doi: 10.7326/0003-4819-151-8-200910200-00004
- 16. Selinger CI, Cooper WA, Al-Sohaily S, Mladenova DN, Pangon L, et al. (2011) Loss of special AT-rich binding protein 1 expression is a marker of poor survival in lung cancer. J Thorac Oncol 6: 1179–1189. doi: 10.1097/jto.0b013e31821b4ce0
- 17. Cai S, Lee CC, Kohwi-Shigematsu T (2006) SATB1 packages densely looped, transcriptionally active chromatin for coordinated expression of cytokine genes. Nat Genet 38: 1278–1288. doi: 10.1038/ng1913
- 18. Fessing MY, Mardaryev AN, Gdula MR, Sharov AA, Sharova TY, et al. (2011) p63 regulates Satb1 to control tissue-specific chromatin remodeling during development of the epidermis. J Cell Biol 194: 825–839. doi: 10.1083/jcb.201101148
- 19. Cai C, Zhang HY, Le JJ, Dong JC, Cui Y, et al. (2010) Inflammatory airway features and hypothalamic-pituitary-adrenal axis function in asthmatic rats combined with chronic obstructive pulmonary disease. Chin Med J (Engl) 123: 1720–1726.
- 20. Yasui D, Miyano M, Cai S, Varga-Weisz P, Kohwi-Shigematsu T (2002) SATB1 targets chromatin remodelling to regulate genes over long distances. Nature 419: 641–645. doi: 10.1038/nature01084
- 21. Baguma-Nibasheka M, Angka HE, Inanlou MR, Kablar B (2007) Microarray analysis of Myf5−/−:MyoD−/− hypoplastic mouse lungs reveals a profile of genes involved in pneumocyte differentiation. Histol Histopathol 22: 483–495.
- 22. Dickinson LA, Joh T, Kohwi Y, Kohwi-Shigematsu T (1992) A tissue-specific MAR/SAR DNA-binding protein with unusual binding site recognition. Cell 70: 631–645. doi: 10.1016/0092-8674(92)90432-c
- 23. Krangel MS (2007) T cell development: Better living through chromatin. Nat Immunol 8: 687–694. doi: 10.1038/ni1484
- 24. van Klaveren RJ, Oudkerk M, Prokop M, Scholten ET, Nackaerts K, et al. (2009) Management of lung nodules detected by volume CT scanning. N Engl J Med 361: 2221–2229. doi: 10.1056/nejmoa0906085
- 25. Soler Artigas M, Loth DW, Wain LV, Gharib SA, Obeidat M, et al. (2011) Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function. Nat Genet 43: 1082–1090.
- 26. Hofman A, van Duijn CM, Franco OH, Ikram MA, Janssen HL, et al. (2011) The rotterdam study: 2012 objectives and design update. Eur J Epidemiol 26: 657–686. doi: 10.1007/s10654-011-9610-5
- 27. Stolk RP, Rosmalen JG, Postma DS, de Boer RA, Navis G, et al. (2008) Universal risk factors for multifactorial diseases: LifeLines: A three-generation population-based study. Eur J Epidemiol 23: 67–74. doi: 10.1007/s10654-007-9204-4
- 28. Rijcken B, Schouten JP, Mensinga TT, Weiss ST, De Vries K, et al. (1993) Factors associated with bronchial responsiveness to histamine in a population sample of adults. Am Rev Respir Dis 147: 1447–1453. doi: 10.1164/ajrccm/147.6_pt_1.1447
- 29. van Diemen CC, Postma DS, Vonk JM, Bruinenberg M, Nolte IM, et al. (2006) Decorin and TGF-beta1 polymorphisms and development of COPD in a general population. Respir Res 7: 89. doi: 10.1186/1465-9921-7-89
- 30. Verschuren WM, Blokstra A, Picavet HS, Smit HA (2008) Cohort profile: The doetinchem cohort study. Int J Epidemiol 37: 1236–1241. doi: 10.1093/ije/dym292
- 31. Nizankowska-Mogilnicka E, Mejza F, Buist AS, Vollmer WM, Skucha W, et al. (2007) Prevalence of COPD and tobacco smoking in malopolska region–results from the BOLD study in poland. Pol Arch Med Wewn 117: 402–410.
- 32. Lamprecht B, McBurnie MA, Vollmer WM, Gudmundsson G, Welte T, et al. (2011) COPD in never smokers: Results from the population-based burden of obstructive lung disease study. Chest 139: 752–763. doi: 10.1378/chest.10-1253
- 33. Becker N, Delorme S, Kauczor HU (2008) LUSI: The german component of the european trial on the efficacy of multislice-CT for the early detection of lung cancer. onkologie 31.
- 34. Vestbo J, Anderson W, Coxson HO, Crim C, Dawber F, et al. (2008) Evaluation of COPD longitudinally to identify predictive surrogate end-points (ECLIPSE). Eur Respir J 31: 869–873. doi: 10.1183/09031936.00111707
- 35. Regan EA, Hokanson JE, Murphy JR, Make B, Lynch DA, et al. (2010) Genetic epidemiology of COPD (COPDGene) study design. COPD 7: 32–43. doi: 10.3109/15412550903499522
- 36. Cho MH, Castaldi PJ, Wan ES, Siedlinski M, Hersh CP, et al. (2011) A genome-wide association study of COPD identifies a susceptibility locus on chromosome 19q13. Hum Mol Genet doi: 10.1093/hmg/ddr524
- 37. Grydeland TB, Dirksen A, Coxson HO, Pillai SG, Sharma S, et al. (2009) Quantitative computed tomography: Emphysema and airway wall thickness by sex, age and smoking. Eur Respir J 34: 858–865. doi: 10.1183/09031936.00167908