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Depts of 1 Cellular and Molecular Physiology, 5 Pediatrics, and 2 Health Evaluation Sciences, The Pennsylvania State University College of Medicine,Hershey, PA, USA. 3 National institute of Respiratory Diseases. 4 Dept of Science National Autonymous University of Mexico, Mexico
CORRESPONDENCE: J. Floros, Dept of Cellular and Molecular Physiology, The Pennsylvania State University College of Medicine, 500 University Drive, H166 Hershey, PA 17033, USA. Fax: 1 7175317667
Keywords: chronic obstructive pulmonary disease, gene interaction, surfactant protein, surfactant proteins A, B and D
Received: May 12, 2000
Accepted May 9, 2001
This work was supported by NIH R37 HL34788 and the General Clinical Research Center at the Pennsylvania State University College of Medicine.
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Abstract |
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 TOP Abstract Patients and methodsResultsDiscussionAcknowledgementsReferences |
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Genotype analysis of surfactant protein (SP)-A, SP-B, SP-B-linked microsatellite, and SP-D marker alleles was performed in patients with COPD (n=97) and smoker (n=82) or nonsmoker (n=99) controls. Univariate and multiple logistic regression analyses were performed.
The regression analysis results between COPD and smokers revealed several COPD susceptibility alleles (AA62_A, B1580_C, D2S388_5), based on an odds ratio (OR >2.5). The predictive ability of this model for developing COPD is good (c=0.926). Allele-allele (B1580_C and D2S388_5) and allele-environment (i.e. smoking) interactions were detected. When smoker controls were compared to nonsmoker controls, marker D2S388_5 appeared to be smoking-independent (p=0.874), whereas marker alleles AA62_A (p=0.045) and B1580_5 (p=0.007) were smoking-dependent. Males were at higher risk (OR=6.05, p=0.001), and smoking (>50 packs·yr–1) increased risk (OR=5.38, p=0.007). Males and alleles of loci flanking SP-B were associated with more severe cases (forced expiratory volume in one second/forced vital capacity ![{els]](/math/els.gif)
40%).
The present results indicate that the surfactant protein alleles may be useful in chronic obstructive pulmonary disease by either predicting the disease in a subgroup and/or by identifying disease subgroups that may be used for therapeutic intervention. These observations should now be confirmed in a larger study, designed according to strict epidemiological criteria.
Chronic obstructive pulmonary disease (COPD) is a major medical problem and a leading cause of morbidity and mortality among the adult population. The disease is characterized by progressive airflow obstruction due to chronic bronchitis, emphysema, or both 1, and some COPD patients develop predominately airway disease and others may have predominantly parenchymal disease. Although the most important risk factor by far for the development of COPD is cigarette smoking, only 10–20% of heavy cigarette smokers develop COPD 1, 2, suggesting that other environmental or genetic factors contribute to COPD. Environmental risk factors include childhood respiratory infections, occupational exposures, ambient air pollution, lower birth weight, and diet 3.
Evidence for genetic susceptibility factors is derived from studies of twins where the lung functional response to cigarette smoking was studied 4, and from studies showing an aggregation of COPD in families 5. Genetic risk factors for COPD may include the inherited deficiency of 
1-antitrypsin in individuals with the Z allele 6, which is uncommon and explains only a very small proportion (<1%) of the COPD cases 1. Genetic polymorphisms in the xenobiotic enzyme 7, and vitamin D binding protein 8 appear be associated with an increased or decreased risk for COPD, respectively.
Although pulmonary surfactant or its components have the potential to be contributors to the pathogenesis of COPD, very little work has been done in this regard. Pulmonary surfactant, a lipoprotein complex, is essential for normal lung function and surfactant, or components of surfactant, play a role in bronchiolar stability, innate host defence, and the regulation of the inflammatory processes in the lung 9. The surfactant proteins (SPs) play important roles in surfactant function, structure, and metabolism 9, and SP-A and SP-D are involved in host defence and/or the regulation of inflammatory processes of the lung.
The human SP-A locus is located on chromosome 10 and consists of two functional genes in opposite transcriptional orientation 10. The human SP-D is linked to the SP-A locus and is located proximal to the centromere at about 80–100 kb from the SP-A2 gene 10. A number of alleles have been characterized for each SP-A gene. The most commonly observed alleles for the SP-A1 gene are the 6A, 6A2, 6A3, 6A4 alleles, and for the SP-A2 gene are the 1A, lA0, 1A1, 1A2, 1A3, lA5 11–13, (unpublished observations). Functional differences between SP-A1 and SP-A2 alleles, and possibly among alleles of each gene have been demonstrated with in vitro expressed human SP-A alleles 14. Moreover, splice variation and/or polymorphisms at the 5' 12 and 3' untranslated regions of these alleles, respectively, point to regulatory differences 15. Several polymorphisms have also been identified for SP-D 13 and an association between an SP-D allele, as well as SP-A alleles and tuberculosis, have been observed 16. The human SP-B locus is located on chromosome 2p12-p11.2. A number of polymorphisms have been characterized for SP-B and some of these have been associated with disease 17–20. In addition, a number of microsatellite markers flanking the SP-B locus have been characterized 21.
Because COPD is characterized by a chronic inflammation and abnormal lung function and is probably the result of complex interactions of environmental and genetic factors, this report has investigated whether genetic variation in surfactant proteins contributes to COPD by assessing any associations with surfactant protein marker alleles. Allele associations may help to genetically identify more homogeneous COPD subgroups to study mechanisms involved in the pathogenesis of COPD, and/or study the response to therapeutic interventions.
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Patients and methods |
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TOPAbstractPatients and methodsResultsDiscussionAcknowledgementsReferences |
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Patients and healthy smoker controls were sequentially enrolled from the Smoking Cessation Program at INER, according to their willingness to participate in the study. Individuals with a smoking history of >10 pack-yrs, forced expiratory volume in one second (FEV1) <70% predicted, FEV1/forced vital capacity (FVC) <70% and with no history suggestive of asthma were considered COPD patients (cases). Subjects with smoking history of >10 pack-yrs, but with FEV1 >70% predicted, and FEV1/FVC >70% were considered smoker controls. In addition, sequential, unrelated nonsmoking healthy blood donors from INER were included. The transfusion service at INER serves the geographical area from which the patients were recruited. Patients and controls had >3 previous generations born in Mexico.
Pulmonary function test
Spirometry
FEV1 and FVC were determined by using a turbine spirometer (Pony Cosmed Inc., Rome, Italy). According to the criteria of the American Thoracic Society (ATS), the best of three acceptable forced expiratory manoeuvres is used to select FEV1 and FVC while the best FEV1 and the best FVC from either tracing are used for the FEV1/FVC ratio 1.
The Pony spirometer used in this study fulfils the equipment recommendations by the ATS in terms of range/accuracy for FVC, FEV1, flow range (0–14 L·s–1), time, resistance and back-pressure. In addition, in patients with COPD, a good concordance was observed between the body plethysmography and spirometric results, which further supports the present data.
Based on the present authors' experience, the readings of the calibration sessions, which were made by the same technicians twice a week, had <3% differences. In addition, a biological control is used (one of the technicians is submitted two- or three-times a week to the test). The accumulated results from this control test over a year indicate that differences in reproducibility are <3%.
Lung volumes and diffusing capacity
Total lung capacity (TLC) and residual volume (RV) were obtained by using a body plethysmograph (MasterLab, Jaegger, Frankfurt, Germany). All subjects were previously familiarized with the equipment before the tests. For carbon monoxide diffusing capacity of the lung (Dl,co) manoeuvres, >2 tests with 5% variability, were considered to be acceptable. Patients with FVC <1 L and/or unable to follow instructions or to hold their breath for 10 s, were not submitted to this test. The coefficient of variation and intraclass correlation coefficient (ICC) for FEV1 and FVC were 3% and 0.98 respectively; for TLC, RV and Dl,co, these were in the range of 5–12% and 0.90–0.92, respectively. For all the pulmonary function tests, the reference values reported by Quanjer 22 were used.
Arterial blood gases
These were obtained through a radial puncture while patients were breathing room air for >30 min and measured in a gasometer (IL 1310, Instrumentation Laboratory Inc., Lexington, MA, USA).
Genotype analysis for surfactant protein-A, -B, and -D using a polymerase chain reaction-based converted restriction fragment length polymorphism method
The converted polymerase chain reaction (PCR) and converted restriction fragment length polymorphism/genotype analysis (cRFLP) approaches are based on the notion that a site containing a single base polymorphism (if it does not represent a natural enzyme recognition site) is converted into a restriction enzyme recognition site by simply using a PCR primer that contains the necessary mismatched base. Because the amplified fragment will contain PCR products from both alleles, it is expected that in the case of homozygotes for either one or the other allele, the PCR products will either both be digested or not by the appropriate enzyme. In the case of heterozygotes, one allele would be digested and the other will remain intact. Following electrophoresis and visualization of the digested PCR products, one can assess the genotype at the particular site for the given individual. For all PCR reactions, except the 11 kb fragment of SP-B (see later), the thermocycler, ERICOMP TwinBlockTM system was used. For the 11 kb, the Perkin Elmer Cetus DNA Thermal Cycle (Applied Biosystems, Foster City, CA, USA) was used.
Genotype analysis
Surfactant protein-A
Genomic deoxyribonucleic acids (DNAs) were used as a template to first amplify a 3.3 kb SP-A1 or SP-A2 specific fragment, which then served as a template for subsequent converted PCR reactions.
cRFLP analysis was then performed for nucleotide changes at codons of five (AAl9, AA50, AA62, AA133, and AA219) and of four (AA9, AA91, AA140 and AA223) amino acids, in order to score SP-A1 and SP-A2 alleles, respectively (fig. 1
), as described in detail by DiAngelo et al. 13.
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) and the scoring of alleles was made according to the DNA patterns as previously described 18, 20.
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), as described previously 20, 21. For each marker, a specific end labelled 32P-
adenosine triphophate primer was used for PCR, the PCR products were run on a 6% polyacrylamine gel containing formamide, the gel dried, and exposed to X-AR film. The scoring of alleles was made according to control allele DNA patterns as previously described 21.
Surfactant protein-D
For SP-D genotyping, two fragments (376 bp and 
1 kb) were amplified from genomic DNA. One (376 bp) contained the codon ATG or ACG for amino acid 11 (DA11), and the other contained the codon ACA or GCA for amino acid 160 (DA160). The underlined nucleotide is the polymorphic nucleotide. Each fragment served as a template for converted PCR at DA11 or DA160 sites, as previously described 13.
Statistical analyses
The analyses performed for the genotype data include univariate analysis and multiple logistic regression analysis. The three groups of individuals are: 1) nonsmoker control, 2) smoker control, and 3) COPD, all of whom were smokers. The genotype fields considered in the analyses are summarized in table 1 and the location and characteristics of these are depicted in figures 1 and 2.
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Multivariate analyses
Assuming no dose-effect for the alleles, multivariate logistic regression models were built using backward selection methods with staying significance level equal to 0.05. The alleles that entered the model were preselected, i.e. only the alleles shown to be significant in the univariate analysis (p<0.1) were considered in the model. Sex, age, and smoking (>50 packs·yr–1) were forced to be included as variables in the model that compares COPD and healthy smokers, because further analysis showed them to be confounders for certain alleles. For smoker/nonsmoker comparison, only sex was included because age was unknown for nonsmokers.
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Results |
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TOPAbstractPatients and methodsResultsDiscussionAcknowledgementsReferences |
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, and the impact of pack-yrs on those who develop COPD and those who do not (p=0.001) is shown in table 3. An association (Chi-squared 16.9, p<0.001) between heavy smokers (>50 packs·yr–1) with COPD (n=36), and without COPD (n=9) was observed. An association between the intensity of smoking and sex (p=0.012) was also observed when smokers with or without COPD were considered. Within the COPD group there was no correlation between the intensity of smoking and severity of COPD, or between sex and severity of COPD.
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) were included in the multivariate analysis. A number of marker alleles were significant. These included one SP-A1 (AA62_A), one SP-B (B1580_C), and one microsatellite (D2S388_5) (table 5). Based on the OR, all three marker alleles, as well as sex (males), age, and smoking (>50 packs·yr–1) appear to be associated with increased risk for COPD, ranging from 1.17-fold (age) to 7.42-fold (AA62_A). Having CCA at the marker locus AA62, instead of CCG, or having 1580 ACT at B, instead of ATT, increases the susceptibility by 7.42- and 3.65-fold, respectively. The transition from A to G at AA62 does not change the encoded amino acid, but the transition of C to T at B1580 changes the encoded amino acid from a threonine to a serine, resulting in the elimination of a potential N-linked glycosylation site.
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Allele interactions and confounding factors
Interaction exists when the presence of marker A would affect the susceptibility of having COPD for individuals with another marker B. Note that in the interaction analysis, again sex, smoking, and age were adjusted for in the COPD/smoker comparison. In the COPD and healthy smoker comparison, an interaction (p=0.055) was observed between B1580_C and D2S388_5. The combined presence of two of the alleles studied here may better predict COPD in certain cases (fig. 3). If both B1580_C and D2S388_5 are present, the OR for COPD is 24.3 (p=0.0004) compared to the situation where both markers are absent (A, fig. 3). No significant differences were observed for situations B and C in figure 3 when either of these was compared to A.
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), assuming no dose effect, for the three alleles (AA62_A, D2S388_5, B1580_C) that remained in the model of COPD versus smokers. The frequency of alleles AA62_A and B1580_C was significantly different between smokers and nonsmoker controls, after adjusting for sex (p=0.045 and p=0.007, respectively, and ORs of 0.337 and 0.346, respectively). However, the frequency of allele D2S388_5 was not significantly different in these two control groups (OR=0.94, p=0.874). These findings indicate that the two control groups (smokers and nonsmokers), based on the marker alleles used in this study, are different but overlapping populations. Also, the results of the smoker/nonsmoker comparison identify the D2S388_5 marker allele as a "smoking-independent" marker allele and the AA62_A and B1580_C as "smoking-dependent", pointing to the complexity of the underlying interactions. How smoking affects these marker alleles remains to be determined.
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In the COPD/smoker comparison, the OR for the AA62_A marker allele in the univariate analysis is 4.09 (table 4), but in the multivariate analysis is 7.42 (table 5), whereas the ORs for B1580_C and D2S388_5 are similar between univariate and multivariate analyses (tables 4 and 5). These observations indicate that the AA62_A marker allele is confounded by some other marker(s). Further analysis indicated that B1580_C may be a confounder for AA62_A, but D2S388_5 is not a confounder for AA62_A.
Chronic obstructive pulmonary disease subgroups based on severity
To determine whether marker alleles are associated with severity in COPD, the COPD population was separated into two subgroups based on FEV1/FVC values. One group included patients (n=46) with FEV1/FVC>40% and the other group, patients (n=47) with FEV1/FVC 40%. Multiple logistic regression analysis was performed that included only marker alleles shown to be significant in the univariate tests. Age and smoking appear to be nonsignificant risk factors, but sex appears to be significant. Therefore, sex was forced as a variable in the model. Two marker alleles (B_18_C and D2S388_4), as well as sex (males) were significant (table 7). All three factors appear, based on ORs, to be associated with severe cases of COPD by approximately 3–4-fold. These results were also confirmed in a multivariate analysis (not shown) that included all marker alleles depicted in table 1, without preselection of significant (p<0.1) marker alleles.
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Discussion |
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TOPAbstractPatients and methodsResultsDiscussionAcknowledgementsReferences |
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The regression analysis data revealed that smoking is an important variable for COPD, and this is consistent with the observed association (p<0.001) between heavy smokers (>50 packs·yr–1) and COPD (table 3). Although, smoking is a major determinant of COPD, only a fraction of smokers develop COPD 1, 2. Smokers with normal FEV1 have been shown to have increased inflammation, but the characteristics of this inflammation differ between smokers who develop COPD and those who do not 25. Genetic factors have been implicated in smoking 26 and perhaps yet unknown genetic factors may determine which subgroup of smokers has increased susceptibility to COPD. The surfactant protein marker alleles may help to distinguish such a subgroup. A subgroup of smokers, identified by marker alleles (AA62_A and B1580_C), have increased susceptibility to COPD, compared to smokers who lack these alleles.
Whether SP-A1 (AA62_A), SP-B (B1580_C), or SP-B-linked microsatellite (D2S388_5) marker loci themselves, or linked loci, contribute to COPD remains to be determined. Of interest, heterozygous SP-B mice have half the amount of SP-B (compared to homozygous SP-B) and under normal conditions, exhibit small physiological lung abnormalities 27. It is possible that certain SP-B alleles may associate with compromised lung function under certain circumstances. For example, the marker allele B1580_C, associated with COPD, encodes a threonine (instead of isoleucine) at the marker locus B1580. A threonine at this marker locus can result in an additional N-linked glycosylation site. Although the functional role of such a change is not known, it may have an impact on the processing of the precursor SP-B molecule resulting in low SP-B content, which in turn may compromise lung function. Conversely, the SP-A1 AA62_A single nucleotide polymorphism, present in alleles 6A2 and 6A3 (fig. 1), and which does not change the encoded amino acid, may identify regulatory differences between AA62_A and other SP-A1 alleles and/or chromosomes or groups of alleles that contribute to COPD. Similarly, the microsatellite D2S388 marker locus is an unknown marker locus and the D2S388_5 marker allele may be linked to a gene that contributes to COPD.
SP-A and SP-B are functionally interactive, because both are necessary for aspects of surfactant such as the structural form of surfactant called tubular myelin 9. It is possible that the functional capabilities of certain surfactant protein allele combinations may compromise, significantly more, host defence and/or alveolar integrity, through networks of additive and/or epistatic interactions 28–30 at the molecular, cellular, or tissue level. However, in the present study, no interaction was observed between AA62_A (or 6A2 and 6A3; fig. 1) and either of the two (B1580_C, D2S388_5) significant markers (not shown), suggesting that the presence of one marker allele does not affect susceptibility to COPD for individuals with the other marker. Conversely, smoking may have an impact on alleles AA62_A and B1580_C, since these alleles appeared to be "smoking-dependent". Although the underlying mechanisms are unknown, genetic factors playing a role in cigarette smoking have been previously suggested 26.
Furthermore, surfactant protein marker alleles may be useful in separating subgroups of COPD based on severity. Two marker alleles on chromosome 2 (B_18_C and D2S2388_4) were associated with severe cases of COPD. Males were also more likely (OR=3.6) to be in the severe (FEV1/FVC 40%) COPD subgroup, which is also consistent with the strong association between males (p<0.0001, table 2) and COPD. Of relevance is a recent preliminary finding where an intron 4 variant of the SP-B gene 17 was observed in higher frequency in acute respiratory failure in COPD of a German population. The frequency of this variant was higher in females in this severe subgroup of COPD 31. These data together, suggest that sex is important in COPD and that different marker alleles may identify increased severity for males and females. Smoking was no longer a variable in the COPD severity subgroups, which is consistent with the lack of interaction of smoking and genotype within the COPD group. These data indicate that the amount of smoking may not play a role in the severity of COPD but it may modify gene-disease interactions, and through this modification, smoking contributes to COPD in individuals with certain genetic backgrounds. In this regard, the surfactant protein marker alleles may be useful in identifying COPD subgroups.
For the markers tested, there is a possibility that some of the results are significant by chance alone. For example, out of the 33 markers considered in table 1, if each marker is tested individually at a statistical significance level of p<0.05, the probability of observing at least one significant marker by chance is 0.82, of three or more markers by chance is 0.23, and of six or more markers by chance is 0.05. Four and six markers were identified in table 4, with p<0.05 from the Chi-squared test and the logistic regression, respectively. Thus, some of the markers found in table 4 may be expected to be due to chance alone and some may not. Although, the probabilities (0.23 and 0.05) are low, indicating that the probability of the "chance" factor alone being a major factor is indeed low, the observations made need to be confirmed in a larger study designed according to strict epidemiological criteria.
In summary, the data presented implicate surfactant proteins in chronic obstructive pulmonary disease, and point to the usefulness of surfactant protein marker alleles in studies of the disease. Because chronic obstructive pulmonary disease is a disease with complex aetiology, it is possible that difficulties exist identifying homogeneous clinical subgroups for study. The surfactant protein marker alleles may be useful in this regard, and contribute to the identification of a significant chronic obstructive pulmonary disease subgroup. Such a subgroup, along with other subgroups may be used, for example, in studies of drug response, or other therapeutic intervention. Furthermore, the use of genetic markers as additional parameters to identify more homogeneous subgroups may facilitate the study of mechanisms involved in the pathogenesis of specified chronic obstructive pulmonary disease subgroups, that may in turn help in the understanding of why, for example, only a fraction of smokers develop the disease.
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Acknowledgements |
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TOPAbstractPatients and methodsResultsDiscussionAcknowledgementsReferences |
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TOPAbstractPatients and methodsResultsDiscussionAcknowledgementsReferences |
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production in the THP-1 cell line. Am J Physiol 2000;278:L946–L954.
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