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[Cancer Research 65, 8158-8165, September 15, 2005]
© 2005 American Association for Cancer Research

Molecular Biology, Pathobiology and Genetics

Five Loci, SLT1 to SLT5, Controlling the Susceptibility to Spontaneously Occurring Lung Cancer in Mice

Daolong Wang and Ming You

Department of Surgery and the Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri

Requests for reprints: Ming You, Department of Surgery and The Alvin J. Siteman Cancer Center, Campus Box 8109, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110. Phone: 314-362-8315; Fax: 314-362-8323; E-mail: youm{at}wustl.edu.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results and Discussion
 References
 
A series of linkage studies was previously conducted to identify quantitative trait loci associated with chemically induced lung tumors. However, little is known of genetic susceptibility to spontaneously occurring lung tumorigenesis (SLT) in mice. In this study, we did a whole-genome linkage disequilibrium analysis for susceptibility to SLT in mice using ~135,900 single-nucleotide polymorphisms (SNPs) from the Roche and Genomic Institute of the Novartis Research Foundation SNP databases. A common set of 13 mouse strains was used, including 10 resistant strains (129X1/SvJ, AKR/J, C3H/HeJ, C57BL/6J, DBA/2J, NZB/BlnJ, CAST/EiJ, SPRET/EiJ, SM/J, and LP/J) and 3 susceptible strains (A/J, BALB/cJ, and NZW/LaCJ). Fisher exact test was used to assess the association between individual SNPs and susceptibility to SLT. Five regions, SLT1 to SLT5, were mapped on chromosomes 6, 7, 8, 19, and X, respectively. SLT1 to SLT5 showed a significant association with SLT under the empirical threshold (P ≤ 0.004) derived from permutation tests. SNP versus SNP association tests indicated that these SLT regions were unlikely to be caused by population substructure. Thus, SLT1 to SLT5 seem to be novel loci controlling the susceptibility to spontaneously occurring lung cancer in mice. Our results provide, for the first time, an insight into the genetic control of spontaneously occurring lung tumorigenesis.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results and Discussion
 References
 
Lung cancer is the leading cause of cancer mortality in both males and females in developed countries (1). Lung cancer development is a multistage process involving successive addition of genetic aberrations (1). Accumulating evidence shows that genetic factors are involved in familial lung tumor development in humans. As a result, identification and characterization of genes responsible for susceptibility or resistance to lung tumorigenesis will be of great interest in cancer detection, prevention, and therapy. Given the phylogenetic proximity, common genetic changes were frequently found in both human and mouse lung tumors (e.g., p53, Kras2, and p16). Thus, the mouse is a valuable model for studying tumor susceptibility, stages of tumor development, and the interaction of genetic and environmental factors that result in predisposition to neoplasia.

Linkage mapping of quantitative trait loci has revealed several regions on mouse chromosomes related to susceptibility and resistance to chemically induced lung cancer (2, 3). Pulmonary adenoma susceptibility locus 1 (Pas1), located on chromosome 6 between markers D6Mit54 and D6Mit304, is a major locus affecting the inheritable predisposition to chemically induced lung tumor development in mice. Other pulmonary adenoma susceptibility loci (i.e., Pas2, Pas3, and Pas4) have been shown to modulate the effect of Pas1. Pas2 was mapped on mouse chromosome 17 between D17Mit23 and D17Mit50; Pas3 on chromosome 19 in the region flanked by D19Mit42 and D19Mit19; and Pas4 on chromosome 9 from D9Mit11 to D9Mit282. We reported a locus on mouse chromosome 10 (D10Mit126) that modified the effect of Pas1 and increased tumor multiplicities. Tripodis et al. (4) reported 30 susceptibility lung cancer (Sluc) loci using recombinant congenic inbred strains of mice and the lung tumor development in response to chemical carcinogens. Moreover, several quantitative trait loci of pulmonary adenoma resistance (Par) to chemical induction were also reported. Par1 locus was mapped to mouse chromosome 11, near the Rara locus, with a log of odds score of 5.3. Par2, located on distal chromosome 18, accounts for 38% of the phenotypic variance in the backcross population, and plays a major role in protection against lung tumor development. Resistance locus Par3, located on chromosome 12, was considered to act synergistically with Par1 (5).

Linkage disequilibrium is defined as the nonrandom association of alleles at linked loci in the population. It is the basis of fine mapping of complex disease loci through whole-genome association studies. In comparison with linkage mapping, linkage disequilibrium mapping can significantly increase the precision of locating a gene of interest (6). In humans, linkage disequilibrium has been successfully used to find the precise location of disease loci (7). Single-nucleotide polymorphisms (SNPs) are changes in a single base at a specific position in the genome. Over the past few years, SNPs have been widely used for the identification of complex disease genes and for pharmacogenetic applications. Depending on where an SNP occurs, it might be responsible, in whole or in part, for the phenotypic difference. With the increasing number of available mouse SNPs, linkage disequilibrium mapping can be used to localize genomic regions responsible for mouse lung tumor susceptibility.

In this study, we conducted a whole-genome linkage disequilibrium analysis for spontaneous lung tumorigenesis (SLT) susceptibility with two recently released SNP databases (containing a total of ~135,900 SNPs): the Roche mouse SNP database and the SNP database of Genomics Institute of the Novartis Research Foundation. Our goal was to identify novel loci specifically predisposed to spontaneously occurring SLT because little is known for the genetic susceptibility to SLT in mice.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results and Discussion
 References
 
Single-nucleotide polymorphism data and susceptibility to spontaneously occurring lung tumorigenesis. The SNP data were downloaded from the Roche database1 and the GNF database.2 As of the beginning of this study, the Roche SNP database contained ~125,000 SNPs on 18 inbred mouse strains, which were from the regions of 2,175 genes across the genome. The GNF database contains ~10,900 evenly distributed SNPs, which were typed for 48 inbred mouse strains. The SNPs from the two data sets together cover the mouse genome at an average density of 18 kb per SNP.

In each of the two data sets, some strains, such as A/J and A/HeJ, BALB/cJ and BALB/cByJ, C57BL/10J, C57BL6J, C57BLKS/J, C57BR/cdJ, and C57L/J, share very recent ancestors and have more than 99% of all the SNPs identical among them. To avoid apparent genetic overrepresentation in the sample that can cause spurious linkage disequilibrium, we retained only one strain from each of the substrain groups. In addition, we had to exclude strains with unknown SLT susceptibility, which led to a common set of 13 strains for both the Roche data and the GNF data. These strains include a group of 10 strains (129X1/SvJ, AKR/J, C3H/HeJ, C57BL/6J, DBA/2J, NZB/BlnJ, CAST/EiJ, SPRET/EiJ, SM/J, and LP/J) resistant to SLT, and a group of 3 susceptible strains (A/J, BALB/cJ, and NZW/LaCJ). The classification of these strains was based on published observations on spontaneously occurring lung tumor incidence frequency (8, 9), the Mouse Tumor Biology Database of the Jackson laboratory,3 and our unpublished observations (Table 1).


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  		Table 1. Mouse strains and susceptibility

 
The SNP data were subjected to further scrutiny. This included the removal of SNPs with less than eight strains typed and SNPs with only one phenotypic category (either susceptible or resistant) after excluding missing data, which resulted in a final set of 110,245 SNPs (99,882 from the Roche data and 10,363 from the GNF data), and thus a reduced coverage density to 22 kb per SNP. In addition, the genomic positions (in base pair) of the SNPs for the two data sets were unified to the latest National Center for Biotechnology Information (NCBI) mouse genome map (Build 33.1).4 The method was to first find all SNPs with an accession in the NCBI dbSNP database, and then use the NCBI positions of these SNPs to infer the positions of those having no accession in the NCBI database by simple interpolation but keeping their original SNP order unchanged.

Statistical analysis. Fisher exact test was applied to assessing associations between each SNP in the combined data set and SLT susceptibility. To do the test, a 2 x 2 contingency table was created for counts of the four possible combinations between each SNP and SLT susceptibility. A two-sided P value for each SNP was obtained for testing hypothesis of no association between the SNP and SLT susceptibility. All Fisher exact tests were conducted using the R statistical package (10).

Despite the fact that P values from Fisher tests by themselves are probabilities for no associations, the overall false-positive rate for genome-wide tests is difficult to obtain. This is because there are no simple relations between tests for linked (dependent) SNPs, and the simple Bonferroni correction seems too stringent for our analysis. Therefore, to control the genome-wide false-positive rate that is inflated by multiple association tests, we conducted permutation tests and evaluated the significances of SNP-SLT associations with an empirical threshold determined by permutation tests (11). Each permutation was done by rearranging the phenotype labels (susceptible or resistant) among all the mouse strains but keeping their SNP data unchanged. In doing so, the true (if any) associations between SNPs and SLT susceptibility would be wiped out. All 286 possible permuted samples were created each with rearranged phenotype data and were analyzed in the same way as for the original data. An empirical null distribution was then generated for genome-wide minimum P values each from the genome-wide Fisher tests with one permuted sample. Based on the empirical distribution, a critical P value for a given genome-wide false-positive rate was determined for testing the significance of each association. Two significance levels were considered: one false-positive per genome-wide scan (suggestive level) and 0.05 false positive per genome-wide scan (significant level; ref. 12).

Population substructure (overrepresentation of certain genetic backgrounds) may lead to spurious linkage disequilibrium and has recently gained considerable attention (13). To assess the influence of population substructure in our analysis, we conducted Fisher exact tests for SNP versus SNP associations between significant SNPs detected under the empirical threshold and all their respective independent SNPs (on all different chromosomes). The P values from all such tests were then used to get a baseline as a genomic control (13) for evaluating the reliability of the linkage disequilibria identified.


   Results and Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results and Discussion
References
 
Determination of thresholds by permutation tests. To conduct genome-wide linkage disequilibrium tests at a given overall false-positive rate, it is necessary to determine the appropriate threshold. In this study, we did permutation tests for all 286 possible permuted samples. The permutation results were first evaluated for the distribution of total numbers of SNPs detected at various nominal significance levels ranging from {alpha} = 0.1 to {alpha} = 0.001. The number of SNPs detected with each permuted sample at each of the significance levels was compared with the total number of SNPs detected with the original data at the same significance level. Figure 1A shows the distribution of total numbers of SNPs detected with all the permuted samples that were larger than or equal to the corresponding total number of SNPs detected with the original data. It is clear from Fig. 1A that with decreased {alpha} value (increased significance level), the total number of SNPs detected with the original data declined, and so did the false-positive rate. When {alpha} was reduced to 0.006, the total number of SNPs detected with the original data was reduced to 167, whereas only 3 of the 286 permuted samples (including the original data set itself) yielded the same or larger number of SNPs, a chance of about 1%. This false-positive rate remained until {alpha} = 0.004. At {alpha} ≤ 0.003, however, no SNPs could be detected with either the original data or any permuted samples. The low overall false-positive rate at high significance levels indicates that associations detected at these significance levels likely showed true associations between the relevant SNPs and SLT susceptibility.



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  		Figure 1. Empirical distributions derived from permutation tests. A, distribution of total numbers of SNPs detected from all permuted samples at 16 successive nominal {alpha}. The numbers above or below the spots are the numbers of markers detected at a given nominal {alpha} with the original data. The proportion of permuted samples was calculated as the number of permuted samples that yielded equal or more markers than that obtained from the original data divided by 286 (number of all possible permuted samples). B, frequency distribution of discrete genome-wide minimum P values.

 
Using the permutation results, an empirical distribution was created for genome-wide minimum P values, each from the analysis of one permuted sample. Due to the nature of the relatively small sample size, the distribution displayed an apparently discrete trend: all the minimum P values fell into six groups of distinct P values from 0.014 to 0.0035 (Fig. 1B). There were 216 permuted samples falling in the largest group with P value = 0.0035, implying that we would not be able to obtain a significant threshold for our association tests, according to the relevant definition mentioned above (12). However, we were able to get a suggestive threshold (one false-positive per genome-wide scan). Taking into account the distribution of total numbers of SNPs detected at different nominal thresholds (Fig. 1A) and possible higher false-positive rate resulting from the small sample size, we selected the relatively stringent threshold P = 0.004 as our suggestive significance level. Based on the empirical distribution, at this threshold, there would be an expectation of about 0.87 false positive per genome-wide scan.

Genome-wide linkage disequilibrium mapping of mouse SLT loci. Genome-wide association tests were conducted with the original combined data set. P values generated from Fisher tests for associations between SLT susceptibility and all the SNPs in the combined data set were plotted against the unified genomic positions of the SNPs in Fig. 2A. With the empirical suggestive threshold (P = 0.004) previously determined, we identified five regions (SLT1-SLT5) on chromosomes 6, 7, 8, 19, and X, respectively. Each region has at least one SNP significantly associated with SLT (Table 2). These regions were extended to cover closely linked SNPs that were associated with SLT susceptibility with P < 0.05. SLT3 on chromosome 8 is 1.7 Mb, containing most of the SNPs (120) significant at the empirical threshold; in SLT5 (10.3 Mb) on chromosome X, seven significant SNPs were detected, each occupying a 1 or 2 Mb consecutive genomic region. Each of the other three SLT regions, ranging from 1.8 to 3.1 Mb, contains one SNP significant at the empirical threshold.



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  		Figure 2. Genome-wide linkage disequilibrium analysis for spontaneous lung cancer susceptibility using the Roche and GNF SNP data sets. A, genome-wide linkage disequilibrium analysis. Each point in the chromosome plots represents an SNP, and was plotted as –log10(p) against its position in megabases. Vertical dashed side, P = 0.0032. Arrows, regions that were significant at the suggestive level. B, haplotypes around SNPs having significant associations with spontaneous lung cancer in the five linkage disequilibrium regions. Each row represents a mouse strain; each column stands for an ordered SNP. Strains above the horizontal line are resistant to spontaneous lung cancer; those below the line are susceptible. Boxed SNPs were significantly or nearly significant associated with lung cancer at the empirical threshold P = 0.004). Dots, missing observations.

 

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  		Table 2. Linkage disequilibrium regions detected for spontaneous lung cancer susceptibility

 
Validation by a genomic control. To examine the likelihood of the detected SLT regions being true linkage disequilibrium, rather than spurious linkage disequilibrium resulting from population substructure, we conducted SNP versus SNP association tests. For each of the 130 SNPs significantly associated with SLT in the five SLT regions, we used Fisher test for its associations with all its respective independent SNPs. The independent SNPs were all those located on different chromosomes from that of the SNP under test, but not in any of the five SLT regions. The same empirical threshold (P = 0.004) was used for testing the significance of the association between each pair of SNPs, and the total number of significant associations was recorded. Our results showed that a total of 357 tests gave a significant P value (P ≤ 0.004), which involved only 62 independent SNPs, a proportion of about 5.7 x 10–4 among all independent SNPs tested (Fig. 3). Moreover, most of these independent SNPs had significant associations with less than 10 of the 130 SNPs from the SLT regions, and none of them had significant associations with more than 40 SNPs from the SLT regions. This is in sharp contrast with the results for SLT susceptibility, which had significant associations with all of the 130 SNPs. This result showed that the associations of SLT susceptibility with the SNPs in the five linkage disequilibrium regions were less likely to be caused by the population substructure of the mouse strains. In other words, these regions are likely the true linkage disequilibrium with the genes affecting the SLT susceptibility.



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  		Figure 3. Association tests between the 130 SNPs in the five linkage disequilibrium regions and their respective independent SNPs. The x axis is for independent SNPs arranged in an arbitrary order. Dots, total number of significant associations that an independent SNP had with the set of the 130 SNPs. The horizontal dashed line was drawn at y = 130, which is the number of significant associations detected for spontaneous lung cancer.

 
Haplotypes in the SLT regions. Figure 2B gives the haplotypes for each of the mouse strains in the five SLT regions. The SLT regions include varied numbers of SNPs: 11 in SLT1, SLT2, and SLT4; 1,293 in SLT3; and 32 in SLT5. The SNPs shown in Fig. 2B for SLT3 and SLT5 were selected to evenly cover the regions due to the high densities of SNPs. It is clear that in each of the five regions, the susceptible strains have haplotypes very distinct from the resistant strains. Significant associations between the SNPs and SLT in the regions often represented complete linkage disequilibrium. However, most of the 130 significant SNPs in the five regions were specified as either intronic SNPs or "locus region" based on the NCBI dbSNP database; none of them were specified as potential functional changes (missense, nonsense, untranslated region, etc.). Thus, we attempted to narrow the list of potential candidates using comparative genomic analyses.

Comparative genomic analysis in the SLT regions. Based on the NCBI genome map (Build 33.1), there are 34, 30, 28, 34, and 86 genes in SLT1, SLT2, SLT3, SLT4, and SLT5, respectively (Table 2). To identify potential candidate genes in each of the regions, we first conducted comparative genomic analyses to obtain the genes that were the most conserved. Using the web-based tool developed by Li et al. (14), we conducted three types of comparisons to identify genes homologous between mouse and other species: (a) comparisons of the proteome of mouse (Mus musculus) with those of all 29 species available in the web-based program, including 14 multicellular organisms (Homo sapiens, Rattus norvegicus, Fugu rubripes, etc.) and 15 unicellular (or unicellular-multicellular) organisms (Chlamydomonas reinhardtii, Cryptococcus neoformans, etc.); (b) comparisons with all the 14 multicellular organisms only; (c) comparisons with all the 15 unicellular organisms only. Because unicellular organisms are more primitive than multicellular organisms, mouse genes homologous with those of unicellular organisms should be more conserved than those homologous with multicellular-organism genes and the latter more conserved than the genes having no homology.

Results showed that the comparisons with all the 29 organisms yielded the smallest number (835) of homologous genes, whereas comparisons with multicellular organisms resulted in the most genes (4,170). The number of genes identified by comparisons with unicellular organisms (927) was a little more than that from comparisons with all organisms. In fact, all the genes from comparisons with all organisms were only a subset of the genes from comparisons with unicellular organisms, and most of the genes homologous with unicellular organisms were also homologous with multicellular organisms. We then compared the genes in each of the SLT regions with the mapped homologous genes from each type of comparison (824 genes with all-organisms homology, 4,088 genes with multicellular homology, and 913 genes with unicellular homology). The results are summarized in Table 3 and Fig. 4. Although SLT1 and SLT2 do not have genes with unicellular homology, there were five genes in SLT1 and one gene in SLT2 that are homologous with genes of multicellular organisms. SLT3 has three genes with both unicellular and multicellular homology, and also has one additional gene homologous with genes of multicellular organisms. SLT4 and SLT5 both have only one gene homologous with genes from both unicellular and multicellular organisms, but five (in SLT4) and seven (in SLT5) additional genes with multicellular homology. If the conservation of a genomic region can be determined by gene homology, SLT3 would be the most conserved region.


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  		Table 3. Homologous genes in the SLT regions based on comparative genomic analyses

 


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  		Figure 4. Physical maps of the five SLT regions. For each SLT region, the thick brown solid arrow (SLT arrow) indicates the SLT region and orientation. On the right side of the SLT arrow are mouse genes and their orientations (open arrows). Orange arrows, genes homologous with multicellular organisms; brown arrows, genes homologous with unicellular organisms. On the left side of the SLT arrow are homologous regions (dashed arrows), if any, and their orientations on the human genome.

 
Except for several predicted genes (such as LOC381788 in SLT1 and LOC245666 in SLT5), most of the 24 genes with varied conservation in the five regions are known genes. The five genes with unicellular homology are Rhou, Afg3l1, and Taf5l in SLT3, Atad1 in SLT4, and Pak3 in SLT5 (Table 3). Rhou (also known as G28K or WRCH1) encodes a protein with homology to the human cell division cycle protein 42/G25K (15). It was found to be differentially expressed in primary kidney, colon, gastric, breast, ovarian, and uterus cancer. Afg3l1 (also known as AFG3) encodes a protein that is targeted to the mitochondria (16), and is possibly related to disorders sharing features with mitochondrial disease syndromes, such as sensorineural deafness, diabetes, and retinopathy. However, there was no published evidence indicating its role in cancer. Taf5l encodes a protein that is a component of the PCAF histone acetylase complex and structurally similar to one of the histone-like TAFs, TAF5. The PCAF histone acetylase complex plays a role in the regulation of transcription, cell cycle progression, and differentiation (17). However, it is not clear whether this gene played a role in tumorigenesis in either humans or mice. Atad1 in SLT4 is a member of the ATPase family with quite disparate cellular activities (18). Pak3 belongs to the family coding for p21-activated kinases that are involved in the control of cytoskeleton dynamics and cell cycle progression (19, 20). In humans, this gene was shown to be responsible for X-linked mental retardation and may indirectly influence mitotic events in breast cancer cells (21). Other genes of interest in the SLT regions that are homologous only with multicellular organisms include Zik1 in SLT2 and Ureb1 in SLT5. Zik1 encodes a zinc finger protein interacting with K protein 1, which was found to be a transcriptional repressor (22). Ureb1 encodes upstream regulatory element binding protein 1e. It was found to be overexpressed in human colorectal cancer (23). Another study suggested that optimal suppression of tumor suppressor p53 transactivation requires tyrosine-phosphorylated Ureb1 (24).

Known cancer genes in the SLT regions. The human homologous regions for the SLT regions were indicated in Fig. 4. Studies on loss of heterozygosity (LOH) have shown that some of the regions entailed frequent loss in human lung cancer, such as 1q (SLT3; ref. 25), 2p (SLT1; ref. 26), and 10q (SLT4; ref. 27). In mice, regions on chromosome 6 (SLT1) also incurred LOH in lung cancer development (28). These results suggest that the related genomic regions are genetically more fragile than other regions in lung cancer development, and possibly harbor lung cancer suppressor genes. According to the list of known cancer genes provided on the Sanger Institute web site,5 four genes in three of the SLT regions are known cancer genes in human. These genes include Fanca in SLT3, Pten and Tnfrsf6 in SLT4, and Lhfp in SLT5. Fanca is part of a multiprotein nuclear Fanconi anemia complex with identical function in cellular responses to DNA damage and germ cell survival (29, 30). In addition to the functions in development of Fanconi anemia, Fanca has also been reported to be possibly involved in pathways for breast cancer, familial pancreatic cancer, and sporadic acute myeloid leukemia. Thus, it is logical to hypothesize that this gene may also play some roles in SLT susceptibility in mice. Pten is a tumor suppressor, coregulating oncogenic cell signaling pathways downstream of receptor tyrosine kinases (31–33). Many recent studies have shown that decreased expression of Pten was related to tumorigenesis of non–small-cell lung cancer in humans (34–36). Gautam et al. (37) found that the expression of Pten is an intermediate step for suppression of lung cancer metastasis induced by overexpression of gene RRM1 in both human and mouse cell lines. This strongly suggests that Pten may also play an important role in inheritable spontaneous lung cancer development of mice. Tnfrsf6 (also known as FAS) encodes the cell surface receptor involved in apoptotic signal transmission in many cell types, including cells of the immune system (38). Tnfrsf6 is silenced in many tumor types, resulting in an inability to respond to proapoptotic signals (39). A recent study showed that Tnfrsf6 was involved in up-regulation of Fas/Fas ligand–mediated apoptosis induced by gossypol in human alveolar lung cancer cell line (40). The Fas/Fas ligand system was also found to be a critical regulator of perinatal alveolar epithelial type II cell apoptosis in mouse lungs (41). Thus, it is reasonable to consider Tnfrsf6 as a promising candidate gene for genetic susceptibility to spontaneous lung cancer in mice. Lhfp (lipoma HMGIC fusion partner) acts as a translocation partner of HMGIC in a lipoma with a t(12;13). It was found to be related to benign and low-malignant lipomatous tumors (42). However, there were no reports showing its relevance in the development of lung and/or lung cancers.


   Acknowledgments
 
Grant support: NIH grants CA099187, CA099147, ES012063, and ES013340.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Dr. Tim Wiltshire for sharing the mouse SNP data. We are grateful to several individuals in Chemoprevention Program of the Siteman Cancer Center for making corrections to this article.


   Footnotes
 
1 http://mousesnp.roche.com Back

2 http://snp.gnf.org/GNF10K/files.html Back

3 http://www.jax.org Back

4 http://www.ncbi.nlm.nih.gov Back

5 http://www.sanger.ac.uk Back

Received 5/ 3/05. Revised 6/21/05. Accepted 6/30/05.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results and Discussion
 References
 

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