Regional differences in the pattern of airway remodeling following chronic allergen exposure in mice

BACKGROUND: Airway remodeling present in the large airways in asthma or asthma models has been associated with airway dysfunction in humans and mice. It is not clear if airways distal to the large conducting airways have similar degrees of airway remodeling following chronic allergen exposure in mice. Our objective was to test the hypothesis that airway remodeling is heterogeneous by optimizing a morphometric technique for distal airways and applying this to mice following chronic exposure to allergen or saline. METHODS: In this study, BALB/c mice were chronically exposed to intranasal allergen or saline. Lung sections were stained for smooth muscle, collagen, and fibronectin content. Airway morphometric analysis of small (0–50000 μm(2)), medium (50000 μm(2)–175000 μm(2)) and large (>175000 μm(2)) airways was based on quantifying the area of positive stain in several defined sub-epithelial regions of interest. Optimization of this technique was based on calculating sample sizes required to detect differences between allergen and saline exposed animals. RESULTS: Following chronic allergen exposure BALB/c mice demonstrate sustained airway hyperresponsiveness. BALB/c mice demonstrate an allergen-induced increase in smooth muscle content throughout all generations of airways, whereas changes in subepithelial collagen and fibronectin content are absent from distal airways. CONCLUSION: We demonstrate for the first time, a systematic objective analysis of allergen induced airway remodeling throughout the tracheobronchial tree in mice. Following chronic allergen exposure, at the time of sustained airway dysfunction, BALB/c mice demonstrate regional differences in the pattern of remodeling. Therefore results obtained from limited regions of lung should not be considered representative of the entire airway tree

Genetic associations in diabetic nephropathy: a meta-analysis

Diabetes mellitus: pathophysiological changes and therap

Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS

Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency–matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10−4–10−8), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits

Haplotype Reconstruction Error as a Classical Misclassification Problem: Introducing Sensitivity and Specificity as Error Measures

BACKGROUND: Statistically reconstructing haplotypes from single nucleotide polymorphism (SNP) genotypes, can lead to falsely classified haplotypes. This can be an issue when interpreting haplotype association results or when selecting subjects with certain haplotypes for subsequent functional studies. It was our aim to quantify haplotype reconstruction error and to provide tools for it. METHODS AND RESULTS: By numerous simulation scenarios, we systematically investigated several error measures, including discrepancy, error rate, and R(2), and introduced the sensitivity and specificity to this context. We exemplified several measures in the KORA study, a large population-based study from Southern Germany. We find that the specificity is slightly reduced only for common haplotypes, while the sensitivity was decreased for some, but not all rare haplotypes. The overall error rate was generally increasing with increasing number of loci, increasing minor allele frequency of SNPs, decreasing correlation between the alleles and increasing ambiguity. CONCLUSIONS: We conclude that, with the analytical approach presented here, haplotype-specific error measures can be computed to gain insight into the haplotype uncertainty. This method provides the information, if a specific risk haplotype can be expected to be reconstructed with rather no or high misclassification and thus on the magnitude of expected bias in association estimates. We also illustrate that sensitivity and specificity separate two dimensions of the haplotype reconstruction error, which completely describe the misclassification matrix and thus provide the prerequisite for methods accounting for misclassification

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Critical Role of Macrophages and Their Activation via MyD88-NFκB Signaling in Lung Innate Immunity to Mycoplasma pneumoniae

Mycoplasma pneumoniae (Mp), a common cause of pneumonia, is associated with asthma; however, the mechanisms underlying this association remain unclear. We investigated the cellular immune response to Mp in mice. Intranasal inoculation with Mp elicited infiltration of the lungs with neutrophils, monocytes and macrophages. Systemic depletion of macrophages, but not neutrophils, resulted in impaired clearance of Mp from the lungs. Accumulation and activation of macrophages were decreased in the lungs of MyD88−/− mice and clearance of Mp was impaired, indicating that MyD88 is a key signaling protein in the anti-Mp response. MyD88-dependent signaling was also required for the Mp-induced activation of NFκB, which was essential for macrophages to eliminate the microbe in vitro. Thus, MyD88-NFκB signaling in macrophages is essential for clearance of Mp from the lungs

Disordered microbial communities in asthmatic airways.

A rich microbial environment in infancy protects against asthma [1], [2] and infections precipitate asthma exacerbations [3]. We compared the airway microbiota at three levels in adult patients with asthma, the related condition of COPD, and controls. We also studied bronchial lavage from asthmatic children and controls.We identified 5,054 16S rRNA bacterial sequences from 43 subjects, detecting >70% of species present. The bronchial tree was not sterile, and contained a mean of 2,000 bacterial genomes per cm(2) surface sampled. Pathogenic Proteobacteria, particularly Haemophilus spp., were much more frequent in bronchi of adult asthmatics or patients with COPD than controls. We found similar highly significant increases in Proteobacteria in asthmatic children. Conversely, Bacteroidetes, particularly Prevotella spp., were more frequent in controls than adult or child asthmatics or COPD patients.The results show the bronchial tree to contain a characteristic microbiota, and suggest that this microbiota is disturbed in asthmatic airways

The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling

Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator