Thymic stromal lymphopoietin (TSLP) is elevated in asthma and triggers dendritic cell-mediated activation of Th2 inflammatory responses. Although TSLP has been shown to be produced mainly by airway epithelial cells, the regulation of epithelial TSLP expression has not been extensively studied. We investigated the expression of TSLP in cytokine- or TLR ligand-treated normal human bronchial epithelial cells (NHBE). The mRNA for TSLP was significantly up-regulated by stimulation with IL-4 (5.5-fold) and IL-13 (5.3-fold), weakly up-regulated by TNF-α, TGF-β, and IFN-β, and not affected by IFN-γ in NHBE. TSLP mRNA was only significantly up-regulated by the TLR3 ligand (dsRNA) among the TLR ligands tested (66.8-fold). TSLP was also induced by in vitro infection with rhinovirus. TSLP protein was detected after stimulation with dsRNA (120 ± 23 pg/ml). The combination of TNF-α and IL-4 produced detectable levels of TSLP protein (40 ± 13 pg/ml). In addition, TSLP was synergistically enhanced by a combination of IL-4 and dsRNA (mRNA; 207-fold, protein; 325 ± 75 pg/ml). The induction of TSLP by dsRNA was dependent upon NF-κB and IFN regulatory factor 3 (IRF-3) signaling via TLR3 as indicated by a study with small interfering RNA. The potent topical glucocorticoid fluticasone propionate significantly suppressed dsRNA-dependent TSLP production in NHBE. These results suggest that the expression of TSLP is induced in airway epithelial cells by stimulation with the TLR3 ligand and Th2 cytokines and that this response is suppressed by glucocorticoid treatment. This implies that respiratory viral infection and the recruitment of Th2 cytokine producing cells may amplify Th2 inflammation via the induction of TSLP in the asthmatic airway.

Background Compositional differences in bronchial bacterial microbiota have been associated with asthma, but it remains unclear whether the findings are attributable to asthma, to aeroallergen sensitization or to inhaled corticosteroid treatment. Objectives To compare the bronchial bacterial microbiota in adults with steroid-naive atopic asthma (AA), with atopy but no asthma (ANA), and non-atopic healthy subjects (HC), and determine relationships of bronchial microbiota to phenotypic features of asthma. Methods Bacterial communities in protected bronchial brushings from 42 AA, 21 ANA, and 21 HC subjects were profiled by 16S rRNA gene sequencing. Bacterial composition and community-level functions inferred from sequence profiles were analyzed for between-group differences. Associations with clinical and inflammatory variables were examined, including markers of type 2-related inflammation and change in airway hyperresponsiveness following six weeks of fluticasone treatment. Results The bronchial microbiome differed significantly among the three groups. Asthmatic subjects were uniquely enriched in members of the Haemophilus, Neisseria., Fusobacterium, Porphyromonas and Sphingomonodaceae, and depleted in members of the Mogibacteriaceae and Lactobacillales. Asthma-associated differences in predicted bacterial functions included involvement of amino acid and short-chain fatty acid metabolism pathways. Subjects with type 2-high asthma harbored significantly lower bronchial bacterial burden. Distinct changes in specific microbiota members were seen following fluticasone treatment. Steroid-responsiveness was linked to differences in baseline compositional and functional features of the bacterial microbiome. Conclusion Even in mild steroid-naive asthma subjects, differences in the bronchial microbiome are associated with immunologic and clinical features of the disease. The specific differences identified suggest possible microbiome targets for future approaches to asthma treatment or prevention.

To address these challenges we introduce here methods for local ancestry inference which leverage the structure of linkage disequilibrium in the ancestral population (LAMP-LD), and incorporate the constraint of Mendelian segregation when inferring local ancestry in nuclear family trios (LAMP-HAP). Our algorithms uniquely combine hidden Markov models (HMMs) of haplotype diversity within a novel window-based framework to achieve superior accuracy as compared with published methods. Further, unlike previous methods, the structure of our HMM does not depend on the number of reference haplotypes but on a fixed constant, and it is thereby capable of utilizing large datasets while remaining highly efficient and robust to over-fitting. Through simulations and analysis of real data from 489 nuclear trio families from the mainland US, Puerto Rico and Mexico, we demonstrate that our methods achieve superior accuracy compared with published methods for local ancestry inference in Latinos.