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Human SCGB1A1 protein has been shown to be significantly reduced in BAL, sputum, and serum from humans with asthma as compared with healthy individuals. However, the mechanism of this reduction and its functional impact have not been entirely elucidated. By mining online datasets, we found that the mRNA of SCGB1A1 was significantly repressed in brushed human airway epithelial cells from individuals with asthma, and this repression appeared to be associated with reduced expression of FOXA2. Consistently, both Scgb1A1 and FoxA2 were downregulated in an ovalbumin-induced mouse model of asthma. Furthermore, compared with wild-type mice, Scgb1a1 knockout mice had increased airway hyperreactivity and inflammation when they were exposed to ovalbumin, confirming the antiinflammatory role of Scgb1a1 in protection against asthma phenotypes. To search for potential asthma-related stimuli of SCGB1A1 repression, we tested T-helper cell type 2 cytokines. Both IL-4 and IL-13 repressed epithelial expression of SCGB1A1 and FOXA2. Importantly, infection of epithelial cells with human rhinovirus similarly reduced expression of these two genes, which suggests that FOXA2 may be the common regulator of SCGB1A1. To establish the causal role of reduced FOXA2 in SCGB1A1 repression, we demonstrated that FOXA2 was required for SCGB1A1 expression at baseline. FOXA2 overexpression was sufficient to drive promoter activity and expression of SCGB1A1 and was also able to restore the repressed SCGB1A1 expression in IL-13-treated or rhinovirus-infected cells. Taken together, these findings suggest that low levels of epithelial SCGB1A1 in asthma are caused by reduced FOXA2 expression.

[Abstract] Objective: Magnetic resonance imaging (MRI)-detected structural features are associated with increased risk of radiographic osteoarthritis (ROA). Specific mitochondrial DNA (mtDNA) haplogroups have been associated with incident ROA. Our objective was to compare the presence of MRI-detected structural features across mtDNA haplogroups among knees that developed incident ROA. Design: Knees from the Osteoarthritis Initiative (OAI) that developed incident ROA during 48 months follow-up were identified from Caucasian participants. mtDNA haplogroups were assigned based on a single base extension assay. MRIs were obtained annually between baseline and 4-year follow-up and scored using the MRI Osteoarthritis Knee Score (MOAKS). The association between mtDNA haplogroups and MRI-detected structural features was estimated using log-binomial regression. Participants who carried haplogroup H served as the reference group. Results: The sample included 255 participants contributing 277 knees that developed ROA. Haplogroups included H (116, 45%), J (17, 7%), T (26, 10%), Uk (61, 24%), and the remaining less common haplogroups ("others") (35, 14%). Knees of participants with haplogroup J had significantly lower risk of medium/large bone marrow lesions (BMLs) in the medial compartment [3.2%, relative risks (RR) = 0.17; 95%CI: 0.05, 0.64; P = 0.009] compared to knees of participants who carried haplogroup H [16.3%], as did knees from participants within the "others" group [2.8%, RR = 0.20; 95%CI: 0.08, 0.55; P = 0.002], over the 4 year follow-up period. Conclusions: mtDNA haplogroup J was associated with lower risk of BMLs in the medial compartment among knees that developed ROA. Our results offer a potential hypothesis to explain the mechanism underlying the previously reported protective association between haplogroup J and ROA.

RNA-sequencing (RNA-seq), based on next generation sequencing (NGS) technologies, has become the preferred tool for transcriptome analysis in the past two decades. Ever maturing and decreasing costs of high-throughput sequencing technologies have led to new types of data such as large-scale RNA-seq and single-cell RNA-seq (scRNA-seq) data. The analysis of these new types of RNA-seq data presents both new opportunities and challenges. In this dissertation, I present three novel statistical works that focus on these types of RNA-seq data, driven by various interests of research. The first project, MDSeq, introduces the first gene expression variability analysis for large-scale RNA-seq count data. MDSeq utilizes a novel reparametrization of the negative binomial distribution to provide flexible generalized linear models (GLMs) on both the mean and dispersion, and simultaneously addresses the challenges of analyzing large-scale RNA-seq data by modeling technical excess of zeros, identifying outliers efficiently, and evaluating differential expressions at biologically interesting levels. The last two works, scDoc and scGSA, are analysis tools for the recently emerging scRNA-seq with different perspectives. scDoc is a statistical tool that accurately and robustly imputes drop-out events in scRNA-seq data. It is the first drop-out imputation method that includes drop-out information when accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. scGSA is a novel gene set analysis tool for scRNA-seq data. Without any prior knowledge about class labels (e.g., label of cell types), which is required by all existing gene set analysis approaches, scGSA can detect significant gene sets relating to biologically meaningful heterogeneity among cells. Through various comprehensive simulation studies, all three proposed methods have demonstrated the highest power compared with other existing methods wh