Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by breathlessness due to airflow obstruction. COPD is the third leading cause of death worldwide. So far, none of the existing pharmacological treatments for COPD can stop the progressive decline in lung function. Drug repurposing is the application of existing approved therapeutic compounds for new disease indications, which may reduce the cost and time of new drug development. So far, there is not any systematic multi-omics data integration for drug repurposing in COPD. The goal of this project is to develop a systems biology pipeline for the identification of biological-relevant gene targets with drug repurposing potential for COPD treatment using multi-omics integration. Here we implemented a computational methodology to identify drug repurposing targets for COPD. We integrated multi-omics COPD data including, genome, transcriptome, proteome, metabolome, interactome data, and drug-target information. A distance-based network model was created to rank the potential candidate genes. Fifty genes were prioritized as COPD signature genes for their overall proximity to signature genes identified at all omics levels. Forty of them may be considered as “druggable” targets. Literature search reported CRCX4 – Plerixafor as one prioritized targets-gene pair for drug repurposing. The bone marrow stimulant Plerixafor is currently being evaluated for COPD treatment in clinical trials, suggesting that our pipeline is finding promising drug repurposing targets. Our work, for the first time, applied a systematic approach integrating multiple omics data to find drug repurposing targets for COPD. / Pharmaceutical Sciences

When people access online health information, unfortunately, they have access to both clinically accurate and inaccurate information that they may then utilize to make informed personal health decisions. This research fills a gap in the literature of online health communities as they relate to chronic obstructive pulmonary disease (COPD). The conduct of this research required a multi-phased and multi-method approach, best presented in three distinct essays. In Essays 1 and 2, data gathering within two online health communities specific to COPD allowed this study to address three research questions: (1) what are the information needs of COPD patients that result in their participation in online health communities; (2) what are the information sources offered to the participants in these online communities; and (3) is the information obtained via those communities credible. Essay 1 harvested data from a moderated website hosted by a non-profit organization for patients with COPD and Essay 2 harvested data from a non-moderated Facebook group also serving this unique group. Data Miner, a Chrome extension designed to extract data, was used to collect data, key words and themes which brought an understanding of the health information needs of participants and identified what health information sources were preferred. Using NIH guidelines, the credibility of sources exchanged were evaluated for both groups. The research presented in Essay 1 showed that COPD patients have health information needs and that a clinically monitored social health online community, that is available 24/7 to answer questions that arise at the time of need, provides much needed support. The research in Essay 2 illustrates the need for healthcare workers to be aware of unmoderated sites and promote these sites for the purpose of socialization only, and not for medical information. Building on the knowledge gained through the data analysis in Essays 1 and 2 and based on the theoretical frameworks established in the health belief model, social exchange theory, and the technology acceptance model, Essay 3 generated a new integrated model that seeks to understand information seeking effectiveness in online health communities was proposed. This model identifies the relationships between the types of disease specific information sought by members of 65 COPD Facebook groups, and member success in acquiring credible and clinically accurate health information to use in making health decisions related to disease management and the development of effective health management behaviors. Structural equation modeling was utilized to analyze survey responses and test the proposed model for statistical significance This study has important implications for health educators and medical professionals that will enhance their understanding of the benefits of online peer health communities and will guide them in providing their patients with an "information prescription" guiding them to clinically accurate and understandable, disease specific health information between office visits and at the patient's time of need.

Chronic lung diseases including Chronic Obstructive Pulmonary Disease (COPD), Idiopathic Pulmonary Fibrosis (IPF) and lung cancer are major causes of morbidity and mortality in the United States due to high incidence and limited therapeutic options. In order to address this critical issue, I have leveraged RNA sequencing and integrative genomics to define disease-associated transcriptomic changes which could be potentially targeted to lead to new therapeutics. We sequenced the lung transcriptome of subjects with IPF (n=19), emphysema (n=19, a subtype of COPD), or neither (n=20). The expression levels of 1770 genes differed between IPF and control lung, and 220 genes differed between emphysema and control lung (p<0.001). Upregulated genes in both emphysema and IPF were enriched for the p53/hypoxia pathway. These results were validated by immunohistochemistry of select p53/hypoxia proteins and by GSEA analysis of independent expression microarray experiments. To identify regulatory events, I constructed an integrative miRNA target prediction and anticorrelation miRNA-mRNA network, which highlighted several miRNA whose expression levels were the opposite of genes differentially expressed in both IPF and emphysema. MiR-96 was a highly connected hub in this network and was subsequently overexpressed in cell lines to validate several potential regulatory connections. Building upon these successful experiments, I next sought to define gene expression changes and the miRNA-mRNA regulatory network in never smoker lung cancer. Large and small RNA was sequenced from matched lung adenocarcinoma tumor and adjacent normal lung tissue obtained from 22 subjects (8 never, 14 current and former smokers). I identified 120 genes whose expression was modified uniquely in never smoker lung tumors. Using a repository of gene-expression profiles associated with small bioactive molecules, several compounds which counter the never smoker tumor signature were identified in silico. Leveraging differential expression information, I again constructed an mRNA-miRNA regulatory network, and subsequently identified a potential never smoker oncomir has-mir-424 and its transcription factor target FOXP2. In this thesis, I have identified genes, pathways and the miRNA-mRNA regulatory network that is altered in COPD, IPF, and lung adenocarcinoma among never smokers. My findings may ultimately lead to improved treatment options by identifying targetable pathways, regulators, and therapeutic drug candidates. / 2017-02-01T00:00:00Z

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京都大学 / 新制・課程博士 / 博士(医学) / 甲第24967号 / 医博第5021号 / 新制||医||1069(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 伊達 洋至, 教授 江木 盛時, 教授 川上 浩司 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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