Integrative-omics of the Disordered COPD Small Airway Epithelium

We aim to use an integrated network systems approach to analyze certain existing small airway epithelium (SAE) omic data sets at the genetic, epigenetic (methylation), gene expression, microRNA and metabolomic levels, to develop an initial model of network connectivities and key network pressure points relevant to SAE biology in health and disease.

Hypothesis: We hypothesize that the disordered differentiation of the SAE that characterizes COPD results from the complex interaction of cigarette smoke components with a hierarchy of genetic, epigenetic, gene expression and metabolomics network interactions as the BC differentiate into a mucociliary epithelium. Specific aim 1. Using an integrated network systems approach to analyze our extensive existing SAE omic data sets at the genetic, epigenetic (methylation), gene expression, microRNA and metabolomics levels, to develop an initial model of network connectivities and key network pressure points relevant to SAE biology in health and disease. Specific aim 2. To refine the model, a comprehensive omics data set will be collected at multiple time points as SAE BC of nonsmokers and COPD smokers differentiate to normal and disordered mucociliary epithelium (respectively) on air-liquid interface (ALI) culture, an in vitro model of SAE differentiation. The computational strategies from aim 1 will be used to improve the model with these data. Specific aim 3. To test and finalize the integrated network model, a parallel omics data set will be generated from BC from nonsmokers, and COPD smokers as they differentiate on ALI under conditions where key hubs will be up- or down-regulated and the differentiation process stressed under conditions mimicking the in vivo SAE environment. The end result will be an integrated systems model of SAE biology and how this is disordered in COPD.