Chronic Obstructive Pulmonary Disease (COPD) remains a leading cause of mortality in the U.S. smoking population. Interestingly, only 15% of smokers develop COPD indicating that there is a strong genetic component to the disease. Mutations in specific genes including SERPINA1, which encodes 1- 1-antitrypsin, are associated with increased risk of developing COPD. Genes exist as DNA (deoxy ribonucleic acid), RNA (ribonucleic acid) and proteins. Using novel computational and experimental techniques that predict and validate RNA structure, we have identified a "RiboSNitch" associated with COPD in the SERPINA1 non-coding region of the mRNA. A RiboSNitch is a structured element in an RNA that adopts an alternative conformation if a specific, disease-associated SNP (Single Nucleotide Polymorphism) is present. We identified mutations associated with COPD in non-coding regions of SERPINA1 that affect the structure of the mRNA untranslated regions (UTRS). Furthermore, these non-coding regions have highly polymorphic splicing patterns, which we have shown affect RNA structure. We propose to further investigate the role of non-coding RNA structure in COPD. Specifically, we will develop highly predictive models of the SERPINA1 mRNA and determine the efects of mutations on structure and function. This will enable us to correlate genotypic information with the structure and function of the regulatory non-coding regions of genes. By determining the functional consequences of RiboSNitch conformational changes on gene regulation in lung and liver cel types (where SERPINA1 is most highly expressed), we will establish the necessary structure/function relationships to obtain a predictive understanding of the role of SERPINA1 mRNA in COPD.
We propose to investigate the role of RNA (Ribonucleic Acid) structural changes in the non-coding regions of Chronic Obstructive Pulmonary Disease (COPD) associated genes. Specifically, we have identified mutations in the untranslated regions (UTRS) of genes that control the elasticity of lung tissue and increase the risk of developing COPD. We will build experimentally validated computational models that will predict the effects of any mutation on these genes and thus improve our ability to determine COPD predisposition based on genetic sequence.
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