New Computational Pipeline to Analyze Reverse Phase Protein Array Data from Adult Rats Exposed to Cigarette Smoke

Recent progress in functional proteomics, particularly in protein arrays such as reverse phase protein array (RPA), allows the measurement of changes in protein contents, modifications in a sensitive, high-throughput and customizable manner while requiring only small amounts of protein samples. Often, the RPA data is not normally distributed, which makes the usual linear regression model, ANOVA, and t-test unsuitable for RPA data analysis. Currently, there is no bioinformatics method specifically designed for RPA analysis. To identify proteins and pathways which play a key role in response to cigarette smoke exposure, a novel computational pipeline for RPA data analysis was developed. In this study, adult rats (n=5/group) were exposed for 28 days to conditioned fresh air (6 h/day) or to cigarette mainstream smoke (3R4F) (6 h/day at 8, 15, or 23 μg nicotine/l), and lungs and respiratory nasal epithelium samples were analyzed by RPA. Significance analysis of microarrays (SAM) was found to be suitable for identification of sets of differentially expressed proteins. The empirical null distribution was generated by 150 random permutations, whereby delta was set to 0.01. To the best of our knowledge, this is the first time that SAM has been used for RPA data analysis. RPA experiment workflow and metadata capture were achieved using the caArray data management system and MAGE-TAB data exchange format with RPA-specific extensions, which enable support for commercial RPA technology and RPA-specific MAGE-TAB files. In conclusion, a novel computational pipeline for RPA data analysis was established. Our pipeline revealed distinct, significant expression changes of proteins that are indicative of various pathways activated in respiratory nasal epithelium and lungs of animals exposed to high dose and medium dose cigarette smoke.