Cabanski_2015_Inflamm.Res_64_471

BACKGROUND: Mouse models are useful for studying cigarette smoke (CS)-induced chronic pulmonary pathologies such as lung emphysema. To enhance translation of large-scale omics data from mechanistic studies into pathophysiological changes, we have developed computational tools based on reverse causal reasoning (RCR). OBJECTIVE: In the present study we applied a systems biology approach leveraging RCR to identify molecular mechanistic explanations of pathophysiological changes associated with CS-induced lung emphysema in susceptible mice.
METHODS: The lung transcriptomes of five mouse models (C57BL/6, ApoE (-/-) , A/J, CD1, and Nrf2 (-/-) ) were analyzed following 5-7 months of CS exposure.
RESULTS: We predicted 39 molecular changes mostly related to inflammatory processes including known key emphysema drivers such as NF-kappaB and TLR4 signaling, and increased levels of TNF-alpha, CSF2, and several interleukins. More importantly, RCR predicted potential molecular mechanisms that are less well-established, including increased transcriptional activity of PU.1, STAT1, C/EBP, FOXM1, YY1, and N-COR, and reduced protein abundance of ITGB6 and CFTR. We corroborated several predictions using targeted proteomic approaches, demonstrating increased abundance of CSF2, C/EBPalpha, C/EBPbeta, PU.1, BRCA1, and STAT1. CONCLUSION: These systems biology-derived candidate mechanisms common to susceptible mouse models may enhance understanding of CS-induced molecular processes underlying emphysema development in mice and their relevancy for human chronic obstructive pulmonary disease.