Transcriptional profiling and targeted proteomics reveals common molecular changes associated with cigarette smoke-induced lung emphysema development in five susceptible mouse strains

Abstract

BackgroundMouse 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).ObjectiveIn 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-κB and TLR4 signaling, and increased levels of TNF-α, 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/EBPα, C/EBPβ, 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00011-015-0820-2) contains supplementary material, which is available to authorized users.

Cigarette Smoke‐Induced Acquired Dysfunction of Cystic Fibrosis Transmembrane Conductance Regulator in the Pathogenesis of Chronic Obstructive Pulmonary Disease

“…These studies clearly imply that acquired CFTR dysfunctions induced by cigarette smoking may contribute to the pathophysiology of COPD. Indeed, this notion was further experimentally supported by several studies in murine models [ 37 , 47 ]. For instance, mice overexpressing the beta subunit of ENaC ( β -ENaC) displayed a decreased CFTR function, along with an airway mucus obstruction and mortality, which were consistent with the role of CFTR in defining manifestations of the severity of COPD [ 37 ].…”

The Apoe−/− mouse model: a suitable model to study cardiovascular and respiratory diseases in the context of cigarette smoke exposure and harm reduction

“…Another study used a systems biology approach together with reverse causal reasoning to investigate the mechanisms associated with emphysema development in five different mouse models, including Apoe −/− mice [ 127 ]. Emphysema is a component of COPD, but it appears to have a strong underlying genetic element in its development because not all smokers develop this disease.…”

Comprehensive systems biology analysis of a 7-month cigarette smoke inhalation study in C57BL/6 mice

“…1 ). The time point for switching was chosen based on the results of a previously conducted inhalation study on C57BL/6 mice indicating that lung inflammation, emphysema, and changed pulmonary function were apparent after 2 months of exposure to 3R4F 8 . Aerosol characterization for the pMRTP and 3R4F cigarettes, demonstrating reduction of a number of harmful and potentially harmful constituents is given in ‘Smoke chemistry’ file [Data Citation 1].…”

“…Typically, 10 animals were used per treatment, exposure duration, and endpoint type because this group size has previously been shown to be reasonable for statistical interpretation of data 8–10 while keeping the animal numbers in line with the expectations of our animal welfare policy. Figure 2 illustrates the power obtained for various sample sizes ( n =7 and 10 per group) as a function of the standardized mean difference by performing a two-tailed two-sample t -test with equal variances and α=5%, the standardized mean difference being defined as the absolute difference between the means of the two groups compared divided by their common standard deviation.…”