Quantitative analysis of proteome dynamics in a mouse model of asthma

Abstract

Asthma is an inflammatory disease of the respiratory system, and a major factor of increasing health care costs worldwide. The molecular actors leading to the development of chronic asthma are not fully understood and require further investigation. The aim of this study was to monitor the proteome dynamics during asthma development from early inflammatory to late fibrotic stages. A mouse asthma model was used to analyse the lung proteome at four time points during asthma development (0weeks=control, 5, 8 and 12weeks of treatment, n=6 each). The model was analysed using lung function tests, immune cell counting and histology. Furthermore, a multi-fraction mass spectrometry-based proteome analysis was performed to achieve a comprehensive coverage and quantification of the lung proteome. At early stages, the mice showed predominant eosinophilic inflammation of the airways, which disappeared at later stages and was replaced by marked airway hyper-reactivity and fibrosis of the airways. 3325 proteins were quantified with 435 proteins found to be significantly differentially abundant between the experimental groups (ANOVA p-value ≤.05, maximum fold change ≥1.5). We applied hierarchical clustering to identify common protein abundance profiles along the asthma development and analysed these clusters using gene ontology annotation and enrichment analysis. We demonstrate the correlation of protein clusters with the course of asthma development, that is eosinophilic inflammation and fibrotic remodelling of the airways. Proteome analysis revealed proteins that were previously described to be important during asthma chronification. Moreover, we identified additional proteins previously not described in the context of asthma. We provide a comprehensive data set of a long-term mouse model of asthma that may contribute to a better understanding and allow new insights into the progression and development of chronic asthma. Data are available via ProteomeXchange with identifier PXD011159.