Quantitative proteomic profiling of extracellular matrix and site-specific collagen post-translational modifications in an model of lung fibrosis.

Lung fibrosis is characterized by excessive deposition of extracellular matrix (ECM), in particular collagens, by fibroblasts in the interstitium. Transforming growth factor-β1 (TGF-β1) alters the expression of many extracellular matrix (ECM) components produced by fibroblasts, but such changes in ECM composition as well as modulation of collagen post-translational modification (PTM) levels have not been comprehensively investigated. Here, we performed mass spectrometry (MS)-based proteomics analyses to assess changes in the ECM deposited by cultured lung fibroblasts from idiopathic pulmonary fibrosis (IPF) patients upon stimulation with transforming growth factor β1 (TGF-β1). In addition to the ECM changes commonly associated with lung fibrosis, MS-based label-free quantification revealed profound effects on enzymes involved in ECM crosslinking and turnover as well as multiple positive and negative feedback mechanisms of TGF-β1 signaling. Notably, the ECM changes observed in this model correlated significantly with ECM changes observed in patient samples. Because collagens are subject to multiple PTMs with major implications in disease, we implemented a new bioinformatic platform to analyze MS data that allows for the comprehensive mapping and site-specific quantitation of collagen PTMs in crude ECM preparations. These analyses yielded a comprehensive map of prolyl and lysyl hydroxylations as well as lysyl glycosylations for 15 collagen chains. In addition, site-specific PTM analysis revealed novel sites of prolyl-3-hydroxylation and lysyl glycosylation in type I collagen. Interestingly, the results show, for the first time, that TGF-β1 can modulate prolyl-3-hydroxylation and glycosylation in a site-specific manner. Taken together, this proof of concept study not only reveals unanticipated TGF-β1 mediated regulation of collagen PTMs and other ECM components but also lays the foundation for dissecting their key roles in health and disease. The proteomic data has been deposited to the ProteomeXchange Consortium the MassIVE partner repository with the data set identifier MSV000082958.