Proteomic analysis of CTGF-activated lung fibroblasts: identification of IQGAP1 as a key player in lung fibroblast migration

Connective tissue growth factor (CTGF, CCN2) is overexpressed in lung fibroblasts isolated from patients with interstitial lung disease (ILD) and systemic sclerosis (SSc, scleroderma) and is considered to be a molecular marker of fibrosis. To understand the significance of elevated CTGF, we investigated the changes in lung fibroblast proteome in response to CTGF overexpression. Using 2-dimensional gel electrophoresis followed by in-gel proteolytic digestion and mass spectrometric analysis, we identified 13 proteins affected by CTGF. Several of the CTGF-induced proteins, such as pro-alpha (I) collagen and cytoskeletal proteins vinculin, moesin, and ezrin, are known to be elevated in pulmonary fibrosis, whereas 9 of 13 proteins have not been studied in pulmonary fibrosis and are, therefore, novel CTGF-responsive molecules that may have important roles in ILD. Our study demonstrates that 1 of the novel CTGF-induced proteins, IQ motif containing GTPase activating protein (IQGAP) 1, is elevated in lung fibroblasts isolated from scleroderma patients with ILD. IQGAP1 is a scaffold protein that plays a pivotal role in regulating migration of endothelial and epithelial cells. Scleroderma lung fibroblasts and normal lung fibroblasts treated with CTGF demonstrated increased rate of migration in a wound healing assay. Depletion of IQGAP1 expression by small interfering RNA inhibited CTGF-induced migration and MAPK ERK1/2 phosphorylation in lung fibroblasts. MAPK inhibitor U0126 decreased CTGF-induced cell migration and did not interfere with CTGF-induced IQGAP1 expression, suggesting that MAPK pathway is downstream of IQGAP1. These findings further implicate the importance of CTGF in lung tissue repair and fibrosis and propose that CTGF-induced migration of lung fibroblasts to the damaged tissue is mediated via IQGAP1 and MAPK signaling pathways.     

Semantic role labeling for protein transport predicates

Background  

Automatic semantic role labeling (SRL) is a natural language processing (NLP) technique that maps sentences to semantic representations. This technique has been widely studied in the recent years, but mostly with data in newswire domains. Here, we report on a SRL model for identifying the semantic roles of biomedical predicates describing protein transport in GeneRIFs – manually curated sentences focusing on gene functions. To avoid the computational cost of syntactic parsing, and because the boundaries of our protein transport roles often did not match up with syntactic phrase boundaries, we approached this problem with a word-chunking paradigm and trained support vector machine classifiers to classify words as being at the beginning, inside or outside of a protein transport role.