In Silico Insight of Endothelin Signaling in Idiopathic Pulmonary Fibrosis (IPF)
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Idiopathic pulmonary fibrosis (IPF), a form of interstitial lung disease (ILD), is characterized by progressive lung scarring with a poor prognosis. Endothelin signalling has emerged as a key player in lung fibrogenesis under various pathological conditions. However, its specific contribution to IPF pathogenesis remains poorly elucidated. We propose that leveraging in silico modelling approaches can provide valuable insights into the role of endothelin signalling in IPF, potentially paving the way for novel therapeutic and diagnostic strategies. We employed datasets curated from the Gene Expression Omnibus (GEO) database. Comprehensive data analyses from lung, bronchoalveolar lavage cells, blood and human primary fibroblast samples from both control and IPF patients were utilized to uncover the expression patterns and clinical correlations of endothelin genes. Furthermore, single-cell RNA-sequencing (scRNA-seq) was leveraged to explore the cellular heterogeneity and specific cell types harboring aberrant endothelin expression in the IPF lung microenvironment. Our analysis revealed a significant changes of endothelin genes expression pattern in IPF patient samples as compared to healthy control. Notably, IPF patients with upregulation of endothelin-1, demonstrated a statistically significant poorer survival. These findings implicate endothelin signalling as a novel and potentially targetable pathway in IPF. Further investigations are warranted to validate these findings and explore the therapeutic potential of modulating endothelin signalling in this lung disease.
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