Transcriptome analysis of IPF fibroblastic foci identifies key pathways involved in fibrogenesis

• Published in: Thorax Vol. 76; no. 1; pp. 73 - 82
• Main Authors: Guillotin, Delphine, Taylor, Adam R, Platé, Manuela, Mercer, Paul F, Edwards, Lindsay M, Haggart, Ross, Miele, Gino, McAnulty, Robin J, Maher, Toby M, Hynds, Robert E, Jamal-Hanjani, Mariam, Marshall, Richard P, Fisher, Andrew J, Blanchard, Andy D, Chambers, Rachel C
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group Ltd and British Thoracic Society 01.01.2021; BMJ Publishing Group LTD
• Subjects: Cancer; Cells, Cultured; Collagen; CRISPR; Disease; Fibroblasts; Fibroblasts - metabolism; Fibroblasts - pathology; Gene expression; Gene Expression Profiling; Gene Expression Regulation; Humans; idiopathic pulmonary fibrosis; Idiopathic Pulmonary Fibrosis - genetics; Idiopathic Pulmonary Fibrosis - metabolism; Idiopathic Pulmonary Fibrosis - pathology; Lasers; Lung - metabolism; Lung - pathology; Metabolism; Patients; Pulmonary fibrosis; Respiratory research; RNA - genetics; Signal Transduction; Statistical analysis; Transcriptome - genetics; Up-Regulation; idiopathic pulmonary fibrosis
• ISSN: 0040-6376; 1468-3296
• DOI: 10.1136/thoraxjnl-2020-214902

IntroductionFibroblastic foci represent the cardinal pathogenic lesion in idiopathic pulmonary fibrosis (IPF) and comprise activated fibroblasts and myofibroblasts, the key effector cells responsible for dysregulated extracellular matrix deposition in multiple fibrotic conditions. The aim of this study was to define the major transcriptional programmes involved in fibrogenesis in IPF by profiling unmanipulated myofibroblasts within fibrotic foci in situ by laser capture microdissection.MethodsThe challenges associated with deriving gene calls from low amounts of RNA and the absence of a meaningful comparator cell type were overcome by adopting novel data mining strategies and by using weighted gene co-expression network analysis (WGCNA), as well as an eigengene-based approach to identify transcriptional signatures, which correlate with fibrillar collagen gene expression.ResultsWGCNA identified prominent clusters of genes associated with cell cycle, inflammation/differentiation, translation and cytoskeleton/cell adhesion. Collagen eigengene analysis revealed that transforming growth factor β1 (TGF-β1), RhoA kinase and the TSC2/RHEB axis formed major signalling clusters associated with collagen gene expression. Functional studies using CRISPR-Cas9 gene-edited cells demonstrated a key role for the TSC2/RHEB axis in regulating TGF-β1-induced mechanistic target of rapamycin complex 1 activation and collagen I deposition in mesenchymal cells reflecting IPF and other disease settings, including cancer-associated fibroblasts.ConclusionThese data provide strong support for the human tissue-based and bioinformatics approaches adopted to identify critical transcriptional nodes associated with the key pathogenic cell responsible for fibrogenesis in situ and further identify the TSC2/RHEB axis as a potential novel target for interfering with excessive matrix deposition in IPF and other fibrotic conditions.