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The interleukin (IL)-1 family of cytokines is strongly associated with systemic sclerosis (SSc) and pulmonary involvement, but the molecular mechanisms are poorly understood. The aim of this study was to assess the role of IL-1α and IL-1β in pulmonary vascular and interstitial remodelling in a mouse model of SSc.IL-1α and IL-1β were localised in lungs of SSc patients and in the fos-related antigen-2 (Fra-2) transgenic (TG) mouse model of SSc. Lung function, haemodynamic parameters and pulmonary inflammation were measured in Fra-2 TG mice with or without 8 weeks of treatment with the IL-1 receptor antagonist anakinra (25 mg·kg-1·day-1). Direct effects of IL-1 on pulmonary arterial smooth muscle cells (PASMCs) and parenchymal fibroblasts were investigated in vitroFra-2 TG mice exhibited increased collagen deposition in the lung, restrictive lung function and enhanced muscularisation of the vasculature with concomitant pulmonary hypertension reminiscent of the changes in SSc patients. Immunoreactivity of IL-1α and IL-1β was increased in Fra-2 TG mice and in patients with SSc. IL-1 stimulation reduced collagen expression in PASMCs and parenchymal fibroblasts via distinct signalling pathways. Blocking IL-1 signalling in Fra-2 TG worsened pulmonary fibrosis and restriction, enhanced T-helper cell type 2 (Th2) inflammation, and increased the number of pro-fibrotic, alternatively activated macrophages.Our data suggest that blocking IL-1 signalling as currently investigated in several clinical studies might aggravate pulmonary fibrosis in specific patient subsets due to Th2 skewing of immune responses and formation of alternatively activated pro-fibrogenic macrophages.
Pubmed ID: 31320452 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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