bioRxivMolecular BiologyPreprint
Myofibroblast lineage mapping and inhibiting subretinal fibrosis by targeting SMAD3 and MRTF pathways via microRNA-24 functional study
Yinga WuiD, Yao TongiD, Katherine G. ByrnesiD, Qi Zhou, Chunmin Dong, Chase Benjamin, Emma Parker, Duran BaoiD, Zhaoyang Ren, Chastain A. Anderson, Rafael L. Ufret-VincentyiD, Yu-Guang He, Ze ZhangiD, David M. HinkleiD, Jing MaiD, Shusheng WangiD
Abstract
Subretinal fibrosis underlies the end-stage pathogenesis of retinal diseases including age-related macular degeneration (AMD). It can disrupt retinal structure and eventually lead to legal blindness by generating contractile force, fibrotic scarring, subretinal hemorrhage, and retinal detachment. Myofibroblasts are the predominant cells critically involved in subretinal fibrosis, however, the cellular contribution to myofibroblasts remains unclear. Here we demonstrate that multiple cell lineages, including macrophages, endothelial cells (EC), retinal pigment epithelial (RPE) cells and pericytes, significantly contribute to myofibroblasts in a laser-induced subretinal fibrosis model. We found microRNA miR-24 is significantly downregulated in the plasma of wet AMD patients. Overexpression of miR-24 represses epithelial-mesenchymal transition (EMT), endothelial-mesenchymal transition (EndMT), and the resulting fibrosis by regulating TGF-{beta}/SMAD3 and PAK4/LIMK2/MRTF pathways. Consistently, a combination of SMAD3 and MRTF inhibitors show superior efficacy to individual inhibitors in repressing fibrosis in vitro and laser-induced subretinal fibrosis in vivo. Together, these suggest the contribution of multiple cell-types in myofibroblast transformation in subretinal fibrosis, and repression of miR-24-regulated TGF-{beta}/SMAD3 and PAK4/LIMK2/MRTF pathways in multiple cell types holds therapeutic potential for treating subretinal fibrosis in AMD and other fibrotic disorders.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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