TGF-β–induced epigenetic deregulation of SOCS3 facilitates STAT3 signaling to promote fibrosis
Clara Dees, … , Georg Schett, Jörg H.W. Distler
Clara Dees, … , Georg Schett, Jörg H.W. Distler
Published January 28, 2020
Citation Information: J Clin Invest. 2020;130(5):2347-2363. https://doi.org/10.1172/JCI122462.
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Research Article Cell biology Immunology

TGF-β–induced epigenetic deregulation of SOCS3 facilitates STAT3 signaling to promote fibrosis

Abstract

Fibroblasts are key effector cells in tissue remodeling. They remain persistently activated in fibrotic diseases, resulting in progressive deposition of extracellular matrix. Although fibroblast activation may be initiated by external factors, prolonged activation can induce an “autonomous,” self-maintaining profibrotic phenotype in fibroblasts. Accumulating evidence suggests that epigenetic alterations play a central role in establishing this persistently activated pathologic phenotype of fibroblasts. We demonstrated that in fibrotic skin of patients with systemic sclerosis (SSc), a prototypical idiopathic fibrotic disease, TGF-β induced the expression of DNA methyltransferase 3A (DNMT3A) and DNMT1 in fibroblasts in a SMAD-dependent manner to silence the expression of suppressor of cytokine signaling 3 (SOCS3) by promoter hypermethylation. Downregulation of SOCS3 facilitated activation of STAT3 to promote fibroblast-to-myofibroblast transition, collagen release, and fibrosis in vitro and in vivo. Reestablishment of the epigenetic control of STAT3 signaling by genetic or pharmacological inactivation of DNMT3A reversed the activated phenotype of SSc fibroblasts in tissue culture, inhibited TGF-β–dependent fibroblast activation, and ameliorated experimental fibrosis in murine models. These findings identify a pathway of epigenetic imprinting of fibroblasts in fibrotic disease with translational implications for the development of targeted therapies in fibrotic diseases.

Authors

Clara Dees, Sebastian Pötter, Yun Zhang, Christina Bergmann, Xiang Zhou, Markus Luber, Thomas Wohlfahrt, Emmanuel Karouzakis, Andreas Ramming, Kolja Gelse, Akihiko Yoshimura, Rudolf Jaenisch, Oliver Distler, Georg Schett, Jörg H.W. Distler

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Figure 2

Regulation of SOCS3 expression by DNA methylation.

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Regulation of SOCS3 expression by DNA methylation. (A) SOCS3 expression ...
(A) SOCS3 expression in SSc fibroblasts incubated with 5-aza. n = 6 fibroblast lines from different donors for qPCR with 2 technical replicates each; n = 5 fibroblast lines from different donors for Western blot with 3 technical replicates each. (B) Effects of 5-aza on SOCS3 expression in SSc and normal fibroblasts. n = 5 fibroblast lines from different donors for qPCR with 2 technical replicates each; n = 5 fibroblast lines from different donors for Western blot with 3 technical replicates each. (C) Promoter methylation of SOCS3 by methylation-specific PCR (MSP) (U, unmethylated; M, methylated PCR) and MeDIP assay in SSc and normal fibroblasts incubated with 5-aza. n = 7 fibroblast lines from different donors for MSP with 3 technical replicates each; n = 5 fibroblast lines from different donors for MeDIP with 4 technical replicates each. (D) SOCS3 expression in normal fibroblasts stimulated with TGF-β and incubated with 5-aza. n = 5 fibroblast lines from different donors for both qPCR and Western blot with 2 technical replicates each. (E and F) Socs3 expression in (E) TBRIact- and (F) bleomycin-induced fibrosis treated with 5-aza. n = 5 mice/group for qPCR with 2 technical replicates each; n = 3 mice/group for Western blot with 3 technical replicates each. (G) Promoter methylation of SOCS3 induced by TGF-β in normal human fibroblasts as analyzed by MSP and MeDIP assay. n = 10 fibroblast lines from different donors for MSP with 2 technical replicates each; n = 5 fibroblast lines from different donors for MeDIP with 4 technical replicates each. Data are depicted as the median with interquartile range. Each dot represents an individual result. One-way ANOVA with Tukey’s range test as post hoc analysis was used for statistical analyses. 0.01 > **P ≥ 0.001; ***P < 0.001.