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TGF-β/Smad signaling in renal fibrosis
Open accessAbstract
TGF-β (transforming growth factor-β) is well identified as a central mediator in renal fibrosis. TGF-β initiates canonical and non-canonical pathways to exert multiple biological effects. Among them, Smad signaling is recognized as a major pathway of TGF-β signaling in progressive renal fibrosis. During fibrogenesis, Smad3 is highly activated, which is associated with the down-regulation of an inhibitory Smad7 via an ubiquitin E3-ligases-dependent degradation mechanism. The equilibrium shift between Smad3 and Smad7 leads to accumulation and activation of myofibroblasts, overproduction of ECM (extracellular matrix), and reduction in ECM degradation in the diseased kidney. Therefore, overexpression of Smad7 has been shown to be a therapeutic agent for renal fibrosis in various models of kidney diseases. In contrast, another downstream effecter of TGF-β/Smad signaling pathway, Smad2, exerts its renal protective role by counter-regulating the Smad3. Furthermore, recent studies demonstrated that Smad3 mediates renal fibrosis by down-regulating miR-29 and miR-200 but up-regulating miR-21 and miR-192. Thus, overexpression of miR-29 and miR-200 or down-regulation of miR-21 and miR-192 is capable of attenuating Smad3-mediated renal fibrosis in various mouse models of chronic kidney diseases (CKD). Taken together, TGF-β/Smad signaling plays an important role in renal fibrosis. Targeting TGF-β/Smad3 signaling may represent a specific and effective therapy for CKD associated with renal fibrosis.
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Metadata
- Authors
- Meng, X.-M., Tang, P. M.-K., Li, J., & Lan, H. Y.
- Venue
- Frontiers in Physiology
- Publisher
- Frontiers Media SA
- Published
- 2015-03-19
- Volume
- 6
- Pages
- 82-82
- ISSN
- 1664-042X (ISSN-L)
- OpenAlex
- W2110886621
- Cited by
- 666
- References
- 119
- FWCI
- 22.39
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.