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TGFβ2 and TGFβ3 isoforms drive fibrotic disease pathogenesis
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TGFβ2 and TGFβ3 isoforms are activated via unique mechanisms and are associated with fibrosis in human diseases.
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“Although studies have long implicated TGFβ1 as the dominant fibrogenic driver, a recent study also implicates TGFβ2 and TGFβ3 as potent fibrogenic stimuli, but which lack the pro-inflammatory effects of TGFβ1 antagonism that have limited the latter’s appeal as a therapeutic target (147).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [147]
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“TGFβ1 was the first, and remains the most potent fibrogenic stimulus towards HSCs and other tissue myofibroblasts, but as noted above, TGFβ2 and TGFβ3 merit consideration as safer therapeutic targets (147).”
From Found in translation—Fibrosis in metabolic dysfunction–associated steatohepatitis (MASH) (2023) · cites this paper as [147]
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del Amo-Maestro, L., Marino‐Puertas, L., Goulas, T., & Gomis‐Rüth, F. X. (2019) · Scientific Reports · doi:10.1038/s41598-019-44943-4 · Cited by 9
Human transforming growth factor beta2 in a tetragonal crystal form
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TGF-β signaling in health and disease
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Metadata
- Authors
- Sun, T., Huang, Z., Liang, W.-C., Yin, J., Lin, W. Y., Wu, J., Vernes, J.-M., Lutman, J., Caplazi, P., Jeet, S., Wong, T., Wong, M., DePianto, D. J., Morshead, K. B., Sun, K.-H., Modrusan, Z., Vander Heiden, J. A., Abbas, A. R., Zhang, H., Xu, M., N’Diaye, E.-N., Roose-Girma, M., Wolters, P. J., Yadav, R., Sukumaran, S., Ghilardi, N., Corpuz, R., Emson, C., Meng, Y. G., Ramalingam, T. R., Lupardus, P., Brightbill, H. D., Seshasayee, D., Wu, Y., & Arron, J. R.
- Affiliations
- Genentech (US)
- Department of Antibody Engineering, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
- Department of OMNI Bioinformatics, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
- University of California, San Francisco (US)
- Corresponding author
- Sun, Tianhe
- Arron, Joseph R.
- ORCID
- Sun, Tianhe
- Huang, Zhiyu
- Liang, Wei-Ching
- Yin, Jianping
- Lin, Wei Yu
- Wu, Jia
- Lutman, Jeff
- Caplazi, Patrick
- Jeet, Surinder
- Wong, Tiffany
- Wong, Manda
- DePianto, Daryle J.
- Morshead, Katrina B.
- Sun, Kai-Hui
- Modrusan, Zora
- Vander Heiden, Jason A.
- Abbas, Alexander R.
- Zhang, Hua
- Xu, Min
- Roose-Girma, Meron
- Wolters, Paul J.
- Yadav, Rajbharan
- Sukumaran, Siddharth
- Ghilardi, Nico
- Corpuz, Racquel
- Emson, Claire
- Meng, Y. Gloria
- Ramalingam, Thirumalai R.
- Lupardus, Patrick
- Brightbill, Hans D.
- Seshasayee, Dhaya
- Wu, Yan
- Arron, Joseph R.
- Venue
- Science Translational Medicine
- Published
- 2021-08-04
- Volume
- 13
- Issue
- 605
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- OpenAlex
- W3188006245
- Cited by
- 134
- References
- 91
- Citations by year
- 2026: 21 · 2025: 33 · 2024: 24 · 2023: 38 · 2022: 16 · 2021: 2
- FWCI
- 6.43
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- Science Translational Medicine (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Genentech (N/A)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › TGF-β signaling in diseases
- Health Sciences › Medicine › Hepatology › Liver physiology and pathology
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Keywords
- TGF-beta2, TGF-beta3, fibrosis, pulmonary fibrosis, liver fibrosis, allosteric inhibition, TGF-β1, conditional knockout mice, co-crystal structure, antifibrotic therapy
- MeSH terms
- Animals, Disease Models, Animal, Female, Fibrosis, Humans, Protein Isoforms, Mice, Transforming Growth Factor beta2, Transforming Growth Factor beta3
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.
Statistics and arithmetic are recomputed, not judged.
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