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An Ovol2-Zeb1 Mutual Inhibitory Circuit Governs Bidirectional and Multi-step Transition between Epithelial and Mesenchymal States
Open accessAbstract
Reversible epithelial-to-mesenchymal transition (EMT) is central to tissue development, epithelial stemness, and cancer metastasis. While many regulatory elements have been identified to induce EMT, the complex process underlying such cellular plasticity remains poorly understood. Utilizing a systems biology approach integrating modeling and experiments, we found multiple intermediate states contributing to EMT and that the robustness of the transitions is modulated by transcriptional factor Ovol2. In particular, we obtained evidence for a mutual inhibition relationship between Ovol2 and EMT inducer Zeb1, and observed that adding this regulation generates a novel four-state system consisting of two distinct intermediate phenotypes that differ in differentiation propensities and are favored in different environmental conditions. We identified epithelial cells that naturally exist in an intermediate state with bidirectional differentiation potential, and found the balance between EMT-promoting and -inhibiting factors to be critical in achieving and selecting between intermediate states. Our analysis suggests a new design principle in controlling cellular plasticity through multiple intermediate cell fates and underscores the critical involvement of Ovol2 and its associated molecular regulations.
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Cited by
Nieto, M. A., Huang, R. Y.-J., Jackson, R. A., & Thiery, J. P. (2016) · Cell · doi:10.1016/j.cell.2016.06.028 · Cited by 4,492
Dong, J., Hu, Y., Fan, X., Wu, X., Mao, Y., Hu, B., Guo, H., Wen, L., & Tang, F. (2018) · Genome Biology · doi:10.1186/s13059-018-1416-2 · Cited by 187
van Staalduinen, J., Baker, D., ten Dijke, P., & van Dam, H. (2018) · Oncogene · doi:10.1038/s41388-018-0378-x · Cited by 158
The multiverse nature of epithelial to mesenchymal transition
Simeone, P., Trerotola, M., Franck, J., Cardon, T., Marchisio, M., Fournier, I., Salzet, M., Maffia, M., & Vergara, D. (2019) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.11.004 · Cited by 98
Transition state characteristics during cell differentiation
Brackston, R. D., Lakatos, E., & Stumpf, M. P. H. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006405 · Cited by 67
Interpreting Single-Cell and Spatial Omics Data Using Deep Neural Network Training Dynamics
Karin, J., Mintz, R., Raveh, B., & Nitzan, M. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00721-5 · Cited by 16
Metadata
- Authors
- Hong, T., Watanabe, K., Ta, C. H., Villarreal-Ponce, A., Nie, Q., & Dai, X.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-11-10
- Volume
- 11
- Issue
- 11
- Pages
- e1004569
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2177744475
- Cited by
- 265
- References
- 0
- FWCI
- 8.45
- Type
- journal article
- Language
- en
- Open access
- gold
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