Critical transitions and tipping points in EMT

Abstract

Background: Phase transition and phase separation as well as their tipping points are penetrating phenomena in biology and are intrinsic properties of biological systems ranging from basic molecule complexes to cells and all way up to entire ecosystems.

Results: For example, phase separation has been established as a key mechanism for biological molecules such as protein or RNA to form membraneless organelles to perform complex biological functions. Phase transitions are commonly observed during cellular differentiation, and generally, there are the tipping points or critical states just before the phase transitions. And the stability of ecosystem and extinction of species are systematic manifestation of phase transitions. All phase transition and phase separation phenomena display switch-like behavior and critical transitions.

Conclusion: Here we summarize the concepts regarding the epithelial-to-mesenchymal transition (EMT) as a type of phase changes and the implication of critical transitions in EMT, and discuss open questions and challenges in this fast-moving field.

Graphical abstract

Keywords

EMT / phase transition / tipping points

References

[1] Stanley, H. E. (1987) Introduction to Phase Transitions and Critical Phenomena, pp. 308. New York: Oxford University Press

[2] Chen, L., Liu, R., Liu, Z. P., Li, M. and Aihara, K. (2012) Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci. Rep., 2, 342

[3] Liu, R., Chen, P., Aihara, K. and Chen, L. (2015) Identifying early-warning signals of critical transitions with strong noise by dynamical network markers. Sci. Rep., 5, 17501

[4] Honig, J. and Spalek, J. (2017) A Primer to the Theory of Critical Phenomena, 1st edition, Waltham, M.A. (ed.). Elsevier

[5] Haldane, A. G. and May, R. M. (2011) Systemic risk in banking ecosystems. Nature, 469, 351–355

[6] Dai, L., Vorselen, D., Korolev, K. S. and Gore, J. (2012) Generic indicators for loss of resilience before a tipping point leading to population collapse. Science, 336, 1175–1177

[7] Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., van de Koppel, J., van de Leemput, I. A., Levin, S. A., van Nes, E. H., (2012) Anticipating critical transitions. Science, 338, 344–348

[8] Veraart, A. J., Faassen, E. J., Dakos, V., van Nes, E. H., Lürling, M. and Scheffer, M. (2012) Recovery rates reflect distance to a tipping point in a living system. Nature, 481, 357–359

[9] Boija, A., Klein, I. A., Sabari, B. R., Dall’Agnese, A., Coffey, E. L., Zamudio, A. V., Li, C. H., Shrinivas, K., Manteiga, J. C., Hannett, N. M., (2018) Transcription factors activate genes through the phase-separation capacity of their activation domains. Cell, 175, 1842–1855.e16

[10] Yang, J. and Weinberg, R. A. (2008) Epithelial-mesenchymal transition: at the crossroads of development and tumor metastasis. Dev. Cell, 14, 818–829

[11] Thiery, J. P., Acloque, H., Huang, R. Y. and Nieto, M. A. (2009) Epithelial-mesenchymal transitions in development and disease. Cell, 139, 871–890

[12] Revenu, C. and Gilmour, D. (2009) EMT 2.0: shaping epithelia through collective migration. Curr. Opin. Genet. Dev., 19, 338–342

[13] Nieto, M. A., Huang, R. Y., Jackson, R. A. and Thiery, J. P. (2016) EMT: 2016. Cell, 166, 21–45

[14] Kalluri, R. and Weinberg, R. A. (2009) The basics of epithelial-mesenchymal transition. J. Clin. Invest., 119, 1420–1428

[15] Hay, E. D. (1995) An overview of epithelio-mesenchymal transformation. Acta Anat., (Basel), 154, 8–20

[16] Chao, Y., Wu, Q., Acquafondata, M., Dhir, R. and Wells, A. (2012) Partial mesenchymal to epithelial reverting transition in breast and prostate cancer metastases. Cancer Microenviron., 5, 19–28

[17] Goetz, H., Melendez-Alvarez, J. R., Chen, L. and Tian, X. J. (2020) A plausible accelerating function of intermediate states in cancer metastasis. PLOS Comput. Biol., 16, e1007682

[18] Scheel, C. and Weinberg, R. A. (2012) Cancer stem cells and epithelial-mesenchymal transition: concepts and molecular links. Semin. Cancer Biol., 22, 396–403

[19] Yu, M., Bardia, A., Wittner, B. S., Stott, S. L., Smas, M. E., Ting, D. T., Isakoff, S. J., Ciciliano, J. C., Wells, M. N., Shah, A. M., (2013) Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science, 339, 580–584

[20] Ryu, H. S., Park, D. J., Kim, H. H., Kim, W. H. and Lee, H. S. (2012) Combination of epithelial-mesenchymal transition and cancer stem cell-like phenotypes has independent prognostic value in gastric cancer. Hum. Pathol., 43, 520–528

[21] Chen, L., Gibbons, D. L., Goswami, S., Cortez, M. A., Ahn, Y. H., Byers, L. A., Zhang, X., Yi, X., Dwyer, D., Lin, W., (2014) Metastasis is regulated via microRNA-200/ZEB1 axis control of tumour cell PD-L1 expression and intratumoral immunosuppression. Nat. Commun., 5, 5241

[22] Liu, Y., Xue, M., Du, S., Feng, W., Zhang, K., Zhang, L., Liu, H., Jia, G., Wu, L., Hu, X., (2019) Competitive endogenous RNA is an intrinsic component of EMT regulatory circuits and modulates EMT. Nat. Commun., 10, 1637

[23] Chang, H., Liu, Y., Xue, M., Liu, H., Du, S., Zhang, L. and Wang, P. (2016) Synergistic action of master transcription factors controls epithelial-to-mesenchymal transition. Nucleic Acids Res., 44, 2514–2527

[24] Taube, J. H., Herschkowitz, J. I., Komurov, K., Zhou, A. Y., Gupta, S., Yang, J., Hartwell, K., Onder, T. T., Gupta, P. B., Evans, K. W., (2010) Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes. Proc. Natl. Acad. Sci. USA, 107, 15449–15454

[25] Javaid, S., Zhang, J., Anderssen, E., Black, J. C., Wittner, B. S., Tajima, K., Ting, D. T., Smolen, G. A., Zubrowski, M., Desai, R., (2013) Dynamic chromatin modification sustains epithelial-mesenchymal transition following inducible expression of Snail-1. Cell Rep., 5, 1679–1689

[26] Tian, X. J., Zhang, H. and Xing, J. (2013) Coupled reversible and irreversible bistable switches underlying TGF-β-induced epithelial to mesenchymal transition. Biophys. J., 105, 1079–1089

[27] Zhang, J., Tian, X. J., Zhang, H., Teng, Y., Li, R., Bai, F., Elankumaran, S. and Xing, J. (2014) TGF-β-induced epithelial-to-mesenchymal transition proceeds through stepwise activation of multiple feedback loops. Sci. Signal., 7, ra91

[28] Liu, Y., Xue, M., Du, S., Feng, W., Zhang, K., Zhang, L., Liu, H., Jia, G., Wu, L., Hu, X., (2019) Competitive endogenous RNA is an intrinsic component of EMT regulatory circuits and modulates EMT. Nat. Commun., 10, 1637

[29] Yang, M., Li, S. N., Anjum, K. M., Gui, L. -X., Zhu, S. -S., Liu, J., Chen, J. -K., Liu, Q. -F., Ye, G. -D., Wang, W. -J., (2013 ) A double-negative feedback loop between Wnt-beta-catenin signaling and HNF4alpha regulates epithelial-mesenchymal transition in hepatocellular carcinoma. J. Cell Sci., 126, 5692–5703

[30] Liu, R., Yu, X., Liu, X., Xu, D., Aihara, K. and Chen, L. (2014) Identifying critical transitions of complex diseases based on a single sample. Bioinformatics, 30, 1579–1586

[31] Chen, P., Liu, R., Li, Y. and Chen, L. (2016) Detecting critical state before phase transition of complex biological systems by hidden Markov model. Bioinformatics, 32, 2143–2150

[32] Liu, X., Chang, X., Liu, R., Yu, X., Chen, L. and Aihara, K. (2017) Quantifying critical states of complex diseases using single-sample dynamic network biomarkers. PLOS Comput. Biol., 13, e1005633

[33] De Craene, B. and Berx, G. (2013) Regulatory networks defining EMT during cancer initiation and progression. Nat. Rev. Cancer, 13, 97–110

[34] Gregory, P. A., Bracken, C. P., Smith, E., Bert, A. G., Wright, J. A., Roslan, S., Morris, M., Wyatt, L., Farshid, G., Lim, Y. Y., (2011) An autocrine TGF-beta/ZEB/miR-200 signaling network regulates establishment and maintenance of epithelial-mesenchymal transition. Mol. Biol. Cell, 22, 1686–1698

[35] Hajra, K. M., Chen, D. Y. and Fearon, E. R. (2002) The SLUG zinc-finger protein represses E-cadherin in breast cancer. Cancer Res., 62, 1613–1618

[36] Comijn, J., Berx, G., Vermassen, P., Verschueren, K., van Grunsven, L., Bruyneel, E., Mareel, M., Huylebroeck, D., van Roy, F. (2001) The two-handed E box binding zinc finger protein SIP1 downregulates E-cadherin and induces invasion. Mol., Cell, 7, 1267–1278

[37] Gregory, P. A., Bert, A. G., Paterson, E. L., Barry, S. C., Tsykin, A., Farshid, G., Vadas, M. A., Khew-Goodall, Y. and Goodall, G. J. (2008) The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nat. Cell Biol., 10, 593–601

[38] Xiao, P., Liu, W. and Zhou, H. (2016) miR-200b inhibits migration and invasion in non-small cell lung cancer cells via targeting FSCN1. Mol. Med. Rep., 14, 1835–1840

[39] Williams, L. V., Veliceasa, D., Vinokour, E. and Volpert, O. V. (2013) miR-200b inhibits prostate cancer EMT, growth and metastasis. PLoS One, 8, e83991

[40] Burk, U., Schubert, J., Wellner, U., Schmalhofer, O., Vincan, E., Spaderna, S. and Brabletz, T. (2008) A reciprocal repression between ZEB1 and members of the miR-200 family promotes EMT and invasion in cancer cells. EMBO Rep., 9, 582–589

[41] Yang, B., Li, M., Tang, W., Liu, W., Zhang, S., Chen, L. and Xia, J. (2018) Dynamic network biomarker indicates pulmonary metastasis at the tipping point of hepatocellular carcinoma. Nat. Commun., 9, 678

[42] Li, M., Li, C., Liu, W.-X., Liu, C., Cui, J., Li, Q., Ni, H., Yang, Y., Wu, C., Chen, C., (2017) Dysfunction of PLA2G6 and CYP2C44-associated network signals imminent carcinogenesis from chronic inflammation to hepatocellular carcinoma. J. Mol. Cell Biol., 9, 489–503

[43] Liu, X., Chang, X., Leng, S., Tang, H., Aihara, K. and Chen, L. (2019) Detection for disease tipping points by landscape dynamic network biomarkers. Natl. Sci. Rev., 6, 775–785

[44] Liu, R., Wang, J., Ukai, M., Sewon, K., Chen, P., Suzuki, Y., Wang, H., Aihara, K., Okada-Hatakeyama, M. and Chen, L. (2019) Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers. J. Mol. Cell. Biol., 11, 649–664

[45] Richard, A., Boullu, L., Herbach, U., Bonnafoux, A., Morin, V., Vallin, E., Guillemin, A., Papili Gao, N., Gunawan, R., Cosette, J., (2016) Single-cell-based analysis highlights a surge in cell-to-cell molecular variability preceding irreversible commitment in a differentiation process. PLoS Biol., 14, e1002585

[46] Lesterhuis, W. J., Bosco, A., Millward, M. J., Small, M., Nowak, A. K. and Lake, R. A. (2017) Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity. Nat. Rev. Drug Discov., 16, 264–272

[47] Li, M., Zeng, T., Liu, R. and Chen, L. (2013) Detecting tissue-specific early-warning signals for complex diseases based on dynamical network biomarkers: study of type-2 diabetes by cross-tissue analysis. Brief. Bioinform., 15, 229–243

[48] Zhao, J., Zhou, Y., Zhang, X. and Chen, L. (2016) Part mutual information for quantifying direct associations in networks. Proc. Natl. Acad. Sci. USA, 113, 5130–5135

[49] Zhang, X., Zhao, J., Hao, J.-K., Zhao, X.-M. and Chen, L. (2015) Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks. Nucleic Acids Res., 43, e31

[50] Ma, H., Leng, S., Aihara, K., Lin, W. and Chen, L. (2018) Randomly distributed embedding making short-term high-dimensional data predictable. Proc. Natl. Acad. Sci. USA, 115, E9994–E10002

[51] Sa, R., Zhang, W., Ge, J., Wei, X., Zhou, Y., Landzberg, D. R., Wang, Z., Han, X., Chen, L. and Yin, H. (2016) Discovering a critical transition state from nonalcoholic hepatosteatosis to nonalcoholic steatohepatitis by lipidomics and dynamical network biomarkers. J. Mol. Cell Biol., 8, 195–206

[52] Jiang, Z., Lu, L., Liu, Y., Zhang, S., Li, S., Wang, G., Wang, P. and Chen, L. (2020) SMAD7 and SERPINE1 as novel dynamic network biomarkers detect and regulate the tipping point of TGF-beta induced EMT. Sci. Bull., 65, 842–853

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature