Dynamics of the feedback loops required for the phenotypic stabilization in the epithelial‐mesenchymal transition

Silveira, Daner A.; Mombach, José C. M.

doi:10.1111/febs.15062

Cited by 23 publications

(25 citation statements)

References 35 publications

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“…Mathematical modeling for this loop has predicted how clonal cells responding to the same EMT-inducing signal can display different phenotypes due to the emergent multistability (the co-existence of multiple steady states/phenotypes), a prediction which was validated experimentally via single-cell analysis of EMP 40 . Various other EMP networks that have been mathematically studied have included various other direct or indirect positive feedback loops such as ZEB1/GRHL2 28 , ZEB1/ESRP1 43 , ZEB1/miR-1199 44 , or miR-34/SNAIL 45 .…”

Section: Discussionmentioning

confidence: 99%

“…Different modeling frameworks have been used to investigate the dynamics of EMP, depending on the size of network. While small-sized networks have typically been modelled via continuous approaches 9,35,[46][47][48][49] , larger networks have been modelled via discrete Boolean approaches due to lack of available kinetic parameters 24,44,50,51 . While continuous models provide a more quantitative mapping of system dynamics but require many kinetic parameters that can become experimentally intractable, Boolean modeling approaches provide a good estimate of qualitative behavior of a biochemical system without requiring a large set of parameters 52 , but are limited in terms of characterizing dynamic properties such as phenotypic plasticity and state transition rates.…”

Section: Discussionmentioning

confidence: 99%

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Identifying inhibitors of epithelial–mesenchymal plasticity using a network topology-based approach

et al. 2020

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Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges, such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial-mesenchymal plasticity (EMP)-an important arm of phenotypic plasticity-through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameteragnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity correlates positively with the number of positive feedback loops in a given network. These results pave a way toward an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis-by reducing the number of positive feedback loops.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identifying inhibitors of epithelial–mesenchymal plasticity using a network topology-based approach

et al. 2020

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“…The proposed signalling network is based on our previously published model for EMT [14], which contemplated the recent biochemical data on the EMT regulatory core composed by the positive circuits SNAIL1/miR-34 and ZEB1/miR-200 and endogenous TGF-β [8]. New experimentally validated circuits were considered in the proposed network such as ZEB1-mediated positive circuits with ESRP1 and CD44s [31], miR-1199 [12], miR-340 [11] and GRHL2 [21].…”

Section: Proposed Signalling Network Driving the Emtmentioning

confidence: 99%

“…Recently, we published a model about the dynamics of the regulatory cores of SNAIL1 and ZEB1 during TGF-βinduced EMT using a logical computational approach [14] based on the experimental work by Zhang et al [8]. The logical method is recognized as a valuable tool to study biological regulatory processes [15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Systems biology approach suggests new miRNAs as phenotypic stability factors in the epithelial–mesenchymal transition

Silveira

Gupta

Mombach

2020

J. R. Soc. Interface.

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The epithelial–mesenchymal transition (EMT) is a cellular programme on which epithelial cells undergo a phenotypic transition to mesenchymal ones acquiring metastatic properties such as mobility and invasion. TGF-β signalling can promote the EMT process. However, the dynamics of the concentration response of TGF-β-induced EMT is not well explained. In this work, we propose a logical model of TGF-β dose dependence of EMT in MCF10A breast cells. The model outcomes agree with experimentally observed phenotypes for the wild-type and perturbed/mutated cases. As important findings of the model, it predicts the coexistence of more than one hybrid state and that the circuit between TWIST1 and miR-129 is involved in their stabilization. Thus, miR-129 should be considered as a phenotypic stability factor. The circuit TWIST1/miR-129 associates with ZEB1-mediated circuits involving miRNAs 200, 1199, 340, and the protein GRHL2 to stabilize the hybrid state. Additionally, we found a possible new autocrine mechanism composed of the circuit involving TGF-β, miR-200, and SNAIL1 that contributes to the stabilization of the mesenchymal state. Therefore, our work can extend our comprehension of TGF-β-induced EMT in MCF10A cells to potentially improve the strategies for breast cancer treatment.

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“…In the present study we model the regulatory role and synergistic action 1 of miR-16 and miR-34a in the G1/S cell cycle checkpoint using Boolean methods [7][8][9][10] .…”

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confidence: 99%

Integrative data modeling from lung and lymphatic cancer predicts functional roles for miR-34a and miR-16 in cell fate regulation

Gupta

Silveira

Barbé-Tuana

et al. 2020

Sci Rep

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MiR-34a and miR-16 coordinately control cell cycle checkpoint in non-small cell lung cancer (NSCLC) cells. In cutaneous T-cell lymphoma (CTCL) cells miR-16 regulates a switch between apoptosis and senescence, however the role of miR-34a in this process is unclear. Both miRNAs share many common targets and experimental evidences suggest that they synergistically control the cell-fate regulation of NSCLC. In this work we investigate whether the coordinate action between miR-34a and miR-16 can explain experimental results in multiple cell lines of NSCLC and CTCL. For that we propose a Boolean model of the G1/S checkpoint regulation contemplating the regulatory influences of both miRNAs. Model validation was performed by comparisons with experimental information from the following cell lines: A549, H460, H1299, MyLa and MJ presenting excellent agreement. The model integrates in a single logical framework the mechanisms responsible for cell fate decision in NSCLC and CTCL cells. From the model analysis we suggest that miR-34a is the main controller of miR-16 activity in these cells. The model also allows to investigate perturbations of single or more molecules with the purpose to intervene in cell fate mechanisms of NSCLC and CTCL cells.

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Dynamics of the feedback loops required for the phenotypic stabilization in the epithelial‐mesenchymal transition

Cited by 23 publications

References 35 publications

Identifying inhibitors of epithelial–mesenchymal plasticity using a network topology-based approach

Identifying inhibitors of epithelial–mesenchymal plasticity using a network topology-based approach

Systems biology approach suggests new miRNAs as phenotypic stability factors in the epithelial–mesenchymal transition

Integrative data modeling from lung and lymphatic cancer predicts functional roles for miR-34a and miR-16 in cell fate regulation

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