Population Heterogeneity in the Epithelial to Mesenchymal Transition Is Controlled by NFAT and Phosphorylated Sp1

Abstract

Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor β (TGF-β) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-β induced EMT to better understand the operational hierarchy of this complex molecular program. We used ordinary differential equations (ODEs) to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-β exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was enhanced following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-β and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-β and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-β treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response.

An Effective Model of the Retinoic Acid Induced HL-60 Differentiation Program

“…We modeled transcription and translation as Ordinary Differential Equations (ODEs), while signaling processes were assumed to quickly equilibrate and were treated as a pseudo steady state system of algebraic equations. The model formulation follows from a previous study of the Epithelial Messecnchymal Transition (EMT) 61 ; in the current study additional attention was paid to the formulation of the transcription and translation rates, and an updated approach was taken to model the regulation of gene expression.…”

Deciphering Epithelial–Mesenchymal Transition Regulatory Networks in Cancer through Computational Approaches

“…This subset of models displayed upregulated NFATc activity and inhibition of NFATc in these models “restored” full EMT. Experimental stimulation with VEGF-A and TGFβ in MCF10A and DLD1 cell lines confirmed this prediction, i.e., the absence of an NFATc inhibitor led to high levels of both E-cadherin and vimentin (hybrid phenotype), and its presence led to a full EMT ( 54 ).…”

“…In fact, consideration of a “core regulatory network” for EMT presupposes such sloppiness in EMT regulation. Recently, Gould et al applied the “sloppy” Pareto optimal ensemble technique (POET) to study TGFβ-induced EMT in the presence and absence of vascular endothelial growth factor A (VEGF-A) ( 54 ). Their literature-derived EMT network consists of 97 nodes and 169 edges and has 251 unknown model parameters.…”

Automatic jargon identifier for scientists engaging with the public and science communication educators

“…Part II (summary) uses 71% high frequency, 20% mid-frequency, and 8% jargon. Excerpts taken from [ 53 ].…”