Gene regulatory network underlying the immortalization of epithelial cells

Abstract

BackgroundTumorigenic transformation of human epithelial cells in vitro has been described experimentally as the potential result of spontaneous immortalization. This process is characterized by a series of cell–state transitions, in which normal epithelial cells acquire first a senescent state which is later surpassed to attain a mesenchymal stem–like phenotype with a potentially tumorigenic behavior. In this paper we aim to provide a system–level mechanistic explanation to the emergence of these cell types, and to the time–ordered transition patterns that are common to neoplasias of epithelial origin. To this end, we first integrate published functional and well–curated molecular data of the components and interactions that have been found to be involved in such cell states and transitions into a network of 41 molecular components. We then reduce this initial network by removing simple mediators (i.e., linear pathways), and formalize the resulting regulatory core into logical rules that govern the dynamics of each of the network components as a function of the states of its regulators.ResultsComputational dynamic analysis shows that our proposed Gene Regulatory Network model recovers exactly three attractors, each of them defined by a specific gene expression profile that corresponds to the epithelial, senescent, and mesenchymal stem–like cellular phenotypes, respectively. We show that although a mesenchymal stem–like state can be attained even under unperturbed physiological conditions, the likelihood of converging to this state is increased when pro–inflammatory conditions are simulated, providing a systems–level mechanistic explanation for the carcinogenic role of chronic inflammatory conditions observed in the clinic. We also found that the regulatory core yields an epigenetic landscape that restricts temporal patterns of progression between the steady states, such that recovered patterns resemble the time–ordered transitions observed during the spontaneous immortalization of epithelial cells, both in vivo and in vitro.ConclusionOur study strongly suggests that the in vitro tumorigenic transformation of epithelial cells, which strongly correlates with the patterns observed during the pathological progression of epithelial carcinogenesis in vivo, emerges from underlying regulatory networks involved in epithelial trans–differentiation during development.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0393-5) contains supplementary material, which is available to authorized users.

A Computational Model of the Endothelial to Mesenchymal Transition

“…While ECs and PCs are fully differentiated cell types, they have the notable capacity to trans-differentiate into each other (Nakagomi et al, 2015;Chen et al, 2016;Jackson et al, 2017), and are also capable of differentiating into hematopoietic stem cells, mesenchymal stem cells, and several other cell types (van Meeteren and Ten Dijke, 2012;Birbrair et al, 2017;Dejana et al, 2017). Notably, ECs differentiate into PCs in a process called endothelial to mesenchymal transition (EndMT), which is very similar to the epithelial-to-mesenchymal transition (EMT) (Lamouille et al, 2014;Méndez-López et al, 2017). Like EMT, EndMT is a reversible process, and the opposite mechanism is denominated mesenchymal-to-endothelial transition (MEnT) (Sánchez-Duffhues et al, 2018).…”

“…Due to the enormous biological and medical importance of angiogenesis and EMT, both processes have been widely explored through the simulation of models at the molecular and cellular levels (Peirce, 2008;Qutub et al, 2009;Lu et al, 2013;Steinway et al, 2014;Heck et al, 2015;Li et al, 2016;Méndez-López et al, 2017;Weinstein et al, 2017;Suzuki et al, 2018). In contrast, to the best of our knowledge, simulation or formal analyses of the molecular mechanism that control EndMT are lacking.…”

Target Control in Logical Models Using the Domain of Influence of Nodes

“…An abundance of recent literature has shown that logical models can capture the emergent behaviors of real biological systems, they can generate predictions that are validated by follow-up experiments and they can predict successful intervention strategies (Li et al, 2004 ; Espinosa-Soto et al, 2004 ; Mendoza, 2006 ; Saez-Rodriguez et al, 2007 ; Naldi et al, 2010 ; Miskov-Zivanov et al, 2013 ; Steinway et al, 2015 ; Albert et al, 2017 ; Gómez Tejeda Zañudo et al, 2017 ). For example, logical models of signaling networks that underlie hallmarks of cancer identified the key mechanisms that yield cancer phenotypes and predicted therapeutic interventions that disrupt these phenotypes; many of these predictions were validated experimentally (Grieco et al, 2013 ; Cohen et al, 2015 ; Méndez-López et al, 2017 ; Khan et al, 2017 ; Kim et al, 2017 ). Discrete and quantitative models are often consistent in capturing the response repertoire of biological networks (e.g., their potential bistability or response to perturbations) (Kraeutler et al, 2010 ; Steinway et al, 2016 ).…”

A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

“…In particular, we propose using a subtype of network models, known as discrete dynamic models, which have been shown to reproduce the qualitative behavior of cancer signaling networks and are constructed solely from the regulatory interactions among the signaling proteins and the combinatorial effect of these regulatory interactions (e.g. positive or negative, additive or multiplicative) (Wang et al, 2012 ; Morris et al, 2010 ; Steinway et al, 2014 ; Udyavar et al, 2017 ; Méndez-López et al, 2017 ; Collombet et al, 2017 ).…”