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Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition
Intracellular signaling pathways are at the core of cellular information processing. The states of these pathways and their inputs determine signaling dynamics and drive cell function. Within a cancerous tumor, many combinations of cell states and microenvironments can lead to dramatic variations in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733964/ https://www.ncbi.nlm.nih.gov/pubmed/33329028 http://dx.doi.org/10.3389/fphys.2020.579117 |
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author | Wade, James D. Lun, Xiao-Kang Zivanovic, Nevena Voit, Eberhard O. Bodenmiller, Bernd |
author_facet | Wade, James D. Lun, Xiao-Kang Zivanovic, Nevena Voit, Eberhard O. Bodenmiller, Bernd |
author_sort | Wade, James D. |
collection | PubMed |
description | Intracellular signaling pathways are at the core of cellular information processing. The states of these pathways and their inputs determine signaling dynamics and drive cell function. Within a cancerous tumor, many combinations of cell states and microenvironments can lead to dramatic variations in responses to treatment. Network rewiring has been thought to underlie these context-dependent differences in signaling; however, from a biochemical standpoint, rewiring of signaling networks should not be a prerequisite for heterogeneity in responses to stimuli. Here we address this conundrum by analyzing an in vitro model of the epithelial mesenchymal transition (EMT), a biological program implicated in increased tumor invasiveness, heterogeneity, and drug resistance. We used mass cytometry to measure EGF signaling dynamics in the ERK and AKT signaling pathways before and after induction of EMT in Py2T murine breast cancer cells. Analysis of the data with standard network inference methods suggested EMT-dependent network rewiring. In contrast, use of a modeling approach that adequately accounts for single-cell variation demonstrated that a single reaction-based pathway model with constant structure and near-constant parameters is sufficient to represent differences in EGF signaling across EMT. This result indicates that rewiring of the signaling network is not necessary for heterogeneous responses to a signal and that unifying reaction-based models should be employed for characterization of signaling in heterogeneous environments, such as cancer. |
format | Online Article Text |
id | pubmed-7733964 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77339642020-12-15 Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition Wade, James D. Lun, Xiao-Kang Zivanovic, Nevena Voit, Eberhard O. Bodenmiller, Bernd Front Physiol Physiology Intracellular signaling pathways are at the core of cellular information processing. The states of these pathways and their inputs determine signaling dynamics and drive cell function. Within a cancerous tumor, many combinations of cell states and microenvironments can lead to dramatic variations in responses to treatment. Network rewiring has been thought to underlie these context-dependent differences in signaling; however, from a biochemical standpoint, rewiring of signaling networks should not be a prerequisite for heterogeneity in responses to stimuli. Here we address this conundrum by analyzing an in vitro model of the epithelial mesenchymal transition (EMT), a biological program implicated in increased tumor invasiveness, heterogeneity, and drug resistance. We used mass cytometry to measure EGF signaling dynamics in the ERK and AKT signaling pathways before and after induction of EMT in Py2T murine breast cancer cells. Analysis of the data with standard network inference methods suggested EMT-dependent network rewiring. In contrast, use of a modeling approach that adequately accounts for single-cell variation demonstrated that a single reaction-based pathway model with constant structure and near-constant parameters is sufficient to represent differences in EGF signaling across EMT. This result indicates that rewiring of the signaling network is not necessary for heterogeneous responses to a signal and that unifying reaction-based models should be employed for characterization of signaling in heterogeneous environments, such as cancer. Frontiers Media S.A. 2020-11-30 /pmc/articles/PMC7733964/ /pubmed/33329028 http://dx.doi.org/10.3389/fphys.2020.579117 Text en Copyright © 2020 Wade, Lun, Zivanovic, Voit and Bodenmiller. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Wade, James D. Lun, Xiao-Kang Zivanovic, Nevena Voit, Eberhard O. Bodenmiller, Bernd Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition |
title | Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition |
title_full | Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition |
title_fullStr | Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition |
title_full_unstemmed | Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition |
title_short | Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition |
title_sort | mechanistic model of signaling dynamics across an epithelial mesenchymal transition |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733964/ https://www.ncbi.nlm.nih.gov/pubmed/33329028 http://dx.doi.org/10.3389/fphys.2020.579117 |
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