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Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis

Epithelial to Mesenchymal Transition (EMT) and its reverse—Mesenchymal to Epithelial Transition (MET) are hallmarks of metastasis. Cancer cells use this reversible cellular programming to switch among Epithelial (E), Mesenchymal (M), and hybrid Epithelial/Mesenchymal (hybrid E/M) state(s) and seed t...

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Autores principales: Rashid, Mubasher, Hari, Kishore, Thampi, John, Santhosh, Nived Krishnan, Jolly, Mohit Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674141/
https://www.ncbi.nlm.nih.gov/pubmed/36346808
http://dx.doi.org/10.1371/journal.pcbi.1010687
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author Rashid, Mubasher
Hari, Kishore
Thampi, John
Santhosh, Nived Krishnan
Jolly, Mohit Kumar
author_facet Rashid, Mubasher
Hari, Kishore
Thampi, John
Santhosh, Nived Krishnan
Jolly, Mohit Kumar
author_sort Rashid, Mubasher
collection PubMed
description Epithelial to Mesenchymal Transition (EMT) and its reverse—Mesenchymal to Epithelial Transition (MET) are hallmarks of metastasis. Cancer cells use this reversible cellular programming to switch among Epithelial (E), Mesenchymal (M), and hybrid Epithelial/Mesenchymal (hybrid E/M) state(s) and seed tumors at distant sites. Hybrid E/M cells are often more aggressive and metastatic than the “pure” E and M cells. Thus, identifying mechanisms to inhibit hybrid E/M cells can be promising in curtailing metastasis. While multiple gene regulatory networks (GRNs) based mathematical models for EMT/MET have been developed recently, identifying topological signatures enriching hybrid E/M phenotypes remains to be done. Here, we investigate the dynamics of 13 different GRNs and report an interesting association between “hybridness” and the number of negative/positive feedback loops across the networks. While networks having more negative feedback loops favor hybrid phenotype(s), networks having more positive feedback loops (PFLs) or many HiLoops–specific combinations of PFLs, support terminal (E and M) phenotypes. We also establish a connection between “hybridness” and network-frustration by showing that hybrid phenotypes likely result from non-reinforcing interactions among network nodes (genes) and therefore tend to be more frustrated (less stable). Our analysis, thus, identifies network topology-based signatures that can give rise to, as well as prevent, the emergence of hybrid E/M phenotype in GRNs underlying EMP. Our results can have implications in terms of targeting specific interactions in GRNs as a potent way to restrict switching to the hybrid E/M phenotype(s) to curtail metastasis.
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spelling pubmed-96741412022-11-19 Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis Rashid, Mubasher Hari, Kishore Thampi, John Santhosh, Nived Krishnan Jolly, Mohit Kumar PLoS Comput Biol Research Article Epithelial to Mesenchymal Transition (EMT) and its reverse—Mesenchymal to Epithelial Transition (MET) are hallmarks of metastasis. Cancer cells use this reversible cellular programming to switch among Epithelial (E), Mesenchymal (M), and hybrid Epithelial/Mesenchymal (hybrid E/M) state(s) and seed tumors at distant sites. Hybrid E/M cells are often more aggressive and metastatic than the “pure” E and M cells. Thus, identifying mechanisms to inhibit hybrid E/M cells can be promising in curtailing metastasis. While multiple gene regulatory networks (GRNs) based mathematical models for EMT/MET have been developed recently, identifying topological signatures enriching hybrid E/M phenotypes remains to be done. Here, we investigate the dynamics of 13 different GRNs and report an interesting association between “hybridness” and the number of negative/positive feedback loops across the networks. While networks having more negative feedback loops favor hybrid phenotype(s), networks having more positive feedback loops (PFLs) or many HiLoops–specific combinations of PFLs, support terminal (E and M) phenotypes. We also establish a connection between “hybridness” and network-frustration by showing that hybrid phenotypes likely result from non-reinforcing interactions among network nodes (genes) and therefore tend to be more frustrated (less stable). Our analysis, thus, identifies network topology-based signatures that can give rise to, as well as prevent, the emergence of hybrid E/M phenotype in GRNs underlying EMP. Our results can have implications in terms of targeting specific interactions in GRNs as a potent way to restrict switching to the hybrid E/M phenotype(s) to curtail metastasis. Public Library of Science 2022-11-08 /pmc/articles/PMC9674141/ /pubmed/36346808 http://dx.doi.org/10.1371/journal.pcbi.1010687 Text en © 2022 Rashid et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rashid, Mubasher
Hari, Kishore
Thampi, John
Santhosh, Nived Krishnan
Jolly, Mohit Kumar
Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis
title Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis
title_full Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis
title_fullStr Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis
title_full_unstemmed Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis
title_short Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis
title_sort network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674141/
https://www.ncbi.nlm.nih.gov/pubmed/36346808
http://dx.doi.org/10.1371/journal.pcbi.1010687
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