dc.contributor.author |
Rashid, Mubasher |
en_US |
dc.contributor.author |
Hari, Kishore |
en_US |
dc.contributor.author |
THAMPI, JOHN |
en_US |
dc.contributor.author |
Santhosh, Nived Krishnan |
en_US |
dc.contributor.author |
Jolly, Mohit Kumar |
en_US |
dc.date.accessioned |
2023-02-28T10:46:13Z |
|
dc.date.available |
2023-02-28T10:46:13Z |
|
dc.date.issued |
2022-11 |
en_US |
dc.identifier.citation |
PLOS Computational Biology, 18(11), e1010687. |
en_US |
dc.identifier.issn |
1553-734X |
en_US |
dc.identifier.issn |
1553-7358 |
en_US |
dc.identifier.uri |
https://doi.org/10.1371/journal.pcbi.1010687 |
en_US |
dc.identifier.uri |
http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7635 |
|
dc.description.abstract |
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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
PUBLIC LIBRARY SCIENCE |
en_US |
dc.subject |
Survival |
en_US |
dc.subject |
Plasticity |
en_US |
dc.subject |
Dormancy |
en_US |
dc.subject |
Cells |
en_US |
dc.subject |
Lung |
en_US |
dc.subject |
2022 |
en_US |
dc.title |
Network topology metrics explaining enrichment of hybrid epithelial/mesenchymal phenotypes in metastasis |
en_US |
dc.type |
Article |
en_US |
dc.contributor.department |
Dept. of Physics |
en_US |
dc.identifier.sourcetitle |
PLOS Computational Biology |
en_US |
dc.publication.originofpublisher |
Foreign |
en_US |