A multiscale model of epigenetic heterogeneity-driven cell fate decision-making
dc.contributor.author
dc.date.accessioned
2020-06-26T06:13:30Z
dc.date.available
2020-06-26T06:13:30Z
dc.date.issued
2019-04-30
dc.identifier.issn
1553-734X
dc.identifier.uri
dc.description.abstract
The inherent capacity of somatic cells to switch their phenotypic status in response to damage stimuli in vivo might have a pivotal role in ageing and cancer. However, how the entry-exit mechanisms of phenotype reprogramming are established remains poorly understood. In an attempt to elucidate such mechanisms, we herein introduce a stochastic model of combined epigenetic regulation (ER)-gene regulatory network (GRN) to study the plastic phenotypic behaviours driven by ER heterogeneity. To deal with such complex system, we additionally formulate a multiscale asymptotic method for stochastic model reduction, from which we derive an efficient hybrid simulation scheme. Our analysis of the coupled system reveals a regime of tristability in which pluripotent stem-like and differentiated steady-states coexist with a third indecisive state, with ER driving transitions between these states. Crucially, ER heterogeneity of differentiation genes is for the most part responsible for conferring abnormal robustness to pluripotent stem-like states. We formulate epigenetic heterogeneity-based strategies capable of unlocking and facilitating the transit from differentiation-refractory (stem-like) to differentiation-primed epistates. The application of the hybrid numerical method validates the likelihood of such switching involving solely kinetic changes in epigenetic factors. Our results suggest that epigenetic heterogeneity regulates the mechanisms and kinetics of phenotypic robustness of cell fate reprogramming. The occurrence of tunable switches capable of modifying the nature of cell fate reprogramming might pave the way for new therapeutic strategies to regulate reparative reprogramming in ageing and cancer
dc.description.sponsorship
This work is supported by a grant of the Obra Social La Caixa Foundation on Collaborative Mathematics awarded to the Centre de Recerca Matemàtica. The authors have been partially funded by the CERCA Programme of the Generalitat de Catalunya. EC is the recipient of a Sara Borrell post-doctoral contract (CD15/00033, Ministerio de Sanidad y Consumo, Fondo de Investigación Sanitaria, Spain). NF-B and TA acknowledge MINECO and AGAUR for funding under grants MTM2015-71509-C2-1-R and 2014SGR1307. TA acknowledges support from MINECO for funding awarded to the Barcelona Graduate School of Mathematics under the “María de Maeztu” programme, grant number MDM-2014-0445. RP-C also acknowledges the UCL Mathematics Clifford Fellowship. This work was supported by grants from MINECO (SAF2016-80639-P) and AGAUR (2014 SGR229) to JAM
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Public Library of Science
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Reproducció digital del document publicat a: https://doi.org/10.1371/journal.pcbi.1006592
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PLoS Computational Biology, 2019, vol. 15, núm. 4, p. e1006592
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Articles publicats (IdIBGi)
dc.rights
Attribution 4.0 International
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dc.subject
dc.title
A multiscale model of epigenetic heterogeneity-driven cell fate decision-making
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.type.peerreviewed
peer-reviewed
dc.identifier.eissn
1553-7358