A multiscale model of epigenetic heterogeneity-driven cell fate decision-making

PLoS Comput Biol. 2019 Apr 30;15(4):e1006592. doi: 10.1371/journal.pcbi.1006592. eCollection 2019 Apr.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aging / physiology
  • Cellular Reprogramming / physiology*
  • Computational Biology / methods
  • Epigenesis, Genetic / physiology*
  • Gene Regulatory Networks / physiology*
  • Humans
  • Models, Biological*
  • Neoplasms / physiopathology
  • Phenotype

Grants and funding

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. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.