Epigenetic regulation of cell fate reprogramming in aging and disease: A predictive computational model

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Understanding the control of epigenetic regulation is key to explain and modify the aging process. Because histone-modifying enzymes are sensitive to shifts in availability of cofactors (e.g. metabolites), cellular epigenetic states may be tied to changing conditions associated with cofactor variability. The aim of this study is to analyse the relationships between cofactor fluctuations, epigenetic landscapes, and cell state transitions. Using Approximate Bayesian Computation, we generate an ensemble of epigenetic regulation (ER) systems whose heterogeneity reflects variability in cofactor pools used by histone modifiers. The heterogeneity of epigenetic metabolites, which operates as regulator of the kinetic parameters promoting/preventing histone modifications, stochastically drives phenotypic variability. The ensemble of ER configurations reveals the occurrence of distinct epi-states within the ensemble. Whereas resilient states maintain large epigenetic barriers refractory to reprogramming cellular identity, plastic states lower these barriers, and increase the sensitivity to reprogramming. Moreover, fine-tuning of cofactor levels redirects plastic epigenetic states to re-enter epigenetic resilience, and vice versa. Our ensemble model agrees with a model of metabolism-responsive loss of epigenetic resilience as a cellular aging mechanism. Our findings support the notion that cellular aging, and its reversal, might result from stochastic translation of metabolic inputs into resilient/plastic cell states via ER systems. Cell reprogramming, a process that allows differentiated cells to re-acquire stem-like properties, is increasingly considered a critical phenomenon in tissue regeneration, aging and cancer. In light of the importance of metabolism in controlling cell fate, we designed a computational model capable of predicting the likelihood of cell reprogramming in response to changes in aging-related metabolites. Our predictive mathematical model improves our understanding of how pathological processes that involve changes in cell plasticity, such as cancer, might be accelerated or attenuated by means of metabolic reprogramming.
Funding Information
  • Obra Social la Caixa (Collaborative Mathematics)
  • Obra Social la Caixa (Collaborative Mathematics)
  • MINECO (MTM2015-71509-C2-1-R)
  • MINECO (MTM2015-71509-C2-1-R)
  • AGAUR (2014SGR1307)
  • AGAUR (2014SGR1307)
  • Sara Borrell (CD15/00033, FIS)
  • Barcelona Graduate School of Mahematics (MDM-2014-0445)
  • MINECO (SAF2016-80639-P)
  • AGAUR (2014 SGR229)

This publication has 82 references indexed in Scilit: