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

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.

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development

“…State-transition theory has a rich mathematical foundation (Pavliotis, 2014) and has been broadly applied in various scientific fields (i.e., chemistry, physics, and biology (Esteban et al, 2018;Folguera-Blasco et al, 2018;Herring et al, 2017;Hormoz et al, 2016;Pastushenko et al, 2018;Zhou et al, 2012)). To our knowledge, it has not yet been applied to study cancer progression from a reference state of health (i.e., normal hematopoiesis) to a state of disease (i.e., leukemia).…”

A multiscale model of epigenetic heterogeneity reveals the kinetic routes of pathological cell fate reprogramming

“…78 2(a) for a schematic representation). Crucially, the precise deconstruction of the highly 79 complex mechanisms through which epigenetic plasticity allows cells to stochastically 80 activate alternative regulatory programs and undergo pathological versus physiological 81 cell fate transitions requires the incorporation of a central role for epigenetic 82 heterogeneity in phenotypic plasticity [18,26] (see Fig. 1 for a schematic representation).…”

“…83 Pour et al [26] have reported evidence that the potential to reprogram is higher within 84 select subpopulations of cells and that preexisting epigenetic heterogeneity can be tuned 85 to make cells more responsive to reprogramming factor induction. Along this line, we 86 recently presented a model of cell fate reprogramming, in which the heterogeneity of 87 epigenetic metabolites, which operates as a regulator of the kinetic parameters 88 promoting/preventing histone modification, stochastically drives phenotypic variability 89 capable of producing cell epistates primed for reprogramming [18]. However, how the 90 variability of both histone-modifying enzymes and their corresponding cofactors might 91 dictate changes in chromatin-related barriers and lead to drastic changes in the 92 transcriptional regulatory networks for cell fate determination was not contemplated.…”

“…Such epigenetic control is often 151 related to alternative covalent modifications of histones. To address the high complexity 152 of ER, we focus on a simpler stochastic model of ER, based on that formulated 153 in [13,18] and [19]. Our model belongs to a wider class of models which consider that Evolution to the silenced state of the pluripotency-regulating gene.…”

“…The positive marks are assumed to be 165 under the effects of a similar positive reinforcement mechanism [16,19]. A full 166 description of the details of the ER model are given in Section S.2 of the S1 File (see 167 also [18]) and S3 Table, where the transition rates for the ER model are given. 168 Under suitable conditions, determined by the activity and abundance of 169 histone-modifying enzymes and co-factors, the positive reinforcement mechanism 170 produces robust bistable behaviour.…”