Metabolic early warning signals of epigenetic tipping points under chromatin modifier competition
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The maintenance of epigenetic landscapes (EL) requires the precise regulation of chromatin-modifying enzymes (ChME). Competition for ChME can lead to degradation of ELs, triggering large-scale changes in the cell fate information contained in EL. Predicting impending epigenetic tipping points (ETP) by identifying early warning signals (EWS) may help to anticipate the onset of cell identity loss during aging and cancer. We have developed a general mathematical framework that incorporates different connectivity patterns generated by the 3D chromatin folding structure to analyze competition-induced ETP in large EL. This framework allows us to measure the sensitivity and robustness of ETP to the availability of metabolic cofactors and to identify potential EWS. Using a dimension reduction method, we derived coarse-grained (CG) equations for the collective observables associated with chromatin modifications. Analysis of the CG system allows the prediction of global transitions that shape the large-scale features of EL, accurately reproduce the corresponding microscopic benchmarks, and reveal the existence of tipping points under conditions of ChME competition. We applied the CG method to predict ETP under different connectivity patterns, including heterogeneous profiles such as those found in Hi-C data. Although a robustness measure for stable EL was derived from the CG dynamics in bistable regimes, sensitivity analysis revealed that metabolic cofactors have the greatest impact on EL robustness. In particular, we identified the metabolic cofactors SAM and acetyl-CoA as potential EWS for the catastrophic loss of hyperacetylated EL induced by ChME competition. The ability to predict global ETP can facilitate the discovery of predictive biomarkers and inform metabolic interventions aimed at limiting and reversing pathological cell fate decisions.
Author summary
Cells maintain their identity through specific patterns of gene expression controlled by structures called “epigenetic landscapes” (EL). Deterioration of these EL can occur over time as the enzymatic machineries responsible for maintaining them compete with each other. When this competition reaches a “tipping point”, cells can undergo critical shifts in cell fate and lose their original identity, leading to aging and cancer. Using mathematical models, the researchers developed a way to accurately predict these tipping points early on, based on “warning signals” derived from the abundance levels of metabolites used by chromatin-modifying enzymes. This work could lead to new methods for detecting when cells are about to lose their identity and open the door to metabolic treatments that prevent or reverse this process in aging and cancer.
