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Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
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Characterising Epigenetic Tipping Points using a Spectral Dimension Reduction Approach.

Tomás Alarcón1,2,3,4, Javier A Menendez5,6, Josep Sardanyés7

  • 1Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, 08012, Spain. talarcon@crm.cat.

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|March 18, 2026
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Summary
This summary is machine-generated.

Predicting epigenetic tipping points (ETPs) is crucial for understanding cell identity loss in aging and cancer. This study develops a mathematical framework to identify early warning signals (EWS) for ETPs, focusing on metabolic cofactors like SAM and acetyl-CoA.

Keywords:
Epigenetic regulationSpectral dimension reductionTipping points

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Area of Science:

  • Systems biology
  • Epigenetics
  • Computational biology

Background:

  • Epigenetic landscapes (ELs) maintain cell fate and identity through precise regulation of chromatin-modifying enzymes (ChME) and metabolic cofactors (McF).
  • Dysregulation of ChME or McF can lead to EL degradation, triggering cell fate changes relevant to aging and cancer.
  • Predicting epigenetic tipping points (ETPs) using early warning signals (EWS) is essential for anticipating cell identity loss.

Purpose of the Study:

  • To develop a general mathematical framework for analyzing competition-induced ETPs in large ELs, considering 3D chromatin folding.
  • To measure the sensitivity and robustness of ETPs to McF availability and identify potential EWS.
  • To predict global transitions and ETPs under various chromatin connectivity patterns.

Main Methods:

  • Developed a mathematical framework incorporating 3D chromatin folding patterns.
  • Employed dimension reduction to derive coarse-grained (CG) equations for chromatin modification observables.
  • Analyzed CG systems to predict global transitions and ETPs, validated against microscopic benchmarks.

Main Results:

  • The CG method accurately predicts global transitions and ETPs under ChME competition and diverse connectivity patterns (e.g., Hi-C data).
  • Sensitivity analysis revealed metabolic cofactors (SAM and acetyl-CoA) significantly impact EL robustness.
  • Identified SAM and acetyl-CoA as potential EWS for the loss of hyperacetylated ELs.

Conclusions:

  • The developed framework enables prediction of global ETPs, offering insights into mechanisms of cell identity loss.
  • Identified specific metabolic cofactors as critical EWS, potentially useful for biomarker discovery.
  • Findings suggest metabolic interventions could limit or reverse pathological cell fate decisions.