Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.03.24.644914

Metabolic early warning signals of epigenetic tipping points under chromatin modifier competition

Alarcon, T.·Menendez, J. A.·Sardanyes, J.
View original source ↗Export metadata ↓

Abstract

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 summaryCells 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.

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alarcon, T., Menendez, J. A., Sardanyes, J.. 2025-03-25. Metabolic early warning signals of epigenetic tipping points under chromatin modifier competition. https://doi.org/10.1101/2025.03.24.644914

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Limit-pushing overexpression reveals constraints on protein abundance

Proteins are often classified as toxic or non-toxic without measuring the abundance reached, leaving constraints on tolerable protein abundance unresolved. We established a limit-pushing approach in Saccharomyces cerevisiae combining strong inducible expression with gTOW-mediated high-copy selection to counteract copy-number compensation while measuring protein abundance and growth. Nearly all of approximately 80 chromosome I proteins severely inhibited growth or reduced viability at sufficiently high abundance. We established IE50, the expression level associated with a 50% reduction in growth rate, to quantify their widely varying overexpression tolerance. IE50 was positively associated with predicted structural order and cytoplasmic localization propensity and negatively associated with sulphur content. Single-cell imaging linked higher tolerance to proteins remaining cytoplasmic without becoming aggregation-positive and revealed abundance-dependent changes in localization and organelle morphology. At extreme abundance, Fun12, Nup60, and Pex22 generated distinct large-scale intracellular states through specific sequence regions. These findings establish overexpression toxicity as a quantitative property linked to protein characteristics and reveal both constraints on tolerable abundance and sequence-dependent capacities for intracellular organization.

systems biology↗
bioRxiv2026

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

systems biology↗
bioRxiv2026

A thermoregulatory design principle for transitions into hypometabolism

Mammals entering torpor or hibernation undergo an abrupt transition from normothermia to hypothermia, yet how thermoregulation enables this switch remains poorly understood. Here, we identify dynamical signatures that precede these transitions and a mathematical principle that can generate them. In fasting-induced torpor in mice, body-temperature fluctuations increased before torpor onset, providing an early-warning signal that tracked proximity to the transition better than temperature decline alone. A heat-balance model showed that reducing how strongly the effective heat-loss coefficient depends on body temperature reorganizes thermoregulatory stability, allowing a low-temperature equilibrium to emerge while the normothermic state remains stable. This organization is consistent with a symmetry-broken pitchfork involving a saddle-node. Similar increases in temperature fluctuations preceded hibernation onset in hamsters. These findings link pre-transition temperature dynamics to changes in the underlying thermoregulatory landscape and provide a framework for detecting and understanding transitions from normothermia to hypothermia.

systems biology↗