Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1801.05766

A model of dynamic stability of H3K9me3 heterochromatin to explain the resistance to reprogramming of differentiated cells

Charly Jehanno·Gilles Flouriot·Pascale Le Goff·Denis Michel
View original source ↗Read source document ↗Export metadata ↓

Abstract

Despite their dynamic nature, certain chromatin marks must be maintained over the long term. This is particulary true for histone 3 lysine 9 (H3K9) trimethylation, that is involved in the maintenance of healthy differentiated cellular states by preventing inappropriate gene expression, and has been recently identified as the most efficient barrier to cellular reprogramming in nuclear transfer experiments. We propose that the capacity of the enzymes SUV39H1/2 to rebind to a minor fraction of their products, either directly or via HP1, contributes to the solidity of this mark through (i) a positive feedback involved in its establishment by the mutual enforcement of H3K9me3 and SUV39H1/2 and then (ii) a negative feedback sufficient to strongly stabilize H3K9me3 heterochromatin in post-mitotic cells by generating local enzyme concentrations capable of counteracting transient bursts of demethylation. This model does not require direct molecular interactions with adjacent nucleosomes and is favoured by a series of additional mechanisms including (i) the protection of chromatin-bound SUV39H1/2 from the turnovers of soluble proteins, which can explain the uncoupling between the cellular contents in SUV39H1 mRNA and protein; (ii) the cooperative dependence on the local density of the H3K9me3 of HP1-dependent heterochomatin condensation and, dispensably (iii) restricted enzyme exchanges with chromocenters confining the reactive bursts of SUV39H1/2 in heterochromatin. This mechanism illustrates how seemingly static epigenetic states can be firmly maintained by dynamic and reversible modifications.

Explore related subjects

q-bio.MN

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Charly Jehanno, Gilles Flouriot, Pascale Le Goff, Denis Michel. 2017-12-24. A model of dynamic stability of H3K9me3 heterochromatin to explain the resistance to reprogramming of differentiated cells. https://doi.org/10.1016/j.bbagrm.2016.11.006.

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

KEEP EXPLORING

Related papers

arXiv2026

Minimality in Reflexive and Stoichiometric Autocatalysis

Autocatalysis, the ability of a chemical subsystem to sustain its own constituents when supplied with sufficient food molecules, has been closely related to the origin of life on Earth. Emerging from Wilhelm Ostwald's considerations about an explicit autocatalytic reaction, different notions of autocatalysis have been developed over the years. The two most prominent are reflexively autocatalytic F-generated sets (RAFs) and stoichiometric autocatalysis. After having shown that each RAFs is, under reasonable conditions, in general stoichiometrically autocatalytic, we examine here the relationship between the two notions of minimality: irreducible RAFs and autocatalytic cores. To this end, we overcome the obstacle that RAFs and stoichiometric autocatalysis have been formalized in distinct systems of chemical reactions, i.e., catalytic reaction systems (CRS) and chemical reaction networks (CRNs), respectively. We show that reactions in a CRS constitute equivalence classes of reactions of the corresponding CRNs w.r.t. their specific catalyzations. Using the fact that CRN and CRS can be canonically identified whenever each CRS reaction is associated with a single catalyzation, we demonstrate that the Kőnig graph of a monocatalyzed, irreducible RAF is composed of strong blocks devoid of food and waste species that are separated by reaction vertices, each of which contains an autocatalytic core. In fact, a single irreducible RAF can, in general, contain exponentially many autocatalytic cores.

q-bio.MN↗
arXiv2026

Implementation of Linear Regression and Linear Interpolation using Reaction Networks

Statistical inference is a fundamental component of data science. In this work, we focus on two classical inference techniques: regression and interpolation. We propose a reaction-network-based framework for implementing linear regression, including both univariate and multivariate settings, as well as linear interpolation. Our approach encodes the outputs of these inference techniques in the steady-state concentrations of species within the reaction network. A key ingredient of the construction is a novel generalized division module capable of handling division involving negative numbers. We validate the proposed framework through in silico implementations on standard synthetic datasets and obtain the expected regression and interpolation outputs.

q-bio.MN↗
arXiv2026

Mapping disease-regulatory flux through eQTL-based causal gene networks: a complex-trait framework applied to coronary artery disease

A substantial proportion of inherited susceptibility to common diseases is mediated through tissue-specific regulatory variation. The omnigenic model hypothesizes that much of this risk arises from distant (trans) regulatory effects that are propagated via gene-regulatory networks (GRNs) from numerous regulatory genes onto a relatively small set of core genes. Two major challenges have hindered an empirical assessment of this model. First, transcriptome-wide association studies primarily assess gene expression that is regulated locally (in cis), and thus miss trans-acting effects. Second, trans regulation has so far been examined only in aggregate, without identifying which regulatory genes transmit disease-associated signals to which target genes. Here, we present a framework that decomposes each gene's disease association into a local cis component and a set of trans components attributable to its upstream regulators, propagated along a directed causal GRN. For each gene, we estimate sparse Bayesian expression models and we assess model performance using out-of-sample prediction. Propagating trans signals through the inferred network yields a disease-regulatory flux map: a directed, signed representation that quantifies the contribution of each regulator to the disease association of each target gene. Applying this framework to seven tissues relevant to coronary artery disease, we demonstrate how genetic variation flows through the GRN to affect disease risk. Outgoing regulatory influences from individual genes are directionally heterogeneous, whereas disease-associated genes tend to integrate convergent input from multiple regulators; these convergent targets are enriched for cardiovascular-related biological processes. Consequently, each gene's disease association can be reinterpreted as a detailed allocation of the disease signal among its contributing regulators.

q-bio.MN↗