Robert Corentin, Prista von Bonhorst Francisco, Dupont Geneviève, Gonze Didier, De Decker Yannick
Nonlinear Physical Chemistry Unit, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Unit of Theoretical Chronobiology, Université Libre de Bruxelles (ULB), Brussels, Belgium.
PLoS One. 2025 Mar 19;20(3):e0316666. doi: 10.1371/journal.pone.0316666. eCollection 2025.
During cell differentiation, identical pluripotent cells undergo a specification process marked by changes in the expression of key genes, regulated by transcription factors that can inhibit the transcription of a competing gene or activate their own transcription. This specification is orchestrated by gene regulatory networks (GRNs), encompassing transcription factors, biochemical reactions, and signalling cascades. Mathematical models for these GRNs have been proposed in various contexts, to replicate observed robustness in differentiation properties. This includes reproducible proportions of differentiated cells with respect to parametric or stochastic noise and the avoidance of transitions between differentiated states. Understanding the GRN components controlling these features is crucial. Our study thoroughly explored an extended version of the Toggle Switch model with auto-activation loops. This model represents cells evolving from common progenitors in one out of two fates (A or B, bistable regime) or, additionally, remaining in their progenitor state (C, tristable regime). Such a differentiation into populations with three distinct cell fates is observed during blastocyst formation in mammals, where inner cell mass cells can remain in that state or differentiate into epiblast cells or primitive endoderm. Systematic analysis revealed that the existence of a stable non-differentiated state significantly impacts the GRN's robustness against parametric variations and stochastic noise. This state reduces the sensitivity of cell populations to parameters controlling key gene expression asymmetry and prevents cells from making transitions after acquiring a new identity. Stochastic noise enhances robustness by decreasing sensitivity to initial expression levels and helping the system escape from the non-differentiated state to differentiated cell fates, making the differentiation more efficient.
在细胞分化过程中，相同的多能细胞会经历一个特化过程，其特征是关键基因表达发生变化，这一过程由转录因子调控，转录因子可抑制竞争基因的转录或激活自身转录。这种特化由基因调控网络（GRN）精心编排，基因调控网络包括转录因子、生化反应和信号级联。在各种情况下都已提出了针对这些基因调控网络的数学模型，以复制在分化特性中观察到的稳健性。这包括相对于参数噪声或随机噪声，分化细胞具有可重复的比例，以及避免在分化状态之间发生转变。了解控制这些特征的基因调控网络组件至关重要。我们的研究深入探讨了具有自激活环的切换开关模型的扩展版本。该模型表示细胞从共同祖细胞开始，以两种命运之一（A或B，双稳态）进化，或者另外保持其祖细胞状态（C，三稳态）。在哺乳动物囊胚形成过程中可观察到这种分化为具有三种不同细胞命运的群体，其中内细胞团细胞可以保持该状态或分化为上胚层细胞或原始内胚层。系统分析表明，稳定的未分化状态的存在会显著影响基因调控网络对参数变化和随机噪声的稳健性。这种状态降低了细胞群体对控制关键基因表达不对称性的参数的敏感性，并防止细胞在获得新身份后发生转变。随机噪声通过降低对初始表达水平的敏感性并帮助系统从未分化状态转变为分化细胞命运，从而增强了稳健性，使分化更加高效。
