Probing the role of stochasticity in a model of the embryonic stem cell: heterogeneous gene expression and reprogramming efficiency.
Probing the role of stochasticity in a model of the embryonic stem cell: heterogeneous gene expression and reprogramming efficiency.
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DOI：
10.1186/1752-0509-6-98
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发表时间：
2012-08-13
影响因子：
--
通讯作者：
Peterson C
中科院分区：
文献类型：
--
作者：
Chickarmane V;Olariu V;Peterson C
胚胎干细胞（ESC）具有自我更新和保持多能性的能力，同时不断提供各种分化细胞类型的来源。在分子水平上理解控制这些特性的因素对于干细胞生物学及其在再生医学中的应用至关重要。特别相关的是阐明那些控制体细胞重编程成ESC的分子相互作用。计算方法可以作为一个框架来探索ESC简化网络的动力学，目的是了解干细胞如何分化以及它们如何从体细胞重新编程。我们提出了一个胚胎干细胞网络的计算模型，其中一组核心转录因子（tf）相互作用并受外部因素诱导。网络动力学的随机处理表明，NANOG异质性是干细胞命运的决定性因素。特别是，我们的研究结果表明，留在基态还是致力于分化状态的决定基本上是随机的，并且可以通过添加外部因素（2i/3i介质）来调节，这些因素具有减少NANOG表达波动的作用。我们的模型还通过过度表达OCT4将承诺细胞重编程为ESC。在这种情况下，我们总结了重要的实验结果，即当OCT4在特定范围内过表达时，重编程效率达到峰值。我们已经证明了基于ESCs中简化的tf网络的随机计算模型如何能够阐明几个关键的观测动力学特征。它解释了(i)观察到的关键调节因子的异质性，（ii）在某些外部刺激条件下ESC的特征，以及（iii）描述了从ESC到分化状态的转变的发生。此外，该模型（iv）为体细胞重编程提供了一个框架，并传达了对重编程效率作为OCT4过表达函数的理解。
Embryonic stem cells (ESC) have the capacity to self-renew and remain pluripotent, while continuously providing a source of a variety of differentiated cell types. Understanding what governs these properties at the molecular level is crucial for stem cell biology and its application to regenerative medicine. Of particular relevance is to elucidate those molecular interactions which govern the reprogramming of somatic cells into ESC. A computational approach can be used as a framework to explore the dynamics of a simplified network of the ESC with the aim to understand how stem cells differentiate and also how they can be reprogrammed from somatic cells. We propose a computational model of the embryonic stem cell network, in which a core set of transcription factors (TFs) interact with each other and are induced by external factors. A stochastic treatment of the network dynamics suggests that NANOG heterogeneity is the deciding factor for the stem cell fate. In particular, our results show that the decision of staying in the ground state or commitment to a differentiated state is fundamentally stochastic, and can be modulated by the addition of external factors (2i/3i media), which have the effect of reducing fluctuations in NANOG expression. Our model also hosts reprogramming of a committed cell into an ESC by over-expressing OCT4. In this context, we recapitulate the important experimental result that reprogramming efficiency peaks when OCT4 is over-expressed within a specific range of values. We have demonstrated how a stochastic computational model based upon a simplified network of TFs in ESCs can elucidate several key observed dynamical features. It accounts for (i) the observed heterogeneity of key regulators, (ii) characterizes the ESC under certain external stimuli conditions and (iii) describes the occurrence of transitions from the ESC to the differentiated state. Furthermore, the model (iv) provides a framework for reprogramming from somatic cells and conveys an understanding of reprogramming efficiency as a function of OCT4 over-expression.
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