Hu Zhirui, Qian Minping, Zhang Michael Q
MOE Key Laboratory of Bioinformatics and Bioinformatics Div, TNLIST /Department of Automation, Tsinghua University, Beijing 100084, China.
BMC Syst Biol. 2011;5 Suppl 2(Suppl 2):S8. doi: 10.1186/1752-0509-5-S2-S8. Epub 2011 Dec 14.
Somatic cells can be reprogrammed to induced-pluripotent stem cells (iPSCs) by introducing few reprogramming factors, which challenges the long held view that cell differentiation is irreversible. However, the mechanism of induced pluripotency is still unknown.
Inspired by the phenomenological reprogramming model of Artyomov et al (2010), we proposed a novel Markov model, stepwise reprogramming Markov (SRM) model, with simpler gene regulation rules and explored various properties of the model with Monte Carlo simulation. We calculated the reprogramming rate and showed that it would increase in the condition of knockdown of somatic transcription factors or inhibition of DNA methylation globally, consistent with the real reprogramming experiments. Furthermore, we demonstrated the utility of our model by testing it with the real dynamic gene expression data spanning across different intermediate stages in the iPS reprogramming process.
The gene expression data at several stages in reprogramming and the reprogramming rate under several typically experiment conditions coincided with our simulation results. The function of reprogramming factors and gene expression change during reprogramming could be partly explained by our model reasonably well.
This lands further support on our general rules of gene regulation network in iPSC reprogramming. This model may help uncover the basic mechanism of reprogramming and improve the efficiency of converting somatic cells to iPSCs.
通过导入少数重编程因子,体细胞可被重编程为诱导多能干细胞(iPSC),这对长期以来认为细胞分化不可逆的观点提出了挑战。然而,诱导多能性的机制仍不清楚。
受Artyomov等人(2010年)现象学重编程模型的启发,我们提出了一种新颖的马尔可夫模型——逐步重编程马尔可夫(SRM)模型,其基因调控规则更简单,并通过蒙特卡罗模拟探索了该模型的各种特性。我们计算了重编程率,结果表明在体细胞转录因子敲低或全局DNA甲基化抑制的条件下重编程率会增加,这与实际重编程实验一致。此外,我们通过用iPS重编程过程中不同中间阶段的真实动态基因表达数据对模型进行测试,证明了我们模型的实用性。
重编程几个阶段的基因表达数据以及几种典型实验条件下的重编程率与我们的模拟结果相符。我们的模型能够较好地合理解释重编程因子的功能以及重编程过程中基因表达的变化。
这进一步支持了我们关于iPSC重编程中基因调控网络的一般规则。该模型可能有助于揭示重编程的基本机制并提高将体细胞转化为iPSC的效率。