Chen Chieh-Chun, Zhu Xin-Guang, Zhong Sheng
Department of Bioengineering, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA.
BMC Genomics. 2008;9 Suppl 1(Suppl 1):S18. doi: 10.1186/1471-2164-9-S1-S18.
Transcription factors (TFs) have multiple combinatorial forms to regulate the transcription of a target gene. For example, one TF can help another TF to stabilize onto regulatory DNA sequence and the other TF may attract RNA polymerase (RNAP) to start transcription; alternatively, two TFs may both interact with both the DNA sequence and the RNAP. The different forms of TF-TF interaction have different effects on the probability of RNAP's binding onto the promoter sequence and therefore confer different transcriptional efficiencies.
We have developed an analytical method to identify the thermodynamic model that best describes the form of TF-TF interaction among a set of TF interactions for every target gene. In this method, time-course microarray data are used to estimate the steady state concentration of the transcript of a target gene, as well as the relative changes of the active concentration for each TF. These estimated concentrations and changes of concentrations are fed into an inference scheme to identify the most compatible thermodynamic model. Such a model represents a particular way of combinatorial control by multiple TFs on a target gene.
Applying this approach to a time-course microarray dataset of embryonic stem cells, we have inferred five interaction patterns among three regulators, Oct4, Sox2 and Nanog, on ten target genes.
转录因子（TFs）具有多种组合形式来调控靶基因的转录。例如，一个转录因子可以帮助另一个转录因子稳定结合到调控DNA序列上，而另一个转录因子可能吸引RNA聚合酶（RNAP）启动转录；或者，两个转录因子可能同时与DNA序列和RNAP相互作用。不同形式的转录因子-转录因子相互作用对RNA聚合酶结合到启动子序列的概率有不同影响，因此赋予不同的转录效率。
我们开发了一种分析方法，用于识别最能描述每个靶基因一组转录因子相互作用中转录因子-转录因子相互作用形式的热力学模型。在该方法中，时间进程微阵列数据用于估计靶基因转录本的稳态浓度，以及每个转录因子活性浓度的相对变化。这些估计的浓度和浓度变化被输入到一个推理方案中，以识别最兼容的热力学模型。这样的模型代表了多个转录因子对靶基因进行组合控制的一种特定方式。
将这种方法应用于胚胎干细胞的时间进程微阵列数据集，我们推断出三个调控因子Oct4、Sox2和Nanog在十个靶基因上的五种相互作用模式。
