Lin Li-Hsieh, Lee Hsiao-Ching, Li Wen-Hsiung, Chen Bor-Sen
Lab of Systems Biology, Department of Electronical Engineering, National Tsing Hua University, 101, Sec 2, Kuang Fu Hsinchu, 300, Taiwan.
BMC Bioinformatics. 2007 Dec 8;8:473. doi: 10.1186/1471-2105-8-473.
Eukaryotic cells have developed mechanisms to respond to external environmental or physiological changes (stresses). In order to increase the activities of stress-protection functions in response to an environmental change, the internal cell mechanisms need to induce certain specific gene expression patterns and pathways by changing the expression levels of specific transcription factors (TFs). The conventional methods to find these specific TFs and their interactivities are slow and laborious. In this study, a novel efficient method is proposed to detect the TFs and their interactivities that regulate yeast genes that respond to any specific environment change.
For each gene expressed in a specific environmental condition, a dynamic regulatory model is constructed in which the coefficients of the model represent the transcriptional activities and interactivities of the corresponding TFs. The proposed method requires only microarray data and information of all TFs that bind to the gene but it has superior resolution than the current methods. Our method not only can find stress-specific TFs but also can predict their regulatory strengths and interactivities. Moreover, TFs can be ranked, so that we can identify the major TFs to a stress. Similarly, it can rank the interactions between TFs and identify the major cooperative TF pairs. In addition, the cross-talks and interactivities among different stress-induced pathways are specified by the proposed scheme to gain much insight into protective mechanisms of yeast under different environmental stresses.
In this study, we find significant stress-specific and cell cycle-controlled TFs via constructing a transcriptional dynamic model to regulate the expression profiles of genes under different environmental conditions through microarray data. We have applied this TF activity and interactivity detection method to many stress conditions, including hyper- and hypo- osmotic shock, heat shock, hydrogen peroxide and cell cycle, because the available expression time profiles for these conditions are long enough. Especially, we find significant TFs and cooperative TFs responding to environmental changes. Our method may also be applicable to other stresses if the gene expression profiles have been examined for a sufficiently long time.
真核细胞已形成应对外部环境或生理变化（应激）的机制。为了在环境变化时增强应激保护功能的活性，细胞内部机制需要通过改变特定转录因子（TFs）的表达水平来诱导某些特定的基因表达模式和途径。寻找这些特定TFs及其相互作用的传统方法既缓慢又费力。在本研究中，我们提出了一种新颖有效的方法来检测调控酵母基因响应任何特定环境变化的TFs及其相互作用。
对于在特定环境条件下表达的每个基因，构建一个动态调控模型，其中模型的系数代表相应TFs的转录活性和相互作用。所提出的方法仅需要微阵列数据和与该基因结合的所有TFs的信息，但它比当前方法具有更高的分辨率。我们的方法不仅可以找到应激特异性TFs，还可以预测它们的调控强度和相互作用。此外，可以对TFs进行排序，以便我们能够识别对应激起主要作用的TFs。同样，它可以对TFs之间的相互作用进行排序，并识别主要的协同TF对。此外，所提出的方案还明确了不同应激诱导途径之间的相互作用和串扰，从而深入了解酵母在不同环境应激下的保护机制。
在本研究中，我们通过构建转录动态模型，利用微阵列数据调控不同环境条件下基因的表达谱，发现了显著的应激特异性和细胞周期控制的TFs。我们已将这种TF活性和相互作用检测方法应用于许多应激条件，包括高渗和低渗休克、热休克、过氧化氢和细胞周期，因为这些条件下可用的表达时间谱足够长。特别是，我们发现了响应环境变化的重要TFs和协同TFs。如果对基因表达谱进行了足够长时间的检测，我们的方法也可能适用于其他应激。
