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Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae.

机译:全基因组预测和表征酿酒酵母中转录因子之间的相互作用。

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Combinatorial regulation by transcription factor complexes is an important feature of eukaryotic gene regulation. Here, we propose a new method for identification of interactions between transcription factors (TFs) that relies on the relationship of their binding sites, and we test it using Saccharomyces cerevisiae as a model system. The algorithm predicts interacting TF pairs based on the co-occurrence of their binding motifs and the distance between the motifs in promoter sequences. This allows investigation of interactions between TFs without known binding motifs or expression data. With this approach, 300 significant interactions involving 77 TFs were identified. These included more than 70% of the known protein-protein interactions. Approximately half of the detected interacting motif pairs showed strong preferences for particular distances and orientations in the promoter sequences. These one dimensional features may reflect constraints on allowable spatial arrangements for protein-protein interactions. Evidence for biological relevance of the observed characteristic distances is provided by the finding that target genes with the same characteristic distances show significantly higher co-expression than those without preferred distances. Furthermore, the observed interactions were dynamic: most of the TF pairs were not constitutively active, but rather showed variable activity depending on the physiological condition of the cells. Interestingly, some TF pairs active in multiple conditions showed preferences for different distances and orientations depending on the condition. Our prediction and characterization of TF interactions may help to understand the transcriptional regulatory networks in eukaryotic systems.
机译:转录因子复合物的组合调控是真核基因调控的重要特征。在这里,我们提出了一种新的鉴定转录因子(TFs)之间相互作用的方法,该方法依赖于它们的结合位点之间的关系,并使用酿酒酵母作为模型系统对其进行测试。该算法根据其结合基序的共现以及启动子序列中基序之间的距离来预测相互作用的TF对。这允许在没有已知结合基序或表达数据的情况下研究TF之间的相互作用。通过这种方法,确定了涉及77个TF的300个重要交互。这些包括超过70%的已知蛋白质-蛋白质相互作用。大约一半的检测到的相互作用基序对显示出对启动子序列中特定距离和方向的强烈偏好。这些一维特征可以反映对蛋白质-蛋白质相互作用的容许空间排列的限制。通过发现具有相同特征距离的靶基因显示出比没有优选距离的基因显着更高的共表达,这一发现为观察到的特征距离的生物学相关性提供了证据。此外,观察到的相互作用是动态的:大多数TF对不是组成性活性的,而是根据细胞的生理状况显示可变的活性。有趣的是,一些在多种条件下活跃的TF对表现出了对不同距离和方向的偏好,具体取决于条件。我们对TF相互作用的预测和表征可能有助于理解真核系统中的转录调控网络。

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