Identifying combinatorial regulation of transcription factors and binding motifs.
Identifying combinatorial regulation of transcription factors and binding motifs.
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DOI：
10.1186/gb-2004-5-8-r56
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发表时间：
2004
期刊：
影响因子：
12.3
通讯作者：
Zhang MQ
中科院分区：
文献类型：
--
作者：
Kato M;Hata N;Banerjee N;Futcher B;Zhang MQ
将染色质免疫沉淀数据与微阵列表达数据和组合tf基序分析相结合的新方法用于系统地鉴定转录因子和基序的组合，并重建酵母细胞周期的新组合调控图谱。转录因子的组合相互作用对基因调控具有重要意义。尽管各种基因组数据集都与这个问题相关，但每个数据集本身提供的证据相对较弱。开发能够整合不同序列、表达和定位数据的方法变得非常重要。在这里，我们使用了一种新的方法，将染色质免疫沉淀（ChIP）数据与微阵列表达数据和组合TF-motif分析相结合。我们系统地识别转录因子和基序的组合。TFs的各种组合涉及多种结合机制。我们重建了一个新的酵母细胞周期组合调控图，其中细胞周期调控可以绘制为一个扩展的TF模块链。我们发现，细胞周期早期的TF和后期的TF的成对组合通常用于控制中间阶段的基因表达。因此，基因表达的不同次数大于转录因子的数量。我们还看到一些TF模块控制分支点（细胞周期进入和退出），并且在适当的信号存在下，它们可以沿着替代途径前进。结合不同的数据源可以提高统计能力，如检测TF相互作用和复合TF结合基序所示。简单细胞周期调控链的原始图像可以扩展为复合调控模块链：不同的模块可能在同一途径中共享一个共同的TF组件，或者一个TF组件与其他途径交叉对话。
A novel method that integrates chromatin immunoprecipitation data with microarray expression data and combinatorial TF-motif analysis was used to systematically identify combinations of transcription factors and of motifs and to reconstruct a new combinatorial regulatory map of the yeast cell cycle. Combinatorial interaction of transcription factors (TFs) is important for gene regulation. Although various genomic datasets are relevant to this issue, each dataset provides relatively weak evidence on its own. Developing methods that can integrate different sequence, expression and localization data have become important. Here we use a novel method that integrates chromatin immunoprecipitation (ChIP) data with microarray expression data and with combinatorial TF-motif analysis. We systematically identify combinations of transcription factors and of motifs. The various combinations of TFs involved multiple binding mechanisms. We reconstruct a new combinatorial regulatory map of the yeast cell cycle in which cell-cycle regulation can be drawn as a chain of extended TF modules. We find that the pairwise combination of a TF for an early cell-cycle phase and a TF for a later phase is often used to control gene expression at intermediate times. Thus the number of distinct times of gene expression is greater than the number of transcription factors. We also see that some TF modules control branch points (cell-cycle entry and exit), and in the presence of appropriate signals they can allow progress along alternative pathways. Combining different data sources can increase statistical power as demonstrated by detecting TF interactions and composite TF-binding motifs. The original picture of a chain of simple cell-cycle regulators can be extended to a chain of composite regulatory modules: different modules may share a common TF component in the same pathway or a TF component cross-talking to other pathways.