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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Genomewide analysis of Drosophila GAGA factor target genes reveals context-dependent DNA binding.
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Genomewide analysis of Drosophila GAGA factor target genes reveals context-dependent DNA binding.

机译：果蝇GAGA因子靶基因的全基因组分析揭示了上下文相关的DNA结合。

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摘要

The association of sequence-specific DNA-binding factors with their cognate target sequences in vivo depends on the local molecular context, yet this context is poorly understood. To address this issue, we have performed genomewide mapping of in vivo target genes of Drosophila GAGA factor (GAF). The resulting list of approximately 250 target genes indicates that GAF regulates many cellular pathways. We applied unbiased motif-based regression analysis to identify the sequence context that determines GAF binding. Our results confirm that GAF selectively associates with (GA)(n) repeat elements in vivo. GAF binding occurs in upstream regulatory regions, but less in downstream regions. Surprisingly, GAF binds abundantly to introns but is virtually absent from exons, even though the density of (GA)(n) is roughly the same. Intron binding occurs equally frequently in last introns compared with first introns, suggesting that GAF may not only regulate transcription initiation, but possibly also elongation. We provide evidence for cooperative binding of GAF to closely spaced (GA)(n) elements and explain the lack of GAF binding to exons by the absence of such closely spaced GA repeats. Our approach for revealing determinants of context-dependent DNA binding will be applicable to many other transcription factors.
机译：体内序列特异性DNA结合因子与其同源靶序列的关联取决于局部分子环境，但对这种环境了解甚少。为了解决这个问题，我们对果蝇GAGA因子（GAF）的体内靶基因进行了全基因组定位。大约250个靶基因的结果列表表明GAF调节许多细胞途径。我们应用了无偏基序回归分析来确定决定GAF结合的序列背景。我们的结果证实，GAF与体内的（GA）（n）重复元件选择性结合。 GAF结合发生在上游调节区域，但在下游区域较少。出人意料的是，即使（GA）（n）的密度大致相同，GAF也能与内含子充分结合，但外显子实际上却不存在。与第一个内含子相比，内含子结合在最后一个内含子中的发生频率相同，这表明GAF不仅可以调节转录起始，而且可能还可以调控延伸。我们提供了GAF与紧密间隔的（GA）（n）元素协同结合的证据，并解释了GAF与外显子的结合缺乏的原因，即缺少这种紧密间隔的GA重复序列。我们揭示背景相关DNA结合决定因素的方法将适用于许多其他转录因子。

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