Quantitative models of the mechanisms that control genome-wide patterns of transcription factor binding during early Drosophila development.
Quantitative models of the mechanisms that control genome-wide patterns of transcription factor binding during early Drosophila development.
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DOI：
10.1371/journal.pgen.1001290
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发表时间：
2011-02-03
期刊：
影响因子：
4.5
通讯作者：
Eisen MB
中科院分区：
文献类型：
--
作者：
Kaplan T;Li XY;Sabo PJ;Thomas S;Stamatoyannopoulos JA;Biggin MD;Eisen MB
在动物发育过程中，驱动复杂基因表达模式的转录因子与数千个基因组区域结合，在结合区域之间的结合数量差异介导了它们的活性。虽然我们现在有工具来表征这些蛋白质的DNA亲和力，并精确地测量它们在体内的全基因组分布，但我们对决定它们结合的地点、时间和程度的力量的理解仍然很原始。在这里，我们使用转录因子结合的热力学模型来评估不同的生物物理力对黑颊果蝇早期胚胎前后模式的五个调节因子结合的贡献。仅基于DNA序列和体外蛋白-DNA亲和力的预测与体内结合的实验测量结果的相关性为~ 0.4。结合5个因子之间的协同性和竞争性，并通过独立模拟每个核的结合来考虑空间模式，对预测精度影响不大。错误的一个主要来源是对体内不发生的结合事件的预测，我们假设这反映了染色质的可及性降低。为了验证这一点，我们将全基因组DNA可及性的实验测量纳入我们的模型，有效地限制了对开放染色质区域的预测结合。这极大地提高了我们的预测，在已知的目标基因中，各种因素的相关性为0.6-0.9。最后，我们使用我们的模型来量化DNA序列、可及性和结合竞争与合作的作用。我们的研究结果表明，在开放染色质区域，结合几乎完全可以通过单个因子的序列特异性来预测，而蛋白质相互作用的作用最小。我们建议，结合实验确定的染色质可及性数据和转录因子结合的简单计算模型，可以用于预测任何动物转录因子的结合前景，具有显著的精度。在发育的早期阶段，调节蛋白结合DNA并控制附近基因的表达，从而驱动发育过程中基因表达的时空模式。但是，人们对决定这些调节蛋白结合位置的生化力量知之甚少。我们收集了果蝇（Drosophila melanogaster）早期发育的几个关键调节因子活动的实验数据，并开发了一种计算方法来预测它们结合的位置和强度。我们发现，单个调节蛋白之间的竞争、合作和其他相互作用对它们的结合影响有限，而DNA对蛋白质结合的整体可及性对所有因子的结合有重大影响。我们的研究结果提出了一种预测调控结合的实用方法，将实验DNA可接近性分析与计算算法相结合，以确定基因组可接近区域之间的结合将发生在哪里。
Transcription factors that drive complex patterns of gene expression during animal development bind to thousands of genomic regions, with quantitative differences in binding across bound regions mediating their activity. While we now have tools to characterize the DNA affinities of these proteins and to precisely measure their genome-wide distribution in vivo, our understanding of the forces that determine where, when, and to what extent they bind remains primitive. Here we use a thermodynamic model of transcription factor binding to evaluate the contribution of different biophysical forces to the binding of five regulators of early embryonic anterior-posterior patterning in Drosophila melanogaster. Predictions based on DNA sequence and in vitro protein-DNA affinities alone achieve a correlation of ∼0.4 with experimental measurements of in vivo binding. Incorporating cooperativity and competition among the five factors, and accounting for spatial patterning by modeling binding in every nucleus independently, had little effect on prediction accuracy. A major source of error was the prediction of binding events that do not occur in vivo, which we hypothesized reflected reduced accessibility of chromatin. To test this, we incorporated experimental measurements of genome-wide DNA accessibility into our model, effectively restricting predicted binding to regions of open chromatin. This dramatically improved our predictions to a correlation of 0.6–0.9 for various factors across known target genes. Finally, we used our model to quantify the roles of DNA sequence, accessibility, and binding competition and cooperativity. Our results show that, in regions of open chromatin, binding can be predicted almost exclusively by the sequence specificity of individual factors, with a minimal role for protein interactions. We suggest that a combination of experimentally determined chromatin accessibility data and simple computational models of transcription factor binding may be used to predict the binding landscape of any animal transcription factor with significant precision. During early stages of development, regulatory proteins bind DNA and control the expression of nearby genes, thereby driving spatial and temporal patterns of gene expression during development. But the biochemical forces that determine where these regulatory proteins bind are poorly understood. We gathered experimental data on the activities of several key regulators of early development of the fruit fly (Drosophila melanogaster) and developed a computational method to predict where and how strongly they will bind. We find that competition, cooperativity, and other interactions among individual regulatory proteins have a limited effect on their binding, while the global accessibility of DNA to protein binding has a significant impact on the binding of all factors. Our results suggest a practical method for predicting regulatory binding by combining experimental DNA accessibility assays with computational algorithms to determine where will binding occur among the accessible regions of the genome.
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影响因子：
7
作者：
Bieda, M;Xu, XQ;Farnham, PJ
通讯作者：
Farnham, PJ
影响因子：
9.9
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通讯作者：
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