A modified bacterial one-hybrid system yields improved quantitative models of transcription factor specificity.
A modified bacterial one-hybrid system yields improved quantitative models of transcription factor specificity.
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DOI：
10.1093/nar/gkr239
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发表时间：
2011-07
影响因子：
14.9
通讯作者：
Stormo GD
中科院分区：
文献类型：
--
作者：
Christensen RG;Gupta A;Zuo Z;Schriefer LA;Wolfe SA;Stormo GD
我们检查了对使用细菌单杂交（B1H）系统恢复的结合位点进行高通量测序的情况，发现与对一小部分所选克隆进行测序的标准方法相比，可以获得改进的转录因子（TF）结合特异性模型。我们可以使用带有约束变异的 B1H 选择方法 (CV-B1H) 的修改版本获得更准确的结合模型。然而，使用 CV-B1H 数据实现这些改进的模型需要开发一种新的分析方法——GRaMS（特异性生长率建模）——该方法将细菌生长率估计为识别序列质量的函数。我们使用 Zif268（一种充分表征的 C2H2 锌指 TF）对这些不同的基序发现方法进行基准测试，在标准 B1H 方法的 28bp 随机文库和 CV-B1H 方法的 6bp 随机文库上测试了 45 种不同的实验条件：五个时间点和三种不同的 IPTG 和 3-AT 浓度。我们发现 GRaMS 分析对于不同的实验参数具有鲁棒性，而其他分析方法根据实验条件给出的结果差异很大。最后，我们证明 CV-B1H 测定可以在液体培养基中进行，其产生的识别模型在质量上与从固体培养基上选择恢复的序列相似。
We examine the use of high-throughput sequencing on binding sites recovered using a bacterial one-hybrid (B1H) system and find that improved models of transcription factor (TF) binding specificity can be obtained compared to standard methods of sequencing a small subset of the selected clones. We can obtain even more accurate binding models using a modified version of B1H selection method with constrained variation (CV-B1H). However, achieving these improved models using CV-B1H data required the development of a new method of analysis—GRaMS (Growth Rate Modeling of Specificity)—that estimates bacterial growth rates as a function of the quality of the recognition sequence. We benchmark these different methods of motif discovery using Zif268, a well-characterized C2H2 zinc-finger TF on both a 28 bp randomized library for the standard B1H method and on 6 bp randomized library for the CV-B1H method for which 45 different experimental conditions were tested: five time points and three different IPTG and 3-AT concentrations. We find that GRaMS analysis is robust to the different experimental parameters whereas other analysis methods give widely varying results depending on the conditions of the experiment. Finally, we demonstrate that the CV-B1H assay can be performed in liquid media, which produces recognition models that are similar in quality to sequences recovered from selection on solid media.
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