Computational identification of diverse mechanisms underlying transcription factor-DNA occupancy.
Computational identification of diverse mechanisms underlying transcription factor-DNA occupancy.
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DOI：
10.1371/journal.pgen.1003571
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发表时间：
2013
期刊：
影响因子：
4.5
通讯作者：
Sinha S
中科院分区：
文献类型：
--
作者：
Cheng Q;Kazemian M;Pham H;Blatti C;Celniker SE;Wolfe SA;Brodsky MH;Sinha S
基于 ChIP 的转录因子 (TF) 占用全基因组检测已成为了解转录调控（尤其是在全球范围内）的强大、高通量方法。这引起了人们对指导 TF-DNA 结合的潜在生化机制的极大兴趣，最终目标是通过计算预测任何细胞条件下 TF 的占用情况。在这项研究中，我们以比以前更大的规模研究了 TF-DNA 结合的各种潜在决定因素的影响。我们使用基于热力学的 TF-DNA 结合模型（称为“STAP”）来分析来自果蝇胚胎发育的 45 个 TF-ChIP 数据集。我们构建了一个交叉验证框架，将基于 ChIP'ed（“主要”）TF 主题的基线模型与更复杂的模型进行比较，其中假设次要 TF 的结合会影响主要 TF 的占用。根据 ChIP 实验时间点的 RNA-SEQ 表达数据选择相互作用 TF 的候选者。我们发现了次级转录因子的合作和对抗效应的广泛证据，并明确量化了这些效应。我们能够识别多种类型的相互作用，包括（1）主要和次要基序之间的长程相互作用（间隔≤150 bp），暗示间接效应，例如染色质重塑，（2）具有特定位点间间距偏差的短程相互作用，暗示直接物理相互作用，以及（3）重叠的结合位点暗示竞争性结合。此外，通过考虑之前报道的 TF 占用率和 DNA 可及性之间的强相关性，我们能够将影响分类为可能由次要 TF 对局部可及性的影响介导的效应和利用与可及性无关的机制的效应。最后，我们进行了体外 Pull-down 测定，以测试基于模型的短程协同相互作用的预测，并发现测试的 8 个 TF 对中有 7 个发生了物理相互作用，并且其中一些相互作用介导了与 DNA 的协同结合。基于染色质免疫沉淀 (ChIP) 的转录因子 (TF) 占用全基因组检测已成为了解转录调控（尤其是在全球范围内）的强大、高通量方法。在这里，我们利用来自果蝇的 45 个 ChIP 芯片和 ChIP-SEQ 数据集来探索 TF-DNA 结合的潜在机制。为此，我们采用生物物理驱动的计算模型，结合 300 多个 TF 基序（结合特异性）以及来自果蝇胚胎不同发育阶段的基因表达和 DNA 可及性数据。我们的研究结果提供了强有力的统计证据，证明 TF-TF 相互作用在塑造全基因组 TF-DNA 结合谱中所发挥的作用，从而指导基因调控。我们的方法使我们不仅能够简单地认识到这种相互作用的存在，还能量化它们对 TF 占用的影响。我们能够将这些效应的可能机制分类为直接物理相互作用与可达性介导的间接相互作用、远程相互作用与短程相互作用、合作性相互作用与对抗性相互作用。我们的分析揭示了最近生成的 ChIP 数据集中存在组合调控的广泛证据，并为未来丰富的综合模型奠定了基础，该模型将根据序列和表达数据预测细胞类型特异性 TF 占用值。
ChIP-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high-throughput method to understand transcriptional regulation, especially on a global scale. This has led to great interest in the underlying biochemical mechanisms that direct TF-DNA binding, with the ultimate goal of computationally predicting a TF's occupancy profile in any cellular condition. In this study, we examined the influence of various potential determinants of TF-DNA binding on a much larger scale than previously undertaken. We used a thermodynamics-based model of TF-DNA binding, called “STAP,” to analyze 45 TF-ChIP data sets from Drosophila embryonic development. We built a cross-validation framework that compares a baseline model, based on the ChIP'ed (“primary”) TF's motif, to more complex models where binding by secondary TFs is hypothesized to influence the primary TF's occupancy. Candidates interacting TFs were chosen based on RNA-SEQ expression data from the time point of the ChIP experiment. We found widespread evidence of both cooperative and antagonistic effects by secondary TFs, and explicitly quantified these effects. We were able to identify multiple classes of interactions, including (1) long-range interactions between primary and secondary motifs (separated by ≤150 bp), suggestive of indirect effects such as chromatin remodeling, (2) short-range interactions with specific inter-site spacing biases, suggestive of direct physical interactions, and (3) overlapping binding sites suggesting competitive binding. Furthermore, by factoring out the previously reported strong correlation between TF occupancy and DNA accessibility, we were able to categorize the effects into those that are likely to be mediated by the secondary TF's effect on local accessibility and those that utilize accessibility-independent mechanisms. Finally, we conducted in vitro pull-down assays to test model-based predictions of short-range cooperative interactions, and found that seven of the eight TF pairs tested physically interact and that some of these interactions mediate cooperative binding to DNA. Chromatin Immunoprecipitation (ChIP)-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high throughput method to understand transcriptional regulation, especially on a global scale. Here, we utilize 45 ChIP-chip and ChIP-SEQ data sets from Drosophila to explore the underlying mechanisms of TF-DNA binding. For this, we employ a biophysically motivated computational model, in conjunction with over 300 TF motifs (binding specificities) as well as gene expression and DNA accessibility data from different developmental stages in Drosophila embryos. Our findings provide robust statistical evidence of the role played by TF-TF interactions in shaping genome-wide TF-DNA binding profiles, and thus in directing gene regulation. Our method allows us to go beyond simply recognizing the existence of such interactions, to quantifying their effects on TF occupancy. We are able to categorize the probable mechanisms of these effects as involving direct physical interactions versus accessibility-mediated indirect interactions, long-range versus short-range interactions, and cooperative versus antagonistic interactions. Our analysis reveals widespread evidence of combinatorial regulation present in recently generated ChIP data sets, and sets the stage for rich integrative models of the future that will predict cell type-specific TF occupancy values from sequence and expression data.
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DOI：
10.1093/bioinformatics/btr064
发表时间：
2011-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Grant CE;Bailey TL;Noble WS
通讯作者：
Noble WS
影响因子：
7
作者：
Gordan, Raluca;Hartemink, Alexander J.;Bulyk, Martha L.
通讯作者：
Bulyk, Martha L.
影响因子：
64.8
作者：
Graveley BR;Brooks AN;Carlson JW;Duff MO;Landolin JM;Yang L;Artieri CG;van Baren MJ;Boley N;Booth BW;Brown JB;Cherbas L;Davis CA;Dobin A;Li R;Lin W;Malone JH;Mattiuzzo NR;Miller D;Sturgill D;Tuch BB;Zaleski C;Zhang D;Blanchette M;Dudoit S;Eads B;Green RE;Hammonds A;Jiang L;Kapranov P;Langton L;Perrimon N;Sandler JE;Wan KH;Willingham A;Zhang Y;Zou Y;Andrews J;Bickel PJ;Brenner SE;Brent MR;Cherbas P;Gingeras TR;Hoskins RA;Kaufman TC;Oliver B;Celniker SE
通讯作者：
Celniker SE
影响因子：
64.5
作者：
Berger, Michael F.;Badis, Gwenael;Hughes, Timothy R.
通讯作者：
Hughes, Timothy R.
影响因子：
2.5
作者：
Anderson, Douglas M.;Beres, Brian J.;Rawls, Alan
通讯作者：
Rawls, Alan