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真核生物转录调控的预测模型揭示了代谢条件下转录因子作用和启动子使用的变化。

Predictive models of eukaryotic transcriptional regulation reveals changes in transcription factor roles and promoter usage between metabolic conditions.

机构信息

Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-41296, Sweden.

Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg SE-41296, Sweden.

出版信息

Nucleic Acids Res. 2019 Jun 4;47(10):4986-5000. doi: 10.1093/nar/gkz253.

DOI:10.1093/nar/gkz253
PMID:30976803
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6547448/
Abstract

Transcription factors (TF) are central to transcriptional regulation, but they are often studied in relative isolation and without close control of the metabolic state of the cell. Here, we describe genome-wide binding (by ChIP-exo) of 15 yeast TFs in four chemostat conditions that cover a range of metabolic states. We integrate this data with transcriptomics and six additional recently mapped TFs to identify predictive models describing how TFs control gene expression in different metabolic conditions. Contributions by TFs to gene regulation are predicted to be mostly activating, additive and well approximated by assuming linear effects from TF binding signal. Notably, using TF binding peaks from peak finding algorithms gave distinctly worse predictions than simply summing the low-noise and high-resolution TF ChIP-exo reads on promoters. Finally, we discover indications of a novel functional role for three TFs; Gcn4, Ert1 and Sut1 during nitrogen limited aerobic fermentation. In only this condition, the three TFs have correlated binding to a large number of genes (enriched for glycolytic and translation processes) and a negative correlation to target gene transcript levels.

摘要

转录因子(TF)是转录调控的核心,但它们通常是在相对孤立的情况下进行研究的,并且没有密切控制细胞的代谢状态。在这里,我们描述了在涵盖多种代谢状态的四个恒化器条件下,15 种酵母 TF 的全基因组结合(通过 ChIP-exo)。我们将这些数据与转录组学和另外六个最近映射的 TF 进行整合,以确定描述 TF 如何在不同代谢条件下控制基因表达的预测模型。TF 对基因调控的贡献预计主要是激活的、累加的,并且通过假设 TF 结合信号的线性效应可以很好地近似。值得注意的是,与简单地将低噪声和高分辨率 TF ChIP-exo 在启动子上的读取相加相比,使用峰发现算法从 TF 结合峰中进行预测的效果要差得多。最后,我们发现了三个 TF(Gcn4、Ert1 和 Sut1)在氮限制有氧发酵过程中具有新的功能作用的迹象。仅在这种情况下,这三个 TF 与大量基因(富含糖酵解和翻译过程)的结合具有相关性,并且与靶基因转录水平呈负相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/4ac8fc719c34/gkz253fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/56945fcc963b/gkz253fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/d7f33f9d51c1/gkz253fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/f5b092c86c9b/gkz253fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/d5e54a40014a/gkz253fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/23b186f111bd/gkz253fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/4ac8fc719c34/gkz253fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/56945fcc963b/gkz253fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/d7f33f9d51c1/gkz253fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/f5b092c86c9b/gkz253fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/d5e54a40014a/gkz253fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/23b186f111bd/gkz253fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e98f/6547448/4ac8fc719c34/gkz253fig6.jpg

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