小柯机器人

海量随机启动子破译真核基因调控逻辑
2019-12-03 12:42

近日,美国麻省理工学院-哈佛大学博德研究所Aviv Regev、Carl G. de Boer等研究人员,利用1亿个随机启动子破译了真核生物基因调控逻辑。该研究于2019年12月2日在线发表于国际学术期刊《自然—生物技术》。

研究人员测量了1亿多个完全随机的合成酵母启动子序列的表达输出。这些序列产生多样的、可重复的表达水平,这可以通过它们偶然包含功能性TF结合位点来解释。

研究人员使用机器学习来构建可解释的转录调控模型,其预测约94%的表达来自独立的测试启动子,而约89%的表达来自天然酵母启动子片段。这些模型使研究人员能够表征每个TF的特异性、活性和与染色质的相互作用。TF活性取决于结合位点链、位置、DNA螺旋面和染色质状况。

值得注意的是,表达水平受弱调控相互作用的影响,这使设计序列研究感到困惑。研究人员的分析表明,完全随机DNA的高通量检测可以提供开发复杂的预测性基因调控模型所需的大数据。

据悉,TF如何破译顺式调控DNA序列以控制基因表达仍不清楚,这主要是因为以往研究中使用的天然和工程序列规模不足。

附:英文原文

Title: Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

Author: Carl G. de Boer, Eeshit Dhaval Vaishnav, Ronen Sadeh, Esteban Luis Abeyta, Nir Friedman, Aviv Regev

Issue&Volume: 2019-12-02

Abstract: How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation.

DOI: 10.1038/s41587-019-0315-8

Source: https://www.nature.com/articles/s41587-019-0315-8

Nature Biotechnology:《自然—生物技术》,创刊于1996年。隶属于施普林格·自然出版集团,最新IF:68.164
官方网址:https://www.nature.com/nbt/
投稿链接:https://mts-nbt.nature.com/cgi-bin/main.plex


本期文章:《自然—生物技术》:Online/在线发表

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