利用大规模平行报告系统和机器学习揭示序列和位置对酵母 uORF 活性的影响。

Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning.

机构信息

Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, United States.

Computational Biology Department, Carnegie Mellon University, Pittsburgh, United States.

出版信息

Elife. 2023 May 25;12:e69611. doi: 10.7554/eLife.69611.

Abstract

Upstream open-reading frames (uORFs) are potent -acting regulators of mRNA translation and nonsense-mediated decay (NMD). While both AUG- and non-AUG initiated uORFs are ubiquitous in ribosome profiling studies, few uORFs have been experimentally tested. Consequently, the relative influences of sequence, structural, and positional features on uORF activity have not been determined. We quantified thousands of yeast uORFs using massively parallel reporter assays in wildtype and ∆ yeast. While nearly all AUG uORFs were robust repressors, most non-AUG uORFs had relatively weak impacts on expression. Machine learning regression modeling revealed that both uORF sequences and locations within transcript leaders predict their effect on gene expression. Indeed, alternative transcription start sites highly influenced uORF activity. These results define the scope of natural uORF activity, identify features associated with translational repression and NMD, and suggest that the locations of uORFs in transcript leaders are nearly as predictive as uORF sequences.

摘要

上游开放阅读框(uORFs)是调节 mRNA 翻译和无义介导的衰变(NMD)的有效调节因子。虽然核糖体图谱研究中普遍存在 AUG 和非 AUG 起始的 uORFs,但很少有 uORFs 经过实验验证。因此,uORF 活性的序列、结构和位置特征的相对影响尚未确定。我们使用大规模平行报告基因检测在野生型和 ∆ 酵母中对数千个酵母 uORFs 进行了定量检测。虽然几乎所有的 AUG uORFs 都是强大的阻遏物,但大多数非 AUG uORFs 对表达的影响相对较弱。机器学习回归模型表明,uORF 序列和转录起始位点内的位置都可以预测它们对基因表达的影响。事实上,替代转录起始位点极大地影响了 uORF 的活性。这些结果定义了自然 uORF 活性的范围,确定了与翻译抑制和 NMD 相关的特征,并表明 uORF 在转录起始位点的位置与 uORF 序列一样具有预测性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3624/10259493/2aa86ce7c9c1/elife-69611-fig1.jpg

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