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酵母细胞周期中翻译过程的动态随机模型。

A dynamical stochastic model of yeast translation across the cell cycle.

作者信息

Seeger Martin, Flöttmann Max, Klipp Edda

机构信息

Humboldt-Universität zu Berlin, Institute of Biology, Theoretical Biophysics, 10099 Berlin, Germany.

出版信息

Heliyon. 2023 Jan 26;9(2):e13101. doi: 10.1016/j.heliyon.2023.e13101. eCollection 2023 Feb.

DOI:10.1016/j.heliyon.2023.e13101
PMID:36793957
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9922973/
Abstract

Translation is a central step in gene expression, however its quantitative and time-resolved regulation is poorly understood. We developed a discrete, stochastic model for protein translation in in a whole-transcriptome, single-cell context. A "base case" scenario representing an average cell highlights translation initiation rates as the main co-translational regulatory parameters. Codon usage bias emerges as a secondary regulatory mechanism through ribosome stalling. Demand for anticodons with low abundancy is shown to cause above-average ribosome dwelling times. Codon usage bias correlates strongly both with protein synthesis rates and elongation rates. Applying the model to a time-resolved transcriptome estimated by combining data from FISH and RNA-Seq experiments, it could be shown that increased total transcript abundance during the cell cycle decreases translation efficiency at single transcript level. Translation efficiency grouped by gene function shows highest values for ribosomal and glycolytic genes. Ribosomal proteins peak in S phase while glycolytic proteins rank highest in later cell cycle phases.

摘要

翻译是基因表达的核心步骤,然而其定量和时间分辨调控仍知之甚少。我们在全转录组、单细胞背景下开发了一种用于蛋白质翻译的离散随机模型。代表平均细胞的“基础情况”表明翻译起始速率是主要的共翻译调控参数。密码子使用偏好通过核糖体停滞成为次要调控机制。对低丰度反密码子的需求导致核糖体停留时间高于平均水平。密码子使用偏好与蛋白质合成速率和延伸速率都密切相关。将该模型应用于通过结合FISH和RNA-Seq实验数据估计的时间分辨转录组,结果表明细胞周期中总转录本丰度增加会降低单个转录本水平的翻译效率。按基因功能分组的翻译效率显示核糖体和糖酵解基因的值最高。核糖体蛋白在S期达到峰值,而糖酵解蛋白在细胞周期后期排名最高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/ccaee03120f1/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/218d9ffb75f3/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/070a147279a4/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/f3cc995d8e79/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/c25661afe10d/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/2510dd94fbfb/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/33a080de7a3f/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/ccaee03120f1/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/218d9ffb75f3/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/070a147279a4/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/f3cc995d8e79/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/c25661afe10d/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/2510dd94fbfb/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/33a080de7a3f/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53cd/9922973/ccaee03120f1/gr007.jpg

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Exact solution of stochastic gene expression models with bursting, cell cycle and replication dynamics.
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FEMS Yeast Res. 2024 Jan 9;24. doi: 10.1093/femsyr/foae011.
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Phys Rev E. 2020 Mar;101(3-1):032403. doi: 10.1103/PhysRevE.101.032403.
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Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells.真核细胞中随机基因表达的详细模型的解析分布。
Proc Natl Acad Sci U S A. 2020 Mar 3;117(9):4682-4692. doi: 10.1073/pnas.1910888117. Epub 2020 Feb 18.
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Novel insights into gene expression regulation during meiosis revealed by translation elongation dynamics.减数分裂过程中基因表达调控的新见解揭示了翻译延伸动态。
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