2025-07-25 テキサス大学オースチン校(UT Austin)
<関連情報>
- https://news.utexas.edu/2025/07/25/new-ai-tool-accelerates-mrna-based-treatments-for-viruses-cancers-genetic-disorders/
- https://www.nature.com/articles/s41587-025-02712-x
- https://www.nature.com/articles/s41587-025-02718-5
哺乳類細胞におけるメッセンジャーRNAの翻訳効率の予測 Predicting the translation efficiency of messenger RNA in mammalian cells
Dinghai Zheng,Logan Persyn,Jun Wang,Yue Liu,Fernando Ulloa-Montoya,Can Cenik & Vikram Agarwal
Nature Biotechnology Published:25 July 2025
DOI:https://doi.org/10.1038/s41587-025-02712-x
Abstract
The mechanisms by which mRNA sequences specify translational control remain poorly understood in mammalian cells. Here we generate a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing more than 140 human and mouse cell types from 3,819 ribosomal profiling datasets. We develop RiboNN, a state-of-the-art multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features. While most earlier models solely considered the 5′ untranslated region (UTR) sequence, RiboNN integrates how the spatial positioning of low-level dinucleotide and trinucleotide features (that is, including codons) influences TE, capturing mechanistic principles such as how ribosomal processivity and tRNA abundance control translational output. RiboNN predicts the translational behavior of base-modified therapeutic RNA and explains evolutionary selection pressures in human 5′ UTRs. Finally, it detects a common language governing mRNA regulatory control and highlights the interconnectedness of mRNA translation, stability and localization in mammalian organisms.
翻訳効率の共変量は、細胞タイプを超えて保存された協調パターンを同定する Translation efficiency covariation identifies conserved coordination patterns across cell types
Yue Liu,Shilpa Rao,Ian Hoskins,Michael Geng,Qiuxia Zhao,Jonathan Chacko,Vighnesh Ghatpande,Kangsheng Qi,Logan Persyn,Jun Wang,Dinghai Zheng,Yochen Zhong,Dayea Park,Elif Sarinay Cenik,Vikram Agarwal,Hakan Ozadam & Can Cenik
Nature Biotechnology Published:25 July 2025
DOI:https://doi.org/10.1038/s41587-025-02718-5
Abstract
Characterizing shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and biological functions. However, it is unclear if such coordination extends to translation. In this study, we uniformly analyze 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of translation efficiency covariation (TEC), identifying coordinated translation patterns across cell types. We nominate candidate mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and uncovers gene functions that are not evident from RNA or protein co-expression. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish TEC as a conserved organizing principle of mammalian transcriptomes. TEC has potential as a predictive marker for gene function and may offer a framework for designing gene expression systems in synthetic biology and biotechnological applications.
