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在真核生物RNA的结构探测数据中观察到的一致特征。

Consistent features observed in structural probing data of eukaryotic RNAs.

作者信息

Yamamura Kazuteru, Asai Kiyoshi, Iwakiri Junichi

机构信息

Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8561, Japan.

出版信息

NAR Genom Bioinform. 2025 Jan 30;7(1):lqaf001. doi: 10.1093/nargab/lqaf001. eCollection 2025 Mar.

DOI:10.1093/nargab/lqaf001
PMID:39885881
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11780854/
Abstract

Understanding RNA structure is crucial for elucidating its regulatory mechanisms. With the recent commercialization of messenger RNA vaccines, the profound impact of RNA structure on stability and translation efficiency has become increasingly evident, underscoring the importance of understanding RNA structure. Chemical probing of RNA has emerged as a powerful technique for investigating RNA structure in living cells. This approach utilizes chemical probes that selectively react with accessible regions of RNA, and by measuring reactivity, the openness and potential of RNA for protein binding or base pairing can be inferred. Extensive experimental data generated using RNA chemical probing have significantly contributed to our understanding of RNA structure in cells. However, it is crucial to acknowledge potential biases in chemical probing data to ensure an accurate interpretation. In this study, we comprehensively analyzed transcriptome-scale RNA chemical probing data in eukaryotes and report common features. Notably, in all experiments, the number of bases modified in probing was small, the bases showing the top 10% reactivity well reflected the known secondary structure, bases with high reactivity were more likely to be exposed to solvent and low reactivity did not reflect solvent exposure, which is important information for the analysis of RNA chemical probing data.

摘要

了解RNA结构对于阐明其调控机制至关重要。随着信使RNA疫苗最近商业化,RNA结构对稳定性和翻译效率的深远影响日益明显,凸显了了解RNA结构的重要性。RNA的化学探测已成为研究活细胞中RNA结构的强大技术。这种方法利用化学探针与RNA的可及区域选择性反应,通过测量反应性,可以推断RNA与蛋白质结合或碱基配对的开放性和可能性。使用RNA化学探测产生的大量实验数据极大地促进了我们对细胞中RNA结构的理解。然而,认识到化学探测数据中潜在的偏差对于确保准确解释至关重要。在本研究中,我们全面分析了真核生物中转录组规模的RNA化学探测数据并报告了共同特征。值得注意的是,在所有实验中,探测中被修饰的碱基数量很少,反应性排名前10%的碱基很好地反映了已知的二级结构,高反应性的碱基更有可能暴露于溶剂中,而低反应性并不反映溶剂暴露情况,这是分析RNA化学探测数据的重要信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/01a33b5c34f8/lqaf001fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/aef94f5cdc1d/lqaf001fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/cc7d70b72248/lqaf001fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/9fd31491783f/lqaf001fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/118307c40a57/lqaf001fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/808d65db1f74/lqaf001fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/01a33b5c34f8/lqaf001fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/aef94f5cdc1d/lqaf001fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/cc7d70b72248/lqaf001fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/9fd31491783f/lqaf001fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/118307c40a57/lqaf001fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/808d65db1f74/lqaf001fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d0a/11780854/01a33b5c34f8/lqaf001fig6.jpg

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Glycosylated queuosines in tRNAs optimize translational rate and post-embryonic growth.tRNA 中的糖基化 queuosines 优化了翻译速度和胚胎后生长。
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Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.RNA 信息学的最新趋势:机器学习和深度学习在 RNA 二级结构预测和 RNA 药物发现中的应用综述。
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