An integrated approach for mapping RNA protein interactions in the ribosome

绘制核糖体中 RNA 蛋白相互作用的综合方法

• 批准号: 22K19291
• 负责人: ナマシヴァヤム パンディアン
• 金额: $ 4.08万
• 依托单位: Kyoto University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Challenging Research (Exploratory)
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-06-30 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22K19291/
• 关键词: Nanopore; Direct RNA sequencing; RNA-protein interactions; Chemical probe; Machine learning; ナノポアシークエンシング; リボソーム; RNA-たんぱく質相互作用; ケミカルプローブ; バイオインフォマティクス

In this fiscal year, we have successfully demonstrated the proof-of-concept (POC) studies to verify that chemical probes can be harnessed in nanopore direct RNA sequencing (dRNA-Seq) platform and can aid in assessing RNA modifications. We published (ACS Chem. Biol. 2022) the part of our results to show that the chemical probe (acrylonitrile) could selectively react with the inosine modification and create a signature mismatch error to deduce the presence of actual inosine modifications. We also deduced the stoichiometry of inosine modification through deviation in signal intensity and trace value. Our methods showed that Adenosine to inosine (A-I) editing could be accurately identified using mismatch error with an accuracy greater than 80% using synthetic RNA and support vector machine learning. Furthermore, we have substituted the need for such a null data set and demonstrated that the acrylonitrile’s selective reactivity could facilitate distinguishing signature mismatch errors from noise. We also modified a `nanoRMS workflow` and showed that chemical probes efficiently alter trace value and signal intensity. We also summarized how machine-learning tools could aid the discovery of natural product-derived small molecules that can serve as targeted therapeutics and probes. Furthermore, the nano-bio interaction of the functionalized quantum dots, which is expected to harbor the bi-functional probe to decipher RNA-protein interaction, was tested using the in vitro and in vivo models. These results form an ideal platform for the integrated approach to mapping RNA dynamics.

在本财政年度，我们成功展示了概念验证（POC）研究，以验证化学探针可用于纳米孔直接RNA测序（dRNA-Seq）平台，并可帮助评估RNA修饰。我们发表了（ACS Chem.Biol.2022）我们的部分结果，以表明化学探针（丙烯腈）可以选择性地与肌苷修饰反应，并产生特征失配错误，以推断实际肌苷修饰的存在。我们还通过信号强度和痕量值的偏差推断了肌苷修饰的化学计量比。我们的方法表明，使用合成RNA和支持向量机学习，可以使用错配误差准确地识别腺苷到肌苷（A-I）编辑，准确率超过80%。此外，我们已经取代了这样一个空数据集的需要，并证明了丙烯腈的选择性反应，可以促进区分签名不匹配的错误从噪声。我们还修改了“纳米RMS工作流程”，并表明化学探针有效地改变了痕量值和信号强度。我们还总结了机器学习工具如何帮助发现天然产物衍生的小分子，这些小分子可以作为靶向治疗剂和探针。此外，使用体外和体内模型测试了功能化量子点的纳米生物相互作用，预期其具有双功能探针以破译RNA-蛋白质相互作用。这些结果形成了一个理想的平台的综合方法来映射RNA动力学。

项目成果

National Chemical Laboratory - Pune(インド)

国家化学实验室 - 浦那（印度）

Reading RNA modifications more precisely

更精确地读取 RNA 修饰

Rutgers University(米国)

罗格斯大学（美国）

University of Zurich/AO Research Institute(スイス)

苏黎世大学/AO 研究所（瑞士）

Targeted transcription therapeutics for skin cell reprogramming

用于皮肤细胞重编程的靶向转录疗法

• 发表时间: 2023
• 作者: Ramasamy Soundhar;Sahayasheela Vinodh J.;Sharma Surbhi;Yu Zutao;Hidaka Takuya;Cai Li;Thangavel Vaijayanthi;Sugiyama Hiroshi;Pandian Ganesh N.;NAMASIVAYAM Ganesh Pandian
• 通讯作者: NAMASIVAYAM Ganesh Pandian