Central European Institute of Technology, Masaryk University, 62500, Brno, Czech Republic.
Faculty of Science, National Centre for Biomolecular Research, Masaryk University, 62500, Brno, Czech Republic.
Sci Rep. 2023 Dec 21;13(1):22895. doi: 10.1038/s41598-023-49757-z.
Argonaute proteins are instrumental in regulating RNA stability and translation. AGO2, the major mammalian Argonaute protein, is known to primarily associate with microRNAs, a family of small RNA 'guide' sequences, and identifies its targets primarily via a 'seed' mediated partial complementarity process. Despite numerous studies, a definitive experimental dataset of AGO2 'guide'-'target' interactions remains elusive. Our study employs two experimental methods-AGO2 CLASH and AGO2 eCLIP, to generate thousands of AGO2 target sites verified by chimeric reads. These chimeric reads contain both the AGO2 loaded small RNA 'guide' and the target sequence, providing a robust resource for modeling AGO2 binding preferences. Our novel analysis pipeline reveals thousands of AGO2 target sites driven by microRNAs and a significant number of AGO2 'guides' derived from fragments of other small RNAs such as tRNAs, YRNAs, snoRNAs, rRNAs, and more. We utilize convolutional neural networks to train machine learning models that accurately predict the binding potential for each 'guide' class and experimentally validate several interactions. In conclusion, our comprehensive analysis of the AGO2 targetome broadens our understanding of its 'guide' repertoire and potential function in development and disease. Moreover, we offer practical bioinformatic tools for future experiments and the prediction of AGO2 targets. All data and code from this study are freely available at https://github.com/ML-Bioinfo-CEITEC/HybriDetector/ .
Argonaute 蛋白在调节 RNA 稳定性和翻译中起着重要作用。AGO2 是主要的哺乳动物 Argonaute 蛋白，已知主要与 microRNA 结合，microRNA 是一小段 RNA“指导”序列家族，并通过“种子”介导的部分互补过程来识别其靶标。尽管进行了大量研究，但 AGO2“指导”-“靶”相互作用的明确实验数据集仍然难以捉摸。我们的研究采用了两种实验方法-AGO2 CLASH 和 AGO2 eCLIP，生成了数千个由嵌合读数验证的 AGO2 靶标位点。这些嵌合读数包含加载 AGO2 的小 RNA“指导”和靶序列，为建模 AGO2 结合偏好提供了强大的资源。我们新颖的分析管道揭示了数千个由 microRNA 驱动的 AGO2 靶标位点，以及大量源自其他小 RNA 片段（如 tRNA、YRNA、snoRNA、rRNA 等）的 AGO2“指导”。我们利用卷积神经网络来训练机器学习模型，这些模型可以准确预测每个“指导”类的结合潜力，并对几个相互作用进行了实验验证。总之，我们对 AGO2 靶标组的全面分析拓宽了我们对其“指导”谱及其在发育和疾病中的潜在功能的理解。此外，我们还提供了实用的生物信息学工具，用于未来的实验和 AGO2 靶标的预测。本研究的所有数据和代码均可在 https://github.com/ML-Bioinfo-CEITEC/HybriDetector/ 上免费获取。
