Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning II

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are still not well understood. In this study, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequence. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis, and confirmed that primary RNA sequence is a major determinant in small RNA secretion. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins, e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. We used exoCLIP to reveal the RNA interactome of HNRNPA2B1 and RBM24 in extracellular vesicles. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into complex processes such as small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications. Overall design: Using a machine learning model to dissect the primary sequences and structural features of small RNAs in the extracellular and intracellular compartments.

小型非编码RNA（small non-coding RNAs）可通过多种机制被分泌，包括经由外泌体分选进入小型细胞外囊泡，以及结合于脂蛋白复合物中完成分泌。然而，调控其分选与分泌的具体分子机制目前仍未被充分阐明。本研究中，我们开发了一款名为ExoGRU的机器学习模型，可基于RNA原始序列预测小型RNA的分泌概率。我们通过ExoGRU引导的诱变实验与合成RNA序列分析，对该模型的性能进行了实验验证，并证实RNA原始序列是决定小型RNA分泌能力的核心因素之一。此外，我们借助ExoGRU揭示了调控小型RNA分泌的顺式与反式作用因子，包括已知的及新发现的RNA结合蛋白（RNA-binding proteins, RBP），例如YBX1、HNRNPA2B1与RBM24。我们还开发了一种名为exoCLIP的新型实验技术，可用于解析无细胞环境中RNA结合蛋白的RNA互作组。我们利用exoCLIP技术解析了细胞外囊泡中HNRNPA2B1与RBM24的RNA互作组。综上，本研究结果证实了机器学习在揭示全新生物学机制方面的应用潜力。该研究不仅加深了我们对小型RNA分泌等复杂生物学过程的理解，其相关研究成果还可应用于治疗学与合成生物学领域。整体实验设计：借助机器学习模型解析细胞外与细胞内腔室中小RNA的原始序列及结构特征。