PDF(Pubmed)
Abstract：
Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRNA binding profiles and spliceosome assembly information to identify modulatory SSO binding sites on pre-mRNA. Using Shapley and out-of-bag analyses we also predicted the identity of specific SFs whose binding to pre-mRNA is blocked by SSOs. This step adds considerable transparency to AI/ML-driven drug discovery and informs biological insights useful in further validation steps. We applied this approach to previously established functional SSOs to retrospectively identify the SFs likely to regulate those events. We then took a prospective validation approach using a novel target in triple negative breast cancer (TNBC), NEDD4L exon 13 (NEDD4Le13). Targeting NEDD4Le13 with an AI/ML-designed SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data.
摘要：
剪接转换寡核苷酸(SSO)是直接作用于pre-mRNA以调节可变剪接(AS)的反义化合物。这项研究证明了人工智能/机器学习(AI/ML)为识别功能、可验证，和治疗SSO。我们使用剪接因子（SF）pre-mRNA结合谱和剪接体组装信息训练XGboost树模型，以识别pre-mRNA上的调节性SSO结合位点。使用Shapley和袋外分析，我们还预测了特定SF的身份，其与前mRNA的结合被SSO阻断。此步骤为AI/ML驱动的药物发现增加了相当大的透明度，并告知在进一步验证步骤中有用的生物学见解。我们将此方法应用于先前建立的功能性SSO，以回顾性地识别可能调节这些事件的SF。然后，我们采用了一种前瞻性验证方法，在三阴性乳腺癌（TNBC）中使用了一种新的靶标，NEDD4L外显子13（NEDD4Le13）。用AI/ML设计的SSO靶向NEDD4Le13通过下调TGFβ途径降低了TNBC细胞的增殖和迁移行为。总的来说,这项研究说明了AI/ML从RNA-seq数据中提取可操作见解的能力。
