Network characterization linc1393 in the maintenance of pluripotency provides the principles for lncRNA targets prediction

• DOI: 10.1016/j.isci.2023.107469
• Publication Date: 2023-07-26T03:30:31Z
• Authors: Weibo Hou; Ming Zong; Qi Zhao; Xu Yang; Jiaming Zhang; Shuanghui Liu; Xuanwen Li; Lijun Chen; Chun Tang; Xinyu Wang; Zhixiong Dong; Meiling Gao; Jianzhong Su; Qingran Kong
• Keywords: Stem cells research; Molecular biology; Science; Machine learning; Q; Epigenetics; Article

Long non-coding RNAs (lncRNAs) have been implicated in diverse biological processes. However, the functional mechanisms have not yet been fully explored. Characterizing the interactions of lncRNAs with chromatin is central to determining their functions but, due to precise and efficient approaches lacking, our understanding of their functional mechanisms has progressed slowly. In this study, we demonstrate that a nuclear lncRNA linc1393 maintains mouse ESC pluripotency by recruiting SET1A near its binding sites, to establish H3K4me3 status and activate the expression of specific pluripotency-related genes. Moreover, we characterized the principles of lncRNA-chromatin interaction and transcriptional regulation. Accordingly, we developed a computational framework based on the XGBoost model, LncTargeter, to predict the targets of a given lncRNA, and validated its reliability in various cellular contexts. Together, these findings elucidate the roles and mechanisms of lncRNA on pluripotency maintenance, and provide a promising tool for predicting the regulatory networks of lncRNAs.