Division of Exercise Physiology, West Virginia University School of Medicine, Morgantown, West Virginia, United States.
Mitochondria, Metabolism, and Bioenergetics Working Group, West Virginia University School of Medicine, Morgantown, West Virginia, United States.
Am J Physiol Cell Physiol. 2024 Aug 1;327(2):C221-C236. doi: 10.1152/ajpcell.00648.2023. Epub 2024 Jun 3.
Extranuclear localization of long noncoding RNAs (lncRNAs) is poorly understood. Based on machine learning evaluations, we propose a lncRNA-mitochondrial interaction pathway where polynucleotide phosphorylase (PNPase), through domains that provide specificity for primary sequence and secondary structure, binds nuclear-encoded lncRNAs to facilitate mitochondrial import. Using FVB/NJ mouse and human cardiac tissues, RNA from isolated subcellular compartments (cytoplasmic and mitochondrial) and cross-linked immunoprecipitate (CLIP) with PNPase within the mitochondrion were sequenced on the Illumina HiSeq and MiSeq, respectively. lncRNA sequence and structure were evaluated through supervised [classification and regression trees (CART) and support vector machines (SVM)] machine learning algorithms. In HL-1 cells, quantitative PCR of PNPase CLIP knockout mutants (KH and S1) was performed. In vitro fluorescence assays assessed PNPase RNA binding capacity and verified with PNPase CLIP. One hundred twelve (mouse) and 1,548 (human) lncRNAs were identified in the mitochondrion with Malat1 being the most abundant. Most noncoding RNAs binding PNPase were lncRNAs, including Malat1. lncRNA fragments bound to PNPase compared against randomly generated sequences of similar length showed stratification with SVM and CART algorithms. The lncRNAs bound to PNPase were used to create a criterion for binding, with experimental validation revealing increased binding affinity of RNA designed to bind PNPase compared to control RNA. The binding of lncRNAs to PNPase was decreased through the knockout of RNA binding domains KH and S1. In conclusion, sequence and secondary structural features identified by machine learning enhance the likelihood of nuclear-encoded lncRNAs binding to PNPase and undergoing import into the mitochondrion. Long noncoding RNAs (lncRNAs) are relatively novel RNAs with increasingly prominent roles in regulating genetic expression, mainly in the nucleus but more recently in regions such as the mitochondrion. This study explores how lncRNAs interact with polynucleotide phosphorylase (PNPase), a protein that regulates RNA import into the mitochondrion. Machine learning identified several RNA structural features that improved lncRNA binding to PNPase, which may be useful in targeting RNA therapeutics to the mitochondrion.
长链非编码 RNA(lncRNA)的核外定位知之甚少。基于机器学习评估,我们提出了一种 lncRNA-线粒体相互作用途径,其中多核苷酸磷酸化酶(PNPase)通过提供对一级序列和二级结构特异性的结构域,将核编码 lncRNA 结合到促进线粒体导入的位置。使用 FVB/NJ 小鼠和人心脏组织,分别对分离的亚细胞区室(细胞质和线粒体)的 RNA 和 PNPase 在线粒体中的交联免疫沉淀(CLIP)进行 Illumina HiSeq 和 MiSeq 测序。通过监督 [分类和回归树(CART)和支持向量机(SVM)]机器学习算法评估 lncRNA 序列和结构。在 HL-1 细胞中,对 PNPase CLIP 敲除突变体(KH 和 S1)进行了定量 PCR。体外荧光测定评估了 PNPase RNA 结合能力,并通过 PNPase CLIP 进行了验证。在小鼠中有 112 个(小鼠)和 1548 个(人)lncRNA 在 线粒体中被鉴定出来,其中 Malat1 最为丰富。与 PNPase 结合的大多数非编码 RNA 是 lncRNA,包括 Malat1。与具有相似长度的随机生成序列相比,与 PNPase 结合的 lncRNA 片段表现出与 SVM 和 CART 算法的分层。使用与 PNPase 结合的 lncRNAs 创建了一个结合标准,实验验证表明,设计用于与 PNPase 结合的 RNA 与对照 RNA 相比,具有更高的结合亲和力。通过敲除 RNA 结合结构域 KH 和 S1,降低了 lncRNA 与 PNPase 的结合。总之,机器学习识别的序列和二级结构特征增强了核编码 lncRNA 与 PNPase 结合并进入线粒体的可能性。长链非编码 RNA(lncRNA)是相对较新的 RNA,其在调节基因表达方面的作用日益突出,主要在细胞核内,但最近在诸如线粒体等区域也有作用。本研究探讨了 lncRNA 如何与多核苷酸磷酸化酶(PNPase)相互作用,PNPase 是一种调节 RNA 导入线粒体的蛋白质。机器学习确定了几个 RNA 结构特征,这些特征提高了 lncRNA 与 PNPase 的结合能力,这可能有助于将 RNA 治疗靶向线粒体。