Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical University, 155 North Nanjing Street, Heping District, Shenyang, 110001, China.
J Transl Med. 2021 Feb 25;19(1):88. doi: 10.1186/s12967-021-02731-7.
tRNA-derived fragments (tRFs) are 14-40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes.
We used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models.
We first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977-0.983) in the training process and an AUC = 0.847 (0.83-0.861) in the test process. The model was applied to all the sites with perfect Watson-Crick complementarity to the seed in the 3' untranslated region (3'-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. We also validated the predictions via quantitative real-time PCR (qRT-PCR). Thirteen potential target genes from the top of the predictions were significantly down-regulated at the mRNA levels by overexpression of the tRFs (tRF-3001a, tRF-3003a or tRF-3009a).
Predictions can be obtained online, tRFTars, freely available at http://trftars.cmuzhenninglab.org:3838/tar/ , which is the first tool to predict targets of tRFs in humans with a user-friendly interface.
tRNA 衍生片段（tRFs）是 14-40 个核苷酸长的小非编码 RNA，来源于特定的 tRNA 切割事件，在许多生物过程中具有关键的调节功能。许多研究表明，tRFs 与 Argonaute（AGO）复合物有关，并以与 miRNA 相同的方式抑制基因表达。然而，目前还没有准确预测 tRF 靶基因的工具。
我们使用交联、连接和杂交（CLASH）的 RNA 测序以及内源性 AGO 结合 RNA 的共价连接（CLEAR）-CLIP 鉴定的 tRF-mRNA 对来评估可能参与 tRF 靶向的特征，包括每个位点的序列上下文和 tRF-mRNA 相互作用。我们应用遗传算法（GA）选择关键特征，并应用支持向量机（SVM）构建 tRF 预测模型。
我们首先确定了全局影响 tRF 靶向的特征。在这些特征中，最重要的是最小自由折叠能（MFE）、位置 8 匹配、tRF-mRNA 双链配对的碱基数和 tRF 的长度，这与之前的发现一致。我们构建的模型在训练过程中的接收者操作特征（ROC）曲线下面积（AUC）为 0.980（0.977-0.983），在测试过程中的 AUC 为 0.847（0.83-0.861）。该模型应用于人类基因组 3' 非翻译区（3'-UTR）中与种子完全互补的所有位点。通过报告基因检测证实了 9 个 tRF 靶/非靶基因中的 7 个。我们还通过定量实时 PCR（qRT-PCR）验证了这些预测。通过过表达 tRF（tRF-3001a、tRF-3003a 或 tRF-3009a），在 mRNA 水平上，从预测结果的前 13 位中筛选出 13 个潜在的靶基因显著下调。
可在线获得预测结果，tRFTars 可免费在 http://trftars.cmuzhenninglab.org:3838/tar/ 获得，这是第一个具有用户友好界面的预测人类 tRF 靶基因的工具。
