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Inferring targeting modes of Argonaute-loaded tRNA fragments.

ABSTRACT: Transfer RNA fragments (tRFs) are an emerging class of small RNA molecules derived from mature or precursor tRNAs. They are found across a wide range of organisms and tissues, in small RNA fraction or loaded to Argonaute in numbers comparable to microRNAs. Their functions and mechanisms of action are largely unknown, and results obtained on individual tRFs are often hard to generalize. Here we predicted binding mechanisms and specific target interaction sites of 26 human Argonaute-loaded tRFs of different types using large-scale meta-analyses of available experimental data. Strikingly, our findings matched all interaction sites detected in a recent experimental screen, confirming the validity of our computational approach. Such sites are primarily located on the 5' end of tRFs and often involve additional binding along the tRF length, similar to microRNAs. Indicative of multiple layers of regulation, diverse regulatory non-coding RNAs comprised a third of the tRF targets, with the rest being protein-coding transcripts. In the latter, coding sequence and 3' UTRs were the likely primary target regions, although we observed interactions of tRFs with 5' UTRs. Another novel phenomenon we report, a large number of putative interactions between tRFs and introns, is compatible with the roles of Argonaute in the nucleus. Further, observed tRF-intron binding modes suggest a mechanism of interaction of tRFs with Argonaute-dependent introns, and we predict here >20 candidate introns of this type. Taken together, these results present tRFs as regulatory molecules with a rich functional spectrum.

SUBMITTER: Guan L

PROVIDER: S-EPMC7549686 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundtRNA-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.MethodsWe 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.ResultsWe 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).ConclusionsPredictions 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.

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Inferring targeting modes of Argonaute-loaded tRNA fragments.

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