DiRT v2.0: An Optimized Pipeline for Detecting Dicistronic tRNA-mRNA Transcripts in Plants

The canonical role of transfer RNAs (tRNAs) in protein synthesis has been extensively characterized; however, recent studies have uncovered novel functions for tRNA as a mediator of long-distance signaling in plants. Several studies have identified dicistronic tRNA-mRNA transcripts that contain a tRNA gene and an adjacent protein-coding gene (PCG) that are transcribed as a single unit. These transcripts are associated with RNA systemic mobility through the plant's vascular tissues, potentially acting as non-cell-autonomous signaling messengers in coordinating development and stress responses. Here, we report a computational pipeline to detect dicistronic tRNA-mRNA transcripts from short-read next-generation RNA-sequencing datasets; to our knowledge, this is the only established pipeline for the systematic identification of such candidates in plants. The dicistronic RNA transcript version 2 (v2) described here improves on the earlier version DiRT v1 by expanding the repertoire of dicistronic transcripts detected to include tRNA-like structures (TLS) as well as functional tRNAs, which were already supported in the pipeline. The updated protocol also includes detection of dicistronic tRNA or TLS sequences within genomic features such as untranslated regions (UTRs). The accurate detection of both tRNAs and UTR-embedded tRNA-like sequences (TLS) is critical, as these RNA structures have been reported to function as mediators of long-distance RNA mobility. Furthermore, as NGS datasets are prone to sequencing artifacts and potential DNA contamination, we improved the pipeline's statistical robustness by including read coverage of flanking intronic regions as a baseline control. To account for potential DNA contamination during RNA-seq library preparation, detected tRNA-mRNA transcripts are deemed as putatively dicistronic only if the coverage of their intergenic region is significantly higher (Student's t-test, FDR < 0.05) than flanking intronic regions. Furthermore, the updated pipeline allows this statistical test to be applied to intronless and single-intron genes. Using this updated protocol, we identified novel tRNA and TLS dicistronic transcripts in both grapevine (Vitis spp. Ruggeri 140) and Arabidopsis thaliana datasets and validated in vitro using RT-PCR. We provide a fast and reliable method to detect dicistronic transcripts that can be applied to any short-read RNA-sequencing dataset, fast-tracking the functional characterization of these newly emerging transcripts. Key features • DiRT version 2.0 (v2) provides a reliable and improved bioinformatics workflow to identify dicistronic tRNA-mRNA transcriptomic features in plants. • The updated workflow improves detection of tRNA-like structures (TLS) and UTRembedded dicistronic tRNA-mRNA candidates. • Applies Student's t-test (FDR < 0.05) and base pair coverage to validate transcript continuity, utilizing neighboring introns as the appropriate biological background control. • DiRT v2 supports prediction of dicistronic candidates from intronless and singleintron protein coding genes (PCG).