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Yague-Sanz, C.

Publications and source records attributed to Yague-Sanz, C..

2 recordsLinked to original sources

RNase III cleavage signals spread across splice junctions enforce sequential processing of co-hosted snoRNAs.

Small nucleolar RNAs (snoRNAs) are a class of non-coding RNA molecules whose precursor transcripts are capped and polyadenylated. However, these end modifications are detrimental to snoRNA function and must be removed, a process typically involving excision from introns and/or endonucleolytic cleavage. In the case of polycistronic RNA precursors that host multiple snoRNAs, the sequence of maturation events is not well understood. Here we report a new mode of maturation concerning snoRNA pairs that are co-hosted in the intron and the adjacent 3 exon of a precursor transcript. For such a pair in the model eukaryotic species Schizosaccharomyces pombe, we identified a double-stranded RNA hairpin folding across the exon-exon junction. The hairpin recruits the RNase III Pac1 that cleaves and destabilizes the precursor transcript while participating in the maturation of the downstream exonic snoRNA, but only after splicing and release of the intronic snoRNA. We propose that such RNase III degradation signal hidden in an exon-exon junction evolved to enforce sequential snoRNA processing. Sequence analysis suggest that this mechanism is conserved in animals and plants.

molecular biology↗

Unbiased and comprehensive identification of viral encoded circular RNAs in a large range of viral species and families

Non-coding RNAs play a significant role in viral infection cycles, with recent attention focused on circular RNAs (circRNAs) originating from various viral families. Notably, these circRNAs have been associated with oncogenesis and alterations in viral fitness. However, identifying their expression has proven more challenging than initially anticipated due to unique viral characteristics. This challenge has the potential to impede progress in our understanding of viral circRNAs. Key hurdles in working with viral genomes include: (1) the presence of repetitive regions that can lead to misalignment of sequencing reads, and (2) unconventional splicing mechanisms that deviate from conserved eukaryotic patterns. To address these challenges, we developed vCircTrappist, a bioinformatic pipeline tailored to identify backsplicing events and pinpoint loci expressing circRNAs in RNA sequencing data. Applying this pipeline, we obtained novel insights from both new and existing datasets encompassing a range of animal and human pathogens belonging to Herpesviridae, Retroviridae, Adenoviridae and Orthomyxoviridae families. Subsequent RT-PCR and Sanger sequencings validated the accuracy of the developed bioinformatic tool for a selection of new candidate viral encoded circRNAs. These findings demonstrate that vCircTrappist is an open and unbiased approach for comprehensive identification of virus-derived circRNAs. Significance StatementCircular RNAs (circRNAs) were revealed to have prominent roles in cellular life in the past decade. They were more recently shown to be expressed by viruses, influencing their infectious cycles and host-pathogen relationship. In this context, viruses that were not previously associated with cellular splicing processes are shown to express circRNAs through unknown mechanisms. These non-canonical circRNAs were already shown to be important in the viral cycle and pathogenesis of the viruses they are encoded from. Here, we propose a bioinformatics pipeline that bypasses the limitations of the existing tools in the identification of viral circRNA. Using this pipeline, we discovered numerous candidates and invite the reader to start its own exploration in the realm of viral encoded circRNAs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/600382v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1003330org.highwire.dtl.DTLVardef@20d366org.highwire.dtl.DTLVardef@116f38forg.highwire.dtl.DTLVardef@1d04a76_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗