Noncanonical small RNA chimeras identified by AGO2-CLASH


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HEJRET Václav VARADARAJAN Nandan Mysore KLIMENTOVÁ Eva GIASSA Ilektra-Chara VAŇÁČOVÁ Štěpánka ALEXIOU Panagiotis

Year of publication 2022
Type Conference abstract
MU Faculty or unit

Central European Institute of Technology

Description Mammalian cells express four AGO proteins (AGO1-4) that associate with miRNAs and act as effectors in RNAi pathways. Whereas miRNAs have long been considered the primary small RNA drivers of RNAi, several recent reports indicated that AGO proteins associate with small RNAs derived from other types of RNAs and suggested that they could serve similar functions like miRNAs. For instance, the CLASH (crosslinking, ligation, and sequencing of hybrids) analysis of AGO1 in Flp-In T-REx 293 cells uncovered a number of tRNA fragments (tRFs) and are predicted to confer post-transcriptional silencing regulation to their targets similar to miRNAs. Here we present the CLASH analysis of AGO2 in HEK293 cells to address the small RNA repertoire and uncover their physiological targets. We developed an optimized bioinformatics approach of chimeric read identification to detect chimeras of high confidence. In this new pipeline, we start with raw sequenced CLASH derived reads and the alignment to both whole genome and to specific noncoding RNA databases gives us potential to observe and annotate a wide scale of various AGO2 driven interactions. We identified 16828 chimeric target sites also supported by non-chimeric reads. 43% of these were miRNA:mRNA pairs. Interestingly, the majority of identified chimeric target interactions appears to be driven by non-miRNA driver molecule fragments (tRNA, rRNA, yRNA, snoRNA, vaultRNA). By using a combination of reporter assays and small RNA inhibitory assays we were able to show that at least some of these non-miRNA driver interactions regulate the steady state level of their targets determined by the CLASH. Identified small RNA:target chimeras were used to train Convolutional Neural Network models that can predict the potential of small RNA:target site binding with above state-of-the-art accuracy.
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