RG4detector, a novel RNA G-quadruplex predictor, uncovers their impact on stress granule formation. Issue 20 (9th November 2022)

• Record Type: Journal Article
• Authors: Turner, Maor; Danino, Yehuda M; Barshai, Mira; Yacovzada, Nancy S; Cohen, Yahel; Olender, Tsviya; Rotkopf, Ron; Monchaud, David; Hornstein, Eran; Orenstein, Yaron
• Is Part Of: Nucleic acids research. Volume 50:Issue 20(2022)
• Page Start: 11426
• Page End: 11441
• Publication Date: 2022-11-09
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkac950
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: RNA G-quadruplexes (rG4s) are RNA secondary structures, which are formed by guanine-rich sequences and have important cellular functions. Existing computational tools for rG4 prediction rely on specific sequence features and/or were trained on small datasets, without considering rG4 stability information, and are therefore sub-optimal. Here, we developed rG4detector, a convolutional neural network to identify potential rG4s in transcriptomics data. rG4detector outperforms existing methods in both predicting rG4 stability and in detecting rG4-forming sequences. To demonstrate the biological-relevance of rG4detector, we employed it to study RNAs that are bound by the RNA-binding protein G3BP1. G3BP1 is central to the induction of stress granules (SGs), which are cytoplasmic biomolecular condensates that form in response to a variety of cellular stresses. Unexpectedly, rG4detector revealed a dynamic enrichment of rG4s bound by G3BP1 in response to cellular stress. In addition, we experimentally characterized G3BP1 cross-talk with rG4s, demonstrating that G3BP1 is a bona fide rG4-binding protein and that endogenous rG4s are enriched within SGs. Furthermore, we found that reduced rG4 availability impairs SG formation. Hence, we conclude that rG4s play a direct role in SG biology via their interactions with RNA-binding proteins and that rG4detector is a novel useful tool for rG4 transcriptomics data analyses. Graphical Abstract: