Background: Circular RNAs (circRNAs) are stable, often highly expressed RNA transcripts with potential to modulate other regulatory RNAs. A few circRNAs have been shown to bind RNA-binding proteins (RBPs); however, little is known about the prevalence and distribution of these interactions in different biological contexts.
Methods: We conduct an extensive screen of circRNA-RBP interactions in the ENCODE cell lines HepG2 and K562. We profile circRNAs in deep-sequenced total RNA samples and analyze circRNA-RBP interactions using a large set of eCLIP data with binding sites of 150 RBPs. We validate interactions for select circRNAs and RBPs by performing RNA immunoprecipitation and functionally characterize our most interesting candidates by conducting knockdown studies followed by RNA-Seq.
Results: We generate a comprehensive catalog of circRNA-RBP interactions in HepG2 and K562 cells. We show that KHSRP binding sites are enriched in flanking introns of circRNAs and that KHSRP depletion affects circRNA biogenesis. We identify circRNAs that are highly covered by RBP binding sites and experimentally validate individual circRNA-RBP interactions. We show that circCDYL, a highly expressed circRNA with clinical and functional implications in bladder cancer, is almost completely covered with GRWD1 binding sites in HepG2 cells, and that circCDYL depletion counteracts the effect of GRWD1 depletion. Furthermore, we confirm interactions between circCDYL and RBPs in bladder cancer cells and demonstrate that circCDYL depletion affects hallmarks of cancer and perturbs the expression of key cancer genes, e.g., TP53. Finally, we show that elevated levels of circCDYL are associated with overall survival of bladder cancer patients.
Conclusions: Our study demonstrates transcriptome-wide and cell-type-specific circRNA-RBP interactions that could play important regulatory roles in tumorigenesis.
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http://dx.doi.org/10.1186/s13073-020-00812-8 | DOI Listing |
BMC Biol
November 2024
School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Background: Due to the ability of circRNA to bind with corresponding RBPs and play a critical role in gene regulation and disease prevention, numerous identification algorithms have been developed. Nevertheless, most of the current mainstream methods primarily capture one-dimensional sequence features through various descriptors, while neglecting the effective extraction of secondary structure features. Moreover, as the number of introduced descriptors increases, the issues of sparsity and ineffective representation also rise, causing a significant burden on computational models and leaving room for improvement in predictive performance.
View Article and Find Full Text PDFInterdiscip Sci
November 2024
School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
Circular RNA (circRNA) has the capacity to bind with RNA binding protein (RBP), thereby exerting a substantial impact on diseases. Predicting binding sites aids in comprehending the interaction mechanism, thereby offering insights for disease treatment strategies. Here, we propose a novel approach based on temporal convolutional network (TCN) and cross multi-head attention mechanism to predict circRNA-RBP binding sites (circTCA).
View Article and Find Full Text PDFPLoS Comput Biol
August 2024
Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China.
Int J Biol Macromol
October 2024
College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China; Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China. Electronic address:
CircRNAs play vital roles in biological system mainly through binding RNA-binding protein (RBP), which is essential for regulating physiological processes in vivo and for identifying causal disease variants. Therefore, predicting interactions between circRNA and RBP is a critical step for the discovery of new therapeutic agents. Application of various deep-learning models in bioinformatics has significantly improved prediction and classification performance.
View Article and Find Full Text PDFComput Biol Med
May 2024
Department of Engineering, University of Cambridge, United Kingdom. Electronic address:
Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availability and accuracy, necessitating advanced approaches.
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