Features Of circRNAs Research Articles

Systematic identification and characterization of exon-intron circRNAs.

Exon-intron circRNAs (EIciRNAs) are a circRNA subclass with retained introns. Global features of EIciRNAs remain largely unexplored, mainly owing to the lack of bioinformatic tools. The regulation of intron retention (IR) in EIciRNAs and the associated functionality also require further investigation. We developed a framework, FEICP, which efficiently detected EIciRNAs from high-throughput sequencing (HTS) data. EIciRNAs are distinct from exonic circRNAs (EcircRNAs) in aspects such as with larger length, localization in the nucleus, high tissue specificity, and enrichment mostly in the brain. Deep learning analyses revealed that compared with regular introns, the retained introns of circRNAs (CIRs) are shorter in length, have weaker splice site strength, and have higher GC content. Compared with retained introns in linear RNAs (LIRs), CIRs are more likely to form secondary structures and show greater sequence conservation. CIRs are closer to the 5'-end, whereas LIRs are closer to the 3'-end of transcripts. EIciRNA-generating genes are more actively transcribed and associated with epigenetic marks of gene activation. Computational analyses and genome-wide CRISPR screening revealed that SRSF1 binds to CIRs and inhibits the biogenesis of most EIciRNAs. SRSF1 regulates the biogenesis of EIciLIMK1, which enhances the expression of LIMK1 in cis to boost neuronal differentiation, exemplifying EIciRNA physiological function. Overall, our study has developed the FEICP pipeline to identify EIciRNAs from HTS data, and reveals multiple features of CIRs and EIciRNAs. SRSF1 has been identified to regulate EIciRNA biogenesis. EIciRNAs and the regulation of EIciRNA biogenesis play critical roles in neuronal differentiation.

MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning

BackgroundcircRNAs play an important role in drug resistance and cancer development. Recently, many studies have shown that the expressions of circRNAs in human cells can affect the sensitivity of cells to therapeutic drugs, thus significantly influencing the therapeutic effects of these drugs. Traditional biomedical experiments required to verify this sensitivity relationship are not only time-consuming but also expensive. Hence, the development of an efficient computational approach that can accurately predict the novel associations between drug sensitivities and circRNAs is a crucial and pressing need.MethodsIn this research, we present a novel computational framework called MNCLCDA, which aims to predict the potential associations between drug sensitivities and circRNAs to assist with medical research. First, MNCLCDA quantifies the similarity between the given drug and circRNA using drug structure information, circRNA gene sequence information, and GIP kernel information. Due to the existence of noise in similarity information, we employ a preprocessing approach based on random walk with restart for similarity networks to efficiently capture the useful features of circRNAs and drugs. Second, we use a mixed neighbourhood graph convolutional network to obtain the neighbourhood information of nodes. Then, a graph-based contrastive learning method is used to enhance the robustness of the model, and finally, a double Laplace-regularized least-squares method is used to predict potential circRNA-drug associations through the kernel matrices in the circRNA and drug spaces.ResultsNumerous experimental results show that MNCLCDA outperforms six other advanced methods. In addition, the excellent performance of our proposed model in case studies illustrates that MNCLCDA also has the ability to predict the associations between drug sensitivity and circRNA in practical situations.ConclusionsAfter a large number of experiments, it is illustrated that MNCLCDA is an efficient tool for predicting the potential associations between drug sensitivities and circRNAs, thereby can provide some guidance for clinical trials.

Identification of immune-responsive circular RNAs in shrimp (Litopenaeus vannamei) upon yellow head virus infection

Circular RNAs (circRNAs) are a subclass of non-coding RNAs (ncRNAs) formed through a process known as back-splicing. They play a crucial role in the genetic regulation of various biological processes. Currently, circRNAs have been identified as participants in the antiviral response within mammalian cells. However, circRNAs in shrimp infected with the yellow head virus (YHV) remain largely unexplored. Therefore, this study aims to identify circRNAs in the hemocytes of Litopenaeus vannamei during YHV infection. We discovered 358 differentially expressed circRNAs (DECs), with 177 of them being up-regulated and 181 down-regulated. Subsequently, eight DECs, including circ_alpha-1-inhibitor 3, circ_CDC42 small effector protein 2, circ_hemicentin 2, circ_integrin alpha V, circ_kazal-type proteinase inhibitor, circ_phenoloxidase 3, circ_related protein rab-8B, and circ_protein toll-like, were randomly selected for analysis of their expression patterns during YHV infection using qRT-PCR. Furthermore, the circRNAs' characteristics were confirmed through PCR, RNase R treatment, and Sanger sequencing, all of which were consistent with the features of circRNAs. These findings contribute to a better understanding of circRNAs’ involvement in the antiviral response in shrimp.

Protein-Related Circular RNAs in Human Pathologies.

Circular RNAs (circRNAs) are a distinct family of RNAs derived from alternative splicing which play a crucial role in regulating gene expression by acting as microRNA (miRNA) and RNA binding protein (RBP) sponges. However, recent studies have also reported the multifunctional potential of these particles. Under different conditions, circRNAs not only regulate protein synthesis, destination, and degradation but can serve as protein scaffolds or recruiters and are also able to produce short peptides with active biological functions. circRNAs are under ongoing investigation because of their close association with the development of diseases. Some circRNAs are reportedly expressed in a tissue- and development stage-specific manner. Furthermore, due to other features of circRNAs, including their stability, conservation, and high abundance in bodily fluids, they are believed to be potential biomarkers for various diseases, including cancers. In this review, we focus on providing a summary of the current knowledge on circRNA–protein interactions. We present the properties and functions of circRNAs, the possible mechanisms of their translation abilities, and the emerging functions of circRNA-derived peptides in human pathologies.

Characterization of Circular RNA and microRNA Profiles in Septic Myocardial Depression: a Lipopolysaccharide-Induced Rat Septic Shock Model.

Septic shock with heart dysfunction is common in intensive care units. However, the mechanism underlying myocardial depression is still unclear. Whether circular RNA (circRNA) or microRNA (miRNA) profiles differ between patients with and without myocardial depression is unknown. We generated a hypodynamic septic shock model induced by lipopolysaccharide (LPS) in adolescent rats. A total of 12 rats were utilized and heart tissue from each was collected. RNA sequencing was performed on left ventricular tissue. We focused on features of circRNAs and miRNAs, predicting their function by bioinformatic analysis and constructing circRNA-associated and miRNA-associated regulatory networks in heart tissue. We detected 851 circRNAs in heart samples, and 11 showed differential expression. A total of 639 annotated miRNAs and 91 novel miRNAs were explored including 78 showing differential expression between the two groups. We then constructed the most comprehensive circRNA-associated and miRNA-associated networks to explore their regulatory relationship in septic heart tissue, and demonstrated that different networks could potentially participate in and regulate the pathological process of sepsis. Furthermore, gene ontology term enrichment indicated miRNAs, and miRNA-mRNA networks could be associated with regulation and metabolic process, or influence cellular functions. The construction of regulator networks could improve the understanding of the basic molecular mechanisms underlying myocardial depression. It will be important for future investigations to ascertain the biological mechanisms present during the development of sepsis-induced myocardial depression to influence approaches to treatment.

Intriguing circles: Conflicts and controversies in circular RNA research.

Circular RNAs (circRNAs) are covalently closed RNA circles without a 5' cap or 3' tail. Since the landmark discovery of ciRS-7/CDR1as functioning as a miR-7 sponge in 2013, circRNAs have become a hot topic in RNA research. CircRNAs have been found to play active roles in cancer, cardiovascular diseases, neurological disorders, and many other diseases. They can function as microRNA (miRNA) sponges, protein scaffolds, and even translation templates. However, as circRNA research expands, many divergent views have emerged. For example, are most circRNAs competent in serving as miRNA sponges? What kinds of circRNAs are most likely to sponge miRNAs? Apart from sponging miRNAs, what could the functions of most circRNAs be? What are the features of circRNAs that are translatable? Many researchers have claimed that circRNAs are abundant, stable, conserved, and specific molecules, which hold great potential in serving as biomarkers. However, circRNA abundance is variable and some circRNAs are abundant while others are not. In addition, their stability and conservation may vary under different circumstances. Furthermore, it is unclear whether circRNA biogenesis is more likely to be regulated by RNA binding proteins or transcription factors. All of these are open questions that remain to be answered by researchers in this field. Discussing and investigating these questions will advance the understanding of this class of novel molecules and may propel inspiring new ideas for future studies. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA in Disease and Development > RNA in Disease RNA Methods > RNA Analyses in Cells RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.