Circular RNAs play a crucial role in cell development and serve as biomarkers in many diseases. Nevertheless, the function of many circular RNAs remains unknown. This function can be inferred from sponging and silencing interactions with micro RNAs and messenger RNAs. We recently proposed a network-based circRNA functional annotation tool, circGPA. However, validation data for RNA interactions are often sparse and predicted interactions contain many false positives. To address this issue, we propose an extended algorithm named circGPAcorr, which uses expression data to weight the interactions, resulting in more precise functional annotation. To assess the significance of the results, the p-value is calculated using reduction to circGPA, a generating-polynomial-based method. We show that the problem is #P-hard, and thus computationally difficult. The circGPAcorr algorithm is tested on publicly available myelodysplastic syndromes expression data, providing gene ontology annotations that align with the literature on myelodysplastic syndromes. At the same time, we demonstrate its performance in the circRNA-disease annotation task.

• Keywords
• CircRNA, Functional annotation, Gene expression, Generating polynomial,
• Publication type
• Journal Article MeSH

BACKGROUND: Current experimental data on RNA interactions remain limited, particularly for non-coding RNAs, many of which have only recently been discovered and operate within complex regulatory networks. Researchers often rely on in-silico interaction detection algorithms, such as TargetScan, which are based on biochemical sequence alignment. However, these algorithms have limited performance. RNA-seq expression data can provide valuable insights into regulatory networks, especially for understudied interactions such as circRNA-miRNA-mRNA. By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy. RESULTS: This paper introduces Pi-GMIFS, an extension of the generalized monotone incremental forward stagewise (GMIFS) regression algorithm that incorporates prior knowledge. The algorithm first estimates prior response values through a prior-only regression, interpolates between these prior values and the original data, and then applies the GMIFS method. Our experimental results on circRNA-miRNA-mRNA regulatory interaction networks demonstrate that Pi-GMIFS consistently enhances precision and recall in RNA interaction prediction by leveraging implicit information from bulk RNA-seq expression data, outperforming the initial prior knowledge. CONCLUSION: Pi-GMIFS is a robust algorithm for inferring acyclic interaction networks when the variable ordering is known. Its effectiveness was confirmed through extensive experimental validation. We proved that RNA-seq data of a representative size help infer previously unknown interactions available in TarBase v9 and improve the quality of circRNA disease annotation.

• Keywords
• Bayesian network, Circular RNA, Functional annotation, Penalized regression, Structure inference,
• MeSH
• Algorithms MeSH
• Gene Regulatory Networks MeSH
• RNA, Circular * genetics   metabolism   MeSH
• Humans MeSH
• Linear Models MeSH
• RNA, Messenger * genetics   metabolism   MeSH
• MicroRNAs * genetics   metabolism   MeSH
• Sequence Analysis, RNA methods   MeSH
• RNA-Seq * methods   MeSH
• Computational Biology methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• RNA, Circular * MeSH
• RNA, Messenger * MeSH
• MicroRNAs * MeSH

Mutations in the splicing factor 3b subunit 1 (SF3B1) gene are frequent in myelodysplastic neoplasms (MDS). Because the splicing process is involved in the production of circular RNAs (circRNAs), we investigated the impact of SF3B1 mutations on circRNA processing. Using RNA sequencing, we measured circRNA expression in CD34+ bone marrow MDS cells. We defined circRNAs deregulated in a heterogeneous group of MDS patients and described increased circRNA formation in higher-risk MDS. We showed that the presence of SF3B1 mutations did not affect the global production of circRNAs; however, deregulation of specific circRNAs was observed. Particularly, we demonstrated that strong upregulation of circRNAs processed from the zinc finger E-box binding homeobox 1 (ZEB1) transcription factor; this upregulation was exclusive to SF3B1-mutated patients and was not observed in those with mutations in other splicing factors or other recurrently mutated genes, or with other clinical variables. Furthermore, we focused on the most upregulated ZEB1-circRNA, hsa_circ_0000228, and, by its knockdown, we demonstrated that its expression is related to mitochondrial activity. Using microRNA analyses, we proposed miR-1248 as a direct target of hsa_circ_0000228. To conclude, we demonstrated that mutated SF3B1 leads to deregulation of ZEB1-circRNAs, potentially contributing to the defects in mitochondrial metabolism observed in SF3B1-mutated MDS.

• Keywords
• SF3B1, ZEB1, circular RNA, myelodysplastic neoplasms, splicing,
• MeSH
• Leukemia, Myeloid, Acute * MeSH
• Phosphoproteins genetics   MeSH
• RNA, Circular genetics   MeSH
• Humans MeSH
• Mutation genetics   MeSH
• Myelodysplastic Syndromes * genetics   MeSH
• RNA Splicing Factors genetics   MeSH
• Transcription Factors genetics   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Phosphoproteins MeSH
• RNA, Circular MeSH
• RNA Splicing Factors MeSH
• SF3B1 protein, human MeSH Browser
• Transcription Factors MeSH