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: Natural killer (NK) cell-based therapies represent a promising approach for acute myeloid leukemia (AML) relapse, yet their efficacy is hindered by immunosuppressive factors such as transforming growth factor beta (TGF-β) in the tumor microenvironment. This study investigated the effects of TGF-β on NK cell cytotoxicity and migration using 2D and 3D co-culture models that mimic the leukemic microenvironment. METHODS: TGF-β production was evaluated in AML-derived leukemic cell lines and mesenchymal stromal cells (hTERT-MSCs) using ELISA. Bulk RNA sequencing (RNA-seq) was performed to analyze global gene expression changes in TGF-β-treated primary human NK cells. NK cell cytotoxicity and migration were assessed in 2D monolayer and 3D spheroid co-cultures containing hTERT-MSCs and leukemic cells using flow cytometry and confocal microscopy. RESULTS: Both leukemic cells and MSCs produced TGF-β, with increased levels observed in MSCs after co-culture with primary AML blasts. RNA sequencing revealed that TGF-β altered key gene pathways associated with NK cell cytotoxicity, adhesion, and migration, supporting its immunosuppressive role. In functional assays, TGF-β exposure significantly reduced NK cell-mediated cytotoxicity in a time-dependent manner and impaired NK cell infiltration into 3D spheroids, particularly in models incorporating MSCs. Additionally, MSCs themselves provided a protective environment for leukemic cells, further reducing NK cell effectiveness in 2D co-cultures. CONCLUSION: TGF-β suppresses both NK cell cytotoxicity and migration, limiting their ability to eliminate leukemic cells and infiltrate the bone marrow niche (BMN). These findings provide novel insights into TGF-β-mediated immune evasion mechanisms and provide important insights for the future design of NK-based immunotherapies and clinical trials.

• Keywords
• 3D models, NK cells, TGFbeta, acute myeloid leukemia, bone marrow niche, immunotherapy,
• 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