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Interferon-alpha competing endogenous RNA network antagonizes microRNA-1270.

ABSTRACT: A new form of circuitry for gene regulation has been identified in which RNAs can crosstalk by competing for shared microRNAs (miRNAs). Such competing endogenous RNAs (ceRNAs) form a network via shared miRNA response elements (MREs) to antagonize miRNA function. We previously reported natural antisense RNA (AS) as an important modulator of interferon-α1 (IFN-α1) mRNA levels by promoting IFN-α1 mRNA stability. We show that IFN-α1 AS forms a ceRNA network with specific IFN-α AS (IFN-α7/-α8/-α10/-α14) and mRNA (IFN-α8/-α10/-α14/-α17) subtypes from the IFN-α gene (IFNA) family to antagonize miRNA-1270 (miR-1270), thereby modulating IFN-α1 mRNA levels. Bioinformatic analysis demonstrated that IFN-α1 AS harbors multiple miR-1270 MREs (MRE-1270s), whose presence was substantiated by miR-1270 overexpression and transfection of antimiR-1270. The antimiR-1270, complementary to the miR-1270 seed region, revealed that IFN-α1 AS likely shares the MRE-1270 with IFN-α1 mRNA and specific IFN-α AS and mRNA subtypes. Subsequent bioinformatic analysis for MRE-1270s showed that IFN-α1 AS and other RNA subtypes shared the 6-mer MRE-1270 site. Further MRE-mapping demonstrated that the total number of MRE-1270s in IFN-α1 AS accounted for approximately 30 % of the miR-1270 population. AntimiR-1270 transfection also caused specific de-repression of five cellular mRNAs, including that of CAPRIN1. These results suggest that IFN-α1 AS, together with specific IFN-α AS and mRNA subtypes, as well as the five cellular mRNAs, participate as competing molecules in the ceRNA network against miR-1270. This coordinated regulatory architecture suggests a vital function for the innate immune system in maintaining precise physiological type I IFN levels via post-transcriptional regulatory mechanisms.

SUBMITTER: Kimura T

PROVIDER: S-EPMC4477080 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOsteoarthritis (OA) is one of the most common diseases in elderly people; however, the correlation between molecular alterations and the occurrence and progression of OA are still not well understood. We conducted this study to investigate the molecular changes in OA via the competing endogenous ribonucleic acid (ceRNA) network.MethodsWe downloaded the messenger RNA (mRNA) data set, GSE48556, the microRNA (miRNA) data set, GSE105027, and the long non-coding (lncRNA) data set, GSE126963 from the Gene Expression Omnibus (GEO) database, and examined the differentially expressed genes (DEGs) in these data sets. Further, we constructed a ceRNA network of the differentially expressed miRNAs, mRNAs, and lncRNAs. To determine the biological functions of the ceRNA network, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Finally, we conducted an immune cell infiltration analysisusing single-sample gene set enrichment analysis to examine the abundance of immune cells in healthy and OA patients, and compared the infiltration of 28 immune cells between the healthy and OA samples. We also analyzed the relationship between the abundance of immune cells and mRNA expression levels in the ceRNA network.ResultsUltimately hsa-mir-425-3p, dual specificity phosphatase 1, and 24 lncRNAs were identified in the ceRNA network. The functional enrichment analyses showed that these lncRNAs, miRNAs, and mRNAs are involved in various significant biological process, such as the regulation of leukocyte migration, Mitogen-Activated Protein (MAP) kinase tyrosine/serine/threonine phosphatase activity, the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway, and osteoclast differentiation, and can also have a strong effect on immune cell infiltration.ConclusionsThe dual-specificity phosphatase 1-specific ceRNA network can be used as a diagnostic tool to assess the progression of OA patients.

Project description:The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA‑sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE‑miRNAs), long non‑coding RNAs (DE‑lncRNAs) and genes (DEGs). Subsequently, lncRNA‑miRNA, miRNA‑gene (that is, miRNA‑mRNA) and gene‑gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA‑mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE‑lncRNAs, 917 DEGs and 30 DE‑miRNAs were screened. The miRNA‑mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand‑receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine‑cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA‑206 and kallikrein‑related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.

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Interferon-alpha competing endogenous RNA network antagonizes microRNA-1270.

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