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      The snoRNA-like lncRNA LNC-SNO49AB drives leukemia by activating the RNA-editing enzyme ADAR1

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          Abstract

          Long noncoding RNAs (lncRNAs) are usually 5′ capped and 3′ polyadenylated, similar to most typical mRNAs. However, recent studies revealed a type of snoRNA-related lncRNA with unique structures, leading to questions on how they are processed and how they work. Here, we identify a novel snoRNA-related lncRNA named LNC-SNO49AB containing two C/D box snoRNA sequences, SNORD49A and SNORD49B; and show that LNC-SNO49AB represents an unreported type of lncRNA with a 5′-end m7G and a 3′-end snoRNA structure. LNC-SNO49AB was found highly expressed in leukemia patient samples, and silencing LNC-SNO49AB dramatically suppressed leukemia progression in vitro and in vivo. Subcellular location indicated that the LNC-SNO49AB is mainly located in nucleolus and interacted with the nucleolar protein fibrillarin. However, we found that LNC-SNO49AB does not play a role in 2′-O-methylation regulation, a classical function of snoRNA; instead, its snoRNA structure affected the lncRNA stability. We further demonstrated that LNC-SNO49AB could directly bind to the adenosine deaminase acting on RNA 1(ADAR1) and promoted its homodimerization followed by a high RNA A-to-I editing activity. Transcriptome profiling shows that LNC-SNO49AB and ADAR1 knockdown respectively share very similar patterns of RNA modification change in downstream signaling pathways, especially in cell cycle pathways. These findings suggest a previously unknown class of snoRNA-related lncRNAs, which function via a manner in nucleolus independently on snoRNA-guide rRNA modification. This is the first report that a lncRNA regulates genome-wide RNA A-to-I editing by enhancing ADAR1 dimerization to facilitate hematopoietic malignancy, suggesting that LNC-SNO49AB may be a novel target in therapy directed to leukemia.

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          Most cited references86

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

            We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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              Regulation of microRNA function in animals

              Since their serendipitous discovery in nematodes, microRNAs (miRNAs) have emerged as key regulators of biological processes in animals. These small RNAs form complex regulatory networks in cell development, differentiation and homeostasis. Deregulation of miRNA function is associated with an increasing number of human diseases, particularly cancer. Recent discoveries have expanded our understanding of how miRNAs are regulated. Here we review the mechanisms that modulate miRNA activity, their stability and their localization through alternative processing, sequence editing, post-translational modifications of Argonaute proteins, viral factors, transport from the cytoplasm and regulation of miRNA–target interactions. We conclude by discussing intriguing open questions to be answered by future research.
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                Author and article information

                Contributors
                wangwt8@mail.sysu.edu.cn
                lsscyq@mail.sysu.edu.cn
                Journal
                Cell Discov
                Cell Discov
                Cell Discovery
                Springer Nature Singapore (Singapore )
                2056-5968
                1 November 2022
                1 November 2022
                2022
                : 8
                : 117
                Affiliations
                [1 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory for Biocontrol, School of Life Sciences, , Sun Yat-sen University, ; Guangzhou, Guangdong China
                [2 ]GRID grid.412615.5, ISNI 0000 0004 1803 6239, The First Affiliated Hospital of Sun Yat-sen University, ; Guangzhou, Guangdong China
                Article
                460
                10.1038/s41421-022-00460-9
                9622897
                36316318
                6cc6cee4-1dd8-4201-b44f-396a839598b7
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 March 2022
                : 18 August 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100011002, National Science Foundation of China | National Natural Science Foundation of China-Yunnan Joint Fund (NSFC-Yunnan Joint Fund);
                Award ID: 82000168
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31870818
                Award Recipient :
                Funded by: National Key R&D Program of China (No. 2021YFA1300502)
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                Article
                Custom metadata
                © The Author(s) 2022

                non-coding rnas,leukaemia
                non-coding rnas, leukaemia

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