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      CircSPI1 acts as an oncogene in acute myeloid leukemia through antagonizing SPI1 and interacting with microRNAs

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          Abstract

          PU.1 (encoded by SPI1) is essential for myeloid development, and inhibition of its expression and activity can lead to acute myeloid leukemia (AML). The precise regulation of PU.1 expression is crucial for the development of AML, and the discovery of circular RNAs (circRNAs) can add a new layer of information on regulation. Here, we found that circSPI1, the circular RNA derived from the SPI1 gene, is highly expressed in AML but not in normal counterparts. Unlike SPI1, a tumor suppressor and being lowly expressed in AML, we demonstrate that circSPI1 acts as an oncogene, evidenced by the observation that circSPI1 knockdown induces myeloid differentiation and apoptosis of AML cells. We provide mechanistic evidence for multiple regulatory roles of circSPI1 in AML progression. On one hand, circSPI1 contributes to myeloid differentiation of AML cells by interacting with the translation initiation factor eIF4AIII to antagonize PU.1 expression at the translation level. On the other hand, circSPI1 contributes to proliferation and apoptosis by interacting with miR-1307-3p, miR-382-5p, and miR-767-5p; this role is uncoupled with SPI1. Finally, we illustrate the clinical significance of circSPI1 by showing that circSPI1-regulated genes are associated with the clinical outcome of AML patients. Our data provide new insight into the complex SPI1 gene regulation now involving circSPI1.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                kankanwang@shsmu.edu.cn
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                19 March 2021
                19 March 2021
                April 2021
                : 12
                : 4
                : 297
                Affiliations
                [1 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, , Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, ; Shanghai, China
                [2 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Sino-French Research Center for Life Sciences and Genomics, Ruijin Hospital, , Shanghai Jiao Tong University School of Medicine, ; Shanghai, China
                Author information
                http://orcid.org/0000-0001-9767-6792
                http://orcid.org/0000-0002-7278-6466
                http://orcid.org/0000-0001-7198-2134
                Article
                3566
                10.1038/s41419-021-03566-2
                7979773
                33741901
                b4b35b5d-469c-429c-a617-6b17c6c53638
                © The Author(s) 2021

                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
                : 10 November 2020
                : 22 February 2021
                : 24 February 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81890994
                Award ID: 81530003
                Award ID: 81911530240
                Award ID: 81770153
                Award ID: 81890994
                Award ID: 81770153
                Award ID: 81530003
                Award ID: 81911530240
                Award ID: 81890994
                Award ID: 81770153
                Award ID: 81530003
                Award ID: 81911530240
                Award ID: 81890994
                Award ID: 81770153
                Award ID: 81530003
                Award ID: 81911530240
                Award Recipient :
                Funded by: This work was supported by the National Key Research and Development Program (No. 2019YFA0905900).
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Cell biology
                mechanisms of disease,transcriptional regulatory elements
                Cell biology
                mechanisms of disease, transcriptional regulatory elements

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