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      Recurrent SRSF2 mutations in MDS affect both splicing and NMD

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          Abstract

          Here, Rahman et al. looked for a specific role of mutant SRSF2 in nonsense-mediated mRNA decay (NMD). They show that SRSF2 Pro95 hot spot mutations elicit enhanced mRNA decay, which is dependent on sequence-specific RNA binding and splicing, and thus provide new insight into the critical effects of SRSF2 mutants in hematologic malignancies.

          Abstract

          Oncogenic mutations in the RNA splicing factors SRSF2, SF3B1, and U2AF1 are the most frequent class of mutations in myelodysplastic syndromes and are also common in clonal hematopoiesis, acute myeloid leukemia, chronic lymphocytic leukemia, and a variety of solid tumors. They cause genome-wide splicing alterations that affect important regulators of hematopoiesis. Several mRNA isoforms promoted by the various splicing factor mutants comprise a premature termination codon (PTC) and are therefore potential targets of nonsense-mediated mRNA decay (NMD). In light of the mechanistic relationship between splicing and NMD, we sought evidence for a specific role of mutant SRSF2 in NMD. We show that SRSF2 Pro95 hot spot mutations elicit enhanced mRNA decay, which is dependent on sequence-specific RNA binding and splicing. SRSF2 mutants enhance the deposition of exon junction complexes (EJCs) downstream from the PTC through RNA-mediated molecular interactions. This architecture then favors the association of key NMD factors to elicit mRNA decay. Gene-specific blocking of EJC deposition by antisense oligonucleotides circumvents aberrant NMD promoted by mutant SRSF2, restoring the expression of PTC-containing transcript. Our study uncovered critical effects of SRSF2 mutants in hematologic malignancies, reflecting the regulation at multiple levels of RNA metabolism, from splicing to decay.

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          Most cited references60

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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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            MEME Suite: tools for motif discovery and searching

            The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://meme.nbcr.net.
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              Probability-based protein identification by searching sequence databases using mass spectrometry data

              Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.
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                Author and article information

                Journal
                Genes Dev
                Genes Dev
                genesdev
                genesdev
                GAD
                Genes & Development
                Cold Spring Harbor Laboratory Press
                0890-9369
                1549-5477
                1 March 2020
                : 34
                : 5-6
                : 413-427
                Affiliations
                [1 ]Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;
                [2 ]Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA;
                [3 ]Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA;
                [4 ]Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
                Author notes
                Corresponding author: krainer@ 123456cshl.edu
                Article
                8711660
                10.1101/gad.332270.119
                7050488
                32001512
                24caf0ab-1881-4182-8438-3871eb8ad04f
                © 2020 Rahman et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genesdev.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 1 September 2019
                : 20 December 2019
                Page count
                Pages: 15
                Funding
                Funded by: National Cancer Institute , open-funder-registry 10.13039/100000054;
                Award ID: P01-CA13106
                Funded by: National Instiutes of Health/National Heart, Lung, and Blood Institute
                Award ID: R01-HL128239
                Funded by: Department of Defense Bone Marrow Failure Research Program
                Award ID: W81XWH-16-1-0059
                Funded by: Starr Foundation , open-funder-registry 10.13039/100009784;
                Award ID: I8-A8-075
                Funded by: Henry and Marilyn Taub Foundation , open-funder-registry 10.13039/100013730;
                Funded by: Leukemia and Lymphoma Society , open-funder-registry 10.13039/100005189;
                Funded by: Pershing Square Sohn Cancer Research Alliance
                Funded by: NCI Cancer Center Support Grant
                Award ID: 5P30CA045508
                Categories
                Research Paper

                leukemia,mutation,myelodysplastic syndromes,nonsense-mediated mrna decay,srsf2,splicing,splicing factor

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