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      Independent transcriptomic and proteomic regulation by type I and II protein arginine methyltransferases

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          Summary

          Protein arginine methyltransferases (PRMTs) catalyze the post-translational monomethylation (Rme1), asymmetric (Rme2a), or symmetric (Rme2s) dimethylation of arginine. To determine the cellular consequences of type I (Rme2a) and II (Rme2s) PRMTs, we developed and integrated multiple approaches. First, we determined total cellular dimethylarginine levels, revealing that Rme2s was ∼3% of total Rme2 and that this percentage was dependent upon cell type and PRMT inhibition status. Second, we quantitatively characterized in vitro substrates of the major enzymes and expanded upon PRMT substrate recognition motifs. We also compiled our data with publicly available methylarginine-modified residues into a comprehensive database. Third, we inhibited type I and II PRMTs and performed proteomic and transcriptomic analyses to reveal their phenotypic consequences. These experiments revealed both overlapping and independent PRMT substrates and cellular functions. Overall, this study expands upon PRMT substrate diversity, the arginine methylome, and the complex interplay of type I and II PRMTs.

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          Highlights

          • Rapid and sensitive direct-injection MS/MS dimethylarginine quantification

          • Quantitative methyltransferase assays on oriented peptide array libraries (OPALs)

          • PTMScan, proteomic, transcriptomic, and phenotypic analysis of PRMT inhibition

          • Compilation of our data and publicly available arginine methylome data

          Abstract

          Molecular biology; Cell biology

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          Most cited references107

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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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            Fiji: an open-source platform for biological-image analysis.

            Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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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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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                11 August 2021
                24 September 2021
                11 August 2021
                : 24
                : 9
                : 102971
                Affiliations
                [1 ]Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY 10461, USA
                [2 ]Department of Chemistry, University of Virginia, Charlottesville, VA 22904, USA
                [3 ]Departments of Chemistry and Pathology, University of Virginia, Charlottesville, VA 22904, USA
                [4 ]Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX, USA
                [5 ]Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Smithville, TX, USA
                [6 ]Graduate Program in Genetics and Epigenetics, The University of Texas MD Anderson UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA
                [7 ]Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
                [8 ]EpiCypher, Inc., Research Triangle Park, NC 27709, USA
                Author notes
                []Corresponding author hongshanchen@ 123456njmu.edu.cn
                [∗∗ ]Corresponding author dfh@ 123456virginia.edu
                [∗∗∗ ]Corresponding author david.shechter@ 123456einsteinmed.org
                [9]

                Present address: Department of Biochemistry and Biophysics and Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA

                [10]

                Present address: Key Laboratory of Cardiovascular & Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, China, 211166

                [11]

                Present address: Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL 60611

                [12]

                These authors contributed equally

                [13]

                Lead contact

                Article
                S2589-0042(21)00939-1 102971
                10.1016/j.isci.2021.102971
                8417332
                34505004
                1c6b6609-c8a0-4fc3-b8be-5bfa123df73d
                © 2021 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 25 June 2020
                : 21 June 2021
                : 9 August 2021
                Categories
                Article

                molecular biology,cell biology
                molecular biology, cell biology

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