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      Type I and II PRMTs inversely regulate post-transcriptional intron detention through Sm and CHTOP methylation

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          Abstract

          Protein arginine methyltransferases (PRMTs) are required for the regulation of RNA processing factors. Type I PRMT enzymes catalyze mono- and asymmetric dimethylation; Type II enzymes catalyze mono- and symmetric dimethylation. To understand the specific mechanisms of PRMT activity in splicing regulation, we inhibited Type I and II PRMTs and probed their transcriptomic consequences. Using the newly developed Splicing Kinetics and Transcript Elongation Rates by Sequencing (SKaTER-seq) method, analysis of co-transcriptional splicing demonstrated that PRMT inhibition resulted in altered splicing rates. Surprisingly, co-transcriptional splicing kinetics did not correlate with final changes in splicing of polyadenylated RNA. This was particularly true for retained introns (RI). By using actinomycin D to inhibit ongoing transcription, we determined that PRMTs post-transcriptionally regulate RI. Subsequent proteomic analysis of both PRMT-inhibited chromatin and chromatin-associated polyadenylated RNA identified altered binding of many proteins, including the Type I substrate, CHTOP, and the Type II substrate, SmB. Targeted mutagenesis of all methylarginine sites in SmD3, SmB, and SmD1 recapitulated splicing changes seen with Type II PRMT inhibition, without disrupting snRNP assembly. Similarly, mutagenesis of all methylarginine sites in CHTOP recapitulated the splicing changes seen with Type I PRMT inhibition. Examination of subcellular fractions further revealed that RI were enriched in the nucleoplasm and chromatin. Taken together, these data demonstrate that, through Sm and CHTOP arginine methylation, PRMTs regulate the post-transcriptional processing of nuclear, detained introns.

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          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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            We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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              Genome engineering using the CRISPR-Cas9 system.

              Targeted nucleases are powerful tools for mediating genome alteration with high precision. The RNA-guided Cas9 nuclease from the microbial clustered regularly interspaced short palindromic repeats (CRISPR) adaptive immune system can be used to facilitate efficient genome engineering in eukaryotic cells by simply specifying a 20-nt targeting sequence within its guide RNA. Here we describe a set of tools for Cas9-mediated genome editing via nonhomologous end joining (NHEJ) or homology-directed repair (HDR) in mammalian cells, as well as generation of modified cell lines for downstream functional studies. To minimize off-target cleavage, we further describe a double-nicking strategy using the Cas9 nickase mutant with paired guide RNAs. This protocol provides experimentally derived guidelines for the selection of target sites, evaluation of cleavage efficiency and analysis of off-target activity. Beginning with target design, gene modifications can be achieved within as little as 1-2 weeks, and modified clonal cell lines can be derived within 2-3 weeks.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                05 January 2022
                2022
                : 11
                : e72867
                Affiliations
                [1 ] Department of Biochemistry, Albert Einstein College of Medicine Bronx United States
                [2 ] Department of Molecular Pharmacology, Albert Einstein College of Medicine Bronx United States
                [3 ] Department of Cell Biology, Albert Einstein College of Medicine Bronx United States
                University of Pennsylvania United States
                Columbia University United States
                University of Pennsylvania United States
                Author information
                https://orcid.org/0000-0001-7809-5888
                https://orcid.org/0000-0001-5330-0486
                https://orcid.org/0000-0003-0521-8677
                https://orcid.org/0000-0001-9073-6641
                https://orcid.org/0000-0002-7692-2496
                https://orcid.org/0000-0002-8107-0523
                https://orcid.org/0000-0001-9388-6004
                Article
                72867
                10.7554/eLife.72867
                8765754
                34984976
                50ade09e-c57b-41a5-aa53-97b2ed617008
                © 2022, Maron et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 06 August 2021
                : 03 January 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R01GM108646
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R01GM57829
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R01GM134379
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100002590, American Lung Association;
                Award ID: LCD-564723
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006984, Irma T. Hirschl Trust;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: S10OD030286
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: P30CA013330
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Cancer Biology
                Cell Biology
                Custom metadata
                Biochemical, transcriptomic, and proteomic analyses reveal that protein arginine methyltransferases post-transcriptionally regulate intron detention—introns that persist in nuclear polyadenylated RNA—through methylation of RNA splicing and processing factors.

                Life sciences
                prmt5,prmt1,retained detained introns,rna processing,snrnp,a549,detained introns,di,mass spectrometry,ptms,post-translational modifications,transcription,sdma,mma,adma,rme2s,rme2a,rme1,prmts,carm1,prmt4,protein arginine methyltransferases,splicing,post-transcriptional processing

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