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      A genetic screen in C. elegans reveals roles for KIN17 and PRCC in maintaining 5’ splice site identity

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          Abstract

          Pre-mRNA splicing is an essential step of eukaryotic gene expression carried out by a series of dynamic macromolecular protein/RNA complexes, known collectively and individually as the spliceosome. This series of spliceosomal complexes define, assemble on, and catalyze the removal of introns. Molecular model snapshots of intermediates in the process have been created from cryo-EM data, however, many aspects of the dynamic changes that occur in the spliceosome are not fully understood. Caenorhabditis elegans follow the GU-AG rule of splicing, with almost all introns beginning with 5’ GU and ending with 3’ AG. These splice sites are identified early in the splicing cycle, but as the cycle progresses and “custody” of the pre-mRNA splice sites is passed from factor to factor as the catalytic site is built, the mechanism by which splice site identity is maintained or re-established through these dynamic changes is unclear. We performed a genetic screen in C. elegans for factors that are capable of changing 5’ splice site choice. We report that KIN17 and PRCC are involved in splice site choice, the first functional splicing role proposed for either of these proteins. Previously identified suppressors of cryptic 5’ splicing promote distal cryptic GU splice sites, however, mutations in KIN17 and PRCC instead promote usage of an unusual proximal 5’ splice site which defines an intron beginning with UU, separated by 1nt from a GU donor. We performed high-throughput mRNA sequencing analysis and found that mutations in PRCC, and to a lesser extent KIN17, changed alternative 5’ splice site usage at native sites genome-wide, often promoting usage of nearby non-consensus sites. Our work has uncovered both fine and coarse mechanisms by which the spliceosome maintains splice site identity during the complex assembly process.

          Author summary

          Pre-messenger RNA splicing is an important regulator of eukaryotic gene expression, changing the content, frame, and functionality of both coding and non-coding transcripts. Our understanding of how the spliceosome chooses where to cut has focused on the initial identification of splice sites. However, our results suggest that the spliceosome also relies on other components in later steps to maintain the identity of the splice donor sites. We are currently in the midst of a “resolution revolution”, with ever-clearer cryo-EM snapshots of stalled complexes, allowing researchers to visualize moments in time in the splicing cycle. These models are illuminating, but do not always elucidate mechanistic functioning of a highly dynamic ribonucleoprotein complex. Therefore, our lab takes a complementary approach, using the power of genetics in a multicellular animal to gain functional insights into the spliceosome. Using a C. elegans genetic screen, we have found novel functional splicing roles for two proteins, KIN17 and PRCC. Mutations in PRCC in particular promote nearby alternative 5’ splice sites at native loci. This work improves our understanding of how the spliceosome maintains the identity of where to cut the pre-mRNA, and thus how genes are expressed and used in multicellular animals.

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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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            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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              Highly accurate protein structure prediction with AlphaFold

              Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: ValidationRole: Visualization
                Role: InvestigationRole: Validation
                Role: Data curationRole: InvestigationRole: SoftwareRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                10 February 2022
                February 2022
                : 18
                : 2
                : e1010028
                Affiliations
                [1 ] Center for Molecular Biology of RNA, Department of Molecular Cell Developmental Biology, University of California, Santa Cruz, California, United States of America
                [2 ] UCSC Genomics Institute, University of California, Santa Cruz, Santa Cruz, California, United States of America
                Ohio State University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-9222-6749
                https://orcid.org/0000-0002-7086-5224
                https://orcid.org/0000-0002-2682-0328
                https://orcid.org/0000-0002-6757-5621
                https://orcid.org/0000-0002-1787-1246
                https://orcid.org/0000-0003-0027-1647
                Article
                PGENETICS-D-21-00896
                10.1371/journal.pgen.1010028
                8865678
                35143478
                d221ccf8-cdb4-4bb5-8e6f-385fce42a611
                © 2022 Suzuki et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 1 July 2021
                : 10 January 2022
                Page count
                Figures: 8, Tables: 2, Pages: 33
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000152, division of molecular and cellular biosciences;
                Award ID: MCB-1613867
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, national institute of general medical sciences;
                Award ID: 5R01GM135221
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, national institute of general medical sciences;
                Award ID: 5T32GM133391
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: T34GM140956
                Award Recipient :
                Research in the Zahler lab was previously supported by a grant from the National Science Foundation (MCB-1613867 to AMZ) and is currently supported by a grant from the National Institutes of Health (5R01GM135221 to AMZ). JMNGLS was supported by the UCSC MCD Graduate Training Grant (5T32GM133391). DRG is supported by the UCSC MARC Program (T34GM140956). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Biology and Life Sciences
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                Genome Complexity
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                2022-02-23
                Raw fastq reads from the high throughput mRNASeq experiments are available now at GEO GSE178335. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE178335 DNA Sequences of raw, ENU mutagenized suppressor strains deposited at the NCBI Sample Read Archive as BioProject PRJNA778860 https://www.ncbi.nlm.nih.gov/sra/PRJNA778860.

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