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      QKI is a critical pre-mRNA alternative splicing regulator of cardiac myofibrillogenesis and contractile function

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          Abstract

          The RNA-binding protein QKI belongs to the hnRNP K-homology domain protein family, a well-known regulator of pre-mRNA alternative splicing and is associated with several neurodevelopmental disorders. Qki is found highly expressed in developing and adult hearts. By employing the human embryonic stem cell (hESC) to cardiomyocyte differentiation system and generating QKI-deficient hESCs (hESCs- QKI del ) using CRISPR/Cas9 gene editing technology, we analyze the physiological role of QKI in cardiomyocyte differentiation, maturation, and contractile function. hESCs- QKI del largely maintain normal pluripotency and normal differentiation potential for the generation of early cardiogenic progenitors, but they fail to transition into functional cardiomyocytes. In this work, by using a series of transcriptomic, cell and biochemical analyses, and the Qki-deficient mouse model, we demonstrate that QKI is indispensable to cardiac sarcomerogenesis and cardiac function through its regulation of alternative splicing in genes involved in Z-disc formation and contractile physiology, suggesting that QKI is associated with the pathogenesis of certain forms of cardiomyopathies.

          Abstract

          RNA binding protein Quaking (QKI) is known for its broad function in pre-mRNA splicing and modification and its association with several neurodevelopmental disorders. Here the authors reveal that QKI-mediated regulation of RNA splicing is indispensable to cardiac development and contractile physiology.

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          Most cited references81

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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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            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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                gyhuang@shmu.edu.cn
                wshou@iu.edu
                sunning@fudan.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                4 January 2021
                4 January 2021
                2021
                : 12
                : 89
                Affiliations
                [1 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Physiology and Pathophysiology, School of Basic Medical Sciences, , Fudan University, ; Shanghai, China
                [2 ]GRID grid.411333.7, ISNI 0000 0004 0407 2968, Shanghai Key Laboratory of Birth Defects, , Children’s Hospital of Fudan University, ; Shanghai, China
                [3 ]GRID grid.257413.6, ISNI 0000 0001 2287 3919, Herman B Wells Center for Pediatric Research, Department of Pediatrics, , Indiana University School of Medicine, ; Indianapolis, IN USA
                [4 ]GRID grid.257413.6, ISNI 0000 0001 2287 3919, Department of Bioinformatics, , Indiana University School of Medicine, ; Indianapolis, IN USA
                [5 ]Institute of Materia Medica and Center of Translational Medicine, College of Pharmacy, Army Medical University, Chongqing, China
                [6 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Center for Stem Cell Biology and Regenerative Medicine, School of Medicine, , Tsinghua University, ; Beijing, China
                [7 ]Institute of Resource Development and Analysis, Kumanoto University, Kumanoto, Japan
                Author information
                http://orcid.org/0000-0001-9015-9864
                http://orcid.org/0000-0001-9653-9227
                http://orcid.org/0000-0001-6002-2226
                http://orcid.org/0000-0001-5538-9605
                http://orcid.org/0000-0002-7861-3646
                Article
                20327
                10.1038/s41467-020-20327-5
                7782589
                33397958
                0b08537b-62e7-4a86-9af2-fad3034e9d64
                © 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
                : 22 December 2019
                : 27 November 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81500241
                Award ID: 31571527
                Award Recipient :
                Funded by: National Children’s Medical Center EK1125180102
                Funded by: FundRef https://doi.org/10.13039/100000050, U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI);
                Award ID: P01HL134599
                Award ID: R01HL145060
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                rna,heart development,cardiology
                Uncategorized
                rna, heart development, cardiology

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