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      Altered Electrical, Biomolecular, and Immunologic Phenotypes in a Novel Patient-Derived Stem Cell Model of Desmoglein-2 Mutant ARVC

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          Abstract

          Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a progressive heart condition which causes fibro-fatty myocardial scarring, ventricular arrhythmias, and sudden cardiac death. Most cases of ARVC can be linked to pathogenic mutations in the cardiac desmosome, but the pathophysiology is not well understood, particularly in early phases when arrhythmias can develop prior to structural changes. Here, we created a novel human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) model of ARVC from a patient with a c.2358delA variant in desmoglein-2 ( DSG2). These DSG2-mutant ( DSG2 Mut) hiPSC-CMs were compared against two wildtype hiPSC-CM lines via immunostaining, RT-qPCR, Western blot, RNA-Seq, cytokine expression and optical mapping. Mutant cells expressed reduced DSG2 mRNA and had altered localization of desmoglein-2 protein alongside thinner, more disorganized myofibrils. No major changes in other desmosomal proteins were noted. There was increased pro-inflammatory cytokine expression that may be linked to canonical and non-canonical NFκB signaling. Action potentials in DSG2 Mut CMs were shorter with increased upstroke heterogeneity, while time-to-peak calcium and calcium decay rate were reduced. These were accompanied by changes in ion channel and calcium handling gene expression. Lastly, suppressing DSG2 in control lines via siRNA allowed partial recapitulation of electrical anomalies noted in DSG2 Mut cells. In conclusion, the aberrant cytoskeletal organization, cytokine expression, and electrophysiology found DSG2 Mut hiPSC-CMs could underlie early mechanisms of disease manifestation in ARVC patients.

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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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              Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

              The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                10 July 2021
                July 2021
                : 10
                : 14
                : 3061
                Affiliations
                [1 ]Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; rhawtho4@ 123456jhmi.edu (R.N.H.); ablazesk@ 123456mit.edu (A.B.); jilowenthal@ 123456jhmi.edu (J.L.); skannan4@ 123456jhmi.edu (S.K.); rteuben1@ 123456jhmi.edu (R.T.); jmorri65@ 123456jhmi.edu (J.M.-M.); kbohele1@ 123456jhmi.edu (K.R.B.)
                [2 ]Medical Scientist Training Program, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
                [3 ]Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; ddisilvestre@ 123456som.umaryland.edu (D.D.); cjames7@ 123456jhmi.edu (C.A.J.)
                [4 ]Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA; jsaffitz@ 123456bidmc.harvard.edu
                [5 ]Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, FL 32306, USA
                [6 ]Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA
                Author notes
                [* ]Correspondence: stephen.chelko@ 123456med.fsu.edu (S.P.C.); gordon.tomaselli@ 123456einsteinmed.org (G.T.); ltung@ 123456jhu.edu (L.T.); Tel.: +1-850-644-2215 (S.P.C.); +1-718-430-2801 (G.T.); +1-410-955-9603 (L.T.)
                Author information
                https://orcid.org/0000-0002-4454-5753
                https://orcid.org/0000-0001-5909-0520
                https://orcid.org/0000-0001-8040-4600
                https://orcid.org/0000-0003-1675-5945
                Article
                jcm-10-03061
                10.3390/jcm10143061
                8306340
                34300226
                3349e7a5-d195-40df-ab95-748801421bae
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 29 May 2021
                : 08 July 2021
                Categories
                Article

                arrhythmogenic cardiomyopathy,arrhythmogenic right ventricular cardiomyopathy,arvc,desmoglein-2,nfκb signaling,patient-derived stem cells,induced pluripotent stem cells

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