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      Genetic Spectrum of Idiopathic Restrictive Cardiomyopathy Uncovered by Next-Generation Sequencing

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          Abstract

          Background

          Cardiomyopathies represent a rare group of disorders often of genetic origin. While approximately 50% of genetic causes are known for other types of cardiomyopathies, the genetic spectrum of restrictive cardiomyopathy (RCM) is largely unknown. The aim of the present study was to identify the genetic background of idiopathic RCM and to compile the obtained genetic variants to the novel signalling pathways using in silico protein network analysis.

          Patients and Methods

          We used Illumina MiSeq setup to screen for 108 cardiomyopathy and arrhythmia-associated genes in 24 patients with idiopathic RCM. Pathogenicity of genetic variants was classified according to American College of Medical Genetics and Genomics classification.

          Results

          Pathogenic and likely-pathogenic variants were detected in 13 of 24 patients resulting in an overall genotype-positive rate of 54%. Half of the genotype-positive patients carried a combination of pathogenic, likely-pathogenic variants and variants of unknown significance. The most frequent combination included mutations in sarcomeric and cytoskeletal genes (38%). A bioinformatics approach underlined the mechanotransducing protein networks important for RCM pathogenesis.

          Conclusions

          Multiple gene mutations were detected in half of the RCM cases, with a combination of sarcomeric and cytoskeletal gene mutations being the most common. Mutations of genes encoding sarcomeric, cytoskeletal, and Z-line-associated proteins appear to have a predominant role in the development of RCM.

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          Most cited references 24

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          dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs.

          The purpose of the dbNSFP is to provide a one-stop resource for functional predictions and annotations for human nonsynonymous single-nucleotide variants (nsSNVs) and splice-site variants (ssSNVs), and to facilitate the steps of filtering and prioritizing SNVs from a large list of SNVs discovered in an exome-sequencing study. A list of all potential nsSNVs and ssSNVs based on the human reference sequence were created and functional predictions and annotations were curated and compiled for each SNV. Here, we report a recent major update of the database to version 3.0. The SNV list has been rebuilt based on GENCODE 22 and currently the database includes 82,832,027 nsSNVs and ssSNVs. An attached database dbscSNV, which compiled all potential human SNVs within splicing consensus regions and their deleteriousness predictions, add another 15,030,459 potentially functional SNVs. Eleven prediction scores (MetaSVM, MetaLR, CADD, VEST3, PROVEAN, 4× fitCons, fathmm-MKL, and DANN) and allele frequencies from the UK10K cohorts and the Exome Aggregation Consortium (ExAC), among others, have been added. The original seven prediction scores in v2.0 (SIFT, 2× Polyphen2, LRT, MutationTaster, MutationAssessor, and FATHMM) as well as many SNV and gene functional annotations have been updated. dbNSFP v3.0 is freely available at http://sites.google.com/site/jpopgen/dbNSFP.
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            Hypertrophic cardiomyopathy: distribution of disease genes, spectrum of mutations, and implications for a molecular diagnosis strategy.

            Hypertrophic cardiomyopathy is an autosomal-dominant disorder in which 10 genes and numerous mutations have been reported. The aim of the present study was to perform a systematic screening of these genes in a large population, to evaluate the distribution of the disease genes, and to determine the best molecular strategy in clinical practice. The entire coding sequences of 9 genes (MYH7, MYBPC3, TNNI3, TNNT2, MYL2, MYL3, TPM1, ACTC, andTNNC1) were analyzed in 197 unrelated index cases with familial or sporadic hypertrophic cardiomyopathy. Disease-causing mutations were identified in 124 index patients ( approximately 63%), and 97 different mutations, including 60 novel ones, were identified. The cardiac myosin-binding protein C (MYBPC3) and beta-myosin heavy chain (MYH7) genes accounted for 82% of families with identified mutations (42% and 40%, respectively). Distribution of the genes varied according to the prognosis (P=0.036). Moreover, a mutation was found in 15 of 25 index cases with "sporadic" hypertrophic cardiomyopathy (60%). Finally, 6 families had patients with more than one mutation, and phenotype analyses suggested a gene dose effect in these compound-heterozygous, double-heterozygous, or homozygous patients. These results might have implications for genetic diagnosis strategy and, subsequently, for genetic counseling. First, on the basis of this experience, the screening of already known mutations is not helpful. The analysis should start by testing MYBPC3 and MYH7 and then focus on TNNI3, TNNT2, and MYL2. Second, in particularly severe phenotypes, several mutations should be searched. Finally, sporadic cases can be successfully screened.
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              Content-rich biological network constructed by mining PubMed abstracts

               Hao Chen,  Burt Sharp (2004)
              Background The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public. Results We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses. Conclusions Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot can be accessed free of charge to academic users.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                23 September 2016
                2016
                : 11
                : 9
                Affiliations
                [1 ]Almazov Federal Medical Research Centre, St. Petersburg, 197341, Russia
                [2 ]Department of Women’s and Children’s Health and Centre for Molecular Medicine, Karolinska Institute, Stockholm, 17176, Sweden
                [3 ]St. Petersburg State Polytechnical University, St. Petersburg, 195251, Russia
                [4 ]Department of BioinformRatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, 85354, Freising, Germany
                [5 ]Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
                [6 ]Institutite of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, 17176, Sweden
                [7 ]Department of Faculty Therapy, Pavlov Medical University, St. Petersburg, 197022, Russia
                [8 ]ITMO University, Institute of Translational Medicine, St. Petersburg, 197101, Russia
                [9 ]National Institute for Health Development, Tallinn, Estonia
                Indiana University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: A Kostareva ES DF IG TS.

                • Data curation: A Kostareva.

                • Formal analysis: GS A Klyushina.

                • Investigation: TV AG TP.

                • Methodology: A Kiselev NS GF.

                • Resources: ES A Kozlenok TS.

                • Software: DN ST AR A Kiselev.

                • Validation: A Kostareva AZ TK.

                • Visualization: TP AG GF DN.

                • Writing – original draft: A Kostareva NS TS GF.

                • Writing – review & editing: A Klyushina GS.

                Article
                PONE-D-16-01559
                10.1371/journal.pone.0163362
                5035084
                27662471
                © 2016 Kostareva 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.

                Page count
                Figures: 2, Tables: 2, Pages: 16
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100009409, Russian Science Support Foundation;
                Award ID: 14-15-00745
                Award Recipient :
                Funded by: ALF Karolinska Institutet/Stockholm country
                Award Recipient :
                Funded by: Government of Russian Federation
                Award ID: Grant 074-U01
                Award Recipient :
                This work was supported by Russian Science Foundation grant number 14-15-00745 (Anna Kostareva); ALF Karolinska Institutet/Stockholm country (Thomas Sejersen); and Government of Russian Federation, Grant 074-U01 (Anna Kostareva).
                Categories
                Research Article
                Medicine and Health Sciences
                Cardiology
                Cardiomyopathies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Cell Membranes
                Membrane Proteins
                Computer and Information Sciences
                Network Analysis
                Protein Interaction Networks
                Biology and Life Sciences
                Biochemistry
                Proteomics
                Protein Interaction Networks
                Biology and Life Sciences
                Biochemistry
                Proteins
                Cytoskeletal Proteins
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Cytoskeleton
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Next-Generation Sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Next-Generation Sequencing
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Next-Generation Sequencing
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Next-Generation Sequencing
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Variant Genotypes
                Custom metadata
                All relevant data are within the paper and its Supporting Information files. All deep sequencing data are available on http://www.ncbi.nlm.nih.gov/bioproject/PRJNA326163.

                Uncategorized

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