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      Altered microRNA and mRNA profiles during heart failure in the human sinoatrial node

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          Abstract

          Heart failure (HF) is frequently accompanied with the sinoatrial node (SAN) dysfunction, which causes tachy-brady arrhythmias and increased mortality. MicroRNA (miR) alterations are associated with HF progression. However, the transcriptome of HF human SAN, and its role in HF-associated remodeling of ion channels, transporters, and receptors responsible for SAN automaticity and conduction impairments is unknown. We conducted comprehensive high-throughput transcriptomic analysis of pure human SAN primary pacemaker tissue and neighboring right atrial tissue from human transplanted HF hearts (n = 10) and non-failing (nHF) donor hearts (n = 9), using next-generation sequencing. Overall, 47 miRs and 832 mRNAs related to multiple signaling pathways, including cardiac diseases, tachy-brady arrhythmias and fibrosis, were significantly altered in HF SAN. Of the altered miRs, 27 are predicted to regulate mRNAs of major ion channels and neurotransmitter receptors which are involved in SAN automaticity (e.g. HCN1, HCN4, SLC8A1) and intranodal conduction (e.g. SCN5A, SCN8A) or both (e.g. KCNJ3, KCNJ5). Luciferase reporter assays were used to validate interactions of miRs with predicted mRNA targets. In conclusion, our study provides a profile of altered miRs in HF human SAN, and a novel transcriptome blueprint to identify molecular targets for SAN dysfunction and arrhythmia treatments in HF.

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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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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              Heart Disease and Stroke Statistics—2020 Update

              Circulation
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                Author and article information

                Contributors
                vadim.fedorov@osumc.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                29 September 2021
                29 September 2021
                2021
                : 11
                : 19328
                Affiliations
                [1 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Department of Physiology and Cell Biology, , The Ohio State University College of Medicine and Wexner Medical Center, ; Columbus, OH 43210-1218 USA
                [2 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Bob and Corrine Frick Center for Heart Failure and Arrhythmia, Dorothy M. Davis Heart & Lung Research Institute, , The Ohio State University College of Medicine and Wexner Medical Center, ; Columbus, OH USA
                [3 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Biomedical Informatics Shared Resources, , The Ohio State University College of Medicine and Wexner Medical Center, ; Columbus, OH USA
                [4 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Department of Surgery, Division of Cardiac Surgery, , The Ohio State University College of Medicine and Wexner Medical Center, ; Columbus, OH USA
                [5 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Department of Internal Medicine, Division of Cardiovascular Medicine, , The Ohio State University College of Medicine and Wexner Medical Center, ; Columbus, OH USA
                [6 ]GRID grid.5379.8, ISNI 0000000121662407, Division of Cardiovascular Sciences, , University of Manchester, ; Manchester, UK
                [7 ]GRID grid.5522.0, ISNI 0000 0001 2162 9631, Department of Anatomy, , Jagiellonian University Medical College, ; Cracow, Poland
                Article
                98580
                10.1038/s41598-021-98580-x
                8481550
                34588502
                1ed0a746-d699-4bd4-b537-11a02095c2fe
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 March 2021
                : 3 September 2021
                Funding
                Funded by: Leducq Foundation
                Award ID: TNE FANTASY 19CVD03
                Award ID: TNE FANTASY 19CVD03
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: HL115580, HL135109
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                translational research,bioinformatics,molecular medicine,cardiovascular diseases
                Uncategorized
                translational research, bioinformatics, molecular medicine, cardiovascular diseases

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