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      RNA Sequencing of Cardiac in a Rat Model Uncovers Potential Target LncRNA of Diabetic Cardiomyopathy

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          Abstract

          Background: Diabetic cardiomyopathy (DCM) is one of the major causes of heart failure in diabetic patients; however, its pathogenesis remains unclear. Long non-coding RNAs (lncRNAs) are involved in the development of various cardiovascular diseases, but little is known in DCM.

          Objective: The present study was conducted to investigate the altered expression signature of lncRNAs and mRNAs by RNA-sequencing and uncovers the potential targets of DCM.

          Methods: A DCM rat model was established, and the genome-wide expression profile of cardiac lncRNAs and mRNAs was investigated in the rat model with and without DCM by RNA-sequencing. Bioinformatics analysis included the co-expression, competitive endogenous RNA (ceRNA) network, and functional enrichment analysis of deregulated lncRNAs and mRNAs.

          Results: A total of 355 lncRNA transcripts and 828 mRNA transcripts were aberrantly expressed. The ceRNA network showed that lncRNA XR_351927.3, ENSRNOT00000089581, XR_597359.2, XR_591602.2, and XR_001842089.1 are associated with the greatest number of differentially expressed mRNAs and AURKB, MELK, and CDK1 may be the potential regulatory targets of these lncRNAs. Functional analysis showed that these five lncRNAs are closely associated with fibration, cell proliferation, and energy metabolism of cardiac myocytes, indicating that these core lncRNAs have high significance in DCM.

          Conclusions: The present study profiled the DCM-specific lncRNAs and mRNAs, constructed the lncRNA-related ceRNA regulatory network, and identified the potential prognostic biomarkers, which provided new insights into the pathogenesis of DCM.

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          Most cited references36

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

              Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                13 April 2022
                2022
                : 13
                : 848364
                Affiliations
                [1] 1 Department of The First Clinical Medical College , Jinan University , Guangzhou, China
                [2] 2 Department of Cardiology , The First Affiliated Hospital of Jinan University , Guangzhou, China
                [3] 3 Central Laboratory , The Dongguan Affiliated Hospital of Jinan University , Binhaiwan Central Hospital of Dongguan , Dongguan, China
                [4] 4 Department of Pathology , The Dongguan Affiliated Hospital of Jinan University , Binhaiwan Central Hospital of Dongguan , Dongguan, China
                [5] 5 Department of Cardiology , The Dongguan Affiliated Hospital of Jinan University , Binhaiwan Central Hospital of Dongguan , Dongguan, China
                Author notes

                Edited by: Amanda Salviano-Silva, University Medical Center Hamburg-Eppendorf, Germany

                Reviewed by: Tarun Pant, Medical College of Wisconsin, United States

                Gang Yuan, Huazhong University of Science and Technology, China

                *Correspondence: Jun Guo, dr.guojun@ 123456163.com ; Jianmin Xiao, xiaokang20082008@ 123456163.com
                [ † ]

                These authors have contributed equally to this work and share first authorship

                This article was submitted to RNA, a section of the journal Frontiers in Genetics

                Article
                848364
                10.3389/fgene.2022.848364
                9044075
                35495145
                cbedd4a1-503f-449d-976f-adf7466d0bda
                Copyright © 2022 Xi, Chen, Dong, Lam, He, Du, Chen, Guo and Xiao.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 04 January 2022
                : 18 March 2022
                Funding
                Funded by: Basic and Applied Basic Research Foundation of Guangdong Province , doi 10.13039/501100021171;
                Award ID: 2021B1515140036
                Categories
                Genetics
                Original Research

                Genetics
                diabetic cardiomyopathy,long non-coding rna,transcriptome sequencing,bioinformatic analyses,cerna

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