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      TBX20 Improves Contractility and Mitochondrial Function During Direct Human Cardiac Reprogramming

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          Background:

          Direct cardiac reprogramming of fibroblasts into cardiomyocytes has emerged as a promising strategy to remuscularize injured myocardium. However, it is insufficient to generate functional induced cardiomyocytes from human fibroblasts using conventional reprogramming cocktails, and the underlying molecular mechanisms are not well studied.

          Methods:

          To discover potential missing factors for human direct reprogramming, we performed transcriptomic comparison between human induced cardiomyocytes and functional cardiomyocytes.

          Results:

          We identified TBX20 (T-box transcription factor 20) as the top cardiac gene that is unable to be activated by the MGT133 reprogramming cocktail ( MEF2C, GATA4, TBX5, and miR-133). TBX20 is required for normal heart development and cardiac function in adult cardiomyocytes, yet its role in cardiac reprogramming remains undefined. We show that the addition of TBX20 to the MGT133 cocktail (MGT+TBX20) promotes cardiac reprogramming and activates genes associated with cardiac contractility, maturation, and ventricular heart. Human induced cardiomyocytes produced with MGT+TBX20 demonstrated more frequent beating, calcium oscillation, and higher energy metabolism as evidenced by increased mitochondria numbers and mitochondrial respiration. Mechanistically, comprehensive transcriptomic, chromatin occupancy, and epigenomic studies revealed that TBX20 colocalizes with MGT reprogramming factors at cardiac gene enhancers associated with heart contraction, promotes chromatin binding and co-occupancy of MGT factors at these loci, and synergizes with MGT for more robust activation of target gene transcription.

          Conclusions:

          TBX20 consolidates MGT cardiac reprogramming factors to activate cardiac enhancers to promote cardiac cell fate conversion. Human induced cardiomyocytes generated with TBX20 showed enhanced cardiac function in contractility and mitochondrial respiration.

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          Most cited references55

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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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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Contributors
                Journal
                Circulation
                Circulation
                CIR
                Circulation
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0009-7322
                1524-4539
                14 September 2022
                15 November 2022
                : 146
                : 20
                : 1518-1536
                Affiliations
                [1]Department of Biomedical Engineering (Y.T., X.G., V.G.F., J.Z., Y.Z.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham.
                [2]Department of Medicine, Division of Hematology and Oncology (S.A., X.Z., R.L.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham.
                [3]O’Neal Comprehensive Cancer Center (S.A., X.Z., R.L.), Heersink School of Medicine, School of Engineering, University of Alabama at Birmingham.
                Author notes
                Correspondence to: Yang Zhou, PhD, Department of Biomedical Engineering, Heersink School of Medicine & School of Engineering, University of Alabama at Birmingham, 1670 University Boulevard, Birmingham, AL 35243 Email yangzhou@ 123456uab.edu
                Rui Lu, PhD, Division of Hematology/Oncology, Heersink School of Medicine, O’Neal Comprehensive Cancer Center, University of Alabama at Birmingham, 1824 6th Avenue South, Birmingham, AL 35243. Email ruilu1@ 123456uabmc.edu
                Author information
                https://orcid.org/0000-0002-3955-6554
                https://orcid.org/0000-0003-1593-2612
                https://orcid.org/0000-0002-9217-5208
                Article
                00005
                10.1161/CIRCULATIONAHA.122.059713
                9662826
                36102189
                9846cae4-4c85-42d7-aad7-2e48bd0aedfd
                © 2022 The Authors.

                Circulation is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDerivs License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made.

                History
                : 8 March 2022
                : 5 August 2022
                Categories
                10014
                10020
                10035
                Original Research Articles
                Custom metadata
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                cellular reprogramming,fibroblasts,heart,myocytes, cardiac,regeneration,transcription factors

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