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      The effects of liraglutide and dapagliflozin on cardiac function and structure in a multi-hit mouse model of heart failure with preserved ejection fraction

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          Abstract

          Aims

          Heart failure with preserved ejection fraction (HFpEF) is a multifactorial disease that constitutes several distinct phenotypes, including a common cardiometabolic phenotype with obesity and type 2 diabetes mellitus. Treatment options for HFpEF are limited, and development of novel therapeutics is hindered by the paucity of suitable preclinical HFpEF models that recapitulate the complexity of human HFpEF. Metabolic drugs, like glucagon-like peptide receptor agonist (GLP-1 RA) and sodium-glucose co-transporter 2 inhibitors (SGLT2i), have emerged as promising drugs to restore metabolic perturbations and may have value in the treatment of the cardiometabolic HFpEF phenotype. We aimed to develop a multifactorial HFpEF mouse model that closely resembles the cardiometabolic HFpEF phenotype, and evaluated the GLP-1 RA liraglutide (Lira) and the SGLT2i dapagliflozin (Dapa).

          Methods and results

          Aged (18–22 months old) female C57BL/6J mice were fed a standardized chow (CTRL) or high-fat diet (HFD) for 12 weeks. After 8 weeks HFD, angiotensin II (ANGII), was administered for 4 weeks via osmotic mini pumps. HFD + ANGII resulted in a cardiometabolic HFpEF phenotype, including obesity, impaired glucose handling, and metabolic dysregulation with inflammation. The multiple hit resulted in typical clinical HFpEF features, including cardiac hypertrophy and fibrosis with preserved fractional shortening but with impaired myocardial deformation, atrial enlargement, lung congestion, and elevated blood pressures. Treatment with Lira attenuated the cardiometabolic dysregulation and improved cardiac function, with reduced cardiac hypertrophy, less myocardial fibrosis, and attenuation of atrial weight, natriuretic peptide levels, and lung congestion. Dapa treatment improved glucose handling, but had mild effects on the HFpEF phenotype.

          Conclusions

          We developed a mouse model that recapitulates the human HFpEF disease, providing a novel opportunity to study disease pathogenesis and the development of enhanced therapeutic approaches. We furthermore show that attenuation of cardiometabolic dysregulation may represent a novel therapeutic target for the treatment of HFpEF.

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          Most cited references81

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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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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Journal
                Cardiovasc Res
                Cardiovasc Res
                cardiovascres
                Cardiovascular Research
                Oxford University Press
                0008-6363
                1755-3245
                01 August 2021
                01 September 2020
                01 September 2020
                : 117
                : 9
                : 2108-2124
                Affiliations
                [1 ] Department of Cardiology, University Medical Center Groningen, University of Groningen , Hanzeplein 1, 9713 GZ Groningen, The Netherlands
                [2 ] Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW), University Medical Center Utrecht , Uppsalalaan 8, 3584CT, Utrecht, The Netherlands
                [3 ] National University Heart Centre , Singapore, Singapore
                Author notes
                Corresponding author. Tel: +31 50 3612355/+31 50 3615340; fax: +31 50 3611347, E-mail: r.a.de.boer@ 123456umcg.nl
                Author information
                https://orcid.org/0000-0002-7736-8204
                https://orcid.org/0000-0001-8627-7139
                https://orcid.org/0000-0001-8192-6670
                https://orcid.org/0000-0002-7119-5381
                https://orcid.org/0000-0002-5417-4415
                https://orcid.org/0000-0002-4775-9140
                Article
                cvaa256
                10.1093/cvr/cvaa256
                8318109
                32871009
                74be143a-5d15-4d15-81d5-ee86e13107f5
                © The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 03 April 2020
                : 03 April 2020
                : 25 August 2020
                : 24 August 2020
                Page count
                Pages: 17
                Funding
                Funded by: Netherlands Heart Foundation Senior Clinical Scientist Grant;
                Award ID: 2019T064
                Funded by: Rosalind Franklin Fellowship;
                Funded by: Novo Nordisk, DOI 10.13039/501100004191;
                Funded by: Mouse Clinic for Cancer and Aging;
                Funded by: Netherlands Organization for Scientific Research;
                Funded by: Netherlands Heart Foundation;
                Award ID: 2014-40
                Award ID: 2017-21
                Award ID: 2017-11
                Award ID: 2018-30
                Funded by: Netherlands Organization for Scientific Research;
                Award ID: 917.13.350
                Funded by: leDucq Foundation, DOI 10.13039/501100001674;
                Funded by: European Research Council, DOI 10.13039/100010663;
                Award ID: CoG 818715
                Funded by: European Union’s Horizon2020 research and innovation program;
                Funded by: Marie Skłodowska-Curie;
                Award ID: 751988
                Categories
                Original Articles
                Cardiac Remodelling and Heart Failure
                AcademicSubjects/MED00200

                Cardiovascular Medicine
                hfpef,liraglutide,dapagliflozin,mouse model,cardiometabolic
                Cardiovascular Medicine
                hfpef, liraglutide, dapagliflozin, mouse model, cardiometabolic

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