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      Age‐Associated Changes in Endothelial Transcriptome and Epigenetic Landscapes Correlate With Elevated Risk of Cerebral Microbleeds

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          Abstract

          Background

          Stroke is a leading global cause of human death and disability, with advanced aging associated with elevated incidences of stroke. Despite high mortality and morbidity of stroke, the mechanisms leading to blood‐brain barrier dysfunction and development of stroke with age are poorly understood. In the vasculature of brain, endothelial cells (ECs) constitute the core component of the blood‐brain barrier and provide a physical barrier composed of tight junctions, adherens junctions, and basement membrane.

          Methods and Results

          We show, in mice, the incidents of intracerebral bleeding increases with age. After isolating an enriched population of cerebral ECs from murine brains at 2, 6, 12, 18, and 24 months, we studied age‐associated changes in gene expression. The study reveals age‐dependent dysregulation of 1388 genes, including many involved in the maintenance of the blood‐brain barrier and vascular integrity. We also investigated age‐dependent changes on the levels of CpG methylation and accessible chromatin in cerebral ECs. Our study reveals correlations between age‐dependent changes in chromatin structure and gene expression, whereas the dynamics of DNA methylation changes are different.

          Conclusions

          We find significant age‐dependent downregulation of the Aplnr gene along with age‐dependent reduction in chromatin accessibility of promoter region of the Aplnr gene in cerebral ECs. Aplnr is associated with positive regulation of vasodilation and is implicated in vascular health. Altogether, our data suggest a potential role of the apelinergic axis involving the ligand apelin and its receptor to be critical in maintenance of the blood‐brain barrier and vascular integrity.

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          Most cited references75

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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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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Contributors
                kshitij.mohan@cardiov.ox.ac.uk
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                23 August 2023
                05 September 2023
                : 12
                : 17 ( doiID: 10.1002/jah3.v12.17 )
                : e031044
                Affiliations
                [ 1 ] Interfaculty Institute of Cell Biology University of Tübingen Tübingen Germany
                [ 2 ] Department of Genetics University of Saarland Saarbrücken Germany
                [ 3 ] Genome Analytics Helmholtz Centre for Infection Research Braunschweig Germany
                [ 4 ] Leibniz Institute on Aging Fritz Lipmann Institute Jena Germany
                [ 5 ] Department of Tissue Morphogenesis Max Planck Institute for Molecular Biomedicine Münster Germany
                [ 6 ] Faculty of Medicine University of Münster Münster Germany
                [ 7 ] International Max Planck Research School “From Molecules to Organisms” Tübingen Germany
                Author notes
                [*] [* ] Correspondence to: Kshitij Mohan, PhD, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom. Email: kshitij.mohan@ 123456cardiov.ox.ac.uk

                Author information
                https://orcid.org/0000-0002-7978-5343
                https://orcid.org/0000-0002-6423-4637
                https://orcid.org/0000-0002-9649-6922
                https://orcid.org/0000-0001-8632-2593
                https://orcid.org/0000-0003-4409-016X
                https://orcid.org/0000-0003-0563-7417
                Article
                JAH38750 JAHA/2023/031044-T
                10.1161/JAHA.123.031044
                10547332
                37609982
                82017a3a-6a24-4f87-863b-d4092b78a4d7
                © 2023 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 30 May 2023
                : 24 July 2023
                Page count
                Figures: 6, Tables: 0, Pages: 19, Words: 11625
                Funding
                Funded by: IMPRS Tübingen
                Funded by: German Research Foundation , doi 10.13039/501100001659;
                Award ID: SFB/TRR 209–314905040
                Funded by: SYSCID
                Award ID: 733100
                Funded by: German Federal Ministry of Research and Education
                Award ID: 031L0101D
                Categories
                Original Research
                Original Research
                Stroke
                Custom metadata
                2.0
                05 September 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.3 mode:remove_FC converted:04.09.2023

                Cardiovascular Medicine
                aging,blood‐brain barrier,endothelial cells,stroke,blood-brain barrier,cerebrovascular disease/stroke,basic science research,vascular biology,vascular disease

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