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      Long noncoding RNA LEENE promotes angiogenesis and ischemic recovery in diabetes models

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          Abstract

          Impaired angiogenesis in diabetes is a key process contributing to ischemic diseases such as peripheral arterial disease. Epigenetic mechanisms, including those mediated by long noncoding RNAs (lncRNAs), are crucial links connecting diabetes and the related chronic tissue ischemia. Here we identify the lncRNA that enhances endothelial nitric oxide synthase (eNOS) expression (LEENE) as a regulator of angiogenesis and ischemic response. LEENE expression was decreased in diabetic conditions in cultured endothelial cells (ECs), mouse hind limb muscles, and human arteries. Inhibition of LEENE in human microvascular ECs reduced their angiogenic capacity with a dysregulated angiogenic gene program. Diabetic mice deficient in Leene demonstrated impaired angiogenesis and perfusion following hind limb ischemia. Importantly, overexpression of human LEENE rescued the impaired ischemic response in Leene-knockout mice at tissue functional and single-cell transcriptomic levels. Mechanistically, LEENE RNA promoted transcription of proangiogenic genes in ECs, such as KDR (encoding VEGFR2) and NOS3 (encoding eNOS), potentially by interacting with LEO1, a key component of the RNA polymerase II–associated factor complex and MYC, a crucial transcription factor for angiogenesis. Taken together, our findings demonstrate an essential role for LEENE in the regulation of angiogenesis and tissue perfusion. Functional enhancement of LEENE to restore angiogenesis for tissue repair and regeneration may represent a potential strategy to tackle ischemic vascular diseases.

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          Most cited references60

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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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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                Journal
                J Clin Invest
                J Clin Invest
                J Clin Invest
                The Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                1 February 2023
                1 February 2023
                1 February 2023
                : 133
                : 3
                : e161759
                Affiliations
                [1 ]Department of Diabetes Complications and Metabolism, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, California, USA.
                [2 ]Department of Bioengineering, UCSD, La Jolla, California, USA.
                [3 ]Irell and Manella Graduate School of Biological Sciences,
                [4 ]Gene Editing and Viral Vector Core, and
                [5 ]Transgenic Mouse Facility, Center for Comparative Medicine, City of Hope, Duarte, California, USA.
                [6 ]Department of Diabetes and Cancer Metabolism and
                [7 ]Department of Immunology & Theranostics, Arthur Riggs Diabetes and Metabolism Research Institute, Center for Comparative Medicine, City of Hope, Duarte, California, USA.
                [8 ]Department of Medicine, UCSD, La Jolla, California, USA.
                [9 ]Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, Texas, USA.
                Author notes
                Address correspondence to: Zhen Bouman Chen, Department of Diabetes & Metabolism Disease Complications, City of Hope, 1500 E Duarte Road, Duarte, California 91010, USA. Phone: 626.218.0662; Email: zhenchen@ 123456coh.org .

                Authorship note: XT, YL, and DY are co–first authors.

                Author information
                http://orcid.org/0000-0003-2271-3725
                http://orcid.org/0000-0002-4541-6320
                http://orcid.org/0000-0002-7412-2380
                http://orcid.org/0000-0001-8145-6701
                http://orcid.org/0000-0002-9506-671X
                http://orcid.org/0000-0003-4494-1788
                http://orcid.org/0000-0002-3291-1090
                Article
                161759
                10.1172/JCI161759
                9888385
                36512424
                b5c2e8f0-121a-4a34-adb9-35da1b3f4fae
                © 2023 Tang et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 May 2022
                : 8 December 2022
                Funding
                Funded by: National Institutes of Health, https://doi.org/10.13039/100000002;
                Award ID: R01 HL145170,DP1DK126138,U01CA200147,UG3 CA256960,R01 HL108735,R01 HL133254,R01 HL106089,R01GM141096,R01DK065073
                Funded by: Chan Zuckerberg Foundation
                Award ID: CZF2019-002444 to
                R01 HL145170 to Z.B.C. and J.P.C., DP1DK126138 and U01CA200147 to S.Z., UG3 CA256960 to S.Z. and Z.B.C., R01 HL108735 to S.C., J.Y.S., and Z.B.C., R01 HL133254 to J.P.C., R01 HL106089 to R.N. and Z.B.C., R01GM141096 to Z.B.C., and R01DK065073 to R.N
                CZF2019-002444 to Z.B.C. and S.Z
                Categories
                Research Article

                angiogenesis,vascular biology,diabetes,endothelial cells,noncoding rnas

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