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      Pharmacological blockade of the EP3 prostaglandin E 2 receptor in the setting of type 2 diabetes enhances β-cell proliferation and identity and relieves oxidative damage

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          Abstract

          Objective

          Type 2 diabetes is characterized by hyperglycemia and inflammation. Prostaglandin E 2, which signals through four G protein-coupled receptors (EP1-4), is a mediator of inflammation and is upregulated in diabetes. We have shown previously that EP3 receptor blockade promotes β-cell proliferation and survival in isolated mouse and human islets ex vivo. Here, we analyzed whether systemic EP3 blockade could enhance β-cell mass and identity in the setting of type 2 diabetes using mice with a spontaneous mutation in the leptin receptor ( Lepr db ).

          Methods

          Four- or six-week-old, db/+, and db/db male mice were treated with an EP3 antagonist daily for two weeks. Pancreata were analyzed for α-cell and β-cell proliferation and β-cell mass. Islets were isolated for transcriptomic analysis. Selected gene expression changes were validated by immunolabeling of the pancreatic tissue sections.

          Results

          EP3 blockade increased β-cell mass in db/db mice through enhanced β-cell proliferation. Importantly, there were no effects on α-cell proliferation. EP3 blockade reversed the changes in islet gene expression associated with the db/db phenotype and restored the islet architecture. Expression of the GLP-1 receptor was slightly increased by EP3 antagonist treatment in db/db mice. In addition, the transcription factor nuclear factor E2-related factor 2 (Nrf2) and downstream targets were increased in islets from db/db mice in response to treatment with an EP3 antagonist. The markers of oxidative stress were decreased.

          Conclusions

          The current study suggests that EP3 blockade promotes β-cell mass expansion in db/db mice. The beneficial effects of EP3 blockade may be mediated through Nrf2, which has recently emerged as a key mediator in the protection against cellular oxidative damage.

          Highlights

          • Systemic blockade of EP3 in vivo in db/db mice increase beta-cell proliferation and mass.

          • EP3 blockade has no effect on alpha-cell proliferation.

          • EP3 blockade restores islet architecture and beta-cell identity genes in db/db mice.

          • Beta cell GLP-1 receptor expression is increased by EP3 blockade.

          • EP3 blockade activates the Nrf2 anti-oxidant pathway.

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          Most cited references69

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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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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Contributors
                Journal
                Mol Metab
                Mol Metab
                Molecular Metabolism
                Elsevier
                2212-8778
                06 October 2021
                December 2021
                06 October 2021
                : 54
                : 101347
                Affiliations
                [1 ]Dept. of Veterans Affairs Tennessee Valley Authority, Nashville, TN, USA
                [2 ]Dept. of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
                [3 ]Diabetes, Obesity and Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [4 ]Dept. of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
                [5 ]Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
                [6 ]Dept. of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
                [7 ]Dept. of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
                [8 ]Program in Cancer Biology, Vanderbilt University, Nashville, TN, USA
                [9 ]Dept. of Medicine, University of Wisconsin, Madison, WI, USA
                [10 ]William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
                Author notes
                []Corresponding author. Dept. of Medicine, Div. of Diabetes, Endocrinology, and Metabolism, Vanderbilt University Medical Center, 2213 Garland Ave., 7465 MRB IV, Nashville, TN 37232-0475, USA. Fax: +615 936 1667. Maureen.gannon@ 123456vumc.org
                [11]

                Karin J. Bosma and Spencer R. Andrei contributed equally to this work.

                Article
                S2212-8778(21)00194-0 101347
                10.1016/j.molmet.2021.101347
                8529552
                34626853
                cc5f77ea-0004-49b5-bf89-a6ab26789da4

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 8 July 2021
                : 2 September 2021
                : 23 September 2021
                Categories
                Original Article

                type 2 diabetes,mouse model,prostaglandin e2,beta cell proliferation,nrf2

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