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      Health benefits attributed to 17α-estradiol, a lifespan-extending compound, are mediated through estrogen receptor α

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          Abstract

          Metabolic dysfunction underlies several chronic diseases, many of which are exacerbated by obesity. Dietary interventions can reverse metabolic declines and slow aging, although compliance issues remain paramount. 17α-estradiol treatment improves metabolic parameters and slows aging in male mice. The mechanisms by which 17α-estradiol elicits these benefits remain unresolved. Herein, we show that 17α-estradiol elicits similar genomic binding and transcriptional activation through estrogen receptor α (ERα) to that of 17β-estradiol. In addition, we show that the ablation of ERα completely attenuates the beneficial metabolic effects of 17α-E2 in male mice. Our findings suggest that 17α-E2 may act through the liver and hypothalamus to improve metabolic parameters in male mice. Lastly, we also determined that 17α-E2 improves metabolic parameters in male rats, thereby proving that the beneficial effects of 17α-E2 are not limited to mice. Collectively, these studies suggest ERα may be a drug target for mitigating chronic diseases in male mammals.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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                Author and article information

                Contributors
                Role: Senior Editor
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                08 December 2020
                2020
                : 9
                : e59616
                Affiliations
                [1 ]Department of Nutritional Sciences, University of Oklahoma Health Sciences Center Oklahoma CityUnited States
                [2 ]Oklahoma Center for Geroscience, University of Oklahoma Health Sciences Center Oklahoma CityUnited States
                [3 ]Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center Oklahoma CityUnited States
                [4 ]The Jackson Laboratory Bar HarborUnited States
                [5 ]Department of Biochemistry and Molecular Biology, Mayo Clinic RochesterUnited States
                [6 ]Department of Cell Biology, University of Oklahoma Health Sciences Center Oklahoma CityUnited States
                [7 ]Dean McGee Eye Institute, University of Oklahoma Health Sciences Center Oklahoma CityUnited States
                [8 ]Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center Oklahoma CityUnited States
                [9 ]Department of Molecular Pharmacology, Albert Einstein College of Medicine New YorkUnited States
                [10 ]Genes & Human Disease Research Program, Oklahoma Medical Research Foundation Oklahoma CityUnited States
                [11 ]Oklahoma City Veterans Affairs Medical Center Oklahoma CityUnited States
                Weill Cornell Medicine United States
                Calico Life Sciences, LLC United States
                Calico Life Sciences, LLC United States
                University of California, San Francisco United States
                Author information
                http://orcid.org/0000-0001-7027-999X
                https://orcid.org/0000-0002-9996-9123
                Article
                59616
                10.7554/eLife.59616
                7744101
                33289482
                0f9b1237-8e2d-46b5-9f04-c290f2adcfff
                © 2020, Mann et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 03 June 2020
                : 07 December 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R00 AG51661
                Award Recipient :
                Funded by: Harold Hamm Diabetes Center;
                Award ID: Pilot Research Funding
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 AG069742
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 AG059430
                Award Recipient :
                Funded by: Veterans Affairs Oklahoma City;
                Award ID: I01BX003906
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007927, University of Oklahoma Health Sciences Center;
                Award ID: P30 EY012190
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 EY030513
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32 AG052363
                Award Recipient :
                Funded by: Einstein Nathan Shock Center;
                Award ID: P30 AG038072
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Medicine
                Custom metadata
                17α-Estradiol, a life-span extending compound, signals through estrogen receptor α (ERα) in the liver and hypothalamus to elicit health benefits in a sex-specific manner.

                Life sciences
                17α-estradiol,aging,estrogen receptor,hypothalamus,metabolism,obesity,mouse,rat
                Life sciences
                17α-estradiol, aging, estrogen receptor, hypothalamus, metabolism, obesity, mouse, rat

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