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      Early transcriptomic response of mouse adrenal gland and Y-1 cells to dexamethasone

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          Abstract

          Glucocorticoids have short- and long-term effects on adrenal gland function and development. RNA sequencing (RNA-seq) was performed to identify early transcriptomic responses to the synthetic glucocorticoid, dexamethasone (Dex), in vitro and in vivo. In total, 1711 genes were differentially expressed in the adrenal glands of the 1-h Dex-treated mice. Among them, only 113 were also considered differentially expressed genes (DEGs) in murine adrenocortical Y-1 cells treated with Dex for 1 h. Gene ontology analysis showed that the upregulated DEGs in the adrenal gland of the 1-h Dex-treated mice were highly associated with the development of neuronal cells, suggesting the adrenal medulla had a rapid response to Dex. Interestingly, only 4.3% of Dex-responsive genes in the Y-1 cell line under Dex treatment for 1 h were differentially expressed under Dex treatment for 24 h. The heatmaps revealed that most early responsive DEGs in Y-1 cells during 1 h of treatment exhibited a transient response. The expression of these genes under treatment for 24 h returned to basal levels similar to that during control treatment. In summary, this research compared the rapid transcriptomic effects of Dex stimulation in vivo and in vitro. Notably, adrenocortical Y-1 cells had a transient early response to Dex treatment. Furthermore, the DEGs had a minimal overlap in the 1-h Dex-treated group in vivo and in vitro.

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          Most cited references61

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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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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

                Author and article information

                Journal
                Endocr Connect
                Endocr Connect
                EC
                Endocrine Connections
                Bioscientifica Ltd (Bristol )
                2049-3614
                23 June 2022
                01 August 2022
                : 11
                : 8
                : e220064
                Affiliations
                [1 ]Department of Anatomy , Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama
                Author notes
                Correspondence should be addressed to C-C J Huang: jeff.huang@ 123456auburn.edu
                Author information
                http://orcid.org/0000-0003-0376-4682
                Article
                EC-22-0064
                10.1530/EC-22-0064
                9346337
                35904237
                e46c9693-e3be-4ad6-8504-1ab7e379255f
                © The authors

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 21 June 2022
                : 23 June 2022
                Categories
                Research

                glucocorticoids,adrenal gland,transcriptome,dexamethasone,y-1 cells

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