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      A RNA-Seq Analysis of the Rat Supraoptic Nucleus Transcriptome: Effects of Salt Loading on Gene Expression

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          Abstract

          Magnocellular neurons (MCNs) in the hypothalamo-neurohypophysial system (HNS) are highly specialized to release large amounts of arginine vasopressin (Avp) or oxytocin (Oxt) into the blood stream and play critical roles in the regulation of body fluid homeostasis. The MCNs are osmosensory neurons and are excited by exposure to hypertonic solutions and inhibited by hypotonic solutions. The MCNs respond to systemic hypertonic and hypotonic stimulation with large changes in the expression of their Avp and Oxt genes, and microarray studies have shown that these osmotic perturbations also cause large changes in global gene expression in the HNS. In this paper, we examine gene expression in the rat supraoptic nucleus (SON) under normosmotic and chronic salt-loading SL) conditions by the first time using “new-generation”, RNA sequencing (RNA-Seq) methods. We reliably detect 9,709 genes as present in the SON by RNA-Seq, and 552 of these genes were changed in expression as a result of chronic SL. These genes reflect diverse functions, and 42 of these are involved in either transcriptional or translational processes. In addition, we compare the SON transcriptomes resolved by RNA-Seq methods with the SON transcriptomes determined by Affymetrix microarray methods in rats under the same osmotic conditions, and find that there are 6,466 genes present in the SON that are represented in both data sets, although 1,040 of the expressed genes were found only in the microarray data, and 2,762 of the expressed genes are selectively found in the RNA-Seq data and not the microarray data. These data provide the research community a comprehensive view of the transcriptome in the SON under normosmotic conditions and the changes in specific gene expression evoked by salt loading.

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          Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms

          The hippocampal expression profiles of wild-type mice and mice transgenic for δC-doublecortin-like kinase were compared with Solexa/Illumina deep sequencing technology and five different microarray platforms. With Illumina's digital gene expression assay, we obtained ∼2.4 million sequence tags per sample, their abundance spanning four orders of magnitude. Results were highly reproducible, even across laboratories. With a dedicated Bayesian model, we found differential expression of 3179 transcripts with an estimated false-discovery rate of 8.5%. This is a much higher figure than found for microarrays. The overlap in differentially expressed transcripts found with deep sequencing and microarrays was most significant for Affymetrix. The changes in expression observed by deep sequencing were larger than observed by microarrays or quantitative PCR. Relevant processes such as calmodulin-dependent protein kinase activity and vesicle transport along microtubules were found affected by deep sequencing but not by microarrays. While undetectable by microarrays, antisense transcription was found for 51% of all genes and alternative polyadenylation for 47%. We conclude that deep sequencing provides a major advance in robustness, comparability and richness of expression profiling data and is expected to boost collaborative, comparative and integrative genomics studies.
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            Revisiting global gene expression analysis.

            Gene expression analysis is a widely used and powerful method for investigating the transcriptional behavior of biological systems, for classifying cell states in disease, and for many other purposes. Recent studies indicate that common assumptions currently embedded in experimental and analytical practices can lead to misinterpretation of global gene expression data. We discuss these assumptions and describe solutions that should minimize erroneous interpretation of gene expression data from multiple analysis platforms. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Classification of human B-ZIP proteins based on dimerization properties.

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                21 April 2015
                2015
                : 10
                : 4
                : e0124523
                Affiliations
                [1 ]Bioinformatics Section, Information Technology and Bioinformatics Program, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, United States of America
                [2 ]Laboratory of Neurochemistry, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, United States of America
                [3 ]School of Clinical Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, England, BS1 3NY
                [4 ]Department of Physiology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia, 50603
                National Institutes of Health, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: HG KRJ YDS YS CCTH MG DM. Performed the experiments: YDS YS MG SZH. Analyzed the data: KRJ. Wrote the paper: KRJ HG CCTH DM. Designed the RNA-Seq experiments: HG KRJ YDS YS. Designed the microarray and qPCR: CCTH MG DM. Performed the laser capture and RNA-Seq experiments: YDS YS. Performed the microarray and qPCR experiments: CCTH MG SZH. Performed the bioinformatic analyses: KRJ.

                [¤]

                Current address: Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America

                Article
                PONE-D-15-04161
                10.1371/journal.pone.0124523
                4405539
                25897513
                69c4629c-6e31-4ca0-afd8-03c0bad058af

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication

                History
                : 9 February 2015
                : 16 March 2015
                Page count
                Figures: 5, Tables: 8, Pages: 28
                Funding
                This research was supported by the Intramural Research Program of the NIH, NINDS (KJ, HG), and by funding from the BHF (RG/11/28714, MG, DM), the BBSRC(BB/J005452/1, CH, DM) and a High Impact Research Chancellory Grant (UM.C/625/1/HIR/MOHE/MED/22 H-20001-E000086) from the University of Malaya (SZH, CH, DM).
                Categories
                Research Article
                Custom metadata
                RNA-seq data are available as Raw fastq files for all libraries can be found in the Short Read Archive (SRA; http://www.ncbi.nlm.nih.gov/Traces/sra/; SRP049482). Raw .CEL files for all microarrays can be found in the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/; WD = GSE3110, SL = GSE65663).

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