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      Defining Global Gene Expression Changes of the Hypothalamic-Pituitary-Gonadal Axis in Female sGnRH-Antisense Transgenic Common Carp (Cyprinus carpio)

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          Abstract

          Background

          The hypothalamic-pituitary-gonadal (HPG) axis is critical in the development and regulation of reproduction in fish. The inhibition of neuropeptide gonadotropin-releasing hormone (GnRH) expression may diminish or severely hamper gonadal development due to it being the key regulator of the axis, and then provide a model for the comprehensive study of the expression patterns of genes with respect to the fish reproductive system.

          Methodology/Principal Findings

          In a previous study we injected 342 fertilized eggs from the common carp ( Cyprinus carpio) with a gene construct that expressed antisense sGnRH. Four years later, we found a total of 38 transgenic fish with abnormal or missing gonads. From this group we selected the 12 sterile females with abnormal ovaries in which we combined suppression subtractive hybridization (SSH) and cDNA microarray analysis to define changes in gene expression of the HPG axis in the present study. As a result, nine, 28, and 212 genes were separately identified as being differentially expressed in hypothalamus, pituitary, and ovary, of which 87 genes were novel. The number of down- and up-regulated genes was five and four (hypothalamus), 16 and 12 (pituitary), 119 and 93 (ovary), respectively. Functional analyses showed that these genes involved in several biological processes, such as biosynthesis, organogenesis, metabolism pathways, immune systems, transport links, and apoptosis. Within these categories, significant genes for neuropeptides, gonadotropins, metabolic, oogenesis and inflammatory factors were identified.

          Conclusions/Significance

          This study indicated the progressive scaling-up effect of hypothalamic sGnRH antisense on the pituitary and ovary receptors of female carp and provided comprehensive data with respect to global changes in gene expression throughout the HPG signaling pathway, contributing towards improving our understanding of the molecular mechanisms and regulative pathways in the reproductive system of teleost fish.

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          Most cited references 78

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

           K Livak,  T Schmittgen (2001)
          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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            Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

             Y. H. Yang (2002)
            There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.
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              Glycoprotein hormones: structure and function.

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                Author and article information

                Affiliations
                [1 ]State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
                [2 ]Graduate School of the Chinese Academy of Sciences, Beijing, China
                Temasek Life Sciences Laboratory, Singapore
                Author notes

                Conceived and designed the experiments: ZZ JX W. Hu. Performed the experiments: JX CZ. Analyzed the data: JX W. Huang DL. Contributed reagents/materials/analysis tools: SL ZZ W. Hu. Wrote the paper: JX W. Huang W. Hu.

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                10 June 2011
                : 6
                : 6
                3112210
                21695218
                PONE-D-10-03140
                10.1371/journal.pone.0021057
                (Editor)
                Xu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Counts
                Pages: 12
                Categories
                Research Article
                Agriculture
                Animal Management
                Transgenic Animals
                Biology
                Anatomy and Physiology
                Endocrine System
                Endocrine Physiology
                Reproductive Endocrinology
                Reproductive System
                Reproductive Physiology
                Computational Biology
                Genomics
                Genome Expression Analysis
                Microarrays
                Genomics
                Genome Expression Analysis
                Zoology
                Ichthyology

                Uncategorized

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