+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Sex chromosome complement regulates expression of mood-related genes

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.



          Studies on major depressive and anxiety disorders suggest dysfunctions in brain corticolimbic circuits, including altered gamma-aminobutyric acid (GABA) and modulatory (serotonin and dopamine) neurotransmission. Interestingly, sexual dimorphisms in GABA, serotonin, and dopamine systems are also reported. Understanding the mechanisms behind these sexual dimorphisms may help unravel the biological bases of the heightened female vulnerability to mood disorders. Here, we investigate the contribution of sex-related factors (sex chromosome complement, developmental gonadal sex, or adult circulating hormones) to frontal cortex expression of selected GABA-, serotonin-, and dopamine-related genes.


          As gonadal sex is determined by sex chromosome complement, the role of sex chromosomes cannot be investigated individually in humans. Therefore, we used the Four Core Genotypes (FCG) mouse model, in which sex chromosome complement and gonadal sex are artificially decoupled, to examine the expression of 13 GABA-related genes, 6 serotonin- and dopamine-related genes, and 8 associated signal transduction genes under chronic stress conditions. Results were analyzed by three-way ANOVA (sex chromosome complement × gonadal sex × circulating testosterone). A global perspective of gene expression changes was provided by heatmap representation and gene co-expression networks to identify patterns of transcriptional activities related to each main factor.


          We show that under chronic stress conditions, sex chromosome complement influenced GABA/serotonin/dopamine-related gene expression in the frontal cortex, with XY mice consistently having lower gene expression compared to XX mice. Gonadal sex and circulating testosterone exhibited less pronounced, more complex, and variable control over gene expression. Across factors, male conditions were associated with a tightly co-expressed set of signal transduction genes.


          Under chronic stress conditions, sex-related factors differentially influence expression of genes linked to mood regulation in the frontal cortex. The main factor influencing expression of GABA-, serotonin-, and dopamine-related genes was sex chromosome complement, with an unexpected pro-disease effect in XY mice relative to XX mice. This effect was partially opposed by gonadal sex and circulating testosterone, although all three factors influenced signal transduction pathways in males. Since GABA, serotonin, and dopamine changes are also observed in other psychiatric and neurodegenerative disorders, these findings have broader implications for the understanding of sexual dimorphism in adult psychopathology.

          Related collections

          Most cited references 75

          • Record: found
          • Abstract: found
          • Article: not found

          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).
            • Record: found
            • Abstract: found
            • Article: not found

            Collective dynamics of 'small-world' networks.

            Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Assortative mixing in networks

               M. Newman (2002)
              A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. We define a measure of assortative mixing for networks and use it to show that social networks are often assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortative network, which we study both analytically and numerically. Within the framework of this model we find that assortative networks tend to percolate more easily than their disassortative counterparts and that they are also more robust to vertex removal.

                Author and article information

                Biol Sex Differ
                Biol Sex Differ
                Biology of Sex Differences
                BioMed Central
                7 November 2013
                : 4
                : 20
                [1 ]Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
                [2 ]Translational Neuroscience Program, University of Pittsburgh, Pittsburgh, PA 15213, USA
                [3 ]Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
                [4 ]Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
                [5 ]Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA
                [6 ]Center For Neuroscience, University of Pittsburgh, Pittsburgh, PA 15261, USA
                Copyright © 2013 Seney et al.; licensee BioMed Central Ltd.

                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 work is properly cited.


                Human biology

                depression, gaba, serotonin, dopamine, four core genotypes mice, anxiety


                Comment on this article