Androgen Affects the Inhibitory Avoidance Memory by Primarily Acting on Androgen Receptor in the Brain in Adolescent Male Rats

Adolescence is the critical postnatal stage for the action of androgen in multiple brain regions. Androgens can regulate the learning/memory functions in the brain. It is known that the inhibitory avoidance test can evaluate emotional memory and is believed to be dependent largely on the amygdala and hippocampus. However, the effects of androgen on inhibitory avoidance memory have never been reported in adolescent male rats. In the present study, the effects of androgen on inhibitory avoidance memory and on androgen receptor (AR)-immunoreactivity in the amygdala and hippocampus were studied using behavioral analysis, Western blotting and immunohistochemistry in sham-operated, orchiectomized, orchiectomized + testosterone or orchiectomized + dihydrotestosterone-administered male adolescent rats. Orchiectomized rats showed significantly reduced time spent in the illuminated box after 30 min (test 1) or 24 h (test 2) of electrical foot-shock (training) and reduced AR-immunoreactivity in amygdala/hippocampal cornu Ammonis (CA1) in comparison to those in sham-operated rats. Treatment of orchiectomized rats with either non-aromatizable dihydrotestosterone or aromatizable testosterone were successfully reinstated these effects. Application of flutamide (AR-antagonist) in intact adolescent rats exhibited identical changes to those in orchiectomized rats. These suggest that androgens enhance the inhibitory avoidance memory plausibly by binding with AR in the amygdala and hippocampus

Quantitative expression profile of distinct functional regions in the adult mouse brain.

The adult mammalian brain is composed of distinct regions with specialized roles including regulation of circadian clocks, feeding, sleep/awake, and seasonal rhythms. To find quantitative differences of expression among such various brain regions, we conducted the BrainStars (B*) project, in which we profiled the genome-wide expression of ∼50 small brain regions, including sensory centers, and centers for motion, time, memory, fear, and feeding. To avoid confounds from temporal differences in gene expression, we sampled each region every 4 hours for 24 hours, and pooled the samples for DNA-microarray assays. Therefore, we focused on spatial differences in gene expression. We used informatics to identify candidate genes with expression changes showing high or low expression in specific regions. We also identified candidate genes with stable expression across brain regions that can be used as new internal control genes, and ligand-receptor interactions of neurohormones and neurotransmitters. Through these analyses, we found 8,159 multi-state genes, 2,212 regional marker gene candidates for 44 small brain regions, 915 internal control gene candidates, and 23,864 inferred ligand-receptor interactions. We also found that these sets include well-known genes as well as novel candidate genes that might be related to specific functions in brain regions. We used our findings to develop an integrated database (http://brainstars.org/) for exploring genome-wide expression in the adult mouse brain, and have made this database openly accessible. These new resources will help accelerate the functional analysis of the mammalian brain and the elucidation of its regulatory network systems

Acute Induction of Eya3 by Late-Night Light Stimulation Triggers TSHβ Expression in Photoperiodism

SummaryLiving organisms detect seasonal changes in day length (photoperiod) [1–3] and alter their physiological functions accordingly to fit seasonal environmental changes. TSHβ, induced in the pars tuberalis (PT), plays a key role in the pathway that regulates vertebrate photoperiodism [4, 5]. However, the upstream inducers of TSHβ expression remain unknown. Here we performed genome-wide expression analysis of the PT under chronic short-day and long-day conditions in melatonin-proficient CBA/N mice, in which the photoperiodic TSHβ expression response is preserved [6]. This analysis identified “short-day” and “long-day” genes, including TSHβ, and further predicted the acute induction of long-day genes by late-night light stimulation. We verified this by advancing and extending the light period by 8 hr, which induced TSHβ expression within one day. In the following genome-wide expression analysis under this acute long-day condition, we searched for candidate upstream genes by looking for expression that preceded TSHβ's, and we identified the Eya3 gene. We demonstrated that Eya3 and its partner Six1 synergistically activate TSHβ expression and that this activation is further enhanced by Tef and Hlf. These results elucidate the comprehensive transcriptional photoperiodic response in the PT, revealing the complex regulation of TSHβ expression and unexpectedly rapid response to light changes in the mammalian photoperiodic system

Transcriptome Tomography for Brain Analysis in the Web-Accessible Anatomical Space

<div><p>Increased information on the encoded mammalian genome is expected to facilitate an integrated understanding of complex anatomical structure and function based on the knowledge of gene products. Determination of gene expression-anatomy associations is crucial for this understanding. To elicit the association in the three-dimensional (3D) space, we introduce a novel technique for comprehensive mapping of endogenous gene expression into a web-accessible standard space: Transcriptome Tomography. The technique is based on conjugation of sequential tissue-block sectioning, all fractions of which are used for molecular measurements of gene expression densities, and the block- face imaging, which are used for 3D reconstruction of the fractions. To generate a 3D map, tissues are serially sectioned in each of three orthogonal planes and the expression density data are mapped using a tomographic technique. This rapid and unbiased mapping technique using a relatively small number of original data points allows researchers to create their own expression maps in the broad anatomical context of the space. In the first instance we generated a dataset of 36,000 maps, reconstructed from data of 61 fractions measured with microarray, covering the whole mouse brain (ViBrism: <a href="http://vibrism.riken.jp/3dviewer/ex/index.html" target="_blank">http://vibrism.riken.jp/3dviewer/ex/index.html</a>) in one month. After computational estimation of the mapping accuracy we validated the dataset against existing data with respect to the expression location and density. To demonstrate the relevance of the framework, we showed disease related expression of Huntington’s disease gene and <i>Bdnf</i>. Our tomographic approach is applicable to analysis of any biological molecules derived from frozen tissues, organs and whole embryos, and the maps are spatially isotropic and well suited to the analysis in the standard space (e.g. Waxholm Space for brain-atlas databases). This will facilitate research creating and using open-standards for a molecular-based understanding of complex structures; and will contribute to new insights into a broad range of biological and medical questions.</p></div

Transcriptome Tomography.

<p>(<b>A</b>) <b>A schematic illustrated using a model material.</b> Two types of data, material shape images (drawn with green lines) and gene expression densities (shown in red) of fractions (indicated with asterisks), are obtained with sectioning, conjugated with block-face imaging and expression density measurement, along three body axes (shown in parentheses). The three series of sectioning are named after orthogonal planes (C, S and H). The densities are assigned to the voxels (pixels on a regular grid in a 3D space) in the images (as shown with +) and subjected to tomographic reconstruction (indicated in purple). A series of the process from one direction needs one material; therefore, at least three genetically identical materials were required. (<b>B</b>) <b>An outline of the technique and the first dataset creation.</b> Two types of data, fraction templates, which are the material shape image (in green) and fraction data, which are gene expression densities measured with microarray (in dotted red), were acquired from the same fractions prepared with a sectioning machine 3D-ISM <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045373#pone.0045373-Yokota1" target="_blank">[12]</a>. The fractions were named “image fractions” for the former data and “material fractions” for the latter (the preparation process seen in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045373#pone.0045373.s007" target="_blank">Video S1</a>). Six fraction templates for the first dataset, two groups of three series sectioned in each of orthogonal and slightly oblique to the orthogonal planes: S/C/H and So/Co/Ho, composed of 9/13/6 and 10/16/7 fractions, respectively, (61 fractions in total as seen in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045373#pone.0045373.s001" target="_blank">Figure S1B</a>), are shown with fraction numbers in Template C: 13 fractions of 1 mm (5 µm×200 sections)-thickness. The pseudo-tomography technique of mapping in a single coordinate space (named ViBrism) including image registration, pseudo-back projection and tomographic reconstruction is shown in the flowchart (see details in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045373#pone.0045373.s001" target="_blank">Figure S1A</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045373#pone.0045373.s005" target="_blank">Text S1</a>). After volume rendering, 3D expression maps for genes (a sample: Slitrk6) are visualized as pseudo-colored expression densities and anatomical images with an 80% cutoff filter (also seen in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045373#pone.0045373.s008" target="_blank">Video S2</a>). Slitrk6 is known to be expressed mostly in the thalamus as shown in the Allen Brain Atlas and BrainStars databases: 2Dand 3D views displayed here are compatible to those data shown below in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045373#pone-0045373-g004" target="_blank">Figure 4A and B</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045373#pone.0045373.s008" target="_blank">VideoS2</a>.</p