Birdsong “Transcriptomics”: Neurochemical Specializations of the Oscine Song System

Abstract

BackgroundVocal learning is a rare and complex behavioral trait that serves as a basis for the acquisition of human spoken language. In songbirds, vocal learning and production depend on a set of specialized brain nuclei known as the song system.Methodology/Principal FindingsUsing high-throughput functional genomics we have identified ∼200 novel molecular markers of adult zebra finch HVC, a key node of the song system. These markers clearly differentiate HVC from the general pallial region to which HVC belongs, and thus represent molecular specializations of this song nucleus. Bioinformatics analysis reveals that several major neuronal cell functions and specific biochemical pathways are the targets of transcriptional regulation in HVC, including: 1) cell-cell and cell-substrate interactions (e.g., cadherin/catenin-mediated adherens junctions, collagen-mediated focal adhesions, and semaphorin-neuropilin/plexin axon guidance pathways); 2) cell excitability (e.g., potassium channel subfamilies, cholinergic and serotonergic receptors, neuropeptides and neuropeptide receptors); 3) signal transduction (e.g., calcium regulatory proteins, regulators of G-protein-related signaling); 4) cell proliferation/death, migration and differentiation (e.g., TGF-beta/BMP and p53 pathways); and 5) regulation of gene expression (candidate retinoid and steroid targets, modulators of chromatin/nucleolar organization). The overall direction of regulation suggest that processes related to cell stability are enhanced, whereas proliferation, growth and plasticity are largely suppressed in adult HVC, consistent with the observation that song in this songbird species is mostly stable in adulthood.Conclusions/SignificanceOur study represents one of the most comprehensive molecular genetic characterizations of a brain nucleus involved in a complex learned behavior in a vertebrate. The data indicate numerous targets for pharmacological and genetic manipulations of the song system, and provide novel insights into mechanisms that might play a role in the regulation of song behavior and/or vocal learning.

Impact of experience-dependent and -independent factors on gene expression in songbird brain

“…Study of gene expression patterns that distinguish individual song nuclei may be uniquely informative about the functional and biological properties of each nucleus. A detailed analysis of HVC gene expression from experiment e09 has been presented previously (23), and we also note a very strong signature in the combined data for another song nucleus, area X. In the WGCNA analysis, module 22 genes are expressed at low levels in this nucleus, whereas module 89 and 91 genes are expressed at high levels ( Fig.…”

“…Through a set of structured collaborations, this array was used to assess variations in gene expression in brain tissue samples contributed by multiple laboratories and representing a diversity of songbird species, brain subregions, developmental stages, and physiological or behavioral states. These collaborations were organized so that individual datasets could be analyzed independently, as shown in several reports to date (23)(24)(25)(26)(27). However, a unique opportunity was anticipated for an integrated global This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, "Biological Embedding of Early Social Adversity: From Fruit Flies to Kindergartners," held December 9-10, 2011, at the Arnold and Mabel Beckman Center of the National Academies of Sciences and Engineering in Irvine, CA.…”

“…Each sample was hybridized to a zebra finch cDNA array along with a universal reference (a pool of zebra finch telencephalic RNA). Independent analyses of seven of the individual experiments have already been published (23)(24)(25)(26)(27), but all data were reanalyzed here under uniform procedures. The data from all 488 arrays, each representing one contributed sample (SI Appendix, Table S2), were first standardized and then assessed for relationships between the samples using principal components analysis (PCA) (SI Appendix, Fig.…”

“…gives the designated number of each experiment, and the other columns show the species, number of birds (n), number of treatment groups (g), age, sex, brain regions, and contrasts evaluated by each experiment. We provide both rigorous Bonferroni-corrected P values and a more liberal empirical standard (raw P < 0.001) derived from previous analyses of individual experiments in this dataset (23)(24)(25)(26)(27). We compared the numbers of significant probes based on raw P values instead of FDR-corrected P values because a cDNA could have the same raw P value in two experiments yet have vastly different FDR P values depending on how many other cDNAs have expression differences in each experiment.…”

Discrete molecular states in the brain accompany changing responses to a vocal signal

“…Further study is needed to determine how gene response patterns may differ by brain region and type of perceptual or motor activity. We have confidence in our overall analysis as we used a carefully qualified large microarray specifically designed to measure genes expressed in the zebra finch brain (17), participated in a centralized pipeline for execution and analysis that has been independently validated in related studies (28)(29)(30), executed a counterbalanced design with 3 treatment groups where the groups shared the same environment, controlling for circadian effects that confound interpretation of some other reports, applied a sensitive 2-stage mixed linear model for statistical analysis, and corroborated the overall accuracy of our microarray measurements with independent in situ hybridization measurements (SI). Moreover, the discrete composition of our 4 distinct gene response sets (Fig.…”