A molecular neuroethological approach for identifying and characterizing a cascade of behaviorally regulated genes

Abstract

Songbirds have one of the most accessible neural systems for the study of brain mechanisms of behavior. However, neuroethological studies in songbirds have been limited by the lack of high-throughput molecular resources and gene-manipulation tools. To overcome these limitations, we constructed 21 regular, normalized, and subtracted full-length cDNA libraries from brains of zebra finches in 57 developmental and behavioral conditions in an attempt to clone as much of the brain transcriptome as possible. From these libraries, approximately 14,000 transcripts were isolated, representing an estimated 4,738 genes. With the cDNAs, we created a hierarchically organized transcriptome database and a large-scale songbird brain cDNA microarray. We used the arrays to reveal a set of 33 genes that are regulated in forebrain vocal nuclei by singing behavior. These genes clustered into four anatomical and six temporal expression patterns. Their functions spanned a large range of cellular and molecular categories, from signal transduction, trafficking, and structural, to synaptically released molecules. With the full-length cDNAs and a lentiviral vector system, we were able to overexpress, in vocal nuclei, proteins of representative singing-regulated genes in the absence of singing. This publicly accessible resource http://songbirdtranscriptome.net can now be used to study molecular neuroethological mechanisms of behavior.

Fig. 1.

Molecular functions and variant analysis. (A) Distribution of putative molecular functions for 1,924 clusters and 2,449 subclusters of zebra finch brain cDNAs that received gene ontology annotations (www.geneontology.org) compared with 27,048 human genes. Genes can be represented in more than one category because of multiple molecular functions, and thus, categories add up to >100%. Human values were obtained from ref. . (B) mRNA variant analysis. Percentage represents the proportion of a specific variant type relative to the total number of variants from 100 randomly selected cDNA clusters containing 256 subclusters and 668 clones. ∗, P < 0.01 from chance distribution (horizontal line; t test across variant types in n = 10 bins of 10 clusters each). Because not all clones have full sequence coverage, the absolute distribution may change when such sequences are present. Colors denote mRNA subdomains quantified. alt, Alternative.

Fig. 2.

In situ hybridizations of singing-regulated genes. Shown are inverse images of autoradiographs; white is mRNA expression. Images are ordered from top to bottom according to four overall expression patterns and from left to right in temporal order of peak expression. Some genes (egr-1, c-fos, c-jun, and Arc) were induced by singing in the smaller vocal nuclei (NIf, Av, and MO), but we could not reliably assess this for all genes. Egr-1 is shown to the left of the brain diagram (Bottom Right) for anatomical reference. A, arcopallium; Av, avalanche; DM, dorsal medial nucleus; LX, lateral AreaX of the striatum; LMO, oval nucleus of the mesopallium; N, nidopallium; NIf, interfacial nucleus of the nidopallium; P, pallidum; RA robust nucleus of the arcopallium; St, striatum. (Scale bar, 2 mm.)

Fig. 3.

Summary of in situ-verified singing-regulated genes. (A) Table of inferred cellular location, molecular function, and biological process based on ontology definitions of homologous genes in other species. The list is organized according to cellular location (nucleus-to-extracellular space), proportion of vocal nuclei, peak time (0.5–3 h), and temporal patterns (types I–VI) of expression. Sim, similar to, at 60–74% protein identity (Appendix section 3.6.6). (B and C) Pie-chart quantifications of cellular location (B) and molecular function (C). (D) Percentage of the 33 genes regulated by singing in each vocal nucleus. The numbers of genes regulated are in parentheses.

Fig. 4.

Examples of the six types of temporal expression patterns. The large graphs show the average mRNA-expression time course in four song nuclei. Bars represent SEM. The small graphs show schematics. Vertical line, time of measured peak expression; horizontal line, peak expression level. Criteria for including a gene as singing-regulated were that it had to have a significant difference at one or more time points relative to silent controls (0 h; ANOVA by post hoc probable least-squares difference test; ∗, P < 0.05; ∗∗, P < 0.01; n = 4 each time point, n = 5 at 0.5 h). Criteria for placing genes in a temporal category were that the gene had to have or not have significant differences in expression among the 0.5-, 1-, or 3-h time points (ANOVA, P < 0.05; specific values not shown).

Fig. 5.

Singing-driven (0.5 h) Penk and c-fos mRNA expression in juvenile (PH44-48) and adult (>PH180) HVC. Shown are adjacent emulsion-dipped sections under dark-field microscopy from representative juvenile and adult animals; white silver grains, mRNA expression; red, Nissl stain; the orientation is the same as that in Fig. 2. (Scale bar, 200 μm.) Quantitative analyses (pixel density of digitized images) of birds (n = 3 juveniles; n = 3 adults) that produced comparable amounts of song (range 260.2–314.7 s) showed no significant difference between juvenile and adults for singing duration (P = 0.332) or c-fos expression (P = 0.215) but a significant difference for Penk expression (P = 0.02; ANOVA by Fisher's probable least-squares difference post hoc test).

Fig. 6.

Protein expression. (A) AreaX of silent and singing birds. Red, Cy3 label. Straight and angled arrows indicate induced protein expression in a cell nucleus in and out of the plane of focus for egr-1 and c-jun and in the cytoplasm and attached neuronal process for ENK, respectively. (Scale bar, 200 μm.) The orientation is the same as that in Fig. 2. (B) Quantitation of pixel intensity in a 2 × 150 μm area by using Photoshop (Adobe Systems, Mountain View, CA) tools. Cell count was not used because we needed a comparable measure across all proteins, and ENK and β-actin are expressed in processes, making individual cell identity difficult (ANOVA by Fisher's probable least-squares difference post hoc test; n = 4 silent and 4 singing birds). (C) Western blots. Antibodies recognize similar protein products in whole brain of finches and rats. Western blot for ENK is shown in Fig. 17, and Western blot for ZENK is shown in ref. .

Fig. 7.

Lentiviral overexpression of full-length cDNAs in zebra finch brain. (A and B) Ectopic expression of full-length eGFP in AreaX and LMAN, respectively, driven by the mammalian UBiC and EF1α promoters. Arrows indicate the injection track. (Ca) Triple label for eGFP (green), Hu (neuronal cytoplasm, red), and DAPI (all cell nuclei, blue). Flat back arrows indicate eGFP in neurons (Hu+); angled back arrows indicate eGFP in glia. (Cb) Quantification of eGFP/Hu double-labeled neurons in 3 × 100-μm areas within 100 μm of the injection site in AreaX expressed from various promoters (n = 3 animals each, 1–2 months). (D) Lentiviral UbiC promoter expression of recombinant zebra finch Gadd45β tagged with FLAG (red) in AreaX, without the bird singing. (E) Lentiviral UbiC promoter expression of recombinant zebra finch ENK (red) in processes of nidopallium neurons above the striatum (St), where ENK is normally not expressed. ENK was detected with a Met-enkephalin antibody, because the FLAG tag was cleaved off during processing of Penk to ENK (Fig. 17B). Transfection after 1 month is shown in A, transfection after 3 months is shown in B and Ca, and transfection after 1 week is shown in D and E. [Scale bars, 500 μm (A); 100 μm (B, Ca, D, and E).]