Exploring the molecular basis of neuronal excitability in a vocal learner.

Background: Vocal learning, the ability to learn to produce vocalizations through imitation, relies on specialized brain circuitry known in songbirds as the song system. While the connectivity and various physiological properties of this system have been characterized, the molecular genetic basis of neuronal excitability in song nuclei remains understudied. We have focused our efforts on examining voltage-gated ion channels to gain insight into electrophysiological and functional features of vocal nuclei. A previous investigation of potassium channel genes in zebra finches (Taeniopygia guttata) revealed evolutionary modifications unique to songbirds, as well as transcriptional specializations in the song system [Lovell PV, Carleton JB, Mello CV. BMC Genomics 14:470 2013]. Here, we expand this approach to sodium, calcium, and chloride channels along with their modulatory subunits using comparative genomics and gene expression analysis encompassing microarrays and in situ hybridization.

Results: We found 23 sodium, 38 calcium, and 33 chloride channel genes (HGNC-based classification) in the zebra finch genome, several of which were previously unannotated. We determined 15 genes are missing relative to mammals, including several genes (CLCAs, BEST2) linked to olfactory transduction. The majority of sodium and calcium but few chloride channels showed differential expression in the song system, among them SCN8A and CACNA1E in the direct motor pathway, and CACNG4 and RYR2 in the anterior forebrain pathway. In several cases, we noted a seemingly coordinated pattern across multiple nuclei (SCN1B, SCN3B, SCN4B, CACNB4) or sparse expression (SCN1A, CACNG5, CACNA1B).

Conclusion: The gene families examined are highly conserved between avian and mammalian lineages. Several cases of differential expression likely support high-frequency and burst firing in specific song nuclei, whereas cases of sparse patterns of expression may contribute to the unique electrophysiological signatures of distinct cell populations. These observations lay the groundwork for manipulations to determine how ion channels contribute to the neuronal excitability properties of vocal learning systems.