Daniel Burkett

Being social animals, we as humans can fully appreciate how disorders affecting speech, language, and our ability to communicate severely degrade quality of life. Language is a complex behavior that exists only in humans, making the study of the underlying molecular components challenging. The vocal learning subcomponent of language, however, is shared by a handful of animal taxa, including, among birds, the zebra finch songbird species. Beyond this, humans and zebra finches share remarkable parallels with respect to vocalization: our neural circuitry, developmental timelines, and a reliance upon the FoxP2 transcription factor are similar, making them the preeminent model system in which to investigate the molecular basis for learned vocalization with hope of drawing meaningful parallels with humans. In this dissertation, I describe the process by which I begin untangling the complicated molecular basis for vocal learning which, in humans, is exemplified by speech and language. Mutations in the FOXP2 gene cause a speech and language disorder. Like humans, zebra finches require FoxP2 to properly learn their vocalizations. FoxP2 is down-regulated concurrent with singing behavior in a basal ganglia brain region, Area X, when zebra finches practice their songs. I overexpressed two major isoforms of FoxP2 in the zebra finch brain at a developmentally significant time point wherein the bird is undergoing the song learning process, breaking the link between FoxP2 and singing behavior. In doing this, I discovered unique roles for each isoform: the full-length version contributes strongly to overall vocal learning and variability while the truncated version exerts a strong effect on variability but does not affect learning. To uncover the molecular basis for these learning and variability phenotypes, I used weighted gene coexpression network analysis on RNA transcripts from Area X and the outlying non-song ventral striatopallidum of animals overexpressing the FoxP2 isoforms or the reporter gene GFP as a control. In the Area X network, modules correlated to singing, learning, and variability. Notably, a large, densely interconnected module positively correlated to learning was discovered. Through comparative network analysis with the non-song juvenile VSP and adult Area X, I discovered the learning related module is present in juvenile VSP but not adult Area X. Further, singing related modules were preserved between juvenile and adult Area X but not between juvenile Area X and VSP. Together, these results indicate a special confluence of singing and learning-related coexpression in juvenile Area X. I then use this information as a model wherein the building blocks of a complex behavior are discrete coexpression patterns. In this case, the combination of “learning” and “singing” coexpression that occur in juvenile Area X drives the learning behavior. The correlation of gene expression to behavior is only useful when both gene expression and behavior are accurately quantified. With the advent of RNA-seq, the quantification of gene expression reached a pinnacle. To quantify behavior, I applied principles of WGCNA to sound spectral data, creating the “Vocal Inventory Clustering Engine”, which generates clusters of bird vocalizations in an unbiased fashion, a task not possible with existing song analysis software. As part of multiple collaborations, I applied this methodology to the ultrasonic vocalizations of mice, creating, for the first time, a software solution for grouping the variable vocal repertoires of rodents into discrete vocalization “types” in an unbiased and semi-automated fashion. To demonstrate VoICE’s utility, I replicated prior work where Cntnap2 deletion diminished the amount of calling behavior in mouse pups then used VoICE to describe how the knockout makes the vocal repertoire more simple. By using the same network-based principles to group and describe both avian and rodent vocalizations, VoICE allows for a cross-species approach to be taken in determining the relationship between genes and behavior.