Learning is enhanced by tailoring instruction to individual genetic differences.

It is widely argued that personalized instruction based on individual differences in learning styles or genetic predispositions could improve learning outcomes. However, this proposition has resisted clear demonstration in human studies, where it is difficult to control experience and quantify outcomes. Here, we take advantage of the tractable nature of vocal learning in songbirds (Lonchura striata domestica) to test the idea that matching instruction to individual genetic predispositions can enhance learning. We use both cross-fostering and computerized instruction with synthetic songs to demonstrate that matching the tutor song to individual predispositions can improve learning across genetic backgrounds. Moreover, we find that optimizing instruction in this fashion can equalize learning differences across individuals that might otherwise be construed as genetically determined. Our results demonstrate potent, synergistic interactions between experience and genetics in shaping song, and indicate the likely importance of such interactions for other complex learned behaviors.

An automated approach to the quantitation of vocalizations and vocal learning in the songbird.

Studies of learning mechanisms critically depend on the ability to accurately assess learning outcomes. This assessment can be impeded by the often complex, multidimensional nature of behavior. We present a novel, automated approach to evaluating imitative learning. Conceptually, our approach estimates how much of the content present in a reference behavior is absent from the learned behavior. We validate our approach through examination of songbird vocalizations, complex learned behaviors the study of which has provided many insights into sensory-motor learning in general and vocal learning in particular. Historically, learning has been holistically assessed by human inspection or through comparison of specific song features selected by experimenters (e.g. fundamental frequency, spectral entropy). In contrast, our approach uses statistical models to broadly capture the structure of each song, and then estimates the divergence between the two models. We show that our measure of song learning (the Kullback-Leibler divergence between two distributions corresponding to specific song data, or, Song DKL) is well correlated with human evaluation of song learning. We then expand the analysis beyond learning and show that Song DKL also detects the typical song deterioration that occurs following deafening. Finally, we illustrate how this measure can be extended to quantify differences in other complex behaviors such as human speech and handwriting. This approach potentially provides a framework for assessing learning across a broad range of behaviors like song that can be described as a set of discrete and repeated motor actions.

Draft genome assembly of the Bengalese finch, Lonchura striata domestica, a model for motor skill variability and learning.

Background: Vocal learning in songbirds has emerged as a powerful model for sensorimotor learning. Neurobehavioral studies of Bengalese finch (Lonchura striata domestica) song, naturally more variable and plastic than songs of other finch species, have demonstrated the importance of behavioral variability for initial learning, maintenance, and plasticity of vocalizations. However, the molecular and genetic underpinnings of this variability and the learning it supports are poorly understood. Findings: To establish a platform for the molecular analysis of behavioral variability and plasticity, we generated an initial draft assembly of the Bengalese finch genome from a single male animal to 151× coverage and an N50 of 3.0 MB. Furthermore, we developed an initial set of gene models using RNA-seq data from 8 samples that comprise liver, muscle, cerebellum, brainstem/midbrain, and forebrain tissue from juvenile and adult Bengalese finches of both sexes. Conclusions: We provide a draft Bengalese finch genome and gene annotation to facilitate the study of the molecular-genetic influences on behavioral variability and the process of vocal learning. These data will directly support many avenues for the identification of genes involved in learning, including differential expression analysis, comparative genomic analysis (through comparison to existing avian genome assemblies), and derivation of genetic maps for linkage analysis. Bengalese finch gene models and sequences will be essential for subsequent manipulation (molecular or genetic) of genes and gene products, enabling novel mechanistic investigations into the role of variability in learned behavior.

Genetic variation interacts with experience to determine interindividual differences in learned song.

Learning reflects the influence of experience on genetically determined circuitry, but little is known about how experience and genetics interact to determine complex learned phenotypes. Here, we used vocal learning in songbirds to study how experience and genetics contribute to interindividual differences in learned song. Previous work has established that such differences in song within a species depend on learning, but in principle some of these differences could also depend on genetic variation. We focused on song tempo, a learned and quantifiable feature that is controlled by central neural circuitry. To identify genetic contributions to tempo we computer-tutored juvenile Bengalese finches (Lonchura striata domestica) from different genetic backgrounds with synthetic songs in which tempo was systematically varied. Computer-tutored birds exhibited unexpectedly strong heritability for song tempo and comparatively weak influence of experience. We then tested whether heritability was fixed and independent of experience by providing a second group of birds with enriched instruction via live social tutoring. Live tutoring resulted in not only a significant increase in the influence of experience on tempo but also a dramatic decrease in the influence of genetics, indicating that enriched instruction could overcome genetic biases evident under computer tutoring. Our results reveal strong heritable genetic contributions to interindividual variation in song tempo but that the degree of heritability depends profoundly on the quality of instruction. They suggest that for more complex learned phenotypes, where it can be difficult to identify and control relevant experiential variables, heritability may similarly be contingent on the specifics of experience.

RTEL-1 enforces meiotic crossover interference and homeostasis.

Meiotic crossovers (COs) are tightly regulated to ensure that COs on the same chromosome are distributed far apart (crossover interference, COI) and that at least one CO is formed per homolog pair (CO homeostasis). CO formation is controlled in part during meiotic double-strand break (DSB) creation in Caenorhabditis elegans, but a second level of control must also exist because meiotic DSBs outnumber COs. We show that the antirecombinase RTEL-1 is required to prevent excess meiotic COs, probably by promoting meiotic synthesis-dependent strand annealing. Two distinct classes of meiotic COs are increased in rtel-1 mutants, and COI and homeostasis are compromised. We propose that RTEL-1 implements the second level of CO control by promoting noncrossovers.

Condensins regulate meiotic DNA break distribution, thus crossover frequency, by controlling chromosome structure.

Meiotic crossover (CO) recombination facilitates evolution and accurate chromosome segregation. CO distribution is tightly regulated: homolog pairs receive at least one CO, CO spacing is nonrandom, and COs occur preferentially in short genomic intervals called hotspots. We show that CO number and distribution are controlled on a chromosome-wide basis at the level of DNA double-strand break (DSB) formation by a condensin complex composed of subunits from two known condensins: the C. elegans dosage compensation complex and mitotic condensin II. Disruption of any subunit of the CO-controlling condensin dominantly changes DSB distribution, and thereby COs, and extends meiotic chromosome axes. These phenotypes are cosuppressed by disruption of a chromosome axis element. Our data implicate higher-order chromosome structure in the regulation of CO recombination, provide a model for the rapid evolution of CO hotspots, and show that reshuffling of interchangeable molecular parts can create independent machines with similar architectures but distinct biological functions.

Meiotic crossover number and distribution are regulated by a dosage compensation protein that resembles a condensin subunit.

Biological processes that function chromosome-wide are not well understood. Here, we show that the Caenorhabditis elegans protein DPY-28 controls two such processes, X-chromosome dosage compensation in somatic cells and meiotic crossover number and distribution in germ cells. DPY-28 resembles a subunit of condensin, a conserved complex required for chromosome compaction and segregation. In the soma, DPY-28 associates with the dosage compensation complex on hermaphrodite X chromosomes to repress transcript levels. In the germline, DPY-28 restricts crossovers. In many organisms, one crossover decreases the likelihood of another crossover nearby, an enigmatic process called crossover interference. In C. elegans, interference is complete: Only one crossover occurs per homolog pair. dpy-28 mutations increase crossovers, disrupt crossover interference, and alter crossover distribution. Early recombination intermediates (RAD-51 foci) increase concomitantly, suggesting that DPY-28 acts to limit double-strand breaks (DSBs). Reinforcing this view, dpy-28 mutations partially restore DSBs in mutants lacking HIM-17, a chromatin-associated protein required for DSB formation. Our work further links dosage compensation to condensin and establishes a new role for condensin components in regulating crossover number and distribution. We propose that both processes utilize a related mechanism involving changes in higher-order chromosome structure to achieve chromosome-wide effects.