Transcriptomics of learning

Learning is a basic and important component of behavior yet we have very little empirical information about the interaction between mechanisms of learning and evolution. In our work, we are testing hypotheses about the neurogenetic mechanisms through which animal learning abilities evolve. We are able to test this directly by using experimentally evolved populations of flies, which differ in learning ability. These populations were previously evolved within the lab by creating worlds with different patterns of change following theoretically predicted effects on which enhanced learning will evolve. How has evolution acted to modulate genes and gene expression in the brain to accomplish the behavioral differences observed in these populations? We report results from work characterizing the differences in gene expression in the brains of populations of Drosophila that evolved in environments favoring learning from paired populations evolving under control conditions. Using olfactory conditioning in the t-maze, we first show that flies which evolved enhanced learning in an oviposition context also have a generalized enhanced learning ability. We dissected brains from flies following experience learning in the tmaze and analyzed pooled samples using RNAseq. We completed a factorial design of comparing the brains of flies from high learning populations with control populations and in each of two conditions: after conditioning and without conditioning. Following differential gene expression analysis, we found differences within known suites of genes as well as novel transcripts. We have also found evidence of predicted trade-offs between immune response and cognitive capacity. We present these data, as well as results from gene ontology analyses. Combining predictions from behavioral ecology with experimental evolution is a powerful approach to assessing the suites of genetic and neurological changes associated with the evolution of complex behavioral traits, like learning. By analyzing the genomic mechanisms of what has evolved under experimental conditions, we can make a great step forward in understanding the evolution of learning and of plasticity in general