ABSTRACTHigh throughput experiments are becoming increasingly common, and scientists must balance hypothesis driven experiments with genome wide data acquisition. We sought to predict novel genes involved in Drosophila learning and long-term memory from existing public high-throughput data. We performed an analysis using PILGRM, which analyzes public gene expression compendia using machine learning. We evaluated the top prediction alongside genes involved in learning and memory in IMP, an interface for functional relationship networks. We identified Grunge/Atrophin (Gug/Atro), a transcriptional repressor, histone deacetylase, as our top candidate. We find, through multiple, distinct assays, that Gug has an active role as a modulator of memory retention in the fly and its function is required in the adult mushroom body. Depletion of Gug specifically in neurons of the adult mushroom body, after cell division and neuronal development is complete, suggests that Gug function is important for memory retention through regulation of neuronal activity, and not by altering neurodevelopment. Our study provides a previously uncharacterized role for Gug as a possible regulator of neuronal plasticity at the interface of memory retention and memory extinction.
Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
