Functional characterization of an insulin-like peptide network that regulates lea

Harvard University, Cambridge MA

Investigators

Linked publications, trials & patents

Abstract

To survive, animals have to optimize their physiological and behavioral responses based on specific environmental cues. Through conserved signaling mechanisms, the insulin/insulin-like peptide (ILP) pathway plays essential roles in this process by regulating development, metabolism and life span in response to internal and external environments. Intriguingly, ILP signaling also regulates learning and memory, suggesting that ILPs act as a link between environment and neural function to generate optimal behavioral outputs. However, the molecular and cellular mechanisms through which ILPs regulate learning remain largely uncharacterized. Many animals, including humans, encode multiple ILPs in their genomes, such as the 40 ILPs in C. elegans, suggesting functional diversity and potential interaction among them. Recently, two C. elegans ILPs, INS-6 and INS-7, are shown to play opposite roles in regulating aversive olfactory learning. The observation that INS-6 inhibits ins-7 in this process and that the expression of these two ILPs are regulated by other ILPs suggest the existence of an ILP network that modulates behavior. Because INS-6 and INS-7 appear to coordinate the animal's behavioral responses with its physiological state, this further suggests that this network's regulation of learning involves environmental context. This R01 will test this hypothesis through the following: (1) define the ILP network composition and architecture that regulates learning; (2) map the cellular circuitry through which this network functions; and (3) demonstrate how environment modulates the activity of the ILP learning network. To address these aims, this R01 will involve high-throughput learning behavioral assays and in vivo calcium imaging of neuronal activities in different ILP mutant backgrounds and under different conditions. Finally, completion of this R01 will illustrate how the ILP network optimizes learning behavior to increase survival under different environments.