Opioid abuse is a serious public health issue. Opiates have lasting physiological effects on reward memory circuitry, which contributes to cravings for the drug and changes the brain's response to other drugs of abuse. However, little is known of the molecular mechanisms underlying these opioid-induced changes. The sheer number and heterogeneity of neurons within reward circuits, combined with their elaborate connectivity, has prevented a deeper understanding of the identity of these lasting molecular alterations. A small but sophisticated brain and impressive array of neurogenetic tools for in vivo analysis have shown the fruit fly, Drosophila melanogaster to be an ideal model for discovery of novel mechanisms underlying the effects of drugs of abuse on the brain. Remarkably, no work has successfully shown that Drosophila are behaviorally responsive to opiates, despite several invertebrate species showing robust responses to opiate treatment. Our data suggest that Drosophila show acute behavioral responses to the synthetic opiate fentanyl, and will self-administer volatilized fentanyl after injury. Here we propose to establish Drosophila as an effective model to understand the neural and molecular mechanisms underlying the motivation to seek fentanyl. Our goal is to use an innovative neurogenetic approach to map Drosophila opioid receptor circuits responsive to acute nociception and to self- administration, and their post-synaptic connections. This work will provide a brain-wide map of expression of opioid receptors at a single cell level, define which of these neurons are involved in reward and aversion, and determine how these circuits are integrated.
In recent years opiate use has increased at a phenomenal rate throughout the U.S. It is critical to obtain a greater understanding of the neural and molecular mechanisms underlying opioid addiction in order to develop more effective treatments. Innovative neurogenetics tools make the fruit fly, Drosophila melanogaster an ideal model to investigate these mechanisms with multi-tiered resolution from genes to circuits to behavior.
