Epac2 in midbrain dopamine neurons contributes to cocaine reinforcement via facilitation of dopamine release
Abstract
Repeated exposure to drugs of abuse results in an upregulation of cAMP signaling in the mesolimbic dopamine system, a molecular adaptation thought to be critically involved in the development of drug dependence. Exchange protein directly activated by cAMP (Epac2) is a major cAMP effector abundantly expressed in the brain. However, it remains unknown whether Epac2 contributes to cocaine reinforcement. Here, we report that Epac2 in the mesolimbic dopamine system promotes cocaine reinforcement via enhancement of dopamine release. Conditional knockout of Epac2 from midbrain dopamine neurons (Epac2-cKO) and the selective Epac2 inhibitor ESI-05 decreased cocaine self-administration in mice under both fixed-ratio and progressive-ratio reinforcement schedules and across a broad range of cocaine doses. In addition, Epac2-cKO led to reduced evoked dopamine release, whereas Epac2 agonism robustly enhanced dopamine release in the nucleus accumbens in vitro. This mechanism is central to the behavioral effects of Epac2 disruption, as chemogenetic stimulation of ventral tegmental area (VTA) dopamine neurons via deschloroclozapine (DCZ)-induced activation of Gs-DREADD increased dopamine release and reversed the impairment of cocaine self-administration in Epac2-cKO mice. Conversely, chemogenetic inhibition of VTA dopamine neurons with Gi-DREADD reduced dopamine release and cocaine self-administration in wild-type mice. Epac2-mediated enhancement of dopamine release may therefore represent a novel and powerful mechanism that contributes to cocaine reinforcement.
Data availability
All data generated or analyzed during this study are included in the manuscript and supporting file; Source Data files have been provided for figures 1-7. Custom code used for the analysis of the fiber photometry data is available at https://github.com/xiaojieliu17/Fiber-photometry.
Article and author information
Author details
Funding
National Institute on Drug Abuse (R01DA035217)
- Qing-song Liu
National Institute on Drug Abuse (R01DA047269)
- Qing-song Liu
National Institute of Mental Health (F30MH115536)
- Casey R Vickstrom
National Institute on Drug Abuse (R01DA050180)
- David A Baker
National Institute on Drug Abuse (F31DA054759)
- Vladislav Friedman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All animal maintenance and use were in accordance with protocols (AUA #2420) approved by the Institutional Animal Care and Use Committee of Medical College of Wisconsin. All surgery was performed under ketamine and xylazine anesthesia, and every effort was made to minimize suffering.
Copyright
© 2022, Liu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,458
- views
-
- 300
- downloads
-
- 6
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.
-
- Neuroscience
Mice can generate a cognitive map of an environment based on self-motion signals when there is a fixed association between their starting point and the location of their goal.