Contribution of D1R-expressing neurons of the dorsal dentate gyrus and Cav1.2 channels in extinction of cocaine conditioned place preference

Cocaine-associated contextual cues can trigger relapse behavior by recruiting the hippocampus. Extinction of cocaine-associated contextual memories can reduce cocaine-seeking behavior, however the molecular mechanisms within the hippocampus that underlie contextual extinction behavior and subsequent reinstatement remain poorly understood. Here, we extend our previous findings for a role of Cav1.2 L-type Ca2+ channels in dopamine 1 receptor (D1R)-expressing cells in extinction of cocaine conditioned place preference (CPP) in adult male mice. We report that attenuated cocaine CPP extinction in mice lacking Cav1.2 channels in D1R-expressing cells (D1cre, Cav1.2fl/fl) can be rescued through chemogenetic activation of D1R-expressing cells within the dorsal dentate gyrus (dDG), but not the dorsal CA1 (dCA1). This is supported by the finding that Cav1.2 channels are required in excitatory cells of the dDG, but not in the dCA1, for cocaine CPP extinction. Examination of the role of S1928 phosphorylation of Cav1.2, a protein kinase A (PKA) site using S1928A Cav1.2 phosphomutant mice revealed no extinction deficit, likely due to homeostatic scaling up of extinction-dependent S845 GluA1 phosphorylation in the dDG. However, phosphomutant mice failed to show cocaine-primed reinstatement which can be reversed by chemogenetic manipulation of excitatory cells in the dDG during extinction training. These findings outline an essential role for the interaction between D1R, Cav1.2, and GluA1 signaling in the dDG for extinction of cocaine-associated contextual memories

Delayed effect of funding rates in HE applications.

[A] Chart showing changes in funding rates in HE applications up to one year after publication of the guidance and starting one year after publication of the guidance. [B] Table summarizing the coefficients from the time series analysis.</p

Delayed effect of application rates in unsolicited HE applications.

[A] Chart showing changes in application rates in unsolicited HE applications up to one year after publication of the guidance and starting one year after publication of the guidance. [B] Table summarizing the coefficients from the time series analysis.</p

Delayed effect in unsolicited HE applications.

[A] Chart showing changes in funding rates in unsolicited HE applications up to one year after publication of the guidance and starting one year after publication of the guidance. [B] Table summarizing the coefficients from the time series analysis. (TIF)</p

Analysis of competing applications that were solicited for HE and non-HE research from FY2010 to FY2020.

Application counts were divided by the total number of applications for HE and non-HE research for each Fiscal Year. (TIF)</p

Illustrated explanation of ITSA graph with counterfactual line added.

The red box outlines pre-policy time series points and the purple box, post-policy points. Arrows are pointing to time = 1, 26 and 35; explanations of their predictions are below. Coefficients are otherwise known as independent slopes (per variable) within the regression model. For the part of the graph outlined above in red, these are the pre-policy time points, therefore, with regard to the regression model, only the baseline constant and time variable are utilized in deriving this point (not intervention or time since intervention). For model 2a above, solicited application rates, the predicted outcome, can be derived using the necessary coefficients and values from the regression table, for example, at time = 1: Predicted Yt = 73.30 Looking at trends after the policy was put into place, outlined in the purple box on the graph above: Immediately after the policy change (May 2016), for this model (2a), time = 26, intervention = 1 and time since = 1: Predicted Yt = 48.16 Looking further out (August 2018), post-policy change, for this model (2a), time = 35, intervention = 1 and time since = 10: Predicted Yt = 45.91 Had the policy not been put into place, a predicted value (counterfactual) can be calculated. This is visualized in the graph above with the dotted blue line continuing the same pre-policy trend/slope. For the same time = 35 example above: Predicted Yt = 52.90 There is a slight difference in the slopes of the before and after the policy change. Therefore, at different time points, the predicted outcome will vary and may not always be higher had the policy not been put into place. (TIF)</p

Analysis of HE and NIH (excluding HE) award rates for competing RPGs from FY2010- FY2020.

Award counts were divided by the total number of applications for HE and non-HE research for each Fiscal Year. (TIF)</p

Application rates in HE applications.

[A] Chart showing changes in application rates in HE applications prior to and following the guidance publication in 2015. [B] Table summarizing the coefficients from the time series analysis.</p