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Altered gating of Kv1.4 in the nucleus accumbens suppresses motivation for reward

  1. Bernadette O'Donovan
  2. Adewale Adeluyi
  3. Erin L Anderson
  4. Robert D Cole
  5. Jill R Turner
  6. Pavel I Ortinski  Is a corresponding author
  1. University of Kentucky, United States
  2. University of South Carolina, United States
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Cite this article as: eLife 2019;8:e47870 doi: 10.7554/eLife.47870

Abstract

Deficient motivation contributes to numerous psychiatric disorders, including withdrawal from drug use, depression, schizophrenia, and others. Nucleus accumbens (NAc) has been implicated in motivated behavior, but it remains unclear whether motivational drive is linked to discrete neurobiological mechanisms within the NAc. To examine this, we profiled cohorts of Sprague-Dawley rats in a test of motivation to consume sucrose. We found that substantial variability in willingness to exert effort for reward was not associated with operant responding under low-effort conditions or stress levels. Instead, effort-based motivation was mirrored by a divergent NAc shell transcriptome with differential regulation at potassium and dopamine signaling genes. Functionally, motivation was inversely related to excitability of NAc principal neurons. Furthermore, neuronal and behavioral outputs associated with low motivation were linked to faster inactivation of a voltage-gated potassium channel, Kv1.4. These results raise the prospect of targeting Kv1.4 gating in psychiatric conditions associated with motivational dysfunction.

https://doi.org/10.7554/eLife.47870.001

Introduction

Dysregulated motivation to pursue previously rewarding stimuli is a feature of multiple psychiatric disorders, including depression, schizophrenia, and withdrawal from substance use. Indeed, response to positive motivational situations has been proposed as one of only five behavioral dimensions that link the entire range of psychiatric diagnoses with underlying neurobiological mechanisms (RDoC framework; Cuthbert, 2015). Daily fluctuations in motivation for reward is a regular and familiar feature of human experience. However, chronically and persistently low motivation is associated with vulnerability to mental illness, including substance use disorders (Carroll et al., 2002; Janowsky et al., 2003; Perry et al., 2007; Radke et al., 2015; Brennan et al., 2001). Despite progress in understanding the neurobiology of reward over the last two decades, it remains unclear whether neuronal activity underlying persistent differences in motivation is functionally distinct from a broader spectrum of signaling events mediating reward processing.

The mesolimbic reward circuit is central to the processing of rewarding environmental stimuli. At the center of this circuit is the nucleus accumbens which integrates affective, spatial, and cognitive signals with approach to reward (Mogenson et al., 1980; Di Chiara, 2002; Di Chiara et al., 2004; Wise, 2004; Saddoris et al., 2015). The principal cells of the nucleus accumbens, striatal projection neurons (SPNs), regulate their firing patterns in a manner that predicts locomotion toward rewards as well as reward omission (Peoples and West, 1996; Nicola et al., 2004a; Roitman et al., 2005; Wan and Peoples, 2006). Action potential output of SPNs is modulated by dopamine, a molecule consistently implicated in behavioral response to motivationally salient stimuli, including action-outcome associations as well as reward avoidance (Akaike et al., 1987; Nicola et al., 2000; Ji and Martin, 2012; Ortinski et al., 2015). Dopamine modulates firing of SPNs in the nucleus accumbens via intracellular messengers coupled to the associated G-protein signaling cascades (Yang et al., 2013; Perez et al., 2006; Valdés-Baizabal et al., 2015; Cantrell et al., 1999; Schiffmann et al., 1995; Bender et al., 2010). Among the prominent dopamine targets are the voltage-gated potassium channels, canonical regulators of action potential output.

Voltage-gated potassium channels comprise the most diverse family of ion channels with over a hundred genes coding for potassium channel subunits in addition to multiple channel regulators. These channels are notably capable of suppressing, facilitating, and shaping action potentials and rhythmic activity throughout the central nervous system (Perez et al., 2006; Ji and Martin, 2014; Jan and Jan, 1997; Kimm et al., 2015; Johnston et al., 2010). For example, in principal neurons of the medial nucleus of the trapezoid body, activation of Kv1 channels increases firing threshold and inhibits firing, while activation of Kv3 channels accelerates action potential repolarization and promotes high firing rates (Johnston et al., 2010). In midbrain dopaminergic neurons, inhibition of Kv2 channels increases action potential firing and decreases afterhyperpolarization (AHP), while activation of the large conductance Ca2+-activated K+ (BK) channel decreases AHP, but has no effect on action potential firing (Kimm et al., 2015). In the striatum, dopamine depletion increases SPN intrinsic excitability and decreases AHP by accelerating the inactivation of the A-type (IA) K+ current (Azdad et al., 2009). The kinetic properties of potassium channel gating have been a subject of intense interest over many decades as the timing of channel activation and inactivation affects potassium channel interactions with other ionic conductances to determine membrane excitability and action potential generation.

Recent evidence suggests a prominent role for K+ channels in reward and motivated behaviors (Han et al., 2013; Gelernter et al., 2014; Cadet et al., 2017). For example, several studies indicate that G protein-gated inwardly rectifying K+ (GIRK) channels regulate neuronal firing and behavioral response to addictive drugs (McCall et al., 2017; Rifkin et al., 2017). In the NAc, where GIRK expression is very low or absent, chronic cocaine treatment has been shown to increase IA and BK channel currents (Hu et al., 2004). Similarly, decreased activation of small-conductance calcium-activated K+ channels in the NAc core has been reported to increase spike output and facilitate motivation to seek alcohol after abstinence (Hopf et al., 2010), while microinjection of a Kv7 agonist into the nucleus accumbens core has been found to reduce alcohol seeking (McGuier et al., 2016). Finally, a genome-wide analysis found rats that compulsively self-administer methamphetamine despite negative consequences (foot shocks) are segregated from non-compulsive methamphetamine takers by both differential RNA expression and DNA hydroxymethylation at a number of genes encoding voltage-gated K+ channels (Cadet et al., 2017). Despite this substantial body of evidence that Kv channels play a role in individual responding for drug reward, it remains unclear whether Kv-regulation of neuronal excitability may account for variability in responding to naturally reinforcing stimuli.

In this study, we test the hypothesis that individual heterogeneity in effort-based motivation is linked to altered intrinsic excitability of nucleus accumbens shell SPNs. We find that the spectrum of behavioral performance on a classical test of effort-based motivation is mirrored by genome-wide transcriptional differences with a major contribution of voltage-gated K+ channels and dopamine related transcripts. Our electrophysiological analyses provide evidence for a specific voltage-gated K+ channel subtype, Kv1.4, as a channel species with potential to specifically target the low motivation phenotype.

Results

Individual differences in motivation for sucrose reward

We began by establishing a behavioral profile of motivation for sucrose reward using a PR schedule of reinforcement in seven cohorts totaling 111 rats (Figure 1, Figure 1—figure supplement 1A). This revealed dramatic variability in PR breakpoints. Across all cohorts, the animals in the top 25% of interquartile distribution (highS rats) reached a mean breakpoint of 270.4 ± 11.3 lever presses, whereas the animals in the bottom 25% of interquartile distribution (lowS rats) reached a mean breakpoint of 57.4 ± 4.6 lever presses for a single sucrose reward (Figure 1A; t(42)=26.35 p<0.0001, unpaired t-test). Consistent with this, the total number of active lever presses per session also differed between highS and lowS rats. HighS rats pressed the active lever an average of 1462 ± 59.2 times, while lowS rats pressed the active lever an average of 296.6 ± 21.2 times per single operant session (Figure 1B; t(42)=26.49 p<0.0001, unpaired t-test). This difference in operant responding corresponded to an average of nine extra sucrose pellets earned in a single session by highS, relative to lowS, rats (Figure 1C; highS: 19.25 ± 0.16 pellets; lowS: 10.9 ± 0.46 pellets, t(42)=16.36 p<0.0001, unpaired t-test). To ensure that responding on PR was a stable behavior, animals were assigned to categories following 3 days of stable responding (<10% variability in active lever presses) on the PR schedule (Figure 1D,E).

Figure 1 with 1 supplement see all
Characterization of behavioral variability on a progressive ratio task.

(A) Scatterplot: Individual breakpoints of rats tested on the PR schedule of reinforcement (N = 111. Black bar: median. Red bars: upper and lower interquartile ranges. Bar histograms: Breakpoints were significantly different between rats in the lowest breakpoint quartile (lowS, N = 23) and rats in the highest breakpoint quartile (highS, N = 21). (B) Averaged over the last three days of the PR schedule, the rats in the lowS group pressed the active lever significantly fewer times and (C) earned significantly fewer pellets. (D) Stability of active lever presses in lowS rats over last 3 days on PR schedule. (E) Stability of active lever presses in highS over last 3 days on PR schedule. (F) Rats in the lowS and highS groups acquired the sucrose self-administration task (FR1→FR3→FR10 schedule) at a similar rate. (G) Mean active lever presses over the last three days of FR10 schedule were not different between groups. (H) Mean pellets earned over the last three days of FR10 schedule were not different between groups. **, p<0.01.

https://doi.org/10.7554/eLife.47870.002

Although classically defined as a test of effort-based motivation, variability in PR performance could reflect differences in ability to learn stimulus-reward associations or locomotor differences in rates of lever responding. To examine this, we analyzed behavioral performance during the fixed-ratio (FR) stage of operant training. The rats that would go on to form lowS and highS group acquired the self-administration task with a similar time course as the FR training progressed from 1 to 3 to 10 lever presses per reward (Figure 1F; [Main group effect: F(1,42)=2.13, p=0.1] two-way ANOVA). Similarly, the animals did not differ in the number of active lever presses (Figure 1G; t(42)=0.62, p=0.5442, unpaired t-test) and total pellets earned (Figure 1H; t(42)=0.93, p=0.36, unpaired t-test) over the final 3 days of FR10 training. There was also no correlation between active lever presses on FR10 and breakpoints achieved on the PR schedule (Figure 1, Figure 1—figure supplement 1B). This indicates that behavioral variability was specific to the high effort conditions imposed by the PR task. Inactive lever presses were not different between lowS and highS rats on either the FR (lowS: 1.1 ± 0.52; highS: 0.75 ± 0.37; t(42)=0.54 p=0.59, unpaired Student’s t-test) or the PR (lowS: 2.9 ± 0.6; highS: 3.9 ± 0.7; t(42)=1.07 p=0.29, unpaired Student’s t-test) schedules of reinforcement suggesting a lack of locomotor differences in approach to lever not associated with reward.

In a subgroup of 16 rats, we also examined whether baseline variability in stress levels may have contributed to PR performance by measuring levels of the stress marker, corticosterone. Corticosterone levels did not differ between rats (Figure 2A) and there was no correlation between individual corticosterone levels and PR breakpoints (Figure 2B; Pearson’s r2 = 0.0005, p=0.93).

Progressive ratio performance does not depend on individual stress levels.

(A) Plasma corticosterone levels do not differ between lowS, highS, and intermediate performance (midS) rats. (B) Breakpoint values on the PR schedule do not correlate with plasma corticosterone levels (N = 5/group).

https://doi.org/10.7554/eLife.47870.004

Altogether, these data indicate that individual variability in motivation for sucrose is dissociable from performance on low-effort, fixed ratio tasks. Furthermore, variability in motivation that we report is not due to differences in acquisition of stimulus-reward associations, locomotor ability, or baseline differences in HPA axis function.

Divergent transcriptome profile in highS and lowS rats

Seeking of natural rewards and behavioral performance on progressive ratio schedules of reinforcement critically relies on activity of the NAc shell (Basso and Kelley, 1999; Reynolds and Berridge, 2001; Kelley and Swanson, 1997; Wirtshafter and Stratford, 2010). To gain a comprehensive view of molecular drivers of individual variability in motivation for sucrose, we performed genome-wide RNA sequencing of NAc shell tissue. Similar levels of genetic variability could be expected among individuals from a single strain of rats. However, we found that the transcriptome profile diverged between, but not within, groups of rats characterized by their behavioral performance on the PR task. The most pronounced differences appeared between lowS and highS rats, while the transcriptome of animals with intermediate performance on the PR schedule (midS) aligned closer to highS, rather than lowS, rats (Figure 3A). Between the lowS and highS groups, a total of 231 transcripts were differentially regulated (Figure 3, Figure 3—source data 1; log2fold values ≥ 0.5 or≤−0.5). We conducted pathway analysis using Reactome (Fabregat et al., 2018; Milacic et al., 2012) for a more mechanistic insight into the function of differentially expressed transcripts. This analysis indicated that dopamine and K+ channel-related transcripts accounted for 3 of the top five gene pathways with significant differences in expression between lowS and highS groups. The other two Reactome pathways identified genes associated with extracellular matrix reorganization and cell growth/division (Figure 3C). The volcano plot in Figure 3B shows differentially enriched genes color-coded in red (upregulated in lowS) and blue (downregulated in lowS) with FDR < 0.05 (horizontal line). Within this pool, we highlight genes known to regulate neuronal activity in the NAc: four genes related to dopamine signaling (Pdyn, Drd1, Penk and Drd2), expressed at significantly lower levels in the lowS group, and six genes related to K+ channel signaling (Hcn4, Kcna4, Kcnab1, Kcnc4, and Kcnv1) that were bi-directionally regulated in the lowS relative to highS group. Overall, RNA sequencing data indicated that behavioral differences in motivation for reward are linked to genomic variability. Particularly prominent were the genes expected to influence NAc neuronal activity and (as in the case of extracellular matrix transcripts) organization of the NAc circuitry.

Divergent transcriptome profile in lowS and highS rats.

(A) A clustergram summary of top differentially expressed genes (DEGs) between lowS, highS, and midS rats. Each column represents RNA sequencing of NAc tissue from a single animal. Log2fold values are color coded red for upregulated genes and blue for downregulated genes. (B) Volcano plot highlighting genes related to K+ channel activity and dopamine signaling in lowS versus highS transcriptome. (C) Pathway analysis showing top mechanistic networks related to divergent motivation for sucrose in lowS and highS rats (N = 3, 4 and 3 for lowS, midS and highS, respectively).

https://doi.org/10.7554/eLife.47870.005
Figure 3—source data 1

Divergent transcriptome profile in highS and lowS rats.

Full gene list of DEGs between lowS vs highS, and lowS vs midS groups, including log2fold change values, p-values, and q-value (p-value adjusted to FDR). Genes with log2fold change of ≥0.5 and FDR < 0.05 and those with log2fold change of ≤0.5 and FDR < 0.05 were considered differentially expressed. Negative log2fc values indicate transcripts that were downregulated in the highS or midS groups relative to the lowS group. Positive log2fc values indicate transcripts that were upregulated in the highS or midS groups relative to the lowS group.

https://doi.org/10.7554/eLife.47870.006

Low motivation for sucrose is linked to increased NAc spike output

Our sequencing data indicate a prominent involvement of voltage-gated K+ channels, powerful regulators of neuronal excitability. In vivo studies from other groups suggest an inverse relationship between NAc spiking activity and reward-oriented behavior (Peoples and West, 1996; Nicola et al., 2004a; Roitman et al., 2005; Wan and Peoples, 2006) (see Discussion). Therefore, we speculated that low effort-based motivation occurred on a background of increased membrane excitability. Using electrophysiological recordings in brain slices we found that indeed, SPNs of lowS rats fired significantly more action potentials across a range of depolarizing current injections than SPNs of highS rats (Figure 4A, [Main group effect: F(1,215)=22.52, p=0.01], two-way RM ANOVA). Other measures of excitability, including membrane resistance, resting membrane potential, rheobase, latency to first spike, spike threshold, spike amplitude and spike half-width did not differ between the groups (Figure 4—figure supplement 1). However, action potential waveform analysis did show decreased spike afterhyperpolarization in cells from lowS animals (Figure 4B; t(16)=2.41 p=0.028, unpaired t-test), consistent with a role for voltage-gated K+ channels.

Figure 4 with 2 supplements see all
Low motivation for sucrose is associated with increased SPN excitability.

(A) Left, representative traces from NAc SPNs in lowS and highS animals at a depolarizing (+200 mV) current step. Right, action potential output across a range of injected current steps is significantly elevated in the lowS group (**, p<0.01, n = 9 N = 5/group). (B) Decreased afterhyperpolarization area in SPNs from lowS animals is consistent with increased action potential firing (n = 9, N = 5/group; *, p<0.05). (C) Left, representative traces of A-type (IA) currents in lowS and highS rats (n = 16 and 14 for lowS and highS, respectively; N = 5/group). Current amplitude was measured at the peak (red arrow). Right, Current-voltage relationship for IA is similar between groups. (D) Top, representative IA traces from lowS and highS animals are amplitude-scaled and overlaid to highlight differences in inactivation kinetics. Bottom, faster IA inactivation kinetics in the lowS animals in a scatterplot of decay times measured from the largest depolarizing peak in each group. (E) Top, representative traces of IBK currents isolated by paxilline. Current amplitude was measured at steady-state (red arrow). Bottom, there is no difference in IBK current-voltage relationship between lowS and highS animals (n = 7, N = 4/group). (F) Top, representative traces of total K+ current blocked by combination of 4-AP and TEA. Bottom, there is no difference in current-voltage relationship for total K+ current amplitude between lowS and highS animals (n = 7, N = 4/group).

https://doi.org/10.7554/eLife.47870.007

Sequencing data highlighted six K+ channel transcripts differentially expressed in lowS and highS rats, four of which (Kcnab1, Kcna4, Kcnc4, and Kcnv1) code for subunits or regulators of K+ channels underlying A-type (IA) currents (Rettig et al., 1994; Tseng-Crank et al., 1990; Dallas et al., 2008; Hugnot et al., 1996). We isolated IA currents using their characteristic property of fast inactivation at depolarized potentials and detected a substantial contribution of these currents to SPN excitability in both lowS and highS groups of animals. Peak IA amplitude was not significantly different between lowS and highS groups (Figure 4C; [Main group effect: F(1,28)=2.64, p=0.12], two-way RM ANOVA) across a range of holding potentials indicating a similar number of channels underlying A-type currents and a similar profile of voltage-dependent activation of these channels. However, action potential output is strongly sensitive to IA inactivation kinetics (Zemel et al., 2018) with faster inactivation expected to increase action potential frequency similarly to the effect of reduction in the number of underlying channels (see Discussion). We found that the decay time of inactivation was 27% faster in lowS relative to highS animals (Figure 4D; t(26)=2.1, p=0.047 t-test). We then looked at currents mediated by the large-conductance Ca2+-activated potassium (BK) channels since they have been shown to interact with IA to influence both afterhyperpolarization and neuronal firing (Kimm et al., 2015; Storm, 1987; Zhang and McBain, 1995). Our slice recordings indicated no difference in BK current amplitude (IBK) between lowS and highS groups (Figure 4E; [Main group effect: F(1,12)=0.04, p=0.83], two-way RM ANOVA). There was also no difference in the total outward K+ conductance measured after application of TEA and 4-AP (Figure 4F; Total K+: [Main group effect: F(1,13)=0.12, p=0.31], two-way RM ANOVA). Finally, recordings from midS animals aligned with findings in highS subjects: spike frequency was lower than the lowS group, but did not differ from highS animals, while IA amplitude, IBK, and total K+ currents did not differ between lowS, midS and highS groups (Figure 4—figure supplement 2). Notably, IA decay time in the midS group was intermediate to that of lowS and highS animals and not significantly different from either group (Figure 4—figure supplement 2C). Overall, our results suggest that a low motivation phenotype is linked to increased spiking of NAc principal neurons. This increased spiking is consistent with faster A-type current inactivation and decreased spike afterhyperpolarization, but is not related to availability of IA and BK channels or overall ion permeability through voltage-gated K+ channels.

Kv1.4 channels modulate SPN excitability selectively in lowS animals

Fast-inactivating A-type currents are mediated by K+ channel proteins encoded by Kcna4, Kcnc4, or Kcnd1-3 genes (Coetzee et al., 1999). The first two of these genes were represented in our sequencing results and code for Kv1.4 and Kv3.4 channels, respectively. Our IA recording protocol does not distinguish between these two IA channels. However, since only Kv1.4, but not Kv3.4, is strongly expressed in the NAc shell (Pessia et al., 1996Weiser et al., 1994), we profiled Kv1.4 activity using an antagonist, UK-78,282. UK-78,282 exhibits ~200 fold selectivity at Kv1.4 over Kv3.4 and 10- to 700-fold selectivity at Kv1.4 over other closely related channels (Kues and Wunder, 1992). The overall effect of acute application of UK-78,282 (100 nM) was to suppress action potential output in lowS, but not highS, SPNs (Figure 5A). This differential effect reversed the excitability profile of lowS relative to highS neurons such that in the presence of UK-78,282, lowS SPNs fired fewer action potentials than highS SPNs (Figure 5B, [Main group effect: F(1,12)=5.819, p=0.032] two-way RM ANOVA); cf. Figure 4A). UK-78,282 had a similar effect on afterhyperpolarization area in both lowS and highS animals (Figure 5C), suggesting that Kv1.4 activity does not account for differences in afterhyperpolarization between lowS and highS group at baseline (cf. Figure 4B). Additionally, UK-78,282 application did not result in significant differences between lowS and highS groups across a battery of excitability measures (Figure 5—figure supplement 1). Similar to highS animals, spike frequency in midS animals was not affected by UK-78,282 (Figure 5—figure supplement 2A). AHP in the midS group and IBK in either lowS, midS, or highS groups was also unaffected (Figure 5—figure supplement 2B–E).

Figure 5 with 2 supplements see all
NAc neurons from lowS animals are uniquely sensitive to Kv1.4 antagonist, UK-78,282.

(A) Action potential frequency was measured after application of UK-78,282 (100 nM) and expressed as percent change from frequency before UK-78,282 in the same cell (n = 7, N = 4/group). UK-72,282 suppresses firing in the lowS group to levels significantly different from baseline (Main drug effect: F(1,6) = 3.7, p=0.024, two-way RM ANOVA). In the highS group, firing after UK-72,282 application is not significantly different from baseline (Main drug effect: F(1,6) = 0.99, p=0.36, two-way RM ANOVA). (B) Firing frequency-current relationship highlights decreased action potential output in the lowS group after UK-78,282 application (*, p<0.05, two-way RM ANOVA). (C) UK-78,282 did not have an effect on AHP area in neurons from either the lowS or the highS groups. (D) Top, Representative traces of IA before and after UK-78,282 (100 nM) application in lowS (left, n = 13, N = 6) and highS (right, n = 14, N = 6) animals. Bottom, Current-voltage relationships indicate suppression of IA current amplitude by UK-78,282 in both groups (**, p<0.01, two-way RM ANOVA). (E) Left, representative IA traces before (baseline) and during UK-78,282 are amplitude-scaled and overlaid to highlight UK-78,282 effect on inactivation kinetics. Right, UK-78,282 increases IA inactivation time constant (τ) in lowS (top, n = 18, N = 6), but not highS (bottom, n = 16, N = 6), group. **, p<0.01, paired Student’s t-test; ns, not significant. The highS data excludes two cells where UK-78,282 increased decay times to anomalous levels (cell 1: from 20.1 ms to 34 ms; cell 2: from 17.4 ms to 49.8 ms). Including these two cells in the analysis did not change statistical interpretation (p=0.23, paired Student’s t-test).

https://doi.org/10.7554/eLife.47870.010

We then evaluated contribution of Kv1.4 to A-type K+ currents. Application of UK-78,282 significantly reduced amplitude of the IA in lowS, highS, and midS rats (lowS: [Main drug effect: F(1, 12)=14.03, p=0.0028]; highS: [Main drug effect: F(1, 13)=17.19, p=0.001] two-way RM ANOVAs; Figure 5D, Figure 5—figure supplement 2F). This was an unexpected result suggesting that reduced Kcna4 transcript in lowS animals did not reduce availability of Kv1.4 at the cell surface relative to highS group. We did observe a difference in voltage-dependence of recorded currents between groups: lowS neurons displayed sensitivity to UK-78,282 across all voltages, including the voltage range subthreshold to action potential firing, whereas IA in highS neurons was suppressed by UK-78,282 only at higher depolarizing voltages (Figure 5D). To follow-up on the observation that A-type currents display faster inactivation kinetics in lowS cells, we measured decay times of UK-78,282-sensitive currents. In lowS animals, application of UK-78,282 increased the time constant of inactivation from 7.32 ± 0.7 ms to 10.57 ± 1.5 ms (Figure 5E, t(17)=3.5, p=0.003, paired t-test). In neurons from highS animals, IA inactivation was insensitive to UK-78,282 with the time constant of 9.07 ± 1.3 ms at baseline and 8.9 ± 1.3 ms after UK-78,282 application (Figure 5E, t(13)=0.35, p=0.73, paired t-test). There was no effect of UK-78,282 on IA decay time in midS animals (Figure 5—figure supplement 2G,t(6)=0.35, p=0.74, paired t-test). We conclude that differences in Kv1.4 gating, rather than number of available channels alters firing of NAc shell SPNs in a manner that may discriminate between behavioral extremes on a progressive ratio task.

Kv1.4 blockade improves PR performance in lowS animals

Given the selective effect of UK-78,282 on neuronal excitability in lowS rats, we investigated whether a similar selectivity can be observed at the behavioral level. To examine this, we microinfused UK-78,282 into the NAc shell of lowS, midS and highS rats and measured its effect on PR performance. A two-way ANOVA with group (lowS, midS, highS) and UK-78,282 concentration (1 nM, 100 nM) as variables revealed a significant effect of group (F(2,16)=153.6, p<0.0001), a significant effect of drug concentration (F(1.97,31.5) = 3.9, p=0.03) and significant interaction (F(4,32)=5.1, p=0.003). To further explore these effects, we analyzed the effect of UK-78,282 infusion on each separate group. The low dose of UK-78,282 (1 nM) had no significant effect on sucrose self-administration in any group. A higher dose of UK-78,282 (100 nM), however, significantly increased breakpoints in the lowS, but not the highS or midS groups (lowS: F(2,21)=9.5, p=0.001; midS: F(2,25)=0.198, p=0.82; highS: F(2,21)=0.5, p=0.64; one-way ANOVAs Figure 6Ai,Bi;Ci). Similarly, active lever responding and pellets earned were increased specifically in the lowS rats (Figure 6Aii,iii, Bii,iii; Cii,iii; active lever lowS: F(2,21)=10.8, p=0.0006; active lever midS: F(2,25)=0.1, p=0.9; active lever highS: F(2,21)=0.05, p=0.95; pellets lowS: F(2,21)=6.3, p=0.007; pellets highS: F(2,25)=0.15, p=0.86; pellets highS: F(2,21)=0.5, p=0.61; one-way ANOVAs). There was no difference in inactive lever presses between UK-78,282 concentrations in the lowS or midS groups (lowS: F(2,21)=0.75, p=0.5; midS: F(2,25)=0.14, p=0.86, one-way ANOVAs), although inactive lever pressing was significantly reduced by UK-78,282 (100 nM) in highS rats (F(2,21)=6.1, p=0.008, one-way ANOVA; Figure 6Aiv, Biv, Civ). The reason for the latter finding is unclear, but it was strongly driven by a single rat robustly responding at baseline (15 inactive lever presses), but not after UK-78,282 treatment (one inactive lever press). Exclusion of this animal did not meaningfully affect highS breakpoint values, number of active lever presses, or pellets earned. Overall, these data show that an antagonist of Kv1.4 channels elevates willingness to exert effort for reward selectively in rats displaying lower motivation.

Figure 6 with 1 supplement see all
Selective effect of Kv1.4 antagonism on PR performance in lowS animals.

(A) In lowS animals (N = 6), microinjection of UK-78,282 into the NAc shell dose-dependently increased: i) breakpoints, ii) active lever presses and iii) pellets earned, but not iv) inactive lever presses. (B) In midS animals (N = 7) neither 1 nM nor 100 nM concentration of UK-78,282 had an effect on i) breakpoints, ii) active lever presses, iii) pellets earned or iv) inactive lever presses following 100 nM UK-78,282 microinjection. (C) In highS animals (N = 6) neither 1 nM nor 100 nM concentration of UK-78,282 had an effect on i) breakpoints, ii) active lever presses, or iii) pellets earned. There was a significant reduction in iv) inactive lever presses following 100 nM UK-78,282 microinjection. **, p<0.01, one-way ANOVAs.

https://doi.org/10.7554/eLife.47870.013

Discussion

Our results show that a lower baseline motivation to expend effort for naturally reinforcing stimuli is linked to altered kinetics of a voltage-gated potassium channel, Kv1.4, in the NAc shell. Faster Kv1.4 inactivation accelerates action potential output of NAc SPNs, despite an apparent lack of changes in the number of functional Kv1.4 channels. Suppression of Kv1.4 activity by UK-78,282 selectively decreases SPN spiking and facilitates motivated behavior in subjects with a lower baseline motivation for reward.

Selective effect of UK-78,282 on lowS Kv1.4

The lowS, but not the highS, phenotype that we describe was sensitive to the microinfusion of 100 nM concentration of UK-78,282 into the NAc. At this concentration, UK-78,282 is expected to be highly specific for Kv1.4. The target with the next nearest affinity, Kv1.3 channel, is blocked by UK-78,282 with an IC50 of 280 nM (Hanson et al., 1999) and is not expressed in the striatum (Kues and Wunder, 1992). We were initially puzzled by the discordant electrophysiological findings with UK-78,282. The compound selectively suppressed action potential firing in the lowS group, but was equally efficacious at blocking the peak of A-type currents in both lowS and highS animals. Our observation that faster inactivation of A-type currents is also uniquely sensitive to UK-78,282 in the lowS group provided clues as to the potential mechanisms involved. For example, inactivation of Kv1.4 is regulated by phosphorylation via the calcium/calmodulin-dependent kinase II (CaMKII) that prolongs inactivation time course (Roeper et al., 1997). We detected no significant differences in expression of any of the four major CaMKII isoform genes (Camk2a, Camk2b, Camk2d, and Camk2g) in the RNA sequencing data. However, a number of reports indicate that D2 dopamine receptors stimulate CaMKII activity (Takeuchi et al., 2002; Shioda and Fukunaga, 2017). If this mechanism were to be involved, then decreased D2 receptor stimulation would lead to decreased CaMKII activity and faster A-type current inactivation. Further, activity of CaMKII is sensitive to the inhibitor proteins encoded by the Camk2n1 and Camk2n2 genes. Increased expression of these genes in the lowS animals would promote faster inactivation. Consistent with this, our sequencing data show significant downregulation of Drd2 and upregulation of Camk2n2 (but not Camk2n1), transcripts in the lowS, relative to the highS, NAc tissue (Figure 3A, Figure 3—source data 1).

Differential assembly of the Kv1 channel tetramer may also play a role in regulating KV1.4 signaling. For example, hetero-tetrameric assembly of Kv1.4 with the delayed rectifier subunits, Kv1.1 or Kv1.2, leads to slower inactivation relative to the Kv1.4 homomer (Po et al., 1993). Kv1.1 and Kv1.2, encoded by Kcna1 and Kcna2 genes, are both expressed in the rat striatum, albeit at lower levels than Kv1.4 (62). Neither Kcna1 nor Kcna2 were differentially expressed in the lowS vs highS sequencing data. However, expression of the regulatory subunit Kvβ1 (Kcnab1 gene) that promotes cell surface expression of Kv1.x heterotetrameric complexes (Manganas and Trimmer, 2000) was markedly lower in the NAc of lowS animals. Lower expression of Kvβ1 is expected to decrease availability of Kv1.x hetero-tetramers and contribute to faster Kv1.4 channel inactivation reported here.

Both behavioral and electrophysiological effects of UK-78,282 in midS animals were aligned closely with effects observed in the highS group. It is intriguing that with regard to possible regulators of Kv1.4 function discussed above, RNA sequencing data from midS animals indicated significantly lower levels of Kcna1 and Kcnab2 transcripts expected to lead to faster inactivation, but decreased Camk2n1 (no change in Camk2n2) levels expected to lead to slower inactivation (Figure 3—source data 1). Electrophysiological data indicated that IA decay time in midS animals at baseline was intermediate to that of lowS and highS groups (Figure 4—figure supplement 2C), however midS animals were insensitive to UK-78,282 at both the IA kinetics and behavioral levels. These findings highlight remarkable flexibility and multiple redundancies that can impact activity of a single channel with possible behavioral consequences. Other possibilities for regulation of Kv1.4 inactivation include interactions with intracellular heme, intracellular pH, protein phosphatases, and membrane lipids (Roeper et al., 1997; Sahoo et al., 2013; Padanilam et al., 2002; Oliver et al., 2004). Direct examination of each of these mechanisms is outside the scope of this work. We can conclude, however, that unique sensitivity of lowS NAc neurons to UK-78,282 is likely conferred by differential interaction of Kv1.4 channels with binding partners or phosphorylation mechanisms rather than functional availability of Kv1.4 channels on the cell surface.

Kv1.4 regulation of neuron firing

Application of UK-78,282 suppressed peak IA amplitude in both lowS and highS animals (Figure 5D). However, there was a difference in voltage-dependence of the enhancement. In the lowS animals, our results indicate equal availability of Kv1.4 across potentials, including in the zone subthreshold to action potential firing. In the highS animals, Kv1.4 availability is greater at suprathreshold potentials. It is not clear what contributes to this difference. The voltage-dependence of activation kinetics of Kv1 family members has been shown to shift in the depolarizing direction by assembly with Kv1.2 subunit (Baronas et al., 2015). Moreover, Kv1.2 has been shown to enhance activation of Kv1.4/1.2 heterotetramers following depolarizing pre-pulses, a phenomenon termed use-dependent activation (Baronas et al., 2015). Such use-dependence may play a role in suppressing action potentials during spike trains and contribute to lower action potential spike frequency observed in highS animals. Slower inactivation of IA in highS animals, discussed above, will also tend to suppress action potential output.

The effect of constitutive Kv1.4 deletion on action potentials has been examined in two previous studies. The first one of these found that cortical pyramidal neurons of Kv1.4-/- mice have shorter action potential width, but similar resting membrane potential, input resistance and rheobase relative to wild-type controls (Carrasquillo et al., 2012). Blockade of Kv4 channels in this study, however, unmasked differences across a broader range of membrane properties, indicating involvement of Kv4-mediated compensatory mechanisms. The second report, found increased firing of suprachiasmatic nucleus neurons using the same line of knock-out mice, but did not evaluate other measures of membrane excitability (Granados-Fuentes et al., 2012). In a study of NAc neurons, suppression of A-type currents by dopamine was also linked to increased firing, although contribution of Kv1.4 to these currents was not specifically examined (Hopf et al., 2003). Increased action potential frequency in Kv1.4-/- mice is consistent with our recordings from SPNs of the NAc in lowS animals, given the RNA sequencing data that indicates decreased levels of Kcna4 transcript in this group. However, we find no differences in action potential width between lowS (1 ± 0.04 ms) and highS (0.99 ± 0.07 ms) groups. Indeed, we observe that lowS and highS groups are similar across a broad spectrum of action potential and intrinsic excitability measures both in the absence and in the presence of UK-78,282 (Figure 4—figure supplement 1, Figure 5—figure supplement 1). Taken together, these observations argue for brain-region specific impact of Kv1.4 on neuronal output that is additionally guided by interactions with other channels or modulatory mechanisms.

Spike frequency and motivation for reward

The relationship between NAc spiking and reward seeking has been a subject of intense interest for decades. There is general support from behavioral pharmacology studies that inhibition of NAc shell promotes seeking of natural reward (Basso and Kelley, 1999; Reynolds and Berridge, 2001; Kelley and Swanson, 1997). There are also strong indications that NAc firing may be modulated by reward-associated cues. For example, reward consumption has been shown to inhibit firing of NAc shell neurons in vivo, but only in the presence of cues predicting reward, whereas sustained increase in NAc firing has been proposed to inhibit operant responding for natural reward (Nicola et al., 2004a). Multiple other behavioral cues regulate NAc firing in vivo, including timing of reward delivery, magnitude of reward, and reward identity (Nicola et al., 2004a; Nicola et al., 2004b; Taha et al., 2007; Krause et al., 2010). Our data are unique in that we report SPNs from animals less motivated to pursue reward to be biased toward higher firing in the slice, in the absence of external behavioral cues.

An obvious suspect for differences in spike output in our lowS and highS datasets is relative abundance of D1-expresing and D2-expressing neurons. D1- and D2-expressing SPNs display distinct electrophysiological properties with D2 neurons showing greater excitability and lower threshold for action potential firing than D1 neurons (Cepeda et al., 2008; Gertler et al., 2008; Ade et al., 2008). Meanwhile, behavioral data provides mixed clues with some evidence supporting D1 SPNs as mediators of positive aspects of reward, and D2 SPNs as mediators of behavioral aversion (Kravitz et al., 2012; Hikida et al., 2010), while others report that signaling at both D1 and D2 SPNs enhances motivation for natural rewards (Soares-Cunha et al., 2016). Relative contribution of D1- and D2- SPNs to our data is not known and we have not directly addressed this question. However, several considerations argue against biased sampling, including random selection of SPNs from the pool of visually identified neurons and similar input resistance and rheobase values, hallmarks of D1 or D2 identity (Gertler et al., 2008; Janssen et al., 2009), between groups. RNA sequencing results indicated reduced expression of both D1 dopamine receptor and D2 dopamine receptor encoding genes in lowS relative to highS animals, rather than a selective reduction of one receptor population. If maintained at the protein level, this reduction highlights additional possibilities for regulation of neuronal output as a function of effort-based motivation.

Effort-based motivation for natural reward is unlikely to depend on a uniformly sustained increase or decrease in firing across all NAc shell SPNs. During a progressive ratio task for a cocaine reinforcer, a transient increase in NAc firing in vivo has been proposed to serve as a behavioral breakpoint signal, potentially driven by cocaine-induced increases in dopamine levels (Nicola and Deadwyler, 2000). However, behavioral output during progressive ratio for cocaine involved some neurons that are excited and others that are inhibited throughout the different phases of a behavioral task (Nicola and Deadwyler, 2000). Indeed, responses combining both excitation and inhibition have been observed in the NAc across many reward-seeking behaviors examined with in vivo electrophysiology. It is possible that such dynamic responses are guided by interactions with a distinct pattern of inputs to or outputs from the NAc and our experiments do not address this contingency. Our data do support the idea that any behavior encoded by NAc shell output will be biased by greater intrinsic likelihood of generating such output by SPNs in lowS, relative to highS, animals.

Conclusions

In summary, we describe a set of neuronal and genetic features associated with motivation to exert effort for natural reward. Lower motivation is linked to a divergent transcriptome profile, and increased SPN output in the NAc shell. Increased SPN output depends on faster inactivation kinetics of Kv1.4 and blockade of Kv1.4 activity selectively increases effort for natural reward in animals displaying low motivation. These results point to modulators of Kv1.4 gating as potential targets in a broad spectrum of psychiatric disorders associated with deficits in motivation.

Materials and methods

Subjects

Male Sprague-Dawley rats (Rattus norvegicus), weighing between 250–300 grams (Taconic Laboratories, Germantown, NY, USA) were individually housed in a colony room, rats were food restricted (20 g normal chow per day) with ad libitum water access and were maintained on a 12 hr/12 hr light/dark cycle, with lights on at 0700 hr. All experimental procedures were followed in accordance with the University of South Carolina School of Medicine and University of Kentucky Institutional Animal Care and Use Committees.

Sucrose self-administration

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Self-administration experiments were conducted in ventilated, sound attenuating operant chambers, equipped with a house light, active and inactive response levers, a pellet dispenser and a food receptacle (Med-Associates Inc, East Fairfield, VT, USA). During the experiment, rats had ad libitum access to water and were fed 20 g of normal chow per day after the operant session. All subjects were trained daily on a fixed-ratio one (FR1) schedule of reinforcement, in which each active lever press delivered a single 45 mg sucrose pellet. Presses on the inactive lever had no programmed consequences. Each sucrose pellet was followed by a 20 s timeout period during which house light went off and lever responses had no scheduled consequences. Once stable responding was achieved under the FR1 schedule, the rats progressed to the FR3 schedule (three active lever presses for one sucrose pellet) and then to FR10 (ten active lever presses for one sucrose pellet). Once stable under the FR10 schedule, subjects were placed on a progressive ratio (PR) schedule of reinforcement during which successive reinforcements could be earned according to an increasing number of lever-presses based on the formula: [5e(pellet # * 0.2)] – 5 (Richardson and Roberts, 1996). The session ended when rats failed to reach the next lever-press criterion within 1 hr. The final ratio achieved was recorded as the ‘breakpoint’ value. Rats were run on the PR schedule until stable responding was achieved. Stable responding under both FR and PR schedules was defined as <10% variability in active lever responses over three consecutive daily sessions.

Tissue collection

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Tissues were harvested from animals 24 hr after the final behavioral session. Trunk blood and tissue punches from the NAc shell region were collected. Punches were flash frozen on dry ice and plasma was separated from trunk blood after centrifugation (3200 rpm) at 4°C. Tissue and plasma were stored at −80°C.

Plasma corticosterone analysis

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Plasma corticosterone was measured using a corticosterone ELISA kit (Enzo Life Sciences, Farmingdale, NY). Plasma was diluted 1:40 and run according to manufacturer’s protocol. Plates were read using a Synergy 2 Multi-Mode plate reader (Bio Tek, Winooski, VT) with Gen5 software (Bio Tek, Winooski, VT).

RNA sequencing

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Total RNA was extracted using TRIzol (Life Technologies) and a RNeasy Mini Kit (Qiagen) according to manufacturer’s instructions. Samples were homogenized and incubated in TRIzol for 5 min before addition of chloroform and vigorous shaking for 30 s. Following a 3 min incubation, samples were centrifuged at 4°C for 10 min at maximum speed (≥10,000 rpm). The aqueous phase was aspirated and transferred to a microcentrifuge tube before addition of 70% EtOH, centrifugation at ≥10,000 rpm for 15 s, and collection of the precipitate from the RNeasy mini column. This step was repeated after adding 700 μL Buffer RW1 and, next, 500 μL Buffer RPE to the mini column. Another 500 μL of Buffer RPE was centrifuged for 2 min before the sample/mini column underwent a 2 min ‘dry’ spin and transferred to the final collection tube. Last, 30 μL DEPC water was used to elute the sample. RNA samples were quantified using NanoDrop Spectrophotometer ND-2000 (Nanodrop Technologies) and checked for quality and degradation by Agilent 2100 Bioanalyzer. All samples were of high quality (RNA integrity numbers between 9.9 and 10). Strand-specific mRNA libraries were prepared using the TruSeq Stranded mRNA Library Prep Kit (Set B, Illumina Inc) and sequenced on the Illumina NextSeq500 in a paired-end mode with read length of 2 × 75 bp.

Sequencing data preprocessing and analyses

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To ensure there were no sequencing errors, raw sequences were checked for quality using FastQC, and then aligned to the rat genome (downloaded from iGenomes, Illumina) using the STAR aligner program (Dobin et al., 2013). Aligned SAM files from STAR were converted to BAM files using SAMtools (Li et al., 2009). BAM files were processed for read summarization using featureCounts (Liao et al., 2014), and the resulting read counts were preprocessed by filtering out low read counts (read counts < 5) in R software. Processed data were then analyzed for differential expression using DESeq2 (Love et al., 2014) in R software. False discovery rate (FDR < 0.05) was used to determine the threshold of p- value for the analysis. Functional annotation/gene ontology analyses for biological function were conducted using the Reactome classification system (https://reactome.org/) accessed in February-March, 2018. Reactome is an open-source, curated database of biological pathways and processes (Fabregat et al., 2018; Milacic et al., 2012).

Electrophysiology

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Rats were decapitated following isoflurane anesthesia 24 hr after the last behavioral session. Brains were rapidly removed and coronal slices (300 μm-thick) containing the NAc were cut using a Vibratome (VT1200S; Leica Microsystems) in an ice-cold aCSF solution in which NaCl was replaced with an equiosmolar concentration of sucrose. ACSF contained the following (in mM): 130 NaCl, 3 KCl, 1.25 NaH2PO4, 26 NaHCO3, 10 glucose, 1 MgCl2, and 2 CaCl2; pH 7.2–7.4, when saturated with 95% O2 and 5% CO2. Slices were incubated in aCSF at 32–34°C for 45 min and kept at 22–25°C thereafter, until transfer to the recording chamber. All solutions had osmolarity between 305 and 315 mOsm. Slices were viewed under an upright microscope (Olympus BX51WI) with infrared differential interference contrast optics and a 40 × water immersion objective. For recordings, the chamber was continuously perfused at a rate of 1–2 ml/min with oxygenated aCSF heated to 32 ± 1°C using an automated temperature controller (Warner Instruments). Recording pipettes were pulled from borosilicate glass capillaries (World Precision Instruments) to a resistance of 4–7 MΩ when filled with the intracellular solution. The intracellular solution contained the following (in mM): 145 potassium gluconate, 2 MgCl2, 2.5 KCl, 2.5 NaCl, 0.1 BAPTA, 10 HEPES, 2 Mg-ATP, and 0.5 GTP-Tris; pH 7.2–7.3, with KOH; osmolarity 280–290 mOsm.

NAc shell SPNs were identified by their morphology and low resting membrane potential (RMP, −70 to −85 mV) and voltage-clamped at −70 mV. Current step protocols (from −500 to +500 pA; 20 pA increments; 500 ms step duration) were run to determine action potential frequency versus current (f-I) relationships. K+ currents were recorded in voltage-clamp mode with 1 mM QX-314 added to the intracellular solution. Following seal rupture, series resistance was compensated (65–75%). Outward currents were evoked by incrementing holding voltage from −90 mV to +40 mV in 10 mV steps. This protocol was then repeated with a 100 ms pre-step to a depolarized potential (−40 mV) at which IA currents are inactivated. Currents recorded after the −40 mV pre-step were subtracted from those recorded without the pre-step in the same cell, yielding IA that was measured at the peak of subtracted current. BK currents were measured at steady-state after subtracting membrane current responses in the presence of BK channel antagonist, paxilline (10 μM), from responses recorded in the absence of paxilline in the same cell. Total K+ currents were defined as those sensitive to combined application of 4-AP (0.5 mM) and TEA (10 mM) as previously described (Ji and Martin, 2014) and measured at steady-state. Drugs were applied via the Y-tube perfusion system modified for optimal solution exchange in brain slices (Hevers and Lüddens, 2002). All data were collected after a minimum of 2 min of drug exposure. Currents were low-pass filtered at 2 kHz and digitized at 20 kHz using a Digidata 1440A acquisition board (Molecular Devices) and pClamp10 software (Molecular Devices). Access resistance (10–30 MΩ) was monitored during recordings by injection of 10 mV hyperpolarizing pulses; data were discarded if access resistance changed >25% over the course of data collection. All analyses were completed using Clampfit 10 (Molecular Devices).

In vivo microinjections

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A subset of rats was implanted with guide cannulas targeting the NAc shell prior to training on a PR schedule of reinforcement. Following isoflurane anesthesia (2–5% isoflurane in O2) animals were placed in a stereotaxic instrument (Kopf Instruments, Tujunga, CA, USA), and guide cannulas targeting the NAc shell were positioned using the following stereotaxic coordinates (in mm from bregma): +1 AP, ± 1 ML, −5 DV. Guide cannulas were fixed to the skull with dental acrylic and stainless-steel obturators were placed inside the cannulas to prevent occlusions. Following a 7 day recovery period, animals proceeded to PR training and once stable PR responding was achieved, they underwent two microinjection sessions. During the first session, animals received a bilateral infusion of either UK-78,282 (1 nM) or UK-78,282 (100 nM) into the NAc shell through microinjectors extending 2 mm below tips of the guide cannulas. Microinfusions were at 0.5 μl/side over 2 min plus one minute of passive diffusion away from cannula tips. The criterion performance on the PR schedule was then re-established over consecutive daily sessions. After that, the animals underwent a second test session during which they received microinjection of a different UK-78,282 concentration. Microinjections of the two UK-78,282 concentrations were counterbalanced between animals and no animal received more than two microinjections. Cannula placements were confirmed histologically by cresyl violet staining (Figure 6—figure supplement 1).

Statistics

Statistical analyses were performed with Excel 2016 (Microsoft) or GraphPad Prism 6 (GraphPad software). For behavioral and electrophysiological experiments, Students t-tests, one-way ANOVAs, or two-way repeated measures ANOVAs followed by Bonferroni post hoc tests were performed as indicated in the text. Sample sizes were determined using G power 3.1.9.4 (effect size = 0.5, alpha = 0.05, power = 0.8). Throughout the manuscript, cell numbers are designated ‘n’, while animal numbers are designated ‘N’. Data were reported as mean ± standard error of the mean and statistical significance thresholds were set at p<0.05.

Data availability

Data generated or analysed during this study are included in the manuscript and supporting files.

References

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Decision letter

  1. Kate M Wassum
    Reviewing Editor; University of California, Los Angeles, United States
  2. Ronald L Calabrese
    Senior Editor; Emory University, United States
  3. ​Kauê Machado Costa
    Reviewer; National Institute on Drug Abuse Intramural Research Program, United States
  4. Lex Kravitz
    Reviewer; Washington University in St Louis, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Altered gating of Kv1.4 in the nucleus accumbens suppresses motivation for reward" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Ronald Calabrese as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: ​Kauê Machado Costa (Reviewer #2); Alexxai Kravitz (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The reviewers found that the study used a strong, multifaceted approach to reveal genetic and physiological underpinnings of population variance in effort-based motivation. The main findings are:

1) There is substantial variability in effort-based motivation.

2) Those with high v. low effort-based motivation have a different transcriptional profile in the NAc shell, including differential expression of 4 genes related to dopamine signaling and 6 related to K+ channels.

3) Those with low v. high effort-based motivation show lower electrically-evoked phasic dopamine release in the NAc shell.

4) NAc shell medium spiny neurons from low motivation rats were more excitable, with smaller AHPs and faster IA inactivation. This was linked to changes in Kv1.4, a transcript downregulated in low motivation rats.

5) Inactivation of Kv1.4 in the NAc shell increased motivation selectively in low motivation rats.

Essential revisions:

1) The sucrose preference test data are underpowered and, moreover, these data do not reflect the hedonic experience of the pellets earned during the progressive ratio task. For that reason, the reviewers suggested removing these data. All reviewers agreed that these data were not critical, and were even tangential to the main point of this study. If they are to be kept in, power should be added and their interpretation shifted to avoid the implication that these data speak to the hedonic experience of the reward in the PR task.

2) The experiments assessing the effects of UK-78,282 on IA inactivation (Figure 6E) are also underpowered. Because this is such a vital component of the study, the reviewers would like to see the N for both highS and lowS subjects increased.

3) Reviewers were also concerned with the power in the FSCV and FSCAV experiments. For the FSCAV data this was especially a problem with the marginal and thus unclear effect. They agreed that these experiments are also not critical to the conclusions drawn here and can be removed. If you choose to keep them in, power should be added so both the evoked FSCV and tonic FSCAV results are more convincing.

4) The reviewers agreed that is not possible to adequately differentiate D1 and D2 MSNs by electrophysiological criteria alone. Thus, the claims in the Discussion, which were not supported with data, of no correlation between putative D1 or D2 identity and electrophysiological responses should be removed and instead the Discussion should make clear to the reader that this remains an open question.

5) Because motivation in the PR task is interpreted as a stable trait-like feature, the reviewers agreed that it would be helpful provide data on the stability of PR performance across days.

6) Please clarify how lowS, midS, and highS groups were defined for individual experiments and report the behavioral data for each smaller cohort.

Reviewer #1:

The data in this report demonstrate substantial variability in motivation to exert effort in a progressive ratio task for food reward. Taking the top and bottom quartile of the subjects, the authors found that those subjects with low PR performance showed a different NAc shell transcriptional profile than those with high performance, had lower phasic dopamine released evoked by electrical stimulation, and higher MSN excitability. The latter was linked to Kv1.4 function and Kv1.4 blockade was found to increase motivation in low PR subjects. The multifaceted approach here is a strength. There are a lot of data here that will be of interest to many researchers to follow up on. The findings are novel and exciting, demonstrating a new potential mechanism of effort-related motivation and therapeutic target. My concerns are noted below.

1) There is substantial variability in PR performance, leading to categorization of low, and high S groups for subsequent experiments, which seems reasonable. But it is not clear within these smaller cohorts used for the RNA-seq, FSCV, physiology, and infusion experiments how low, mid, and high were defined. Was this top and bottom 25% within each smaller cohort, or within the larger group? This should be made clear and the behavioral data for each smaller cohort should be shown, perhaps in a supplementary figure.

2) Because motivation in the PR task is interpreted as a stable trait-like feature, it would be helpful to show that it was consistent within subject across days.

3) To say that Kv1.4 blockade 'rescues' a deficit in lowS animals is a bit of an overstatement, because even with the blockade breakpoint, presses, and earned pellets remain lower in the lowS than the midS group. To make this clear, the midS data should be included in the main Figure 7. Moreover, to make an interpretation that Kv1.4 blockade had an effect in one group but not another, the data should be analyzed together in a 2 way ANOVA with variables group (low, mid, high S) and drug dose. A significant interaction would justify the individual group ANOVAs evaluating dose within each group.

4) The FSCV methods are not adequately described. The frequency of MFB electrical stimulation needs to be provided. There should be some description of how electrode placements were optimized. The FSCAV methods are not provided. Details on how the FSCV data were analyzed, including how the max was determined, how the half-life was calculated, and how transient events were defined and calculated, need to be provided.

5) The 'hedonic preference' findings lead to the, perhaps unintended, implication that PR performance and reward hedonic experience are not related. But sucrose preference in this two bottle test is not reflective of the hedonic preference of the pellets earned in the PR task and so this interpretation could be somewhat misleading. I suggest referring to this as sucrose preference, as is more common, and walking back conclusions that PR and hedonia were unrelated.

6) The N for the FSCV data are somewhat low in the lowS group (N=5), which becomes a problem for the marginal tonic DA effect. I'm concerned the data are not sufficiently powered to detect an effect. If these data are going to be shown, power should be added or a power analysis provided to indicate these experiments were sufficiently powered.

Reviewer #2:

In their study, O'Donovan et al. set out to identify potential genetic and physiological underpinnings of population variance in effort-based motivation. They use PR breakpoints as an index for this behavioral variable in a naïve cohort of Sprague-Dawley rats, focusing on differences between animals at the top (highS) and bottom (lowS) quartiles of the PR breakpoint distribution. First, they showed that these groups were similar in their FR responses, sucrose preference (but see comment below) and stress hormone levels. Authors performed a genome-wide RNA sequencing of tissue samples from the nucleus accumbens (NAc) of these groups as a broad-scope strategy for identifying differentially expressed transcripts. Out of the 164 identified genes differentially expressed in low- versus high-motivation rats, the authors found four genes related to dopamine signaling and six related to K+ channels. Using in vivo FSCV, they show that lowS rats have lower evoked dopamine transients in the NAc shell. Furthermore, striatal projection neurons (SPNs) from lowS rats were more excitable, with smaller AHPs and faster IA inactivation. They pharmacologically demonstrate that this phenotype is linked to changes in Kv1.4 (KCNA4), which was identified as a downregulated transcript in their RNA screening. The changes in Kv1.4 currents seen in lowS versus highS animals were not simply related to current amplitude, as would perhaps have been expected based on the RNA screening alone, but involved a specific acceleration of IA inactivation that was selectively impacted by blockade with UK-78,282. The authors speculate on the potential mechanism underlying this finding but chose not to explore it within this study. Finally, they pharmacologically inactivate Kv1.4 within the NAc shell in vivo, resulting in a dose-dependent increase in motivation selectively for the lowS rats, establishing a causal link between gene expression variability, Kv1.4 channels, SPN excitability in the NAc shell and behavioral motivation.

Overall, the study is very interesting, well conducted and of high relevance. The approach used to isolate potential underpinnings for behavioral variability is at the forefront of the field. The core of the presented data is quite adequate, and the findings are truly exciting. However, a few key issues need to be addressed.

One major concern is that the sucrose preference test is woefully underpowered. It seems as if only 3 highS and 3 or 4 lowS rats were included in this test, and there is a clear trend towards the lowS animals having a higher sucrose preference. This is particularly important given the role of specific subregions of the NAc shell in mediating hedonic preference vs. incentive salience (as established in the work of Kent Berridge and others). The authors unfortunately would need to repeat this test, specifically with more highS and lowS rats, in order to have a conclusive result and rule out potential differences in sucrose preference.

The authors also claim to have clustered their recorded cells into putative D1- and D2-expressing SPNs based on their electrophysiological properties and saw no difference in "electrophysiological responses at baseline, or following drug application" between the groups, but this data is not shown. This analysis and its results must be disclosed, as well as the relative number of putative D1 and D2 cells in each experiment, wherever this is possible. Any differential effect between these two cells types, as well as potential sampling biases, could lead to major changes in the interpretation of the results. Results reported in Figure 6E are of particular concern, given that the selective effect of UK-78,282 on IA inactivation in the lowS group (a key finding of the study) is "demonstrated" with less than 6 cells in each group, which is already a relatively underpowered experiment without considering a potential asymmetry in the sampling of D1 vs. D2 subpopulations.

Reviewer #3:

The manuscript by O'Donovan and colleagues addresses an important question of individual variability among rats on motivational tasks, specifically here in the progressive ratio task. Variance is often high on these tasks, and reasons for this inter-animal variance are not known. In an elegant set of studies, the authors link this variance to changes in the transcriptional landscape of the nucleus accumbens, and drill down further to link it to changes in potassium channel expression. They highlight one channel in particular, Kv1.4, and perform extensive electrophysiology to show that it is differentially active in low vs. high motivated rats. This channel is expressed highly in both dorsal and ventral striatum, and the authors further demonstrate that pharmacological manipulations of its function can increase motivation in rats that are on the low end of PR responding. Overall, I found the study well designed and experimentally sound, and the discussion of the questions and implications were both interesting and appropriate. I have only suggestions for improvement.

1) In Figure 1, they show that the differences in PR were not observable in the preceding FR sessions. They argue this by looking at average FR responding in the same rats before they were trained on the PR. A more sensitive test of this question would be to look at a correlation between FR responding and PR responding in the same animals. I would encourage the authors to look into this.

2) The paper proposes to find a mechanism underlying variance in motivational drive on the PR task. As they note in their Introduction, motivation can fluctuate between days. The rats here were tested on PR until responding was stable, so the authors have data on whether these differences in PR responding were indeed stable within animals across days. They currently present PR data as the average of the last 3 days of PR training. Can they perform correlations among these days (i.e.: Days 1 vs. 2, 1 vs. 3, 2 vs. 3) to confirm that high responding on the PR task is a stable behavioral trait across days?

3) I was unclear how rats were parsed for RNA analysis, FSCV, and ephys experiments. Were these all from the original group in Figure 1A? Were these solely the lowS and highS animals identified in Figure 1A? Can the authors clarify how they divided these animals up and chose which individuals were used for each assay? A schematic of the overall experimental flowchart would be helpful for wrapping my head around it, as there were many rats that ended up in several different experiments.

4) For the ephys, can the authors clearly label how many cells from how many rats were recorded for each experiment?

5) The final section of the Results is titled, "Kv1.4 blockade rescues deficient PR performance in lowS animals". The word "rescue" also appears in their impact statement, but I think this word is too strong considering their highS rats have breakpoints of ~250, and their lowS rats have breakpoints of ~50 which were increased to ~80 with Kv1.4 blockade. Even with Kv1.4 blockade these rats would be in the "lowS" group based on the distribution in Figure 1A. The authors should be more nuanced in their description of the effect as a "rescue".

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Altered gating of Kv1.4 in the nucleus accumbens suppresses motivation for reward" for further consideration at eLife. Your revised article has been favorably evaluated by Ronald Calabrese as the Senior Editor and three reviewers, one of whom is a member of our Board of Reviewing Editors.

The manuscript has been improved but there are two small remaining issues that need to be addressed before acceptance, as outlined below:

First, please address the concerns about statistical reporting noted by reviewer #1.

Second, given that there has been some author changes (Srimal Samaranayake and Parastoo Hashemi removed and Robert Cole added), please confirm in your cover letter that all authors, including those removed, are aware of and have approved of these changes.

Reviewer #1:

The authors have addressed most of my concerns and the manuscript is improved. I have only one remaining issue which is that there are still a few instances where statistics need to be clarified. There are still instances where only a p value is shown without the full statistical reporting, e.g., in the second paragraph of the subsection “Individual differences in motivation for sucrose reward”, all of the text for Figure 4—figure supplement 1. The full statistic needs to be shown in all these cases. In many cases, e.g. in the subsection “Low motivation for sucrose is linked to increased NAc spike output”, a two -way ANOVA is used to analyze the data, but only one F statistic is reported, leaving it in some cases unclear whether this F statistic is one of the main effects or the interaction. In these cases, please include the full ANOVA results (both main effects and interaction), or otherwise clarify which part of the ANOVA is being reported. I would be preferable to report actual p values in all cases rather than p<0.05 or p<0.01.

Reviewer #2:

The authors have appropriately responded to all of my comments in their revised manuscript. I recommend that the paper should be accepted.

Reviewer #3:

The authors have substantially modified their manuscript and have fully addressed my concerns. I support publication of this manuscript in its present form.

https://doi.org/10.7554/eLife.47870.017

Author response

Essential revisions:

1) The sucrose preference test data are underpowered and, moreover, these data do not reflect the hedonic experience of the pellets earned during the progressive ratio task. For that reason, the reviewers suggested removing these data. All reviewers agreed that these data were not critical, and were even tangential to the main point of this study. If they are to be kept in, power should be added and their interpretation shifted to avoid the implication that these data speak to the hedonic experience of the reward in the PR task.

Sucrose preference data have been removed.

2) The experiments assessing the effects of UK-78,282 on IA inactivation (Figure 6E) are also underpowered. Because this is such a vital component of the study, the reviewers would like to see the N for both highS and lowS subjects increased.

We have increased the animal and recorded cell numbers for the effects of UK-78,282 on IA characterization for both highS and lowS subjects. These data are now based on 13-18 cells from 6 animals in each group and presented in the revised Figure 5D-E.

3) Reviewers were also concerned with the power in the FSCV and FSCAV experiments. For the FSCAV data this was especially a problem with the marginal and thus unclear effect. They agreed that these experiments are also not critical to the conclusions drawn here and can be removed. If you choose to keep them in, power should be added so both the evoked FSCV and tonic FSCAV results are more convincing.

We have removed the FSCV and FSCAV experiments from the manuscript.

4) The reviewers agreed that is not possible to adequately differentiate D1 and D2 MSNs by electrophysiological criteria alone. Thus, the claims in the Discussion, which were not supported with data, of no correlation between putative D1 or D2 identity and electrophysiological responses should be removed and instead the Discussion should make clear to the reader that this remains an open question.

We agree with the reviewers that D1/D2 identity cannot be unequivocally determined by electrophysiological criteria alone. We have extensively revised the Discussion and now state explicitly that relative contribution of D1 vs. D2 cells to our data is not known. Subsection “Spike frequency and motivation for reward”, second paragraph.

5) Because motivation in the PR task is interpreted as a stable trait-like feature, the reviewers agreed that it would be helpful provide data on the stability of PR performance across days.

This is an important point and we have revised Figure 1 to highlight stability of behavioral performance over the last 3 days of progressive ratio testing in individual lowS and highS animals. Figure 1D, E.

6) Please clarify how lowS, midS, and highS groups were defined for individual experiments and report the behavioral data for each smaller cohort.

The subjects for this study were obtained from seven different cohorts. lowS and highS groups were defined by the top and bottom quartile of the PR breakpoint interquartile distribution within each cohort. This is now illustrated in a new Figure 1—figure supplement 1A. The individual cohorts that provided subjects for RNA-seq, electrophysiology and infusion experiments are reported in the figure legend.

Reviewer #1:

[…] 1) There is substantial variability in PR performance, leading to categorization of low, and high S groups for subsequent experiments, which seems reasonable. But it is not clear within these smaller cohorts used for the RNA-seq, FSCV, physiology, and infusion experiments how low, mid, and high were defined. Was this top and bottom 25% within each smaller cohort, or within the larger group? This should be made clear and the behavioral data for each smaller cohort should be shown, perhaps in a supplementary figure.

Please see essential revision #6.

2) Because motivation in the PR task is interpreted as a stable trait-like feature, it would be helpful to show that it was consistent within subject across days.

Please see essential revision #5.

3) To say that Kv1.4 blockade 'rescues' a deficit in lowS animals is a bit of an overstatement, because even with the blockade breakpoint, presses, and earned pellets remain lower in the lowS than the midS group. To make this clear, the midS data should be included in the main Figure 7. Moreover, to make an interpretation that Kv1.4 blockade had an effect in one group but not another, the data should be analyzed together in a 2 way ANOVA with variables group (low, mid, high S) and drug dose. A significant interaction would justify the individual group ANOVAs evaluating dose within each group.

We agree with these excellent points and addressed them in the revised manuscript as follows: 1) we refrain from using ‘rescue’ to describe the results of Kv1.4 blockade findings. Results subsection “Kv1.4 blockade improves PR performance in lowS animals”: 2) we included midS data in the revised Figure 6B.3)The effect of UK-78,282 across lowS, midS, and highS groups was analyzed by two-way ANOVA, which revealed significant effects of group and drug treatment, and significant interaction. These analyses have been added to Results subsection “Kv1.4 blockade improves PR performance in lowS animals”.

4) The FSCV methods are not adequately described. The frequency of MFB electrical stimulation needs to be provided. There should be some description of how electrode placements were optimized. The FSCAV methods are not provided. Details on how the FSCV data were analyzed, including how the max was determined, how the half-life was calculated, and how transient events were defined and calculated, need to be provided.

FSCV and FSCAV experiments have been removed from the manuscript. Please see essential revision #3.

5) The 'hedonic preference' findings lead to the, perhaps unintended, implication that PR performance and reward hedonic experience are not related. But sucrose preference in this two bottle test is not reflective of the hedonic preference of the pellets earned in the PR task and so this interpretation could be somewhat misleading. I suggest referring to this as sucrose preference, as is more common, and walking back conclusions that PR and hedonia were unrelated.

We have removed results and interpretation of the sucrose preference experiments. Please see essential revision #1.

6) The N for the FSCV data are somewhat low in the lowS group (N=5), which becomes a problem for the marginal tonic DA effect. I'm concerned the data are not sufficiently powered to detect an effect. If these data are going to be shown, power should be added or a power analysis provided to indicate these experiments were sufficiently powered.

FSCV and FSCAV experiments have been removed from the manuscript. Please see essential revision #3.

Reviewer #2:

[…] Overall, the study is very interesting, well conducted and of high relevance. The approach used to isolate potential underpinnings for behavioral variability is at the forefront of the field. The core of the presented data is quite adequate, and the findings are truly exciting. However, a few key issues need to be addressed.

One major concern is that the sucrose preference test is woefully underpowered. It seems as if only 3 highS and 3 or 4 lowS rats were included in this test, and there is a clear trend towards the lowS animals having a higher sucrose preference. This is particularly important given the role of specific subregions of the NAc shell in mediating hedonic preference vs. incentive salience (as established in the work of Kent Berridge and others). The authors unfortunately would need to repeat this test, specifically with more highS and lowS rats, in order to have a conclusive result and rule out potential differences in sucrose preference.

We have removed results and interpretation of the sucrose preference experiments. Please see essential revision #1.

The authors also claim to have clustered their recorded cells into putative D1- and D2-expressing SPNs based on their electrophysiological properties and saw no difference in "electrophysiological responses at baseline, or following drug application" between the groups, but this data is not shown. This analysis and its results must be disclosed, as well as the relative number of putative D1 and D2 cells in each experiment, wherever this is possible. Any differential effect between these two cells types, as well as potential sampling biases, could lead to major changes in the interpretation of the results.

We excluded putative D1/D2 data clustering results from the revised Discussion, but indicate reasons why we consider sampling bias unlikely. Please see essential revision #4 and Discussion subsection “Spike frequency and motivation for reward”.

Results reported in Figure 6E are of particular concern, given that the selective effect of UK-78,282 on IA inactivation in the lowS group (a key finding of the study) is "demonstrated" with less than 6 cells in each group, which is already a relatively underpowered experiment without considering a potential asymmetry in the sampling of D1 vs. D2 subpopulations.

We have increased the number of cells and animals in electrophysiological experiments with UK78,282 and included them in the revised Figure 5D-E. Please see essential revision #2.

Reviewer #3:

[…] Overall, I found the study well designed and experimentally sound, and the discussion of the questions and implications were both interesting and appropriate. I have only suggestions for improvement.

1) In Figure 1, they show that the differences in PR were not observable in the preceding FR sessions. They argue this by looking at average FR responding in the same rats before they were trained on the PR. A more sensitive test of this question would be to look at a correlation between FR responding and PR responding in the same animals. I would encourage the authors to look into this.

Thank you for the idea. The revised manuscript includes correlation between FR and PR responding in individual animals in Figure 1—figure supplement 1B and Results subsection “Individual differences in motivation for sucrose reward”, second paragraph.

2) The paper proposes to find a mechanism underlying variance in motivational drive on the PR task. As they note in their Introduction, motivation can fluctuate between days. The rats here were tested on PR until responding was stable, so the authors have data on whether these differences in PR responding were indeed stable within animals across days. They currently present PR data as the average of the last 3 days of PR training. Can they perform correlations among these days (i.e.: Days 1 vs. 2, 1 vs. 3, 2 vs. 3) to confirm that high responding on the PR task is a stable behavioral trait across days?

We now include data demonstrating behavioral stability of PR over the last three days of PR training for each lowS and highS animal in Figures 1D and E.

3) I was unclear how rats were parsed for RNA analysis, FSCV, and ephys experiments. Were these all from the original group in Figure 1A? Were these solely the lowS and highS animals identified in Figure 1A? Can the authors clarify how they divided these animals up and chose which individuals were used for each assay? A schematic of the overall experimental flowchart would be helpful for wrapping my head around it, as there were many rats that ended up in several different experiments.

Please see essential revision #6.

4) For the ephys, can the authors clearly label how many cells from how many rats were recorded for each experiment?

Cell and animal number information has been added throughout the manuscript.

5) The final section of the Results is titled, "Kv1.4 blockade rescues deficient PR performance in lowS animals". The word "rescue" also appears in their impact statement, but I think this word is too strong considering their highS rats have breakpoints of ~250, and their lowS rats have breakpoints of ~50 which were increased to ~80 with Kv1.4 blockade. Even with Kv1.4 blockade these rats would be in the "lowS" group based on the distribution in Figure 1A. The authors should be more nuanced in their description of the effect as a "rescue".

We changed the title of the final section of the Results to “Kv1.4 blockade improved PR performance in lowS animals” and refrain from unwarranted use of “rescue” in the revised impact statement and throughout the revised manuscript.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

The manuscript has been improved but there are two small remaining issues that need to be addressed before acceptance, as outlined below:

First, please address the concerns about statistical reporting noted by reviewer #1.

Second, given that there has been some author changes (Srimal Samaranayake and Parastoo Hashemi removed and Robert Cole added), please confirm in your cover letter that all authors, including those removed, are aware of and have approved of these changes.

In this revised version, we addressed the statistical reporting comments raised by reviewer #1.

We have also removed two authors: Srimal Samaranayake and Parastoo Hashemi. These two authors provided fast-scan cyclic voltammetry data for the original submission. The voltammetry data have been removed from the revised version per reviewers’ suggestion. Both Dr. Samaranayake and Dr. Pashemi are aware that their names have been removed from the author list and approved this change. Additionally, in the revised version, Robert Cole was added to the author list. Dr. Cole was involved in experiments essential for the timely completion of the revised work. Dr. Cole is aware of and approves his addition to the author list of the revised manuscript.

https://doi.org/10.7554/eLife.47870.018

Article and author information

Author details

  1. Bernadette O'Donovan

    Department of Neuroscience, University of Kentucky, Lexington, United States
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3591-0560
  2. Adewale Adeluyi

    Department of Drug Discovery and Biomedical Sciences, South Carolina College of Pharmacy, University of South Carolina, Columbia, United States
    Contribution
    Data curation, Software, Formal analysis, Visualization, Methodology
    Competing interests
    No competing interests declared
  3. Erin L Anderson

    Department of Drug Discovery and Biomedical Sciences, South Carolina College of Pharmacy, University of South Carolina, Columbia, United States
    Contribution
    Formal analysis, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4731-001X
  4. Robert D Cole

    Department of Neuroscience, University of Kentucky, Lexington, United States
    Contribution
    Data curation, Methodology, was added to the manuscript at the review stage, and provided essential contributions to acquisition and curation of the new data requested by the reviewers of the original submission
    Competing interests
    No competing interests declared
  5. Jill R Turner

    College of Pharmacy, University of Kentucky, Lexington, United States
    Contribution
    Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
  6. Pavel I Ortinski

    Department of Neuroscience, University of Kentucky, Lexington, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    pavel.ortinski@uky.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0814-4490

Funding

National Institute on Drug Abuse (DA031747)

  • Pavel I Ortinski

National Institute on Drug Abuse (DA041513)

  • Pavel I Ortinski

National Institute on Drug Abuse (DA032681)

  • Jill R Turner

National Institute on Drug Abuse (DA044311)

  • Jill R Turner

National Institute on Drug Abuse (DA016176)

  • Robert D Cole

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

Funding Support: This work was supported by the National Institutes of Health grants K01DA031747, R01DA041513 (PIO), R00DA032681, R01DA044311 (JRT), T32DA016176 (RDC).

Ethics

Animal experimentation: All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols #2324-101167-120116 of the University of South Carolina and #2018-3132 of the University of Kentucky.

Senior Editor

  1. Ronald L Calabrese, Emory University, United States

Reviewing Editor

  1. Kate M Wassum, University of California, Los Angeles, United States

Reviewers

  1. ​Kauê Machado Costa, National Institute on Drug Abuse Intramural Research Program, United States
  2. Lex Kravitz, Washington University in St Louis, United States

Publication history

  1. Received: April 23, 2019
  2. Accepted: August 22, 2019
  3. Version of Record published: September 5, 2019 (version 1)

Copyright

© 2019, O'Donovan et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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