Research Articles, Cellular/Molecular

Muscarinic Receptor Activation Preferentially Inhibits Rebound in Vulnerable Dopaminergic Neurons

Megan L. Beaver and Rebekah C. Evans
Journal of Neuroscience 16 April 2025, 45 (16) e1443242025; https://doi.org/10.1523/JNEUROSCI.1443-24.2025

Abstract

Dopaminergic subpopulations of the substantia nigra pars compacta (SNc) differentially degenerate in Parkinson's disease and are characterized by unique electrophysiological properties. The vulnerable population expresses a T-type calcium channel-mediated afterdepolarization (ADP) and shows rebound activity upon release from inhibition, whereas the resilient population does not have an ADP and is slower to fire after hyperpolarization. This rebound activity can trigger dopamine release in the striatum, an important component of basal ganglia function. Using whole-cell patch-clamp electrophysiology on ex vivo slices from adult mice of both sexes, we find that muscarinic activation with the nonselective muscarinic agonist oxotremorine inhibits rebound activity more strongly in vulnerable versus resilient SNc neurons. Here, we show that this effect depends on the direct activation of muscarinic receptors on the SNc dopaminergic neurons. Through a series of pharmacological and transgenic knock-out experiments, we tested whether the muscarinic inhibition of rebound was mediated through the canonical rebound-related ion channels: T-type calcium channels, hyperpolarization-activated cation channels (HCN), and A-type potassium channels. We find that muscarinic receptor activation inhibits HCN-mediated current (Ih) in vulnerable SNc neurons but that Ih activity is not necessary for the muscarinic inhibition of rebound activity. Similarly, we find that oxotremorine inhibits rebound activity independently of T-type calcium channels and A-type potassium channels. Together these findings reveal new principles governing acetylcholine and dopamine interactions, showing that muscarinic receptors directly affect SNc rebound activity in the midbrain at the somatodendritic level and differentially modify information processing in distinct SNc subpopulations.

Significance Statement

Dopaminergic neurons in the substantia nigra pars compacta (SNc) can be divided into functional subpopulations with distinct basal ganglia connectivity and different degeneration patterns in Parkinson's disease. We show that the vulnerable and resilient subpopulations of SNc dopaminergic neurons are differentially modulated by muscarinic receptor activation. Specifically, muscarinic receptor activation inhibits rebound activity more strongly in the vulnerable SNc neurons than in the resilient. We find that this inhibition occurs through a noncanonical rebound-related pathway and is not mediated through the channels best known for modulating rebound in midbrain dopaminergic neurons. These findings are important because they reveal novel acetylcholine–dopamine interactions that occur in the midbrain and affect information processing in distinct basal ganglia circuits.

Introduction

Dopaminergic neurons of the midbrain play a significant role in behaviors including aversion, reward learning, and voluntary movement. Degeneration of the dopamine neurons of the substantia nigra pars compacta (SNc) is responsible for many of the symptoms associated with Parkinson's disease (PD). However, SNc neurons do not degenerate uniformly. Two populations of cells, mapped along the dorsal-ventral axis, can also be defined by their vulnerability or resilience to degeneration in PD (Yamada et al., 1990; Fearnley and Lees, 1991; Gibb and Lees, 1991; Damier et al., 1999). These ventral- and dorsal-tier populations are involved in different basal ganglia circuits (Evans et al., 2020) and process information in distinct ways (Evans et al., 2017). The ventral tier, which is more prone to degeneration, contains dopaminergic neurons that express aldehyde dehydrogenase 1a1 but not calbindin (Poulin et al., 2014, 2020; Wu et al., 2019; Carmichael et al., 2021). These vulnerable neurons also have a distinct electrophysiological signature in that they display an afterdepolarization (ADP) when activated from hyperpolarized potentials (Evans et al., 2017). This ADP is mediated by a high number of T-type calcium channels, which, in combination with a large number of hyperpolarization-activated cation (HCN) channels, enhance rebound firing (Neuhoff et al., 2002; Evans et al., 2017). Previous studies have identified rebound activity as a mode of specialized dopaminergic information processing that is unique to the ventral tier of the SNc and has been observed both in vivo and in ex vivo slices (Fiorillo et al., 2013b; Evans et al., 2020).

Multiple ion channels are responsible for mediating the rebound response, including T-type calcium channels, HCN channels, and A-type potassium channels. T-type calcium and HCN channels, which are activated at hyperpolarized potentials, work to rapidly depolarize the cell after inhibition is released (Mercuri et al., 1995; Neuhoff et al., 2002; Amendola et al., 2012; Evans et al., 2017). This rapid depolarization is countered by A-type potassium channels whose outward current prolongs the rebound delay as they inactivate (Tarfa et al., 2017). These three channel types can be actively suppressed or enhanced by neuromodulators (Hildebrand et al., 2007; Gambardella et al., 2012; Gantz and Bean, 2017), suggesting that rebound activity in dopamine neurons is a dynamically modulated characteristic.

One neuromodulator, acetylcholine, is of particular interest as it is known to have important interactions with the dopaminergic system. Extensive previous literature details the influence of striatal cholinergic interneurons over dopamine release in the striatum (Zhou et al., 2001; Zhang and Sulzer, 2004; Pakhotin and Bracci, 2007; Threlfell et al., 2010; Nelson et al., 2014; Shin et al., 2015; Kramer et al., 2022; Razidlo et al., 2022; Krok et al., 2023). However, less is known about the influence of acetylcholine, especially muscarinic receptor activation, on dopaminergic cell bodies and dendrites in the midbrain. Most dopaminergic neurons in the SNc, regardless of subtype, express M5 muscarinic receptors (Weiner et al., 1990). These are Gq-protein-coupled receptors that have been shown to increase intracellular Ca2+ in SNc dopamine neurons (Foster et al., 2014) and alter action potential characteristics (Scroggs et al., 2001). In vivo, muscarinic receptors in the midbrain mediate long-lasting dopamine release in the striatum (Forster and Blaha, 2003; Steidl et al., 2011) and M5-specific modulation alters effort-choice and depression-related behaviors (Nunes et al., 2020, 2023). However, the influence that these M5 receptors have on the intrinsic properties and rebound activity of the different SNc subpopulations has not been comprehensively investigated.

Here we combine whole-cell patch-clamp electrophysiology and pharmacology to evaluate the effects of muscarinic receptor activation on SNc subpopulations. We find that muscarinic activation strongly reduces rebound activity in the vulnerable SNc neuronal subtype but only weakly reduces it in the more resilient SNc neuronal subtype. By selectively blocking the channels known to mediate SNc rebound activity, we show that muscarinic activation of SNc neurons inhibits rebound activity through a noncanonical mechanism.

Materials and Methods

Animal use

All animal handling and procedures were approved by the Animal Care and Use Committee for Georgetown University. Dopamine transporter (DAT)-cre/Ai9 mice [B6.SJL-Slc6a3tm1.1(cre)Bkmn/J, JAX #006660 (Bäckman et al., 2006)/B6.Cg-Gt(ROSA)26Sortm9(CAG-tdTomato)Hze/J, JAX #007909 (Madisen et al., 2010)] of either sex were used at age postnatal day (P) >60 (average age, 120 ± 9 d). CaV3.3 KO mice (Cacna1i−/− on C57BL/6J background; Courtesy of Broad Institute of MIT and Harvard; Ghoshal et al., 2020) were used where specified (average age, 41 ± 3 d).

Slice preparation

Mice were anesthetized with inhaled isoflurane and transcardially perfused with an ice-cold, oxygenated, glycerol-based modified artificial cerebrospinal fluid (aCSF) solution containing the following (in mμ): 198 glycerol, 2.5 KCl, 1.2 NaH2PO4, 25 NaHCO3, 20 HEPES, 10 glucose, 10 MgCl2, and 0.5 CaCl2. Mice were then decapitated and brains extracted. Coronal midbrain slices (200 µm) containing the substantia nigra region were prepared using a vibratome (Leica VT 1200S) and incubated for 30 min in heated (34°C) oxygenated holding aCSF containing the following (in mμ): 92 NaCl, 30 NaHCO3, 1.2 NaH2PO4, 2.5 KCl, 35 glucose, 20 HEPES, 2 MgCl2, 2 CaCl2, 5 Na-ascorbate, 3 Na-pyruvate, and 2 thiourea, as in Evans et al. (2017). Slices, in their holding chamber, were incubated at room temperature for at least 30 min.

Electrophysiological recordings

Slices were hemisectioned and continuously superfused at ∼2–3 ml/min with warm (34°C), oxygenated extracellular recording solution containing the following (in mμ): 125 NaCl, 25 NaHCO3, 3.5 KCl, 1.25 NaH2PO4, 10 glucose, 1 MgCl2, and 2 CaCl2. Neurons were visualized with a 40× objective using a Prior OpenStand Olympus microscope equipped with a scientific CMOS camera (Hamamatsu ORCA-spark).

Whole-cell recordings were made using borosilicate pipettes (2–5 MΩ) pulled with a Flaming/Brown micropipette puller (Sutter Instrument) and filled with internal recording solution containing the following (in mμ): 121.5 KMeSO3, 9 NaCl, 9 HEPES, 1.8 MgCl2, 14 phosphocreatine, 4 Mg-ATP, 0.3 Na-GTP, 0.1 CaCl2, and 0.5 EGTA adjusted to a pH value of ∼7.35 with KOH. All salts were purchased from Sigma-Aldrich.

Signals were digitized with an Axon Digidata 1550B interface, amplified by a MultiClamp 700B amplifier, and acquired using Clampex11.2 software (Molecular Devices). Data were sampled in current clamp at 10 kHz and in voltage clamp at 100 kHz with filtering at 5 kHz. Data were analyzed using custom procedures in Igor Pro (WaveMetrics).

All recordings were performed in dopaminergic neurons which were targeted by their anatomic location and presence of TdTomato, where applicable, and identified based on various electrophysiological characteristics, such as the firing frequency (<5 Hz) and presence of HCN-mediated sag. Ventral-tier (vulnerable) SNc neurons were identified by the presence of the distinctive ADP (Evans et al., 2017). Each slice was used for only one drug wash-on series (one cell).

Drugs

Patch-clamp recordings were performed in the presence of synaptic blockers [10 µμ gabazine (Tocris Bioscience), 1 µμ CGP-35348 (Tocris Bioscience), 5 µμ NBQX (Tocris Bioscience), and 50 µμ D-AP5 (Hello Bio)], unless otherwise specified. As indicated, we used 3 µμ oxotremorine (Sigma-Aldrich), 10 µμ atropine (Sigma-Aldrich), 1 µμ TTA-P2 (Alomone Labs), 10 µμ ZD7288 (Hello Bio), and/or 100 nμ AmmTx3 (Alomone Labs). All drugs were prepared as aliquots in water or DMSO.

Data analysis

Data were analyzed using Igor Pro (WaveMetrics) and GraphPad Prism. Statistical significance in two group comparisons was determined using Wilcoxon rank-sum tests (unpaired) or Wilcoxon signed-rank tests (WSRT; paired). Statistical significance in three or more group comparisons was determined using Kruskal–Wallis tests followed by Dunn's multiple-comparisons tests. Box plots show median, 25th and 75th percentiles (boxes), and 9th and 91st percentiles (whiskers). Descriptive statistics of box plots are reported as median (interquartile range). Graphs represent averages with shading indicating ±standard error of the mean (SEM), and descriptive statistics are reported as such. For each treatment group, n indicates number of cells, with no more than one cell per slice or three cells per treatment condition from a single mouse.

We evaluated rebound using several different measures in order to provide a comprehensive understanding in the changes in rebound activity elicited by muscarinic receptor activation. We recorded rebound and the ADP in a current-clamp protocol that hyperpolarizes the cell to approximately −80 mV, stimulates a single action potential from hyperpolarization, and then releases the hyperpolarization (Fig. 1A). We determined the rebound slope to be the most reliable measure of rebound, measured as the slope of depolarization to the first action potential when released from hyperpolarization (Fig. 1B, green line). Next, we measured the rebound delay—the time it takes the cell to fire an action potential once released from hyperpolarization (Fig. 1B, orange line). If a cell did not fire an action potential within 3 s of repolarization (the duration of the recording), the rebound delay was recorded as 3 s. Not all cells consistently fire action potentials during the rebound period. However, these cells do show a characteristic and measurable depolarizing slope, even if they do not reach threshold to fire an action potential. Finally, when possible, we measured rebound frequency as the frequency of the first two spikes upon release from hyperpolarization. If the first two spikes do not occur within the 3 s rebound period or there is only one spike, the rebound frequency was recorded as zero. Changes in the ADP were measured as the area under the curve (AUC) from the time of the action potential until the membrane potential returned to the hyperpolarized baseline (preV; Fig. 1C, dashed purple line), as is shown by the shaded region in Figure 1C. Drugs were added sequentially to the bath solution, beginning with synaptic blockers (as applicable), followed by any other drug (e.g., atropine or TTA-P2, as applicable), ending with oxotremorine (Fig. 1D). Gaps in the time course between conditions were when additional protocols were run to measure intrinsic cellular properties (e.g., IV curves). Control data (synaptic blockers + OxoM) were collected concurrently over the course of the project and are presented in multiple figures, as indicated.

Figure 1.
Figure 1.

Detailed methodology. A, Diagram of the current-clamp protocol used to evoke rebound and the ADP (top). Sample trace illustrating the current-clamp protocol (bottom). B, Enlargement of the rebound period following release from hyperpolarization (red rectangle in A), showing the measurement of rebound slope (green line) and rebound delay (purple line). C, Enlargement of the ADP (blue rectangle in A), measured by the area under the curve (gray shading) that is defined by the hyperpolarized baseline (purple line). D, Example rebound slope data showing the sequential application of drugs used in all experiments. In these specific data, drug 1 is TTA-P2.

Results

Oxotremorine inhibits rebound of SNc neurons through postsynaptic muscarinic receptors

To investigate the effects of muscarinic acetylcholine receptor activation on SNc neurons, we performed whole-cell patch-clamp electrophysiology on coronal slices from DAT-cre/Ai9 mice. In these slices, dopaminergic SNc neurons were identified by their red fluorescence and divided into subpopulations based on the presence or absence of an electrophysiologically recorded calcium-mediated afterdepolarization (ADP) when stimulated from a hyperpolarized potential, as in Evans et al. (2017) (Fig. 1C). During current-clamp recordings of each dopaminergic SNc neuron, we washed on 3 µμ oxotremorine (OxoM), a nonselective muscarinic agonist, to activate muscarinic receptors. We found that OxoM reliably decreased the rebound activity of ADP-expressing SNc neurons (Fig. 2A,B). We evaluated the effect of muscarinic activation on SNc neuron characteristics in three ways: (1) rebound slope [post-OxoM: 72.70 ± 3.16% of baseline; 0.11 (0.05) V/s pre-OxoM vs 0.08 (0.03) V/s post-OxoM: p = 0.001, WSRT, n = 11], (2) rebound delay [post-OxoM: 1,473.13 ± 405.74% of baseline; 0.12 (0.04) s pre-OxoM vs 2.86 (2.57) s post-OxoM: p = 0.004, WSRT, n = 9], and (3) area under the curve (AUC) of the ADP [post-OxoM: 77.25 ± 6.63% of baseline; 4.05 (2.57) mV*s pre-OxoM vs 3.25 (1.51) mV*s post-OxoM: p = 0.019, WSRT, n = 11]. The ADP is elicited by stimulating an action potential from a hyperpolarized potential (reaching approximately −80 mV). Rebound slope and rebound delay are measured when releasing the cell from a hyperpolarized potential (Fig. 1; see Materials and Methods for details). We also found that OxoM decreased the hyperpolarized baseline [preV; post-OxoM: −3.70 ± 0.38 mV from baseline; −74.08 (8.76) mV pre-OxoM vs −78.52 (9.93) mV post-OxoM: p = 0.001, WSRT, n = 11].

Figure 2.
Figure 2.

Oxotremorine (OxoM) inhibits rebound of SNc neurons through postsynaptic muscarinic receptors. A, Normalized rebound slope (top), rebound delay (second), ADP area under the curve (AUC, third), and hyperpolarized baseline (preV, bottom) as a function of time. Data presented as average ± SEM. B, Box plots of non-normalized data representing individual cell averages before (baseline) and after (shaded region in A) application of OxoM. C, Sample traces of rebound (top) and the ADP (bottom) before (light) and after (dark) application of OxoM with no drug (green), synaptic blockers (SB; blue), or SB + atropine (red) in the bath solution. D, Box plots showing intrinsic characteristics of cells in SB before (light blue) and after (dark blue) application of OxoM.

To test whether OxoM inhibited dopaminergic rebound activity by altering presynaptic glutamatergic or GABAergic inputs to the recorded cell, we applied OxoM in the presence of synaptic blockers (SB; 10 µμ gabazine, 1 µμ CGP-35348, 5 µμ NBQX, and 50 µμ AP5; Fig. 2A,B). In the presence of synaptic blockers, OxoM consistently reduced rebound slope [post-OxoM: 76.72 ± 3.54%; 0.13 (0.03) V/s pre-OxoM vs 0.09 (0.03) V/s post-OxoM: p < 0.001, WSRT, n = 15], increased rebound delay [post-OxoM: 969.30 ± 278.81%; 0.12 (0.05) s pre-OxoM vs 1.10 (1.84) s post-OxoM: p = 0.001, WSRT, n = 11], and reduced ADP size [post-OxoM: 82.14 ± 3.75%; 3.19 (1.19) mV*s pre-OxoM vs 2.77 (0.77) mV*s post-OxoM: p < 0.001, WSRT, n = 15]. Also, synaptic blockers did not prevent the effects of OxoM on the hyperpolarized baseline [post-OxoM: −1.98 ± 0.55 mV; −78.29 (7.84) mV pre-OxoM vs −80.47 (7.41) mV post-OxoM: p = 0.002, WSRT, n = 15]. These findings show that the effect of OxoM on dopaminergic rebound properties is a direct effect on the postsynaptic SNc neuron, rather than an effect on presynaptic neurotransmitter release.

To determine whether the OxoM-mediated rebound inhibition required postsynaptic muscarinic receptor activation, we applied OxoM in the presence of the muscarinic receptor antagonist atropine (10 µM; Fig. 2A,B). In the presence of atropine (Atp.), OxoM did not affect rebound slope [post-OxoM: 97.93 ± 5.00%; 0.14 (0.03) V/s pre-OxoM vs 0.13 (0.02) V/s post-OxoM: p = 0.625, WSRT, n = 10], rebound delay [post-OxoM: 151.49 ± 50.81%; 0.11 (0.04) s pre-OxoM vs 0.12 (0.05) s post-OxoM: p = 0.438, WSRT, n = 6], or ADP size [post-OxoM: 113.66 ± 11.96%; 3.20 (2.14) mV*s pre-OxoM vs 3.60 (2.48) mV*s post-OxoM: p = 0.322, WSRT, n = 10]. In atropine, OxoM minimally affected the hyperpolarized baseline [post-OxoM: −2.34 ± 1.22 mV; −81.45 (7.86) mV pre-OxoM vs −84.10 (6.19) mV post-OxoM: p = 0.084, WSRT, n = 10]. This finding shows that OxoM reduces rebound properties through activation of muscarinic acetylcholine receptors.

Further, there was no significant change in spontaneous activity of these cells in synaptic blockers with the application of OxoM (Fig. 2D), as measured by firing frequency [2.93 (2.88) Hz pre-OxoM vs 3.45 (3.23) Hz post-OxoM: p = 0.252, WSRT, n = 15], resting membrane potential [−49.90 (6.83) mV pre-OxoM vs −48.16 (7.77) mV post-OxoM: p = 0.095, WSRT, n = 15], and input resistance [512.99 (579.63) MΩ pre-OxoM vs 491.56 (356.73) MΩ post-OxoM: p = 0.375, WSRT, n = 7]. Together, these results indicate that OxoM reduces dopaminergic rebound activity through actions on postsynaptic muscarinic receptors.

Inhibition of rebound by OxoM is strongest in ventral-tier SNc neurons

The population of neurons in the ventral tier of the SNc has a higher expression of T-type calcium channels (TTCCs) and HCN channels (Mercuri et al., 1995; Neuhoff et al., 2002; Evans et al., 2017). As a result, these neurons demonstrate the TTCC-mediated ADP and stronger rebound activity compared with their dorsal-tier non-ADP counterparts (Fig. 3A). We wanted to determine whether the inhibition of rebound by OxoM was unique to ADP cells or if rebound activity in non-ADP cells was also inhibited by muscarinic activation. In non-ADP cells, we found that OxoM application slightly inhibited rebound slope [post-OxoM: 88.10 ± 3.47%; 0.11 (0.03) V/s pre-OxoM vs 0.09 (0.03) V/s post-OxoM: p = 0.003, WSRT, n = 16], slightly increased rebound delay [post-OxoM: 201.86 ± 60.41%; 0.13 (0.04) s pre-OxoM vs 0.14 (0.11) s post-OxoM: p < 0.001, WSRT, n = 16], slightly reduced rebound frequency [post-OxoM: 66.69 ± 7.67%; 11.58 (4.79) Hz pre-OxoM vs 10.10 (6.26) Hz post-OxoM: p < 0.001, WSRT, n = 16], and decreased the hyperpolarized baseline [post-OxoM: −2.78 ± 0.49 mV; −75.39 (1.16) mV pre-OxoM vs −78.09 (1.31) mV post-OxoM: p < 0.001, WSRT, n = 16], as shown in Figure 3C,D. Interestingly, although there was an effect of OxoM on the non-ADP neurons, it is much weaker than the effect of OxoM on the ADP neurons (Fig. 3; note: ADP neuron data repeated from Fig. 2). In fact, the effect of OxoM was significantly stronger on the rebound activity of ADP cells compared with that of non-ADP cells as measured by percent reduction in rebound slope [75.61 (15.70)% ADP vs 87.91 (17.28)% non-ADP: p = 0.019, Wilcoxon rank-sum] and rebound delay [1,082.00 (1,373.53)% ADP vs 116.94 (10.39)% non-ADP: p = 0.003, Wilcoxon rank-sum], though there was not a significant difference in the effect of OxoM on rebound frequency [6.83 (82.97)% ADP vs 70.38 (22.35)% non-ADP: p = 0.162, Wilcoxon rank-sum]. No difference was observed in the change in hyperpolarized baseline between ADP and non-ADP groups [−1.60 (2.58) mV ADP vs −2.42 (2.06) mV non-ADP: p = 0.264, Wilcoxon rank-sum; Fig. 3E]. Because the non-ADP neurons have reduced rebound activity compared with the ADP neurons at baseline (Evans et al., 2017), it is important to consider whether the apparent reduced efficacy of OxoM is due to a ceiling effect. While we address this more thoroughly later in this manuscript, the side-by-side comparison of the non-normalized data in Figure 3D shows that rebound delay is even slower in ADP neurons after OxoM than in the non-ADP neurons after OxoM. This indicates that the differential effect of OxoM on these two populations is not due to the non-ADP neurons being closer to a “rebound ceiling” at baseline than the ADP neurons. Therefore, we conclude that muscarinic activation strongly inhibits rebound in ADP cells but only weakly affects rebound in non-ADP cells.

Figure 3.
Figure 3.

Muscarinic activation differentially inhibits rebound in SNc subpopulations. A, Sample traces of ADP (blue) versus non-ADP (orange) cells showing an action potential elicited from a hyperpolarized baseline (top) and rebound after release from hyperpolarization (bottom). B, Sample trace of rebound in a non-ADP cell in SB before (light orange) and after (dark orange) application of OxoM. C, Normalized rebound slope (top), rebound delay (second), rebound frequency (middle), and hyperpolarized baseline (bottom) as a function of time. Data presented as average ± SEM. D, Box plots of non-normalized data representing individual cell averages before (baseline) and after (shaded region in C) application of OxoM. In C and D, ADP neuron data is repeated from Figure 2 for side-by-side comparison. E, Box plots comparing normalized data of ADP versus non-ADP cells in SB + OxoM. Left to right, Rebound slope, rebound delay, rebound frequency, and hyperpolarized baseline.

OxoM inhibits rebound independent of T-type calcium channels

The ventrally located cells of the SNc contain large amounts of TTCCs which mediate rebound firing and the ADP (Evans et al., 2017). Dopaminergic SNc neurons selectively express the M5 muscarinic receptor which is Gq coupled (Weiner et al., 1990; Offermanns et al., 1994; Caulfield and Birdsall, 1998). Interestingly, Gq-coupled muscarinic receptors have been shown to inhibit TTCCs in cultured cells (Hildebrand et al., 2007). Because muscarinic activation inhibits rebound in the ADP cells more strongly than in the non-ADP cells, we hypothesized that OxoM inhibits rebound activity by inhibiting TTCCs. To test this, we applied OxoM in the presence of TTA-P2 (1 µM), a pan-TTCC blocker. On its own, TTA-P2 completely eliminated the ADP and reduced rebound activity, as demonstrated previously (Evans et al., 2017). Surprisingly, however, the presence of TTA-P2 did not occlude the inhibitory effect of OxoM on rebound activity (Fig. 4A,B). In the presence of TTA-P2, OxoM significantly decreased rebound slope [post-OxoM: 78.65 ± 3.41%; 0.10 (0.03) V/s pre-OxoM vs 0.08 (0.03) V/s post-OxoM: p = 0.002, WSRT, n = 10], increased rebound delay [post-OxoM: 431.95 ± 250.73%; 0.14 (0.02) s pre-OxoM vs 0.20 (1.57) s post-OxoM: p = 0.008, WSRT, n = 8], decreased rebound frequency [post-OxoM: 47.12 ± 14.69%; 9.98 (9.63) Hz pre-OxoM vs 3.95 (5.65) Hz post-OxoM: p = 0.031, WSRT, n = 7], and decreased hyperpolarized baseline [post-OxoM: 1.78 ± 0.46 mV; −80.60 (8.42) mV pre-OxoM vs −80.88 (8.05) mV post-OxoM: p = 0.010, WSRT, n = 10]. These data show that OxoM inhibits rebound through a non-TTCC mechanism.

Figure 4.
Figure 4.

Muscarinic inhibition of rebound and the ADP of ventral-tier SNc neurons is not mediated by T-type calcium channels. A, Normalized rebound slope (top), rebound delay (second), rebound frequency (third), and hyperpolarized baseline (bottom) as a function of time. Data presented as average ± SEM. Control (SB) data is repeated from Figure 2 for side-by-side comparison. Inset, Sample traces of rebound in SB + TTA-P2 before (light purple) and after (dark purple) application of OxoM. Calibration: 20 mV, 100 ms. B, Box plots of non-normalized data representing individual cell averages before (baseline) and after (shaded region in A) application of OxoM. C, Normalized rebound slope (top), rebound delay (second), rebound frequency (third), ADP AUC (fourth), and hyperpolarized baseline (bottom) as a function of time. Data presented as average ± SEM. Inset, Sample traces of rebound (top) and the ADP (bottom) before (light indigo) and after (dark indigo) application of OxoM in CaV3.3 KO mice. Calibration: 20 mV, 100 ms. D, Box plots of non-normalized data representing individual cell averages before (baseline) and after (shaded region in C) application of OxoM.

Because TTA-P2 completely abolished the ADP, we were not able to use ADP size as a measure in these experiments. As ADP size would be the measure most sensitive to an OxoM effect on TTCCs, we decided to investigate whether OxoM may be selectively inhibiting one subtype of TTCC. Of the three members of the TTCC family, CaV3.3 channels display slower activation and inactivation kinetics than the CaV3.1 and CaV3.2 subtypes (McRory et al., 2001; Chemin et al., 2002). Though the presence of CaV3.3 in SNc neurons is controversial (Dryanovski et al., 2013; Dufour et al., 2014; Poetschke et al., 2015; Guzman et al., 2018; Benkert et al., 2019), there is clear evidence that Gq-coupled muscarinic receptors can inhibit CaV3.3 in cultured cells (Hildebrand et al., 2007). We hypothesized that selective inhibition of CaV3.3 by OxoM could be responsible for the observed decrease in rebound and ADP size. Using a CaV3.3 knock-out mouse (Cacna1i−/−; Ghoshal et al., 2020), we performed the same electrophysiology experiments with application of OxoM. Dopaminergic neurons were identified by their characteristic intrinsic firing patterns, HCN-mediated sag, and, most importantly, the ADP, which is unique to dopamine neurons in the ventral tier of the SNc (Evans et al., 2017). We observed no difference in ADP size at baseline between knock-out and wild-type conditions [3.59 (2.18) mV*s KO pre-OxoM vs 4.05 (2.57) mV*s WT pre-OxoM: p = 0.314, Wilcoxon rank-sum], suggesting that the CaV3.3 subtype does not play a strong, if any, role in this dopamine neuron activity. However, this also allows us to test the effect of OxoM on ADP size. These experiments were performed without synaptic blockers in the bath solution. We found that OxoM reduced rebound activity in ADP cells in CaV3.3 KO mice similarly to DAT-cre/Ai9 mice (Fig. 4C,D). In the CaV3.3 KO, OxoM significantly decreased the rebound slope [post-OxoM: 71.19 ± 3.87%; 0.15 (0.03) V/s pre-OxoM vs 0.10 (0.03) V/s post-OxoM: p = 0.002, WSRT, n = 10], increased rebound delay [post-OxoM: 560.69 ± 296.44%; 0.09 (0.02) s pre-OxoM vs 0.11 (0.79) s post-OxoM: p = 0.004, WSRT, n = 9], and decreased rebound frequency [post-OxoM: 16.44 ± 8.45%; 15.41 (12.50) Hz pre-OxoM vs 1.22 (7.72) Hz post-OxoM: p = 0.016, WSRT, n = 7]. Further, OxoM decreased the ADP size [post-OxoM: 73.83 ± 5.78%; 3.59 (2.18) mV*s pre-OxoM vs 2.66 (1.22) mV*s post-OxoM: p = 0.004, WSRT, n = 10] but surprisingly did not affect the hyperpolarized baseline [post-OxoM: 0.57 ± 0.72 mV; −77.97 (2.64) mV pre-OxoM vs −79.98 (6.65) mV post-OxoM: p = 0.322, WSRT, n = 10], unlike DAT-cre/Ai9 animals. This contrasting effect on the hyperpolarized baseline could be explained by different mouse strains; however, this interpretation is limited without littermate controls for comparison. While the mechanism underlying this difference in effect on the hyperpolarized baseline is not understood, these results show that the effect of OxoM on rebound measures is not related to or dependent on OxoM's slight augmentation of the hyperpolarized baseline membrane potential. While the lack of littermate controls limits the conclusions that can be drawn from this experiment, we found that OxoM significantly inhibits rebound and the ADP across all measures in electrophysiologically identified SNc neurons of Cav3.3 KO mice. Therefore, based on this result, in combination with the TTA-P2 pharmacological experiments, we concluded that OxoM's effect on rebound is not mediated by TTCCs.

OxoM inhibition of HCN channels is not the mechanism of reduced rebound

The hyperpolarization-activated cation current (Ih) is mediated by HCN channels and plays a role in rebound firing, as it is activated by hyperpolarization and slow to turn off following return to resting membrane potential when released from inhibition (Mercuri et al., 1995; Neuhoff et al., 2002). In voltage-clamp recordings before and after application of OxoM, we find that Ih is inhibited by muscarinic activation. Cells were held at −60 mV, and Ih currents were elicited with 1 s voltage steps (ranging from −50 to −120 mV in 5 mV increments) followed by a 500 ms voltage step to −120 mV to measure the tail currents (Fig. 5A). Normalized tail current amplitude was plotted as the function of the test potentials and fitted with the Boltzmann equation (Fig. 5B). There was a significant decrease in the voltage for half-maximal activation (V50) of Ih in control versus OxoM conditions [Fig. 5C; −97.71 (4.97) mV pre-OxoM vs −104.06 (8.61) mV post-OxoM: p = 0.016, WSRT, n = 7].

Figure 5.
Figure 5.

Changes in HCN channel activation are not responsible for muscarinic inhibition of rebound in ventral-tier SNc neurons. A, Sample traces of HCN-mediated current measured in voltage clamp, in SB before (light blue, top) and after (dark blue, bottom) application of OxoM. B, Normalized activation curves of Ih tail current before (top left) and after (bottom left) application of OxoM, shown combined on right. Data presented as individual cells and their averages (left) and average ± SEM (right). C, Box plot showing V50 of Ih in SB before (light blue) and after (dark blue) application of OxoM. D, Sample traces of rebound (top) and the ADP (bottom) in SB before (light teal) and after (teal) application of ZD7288. E, Sample traces of rebound (top) and the ADP (bottom) in SB + ZD7288 before (teal) and after (dark teal) application of OxoM. F, Normalized rebound slope (top), rebound delay (second), rebound frequency (third), ADP AUC (fourth), and hyperpolarized baseline (bottom) as a function of time. Control (SB) data is repeated from Figure 2 for side-by-side comparison. Data presented as average ± SEM. G, Box plots of non-normalized data representing individual cell averages before (baseline) and after (shaded region in F) application of OxoM.

Because the ventral-tier, ADP-expressing SNc dopaminergic neurons also show larger Ih versus dorsal-tier SNc neurons (Neuhoff et al., 2002), we hypothesized that OxoM selectively inhibits rebound in the ventral-tier SNc because of its inhibition of Ih. To test this, we applied the HCN channel blocker ZD7288 (ZD; 10 µM) prior to OxoM application. We found that OxoM-mediated inhibition of rebound was maintained even when HCN channels were blocked (Fig. 5F,G). In the presence of ZD7288, OxoM significantly decreased rebound slope [post-OxoM: 83.22 ± 6.39%; 0.15 (0.07) V/s pre-OxoM vs 0.12 (0.05) V/s post-OxoM: p = 0.009, WSRT, n = 17] and increased rebound delay [post-OxoM: 1,007.41 ± 386.57%; 0.13 (0.06) s pre-OxoM vs 1.22 (2.13) s post-OxoM: p = 0.002, WSRT, n = 10], though there was no significant decrease in rebound frequency [post-OxoM: 4.97 ± 3.48%; 1.74 (11.52) Hz pre-OxoM vs 0.00 (1.14) Hz post-OxoM: p = 0.063, WSRT, n = 5]. These results demonstrate that although OxoM inhibits Ih, this inhibition is not responsible for OxoM's reduction of rebound activity. Further, blocking Ih did not inhibit the effect of OxoM decreasing the ADP AUC [post-OxoM: 90.13 ± 8.43%; 7.13 (5.61) mV*s pre-OxoM vs 5.74 (5.17) mV*s post-OxoM: p = 0.007, WSRT, n = 17], though there was no effect on the hyperpolarized baseline [post-OxoM: −1.23 ± 1.04 mV; −89.10 (9.91) mV pre-OxoM vs −90.80 (11.53) mV post-OxoM: p = 0.263, WSRT, n = 17]. Interestingly, the time course of OxoM's effect on rebound slope was distinctly different when Ih was blocked versus not. Specifically, in Figure 5F, there is a transient increase in rebound slope within the first 2 min of OxoM application prior to the long-lasting decrease in rebound slope. This difference in time course may indicate that the OxoM-mediated shift in the Ih activation curve may be responsible for the early OxoM-induced reduction in rebound but that a separate mechanism mediates the long-term effects of OxoM on dopamine rebound activity.

Blocking A-type potassium channels enhances the effect of OxoM on rebound activity

Previous work has shown that the A-type potassium current (IA) also plays a role in modulating rebound firing in SNc neurons (Amendola et al., 2012; Tarfa et al., 2017). A-type potassium channels are activated when the cell depolarizes after a period of hyperpolarization. This slows the rebound depolarization and reduces rebound firing. IA has an opposite influence from Ih and TTCCs on rebound activity, and blocking this current completely eliminates the rebound delay. In Figure 6, we applied OxoM in the presence of the A-type potassium channel blocker AmmTx3 (100 nM). Because AmmTx3 enhanced rebound firing so strongly, it was not possible to measure the rebound slope of the first rebound spike, as it occurred immediately upon release from hyperpolarization (Fig. 6C, bottom left). For this same reason we were unable to measure changes in rebound delay. Here, we instead measured the slope between the first (immediate) and second rebound action potentials, while measuring the rebound frequency of the first two spikes after release from hyperpolarization. We found that OxoM had an enhanced effect on rebound activity in the presence of AmmTx3 (Fig. 6A,B). With A-type potassium channels blocked, OxoM significantly reduced rebound slope [post-OxoM: 52.52 ± 5.74%; 0.20 (0.06) V/s pre-OxoM vs 0.10 (0.07) V/s post-OxoM: p = 0.016, WSRT, n = 7], the size of the ADP [post-OxoM: 80.21 ± 6.47%; 4.40 (2.32) mV*s pre-OxoM vs 3.34 (2.05) mV*s post-OxoM: p = 0.031, WSRT, n = 7], and the hyperpolarized baseline [post-OxoM: −2.64 ± 0.64 mV; −81.02 (6.01) mV pre-OxoM vs −82.29 (9.10) mV post-OxoM: p = 0.016, WSRT, n = 7]. There was not a significant decrease in rebound frequency [post-OxoM: 4.95 ± 0.40%; 19.10 (7.20) Hz pre-OxoM vs 1.00 (0.39) Hz post-OxoM: p = 0.125, WSRT, n = 4]. These experiments show that OxoM does not inhibit rebound by enhancing A-type potassium current activity.

Figure 6.
Figure 6.

Muscarinic inhibition of rebound and the ADP of ventral-tier SNc neurons is not mediated by A-type potassium channels. A, Normalized rebound slope (top), rebound frequency (second), ADP AUC (third), and hyperpolarized baseline (bottom) as a function of time. Control (SB) data is repeated from Figure 2 for side-by-side comparison. Data presented as average ± SEM. B, Box plots of non-normalized data representing individual cell averages before (baseline) and after (shaded region in A) application of OxoM. C, Left, Sample traces of rebound in SB (light pink, top) and SB + AmmTx (pink, bottom). Right, Sample trace of rebound in SB + AmmTx + OxoM (dark pink). D, Box plot of normalized rebound slope across pharmacological conditions.

Comparing the effect of OxoM on rebound slope across pharmacological conditions (Fig. 6D), there is no difference between groups blocking HCN channels (SB + OxoM vs SB + ZD7288 + OxoM: p > 0.999, Kruskal–Wallis with Dunn's) or T-type calcium channels (SB + OxoM vs SB + TTA-P2 + OxoM: p > 0.999, Kruskal–Wallis with Dunn's). Interestingly, OxoM has an enhanced effect on rebound slope when blocking A-type potassium channels (SB + OxoM vs SB + AmmTx + OxoM: p = 0.014, Kruskal–Wallis with Dunn's), which supports the idea that Gq-coupled muscarinic receptor activity may actually inhibit IA in dopaminergic neurons (Gantz and Bean, 2017). These data indicate that muscarinic activation may exert opposing effects on rebound activity in SNc dopaminergic neurons through multiple distinct mechanisms.

Simultaneous blockade of HCN and T-type calcium channels is not sufficient to occlude OxoM reduction of rebound

The two most prominent electrophysiological differences between ventral- and dorsal-tier SNc neurons are enhanced Ih and TTCCs in the ventral, ADP-expressing neurons (Neuhoff et al., 2002; Evans et al., 2017). In addition, previous research has shown synergistic activity between HCN channel activity and other intrinsic ion channels (Cobb-Lewis et al., 2023). Therefore, we hypothesized that HCN channels and TTCCs may together mediate the effects of OxoM on rebound. We performed experiments simultaneously blocking both channel types to determine if their cumulative effects occlude those of OxoM. We applied OxoM in the presence of TTA-P2 and ZD7288 and found that blocking both T-type current and Ih concurrently did not reduce the effect of OxoM on rebound activity (Fig. 7C–E). When combined, TTA-P2 + ZD7288 have drastic inhibitory effects on rebound (Fig. 7A). Application of OxoM further inhibited rebound slope [post-OxoM: 66.17 ± 6.66%; 0.08 (0.04) V/s pre-OxoM vs 0.06 (0.05) V/s post-OxoM: p = 0.008, WSRT, n = 8] and rebound frequency [post-OxoM: 39.63 ± 15.40%; 4.10 (3.60) Hz pre-OxoM vs 1.28 (5.21) Hz post-OxoM: p = 0.008, WSRT, n = 8], while increasing rebound delay [post-OxoM: 281.33 ± 93.84%; 0.30 (0.54) s pre-OxoM vs 1.46 (2.65) s post-OxoM: p = 0.008, WSRT, n = 8]. OxoM did not have any further effect on the hyperpolarized baseline [post-OxoM: −1.49 ± 1.60 mV; −85.53 (7.08) mV pre-OxoM vs −86.97 (11.22) mV post-OxoM: p = 0.195, WSRT, n = 8]. These effects are comparable with bath solution only containing synaptic blockers [rebound slope 75.61 (15.70)% SB + OxoM vs 67.93 (30.19)% SB + TTA + ZD + OxoM: p = 0.265, Wilcoxon rank-sum; hyperpolarized baseline −1.60 (2.58) mV SB + OxoM vs −2.42 (1.40) mV SB + TTA + ZD + OxoM: p = 0.636, Wilcoxon rank-sum]. Because TTA-P2 also eliminated the ADP, we could not measure the effect of OxoM on ADP size. Together, these experiments show that neither TTCCs nor HCN channels, alone or in combination, mediate the effect of OxoM on dopaminergic rebound activity.

Figure 7.
Figure 7.

Simultaneous inhibition of T-type calcium and HCN channels does not occlude the effects of OxoM on rebound. A, Sample traces of rebound in SB (light fuchsia, left), SB + TTA-P2 + ZD7288 (fuchsia, middle), and SB + TTA-P2 + ZD7288 + OxoM (dark fuchsia, right). B, Sample traces of rebound in a non-ADP cell in SB (orange) versus an ADP cell in SB + TTA-P2 + ZD7288 (fuchsia). C, Normalized rebound slope (top) and hyperpolarized baseline (bottom) as a function of time. Data presented as average ± SEM. D, Box plots of normalized data representing individual cell averages after (shaded region in C) application of OxoM. In C and D, control (SB) data is repeated from Figure 2 for side-by-side comparison. E, Box plots of non-normalized data showing rebound slope (top left), hyperpolarized baseline (bottom left), rebound delay (top right), and rebound frequency (bottom right) of ADP cells in SB + TTA-P2 + ZD7288 (fuchsia) and SB + TTA-P2 + ZD7288 + OxoM (dark fuchsia) and non-ADP cells (repeated from Fig. 3) in SB (light orange) and SB + OxoM (dark orange).

By inhibiting HCN and T-type calcium channels, we eliminate the major factors that differentiate ADP and non-ADP cells electrophysiologically, functionally turning the ADP neurons into non-ADP neurons with regard to rebound activity. However, even when the ADP cells are pharmacologically put into a “low-rebound” state, OxoM still strongly further reduces all measures of rebound activity in these cells (Fig. 7E). Comparing this data with the non-normalized data in non-ADP neurons (originally presented in Fig. 3) side by side in Figure 7E demonstrates that the limited effect of OxoM on non-ADP cells is not due to floor or ceiling effects. With Ih and TTCCs blocked, the ADP neurons have even lower rebound activity than the baseline state of non-ADP neurons; however, OxoM still further reduces rebound in these ADP neurons as shown by the change in rebound slope [67.93 (30.19)% ADP SB + TTA + ZD + OxoM vs 87.91 (17.28)% non-ADP SB + OxoM: p = 0.011, Wilcoxon rank-sum]. Therefore, we conclude that additional factors, other than HCN and TTCC expression, differentiate these two populations and that at least one of these factors is differentially affected by muscarinic activation.

Discussion

Here we found that muscarinic activation inhibits rebound in ventral-tier SNc neurons more strongly than in dorsal-tier SNc neurons. We found that this rebound inhibition is a direct result of muscarinic receptor activation and is not mediated through the modulation of presynaptic neurotransmitter release. Counter to our original hypotheses, we found that muscarinic activation does not inhibit rebound in SNc neurons by inhibiting T-type calcium channels or hyperpolarization-activated cation channels either alone or in combination. Finally, we found that blocking A-type potassium channels enhanced muscarinic-mediated inhibition of rebound. Therefore, we conclude that muscarinic activation inhibits rebound in SNc neurons through a noncanonical rebound mechanism.

The ventral and dorsal tiers of the SNc differ in molecular markers (Poulin et al., 2014, 2020; Wu et al., 2019), electrophysiological characteristics (Neuhoff et al., 2002; Evans et al., 2017), and circuit connectivity (Evans et al., 2020). One prominent difference between these populations is their differential ability to rebound. The ventral-tier neurons can be considered “rebound-ready” because of the strong expression of TTCCs and HCN channels (Neuhoff et al., 2002; Evans et al., 2017). Our finding that muscarinic activation differentially affects the ADP-expressing (ventral) and non-ADP expressing (dorsal) SNc neurons is further evidence that these two populations process information in unique ways. Dopaminergic neuron rebound activity has been reported in vivo in primates and rodents (Fiorillo et al., 2013a,b; Gut et al., 2022; Dong et al., 2024) and has been hypothesized to function as a safety or relief signal after an aversive stimulus (Wang and Tsien, 2011; Budygin et al., 2012; Fiorillo et al., 2013a; Lerner et al., 2015; de Jong et al., 2019). The dynamic modulation of rebound activity by muscarinic receptors is an important component in the acetylcholine–dopamine interactions that occur in the midbrain and can ultimately influence dopamine release in other brain structures, such as the striatum.

In physiological conditions, the main source of acetylcholine release in the SNc is from the cholinergic neurons of the pedunculopontine nucleus (PPN; Clarke et al., 1987; Mena-Segovia et al., 2008; Dautan et al., 2016; Xiao et al., 2016; Estakhr et al., 2017), a brainstem structure that is involved in the coordination of movement and motor learning (Roseberry et al., 2016; Li and Spitzer, 2020; Dautan et al., 2021). Cholinergic axons have been identified in the SNc, particularly in the dendron bouquets specific to the ventral tier (Crittenden et al., 2016), and muscarinic receptor activation in the SNc is critical for PPN stimulation to generate long-lasting dopamine signals in the striatum (Forster and Blaha, 2003). Future work is needed to fully dissect the influence of endogenous acetylcholine released from the PPN onto the SNc and to determine whether M5 muscarinic receptor activation reduces intrinsic dopamine rebound activity in vivo.

Rebound activity in dopaminergic neurons is controlled by three main channels: T-type calcium channels, hyperpolarization-activated cation channels, and A-type potassium channels (Neuhoff et al., 2002; Amendola et al., 2012; Evans et al., 2017; Tarfa et al., 2017). The SNc receives strong inhibitory input from multiple basal ganglia nuclei (Saitoh et al., 2004; McGregor et al., 2019; Evans, 2022; Gut et al., 2022) which hyperpolarize the membrane and recruit these cation channels. Previous work has shown that Gq-coupled muscarinic receptors can inhibit TTCCs and do so particularly strongly for the CaV3.3 TTCC subtype in cultured cells (Hildebrand et al., 2007). Previous work has also shown that muscarinic receptor activation inhibits HCN channels in striatal cholinergic neurons (Zhao et al., 2016) but enhances it in vestibular ganglion neurons (Bronson and Kalluri, 2023). Here we show that in SNc dopaminergic neurons, muscarinic activation inhibits HCN activity (Fig. 5). Together, these findings would suggest that M5 receptor activation reduces rebound activity through TTCCs and HCN channels. Surprisingly, we found that the inhibition of these channels was not necessary for muscarinic receptor activation to inhibit rebound in SNc dopaminergic neurons. While the lack of littermate controls limits interpretations of the Cav3.3 KO experiments, our findings that blocking all TTCCs with TTA-P2 does not occlude or reduce the muscarinic reduction of rebound shows that TTCCs are not necessary for this inhibition to occur. On the other hand, previous work has shown that Gq-coupled receptors inhibit A-type potassium channels in dissociated dopaminergic neurons (Gantz and Bean, 2017). Because A-type activation reduces rebound activity, this result suggests that muscarinic activation would enhance rebound by inhibiting A-type channels. However, we see that muscarinic activation reduces rebound and blocking A-type channels actually enhances this effect. Therefore, our findings support the idea that muscarinic activation causes multiple distinct physiological changes in SNc neurons to both positively and negatively influence rebound.

Previous work has found that brief (seconds to minutes) application of OxoM to brain slices increases neural firing and somatic calcium in dopaminergic neurons (Gronier and Rasmussen, 1998; Foster et al., 2014). In contrast, our experiments did not show a significant OxoM effect on tonic firing of either ADP-expressing or non-ADP-expressing SNc neurons. This discrepancy may be due to the difference in timing of the OxoM application (short vs long exposure), the difference in OxoM concentration (10 vs 3 µM), or the different electrophysiological technique used (perforated patch vs whole cell). Interestingly, another study found that transient and long-lasting muscarinic stimulation caused opposing neural responses in dopaminergic neurons (Fiorillo and Williams, 2000). These studies highlight the complex interactions between acetylcholine and dopamine in the midbrain.

Together our findings reveal a previously unknown acetylcholine–dopamine interaction that occurs in the midbrain. The selective inhibition of rebound in the vulnerable SNc subpopulation by muscarinic receptor activation is important for our understanding of the complex interplay between the dopaminergic and cholinergic systems of the healthy brain. Because the dopaminergic neurons of the SNc and their cholinergic inputs from the brainstem degenerate in Parkinson's disease (Yamada et al., 1990; Rinne et al., 2008; Sébille et al., 2019), the endogenous activation of M5 muscarinic receptors on SNc neurons is likely to be disrupted in this disorder. Future experiments will be critical for understanding how acetylcholine–dopamine interactions in the midbrain are altered in pathological conditions.

Footnotes

  • This work was supported by the American Parkinson’s Disease Association Research Grant 2021APDA00RG00000209666, Parkinson’s Foundation Stanley Fahn Junior Faculty Award #PF-SF-JFA-1040267, and BRAIN Initiative K99/R00 award #R00NS112417 awarded to R.C.E. and by a National Institute of General Medical Sciences T32 predoctoral fellowship GM142520 awarded to M.L.B. We thank Dr. John Partridge and members of the Evans Lab for feedback on earlier versions of this manuscript.

  • The authors declare no competing financial interests.

  • Correspondence should be addressed to Rebekah C. Evans at re285{at}georgetown.edu.

References

  1. ↵
    1. Amendola J,
    2. Woodhouse A,
    3. Martin-Eauclaire M-F,
    4. Goaillard J-M
    (2012) Ca2+/cAMP-sensitive covariation of IA and IH voltage dependences tunes rebound firing in dopaminergic neurons. J Neurosci 32:2166–2181. https://doi.org/10.1523/JNEUROSCI.5297-11.2012 pmid:22323729
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Bäckman CM,
    2. Malik N,
    3. Zhang Y,
    4. Shan L,
    5. Grinberg A,
    6. Hoffer BJ,
    7. Westphal H,
    8. Tomac AC
    (2006) Characterization of a mouse strain expressing Cre recombinase from the 3′ untranslated region of the dopamine transporter locus. Genesis 44:383–390. https://doi.org/10.1002/dvg.20228
    OpenUrlCrossRefPubMed
  3. ↵
    1. Benkert J, et al.
    (2019) Cav2.3 channels contribute to dopaminergic neuron loss in a model of Parkinson’s disease. Nat Commun 10:5094. https://doi.org/10.1038/s41467-019-12834-x pmid:31704946
    OpenUrlCrossRefPubMed
  4. ↵
    1. Bronson D,
    2. Kalluri R
    (2023) Muscarinic acetylcholine receptors modulate HCN channel properties in vestibular ganglion neurons. J Neurosci 43:902–917. https://doi.org/10.1523/JNEUROSCI.2552-21.2022 pmid:36604171
    OpenUrlAbstract/FREE Full Text
  5. ↵
    1. Budygin EA,
    2. Park J,
    3. Bass CE,
    4. Grinevich VP,
    5. Bonin KD,
    6. Wightman RM
    (2012) Aversive stimulus differentially triggers subsecond dopamine release in reward regions. Neuroscience 201:331–337. https://doi.org/10.1016/j.neuroscience.2011.10.056 pmid:22108611
    OpenUrlCrossRefPubMed
  6. ↵
    1. Carmichael K,
    2. Evans RC,
    3. Lopez E,
    4. Sun L,
    5. Kumar M,
    6. Ding J,
    7. Khaliq ZM,
    8. Cai H
    (2021) Function and regulation of ALDH1A1-positive nigrostriatal dopaminergic neurons in motor control and Parkinson’s disease. Front Neural Circuits 15:644776. https://doi.org/10.3389/fncir.2021.644776 pmid:34079441
    OpenUrlCrossRefPubMed
  7. ↵
    1. Caulfield MP,
    2. Birdsall NJM
    (1998) International union of pharmacology. XVII. Classification of muscarinic acetylcholine receptors. Pharmacol Rev 50:279–290. https://doi.org/10.1016/S0031-6997(24)01362-0
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Chemin J,
    2. Monteil A,
    3. Perez-Reyes E,
    4. Bourinet E,
    5. Nargeot J,
    6. Lory P
    (2002) Specific contribution of human T-type calcium channel isotypes (α1G, α1H and α1I) to neuronal excitability. J Physiol 540:3–14. https://doi.org/10.1113/jphysiol.2001.013269 pmid:11927664
    OpenUrlCrossRefPubMed
  9. ↵
    1. Clarke PBS,
    2. Hommer DW,
    3. Pert A,
    4. Skirboll LR
    (1987) Innervation of substantia nigra neurons by cholinergic afferents from pedunculopontine nucleus in the rat: neuroanatomical and electrophysiological evidence. Neuroscience 23:1011–1019. https://doi.org/10.1016/0306-4522(87)90176-X
    OpenUrlCrossRefPubMed
  10. ↵
    1. Cobb-Lewis DE,
    2. Sansalone L,
    3. Khaliq ZM
    (2023) Contributions of the sodium leak channel NALCN to pacemaking of medial ventral tegmental area and substantia nigra dopaminergic neurons. J Neurosci 43:6841–6853. https://doi.org/10.1523/JNEUROSCI.0930-22.2023 pmid:37640554
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Crittenden JR,
    2. Tillberg PW,
    3. Riad MH,
    4. Shima Y,
    5. Gerfen CR,
    6. Curry J,
    7. Housman DE,
    8. Nelson SB,
    9. Boyden ES,
    10. Graybiel AM
    (2016) Striosome–dendron bouquets highlight a unique striatonigral circuit targeting dopamine-containing neurons. Proc Natl Acad Sci U S A 113:11318–11323. https://doi.org/10.1073/pnas.1613337113 pmid:27647894
    OpenUrlAbstract/FREE Full Text
  12. ↵
    1. Damier P,
    2. Hirsch EC,
    3. Agid Y,
    4. Graybiel AM
    (1999) The substantia nigra of the human brain. II. Patterns of loss of dopamine-containing neurons in Parkinson’s disease. Brain 122:1437–1448. https://doi.org/10.1093/brain/122.8.1437
    OpenUrlCrossRefPubMed
  13. ↵
    1. Dautan D, et al.
    (2016) Segregated cholinergic transmission modulates dopamine neurons integrated in distinct functional circuits. Nat Neurosci 19:1025–1033. https://doi.org/10.1038/nn.4335 pmid:27348215
    OpenUrlCrossRefPubMed
  14. ↵
    1. Dautan D,
    2. Kovács A,
    3. Bayasgalan T,
    4. Diaz-Acevedo MA,
    5. Pal B,
    6. Mena-Segovia J
    (2021) Modulation of motor behavior by the mesencephalic locomotor region. Cell Rep 36:109594. https://doi.org/10.1016/j.celrep.2021.109594 pmid:34433068
    OpenUrlCrossRefPubMed
  15. ↵
    1. de Jong JW,
    2. Afjei SA,
    3. Dorocic IP,
    4. Peck JR,
    5. Liu C,
    6. Kim CK,
    7. Tian L,
    8. Deisseroth K,
    9. Lammel S
    (2019) A neural circuit mechanism for encoding aversive stimuli in the mesolimbic dopamine system. Neuron 101:133–151.e7. https://doi.org/10.1016/j.neuron.2018.11.005 pmid:30503173
    OpenUrlCrossRefPubMed
  16. ↵
    1. Dong J,
    2. Wang L,
    3. Sullivan BT,
    4. Sun L,
    5. Chang L,
    6. Smith VMM,
    7. Ding J,
    8. Le W,
    9. Gerfen CR,
    10. Cai H
    (2024) Patch and matrix striatonigral neurons differentially regulate locomotion. 2024.06.12.598675.
  17. ↵
    1. Dryanovski DI,
    2. Guzman JN,
    3. Xie Z,
    4. Galteri DJ,
    5. Volpicelli-Daley LA,
    6. Lee VM-Y,
    7. Miller RJ,
    8. Schumacker PT,
    9. Surmeier DJ
    (2013) Calcium entry and α-synuclein inclusions elevate dendritic mitochondrial oxidant stress in dopaminergic neurons. J Neurosci 33:10154–10164. https://doi.org/10.1523/JNEUROSCI.5311-12.2013 pmid:23761910
    OpenUrlAbstract/FREE Full Text
  18. ↵
    1. Dufour MA,
    2. Woodhouse A,
    3. Goaillard J-M
    (2014) Somatodendritic ion channel expression in substantia nigra pars compacta dopaminergic neurons across postnatal development. J Neurosci Res 92:981–999. https://doi.org/10.1002/jnr.23382
    OpenUrlCrossRefPubMed
  19. ↵
    1. Estakhr J,
    2. Abazari D,
    3. Frisby K,
    4. McIntosh JM,
    5. Nashmi R
    (2017) Differential control of dopaminergic excitability and locomotion by cholinergic inputs in mouse substantia nigra. Curr Biol 27:1900–1914.e4. https://doi.org/10.1016/j.cub.2017.05.084 pmid:28648825
    OpenUrlCrossRefPubMed
  20. ↵
    1. Evans RC
    (2022) Dendritic involvement in inhibition and disinhibition of vulnerable dopaminergic neurons in healthy and pathological conditions. Neurobiol Dis 172:105815. https://doi.org/10.1016/j.nbd.2022.105815 pmid:35820645
    OpenUrlCrossRefPubMed
  21. ↵
    1. Evans RC,
    2. Twedell EL,
    3. Zhu M,
    4. Ascencio J,
    5. Zhang R,
    6. Khaliq ZM
    (2020) Functional dissection of basal ganglia inhibitory inputs onto substantia nigra dopaminergic neurons. Cell Rep 32:108156. https://doi.org/10.1016/j.celrep.2020.108156 pmid:32937133
    OpenUrlCrossRefPubMed
  22. ↵
    1. Evans RC,
    2. Zhu M,
    3. Khaliq ZM
    (2017) Dopamine inhibition differentially controls excitability of substantia nigra dopamine neuron subpopulations through T-type calcium channels. J Neurosci 37:3704–3720. https://doi.org/10.1523/JNEUROSCI.0117-17.2017 pmid:28264982
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Fearnley JM,
    2. Lees AJ
    (1991) Ageing and Parkinson’s disease: substantia nigra regional selectivity. Brain 114:2283–2301. https://doi.org/10.1093/brain/114.5.2283
    OpenUrlCrossRefPubMed
  24. ↵
    1. Fiorillo CD,
    2. Song MR,
    3. Yun SR
    (2013a) Multiphasic temporal dynamics in responses of midbrain dopamine neurons to appetitive and aversive stimuli. J Neurosci 33:4710–4725. https://doi.org/10.1523/JNEUROSCI.3883-12.2013 pmid:23486944
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Fiorillo CD,
    2. Yun SR,
    3. Song MR
    (2013b) Diversity and homogeneity in responses of midbrain dopamine neurons. J Neurosci 33:4693–4709. https://doi.org/10.1523/JNEUROSCI.3886-12.2013 pmid:23486943
    OpenUrlAbstract/FREE Full Text
  26. ↵
    1. Fiorillo CD,
    2. Williams JT
    (2000) Cholinergic inhibition of ventral midbrain dopamine neurons. J Neurosci 20:7855–7860. https://doi.org/10.1523/JNEUROSCI.20-20-07855.2000 pmid:11027251
    OpenUrlAbstract/FREE Full Text
  27. ↵
    1. Forster GL,
    2. Blaha CD
    (2003) Pedunculopontine tegmental stimulation evokes striatal dopamine efflux by activation of acetylcholine and glutamate receptors in the midbrain and pons of the rat. Eur J Neurosci 17:751–762. https://doi.org/10.1046/j.1460-9568.2003.02511.x
    OpenUrlCrossRefPubMed
  28. ↵
    1. Foster DJ,
    2. Gentry PR,
    3. Lizardi-Ortiz JE,
    4. Bridges TM,
    5. Wood MR,
    6. Niswender CM,
    7. Sulzer D,
    8. Lindsley CW,
    9. Xiang Z,
    10. Conn PJ
    (2014) M5 receptor activation produces opposing physiological outcomes in dopamine neurons depending on the receptor’s location. J Neurosci 34:3253–3262. https://doi.org/10.1523/JNEUROSCI.4896-13.2014 pmid:24573284
    OpenUrlAbstract/FREE Full Text
  29. ↵
    1. Gambardella C,
    2. Pignatelli A,
    3. Belluzzi O
    (2012) The h-current in the substantia nigra pars compacta neurons: a re-examination. PLoS One 7:e52329. https://doi.org/10.1371/journal.pone.0052329 pmid:23284989
    OpenUrlCrossRefPubMed
  30. ↵
    1. Gantz SC,
    2. Bean BP
    (2017) Cell-autonomous excitation of midbrain dopamine neurons by endocannabinoid-dependent lipid signaling. Neuron 93:1375–1387.e2. https://doi.org/10.1016/j.neuron.2017.02.025 pmid:28262417
    OpenUrlCrossRefPubMed
  31. ↵
    1. Ghoshal A, et al.
    (2020) Effects of a patient-derived de novo coding alteration of CACNA1I in mice connect a schizophrenia risk gene with sleep spindle deficits. Transl Psychiatry 10:29. https://doi.org/10.1038/s41398-020-0685-1 pmid:32066662
    OpenUrlCrossRefPubMed
  32. ↵
    1. Gibb WR,
    2. Lees AJ
    (1991) Anatomy, pigmentation, ventral and dorsal subpopulations of the substantia nigra, and differential cell death in Parkinson’s disease. J Neurol Neurosurg Psychiatry 54:388–396. https://doi.org/10.1136/jnnp.54.5.388 pmid:1865199
    OpenUrlAbstract/FREE Full Text
  33. ↵
    1. Gronier B,
    2. Rasmussen K
    (1998) Activation of midbrain presumed dopaminergic neurones by muscarinic cholinergic receptors: an in vivo electrophysiological study in the rat. Br J Pharmacol 124:455–464. https://doi.org/10.1038/sj.bjp.0701850 pmid:9647468
    OpenUrlCrossRefPubMed
  34. ↵
    1. Gut NK,
    2. Yilmaz D,
    3. Kondabolu K,
    4. Huerta-Ocampo I,
    5. Mena-Segovia J
    (2022) Selective inhibition of goal-directed actions in the mesencephalic locomotor region. BioRxiv 2022.01.18.476772). https://doi.org/10.1101/2022.01.18.476772
  35. ↵
    1. Guzman JN,
    2. Ilijic E,
    3. Yang B,
    4. Sanchez-Padilla J,
    5. Wokosin D,
    6. Galtieri D,
    7. Kondapalli J,
    8. Schumacker PT,
    9. Surmeier DJ
    (2018) Systemic isradipine treatment diminishes calcium-dependent mitochondrial oxidant stress. J Clin Invest 128:2266–2280. https://doi.org/10.1172/JCI95898 pmid:29708514
    OpenUrlCrossRefPubMed
  36. ↵
    1. Hildebrand ME,
    2. David LS,
    3. Hamid J,
    4. Mulatz K,
    5. Garcia E,
    6. Zamponi GW,
    7. Snutch TP
    (2007) Selective inhibition of Cav3.3T-type calcium channels by Gαq/11-coupled muscarinic acetylcholine receptors *. J Biol Chem 282:21043–21055. https://doi.org/10.1074/jbc.M611809200
    OpenUrlAbstract/FREE Full Text
  37. ↵
    1. Kramer PF,
    2. Brill-Weil SG,
    3. Cummins AC,
    4. Zhang R,
    5. Camacho-Hernandez GA,
    6. Newman AH,
    7. Eldridge MAG,
    8. Averbeck BB,
    9. Khaliq ZM
    (2022) Synaptic-like axo-axonal transmission from striatal cholinergic interneurons onto dopaminergic fibers. Neuron 110:2949–2960.e4. https://doi.org/10.1016/j.neuron.2022.07.011 pmid:35931070
    OpenUrlCrossRefPubMed
  38. ↵
    1. Krok AC,
    2. Maltese M,
    3. Mistry P,
    4. Miao X,
    5. Li Y,
    6. Tritsch NX
    (2023) Intrinsic dopamine and acetylcholine dynamics in the striatum of mice. Nature 621:543–549. https://doi.org/10.1038/s41586-023-05995-9 pmid:37558873
    OpenUrlCrossRefPubMed
  39. ↵
    1. Lerner TN, et al.
    (2015) Intact-brain analyses reveal distinct information carried by SNc dopamine subcircuits. Cell 162:635–647. https://doi.org/10.1016/j.cell.2015.07.014 pmid:26232229
    OpenUrlCrossRefPubMed
  40. ↵
    1. Li H,
    2. Spitzer NC
    (2020) Exercise enhances motor skill learning by neurotransmitter switching in the adult midbrain. Nat Commun 11:2195. https://doi.org/10.1038/s41467-020-16053-7 pmid:32366867
    OpenUrlCrossRefPubMed
  41. ↵
    1. Madisen L, et al.
    (2010) A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat Neurosci 13:133–140. https://doi.org/10.1038/nn.2467 pmid:20023653
    OpenUrlCrossRefPubMed
  42. ↵
    1. McGregor MM,
    2. McKinsey GL,
    3. Girasole AE,
    4. Bair-Marshall CJ,
    5. Rubenstein JLR,
    6. Nelson AB
    (2019) Functionally distinct connectivity of developmentally targeted striosome neurons. Cell Rep 29:1419–1428.e5. https://doi.org/10.1016/j.celrep.2019.09.076 pmid:31693884
    OpenUrlCrossRefPubMed
  43. ↵
    1. McRory JE,
    2. Santi CM,
    3. Hamming KS,
    4. Mezeyova J,
    5. Sutton KG,
    6. Baillie DL,
    7. Stea A,
    8. Snutch TP
    (2001) Molecular and functional characterization of a family of rat brain T-type calcium channels. J Biol Chem 276:3999–4011. https://doi.org/10.1074/jbc.M008215200
    OpenUrlAbstract/FREE Full Text
  44. ↵
    1. Mena-Segovia J,
    2. Winn P,
    3. Bolam JP
    (2008) Cholinergic modulation of midbrain dopaminergic systems. Brain Res Rev 58:265–271. https://doi.org/10.1016/j.brainresrev.2008.02.003
    OpenUrlCrossRefPubMed
  45. ↵
    1. Mercuri NB,
    2. Bonci A,
    3. Calabresi P,
    4. Stefani A,
    5. Bernardi G
    (1995) Properties of the hyperpolarization-activated cation current lh in rat midbrain dopaminergic neurons. Eur J Neurosci 7:462–469. https://doi.org/10.1111/j.1460-9568.1995.tb00342.x
    OpenUrlCrossRefPubMed
  46. ↵
    1. Nelson AB,
    2. Hammack N,
    3. Yang CF,
    4. Shah NM,
    5. Seal RP,
    6. Kreitzer AC
    (2014) Striatal cholinergic interneurons drive GABA release from dopamine terminals. Neuron 82:63–70. https://doi.org/10.1016/j.neuron.2014.01.023 pmid:24613418
    OpenUrlCrossRefPubMed
  47. ↵
    1. Neuhoff H,
    2. Neu A,
    3. Liss B,
    4. Roeper J
    (2002) Ih channels contribute to the different functional properties of identified dopaminergic subpopulations in the midbrain. J Neurosci 22:1290–1302. https://doi.org/10.1523/JNEUROSCI.22-04-01290.2002 pmid:11850457
    OpenUrlAbstract/FREE Full Text
  48. ↵
    1. Nunes EJ,
    2. Kebede N,
    3. Haight JL,
    4. Foster DJ,
    5. Lindsley CW,
    6. Conn PJ,
    7. Addy NA
    (2023) Ventral tegmental area M5 muscarinic receptors mediate effort-choice responding and nucleus accumbens dopamine in a sex-specific manner. J Pharmacol Exp Ther 385:146–156. https://doi.org/10.1124/jpet.122.001438 pmid:36828630
    OpenUrlAbstract/FREE Full Text
  49. ↵
    1. Nunes EJ,
    2. Rupprecht LE,
    3. Foster DJ,
    4. Lindsley CW,
    5. Conn PJ,
    6. Addy NA
    (2020) Examining the role of muscarinic M5 receptors in VTA cholinergic modulation of depressive-like and anxiety-related behaviors in rats. Neuropharmacology 171:108089. https://doi.org/10.1016/j.neuropharm.2020.108089 pmid:32268153
    OpenUrlCrossRefPubMed
  50. ↵
    1. Offermanns S,
    2. Wieland T,
    3. Homann D,
    4. Sandmann J,
    5. Bombien E,
    6. Spicher K,
    7. Schultz G,
    8. Jakobs K
    (1994) Transfected muscarinic acetylcholine receptors selectively couple to Gi-type G proteins and Gq/11. Mol Pharmacol 45:890–898. https://doi.org/10.1016/S0026-895X(25)10203-4
    OpenUrlAbstract
  51. ↵
    1. Pakhotin P,
    2. Bracci E
    (2007) Cholinergic interneurons control the excitatory input to the striatum. J Neurosci 27:391–400. https://doi.org/10.1523/JNEUROSCI.3709-06.2007 pmid:17215400
    OpenUrlAbstract/FREE Full Text
  52. ↵
    1. Poetschke C,
    2. Dragicevic E,
    3. Duda J,
    4. Benkert J,
    5. Dougalis A,
    6. DeZio R,
    7. Snutch TP,
    8. Striessnig J,
    9. Liss B
    (2015) Compensatory T-type Ca2+ channel activity alters D2-autoreceptor responses of substantia nigra dopamine neurons from Cav1.3 L-type Ca2+ channel KO mice. Sci Rep 5:13688. https://doi.org/10.1038/srep13688 pmid:26381090
    OpenUrlCrossRefPubMed
  53. ↵
    1. Poulin J-F,
    2. Gaertner Z,
    3. Moreno-Ramos OA,
    4. Awatramani R
    (2020) Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches. Trends Neurosci 43:155–169. https://doi.org/10.1016/j.tins.2020.01.004 pmid:32101709
    OpenUrlCrossRefPubMed
  54. ↵
    1. Poulin J-F,
    2. Zou J,
    3. Drouin-Ouellet J,
    4. Kim K-YA,
    5. Cicchetti F,
    6. Awatramani RB
    (2014) Defining midbrain dopaminergic neuron diversity by single-cell gene expression profiling. Cell Rep 9:930–943. https://doi.org/10.1016/j.celrep.2014.10.008 pmid:25437550
    OpenUrlCrossRefPubMed
  55. ↵
    1. Razidlo JA,
    2. Fausner SML,
    3. Ingebretson AE,
    4. Wang LC,
    5. Petersen CM,
    6. Mirza S,
    7. Swank IN,
    8. Alvarez VA,
    9. Lemos JC
    (2022) Chronic loss of muscarinic M5 receptor function manifests disparate impairments in exploratory behavior in male and female mice despite common dopamine regulation. J Neurosci 42:6917–6930. https://doi.org/10.1523/JNEUROSCI.1424-21.2022 pmid:35896424
    OpenUrlAbstract/FREE Full Text
  56. ↵
    1. Rinne JO,
    2. Ma SY,
    3. Lee MS,
    4. Collan Y,
    5. Röyttä M
    (2008) Loss of cholinergic neurons in the pedunculopontine nucleus in Parkinson’s disease is related to disability of the patients. Parkinsonism Relat Disord 14:553–557. https://doi.org/10.1016/j.parkreldis.2008.01.006
    OpenUrlCrossRefPubMed
  57. ↵
    1. Roseberry TK,
    2. Lee AM,
    3. Lalive AL,
    4. Wilbrecht L,
    5. Bonci A,
    6. Kreitzer AC
    (2016) Cell-type-specific control of brainstem locomotor circuits by basal ganglia. Cell 164:526–537. https://doi.org/10.1016/j.cell.2015.12.037 pmid:26824660
    OpenUrlCrossRefPubMed
  58. ↵
    1. Saitoh K,
    2. Isa T,
    3. Takakusaki K
    (2004) Nigral GABAergic inhibition upon mesencephalic dopaminergic cell groups in rats. Eur J Neurosci 19:2399–2409. https://doi.org/10.1111/j.0953-816X.2004.03337.x
    OpenUrlCrossRefPubMed
  59. ↵
    1. Scroggs RS,
    2. Cardenas CG,
    3. Whittaker JA,
    4. Kitai ST
    (2001) Muscarine reduces calcium-dependent electrical activity in substantia nigra dopaminergic neurons. J Neurophysiol 86:2966–2972. https://doi.org/10.1152/jn.2001.86.6.2966
    OpenUrlCrossRefPubMed
  60. ↵
    1. Sébille SB, et al.
    (2019) Normal and pathological neuronal distribution of the human mesencephalic locomotor region. Mov Disord 34:218–227. https://doi.org/10.1002/mds.27578
    OpenUrlCrossRefPubMed
  61. ↵
    1. Shin JH,
    2. Adrover MF,
    3. Wess J,
    4. Alvarez VA
    (2015) Muscarinic regulation of dopamine and glutamate transmission in the nucleus accumbens. Proc Natl Acad Sci U S A 112:8124–8129. https://doi.org/10.1073/pnas.1508846112 pmid:26080439
    OpenUrlAbstract/FREE Full Text
  62. ↵
    1. Steidl S,
    2. Miller AD,
    3. Blaha CD,
    4. Yeomans JS
    (2011) M5 muscarinic receptors mediate striatal dopamine activation by ventral tegmental morphine and pedunculopontine stimulation in mice. PLoS One 6:e27538. https://doi.org/10.1371/journal.pone.0027538 pmid:22102904
    OpenUrlCrossRefPubMed
  63. ↵
    1. Tarfa RA,
    2. Evans RC,
    3. Khaliq ZM
    (2017) Enhanced sensitivity to hyperpolarizing inhibition in mesoaccumbal relative to nigrostriatal dopamine neuron subpopulations. J Neurosci 37:3311–3330. https://doi.org/10.1523/JNEUROSCI.2969-16.2017 pmid:28219982
    OpenUrlAbstract/FREE Full Text
  64. ↵
    1. Threlfell S,
    2. Clements MA,
    3. Khodai T,
    4. Pienaar IS,
    5. Exley R,
    6. Wess J,
    7. Cragg SJ
    (2010) Striatal muscarinic receptors promote activity dependence of dopamine transmission via distinct receptor subtypes on cholinergic interneurons in ventral versus dorsal striatum. J Neurosci 30:3398–3408. https://doi.org/10.1523/JNEUROSCI.5620-09.2010 pmid:20203199
    OpenUrlAbstract/FREE Full Text
  65. ↵
    1. Wang DV,
    2. Tsien JZ
    (2011) Convergent processing of both positive and negative motivational signals by the VTA dopamine neuronal populations (Tanimoto H, ed). PLoS One 6:e17047. https://doi.org/10.1371/journal.pone.0017047 pmid:21347237
    OpenUrlCrossRefPubMed
  66. ↵
    1. Weiner DM,
    2. Levey AI,
    3. Brann MR
    (1990) Expression of muscarinic acetylcholine and dopamine receptor mRNAs in rat basal ganglia. Proc Natl Acad Sci U S A 87:7050–7054. https://doi.org/10.1073/pnas.87.18.7050 pmid:2402490
    OpenUrlAbstract/FREE Full Text
  67. ↵
    1. Wu J, et al.
    (2019) Distinct connectivity and functionality of aldehyde dehydrogenase 1a1-positive nigrostriatal dopaminergic neurons in motor learning. Cell Rep 28:1167–1181.e7. https://doi.org/10.1016/j.celrep.2019.06.095 pmid:31365862
    OpenUrlCrossRefPubMed
  68. ↵
    1. Xiao C,
    2. Cho JR,
    3. Zhou C,
    4. Treweek JB,
    5. Chan K,
    6. McKinney SL,
    7. Yang B,
    8. Gradinaru V
    (2016) Cholinergic mesopontine signals govern locomotion and reward through dissociable midbrain pathways. Neuron 90:333–347. https://doi.org/10.1016/j.neuron.2016.03.028 pmid:27100197
    OpenUrlCrossRefPubMed
  69. ↵
    1. Yamada T,
    2. McGeer PL,
    3. Baimbridge KG,
    4. McGeer EG
    (1990) Relative sparing in Parkinson’s disease of substantia nigra dopamine neurons containing calbindin-D28K. Brain Res 526:303–307. https://doi.org/10.1016/0006-8993(90)91236-A
    OpenUrlCrossRefPubMed
  70. ↵
    1. Zhang H,
    2. Sulzer D
    (2004) Frequency-dependent modulation of dopamine release by nicotine. Nat Neurosci 7:581–582. https://doi.org/10.1038/nn1243
    OpenUrlCrossRefPubMed
  71. ↵
    1. Zhao Z,
    2. Zhang K,
    3. Liu X,
    4. Yan H,
    5. Ma X,
    6. Zhang S,
    7. Zheng J,
    8. Wang L,
    9. Wei X
    (2016) Involvement of HCN channel in muscarinic inhibitory action on tonic firing of dorsolateral striatal cholinergic interneurons. Front Cell Neurosci 10:71. https://doi.org/10.3389/fncel.2016.00071 pmid:27047336
    OpenUrlCrossRefPubMed
  72. ↵
    1. Zhou F-M,
    2. Liang Y,
    3. Dani JA
    (2001) Endogenous nicotinic cholinergic activity regulates dopamine release in the striatum. Nat Neurosci 4:1224–1229. https://doi.org/10.1038/nn769
    OpenUrlCrossRefPubMed
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Muscarinic Receptor Activation Preferentially Inhibits Rebound in Vulnerable Dopaminergic Neurons
Megan L. Beaver, Rebekah C. Evans
Journal of Neuroscience 16 April 2025, 45 (16) e1443242025; DOI: 10.1523/JNEUROSCI.1443-24.2025
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