Manipulating midbrain dopamine neurons and reward-related behaviors with light-controllable nicotinic acetylcholine receptors

Cuttoli, Romain Durand-de; Mondoloni, Sarah; Marti, Fabio; Lemoine, Damien; Nguyen, Claire; Naudé, Jérémie; d’Izarny-Gargas, Thibaut; Pons, Stéphanie; Maskos, Uwe; Trauner, Dirk; Krämer, Richard; Fauré, Philippe; Mourot, Alexandre

doi:10.7554/elife.37487

Cited by 47 publications

(59 citation statements)

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“…Furthermore, it is still unclear whether the cholinergic receptors in VTA dopamine neurons are required for learning the uncertainty bonus or solely for operating the bonus during action selection. These two alternatives could be differentiated experimentally via genetic-chemical manipulations rendering the cholinergic receptors light-controllable [Durand-de Cuttoli et al, 2018]. If the receptors are switched off during the initial sessions in which animals learn the statistics of reward delivery, and then switched on again, we should be able to find out whether the uncertainty seeking behavior appears rapidly or requires additional learning.…”

Section: Discussionmentioning

confidence: 99%

Modeling Uncertainty-Seeking Behavior Mediated by Cholinergic Influence on Dopamine

Belkaid

Krichmar

2019

Preprint

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Recent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projection to dopamine neurons -another neuromodulatory system known for its major implication in reinforcement learning and decision-making. In this paper, we propose a leaky-integrate-and-fire model of this mechanism. It implements a softmax-like selection with an uncertainty bonus by a cholinergic drive to dopaminergic neurons, which in turn influence synaptic currents of downstream neurons. The model is able to reproduce experimental data in two decision-making tasks. It also predicts that i) in the absence of cholinergic input, dopaminergic activity would not correlate with uncertainty, and that ii) the adaptive advantage brought by the implemented uncertainty-seeking mechanism is most useful when sources of reward are not highly uncertain. Moreover, this modeling work allows us to propose novel experiments which might shed new light on the role of acetylcholine in both random and directed exploration. Overall, this study thus contributes to a more comprehensive understanding of the roles of the cholinergic system and its involvement in decision-making in particular.

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Section: Discussionmentioning

confidence: 99%

Modeling Uncertainty-Seeking Behavior Mediated by Cholinergic Influence on Dopamine

Belkaid

Krichmar

2019

Preprint

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“…Cellspecific neuropharmacology approaches provide the ability to test the function of receptors on specific types of neurons with unprecedented cellular precision (e.g. pre-vs. post-synaptic cells, or two different cell types within the same circuit) [3,4]. They also offer a unique opportunity to test the benefits of cell-targeted drugs for neurological and neuropsychiatric disorders.…”

Section: Probing the Nervous System With Cell-targeted Drugsmentioning

confidence: 99%

“…The PTL approach has proven to be highly versatile. It has been applied to potassium channels [32][33][34], ionotropic [35][36][37] and metabotropic [38] glutamate receptors, nAChRs [4,39], GABA A Rs [40,41], dopamine receptors [42] as well as P2X receptors [43]. It was used to probe neurotransmission in various neuronal settings, both ex and in vivo in zebrafish and mice [1].…”

Section: Membrane-tethered Ligandsmentioning

confidence: 99%

“…In zebrafish, the photoswitch can simply be added to the swimming water, but in mice it has to be locally delivered. Despite this drawback, the PTL approach has been applied in the living mouse, notably for restoring vision to blind mice [44], for manipulating action potential firing [45] and GABAergic inhibition [41] in the visual cortex, or for controlling nicotinic transmission in the ventral tegmental area and addiction-related behaviors [4]. One potential shortcoming of PTLs is their non-selective attachment to endogenous cysteines, even though no adverse effect has been observed so far [4,41].…”

Section: Membrane-tethered Ligandsmentioning

confidence: 99%

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Cell-Specific Neuropharmacology

Mondoloni

Cuttoli

Mourot

2019

Trends in Pharmacological Sciences

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Neuronal communication involves a multitude of neurotransmitters and an outstanding diversity of receptors and ion channels. Linking the activity of cell surface receptors and ion channels in defined neural circuits to brain states and behaviors has been a key challenge in neuroscience, since cell-targeting is not possible with traditional neuropharmacology. We review here recent technologies that enable the effect of drugs to be restricted to specific cell types, thereby allowing acute manipulation of the brain's own proteins with circuit specificity. We highlight the importance of developing cell-specific neuropharmacology strategies for decoding the nervous system with molecular-and circuit-precision, and for developing future therapeutics with reduced side effects. Highlights Receptor-tethered ligand Linker Anchor Ligand Membrane-tethered ligand D. FIGURE 1 Active drug Intracellular delivery approaches E. Inactive drug Native Receptors/Channels Drug Endogenous GPCR Targeted ion channel Endogenous ion channel Drug

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“…Furthermore, the presence of nicotinic acetylcholine receptors (nAChRs) in the VTA (Pontieri et al, 1996; Maskos et al, 2005; Changeux, 2010; Faure et al, 2014) provides a potential common route for acetylcholine (ACh) and nicotine (Nic) in modulating dopamine activity during a Pavlovian-conditioning task. Particularly, the high-affinity α4β2 subunit-containing nAChRs desensitizing relatively slowly (≃ sec) and located post-synaptically on VTA DA and GABA neurons have been shown to have the most prominent role in nicotine-induced DAergic bursting activity and self-administration, as suggested by mouse knock-out experiments (Maskos et al, 2005; Changeux, 2010; Faure et al, 2014) and recent direct optogenetic modulation of these somatic receptors (Durand-de Cuttoli et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Nicotinic and Cholinergic Modulation of Reward Prediction Error Computations in the Ventral Tegmental Area: a Minimal Circuit Model

Deperrois

Gutkin

2018

Preprint

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2 Dopamine (DA) neurons in the ventral tegmental area (VTA) are thought to encode reward 3 prediction errors (RPE) by comparing actual and expected rewards. In recent years, much work 4has been done to identify how the brain uses and computes this signal.While several lines of 5 evidence suggest the the interplay of he DA and the inhibitory interneurons in the VTA implements 6 the RPE computaiton, it still remains unclear how the DA neurons learn key quantities, for 7 example the amplitude and the timing of primary rewards during conditioning tasks. Furthermore, 8 exogenous nicotine and endogenous acetylcholine, acting on both VTA DA and GABA (γ -9 aminobutyric acid) neurons via nicotinic-acetylcholine receptors (nAChRs), also likely affect these 10 computations. To explore the potential circuit-level mechanisms for RPE computations during 11 classical-conditioning tasks, we developed a minimal computational model of the VTA circuitry. 12 The model was designed to account for several reward-related properties of VTA afferents and 13 recent findings on VTA GABA neuron dynamics during conditioning. 14 With our minimal model, we showed that the RPE can be learned by a two-speed process 15 computing reward timing and magnitude. Including a model of nAChR-mediated currents in 16 the VTA DA-GABA circuit, we also showed that nicotine should reduce the acetylcholine action 17 on the VTA GABA neurons by receptor desensitization and therefore potentially boost the DA 18 responses to reward information. Together, our results delineate the mechanisms by which 19 RPE are computed in the brain, and suggest a hypothesis on nicotine-mediated effects on 20 reward-related perception and decision-making. 21 Keywords: dopamine, reward-prediction error, ventral tegmental area, acetylcholine, nicotine 22outcomes (rewards, punishments, etc). The difference between prediction and outcome is the prediction 24 1 Deperrois and GutkinNicotinic Modulation of Dopaminergic Reward Computation Circuitry error, which in turn can serve as a teaching signal to allow the animal to update its predictions and render 25 previously neutral stimuli predictive of rewards into reinforcers of behavior. Particularly, the dopamine 26 (DA) neuron activity in the Ventral Tegmental Area (VTA) have been shown to encode the reward prediction 27 error (RPE), or the difference between the actual reward the animal receives and the expected reward 28 classical conditioning with appetitive rewards, unexpected rewards elicit strong transient increases in VTA 31 DA neuron activity, but as a cue fully predicts the reward, the same reward produces little or no DA neurons 32 response. Finally, after learning, if the reward is omitted, DA neurons pause their firing at the moment 33 reward is expected (Schultz et al., 1997; Schultz, 1998; Keiflin and Janak, 2015; Watabe-Uchida et al., 34 2017). Thus DA neurons should either receive or compute the RPE. While several lines of evidence have 35 pointed towards the RPE being computed by the VTA local circu...

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Manipulating midbrain dopamine neurons and reward-related behaviors with light-controllable nicotinic acetylcholine receptors

Cited by 47 publications

References 57 publications

Modeling Uncertainty-Seeking Behavior Mediated by Cholinergic Influence on Dopamine

Modeling Uncertainty-Seeking Behavior Mediated by Cholinergic Influence on Dopamine

Cell-Specific Neuropharmacology

Nicotinic and Cholinergic Modulation of Reward Prediction Error Computations in the Ventral Tegmental Area: a Minimal Circuit Model

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