We can learn a surprising amount about how the brain forms memories by studying the humble fruit fly. These insects can learn to associate odors with positive or negative experiences, allowing them to then seek out ‘rewarded’ odors and avoid ‘punished’ ones.

This association takes place in a brain region called the mushroom body, and it involves two types of neurons: Kenyon cells, which detect odors, and MBONs, which lead to approach or avoidance behaviors. When Kenyon cells detect an odor accompanying an unpleasant event, they weaken their connections with the MBONs that trigger approach behaviors. This prevents the fly from coming close to that odor in the future.

Kenyon cells exchange signals with other neurons using a chemical called acetylcholine, which attaches onto the cells through two types of receptors: nicotinic and muscarinic. Studies in fruit fly larvae suggest that muscarinic receptors are required in Kenyon cells for the insects to learn how to associate odors with unpleasant experiences.

Bielopolski et al. now show that this is also the case in adult flies. Surprisingly, while acetylcholine usually excites fly neurons, activating muscarinic receptors inhibits Kenyon cells rather than exciting them. Labeled muscarinic receptors revealed that the receptors act within the input region of Kenyon cells. Moreover, reducing the levels of muscarinic receptors inside the cells stops flies from associating an odor with a mild electric shock. This manipulation also prevents the learning experience from weakening connections from Kenyon cells onto an MBON that triggers approach behavior. This suggests that allowing these changes in connectivity might be why muscarinic receptors are important for memory.

Understanding how memory works in flies can reveal basic principles that apply to many species, including humans. Such knowledge could ultimately help us improve the memory of patients with dementia, but also inspire better algorithms for artificial intelligence.

Animals learn to modify their behavior based on past experience by changing connection strengths between neurons, and this synaptic plasticity is often regulated by metabotropic receptors. In particular, neurons commonly express both ionotropic and metabotropic receptors for the same neurotransmitter, where the two may mediate different functions (e.g. direct excitation/inhibition vs. synaptic plasticity). In mammals, where glutamate is the principal excitatory neurotransmitter, metabotropic glutamate receptors (mGluRs) have been widely implicated in synaptic plasticity and memory (Jörntell and Hansel, 2006; Lüscher and Huber, 2010). Given the complexity of linking behavior to artificially induced plasticity in brain slices (Schonewille et al., 2011; Yamaguchi et al., 2016), it would be useful to study the role of metabotropic receptors in learning in a simpler genetic model system with a clearer behavioral readout of synaptic plasticity. One such system is Drosophila, where powerful genetic tools and well-defined anatomy have yielded a detailed understanding of the circuit and molecular mechanisms underlying associative memory (Busto et al., 2010; Cognigni et al., 2018; Hige, 2018). The principal excitatory neurotransmitter in Drosophila is acetylcholine, but, surprisingly, little is known about the function of metabotropic acetylcholine signaling in synaptic plasticity or neuromodulation in Drosophila. Here, we address this question using olfactory associative memory.

Flies can learn to associate an odor (conditioned stimulus, CS) with a positive (sugar) or a negative (electric shock) unconditioned stimulus (US), so that they later approach ‘rewarded’ odors and avoid ‘punished’ odors. This association is thought to be formed in the presynaptic terminals of the ~2000 Kenyon cells (KCs) that make up the mushroom body (MB), the fly’s olfactory memory center (Busto et al., 2010; Cognigni et al., 2018; Hige, 2018). These KCs are activated by odors via second-order olfactory neurons called projection neurons (PNs). Each odor elicits responses in a sparse subset of KCs (Campbell et al., 2013; Lin et al., 2014) so that odor identity is encoded in which KCs respond to each odor. When an odor (CS) is paired with reward/punishment (US), an odor-specific set of KCs is activated at the same time that dopaminergic neurons (DANs) release dopamine onto KC presynaptic terminals. The coincident activation causes long-term depression (LTD) of synapses from the odor-activated KCs onto mushroom body output neurons (MBONs) that lead to approach or avoidance behavior (Aso and Rubin, 2016; Aso et al., 2014b; Cohn et al., 2015; Hige et al., 2015; Owald et al., 2015; Perisse et al., 2016; Séjourné et al., 2011). In particular, training specifically depresses KC-MBON synapses of the ‘wrong’ valence (e.g. odor-punishment pairing depresses odor responses of MBONs that lead to approach behavior), because different pairs of ‘matching’ DANs/MBONs (e.g. punishment/approach, reward/avoidance) innervate distinct regions along KC axons (Aso et al., 2014a).

Both MB input (PNs) and output (KCs) are cholinergic (Barnstedt et al., 2016; Yasuyama and Salvaterra, 1999), and KCs express both ionotropic (nicotinic) and metabotropic (muscarinic) acetylcholine receptors (Crocker et al., 2016; Croset et al., 2018; Davie et al., 2018; Shih et al., 2019). The nicotinic receptors mediate fast excitatory synaptic currents (Su and O'Dowd, 2003), while the physiological function of the muscarinic receptors is unknown. Muscarinic acetylcholine receptors (mAChRs) are G-protein-coupled receptors; out of the three mAChRs in Drosophila (mAChR-A, mAChR-B and mAChR-C), mAChR-A (also called Dm1, mAcR-60C or mAChR) is the most closely homologous to mammalian mAChRs (Collin et al., 2013). Mammalian mAChRs are typically divided between ‘M₁-type’ (M₁/M₃/M₅), which signal via G_q and are generally excitatory, and ‘M₂-type’ (M₂/M₄), which signal via G_i/o and are generally inhibitory (Caulfield and Birdsall, 1998). Drosophila mAChR-A seems to use ‘M₁-type’ signaling: when heterologously expressed in Chinese hamster ovary (CHO) cells, it signals via G_q protein (Collin et al., 2013; Ren et al., 2015) to activate phospholipase C, which produces inositol trisphosphate to release Ca²⁺ from internal stores.

Recent work indicates that mAChR-A is required for aversive olfactory learning in Drosophila larvae, as knocking down mAChR-A expression in KCs impairs learning (Silva et al., 2015). However, it is unclear whether mAChR-A is involved in olfactory learning in adult Drosophila, given that mAChR-A is thought to signal through G_q, and in adult flies G_q signaling downstream of the dopamine receptor Damb promotes forgetting, not learning (Berry et al., 2012; Himmelreich et al., 2017). Moreover, it is unknown how mAChR-A affects the activity or physiology of KCs, where it acts (at KC axons or dendrites or both), and how these effects contribute to olfactory learning.

Here, we show that mAChR-A is required in KCs for aversive olfactory learning in adult Drosophila. Surprisingly, genetic and pharmacological manipulations of mAChR-A suggest that mAChR-A is inhibitory and acts on KC dendrites. Moreover, mAChR-A knockdown impairs the learning-associated depression of odor responses in an MB output neuron, MB-MVP2, that is required for aversive memory retrieval. We suggest that dendritically acting mAChR-A is required for synaptic depression between KCs and their outputs.

Here, we show that mAChR-A is required in γ KCs for aversive olfactory learning and short-term memory in adult Drosophila. Knocking down mAChR-A increases KC odor responses, while the mAChR-A agonist muscarine suppresses KC activity. Knocking down mAChR-A prevents aversive learning from reducing responses of the MB output neuron MB-MVP2 to the conditioned odor, suggesting that mAChR-A is required for the learning-related depression of KC->MBON synapses.

Why is mAChR-A only required for aversive learning in γ KCs, not αβ or α′β′ KCs? Although our mAChR-A MiMIC gene trap agrees with single-cell transcriptome analysis that α′β′ KCs express less mAChR-A than do γ and αβ KCs (Croset et al., 2018; Davie et al., 2018), transcriptome analysis indicates that α′β′ KCs do express some mAChR-A (Figure 2—figure supplement 1). Moreover, γ and αβ KCs express similar levels of mAChR-A (Crocker et al., 2016). It may be that the RNAi knockdown is less efficient at affecting the physiology of αβ and α′β′ KCs than γ KCs, whether because the knockdown is less efficient at reducing protein levels, or because αβ and α′β′ KCs have different intrinsic properties or a different function of mAChR-A such that 40% of normal mAChR-A levels is sufficient in αβ and α′β′ KCs but not γ KCs. This interpretation is supported by our finding that mAChR-A RNAi knockdown significantly increases odor responses only in the γ lobe, not the αβ or α′β′ lobes. Alternatively, γ, αβ and α′β′ KCs are thought to be important mainly for short-term memory, long-term memory, and memory consolidation, respectively (Guven-Ozkan and Davis, 2014; Krashes et al., 2007); as we only tested short-term memory, mAChR-A may carry out the same function in all KCs, but only its role in γ KCs is required for short-term (as opposed to long-term) memory. Indeed, the key plasticity gene DopR1 is required in γ, not αβ or α′β′ KCs, for short-term memory (Qin et al., 2012). It may be that mAChR-A is required in non-γ KC types for other forms of memory besides short-term aversive memory, such as appetitive conditioning or other phases of memory like long-term memory. Our finding that mAChR-A is required in γ KCs for aversive short-term memory is consistent with our finding that mAChR-A knockdown in KCs disrupts training-induced depression of odor responses in MB-MVP2, an MBON postsynaptic to γ KCs required for aversive short-term memory (Perisse et al., 2016). However, the latter finding does not rule out the possibility that other MBONs postsynaptic to non-γ KCs may also be affected by mAChR-A knockdown in KCs.

mAChR-A seems to inhibit KC odor responses, because knocking down mAChR-A increases odor responses in the calyx and γ lobe, while activating mAChR-A with bath or local application of muscarine decreases KC odor responses. Some details differ between the genetic and pharmacological results. In particular, while mAChR-A knockdown mainly affects γ KCs, with other subtypes inconsistently affected, muscarine reduces responses in all KC subtypes. What explains these differences? mAChR-A might be weakly activated in physiological conditions, in which case gain of function would cause a stronger effect than loss of function. Similarly, pharmacological activation of mAChR-A is likely a more drastic manipulation than a 60% reduction of mAChR-A mRNA levels. Although we cannot entirely rule out network effects from muscarine application, the effect of muscarine does not stem from PNs or APL (Figure 5C,D) and locally applied muscarine would have little effect on neurons outside the mushroom body.

How does mAChR-A inhibit odor-evoked Ca²⁺ influx in KCs? Given that mAChR-A signals through G_q when expressed in CHO cells (Ren et al., 2015), that muscarinic G_q signaling normally increases excitability in mammals (Caulfield and Birdsall, 1998), and that pan-neuronal artificial activation of G_q signaling in Drosophila larvae increases overall excitability (Becnel et al., 2013), it may be surprising that mAChR-A inhibits KCs. However, G_q signaling may exert different effects on different neurons in the fly brain, and some examples exist of inhibitory G_q signaling by mammalian mAChRs. M₁/M₃/M₅ receptors acting via G_q can inhibit voltage-dependent Ca²⁺ channels (Gamper et al., 2004; Kammermeier et al., 2000; Keum et al., 2014; Suh et al., 2010), reduce voltage-gated Na +currents (Cantrell et al., 1996), or trigger surface transport of KCNQ channels (Jiang et al., 2015), thus increasing inhibitory K⁺ currents. Drosophila mAChR-A may inhibit KCs through similar mechanisms.

What is the source of ACh which activates mAChR-A and modulates odor responses? In the calyx, cholinergic PNs are certainly a major source of ACh. However, KCs themselves are cholinergic (Barnstedt et al., 2016) and release neurotransmitter in both the calyx and lobes (Christiansen et al., 2011). KCs form synapses on each other in the calyx (Zheng et al., 2018), possibly allowing mAChR-A to mediate lateral inhibition, in conjunction with the lateral inhibition provided by the GABAergic APL neuron (Lin et al., 2014).

What function does mAChR-A serve in learning and memory? Our results indicate that mAChR-A knockdown prevents the learning-associated weakening of KC-MBON synapses, in particular for MBON-γ1pedc>α/β, aka MB-MVP2 (Figure 7). One potential explanation is that the increased odor-evoked Ca²⁺ influx observed in knockdown flies increases synaptic release, which overrides the learning-associated synaptic depression. However, increased odor-evoked Ca²⁺ influx per se is unlikely on its own to straightforwardly explain a learning defect, because other genetic manipulations that increase odor-evoked Ca²⁺ influx in KCs either have no effect on, or even improve, olfactory learning. For example, knocking down GABA synthesis in the inhibitory APL neuron increases odor-evoked Ca²⁺ influx in KCs (Lei et al., 2013; Lin et al., 2014) and improves olfactory learning (Liu and Davis, 2009).

The most intuitive explanation would be that mAChR-A acts at KC synaptic terminals in KC axons to help depress KC-MBON synapses. Yet overexpressed mAChR-A localizes to KC dendrites, not axons, and functionally rescues mAChR-A hypomorphic mutants, showing that dendritic mAChR-A suffices for its function in learning and memory. Does this show that mAChR-A has no role in KC axons? Our inability to detect GFP expressed from the mAChR-A MiMIC gene trap suggests that normally there may only be a small amount of mAChR-A in KCs. It may be that with mAChR-A-FLAG overexpression, the correct (undetectable) amount of mAChR-A is trafficked to and functions in axons, but due to a bottleneck in axonal transport, the excess tagged mAChR-A is trapped in KC dendrites. While our results do not rule out this possibility, a general bottleneck in axonal transport seems unlikely as many overexpressed proteins are localized to KC axons (Trunova et al., 2011). We feel it is more parsimonious to take the dendritic localization of mAChR-A-FLAG at face value and infer that mAChR-A functions in KC dendrites.

How can mAChR-A in KC dendrites affect synaptic plasticity in KC axons? mAChR-A signaling might change the shape or duration of KC action potentials (Allen and Burnstock, 1990; Ghamari-Langroudi and Bourque, 2004), an effect that could potentially propagate to KC axon terminals (Juusola et al., 2007; Shu et al., 2006). Such changes in the action potential waveform may not be detected by calcium imaging, but could potentially affect a ‘coincidence detector’ in KC axons that detects when odor (i.e. KC activity) coincides with reward/punishment (i.e. dopamine). This coincidence detector is generally believed to be the Ca²⁺-dependent adenylyl cyclase rutabaga (Levin et al., 1992). Changing the waveform of KC action potentials could potentially affect local dynamics of Ca²⁺ influx near rutabaga molecules. In addition, rutabaga mutations do not abolish learning (mutants have ~40–50% of normal learning scores) (Yildizoglu et al., 2015), so there may be additional coincidence detection mechanisms affected by action potential waveforms. Testing this idea would require a better understanding of biochemical events underlying learning at KC synaptic terminals.

Alternatively, mAChR-A’s effects on synaptic plasticity may not occur acutely. Although we ruled out purely developmental effects of mAChR-A through adult-only RNAi expression (Figure 1E), knocking out mAChR-A for several days in adulthood might still affect KC physiology in a not-entirely-acute way. For example, as with other G-protein-coupled receptors (Wang and Zhuo, 2012), muscarinic receptors can affect gene expression (von der Kammer et al., 1998), which could have wide-ranging effects on KC physiology, for example action potential waveform, expression of key genes required for synaptic plasticity, etc. Another intriguing possibility is suggested by an apparent paradox: both mAChR-A and the dopamine receptor Damb signal through G_q (Himmelreich et al., 2017), but mAChR-A promotes learning while Damb promotes forgetting (Berry et al., 2012). How can G_q mediate apparently opposite effects? Perhaps G_q signaling aids both learning and forgetting by generally rendering synapses more labile. Indeed, although damb mutants retain memories for longer than wildtype, their initial learning is slightly impaired (Berry et al., 2012); damb mutant larvae are also impaired in aversive olfactory learning (Selcho et al., 2009). Although one study reports that knocking down G_q in KCs did not impair initial memory (Himmelreich et al., 2017), the G_q knockdown may not have been strong enough; also, that study shocked flies with 90 V shocks, which also gives normal learning in mAChR-A knockdown flies (Figure 1—figure supplement 1).

Such hypotheses posit that mAChR-A regulates synaptic plasticity ‘competence’ rather than participating directly in the plasticity mechanism itself. Why should synaptic plasticity competence be controlled by an activity-dependent mechanism? It is tempting to speculate that mAChR-A may allow a kind of metaplasticity (Abraham, 2008) in which exposure to odors (hence activation of mAChR-A in KCs) makes flies’ learning mechanisms more sensitive. Indeed, mAChR-A is required for learning with moderate (50 V) shocks, not severe (90 V) shocks. Future studies may further clarify how muscarinic signaling contributes to olfactory learning.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1-8, Figure 1—figure supplement 1, and Figure 8—figure supplement 1.

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Author details

1. Noa Bielopolski

   Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

2. Hoger Amin

   Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Anthi A Apostolopoulou and Eyal Rozenfeld

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7884-4815

3. Anthi A Apostolopoulou

   Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Contributed equally with

   Hoger Amin and Eyal Rozenfeld

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8174-4372

4. Eyal Rozenfeld

   Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

   Contribution

   Software, Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Contributed equally with

   Hoger Amin and Anthi A Apostolopoulou

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5316-0495

5. Hadas Lerner

   Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

   Contribution

   Formal analysis, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

6. Wolf Huetteroth

   Institute for Biology, University of Leipzig, Leipzig, Germany

   Contribution

   Investigation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3421-9935

7. Andrew C Lin

   Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom

   Contribution

   Conceptualization, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Moshe Parnas

   For correspondence

   andrew.lin@sheffield.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6310-9765

8. Moshe Parnas

   1. Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
   2. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

   Contribution

   Conceptualization, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing, Initiated the project

   Contributed equally with

   Andrew C Lin

   For correspondence

   mparnas@tauex.tau.ac.il

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9726-1511

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