Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement

  1. Yoshinori Aso
  2. Daichi Yamada
  3. Daniel Bushey
  4. Karen L Hibbard
  5. Megan Sammons
  6. Hideo Otsuna
  7. Yichun Shuai
  8. Toshihide Hige

  • Curated by eLife

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    This study provides important new insights into how learning affects behavior in the Drosophila model. Using a combination of connectomics, neurophysiology, and behavioral analysis, a small group of neurons in the Drosophila brain that integrates learned odor valences and promotes odor tracking by driving upwind orientation and movement is described. The study's conclusion is supported by convincing evidence and rigorous quantitative analysis. Insights from the neural circuit mechanism that translates learning-induced plasticity into appropriate behavioral actions will be of broad interest to neuroscientists.

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Abstract

How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila , multiple compartments of the mushroom body act in parallel to assign a valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After formation of appetitive memory, UpWiNs acquire enhanced response to reward-predicting odors as the response of the inhibitory presynaptic MBON undergoes depression. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was terminated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.

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  1. Version published to 10.7554/elife.85756 on eLife
  2. eLife

    Author Response

    Reviewer #2 (Public Review):

    Associative learning assigns valence to sensory cues paired with reward or punishment. Brain regions such as the amygdala in mammals and the mushroom body in insects have been identified as primary sites where valence assignment takes place. However, little is known about the neural mechanisms that translate valence-specific activity in these brain regions into appropriate behavioral actions. This study identifies a small set of upwind neurons (UpWiNs) in the Drosophila brain that receive direct inputs from two mushroom body output neurons (MBONs) representing opposite valences. Through a series of behavioral, imaging, and electrophysiological experiments, the authors show that UpWiNs are differentially regulated by the two MBONs, i.e., inhibited by the glutamatergic MBON-α1(encoding negative valence) while activated by the cholinergic MBON-α3 (encoding positive valence). They also show that UpWiNs control the wind-directed behavior of flies. Activation of UpWiNs is sufficient to drive flies to orient and move upwind, and inhibition of UpWiNs reduces flies' upwind movement toward the source of reward-predicting odors (CS+). These results, together with existing knowledge about the function of the mushroom body in memory processing, suggest an appealing model in which reward learning decreases and increases the responses of MBON-α1 and MBON-α3 to the CS+ odor, respectively, and these changes cause UpWiNs to respond more strongly to the CS+ odor and drive upwind locomotion. Interestingly, in the final part of the results, the authors reveal a wind-independent function of UpWiNs: increasing the probability that flies will revisit the site where UpWiNs were activated. Thus, UpWiNs guide learned reward-seeking behavior with and without airflow. Although the mushroom body has been extensively studied for its role in learning and memory, the downstream neural circuits that read the information from the mushroom body to guide memory-driven behaviors remain poorly characterized. This study provides an important piece of the puzzle for this knowledge gap.

    Strength

    1. Memory studies have predominantly relied on binary choice (go or no-go) assays as measures of memory performance. While these assays are convenient and efficient, they fall short of providing a comprehensive understanding of underlying behavioral structures. In an effort to overcome this limitation, the current study used video recording and tracking software to delve deeper into memory-guided behavior. This innovative approach allowed the authors to uncover novel neurons and examine their contribution to behavior with a level of detail not possible with binary choice assays.
    1. This study used electron microscopy-based Drosophila hemibrain connectome data to reveal the synaptic connection between UpWiNs and MBON-α1 and MBON-α3. Using this method, the study shows that a single UpWiN receives direct input from both MBON-α1 and MBON- α3, which is confirmed by a functional imaging experiment. The connectome dataset also reveals several neurons downstream of UpWiNs, opening avenues for further research into the neural mechanisms linking memory and behavior.

    Weakness

    1. The authors repeatedly state in the manuscript that MBON-α1 and MBON-α3 convey appetitive or aversive memories, respectively. This assertion may not be entirely accurate. Evidence from sugar reward conditioning experiments suggests that MBON-α3 is potentiated and required for sugar reward memory retrieval. Therefore, the compartmentalization for appetitive and aversive memories appears not as obvious at the level of MBONs.

    What we intended was that activation of DANs in these compartments can induce aversive and appetitive memories, respectively, when paired with odors, and that these are the sole output pathway from these compartments to read out the memories in these compartments. As we previously proposed (Aso et al., 2014a eLife), these MBONs can integrate inputs from MBONs of other compartments and their activity can reflect appetitive memory stored as synaptic plasticity in other compartments. Since DANs in the α3 compartment respond to heat, bitter and electric shock but not sugar, the observation that MBON-α3 acquires an enhanced CS+ odor response after appetitive conditioning is presumably due to these intercompartmental connections rather than plasticity of KC-MBON synapses in the α3 compartment. In any case, the fact that excitatory activity of MBON-α1 and MBON-α3 conveys opposite valence of memory still holds true since appetitive conditioning induces depression and potentiation of odor responses, respectively.

    To clarify this point, we now cited related literature in the following sentence in the final paragraph of Introduction: “UpWiNs receive inputs from several types of lateral horn neurons and integrate inhibitory and excitatory inputs from MBON-α1 and MBON-α3, which are the output neurons of MB compartments that store long-lasting appetitive or aversive memories, respectively (Aso and Rubin, 2016; Ichinose et al., 2015; Jacob and Waddell, 2022a; Pai et al., 2013; Yamagata et al., 2015).”

    1. This study did not conclusively establish the importance of the MBON-α1/α3 to UpWiN pathways in memory-driven behavior. In the experiments shown in Figure 5, flies were trained to associate the activation of reward-related DANs with a specific odor (CS+). After conditioning, UpWiNs were observed to show enhanced responses to the CS+ odor. However, the results should be interpreted with caution because the driver line used to activate DANs (R58E02-LexAp65) labels not only DANs projecting to the MBON-α1 compartment, but all DANs in the protocerebral anterior medial (PAM) cluster. Thus, it remains unclear to what extent the observed enhanced responses are influenced by changes in inhibitory inputs from MBON-α1. While UpWiNs have been shown to play a critical role in the expression of sugar reward memory (Figure 7), it should be noted that UpWiNs receive inputs from multiple upstream neurons, making it difficult to accurately assess the contribution of MBON-α1/α3 to UpWiN pathways in UpWiN recruitment. Further research is needed to fully address this issue.

    We totally agree with this point and added a sentence to explain an alternative mechanism. “This enhancement of CS+ response can be most easily explained as an outcome of disinhibition from MBON-α1 whose output had been decreased by memory formation; MBON-α1 is inhibitory to UpWiNs (Figure 4B) and MBON-α1 response to the CS+ is reduced following the same training protocol (Yamada et al. 2023). In addition to such a mechanism, plasticity in the β1 compartment may contribute to the enhanced CS+ response in UpWiNs because the driver R58E02 contains DANs in the β1 and glutamatergic MBON from the β1 directly synapse on the dendrites of MBON-α1 and MBON-α3. “

    1. UpWind neurons (UpWiNs) were so named because their activation promotes upwind locomotion. However, when activated in the absence of airflow, flies show increased locomotor speed and an increased probability of revisiting the same location (Figure 7 and Figure 7-figure supplement 1). The revisiting behavior can be observed during the activation of UpWiNs, which is distinct from the local search behavior that typically begins after a reward stimulus is turned off (e.g., Gr64f-GAL4 results in Figure 7-figure supplement 1).

    Return probability was calculated within a 15-s time window. High return probability during LED ON period (10-20s) in Figure 7-figure supplement 1 does not necessarily mean that flies returned during LED ON period. If a fly is at the position A when t=10s, to be counted as “returned”, it needs to move more than 10mm away from A and move back to the position less than 3mm distance from A by t=25s. In the case of sugar sensory neuron activation with Gr64f-GAL4, the peak of return probability is shifted toward a later time point because flies stop and extend proboscis during activation period.

    Because revisiting a location can also be a consequence of repeated turns, it seems more accurate to describe UpWiNs as controlling the speed and likelihood of turns and promoting upwind movement by integrating with neurons that sense the direction of airflow.

    The return probability plotted in Figure 7E is probability of return to the position at the end of LED period within 15s post LED period when angular speed of SS33917>CsChrimson and SS33918>CsChrimson flies are identical to empty-split-GAL4>CsChrimson control flies (Figure 7-figure supplement 1). Thus, revisiting behavior cannot be explained by a simple increase in turing probability.

    Although functions of UpWiNs are not limited to promotion of wind-directed walking, we still think that the “UpWind Neurons” is a practical name for broad readers and oral communications at the current stage of investigations, because EM neuron IDs and names (SMP348, SMP353, SMP354, SLP399 and SLP400) are too lengthy and do not contain any functional information. We initially defined a set of 11 neurons labeled by SS33197 split-GAL4 as “UpWind Neurons (UpWiNs)” based on initial optogenetic screening (Figure 2A). We found other driver lines for mushroom body interneuron cell types that can promote release of dopamine and more robust returning phenotype (e.g. SS49755), but SS33917 remained to be the champion driver line for upwind locomotion phenotype.

    Reviewer #3 (Public Review):

    Aso et al. provide insight into how learned valences are transformed into concrete memory-driven actions, using a diverse set of proven techniques.

    Here the authors use a four-armed arena to evaluate flies' preference for a reward-predicting odor and measure upwind locomotion. This behavioral paradigm was combined with the photoactivation of different memory-eliciting neurons, revealing that appetitive memories stored in different compartments of the mushroom bodies (center of olfactory memory) induce different levels of upwind locomotion. The authors then proceed to a non-exhaustive optogenetic screen of the neurons located downstream of the output neurons of the mushroom bodies (MBONs) and identify a group of 8-11 Cholinergic neurons promoting significant changes in upwind locomotion, the UpWins. By combining confocal immunolabelling of these neurons with electron microscope images, they manage to establish the UpWins' connectome within themselves and with the MBONs. Then, using two in vivo cell recording techniques, electrophysiology, and calcium imaging, they define that UpWins integrate both inhibitory and excitatory synaptic inputs from the MBONs encoding appetitive and aversive memory, respectively. In addition, they show that the UpWins' response to a reward-predicting odor is increased after appetitive training. On a behavioral level, the authors establish that the UpWins respond to wind direction only and are not involved in lower-level motor parameters, such as turning direction and acceleration. Finally, they demonstrate that the UpWins' activity is necessary for long-term appetitive memory retrieval, and even suggest a broader role for the UpWins in olfactory navigation, as their photoactivation increases the probability of revisiting behavior. In the end, the authors state that they provide new insights into how memory is translated into concrete behavior, which is fully supported by their data. Altogether, the authors present a pretty complete study that provides very interesting and reliable data, and that opens a new field of investigation into memory-driven behaviors.

    Strengths of the study:

    • To support their conclusions, the authors provide detailed data from different levels of analysis (behavioral, cellular, and molecular), using multiple sophisticated techniques.
    • The measurement of multiple parameters in the behavioral analysis supports the strong changes in upwind locomotion. In addition, taken individually these parameters provide precise insights into how upwind locomotion changes, and allow the authors to more precisely define the role of the UpWins.
    • The authors use split-Gal4 drivers instead of Gal4, allowing them to better refine neuron labelling.

    The authors discussed and investigated all possible biases, making their data very reliable. For example, they demonstrated that the phenotypes observed in the behavioral assay were wind-directed behaviors and could not be explained by bias avoidance of the arena's center area.

    Limitations of the study:

    • In the absence of more precise drivers, the UpWins' labelling lacks precision. For example, there is no way to know exactly which UpWin is responding in the electrophysiological experiment presented in Figure 4.

    We have ongoing efforts to generate split-GAL4 and split-LexA driver lines for specific subsets of UpWiN neurons, but the data using those lines are not ready for this manuscript. However, we would like to point out that historically, identification of a group of neurons with striking phenotype has been foundational to promote follow-up studies. A good example is P1 neurons for courtship behavior.

    • The screening of neurons located downstream of the MBONs is not exhaustive, meaning that other groups of neurons might be involved in memory-driven upwind locomotion. Although, it does not diminish the authors' conclusions.

    The UpWiNs is certainly not the only one cell type for mediating memory-driven upwind locomotion, since our and other groups’ studies (e.g. Matheson et al., 2022; PMCID: PMC9360402) identified a collection of cell types that can promote upwind locomotion upon optogenetic activation.

    In 2021, we released images and driver lines of a larger collection of split-GAL4 driver lines at https://splitgal4.janelia.org. We are preparing a manuscript to provide anatomical descriptions of these lines. This collection of new drivers will help elucidate more comprehensive views of circuits for memory-driven actions.

    • All data were obtained with walking flies. So far, there have been no experiments on flying flies.

    This is an intriguing question and we mentioned in Discussion that “Our study was limited to walking behaviors, and the role of UpWiNs in flight behaviors remains to be investigated.”

  3. eLife

    eLife assessment

    This study provides important new insights into how learning affects behavior in the Drosophila model. Using a combination of connectomics, neurophysiology, and behavioral analysis, a small group of neurons in the Drosophila brain that integrates learned odor valences and promotes odor tracking by driving upwind orientation and movement is described. The study's conclusion is supported by convincing evidence and rigorous quantitative analysis. Insights from the neural circuit mechanism that translates learning-induced plasticity into appropriate behavioral actions will be of broad interest to neuroscientists.

  4. eLife

    Reviewer #1 (Public Review):

    The study of Aso and colleagues seeks to understand how learned information is steering motor output. Using an artificial training paradigm consisting of odor presentation combined with dopamine neuron activation, they identify upwind orientation as an important parameter of appetitive memory recall (as has been shown before - e.g. Handler 2019). Using the Drosophila genetic targeting library and optogenetic activation, they identify several populations of neurons responsible for upwind orientation by analyzing freely moving animals in an airflow chamber. They concentrate on a specific subset, which they call upwind neurons (UPWINs), and which they can anatomically link downstream to the flies' memory center, the mushroom body (MB), building on the ultrastructural connectome brain atlas. In combination with electrophysiology, in-vivo calcium imaging, and memory assays, they successfully show that (1) UPWINs promote upwind orientation including acceleration of angular speed and bias turning towards the upwind direction, (2) UPWINs receive excitatory and inhibitory input from specific parts of the MB, (3) UPWINs increase odor-evoked activity upon (artificial) appetitive training and (4) appetitive memory recall is impaired when blocking UPWIN neurons only during the memory test.

    The authors use state-of-the-art techniques combining tools like optogenetics, connectome analysis as well as electrophysiology, in-vivo calcium imaging, and memory/behavioral assays tracking individual flies. It provides new insights into mushroom body memory retrieval circuits and how they integrate with information from other brain areas. However, some concerns remain regarding some claims of the paper. The timeline of the behavioral and the physiological experiments differ. It is therefore difficult to define the memory phases when upwind orientation is important for recall. Moreover, one main conclusion the authors draw from their data is that upwind orientation is promoted by disinhibition from a specific MB output connection, however, physiological evidence of this effect is missing. The UPWINs seem to have a more complex function in behavioral control beyond memory recall. The fact that optogenetic UPWIN activation is leading to upwind orientation only in starved flies together with the fact that flies show a high returning probability even without any odor present suggests a functional role in state-dependent exploration behavior.

  5. eLife

    Reviewer #2 (Public Review):

    Associative learning assigns valence to sensory cues paired with reward or punishment. Brain regions such as the amygdala in mammals and the mushroom body in insects have been identified as primary sites where valence assignment takes place. However, little is known about the neural mechanisms that translate valence-specific activity in these brain regions into appropriate behavioral actions. This study identifies a small set of upwind neurons (UpWiNs) in the Drosophila brain that receive direct inputs from two mushroom body output neurons (MBONs) representing opposite valences. Through a series of behavioral, imaging, and electrophysiological experiments, the authors show that UpWiNs are differentially regulated by the two MBONs, i.e., inhibited by the glutamatergic MBON-α1(encoding negative valence) while activated by the cholinergic MBON-α3 (encoding positive valence). They also show that UpWiNs control the wind-directed behavior of flies. Activation of UpWiNs is sufficient to drive flies to orient and move upwind, and inhibition of UpWiNs reduces flies' upwind movement toward the source of reward-predicting odors (CS+). These results, together with existing knowledge about the function of the mushroom body in memory processing, suggest an appealing model in which reward learning decreases and increases the responses of MBON-α1 and MBON-α3 to the CS+ odor, respectively, and these changes cause UpWiNs to respond more strongly to the CS+ odor and drive upwind locomotion. Interestingly, in the final part of the results, the authors reveal a wind-independent function of UpWiNs: increasing the probability that flies will revisit the site where UpWiNs were activated. Thus, UpWiNs guide learned reward-seeking behavior with and without airflow. Although the mushroom body has been extensively studied for its role in learning and memory, the downstream neural circuits that read the information from the mushroom body to guide memory-driven behaviors remain poorly characterized. This study provides an important piece of the puzzle for this knowledge gap.

    Strength

    1. Memory studies have predominantly relied on binary choice (go or no-go) assays as measures of memory performance. While these assays are convenient and efficient, they fall short of providing a comprehensive understanding of underlying behavioral structures. In an effort to overcome this limitation, the current study used video recording and tracking software to delve deeper into memory-guided behavior. This innovative approach allowed the authors to uncover novel neurons and examine their contribution to behavior with a level of detail not possible with binary choice assays.

    2. This study used electron microscopy-based Drosophila hemibrain connectome data to reveal the synaptic connection between UpWiNs and MBON-α1 and MBON-α3. Using this method, the study shows that a single UpWiN receives direct input from both MBON-α1 and MBON- α3, which is confirmed by a functional imaging experiment. The connectome dataset also reveals several neurons downstream of UpWiNs, opening avenues for further research into the neural mechanisms linking memory and behavior.

    Weakness

    1. The authors repeatedly state in the manuscript that MBON-α1 and MBON-α3 convey appetitive or aversive memories, respectively. This assertion may not be entirely accurate. Evidence from sugar reward conditioning experiments suggests that MBON-α3 is potentiated and required for sugar reward memory retrieval. Therefore, the compartmentalization for appetitive and aversive memories appears not as obvious at the level of MBONs.

    2. This study did not conclusively establish the importance of the MBON-α1/α3 to UpWiN pathways in memory-driven behavior. In the experiments shown in Figure 5, flies were trained to associate the activation of reward-related DANs with a specific odor (CS+). After conditioning, UpWiNs were observed to show enhanced responses to the CS+ odor. However, the results should be interpreted with caution because the driver line used to activate DANs (R58E02-LexAp65) labels not only DANs projecting to the MBON-α1 compartment, but all DANs in the protocerebral anterior medial (PAM) cluster. Thus, it remains unclear to what extent the observed enhanced responses are influenced by changes in inhibitory inputs from MBON-α1. While UpWiNs have been shown to play a critical role in the expression of sugar reward memory (Figure 7), it should be noted that UpWiNs receive inputs from multiple upstream neurons, making it difficult to accurately assess the contribution of MBON-α1/α3 to UpWiN pathways in UpWiN recruitment. Further research is needed to fully address this issue.

    3. UpWind neurons (UpWiNs) were so named because their activation promotes upwind locomotion. However, when activated in the absence of airflow, flies show increased locomotor speed and an increased probability of revisiting the same location (Figure 7 and Figure 7-figure supplement 1). The revisiting behavior can be observed during the activation of UpWiNs, which is distinct from the local search behavior that typically begins after a reward stimulus is turned off (e.g., Gr64f-GAL4 results in Figure 7-figure supplement 1). Because revisiting a location can also be a consequence of repeated turns, it seems more accurate to describe UpWiNs as controlling the speed and likelihood of turns and promoting upwind movement by integrating with neurons that sense the direction of airflow.

  6. eLife

    Reviewer #3 (Public Review):

    Aso et al. provide insight into how learned valences are transformed into concrete memory-driven actions, using a diverse set of proven techniques.

    Here the authors use a four-armed arena to evaluate flies' preference for a reward-predicting odor and measure upwind locomotion. This behavioral paradigm was combined with the photoactivation of different memory-eliciting neurons, revealing that appetitive memories stored in different compartments of the mushroom bodies (center of olfactory memory) induce different levels of upwind locomotion. The authors then proceed to a non-exhaustive optogenetic screen of the neurons located downstream of the output neurons of the mushroom bodies (MBONs) and identify a group of 8-11 Cholinergic neurons promoting significant changes in upwind locomotion, the UpWins. By combining confocal immunolabelling of these neurons with electron microscope images, they manage to establish the UpWins' connectome within themselves and with the MBONs. Then, using two in vivo cell recording techniques, electrophysiology, and calcium imaging, they define that UpWins integrate both inhibitory and excitatory synaptic inputs from the MBONs encoding appetitive and aversive memory, respectively. In addition, they show that the UpWins' response to a reward-predicting odor is increased after appetitive training. On a behavioral level, the authors establish that the UpWins respond to wind direction only and are not involved in lower-level motor parameters, such as turning direction and acceleration. Finally, they demonstrate that the UpWins' activity is necessary for long-term appetitive memory retrieval, and even suggest a broader role for the UpWins in olfactory navigation, as their photoactivation increases the probability of revisiting behavior. In the end, the authors state that they provide new insights into how memory is translated into concrete behavior, which is fully supported by their data. Altogether, the authors present a pretty complete study that provides very interesting and reliable data, and that opens a new field of investigation into memory-driven behaviors.

    Strengths of the study:

    - To support their conclusions, the authors provide detailed data from different levels of analysis (behavioral, cellular, and molecular), using multiple sophisticated techniques.

    - The measurement of multiple parameters in the behavioral analysis supports the strong changes in upwind locomotion. In addition, taken individually these parameters provide precise insights into how upwind locomotion changes, and allow the authors to more precisely define the role of the UpWins.

    - The authors use split-Gal4 drivers instead of Gal4, allowing them to better refine neuron labelling.

    The authors discussed and investigated all possible biases, making their data very reliable. For example, they demonstrated that the phenotypes observed in the behavioral assay were wind-directed behaviors and could not be explained by bias avoidance of the arena's center area.

    Limitations of the study:

    - In the absence of more precise drivers, the UpWins' labelling lacks precision. For example, there is no way to know exactly which UpWin is responding in the electrophysiological experiment presented in Figure 4.

    - The screening of neurons located downstream of the MBONs is not exhaustive, meaning that other groups of neurons might be involved in memory-driven upwind locomotion. Although, it does not diminish the authors' conclusions.

    - All data were obtained with walking flies. So far, there have been no experiments on flying flies.

  7. Version published to 10.1101/2022.12.21.521497v2 on bioRxiv
  8. Version published to 10.1101/2022.12.21.521497v1 on bioRxiv