Obesity is a major health problem affecting over 650 million adults worldwide. It is typically caused by overeating high-energy foods, which often contain a lot of sugar. Consuming sugary foods triggers the production of a reward signal called dopamine in the brains of insects and mammals, which reinforces sugar-consuming behavior. The brain balances this with a process called ‘sensory-enhanced satiety’, which makes foods that provide a stronger sensation of sweetness better at reducing hunger and further eating.

High-energy food was scarce for most of human evolution, but over the past century sugar has become readily available in our diet leading to an increase in obesity. Last year, a study in fruit flies reported that a sugary diet reduces the sensitivity to sweet flavors, which leads to overeating and weight gain. It appears that this sensitivity is linked to the effectiveness of sensory-enhanced satiety. However, the mechanism linking diets high in sugar and overeating is still poorly understood. One hypothesis is that fruit flies estimate the energy content of food based on the degree of dopamine released in response to the sugar.

May et al. compared the responses of neurons in fruit flies fed a normal diet to those in flies fed a diet high in sugar. As expected, both groups activated the neurons involved in the dopamine reward response when they tasted sugar. However, when the flies were on a sugar-heavy diet, these neurons were less active. This was because the neurons responsible for tasting sweetness were activated less in flies fed a high-sugar diet, leading to a lowered response by the neurons that produce dopamine. The flies in these experiments were genetically engineered so that the dopamine-producing neurons could be artificially activated in response to light, a technique called optogenetics. When May et al. applied this technique to the flies on a sugar-heavy diet, they were able to stop these flies from overeating.

These findings provide further evidence to support the idea that a sugary diet reduces the brain’s sensitivity to overeating. Given the significant healthcare cost of obesity to society, this improved understanding could help public health initiatives focusing on manufacturing food that is lower in sugar.

Consumption of diets high in sugar and fat decreases the perception of taste stimuli, influencing food preference and promoting food intake (Bartoshuk et al., 2006; Sartor et al., 2011; Ahart et al., 2019; May et al., 2019; Weiss et al., 2019; Kaufman et al., 2018). Recent studies have examined the effects of these diets on the sensitivity of the peripheral taste system and the intensity of taste experience (May et al., 2019; Maliphol et al., 2013; Kaufman et al., 2018; Weiss et al., 2019), but how exactly taste deficits increase feeding behavior is not known. Orosensory signals determine the palatability or ‘liking’ for foods (Berridge and Kringelbach, 2015), but they also promote meal termination via a process called ‘sensory-enhanced (or mediated) satiety’ (Chambers et al., 2015). Indeed, foods that provide longer and more intense sensory exposure are more satiating, reducing hunger and subsequent test-meal intake in humans (Yeomans, 2017; Bolhuis et al., 2011; Ramaekers et al., 2014; Viskaal-van Dongen et al., 2011; Cecil et al., 1998; Forde et al., 2013). Specifically, sensory signals are thought to function early in the satiety cascade (Blundell et al., 1987) by promoting satiation and bringing the on-going eating episode to an end (Blundell et al., 2010; Bellisle and Blundell, 2013). This is in contrast to nutrient-derived signals, which develop more slowly and consolidate satiety by inhibiting further eating after the end of a meal (Blundell et al., 2010; Bellisle and Blundell, 2013). We reasoned that if orosensory attributes like taste intensity are important to curtail a feeding event, then diet-dependent changes in taste sensation could promote feeding by impairing sensory-enhanced satiation. Here we investigated the relationship between diet composition – specifically high dietary sugar – the central processing of sweet taste signals, and satiation by exploiting the simple taste system and the conserved neurochemistry of the fruit fly D. melanogaster.

Like humans and rodents, D. melanogaster flies exposed to palatable diets rich in sugar or fat overconsume, gain weight, and become at-risk for obesity and develop phenotypes associated with metabolic syndrome (Musselman and Kühnlein, 2018). We recently showed that, in addition to promoting feeding by increasing meal size, consumption of high dietary sugar decreased the electrophysiological and calcium responses of the Gr64f+ sweet sensing neurons to sweet stimuli, independently of weight gain (May et al., 2019). These physiological changes in the Gr64f+ cells reduced the fruit flies’ taste sensitivity and response intensity. Opto- and neurogenetics manipulations to correct the responses of the Gr64f+ neurons to sugar prevented animals exposed to high dietary sugar from overfeeding and restored normal meal size (May et al., 2019). Thus, the diet-dependent dulling in sweet taste causes higher feeding in flies, but how does this happen? How do alterations in the peripheral sensory neurons modulate a behavior as complex as feeding? To better understand how this occurs, we decided to examine the effects of high dietary sugar and taste changes in the central processing of sweet stimuli by dopaminergic neurons (DANs). Indeed, while the neural pathways that bring sensory information from the periphery to higher order brain regions are unique across organisms, dopaminergic circuits process sweet taste information in humans, rodents, and fruit flies. Interestingly, the reinforcing effects of sugar taste and nutrient properties are relayed via distinct dopaminergic pathways in these organisms (Yamagata et al., 2015; Huetteroth et al., 2015; Tellez et al., 2016; Thanarajah et al., 2019). In flies, DANs in the Protocerebral Anterior Medial (PAM) cluster respond to the sweet sensory properties to signal sugar reward (Burke et al., 2012; Liu et al., 2012), reinforce short term appetitive memories (Yamagata et al., 2015; Huetteroth et al., 2015), and promote foraging and intake (Tsao et al., 2018; Musso et al., 2019). We hypothesized that diet-dependent impairments in the peripheral responses to sugar could influence the way sweet taste information is transduced through PAM-DANs to affect feeding behavior and obesity risk.

Here we show that in flies fed a high sugar diet the presynaptic responses of a specific subset of PAM DANs to sweet taste are decreased and delayed. These changes are specific to sweet stimuli and mediated by high dietary sugar. Further, we report that the reduction in the central processing of sweet taste information increases the duration and size of meals: closed-loop optogenetic stimulation of a specific set of PAM DANs corrected meal size, duration, and feeding rate. Together, our results argue that diet-dependent alterations in the central processing of sweet sensory responses delay meal termination by impairing the process of sensory-enhanced satiation.

In this study we found that diet-dependent changes in sensory perception promote feeding and weight gain by impairing the central dopaminergic processing of sweet taste information. When animals consume a high sugar diet, the responses to sweet taste of a distinct population of PAM DANs innervating the β’2 compartment of the MB are decreased and delayed. These alterations in dopaminergic processing increase the eating rate and extend the duration of meals, leading to attenuated satiation, higher feeding, and weight gain (Figure 5E). Interestingly, we observed a reduction in PAM DAN responses only when flies ate diets that resulted in sweet taste deficits; consumption of an equal calorically-rich lard diet that did not impact taste had no effect on the PAM DANs responses. Similarly, animals fed high dietary sugar exhibited differences in PAM-β’2 responses to sweet, but not water taste stimuli, reinforcing the idea that PAM DAN alterations occur because of lower signal transmission from the sensory neurons (Figure 5E). Indeed, correcting sweet taste deficits by feeding fruit flies an inhibitor of the enzyme O-GlcNAc Transferase – which we previously found to be required for taste impairments– prevented impairments in PAM-β’2 responses, although we cannot exclude that this could also be due to its effect beyond the sensory neurons (but not in the MB301B neurons, Figure 3—figure supplement 2A). Here, we propose a model where diet-dependent changes in taste intensity and sensitivity reduce the central processing of sensory stimuli to cause weaker and attenuated satiation. Interestingly, our anatomical analysis of several PAM-β’2 DANs lines showed that the neurons labeled by MB301B minimally overlap with other split-GAL4 lines purported to express in β’2 (A degree of overlap is not unexpected and does not necessarily imply that the neurons accessed by these driver lines perform identical functions.) MB301B is largely distinct from these other lines both ventrally and anteriorly. Ventral expression of MB301B enters the β2 compartment, which has been implicated in nutrient reward; however, this compartment is not responsive to sweetness and its activation still resulted in diet-induced obesity. Thus, MB301B expression in anterior β’2 could represent an unique MB compartment that is part of a circuit dedicated to sweet taste processing and feeding behavior.

A weakness of the current study is that we were unable to follow the transmission of the taste signal from the primary sensory neurons through the different circuits that eventually communicate with PAM. Studies that will identify taste projection neurons genetically will allow us to further probe this point in the future. Further, while the split-GAL4 line used in this study expresses in PAM-β2β’2 neurons in the central brain, it also labels a few projections in the ventral nerve cord/SEZ, which could also contribute to some of the effects measured here. Interestingly, two studies previously found that neurogenetic or closed-loop optogenetic activation of MB301B PAM-β2β’2 neurons resulted in an increase in foraging behavior (Tsao et al., 2018) and higher feeding to both water and sucrose substrates (Musso et al., 2019), respectively. We also measured an increase in feeding behavior with closed-loop optogenetic activation of MB301B neurons in flies fed a control diet (5% sucrose), confirming these observations. However, applying the same optogenetic protocol when animals consumed a high sugar diet resulted in lower eating and a protection from diet-induced obesity. Since these neurons have lower activity in flies on a high sugar diet, we propose that the optogenetic stimulation in this context functions as a normalization of the activity, rather than activation in the absence of the stimulus. This suggests that variations in relative PAM DANs activity, rather than their absolute output, may modulate feeding behavior in flies exposed to high dietary sugar. It will also be interesting to ask how the activity of these neurons relates to other aspects of feeding behavior, such as the acceptance of low quality or bitter foods.

Studies in rodents and humans have delineated the importance of sensory signals to modulate satiation and terminate meals. This process, termed sensory-enhanced satiation (Chambers et al., 2015), plays an early role in the satiety cascade before post-oral nutrient-derived signals consolidate satiety (Bellisle and Blundell, 2013; Blundell et al., 1987). Data show that higher sensory intensity and oral exposure promote stronger satiation (Bolhuis et al., 2011; Ramaekers et al., 2014). For example, high sensory characteristics, such as saltiness and sweetness, enhanced the satiating effect of both low and high energy test drinks (Yeomans and Chambers, 2011; Yeomans et al., 2014), decreased consumption of pasta sauce (Yeomans, 1998; Yeomans, 1996), yoghurt (Vickers et al., 2001) and tea (Vickers and Holton, 1998). However, the neural basis for this phenomenon is unknown. Here we characterized the circuit-based mechanisms of sensory-enhanced satiation by exploiting the simplicity of the fruit fly system. We show that sensory-enhanced satiation involves the central dopaminergic processing of peripheral sweet taste stimuli by a dedicated group of PAM-β’2 neurons. Given the role of PAM DANs transmission in reinforcing appetitive memories (Burke et al., 2012; Liu et al., 2012), this discovery is significant because it suggests that satiation may involve a learning or rewarding component and that diet composition may direct food intake by influencing this aspect. Indeed, sensory cues function as a predictor of nutrient density and set expectations for how filling different types of foods should be (Chambers et al., 2015; McCrickerd and Forde, 2016; Yeomans, 2017). This information could be used to modulate the feeding rate during the meal and initiate the process of meal termination without relying uniquely on nutrient-derived cues, which arrive later (Bellisle and Blundell, 2013; Blundell et al., 1987). The idea that sensory cues could set cognitive expectations about the fullness of future meals is also in line with the known roles of DA in promoting the formation of appetitive memories. In fruit flies, PAM DANs promote the formation of short-term associative memories based on taste and long-term associative memories based on nutrient density by modulating plasticity of the postsynaptic Mushroom Body Output Neurons (MBONs) (Cohn et al., 2015; Owald et al., 2015). MBONs are, in turn, connected to pre-motor areas like the Central Complex (Aso et al., 2014a) – the fly genetic and functional analog of the basal ganglia (Strausfeld and Hirth, 2013) – providing an anatomical route to modulate aspects of feeding such as proboscis extension (Chia and Scott, 2019), the analogue of licking or chewing rate. Interestingly, some MBONs receive input from both the taste (β’2) and nutrient (γ5) compartments, raising the possibility that sensory and nutrient memories may be integrated in the same cells to regulate different aspects of the satiety cascade (satiation vs. satiety). In flies, the mode and timing of DA delivery onto the MBONs is critical to establish the strength and valence of the associations (Handler et al., 2019). The delay and decrease we measured in animals on a high sugar diet could impair MBON synaptic plasticity and the formation of new appetitive memories (Cohn et al., 2015; Owald et al., 2015). If this is the case, we would expect that flies on this diet may be insensitive to new learning, use old food memories to predict the filling effects of the meal, and thus overshoot their food intake. This is consistent with the idea elegantly espoused by Kroemer and Small, 2016 who explain the decrease in DA transmission with diet or obesity in a reinforcement learning framework. A different possibility, however, is that alterations in PAM DAN processing are not related to reinforcement learning per se, but instead to a decrease in overall reward receipt. In this light, sensory signals would cue reward not learning, and the pleasure experienced during eating would promote satiation and curb food intake. The idea that decreases in the sensitivity of the reward system increases food intake has been described as the ‘reward deficit’ theory of obesity (Wang et al., 2002), which also draws a parallel between the effects of drugs of abuse and that of sugar on the brain. Our results are consistent with both reinforcement learning and reward deficit scenarios, as well as with other integrated theories of obesity (Stice and Yokum, 2016); future experiments examining the role of circuits downstream of PAMs, and especially the involvement of MBONs, will differentiate between these possibilities. In addition to contributing to the current body of evidence connecting diet with DA alterations in mammals (DiFeliceantonio and Small, 2019; Kroemer and Small, 2016; Geiger et al., 2009; Friend et al., 2017; van de Giessen et al., 2013), our results also show that at least some of these alterations are due to diet, and not obesity. In particular, we speculate that some of the changes in DA transmission observed with diet exposure in rodents and humans may be due to impairments in sensory processing, since humans and rodents also process the taste and nutritive properties of sugar separately (Tellez et al., 2016; Thanarajah et al., 2019). It will be particularly interesting to test whether mimicking the effects of a high sugar diet on these DANs using optogenetics will promote feeding behavior.

In conclusion, our experiments demonstrate that by reducing peripheral taste sensation, a high sugar diet impairs the central DA processing of sensory signals and weakens satiation. These studies forge a causal link between sugar – a key component of processed foods – taste sensation, and weakened satiation, consistent with the fact that humans consume more calories when their diets consist of processed foods (Hall et al., 2019). Given the importance of sensory changes in initiating this cascade of circuit dysfunction, understanding how diet composition mechanistically affects taste is imperative to understand how the food environment directs feeding behavior and metabolic disease.

All data generated or analyzed during this study are included in the manuscript and supporting files.

1. Preprint

   1. Ahart ZC
   2. Martin LE
   3. Kemp BR
   4. Banik DD
   5. Roberts S
   6. Torregrossa A
   7. Medler K

   (2019) Differential effects of diet and weight on taste responses in Diet-Induced obese mice

   bioRxiv.

   https://doi.org/10.1101/564211

   • Google Scholar
2. 1. Aso Y
   2. Sitaraman D
   3. Ichinose T
   4. Kaun KR
   5. Vogt K
   6. Belliart-Guérin G
   7. Plaçais PY
   8. Robie AA
   9. Yamagata N
   10. Schnaitmann C
   11. Rowell WJ
   12. Johnston RM
   13. Ngo TT
   14. Chen N
   15. Korff W
   16. Nitabach MN
   17. Heberlein U
   18. Preat T
   19. Branson KM
   20. Tanimoto H
   21. Rubin GM

   (2014a) Mushroom body output neurons encode Valence and guide memory-based action selection in Drosophila

   eLife 3:e04580.

   https://doi.org/10.7554/eLife.04580

   • PubMed
   • Google Scholar
3. 1. Aso Y
   2. Hattori D
   3. Yu Y
   4. Johnston RM
   5. Iyer NA
   6. Ngo TT
   7. Dionne H
   8. Abbott LF
   9. Axel R
   10. Tanimoto H
   11. Rubin GM

   (2014b) The neuronal architecture of the mushroom body provides a logic for associative learning

   eLife 3:e04577.

   https://doi.org/10.7554/eLife.04577

   • PubMed
   • Google Scholar
4. 1. Aso Y
   2. Rubin GM

   (2016) Dopaminergic neurons write and update memories with cell-type-specific rules

   eLife 5:e16135.

   https://doi.org/10.7554/eLife.16135

   • PubMed
   • Google Scholar
5. 1. Bartoshuk LM
   2. Duffy VB
   3. Hayes JE
   4. Moskowitz HR
   5. Snyder DJ

   (2006) Psychophysics of sweet and fat perception in obesity: problems, solutions and new perspectives

   Philosophical Transactions of the Royal Society B: Biological Sciences 361:1137–1148.

   https://doi.org/10.1098/rstb.2006.1853

   • PubMed
   • Google Scholar
6. 1. Bellisle F
   2. Blundell JE

   (2013) 1 - Satiation, Satiety: Concepts and Organisation of Behaviour

   Satiation, Satiety and the Control of Food Intake pp. 3–11.

   https://doi.org/10.1533/9780857098719.1.3

   • Google Scholar
7. 1. Berridge KC
   2. Kringelbach ML

   (2015) Pleasure systems in the brain

   Neuron 86:646–664.

   https://doi.org/10.1016/j.neuron.2015.02.018

   • PubMed
   • Google Scholar
8. Software

   1. Blundell JE
   2. Rogers PJ
   3. Hill AJ

   (1987) Evaluating the Satiating Power of Foods: Implications for Acceptance and Consumption

   Food Acceptance and Nutrition .

   http://agris.fao.org/agris-search/search.do?recordID=US201302689288
9. 1. Blundell J
   2. de Graaf C
   3. Hulshof T
   4. Jebb S
   5. Livingstone B
   6. Lluch A
   7. Mela D
   8. Salah S
   9. Schuring E
   10. van der Knaap H
   11. Westerterp M

   (2010) Appetite control: methodological aspects of the evaluation of foods

   Obesity Reviews 11:251–270.

   https://doi.org/10.1111/j.1467-789X.2010.00714.x

   • PubMed
   • Google Scholar
10. 1. Bolhuis DP
    2. Lakemond CM
    3. de Wijk RA
    4. Luning PA
    5. Graaf C

    (2011) Both longer oral sensory exposure to and higher intensity of saltiness decrease ad libitum food intake in healthy normal-weight men

    The Journal of Nutrition 141:2242–2248.

    https://doi.org/10.3945/jn.111.143867

    • PubMed
    • Google Scholar
11. 1. Burke CJ
    2. Huetteroth W
    3. Owald D
    4. Perisse E
    5. Krashes MJ
    6. Das G
    7. Gohl D
    8. Silies M
    9. Certel S
    10. Waddell S

    (2012) Layered reward signalling through octopamine and dopamine in Drosophila

    Nature 492:433–437.

    https://doi.org/10.1038/nature11614

    • Google Scholar
12. 1. Cecil JE
    2. Francis J
    3. Read NW

    (1998) Relative contributions of intestinal, gastric, oro-sensory influences and information to changes in appetite induced by the same liquid meal

    Appetite 31:377–390.

    https://doi.org/10.1006/appe.1998.0177

    • PubMed
    • Google Scholar
13. 1. Chambers L
    2. McCrickerd K
    3. Yeomans MR

    (2015) Optimising foods for satiety

    Trends in Food Science & Technology 41:149–160.

    https://doi.org/10.1016/j.tifs.2014.10.007

    • Google Scholar
14. Preprint

    1. Chia J
    2. Scott K

    (2019) Activation of specific mushroom body output neurons inhibits Proboscis extension and feeding behavior

    bioRxiv.

    https://doi.org/10.1101/768960

    • Google Scholar
15. 1. Cohn R
    2. Morantte I
    3. Ruta V

    (2015) Coordinated and compartmentalized neuromodulation shapes sensory processing in Drosophila

    Cell 163:1742–1755.

    https://doi.org/10.1016/j.cell.2015.11.019

    • PubMed
    • Google Scholar
16. 1. DiFeliceantonio AG
    2. Small DM

    (2019) Dopamine and diet-induced obesity

    Nature Neuroscience 22:1–2.

    https://doi.org/10.1038/s41593-018-0304-0

    • PubMed
    • Google Scholar
17. 1. Dus M
    2. Lai JS
    3. Gunapala KM
    4. Min S
    5. Tayler TD
    6. Hergarden AC
    7. Geraud E
    8. Joseph CM
    9. Suh GS

    (2015) Nutrient sensor in the brain directs the action of the Brain-Gut Axis in Drosophila

    Neuron 87:139–151.

    https://doi.org/10.1016/j.neuron.2015.05.032

    • PubMed
    • Google Scholar
18. 1. Forde CG
    2. van Kuijk N
    3. Thaler T
    4. de Graaf C
    5. Martin N

    (2013) Oral processing characteristics of solid savoury meal components, and relationship with food composition, sensory attributes and expected satiation

    Appetite 60:208–219.

    https://doi.org/10.1016/j.appet.2012.09.015

    • PubMed
    • Google Scholar
19. 1. Friend DM
    2. Devarakonda K
    3. O'Neal TJ
    4. Skirzewski M
    5. Papazoglou I
    6. Kaplan AR
    7. Liow JS
    8. Guo J
    9. Rane SG
    10. Rubinstein M
    11. Alvarez VA
    12. Hall KD
    13. Kravitz AV

    (2017) Basal ganglia dysfunction contributes to physical inactivity in obesity

    Cell Metabolism 25:312–321.

    https://doi.org/10.1016/j.cmet.2016.12.001

    • PubMed
    • Google Scholar
20. 1. Geiger BM
    2. Haburcak M
    3. Avena NM
    4. Moyer MC
    5. Hoebel BG
    6. Pothos EN

    (2009) Deficits of mesolimbic dopamine neurotransmission in rat dietary obesity

    Neuroscience 159:1193–1199.

    https://doi.org/10.1016/j.neuroscience.2009.02.007

    • PubMed
    • Google Scholar
21. 1. Hall KD
    2. Ayuketah A
    3. Brychta R
    4. Cai H
    5. Cassimatis T
    6. Chen KY
    7. Chung ST
    8. Costa E
    9. Courville A
    10. Darcey V
    11. Fletcher LA
    12. Forde CG
    13. Gharib AM
    14. Guo J
    15. Howard R
    16. Joseph PV
    17. McGehee S
    18. Ouwerkerk R
    19. Raisinger K
    20. Rozga I
    21. Stagliano M
    22. Walter M
    23. Walter PJ
    24. Yang S
    25. Zhou M

    (2019) Ultra-Processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake

    Cell Metabolism 30:67–77.

    https://doi.org/10.1016/j.cmet.2019.05.008

    • PubMed
    • Google Scholar
22. 1. Handler A
    2. Graham TGW
    3. Cohn R
    4. Morantte I
    5. Siliciano AF
    6. Zeng J
    7. Li Y
    8. Ruta V

    (2019) Distinct dopamine receptor pathways underlie the temporal sensitivity of associative learning

    Cell 178:60–75.

    https://doi.org/10.1016/j.cell.2019.05.040

    • PubMed
    • Google Scholar
23. 1. Huetteroth W
    2. Perisse E
    3. Lin S
    4. Klappenbach M
    5. Burke C
    6. Waddell S

    (2015) Sweet taste and nutrient value subdivide rewarding dopaminergic neurons in Drosophila

    Current Biology 25:751–758.

    https://doi.org/10.1016/j.cub.2015.01.036

    • PubMed
    • Google Scholar
24. 1. Inagaki HK
    2. Jung Y
    3. Hoopfer ED
    4. Wong AM
    5. Mishra N
    6. Lin JY
    7. Tsien RY
    8. Anderson DJ

    (2014) Optogenetic control of Drosophila using a red-shifted channelrhodopsin reveals experience-dependent influences on courtship

    Nature Methods 11:325–332.

    https://doi.org/10.1038/nmeth.2765

    • PubMed
    • Google Scholar
25. 1. Kaufman A
    2. Choo E
    3. Koh A
    4. Dando R

    (2018) Inflammation arising from obesity reduces taste bud abundance and inhibits renewal

    PLOS Biology 16:e2001959.

    https://doi.org/10.1371/journal.pbio.2001959

    • PubMed
    • Google Scholar
26. 1. Kiragasi B
    2. Wondolowski J
    3. Li Y
    4. Dickman DK

    (2017) A presynaptic glutamate receptor subunit confers robustness to neurotransmission and homeostatic potentiation

    Cell Reports 19:2694–2706.

    https://doi.org/10.1016/j.celrep.2017.06.003

    • PubMed
    • Google Scholar
27. 1. Kroemer NB
    2. Small DM

    (2016) Fuel not fun: reinterpreting attenuated brain responses to reward in obesity

    Physiology & Behavior 162:37–45.

    https://doi.org/10.1016/j.physbeh.2016.04.020

    • PubMed
    • Google Scholar
28. 1. Kwon JY
    2. Dahanukar A
    3. Weiss LA
    4. Carlson JR

    (2011) Molecular and cellular organization of the taste system in the Drosophila larva

    Journal of Neuroscience 31:15300–15309.

    https://doi.org/10.1523/JNEUROSCI.3363-11.2011

    • PubMed
    • Google Scholar
29. 1. LeDue EE
    2. Chen YC
    3. Jung AY
    4. Dahanukar A
    5. Gordon MD

    (2015) Pharyngeal sense organs drive robust sugar consumption in Drosophila

    Nature Communications 6:6667.

    https://doi.org/10.1038/ncomms7667

    • PubMed
    • Google Scholar
30. 1. Lin S
    2. Owald D
    3. Chandra V
    4. Talbot C
    5. Huetteroth W
    6. Waddell S

    (2014) Neural correlates of water reward in thirsty Drosophila

    Nature Neuroscience 17:1536–1542.

    https://doi.org/10.1038/nn.3827

    • Google Scholar
31. 1. Liu C
    2. Plaçais PY
    3. Yamagata N
    4. Pfeiffer BD
    5. Aso Y
    6. Friedrich AB
    7. Siwanowicz I
    8. Rubin GM
    9. Preat T
    10. Tanimoto H

    (2012) A subset of dopamine neurons signals reward for odour memory in Drosophila

    Nature 488:512–516.

    https://doi.org/10.1038/nature11304

    • PubMed
    • Google Scholar
32. 1. Maliphol AB
    2. Garth DJ
    3. Medler KF

    (2013) Diet-induced obesity reduces the responsiveness of the peripheral taste receptor cells

    PLOS ONE 8:e79403.

    https://doi.org/10.1371/journal.pone.0079403

    • PubMed
    • Google Scholar
33. 1. May CE
    2. Vaziri A
    3. Lin YQ
    4. Grushko O
    5. Khabiri M
    6. Wang QP
    7. Holme KJ
    8. Pletcher SD
    9. Freddolino PL
    10. Neely GG
    11. Dus M

    (2019) High dietary sugar reshapes sweet taste to promote feeding behavior in Drosophila melanogaster

    Cell Reports 27:1675–1685.

    https://doi.org/10.1016/j.celrep.2019.04.027

    • PubMed
    • Google Scholar
34. Software

    1. May CE

    (2020) May_et_al_optoFLIC, version a7cc474

    GitHub.

    https://github.com/chrismayumich/May_et_al_optoFLIC
35. 1. McCrickerd K
    2. Forde CG

    (2016) Sensory influences on food intake control: moving beyond palatability

    Obesity Reviews 17:18–29.

    https://doi.org/10.1111/obr.12340

    • Google Scholar
36. 1. Milyaev N
    2. Osumi-Sutherland D
    3. Reeve S
    4. Burton N
    5. Baldock RA
    6. Armstrong JD

    (2012) The virtual fly brain browser and query interface

    Bioinformatics 28:411–415.

    https://doi.org/10.1093/bioinformatics/btr677

    • PubMed
    • Google Scholar
37. 1. Musselman LP
    2. Kühnlein RP

    (2018) Drosophila as a model to study obesity and metabolic disease

    The Journal of Experimental Biology 221:jeb163881.

    https://doi.org/10.1242/jeb.163881

    • PubMed
    • Google Scholar
38. 1. Musso PY
    2. Junca P
    3. Jelen M
    4. Feldman-Kiss D
    5. Zhang H
    6. Chan RC
    7. Gordon MD

    (2019) Closed-loop optogenetic activation of peripheral or central neurons modulates feeding in freely moving Drosophila

    eLife 8:e45636.

    https://doi.org/10.7554/eLife.45636

    • PubMed
    • Google Scholar
39. 1. Nitabach MN

    (2006) Electrical hyperexcitation of lateral ventral pacemaker neurons desynchronizes downstream circadian oscillators in the fly circadian circuit and induces multiple behavioral periods

    Journal of Neuroscience 26:479–489.

    https://doi.org/10.1523/JNEUROSCI.3915-05.2006

    • Google Scholar
40. 1. Owald D
    2. Felsenberg J
    3. Talbot CB
    4. Das G
    5. Perisse E
    6. Huetteroth W
    7. Waddell S

    (2015) Activity of defined mushroom body output neurons underlies learned olfactory behavior in Drosophila

    Neuron 86:417–427.

    https://doi.org/10.1016/j.neuron.2015.03.025

    • PubMed
    • Google Scholar
41. 1. Pfeiffer BD
    2. Ngo TT
    3. Hibbard KL
    4. Murphy C
    5. Jenett A
    6. Truman JW
    7. Rubin GM

    (2010) Refinement of tools for targeted gene expression in Drosophila

    Genetics 186:735–755.

    https://doi.org/10.1534/genetics.110.119917

    • PubMed
    • Google Scholar
42. 1. Poskanzer KE
    2. Marek KW
    3. Sweeney ST
    4. Davis GW

    (2003) Synaptotagmin I is necessary for compensatory synaptic vesicle endocytosis in vivo

    Nature 426:559–563.

    https://doi.org/10.1038/nature02184

    • PubMed
    • Google Scholar
43. 1. Ramaekers MG
    2. Luning PA
    3. Ruijschop RM
    4. Lakemond CM
    5. Bult JH
    6. Gort G
    7. van Boekel MA

    (2014) Aroma exposure time and Aroma concentration in relation to satiation

    British Journal of Nutrition 111:554–562.

    https://doi.org/10.1017/S0007114513002729

    • PubMed
    • Google Scholar
44. 1. Ro J
    2. Harvanek ZM
    3. Pletcher SD

    (2014) FLIC: high-throughput, continuous analysis of feeding behaviors in Drosophila

    PLOS ONE 9:e101107.

    https://doi.org/10.1371/journal.pone.0101107

    • PubMed
    • Google Scholar
45. 1. Sartor F
    2. Donaldson LF
    3. Markland DA
    4. Loveday H
    5. Jackson MJ
    6. Kubis HP

    (2011) Taste perception and implicit attitude toward sweet related to body mass index and soft drink supplementation

    Appetite 57:237–246.

    https://doi.org/10.1016/j.appet.2011.05.107

    • PubMed
    • Google Scholar
46. 1. Shiraiwa T
    2. Carlson JR

    (2007) Proboscis extension response (PER) Assay inDrosophila

    Journal of Visualized Experiments : JoVE 193.

    https://doi.org/10.3791/193

    • PubMed
    • Google Scholar
47. 1. Stice E
    2. Yokum S

    (2016) Neural vulnerability factors that increase risk for future weight gain

    Psychological Bulletin 142:447–471.

    https://doi.org/10.1037/bul0000044

    • PubMed
    • Google Scholar
48. 1. Strausfeld NJ
    2. Hirth F

    (2013) Deep homology of arthropod central complex and vertebrate basal ganglia

    Science 340:157–161.

    https://doi.org/10.1126/science.1231828

    • PubMed
    • Google Scholar
49. 1. Tellez LA
    2. Han W
    3. Zhang X
    4. Ferreira TL
    5. Perez IO
    6. Shammah-Lagnado SJ
    7. van den Pol AN
    8. de Araujo IE

    (2016) Separate circuitries encode the hedonic and nutritional values of sugar

    Nature Neuroscience 19:465–470.

    https://doi.org/10.1038/nn.4224

    • PubMed
    • Google Scholar
50. 1. Tennessen JM
    2. Barry WE
    3. Cox J
    4. Thummel CS

    (2014) Methods for studying metabolism in Drosophila

    Methods 68:105–115.

    https://doi.org/10.1016/j.ymeth.2014.02.034

    • PubMed
    • Google Scholar
51. 1. Thanarajah SE
    2. Backes H
    3. DiFeliceantonio AG
    4. Albus K
    5. Cremer AL
    6. Hanssen R
    7. Lippert RN
    8. Cornely OA
    9. Small DM
    10. Brüning JC
    11. Tittgemeyer M

    (2019) Food intake recruits orosensory and Post-ingestive dopaminergic circuits to affect eating desire in humans

    Cell Metabolism 29:695–706.

    https://doi.org/10.1016/j.cmet.2018.12.006

    • PubMed
    • Google Scholar
52. 1. Tsao CH
    2. Chen CC
    3. Lin CH
    4. Yang HY
    5. Lin S

    (2018) Drosophila mushroom bodies integrate hunger and satiety signals to control innate food-seeking behavior

    eLife 7:e35264.

    https://doi.org/10.7554/eLife.35264

    • PubMed
    • Google Scholar
53. 1. van de Giessen E
    2. la Fleur SE
    3. Eggels L
    4. de Bruin K
    5. van den Brink W
    6. Booij J

    (2013) High fat/carbohydrate ratio but not total energy intake induces lower striatal dopamine D2/3 receptor availability in diet-induced obesity

    International Journal of Obesity 37:754–757.

    https://doi.org/10.1038/ijo.2012.128

    • Google Scholar
54. Preprint

    1. Vaziri A
    2. Khabiri M
    3. Brendan T

    (2020) The polycomb repressive complex 2.1 links the food environment to a persistent neural state

    bioRxiv.

    https://doi.org/10.1101/2020.03.25.007773

    • Google Scholar
55. 1. Vickers Z
    2. Holton E
    3. Wang J

    (2001) Effect of ideal–relative sweetness on yogurt consumption

    Food Quality and Preference 12:521–526.

    https://doi.org/10.1016/S0950-3293(01)00047-7

    • Google Scholar
56. 1. Vickers Z
    2. Holton E

    (1998) A comparison of taste test ratings, repeated consumption, and postconsumption ratings of different strengths of iced tea

    Journal of Sensory Studies 13:199–212.

    https://doi.org/10.1111/j.1745-459X.1998.tb00083.x

    • Google Scholar
57. 1. Viskaal-van Dongen M
    2. Kok FJ
    3. de Graaf C

    (2011) Eating rate of commonly consumed foods promotes food and energy intake

    Appetite 56:25–31.

    https://doi.org/10.1016/j.appet.2010.11.141

    • PubMed
    • Google Scholar
58. 1. Wang GJ
    2. Volkow ND
    3. Fowler JS

    (2002) The role of dopamine in motivation for food in humans: implications for obesity

    Expert Opinion on Therapeutic Targets 6:601–609.

    https://doi.org/10.1517/14728222.6.5.601

    • PubMed
    • Google Scholar
59. 1. Weiss MS
    2. Hajnal A
    3. Czaja K
    4. Di Lorenzo PM

    (2019) Taste responses in the nucleus of the solitary tract of awake obese rats are blunted compared with those in lean rats

    Frontiers in Integrative Neuroscience 13:35.

    https://doi.org/10.3389/fnint.2019.00035

    • PubMed
    • Google Scholar
60. 1. Yamagata N
    2. Ichinose T
    3. Aso Y
    4. Plaçais PY
    5. Friedrich AB
    6. Sima RJ
    7. Preat T
    8. Rubin GM
    9. Tanimoto H

    (2015) Distinct dopamine neurons mediate reward signals for short- and long-term memories

    PNAS 112:578–583.

    https://doi.org/10.1073/pnas.1421930112

    • PubMed
    • Google Scholar
61. 1. Yeomans MR

    (1996) Palatability and the micro-structure of feeding in humans: the appetizer effect

    Appetite 27:119–133.

    https://doi.org/10.1006/appe.1996.0040

    • PubMed
    • Google Scholar
62. 1. Yeomans MR

    (1998) Taste, palatability and the control of appetite

    Proceedings of the Nutrition Society 57:609–615.

    https://doi.org/10.1079/PNS19980089

    • PubMed
    • Google Scholar
63. 1. Yeomans MR
    2. McCrickerd K
    3. Brunstrom JM
    4. Chambers L

    (2014) Effects of repeated consumption on sensory-enhanced satiety

    British Journal of Nutrition 111:1137–1144.

    https://doi.org/10.1017/S0007114513003474

    • PubMed
    • Google Scholar
64. 1. Yeomans MR

    (2017) Introducing sensory and cognitive influences on satiation and satiety

    Flavor, Satiety and Food Intake pp. 1–12.

    https://doi.org/10.1002/9781119044970.ch1

    • Google Scholar
65. 1. Yeomans MR
    2. Chambers L

    (2011) Satiety-relevant sensory qualities enhance the satiating effects of mixed carbohydrate-protein preloads

    The American Journal of Clinical Nutrition 94:1410–1417.

    https://doi.org/10.3945/ajcn.111.011650

    • PubMed
    • Google Scholar

Acceptance summary:

In this manuscript, May et al. elaborate a central mechanism that potentially underlies increased sugar consumption in flies chronically fed high-sugar diets. They show that a high sugar diet, which decreases synaptic release from axon terminals of sugar responsive Gr64f neurons occurs together with decreased sucrose-induced activity in a PAM subclass of dopaminergic neurons that innervate the β'2 lobe of the Mushroom Body (MB). This reduction is DA neuron activity is shown in response to high sugar diet but not affected following high fat diet, arguing that the decrease in sugar evoked dopamine release accounts for a compensatory increase in consumption.

Decision letter after peer review:

Thank you for submitting your article "Dietary sugar inhibits satiation by decreasing the central processing of sweet taste" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Mani Ramaswami as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Catherine Dulac as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Alex C Keene (Reviewer #3).

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

Summary:

Previous work by the authors May et al. (2019) showed that prolonged high-sugar diets resulted in increased food consumption together reduced sugar-evoked sensory responses in Gr64f neurons. Strikingly, increased feeding behaviour could be corrected by genetic or ontogenetic manipulations that increased Gr64f neuron response to sugar. In this manuscript, May et al. examine the central brain mechanisms underlying increased food consumption in flies chronically fed high-sugar diets. Specifically, the authors investigate how high sugar diet affects the function of Dopamine (DA) neurons that innervate the β'2 lobe of the Mushroom Body and the consequences of such functional changes in DA neuron activity. They show that a high sugar diet, which decreases synaptic release from axon terminals of Gr64f neurons in the SEZ, occurs together with decreased sucrose-induced activity in PAM-DA neurons that innervate the β'2 lobe of the Mushroom Body (MB). This reduction is DA neuron activity is shown in response to high sugar diet but not affected following high fat diet. Moreover, activating PAM-DA-β'2 neurons in flies that are kept on high sugar diet, rescues the diet induced obesity phenotype. The experiments are scientifically sound and the manuscript is well written. However, there are additional experiments authors need to do to support and clarify their findings and conclusions, particularly pertaining to the roles and mechanism of action of PAM-DA-β'2 neurons.

Essential revisions:

1) It is surprising that neuronal activation via some split-GAL4s that label β'2 lobes do not rescue the phenotype like MB301B. This needs to be rationalized. For instance the authors could perform additional experiments to clarify which β'2 neurons are labelled in MB301B line and show these neurons are not labelled in other splits. Such an anatomical characterization of different split-GAL4s innervating the β'2 should be made easier because all of these lines are registered to a standard brain atlas at Janelia, which should help one determine whether MB301B labels different DANs compared to the splits that do not rescue the phenotype. The expression pattern of MB301B on the Janelia Split Database suggests that this line also labels neurons outside of the Mushroom body; there are ascending or descending tracks seen in the ventral nerve cord. Could activation phenotypes seen in MB301 be due to these neurons that are not in the MB? The text should be revised to include a potential explanation for how dopamine release by different dopaminergic neurons onto the same target cells could have different acute effects on behavior.

2) Tsao et al. (2018) show that MB301 neuron activation leads to increased food search behavioural and their silencing in hungry flies results in reduced food search behaviour. This appears to contrast with findings here that the decreased activity in MB301 neurons (due to high sugar diet) causes an increase in food intake behavior over time. How do these two results fit together? This issue should be explicitly addressed. For instance it would be useful to know what impact of chronic activation of β'2 DANs is on feeding behaviour. Current activation protocol used in the experiments only activate the β'2 DANs when flies are eating. One would argue that chronic activation might lead to increased feeding behaviour potentially reconciling the two studies?

3) One point of concern is that the authors do not describe upstream or downstream neurons to the identified PAM-β' neurons. It is true that their upstream neurons are poorly defined, but it would be useful to know how responses of MBs or MB output neurons are impacted by feeding protocol and influence feeding behavior. The authors should therefore test if optogenetic activation or inhibition of MBONs that innervate the β'2. will rescue the high sugar diet phenotype as predicted by their analysis of PAM-β'2 DANs.

4) Several observations shown in in Figure 2 and 3 argue that the decrease in high sucrose-evoked DA neuron activity is directly linked to the altered responses in Gr64f neurons. This is nice to see but not unexpected as reduced Gr64f neurons output would be expected to decrease stimulation of downstream DA neurons. However the effect on Gr64f neuron synaptic release is far more drastic that the effect of high sugar diet on β'2 DA activity (50% suppression). How do the authors explain this difference?

5) In the first paragraph of the Results, the term "30% sugar" should be changed to "30% sucrose" if indeed sucrose is the only sugar that is used in these taste neuron stimulation experiments. (If not, the types of sugar should be indicated.) Is decreased synaptic release in Gr64f neurons observed in response to all sugars or only to sucrose?

6) What does "n number" indicate in these experiments? Is it the number of animals or the number of trials? In the control group, the fluorescence changes seem to have a bigger variation (the distribution looks like a bimodal distribution) then the high sugar diet experimental group. Is the variation coming from trial to trial differences or in between animal differences? I think this is an important point to clarify. Also, authors are using ∆F/F₀ AUC values to compare the results of the experimental group and the control group. They don't explain why they choose to use this metric compared to peak ∆F/F₀ value that is generally used to quantify fluorescent indicator responses. In the Materials and methods, it is also not clear how they choose the baseline F₀. "For each fly, ∆F/F₀ was calculated from a baseline of 10 samples recorded just prior to the stimulus". What does sample mean here? Do the authors mean data points? What is the speed of data collection? Please clarify and explain.

7) Several additional control or background experiments will be useful to better understand how the identified DANs regulate feeding behaviour. While all do not need to be addressed experimentally, at least the underlying ambiguities should be explicitly addressed in the text.

a) The conclusions would be strengthened with the inclusion of additional concentrations of sucrose. Does reduced responsiveness occur across concentrations or only to high concentrations of sugar? After being returned to normal diet for a number of days, does DAN activity return to normal?

b) Will PAM2 activation affect acute feeding behaviour in control flies at different concentrations (low-high) of sugar? (This seems important anyway.)

c) Could PAM2 activation affect acute feeding behaviour in control flies – potentially overriding low concentration of bitter compounds?

d) Will blocking the β'2 PAM neurons in control-diet fed flies result in increased feeding behaviour as predicted and concluded by the authors?

8) To really show that OGT works via its role in sensory neurons, the authors should ideally use targeted RNA interference to knockdown OGT in Gr64f+ neurons (which was used in their previous publication) and in the insulin producing cells, in order to make the point that changes in PAM-β'2 responses to sugar taste occur via changes in the sweet sensory neurons output rather than because of the metabolic side-effects of high nutrient density. Otherwise the conclusion should be duly qualified.

9) Please discuss the genetic background. It states that W1118;Canton-S was a control. Were lines outcrossed to this strain? Where did the control strain come from?

Essential revisions:

1) It is surprising that neuronal activation via some split-GAL4s that label β'2 lobes do not rescue the phenotype like MB301B. This needs to be rationalized. For instance the authors could perform additional experiments to clarify which β'2 neurons are labelled in MB301B line and show these neurons are not labelled in other splits. Such an anatomical characterization of different split-GAL4s innervating the β'2 should be made easier because all of these lines are registered to a standard brain atlas at Janelia, which should help one determine whether MB301B labels different DANs compared to the splits that do not rescue the phenotype. The expression pattern of MB301B on the Janelia Split Database suggests that this line also labels neurons outside of the Mushroom body; there are ascending or descending tracks seen in the ventral nerve cord. Could activation phenotypes seen in MB301 be due to these neurons that are not in the MB? The text should be revised to include a potential explanation for how dopamine release by different dopaminergic neurons onto the same target cells could have different acute effects on behavior.

Thank you for this suggestion. We tested additional PAM DANs that innervated the same regions to see if the population labeled by MB301B was unique in rescuing the obesity phenotype. While MB301B, MB109B, and MB032B all innervate the β'2 lobe, they are considered distinct DANs – PAM #3, PAM #2, and PAM#6 respectively– that target different downstream cells (Aso et al., 2014). Thus, while innervation of the β'2 compartment is necessary, likely because that is where taste information arrives (Huetteroth et al., 2015; Yamagata et al., 2015), our data suggest that it is not sufficient, and that MB301B PAMs may have a special function. We agree that registering the lines and making this point clear is really critical for future studies, so we have used the Virtual Fly Brain (Milyaev et al., 2012) to do this. These data are now in Figure 4—figure supplement 2 and text was added to the Discussion to explain this point. Unfortunately, there is a glitch in the software and a comparison with MB109B (PAM #2) was not possible. We contacted Greg Jefferis about this and he said that this fix is complicated and would take a long time if at all possible, thus we have not included this particular line in the figure, but all the others are present, and this line is already considered distinct from MB301B (PAM#3) in Aso et al., 2014. We have found that MB301B does not have a perfect overlap with the other lines we tested and that it seems to have more ventral and anterior innervation in the mushroom body.

The reviewers are correct that we cannot exclude the possibility that VNC neurons have an effect on our phenotype. We have revised the text to address this.

2) Tsao et al., 2018 show that MB301 neuron activation leads to increased food search behavioural and their silencing in hungry flies results in reduced food search behaviour. This appears to contrast with findings here that the decreased activity in MB301 neurons (due to high sugar diet) causes an increase in food intake behavior over time. How do these two results fit together? This issue should be explicitly addressed. For instance it would be useful to know what impact of chronic activation of β'2 DANs is on feeding behaviour. Current activation protocol used in the experiments only activate the β'2 DANs when flies are eating. One would argue that chronic activation might lead to increased feeding behaviour potentially reconciling the two studies?

We apologize for the oversight in discussing Tsao et al. (2018) and Musso et al. (2019), we should have addressed their findings. Tsao et al. found that acute activation of MB301B neurons with TrpA1 increased food seeking behavior, and that inactivation with shibire^ts had the opposite effect. Musso et al. found that acute, closed loop optogenetic activation of the same line induced an increase in feeding interactions. Both of these studies were done in fasted flies and for a short time duration. In new experiments now described in Figure 4—figure supplement 1, we also found that closed loop activation of MB301B neurons in flies fed 5% sucrose on the FLIC for two days, led to higher feeding interactions. Thus, these neurons are clearly involved in feeding and food seeking behavior, and our data agrees with the overall findings of the two manuscripts when flies are on a control diet. However, we think that this is a distinct phenomenon than that described here, because in our manuscript we used optogenetic activation to “normalize” the decrease in sweet-taste responses in the presence of the stimulus, rather than to activate the neurons from their “resting” state in the absence of stimulation. The fact that we see a specific high sugar diet x MB301B effect suggests that our experiments are testing fundamentally different behaviors, even if the manipulations and the assays are similar (although our flies are not fasted and the FLIC runs for days, not minutes, and the animals are on a sugar diet). For example, in Musso et al., activation of MB301B induced more feeding interactions to both water and sugar, whereas in our case, the dietary environment in which the flies were on the FLIC, (5% vs. 20%) gave rise to different behaviors (increasing vs. stable feeding). Further, Musso et al. also tested MB056B: this line had an identical phenotype to MB301 in their assay, increasing the number of feeding interactions to both water and sugar on the FLYPAD, but the same line did not rescue diet-induced obesity in our experiments. Together these suggest that we are uncovering a circuit “state” by environment interaction, which is different from testing whether these neurons are sufficient for feeding. We have added this experiment and point to the Discussion. There we speculate that this state is connected to the expected level of reward (from sweetness), and that stable processing of the taste signal centrally is essential to stabilize eating. Thus the relative changes in PAM signaling, rather than their absolute value, is important. This seems to fit well with current interpretations of DAN function. In this scenario, when we stimulate the MB301B DANs in flies on a high sugar diet we are addressing the change that occurs with diet and keeping their activity consistent.

3) One point of concern is that the authors do not describe upstream or downstream neurons to the identified PAM-B' neurons. It is true that their upstream neurons are poorly defined, but it would be useful to know how responses of MBs or MB output neurons are impacted by feeding protocol and influence feeding behavior. The authors should therefore test if optogenetic activation or inhibition of MBONs that innervate the β'2. will rescue the high sugar diet phenotype as predicted by their analysis of PAM-β'2 DANs.

We agree with the reviewer/s that this is an incredibly interesting question, but we disagree that it should be answered in the current manuscript. First, we feel that this question deserves its own in-depth study, where the impact of DANs alterations on MBON plasticity, learning and memory, feeding and obesity are investigated. Indeed, this is the thesis project of another PhD student in the lab, - who has been setting up these assays and tackling different parts of this question over the last 2 years. 4) Several observations shown in in Figure 2 and 3 argue that the decrease in high sucrose-evoked DA neuron activity is directly linked to the altered responses in Gr64f neurons. This is nice to see but not unexpected as reduced Gr64f neurons output would be expected to decrease stimulation of downstream DA neurons. However the effect on Gr64f neuron synaptic release is far more drastic that the effect of high sugar diet on β'2 DA activity (50% suppression). How do the authors explain this difference?

We believe this is due to the differences in the dynamic ranges of the fluorescent probes. GCaMP6 more faithfully reports even small fluctuations of intracellular calcium, while syb-phluorin is unable to capture smaller fluctuations in signal from vesicular release. For example, we measured a 30% decrease in the Gr64f+ neural response to 30% sucrose stimulation of the proboscis in May et al., 2019, which is consistent with the GCaMP6 fluorescence changes measured in the MB301B neurons here. However, it is also possible that some of the “decrease” in the sweet signal is compensated for along the way. Unfortunately, without a genetically defined anatomy of the taste processing circuit between the Gr64f+ and PAM neurons, we cannot answer this question.

5) In the first paragraph of the Results, the term "30% sugar" should be changed to "30% sucrose" if indeed sucrose is the only sugar that is used in these taste neuron stimulation experiments. (If not, the types of sugar should be indicated). Is decreased synaptic release in Gr64f neurons observed in response to all sugars or only to sucrose?

Thank you for pointing this out, we changed it to 30% sucrose. We never published the data, but found that a high sucrose diet led to a decrease in taste responses to D-glucose and L-glucose.

6) What does "n number" indicate in these experiments? Is it the number of animals or the number of trials? In the control group, the fluorescence changes seem to have a bigger variation (the distribution looks like a bimodal distribution) then the high sugar diet experimental group. Is the variation coming from trial to trial differences or in between animal differences? I think this is an important point to clarify. Also, authors are using ∆F/F₀ AUC values to compare the results of the experimental group and the control group. They don't explain why they choose to use this metric compared to peak ∆F/F₀ value that is generally used to quantify fluorescent indicator responses. In the Materials and methods, it is also not clear how they choose the baseline F₀. "For each fly, ∆F/F₀ was calculated from a baseline of 10 samples recorded just prior to the stimulus". What does sample mean here? Do the authors mean data points? What is the speed of data collection? Please clarify and explain.

We have answered the questions below and further clarified it in the Materials and methods.

For calcium imaging experiments, n counts each ROI, of which there are 2 per fly

For the FLIC, n counts individual flies

For metabolic assays, n counts single homogenates of two flies each

The variation in calcium imaging comes from between animal differences. When we compared the Peak % ∆F/F₀ responses to sucrose on different days of imaging we found no differences (One-way ANOVA with Kruskal-Wallis multiple comparisons). Although each animal was placed on the diet at the same time and treated identically, there are still variations in how much they eat relatively to one another per day, the extent of their taste changes, and also in their metabolism (May et al., 2019).

We chose to calculate ∆F/F₀ AUC instead of peak ∆F/F₀ for the synaptophluorin imaging because looking at individual traces it was often difficult to unambiguously define a peak. Thus we felt that ∆F/F₀ AUC would be a more direct way to show these data.

We changed “sample”, to “frame”, one frame in our experiments was captured at 4Hz as listed under methods.

7) Several additional control or background experiments will be useful to better understand how the identified DANs regulate feeding behaviour. While all do not need to be addressed experimentally, at least the underlying ambiguities should be explicitly addressed in the text.

a) The conclusions would be strengthened with the inclusion of additional concentrations of sucrose. Does reduced responsiveness occur across concentrations or only to high concentrations of sugar? After being returned to normal diet for a number of days, does DAN activity return to normal?

We have carried out the calcium imaging experiments with 5% sucrose stimulation to the proboscis. We see a similar decrease in calcium responses to this lower concentration when flies are fed a high sugar diet. This is consistent with the findings that PER is decreased to both low (1% and 5%), mid (10%), and high (20% and 30%) sucrose (May et al., 2019). These data are now included in Figure 3—figure supplement 1C and D.

We were unable to carry out recovery experiments, but have a manuscript on bioRxiv showing that taste deficits last even after flies were moved to a control diet for 10 days

(doi.org/10.1101/2020.03.25.007773). As such, we hypothesize that PAM responses should also remain low. We plan to follow up independently on these findings and look at the effects of long term changes in taste and reward signaling on feeding behavior and obesity.

b) Will PAM2 activation affect acute feeding behaviour in control flies at different concentrations (low-high) of sugar? (This seems important anyway.)

We have conducted OPTOFLIC experiments with 5% food for two days (see point #2). We find that acute activation of flies on a control diet and fed 5% sucrose food (similar to the control diet concentration) on the FLIC results in an increase in feeding behavior, as others have reported (Musso et al., 2019 showed increased feeding with both water and sugar). We have added these data to Figure 4—figure supplement 1 and this point to the Discussion.

c) Could PAM2 activation affect acute feeding behaviour in control flies – potentially overriding low concentration of bitter compounds?

MB301B activation does increase feeding behavior acutely (see response to point #2), but we have not tested whether it can overcome bitter compounds; while this is an interesting question, we feel that given the current circumstances, this is not directly related to the central hypothesis of the study. We have added this point to the Discussion, however.

d) Will blocking the β'2 PAM neurons in control-diet fed flies result in increased feeding behaviour as predicted and concluded by the authors?

We have carried out preliminary optogenetic inhibition experiments using UAS-GtACR. Using a moderately sweet food, 8% sucrose, we have observed an increase in feeding behavior in MB301B > UAS-GtACR flies but were unable to complete more experiments due to COVID-19. There is a long history and debate on the role of DANs inhibition in feeding behavior. Expectations from D2R genetic studies and the reward deficit theory would argue that deficits in DAN signaling should result in increased eating, but many observations show that inhibition of DANs via pharmacology decreases feeding instead. This is likely because the levels of inhibition and the context in which this is delivered (the dietary history and experience of the animal) matter. Given that we were not able to address these points to the level of depth they deserve, and that GtACR doesn’t have the consistency in performance that CsReacher or Chrimson have, we would prefer not to add them to the main manuscript at this time.

8) To really show that OGT works via its role in sensory neurons, the authors should ideally use targeted RNA interference to knockdown OGT in Gr64f+ neurons (which was used in their previous publication) and in the insulin producing cells, in order to make the point that changes in PAM-β'2 responses to sugar taste occur via changes in the sweet sensory neurons output rather than because of the metabolic side-effects of high nutrient density. Otherwise the conclusion should be duly qualified.We agree with the reviewers, and this was the experiment we originally planned, but we have been unable to build a stock with the 5 transgenes in part because of impossibility of recombining some of them, since they were integrated in the same attp site (2 copies each of the split GAL4s to ensure expression of both UAS, UAS-GCaMP,UAS-OGT RNAi). We have modified the text to point out this caveat in the text and in the Discussion. We have found that knock down of OGT has no effect on fat accumulation in MB301B neurons alone (Figure 3—figure supplement 2).

9) Please discuss the genetic background. It states that W1118;Canton-S was a control. Were lines outcrossed to this strain? Where did the control strain come from?

The w1118-Cs strain was made from Anne Simon in the Benzer lab by backcrossing w1118 to Canton S for 10 generations. This information is now in the Materials and methods.

Author details

1. Christina E May

   1. The Neuroscience Graduate Program, The University of Michigan, Ann Arbor, United States
   2. Department of Molecular, Cellular and Developmental Biology, College of Literature, Science, and the Arts, The University of Michigan, Ann Arbor, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing, Conducted all the experiments, with the exception of PER, Helped write the manuscript

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9366-8592

2. Julia Rosander

   The Undergraduate Program in Neuroscience, College of Literature, Science, and the Arts, The University of Michigan, Ann Arbor, United States

   Contribution

   Investigation, Helped with the TAG measurements

   Competing interests

   No competing interests declared

3. Jennifer Gottfried

   The Undergraduate Program in Neuroscience, College of Literature, Science, and the Arts, The University of Michigan, Ann Arbor, United States

   Contribution

   Investigation, Helped with the TAG measurements

   Competing interests

   No competing interests declared

4. Evan Dennis

   The Undergraduate Program in Neuroscience, College of Literature, Science, and the Arts, The University of Michigan, Ann Arbor, United States

   Contribution

   Investigation, Helped with the TAG measurements

   Competing interests

   No competing interests declared

5. Monica Dus

   1. The Neuroscience Graduate Program, The University of Michigan, Ann Arbor, United States
   2. Department of Molecular, Cellular and Developmental Biology, College of Literature, Science, and the Arts, The University of Michigan, Ann Arbor, United States
   3. The Undergraduate Program in Neuroscience, College of Literature, Science, and the Arts, The University of Michigan, Ann Arbor, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing, Carried out the PER experiments and supervised the project, Wrote the manuscript

   For correspondence

   mdus@umich.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1465-9028

• 3,912

  Page views

• 459

  Downloads

• 34

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.