Deciding what and how much to eat is a complex biological process which involves balancing many types of information such as the levels of internal energy storage, the amount of food previously available in the environment, the perceived value of certain food items, and how these are remembered.

At the molecular level, food contains carbohydrates that are broken down to produce glucose, which is then delivered to cells under the control of a hormone called insulin. There, glucose molecules are either immediately used or stored as glycogen until needed. Insulin signalling is also known to interact with the brain’s decision-making systems that control eating behaviors; however, how our brains balance food intake with energy storage is poorly understood. Berger et al. set out to investigate this question using fruit flies as an experimental model.

These insects also produce insulin-like molecules which help to relay information about glycogen levels to the brain’s decision-making system. In particular, these signals reach a population of neurons that produce a messenger known as octopamine similar to the human noradrenaline, which helps regulate how much the flies find consuming certain types of foods rewarding. Berger et al. were able to investigate the role of octopamine in helping to integrate information about internal and external resource levels, memory formation and the evaluation of different food types.

When the insects were fed normally, increased glycogen levels led to foods rich in carbohydrates being rated as less rewarding by the decision-making cells, and therefore being consumed less. Memories related to food intake were also short-lived – in other words, long-term ‘food memory’ was suppressed, re-setting the whole system after every meal.

In contrast, long periods of starvation in insects with high carbohydrates resources produced a stable, long-term memory of food and hunger which persisted even after the flies had fed again. This experience also changed their food rating system, with highly nutritious foods no longer being perceived as sufficiently rewarding. As a result, the flies overate.

This study sheds new light on the mechanisms our bodies may use to maintain energy reserves when food is limited. The persistence of ‘food memory’ after long periods of starvation may also explain why losing weight is difficult, especially during restrictive diets. In the future, Berger et al. hope that this knowledge will contribute to better strategies for weight management.

To ensure survival, the internal energy status of an animal needs to be adjusted to the energy expenditure of the organism and the availability of external food. Increased storage of energy correlates with increased food intake in the past. Dysregulation of food intake can lead to diseases such as obesity and diabetes. Sucrose is a carbohydrate enriched in Western diets, and the breakdown product of sucrose – glucose – can be stored in the organism as glycogen, primarily in the liver and muscles. Elevated glycogen levels are a hallmark of glycogen storage diseases that are accompanied by defects in the liver, muscles, and brain (Ellingwood and Cheng, 2018). Similar to vertebrates, the fruit fly Drosophila melanogaster uses glucose as its primary energy source and stores glycogen primarily in the muscles – the main site of energy expenditure – and the fat body – the equivalent of the vertebrate liver (Gáliková and Klepsatel, 2023; Wigglesworth, 1949). As in vertebrates, glycogen levels are also found in the brain (Yamada et al., 2018).

In Drosophila, the monoamine octopamine – functionally related to noradrenalin in vertebrates – is involved in the regulation of energy homeostasis. In tyramine-β-hydroxylase (Tβh) mutants lacking the neurotransmitter octopamine, the glucose and trehalose concentrations in the hemolymph change less upon starvation, and the life span is extended (Damrau et al., 2017; Li et al., 2016). Tβh mutants show an increased threshold to respond to sucrose with the extension of their proboscis when sucrose is offered to their tarsi – a structure that contains gustatory receptor neurons. The reduced responsiveness to sucrose correlates with a reduced sucrose intake in Tβh mutants (Li et al., 2016; Scheiner et al., 2014). Octopaminergic neurons potentiate the response of sugar-sensing gustatory receptor neurons in satiated flies, suggesting that octopaminergic neurons regulate feeding behavior by changing the sensitivity of taste receptor neurons (Youn et al., 2018). The reduced responsiveness to sucrose is also thought to be responsible for the defects observed in habituation in Tβh mutants (Scheiner et al., 2014). In addition to their changes in simple forms of neuronal plasticity, Tβh mutants fail to form a positive association with a food reward in a classical olfactory conditioning paradigm. Flies quickly learn to associate an odorant with sucrose, and Tβh mutants do not show this positive association directly after learning (Schwaerzel et al., 2003). However, when a longer time after training has elapsed, memory appears (Das et al., 2014). The release of octopamine mediates the reinforcing effect of sweet taste in short-term memory (STM) and the anesthesia-resistant form of long-term memory (LTM) (Burke et al., 2012; Huetteroth et al., 2015; Wu et al., 2013). The question arises how the internal energy supply is integrated into the formation of food-related memories and feeding behavior in Tβh mutants.

The peptide hormone insulin regulates glycose homeostasis at the cellular level (Saltiel and Kahn, 2001). Insulin and its Drosophila counterpart, insulin-like peptides, perform their functions through well-conserved signal transduction cascades (Chatterjee and Perrimon, 2021; Inoue et al., 2018). In vertebrates, in addition to its function in fat tissue and muscles, the insulin receptor is broadly expressed in the brain and regulates neuronal plasticity (Nakai et al., 2022). For example, in rats, reduced insulin receptor function in the hypothalamus results in loss of long-term potentiation and impaired spatial memory (Grillo et al., 2015). In Drosophila, the insulin receptor substrate Chico is required for the development of mushroom bodies, a brain region required for learning and memory, and loss of Chico results in learning defects (Naganos et al., 2012). Disrupting the function of the insulin receptor in the mushroom bodies also results in learning defects, whereas the insulin receptor and Chico are required in the ellipsoid body for protein biosynthesis-dependent LTM (Chambers et al., 2015). While there is evidence that insulin receptor signaling regulates cellular physiology, it is not clear how the internal energy supply of the animal influences the formation of food-related memories.

To analyze the relationship between energy status, the evaluation of a food reward, and the formation of memory and food intake, we used the olfactory associative learning and memory paradigm. Using nutrients as a positive reward allows for investigating how the internal energy status influences the evaluation of the reinforcer and in turn learning and memory. As a step to understand the interconnection between reward evaluation, nutrient intake, and the formation of food-related memories, we analyzed the function of octopamine in the regulation of internal energy homeostasis, learning and memory, and food intake.

Starvation increases learning and memory performance when using a food-related reward (Colomb et al., 2009). However, how the internal energy status is integrated into the evaluation of the reward and the stability of food-related memories and whether there is a correlation between the perceptions of the food reward, food-related memories, and food intake are not clear.

The duration of hunger influences the strength and stability of memories

To investigate whether changes in internal energy storage influence food-related memories, we used the olfactory associative learning and memory paradigm using sucrose as a food-related reinforcer (Schwaerzel et al., 2003). In the paradigm, flies learn to associate an odorant with sucrose and are later tested on whether they remember the association. A prerequisite for this association to be made is that the animals are hungry. During the training, starved flies were exposed for 2 min to the unconditioned odorant, 4-methylcyclohexanol (MCH), followed by a 2 min exposure to a second odorant, 3-octanol (3-OCT), that was paired with the reward of 2 M sucrose. After the training, flies were given a choice between the two odorants (Figure 1A). Normally, flies prefer the rewarded odorant already 2 min after the training. To avoid a bias towards one odorant, in a reciprocal experiment, the first odorant was rewarded. The performance index presents the average positive association between the reward and both odorants and describes whether information is learnt and remembered. To ensure that observed differences in learning and memory were not due to changes in odorant perception, odorant evaluation, or sucrose sensitivity, different fly populations of the same genotypes were tested for their odorant acuity, odorant, and sucrose preference (Figure 1—source data 1). The flies of the different genotypes sensed the odorants and evaluated them as similar salient in comparison. This is important to avoid bias where flies have to choose between the two odorants after training. They also sensed sucrose. We next determined whether the differences in sucrose preference influence sucrose intake during training. Therefore, we measured the intake of food-colored sucrose of starved flies in the behavioral set up. After 2 min, we evaluated whether there was dyed food in the abdomen of the fly (Figure 1—source data 1). Flies of both genotypes fed sucrose within 2 min and there was no difference between w¹¹¹⁸ and Tβh^nM18 flies.

Figure 1

Download asset Open asset

Figure 1—source data 1

The raw data and sensory acuity data of Figure 1 and related supplement.

https://cdn.elifesciences.org/articles/88247/elife-88247-fig1-data1-v1.xlsx

Download elife-88247-fig1-data1-v1.xlsx

To alter the internal energy status, flies were starved either for 16 hr or for 40 hr before the training (Figure 1). We used 3- to 5-day-old male flies to minimize differences in body weight and control for differences in food preferences. We included Tβh^nM18 mutants lacking octopamine in the experiments since they showed defects in energy metabolism and sucrose reward learning (Li et al., 2016; Schwaerzel et al., 2003). Flies that were starved for 16 hr established a positive association between the odorant and the reward. They remembered this association for 2 min, but not for 6 hr or 24 hr. Consistent with previous results, learning and memory performance was significantly improved by prolonged starvation (Colomb et al., 2009). The memory was also remembered for longer since it was still detectable 6 hr after training. The Tβh^nM18 mutants starved for 16 hr did not show 2 min memory but emerged memory 6 hr later that lasted up to 24 hr (Das et al., 2014; Schwaerzel et al., 2003). In contrast, Tβh^nM18 mutants remembered the rewarded odorant after only 2 min following starvation extended to 40 hr. The memory was still detectable after 6 hr and 24 hr (Figure 1B). We repeated similar experiments using a lower concentration of 0.15 M sucrose as reinforcer (Figure 1C). In contrast to the experiments using 2 M sucrose as a reward, 16 hr-starved Tβh^nM18 mutants form a negative association with 0.15 M sucrose directly after the training, which turns into a positive association when the flies were starved longer 40 hr. Again, prolonged starvation significantly increased learning and memory performance in controls and Tβh^nM18 mutants. Thus, the length of starvation and the reinforcer strength influence appetitive memory strength.

Next, we investigated how starvation affects the stability of memory. Protein synthesis-dependent LTM is still labile immediately after training and can be blocked by 4°C cold-shock anesthesia immediately after training. After consolidation, LTM is cold-shock-resistant and insensitive to a cold shock 1 hr before the test (Krashes and Waddell, 2008). To investigate how hunger influences the stability of food-related memories, we administered cold-shock anesthesia immediately after the training and 1 hr before the test and analyzed the effect on memory formation in differently starved control and Tβh^nM18 flies (Figure 2A). We reduced the time for the memory test after training from 6 hr to 3 hr to be able to detect memory in 16 hr-starved flies. The memory was completely abolished by a cold shock directly after training or shortly before the test. Prolonging the starvation period resulted in memory that was sensitive to cold shock directly after training but not shortly before the test. In contrast, 16 hr-starved Tβh^nM18 mutants developed memory that was sensitive to cold shock directly after training and cold-shock-insensitive shortly before the test. The results, together with the observed memory 24 hr later of Tβh^nM18 after the same training (Figure 1B), support that Tβh^nM18 mutant develop LTM. Longer periods of starvation in Tβh^nM18 mutants resulted in anesthesia-resistant LTM.

Figure 2 with 1 supplement see all

Download asset Open asset

Figure 2—source data 1

The raw data and sensory acuity data of Figure 2 and related supplement.

https://cdn.elifesciences.org/articles/88247/elife-88247-fig2-data1-v1.xlsx

Download elife-88247-fig2-data1-v1.xlsx

To further confirm that the observed memory in 16 hr-starved Tβh^nM18 is indeed LTM, we wanted selectively inhibit LTM in Tβh^nM18 mutants and analyze their food-related memory (Figure 2B). We analyzed memory performance 6 hr after training as control flies show no memory at this time point (Figures 1B and 2B). The R15A04-Gal4 driver targets dopaminergic neurons of the PAM cluster, which are specifically required for appetitive LTM but not STM (Yamagata et al., 2015). Blocking the function of these dopaminergic neurons immediately after training using a temperature-sensitive shibire transgene (UAS-shi^ts) and a 30 min heat pulse of 31°C resulted in loss of memory in Tβh^nM18 mutants (Figure 2B). Control flies carrying one copy of the Gal4 transgene showed no memory. Without heat shock, the Tβh^nM18 mutants carrying the UAS-shi^ts, the R15A04-Gal4, or both transgenes developed memory (Figure 2—figure supplement 1). Thus, although they do not show memory immediately after training, Tβh^nM18 mutants that were briefly starved develop LTM.

These results, together with the course of memory loss, show that lightly hungry flies form a memory that lasts up to 3 hr and is sensitive to anesthesia (Figure 2D). Since no memory can be observed 24 hr later, this indicates that it is an anesthesia-sensitive intermediate memory (ITM). Prolonged starvation results in anesthesia-resistant ITM (Figure 2D). In contrast, mildly starved Tβh^nM18 mutants form a LTM that is sensitive to anesthesia-sensitive and detectable 24 hr after the training. Prolonged starvation in Tβh^nM18 mutants leads to anesthesia-resistant LTM (Figure 2D).

Thus, the longer the starvation lasts, the more stable the memory becomes. Depending on the duration of starvation after the same training phase, the animals first form an STM, then anesthesia-sensitive or insensitive ITM. Mildly starved Tβh^nM18 mutants do not form an STM, but a LTM and after prolonged starvation, short-term and anesthesia-resistant memory.

Octopamine is a negative regulator of memory

Since the Tβh^nM18 mutants lack the neurotransmitter octopamine and form LTM, we next investigated whether octopamine normally suppresses LTM. To do so, we first blocked the function of octopamine receptors in controls immediately after the training by feeding the octopamine antagonist epinastine for 1 hr and analyzed memory 5 hr later (Figure 3A). If the octopamine receptor function is required for LTM, prolonged memory should also occur in control flies that normally show no memory after 16 hr of starvation. This was the case. Consistent with the idea that octopamine is a negative regulator of LTM, feeding octopamine to Tβh^nM18 mutants immediately after training blocked LTM (Figure 3A). To analyze whether octopamine is also able to block STM, we fed octopamine prior to training to control flies (Figure 3B). A short pulse of octopamine before the training inhibits STM.

Figure 3 with 1 supplement see all

Download asset Open asset

Figure 3—source data 1

The raw data of Figure 3 and related supplement.

https://cdn.elifesciences.org/articles/88247/elife-88247-fig3-data1-v1.xlsx

Download elife-88247-fig3-data1-v1.xlsx

We were wondering whether octopamine could reduce the appetite in starved animals and thereby reducing the value of the food reward. To investigate this, we fed starved flies with a short octopamine pulse and assayed their sucrose intake after 3 hr (Figure 3—figure supplement 1). Flies fed with octopamine showed a significant reduction of sucrose intake, but not flies fed with a similar amount of tyramine or a tenfold increase in tyramine concentration. Therefore, it is possible that increased octopamine content influences how food is evaluated. This is consistent with a function of octopamine as a signal for food or high internal energy levels. Taken together, octopamine is a negative regulator of appetitive LTM and STM (Figure 3C).

Starvation influences sucrose consumption preference

Starvation reduces the internal energy storage. The reduction could lead to a reassessment of the external food cues and increased food consumption to restore the energy supplies. First, we investigated how starvation reduces the glycogen storage in whole animals (Figure 4A). In controls and Tβh^nM18 mutants, starvation reduces the glycogen level. However, non-starved Tβh^nM18 mutant males had significantly higher glycogen levels at baseline. After 40 hr of starvation, the glycogen levels of Tβh^nM18 mutants were still higher than those of controls that were starved for the amount of time.

Figure 4

Download asset Open asset

Figure 4—source data 1

Raw data related to Figure 4.

https://cdn.elifesciences.org/articles/88247/elife-88247-fig4-data1-v1.xlsx

Download elife-88247-fig4-data1-v1.xlsx

Adult Tβh^nM18 mutants have a reduced sucrose intake (Li et al., 2016; Scheiner et al., 2014). The assessment of external food sources might be reflected in the choice of food. To examine whether hunger influences the evaluation of an external food stimulus, we starved flies and determined the preference for consuming sucrose over protein-enriched food using the capillary feeder assay (Figure 4B). Non-starved flies reared on standard fly food preferred sucrose over protein-enriched food, but Tβh^nM18 mutants to a significantly lesser extent than controls. Also, 18 hr of starvation resulted in a decreased sucrose preference in controls and Tβh^nM18 mutants, but to a significantly lower level in Tβh^nM18 mutants. Extending the starvation period to 40 hr resulted in a similar sucrose consumption in control and mutant flies.

To further investigate whether Tβh^nM18 mutants integrate their internal glycogen storage into their feeding preference, we deprived male flies of sucrose- or protein-enriched food by feeding them 5% sucrose, 5% yeast, or standard fly food for 3 d. The food preference was analyzed after deprivation (Figure 4C). As controls, we included mated females in our analysis as they have different nutritional requirements due to their mating status (Ribeiro and Dickson, 2010; Vargas et al., 2010). Male flies fed standard food for 3 d strongly preferred sucrose over protein-enriched food, with Tβh^nM18 mutants showing a significant reduction. Yeast-deprived and sucrose-deprived Tβh^nM18 mutant showed a significantly reduced preference for sucrose. In mated females, no differences were observed in food preference between controls and the Tβh^nM18 mutant. In conclusion, the reduced preference for sucrose consumption correlates in Tβh^nM18 with increased glycogen levels. Furthermore, Tβh^nM18 mutants can sense the reduction of specific internal energy stores and change their food preferences accordingly.

Internal glycogen storage influences sucrose-related memories

Since memory performance increases when the internal energy supply is reduced by starvation, we wanted to investigate whether the internal energy supply influences memory performance. In Drosophila, glycogen is mainly found in the fat bodies – the major energy storage organ – and the muscles, a major site of energy expenditure (Wigglesworth, 1949). Glycogen synthase and glycogen phosphorylase control glycogen content in the body (Figure 5A). Knockdown of glycogen synthase using GlyS^HMS01279-RNAi efficiently reduced glycogen levels in larvae, while knockdown of glycogen phosphorylase using GlyP^HMS00032-RNAi efficiently increased glycogen levels (Yamada et al., 2018). We altered glycogen levels in the muscles using the mef2-Gal4 driver (Ranganayakulu et al., 1998) and/or the fat bodies using the FB-Gal4 driver (Grönke et al., 2003) and analyzed the effect of altered glycogen levels on STM (Figure 5). We used PAS staining to confirm the down- or upregulation of glycogen in larval muscles or fat bodies, respectively (Figure 5; Yamada et al., 2018). In addition, we quantified the glycogen levels in the bodies of adult flies (Figure 5—figure supplement 1). As we could not always detect differences in glycogen content in whole flies, we also analyzed the glycogen level in the muscle-rich thorax or fat body-rich abdomen (Figure 5—figure supplement 1). The knockdown of glycogen phosphorylase increased glycogen levels, and the knockdown of glycogen synthase reduced glycogen levels.

Figure 5 with 2 supplements see all

Download asset Open asset

Figure 5—source data 1

The raw data and sensory acuity data of Figure 5 and related supplement.

https://cdn.elifesciences.org/articles/88247/elife-88247-fig5-data1-v1.xlsx

Download elife-88247-fig5-data1-v1.xlsx

Increasing glycogen levels in the muscles had no effect on the STM of the 16 hr-starved flies, but decreasing glycogen levels significantly improved memory performance (Figure 5B). Increasing or decreasing glycogen levels in the fat bodies had no effect on memory performance (Figure 5C). When the muscles and fat bodies glycogen levels were significantly increased, flies showed a reduced memory to odorants paired with sucrose. An increase in memory performance was observed when glycogen levels were significantly reduced in both tissues (Figure 5D). Recently, it has been shown that energy metabolism in mushroom bodies is important for the formation of LTM (Plaçais et al., 2017). To analyze the function of the mushroom bodies, the expression of GlyS^HMS01279-RNAi under the control of the Mef2-Gal4 driver was repressed in the mushroom bodies using the mb247-Gal80 driver (Krashes et al., 2007). The memory was still increased (Figure 5—figure supplement 2). Conversely, the reduction of GlyS^HMS01279-RNAi using the mb247-Gal4 driver targeting the mushroom bodies (Zars et al., 2000) did not alter STM (Figure 5—figure supplement 2). Thus, low levels of glycogen in the muscles after starvation positively influence appetitive STM, whereas high levels of glycogen in the muscles and fat body decrease reduce STM.

Internal glycogen levels reduce sucrose-related memories in Tβh mutants

The increased glycogen levels in Tβh^nM18 mutants could be responsible for the reduced STM. To determine whether the reduction in glycogen levels in the muscles or fat bodies restores STM, we expressed GlyS^HMS01279-RNAi under the control of the mef2-Gal4 or FB-Gal4 driver in Tβh^nM18 mutants and analyzed STM (Figure 6A). Neither the reduction in the muscles nor the reduction in fat bodies of Tβh^nM18 mutants improved STM. Only when glycogen was reduced in both tissues did the Tβh^nM18 mutants show improved STM compared to controls. Thus, Tβh^nM18 mutant flies can form appetitive STM similar to controls when the energy storage is sufficiently reduced. Next, we analyzed whether male Tβh^nM18 mutants can form STM to other nutrients than carbohydrates by using a protein-enriched diet in the form of 5% yeast as a positive reinforcer (Figure 6B). To determine whether there is a difference in the evaluation of protein as a food source between male flies and Tβh^nM18 mutants, we measured yeast intake in non-starved and starved flies (Figure 6—figure supplement 1). Non-starved Tβh^nM18 mutant males have a significantly higher protein intake than controls. However, after 16 hr of starvation, the protein intake was comparable to that of control flies. When 5% yeast was used as a food reward, male Tβh^nM18 mutants showed comparable STM levels controls (Figure 6B).

Figure 6 with 1 supplement see all

Download asset Open asset

Figure 6—source data 1

The raw data and sensory acuity data of Figure 6 and related supplement.

https://cdn.elifesciences.org/articles/88247/elife-88247-fig6-data1-v1.xlsx

Download elife-88247-fig6-data1-v1.xlsx

To further analyze the influence of the internal energy status on memory performance, we took advantage of the observation that female flies have different nutritional requirements depending on their mating status (Ribeiro and Dickson, 2010; Vargas et al., 2010). Virgin females showed a higher consumption preference for sucrose than mated females when given the choice between yeast and sucrose (Figure 4C), demonstrating a difference in internal energy demands and suggesting a difference in valence for different diets. We tested virgin and mated female flies of controls and Tβh^nM18 mutants for STM with 2 M sucrose as reinforcer (Figure 6C). Virgin females remembered the odorant paired with sucrose significantly better than mated females. This was also true for Tβh^nM18 mutant females. Thus, the internal energy status influences how the reward is evaluated in learning and memory.

Insulin-like signaling in octopaminergic neurons regulates STM

How is the internal energy status integrated into the reward system? Insulin-like signaling regulates glycogen levels in invertebrates and vertebrates (Semaniuk et al., 2021). Loss of the insulin receptor results in more circulating sugar but not increased glycogen levels (Shingleton et al., 2005). Thus, the insulin receptor might be a good candidate that links the internal energy level to reinforcing neurons. First, we analyzed whether the insulin receptor is expressed in octopaminergic reward neurons in the brain (Figure 7A). To detect the expression of an activated insulin receptor, we used an insulin antibody that recognizes the phosphorylated form of the insulin receptor (InR). This region is highly conserved between humans and flies. First, we tested whether the antibody indeed recognized the activated insulin receptor. Therefore, we overexpressed the activated insulin receptor using the UAS-InR.A1325D transgene under the control of the dTdc2-Gal4 driver (Figure 7—figure supplement 1). The InR.A1325D protein variant mimics the human V938D protein variant, which is constitutively active (Longo et al., 1992) and the dTdc2-Gal4 driver targets octopaminergic reward neurons (Busch et al., 2009). The expression of activated InR resulted in increased immunoreactivity (Figure 7—figure supplement 1). The InR antibody therefore detects the activated InR. In the brain, activated InR is broadly expressed in a punctate manner and more specifically in the soma of dTdc2-Gal4-targeted neurons (Figure 7A). To uncouple energy sensing via the insulin receptor in reward neurons, we expressed UAS-InR.K1409A under the control of the dTdc2-Gal4 driver. The transgene encodes a dominant negative variant of the InR (InR^DN) and disrupts with InR function (Wu et al., 2005). In animals starved for 16 hr, the expression of InR^DN under the control of the dTdc2-Gal4 driver did not alter STM, but uncoupling of InR-dependent energy sensing in reward neurons in Tβh^nM18 restored STM to normal levels (Figure 7B and C).

Figure 7 with 1 supplement see all

Download asset Open asset

Figure 7—source data 1

The raw data and sensory acuity data of Figure 7.

https://cdn.elifesciences.org/articles/88247/elife-88247-fig7-data1-v1.xlsx

Download elife-88247-fig7-data1-v1.xlsx

To investigate whether the improved STM also affects the emerging LTM of the Tβh^nM18 mutants, we performed cold shock experiments in Tβh^nM18 mutants in which InR^DN was expressed in octopaminergic reward neurons (Figure 7D). The 3 hr memory in controls is cold shock sensitive, but the memory in Tβh^nM18 mutants is cold shock insensitive, supporting the idea that the mutants formed anesthesia-resistant memory.

Given that octopamine is a negative regulator of memory and that it is still absent in Tβh^nM18 mutants with blocked insulin signaling on octopaminergic reward neurons, it is not surprising that ARM is still observed in the mutants.

The results can be described with the following model (Figure 7E). Insulin receptor signaling on reward neurons integrated the internal energy status into memory formation. When energy levels are high enough due to increased glycogen levels or food intake, the insulin pathway suppresses the formation of STM. In return, the system releases octopamine to suppress the formation of food-related LTM. In mildly starved Tβh^nM18 mutants, the integration of the internal energy status into the reward neurons is still intact. In addition, the level of the internal energy supply combined with the loss of octopamine is still high enough to support the formation of protein biosynthesis-dependent LTM. In Tβh^nM18 mutants, two functionally different memory traces are formed after training, an insulin receptor-sensitive appetitive STM and an LTM. In starved control animals, the glycogen levels are more severely depleted and octopamine is still present. This supports the formation of STM and an intermediate form of memory (ITM).

Increased starvation results in overconsumption in Tβh^nM18 mutants

To analyze whether the increased glycogen levels and the reduced sucrose reward of Tβh^nM18 mutants correlate with food consumption, the energy requirements in flies under different starvation conditions were analyzed by measuring food intake (Figure 8). Control flies starved for shorter or longer periods consumed similar amounts of 5% sucrose. In contrast, Tβh^nM18 mutants starved for 16 hr consumed significantly less sucrose than controls. Longer-starved mutants consumed approximately 34% more sucrose (Figure 8B). After longer starvation, the glycogen levels in Tβh^nM18 mutants were still higher than those in controls (Figure 4A). Therefore, they overconsumed sucrose. The overconsumption was independent of the diet as they showed similar overconsumption when fed with a solution containing 5% yeast and 5% sucrose (Figure 8B). To investigate whether the internal energy status is also integrated into the feeding behavior by the octopaminergic reward neurons, we blocked insulin signaling in reward neurons in 16 hr-starved Tβh^nM18 mutants and analyzed sucrose consumption (Figure 8C). The reduced sucrose consumption of Tβh^nM18 mutants was significantly improved compared to controls. Thus, the octopaminergic reward system integrates via insulin receptor signaling the internal energy level into the regulation of food consumption. The mechanism is still intact in Tβh^nM18 mutants.

Figure 8

Download asset Open asset

Figure 8—source data 1

The raw data related of Figure 8.

https://cdn.elifesciences.org/articles/88247/elife-88247-fig8-data1-v1.xlsx

Download elife-88247-fig8-data1-v1.xlsx

Depending on the duration of starvation, flies develop STM and an anesthesia-sensitive intermediate type of a food-related memory. The extending the hunger period, memory formation shifts to an anesthesia-resistant ITM. Presentation of the reward leads to the release of octopamine and thus the suppression of LTM. The shifting of different memories phases in response to different energy supplies has been also described for memories that are formed of aversive non-food-related memories. Depending on the reduction of the internal energy supply, flies initially form protein-dependent aversive olfactory LTM and later ARM, a protein synthesis-independent form of memory (Wu et al., 2005). In times of energy shortage, the organism exchanges the costly protein biosynthesis-dependent LTM for the ‘less costly’ protein biosynthesis-independent ARM (Mery and Kawecki, 2005). The effect of memory transition can also be observed at the level of appetitive LTM. In Tβh^nM18 mutants, mild starvation, along with elevated glycogen levels, leads to the formation of LTM and prolonged starvation leads to the formation of anesthesia-resistant LTM. Furthermore, Tβh^nM18 mutants form only anesthesia-sensitive aversive memory (Wu et al., 2013). In contrast, Tβh^nM18 mutants form cold-shock-resistant LTM. Therefore, Tβh^nM18 mutants are not defective in appetitive anesthesia-resistant types of memories rather the internal energy status defines how quickly anesthesia-resistant appetitive memories forms.

All data generated or analysed during this study are included in the manuscript and supporting files. All data related to figures are included in the Figure 1—source data 1 to Figure 8—source data 1.

1. 1. Adel M
   2. Griffith LC

   (2021) The role of dopamine in associative learning in Drosophila: an updated unified model

   Neuroscience Bulletin 37:831–852.

   https://doi.org/10.1007/s12264-021-00665-0

   • PubMed
   • Google Scholar
2. 1. Berridge KC

   (2009) “Liking” and “wanting” food rewards: brain substrates and roles in eating disorders

   Physiology & Behavior 97:537–550.

   https://doi.org/10.1016/j.physbeh.2009.02.044

   • PubMed
   • Google Scholar
3. 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

   • PubMed
   • Google Scholar
4. 1. Busch S
   2. Selcho M
   3. Ito K
   4. Tanimoto H

   (2009) A map of octopaminergic neurons in the Drosophila brain

   The Journal of Comparative Neurology 513:643–667.

   https://doi.org/10.1002/cne.21966

   • PubMed
   • Google Scholar
5. 1. Chambers DB
   2. Androschuk A
   3. Rosenfelt C
   4. Langer S
   5. Harding M
   6. Bolduc FV

   (2015) Insulin signaling is acutely required for long-term memory in Drosophila

   Frontiers in Neural Circuits 9:8.

   https://doi.org/10.3389/fncir.2015.00008

   • PubMed
   • Google Scholar
6. 1. Chatterjee N
   2. Perrimon N

   (2021) What fuels the fly: Energy metabolism in Drosophila and its application to the study of obesity and diabetes

   Science Advances 7:eabg4336.

   https://doi.org/10.1126/sciadv.abg4336

   • PubMed
   • Google Scholar
7. 1. Claßen G
   2. Scholz H

   (2018) Octopamine shifts the behavioral response from indecision to approach or aversion in Drosophila melanogaster

   Frontiers in Behavioral Neuroscience 12:131.

   https://doi.org/10.3389/fnbeh.2018.00131

   • PubMed
   • Google Scholar
8. 1. Cole SH
   2. Carney GE
   3. McClung CA
   4. Willard SS
   5. Taylor BJ
   6. Hirsh J

   (2005) Two functional but noncomplementing Drosophila tyrosine decarboxylase genes: distinct roles for neural tyramine and octopamine in female fertility

   The Journal of Biological Chemistry 280:14948–14955.

   https://doi.org/10.1074/jbc.M414197200

   • PubMed
   • Google Scholar
9. 1. Colomb J
   2. Kaiser L
   3. Chabaud MA
   4. Preat T

   (2009) Parametric and genetic analysis of Drosophila appetitive long-term memory and sugar motivation

   Genes, Brain, and Behavior 8:407–415.

   https://doi.org/10.1111/j.1601-183X.2009.00482.x

   • PubMed
   • Google Scholar
10. 1. Damrau C
    2. Toshima N
    3. Tanimura T
    4. Brembs B
    5. Colomb J

    (2017) Octopamine and tyramine contribute separately to the counter-regulatory response to sugar deficit in Drosophila

    Frontiers in Systems Neuroscience 11:100.

    https://doi.org/10.3389/fnsys.2017.00100

    • PubMed
    • Google Scholar
11. 1. Das G
    2. Klappenbach M
    3. Vrontou E
    4. Perisse E
    5. Clark CM
    6. Burke CJ
    7. Waddell S

    (2014) Drosophila learn opposing components of a compound food stimulus

    Current Biology 24:1723–1730.

    https://doi.org/10.1016/j.cub.2014.05.078

    • PubMed
    • Google Scholar
12. 1. Diegelmann S
    2. Jansen A
    3. Jois S
    4. Kastenholz K
    5. Velo Escarcena L
    6. Strudthoff N
    7. Scholz H

    (2017) The CApillary FEeder assay measures food intake in Drosophila melanogaster

    Journal of Visualized Experiments 1:55024.

    https://doi.org/10.3791/55024

    • PubMed
    • Google Scholar
13. 1. Ellingwood SS
    2. Cheng A

    (2018) Biochemical and clinical aspects of glycogen storage diseases

    The Journal of Endocrinology 238:R131–R141.

    https://doi.org/10.1530/JOE-18-0120

    • PubMed
    • Google Scholar
14. 1. Fujita M
    2. Tanimura T

    (2011) Drosophila evaluates and learns the nutritional value of sugars

    Current Biology 21:751–755.

    https://doi.org/10.1016/j.cub.2011.03.058

    • PubMed
    • Google Scholar
15. 1. Gáliková M
    2. Klepsatel P

    (2023) Endocrine control of glycogen and triacylglycerol breakdown in the fly model

    Seminars in Cell & Developmental Biology 138:104–116.

    https://doi.org/10.1016/j.semcdb.2022.03.034

    • PubMed
    • Google Scholar
16. 1. Grillo CA
    2. Piroli GG
    3. Lawrence RC
    4. Wrighten SA
    5. Green AJ
    6. Wilson SP
    7. Sakai RR
    8. Kelly SJ
    9. Wilson MA
    10. Mott DD
    11. Reagan LP

    (2015) Hippocampal insulin resistance impairs spatial learning and synaptic plasticity

    Diabetes 64:3927–3936.

    https://doi.org/10.2337/db15-0596

    • Google Scholar
17. 1. Grönke S
    2. Beller M
    3. Fellert S
    4. Ramakrishnan H
    5. Jäckle H
    6. Kühnlein RP

    (2003) Control of fat storage by a Drosophila PAT domain protein

    Current Biology 13:603–606.

    https://doi.org/10.1016/s0960-9822(03)00175-1

    • PubMed
    • Google Scholar
18. 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
19. 1. Inagaki HK
    2. Ben-Tabou de-Leon S
    3. Wong AM
    4. Jagadish S
    5. Ishimoto H
    6. Barnea G
    7. Kitamoto T
    8. Axel R
    9. Anderson DJ

    (2012) Visualizing neuromodulation in vivo: TANGO-mapping of dopamine signaling reveals appetite control of sugar sensing

    Cell 148:583–595.

    https://doi.org/10.1016/j.cell.2011.12.022

    • Google Scholar
20. 1. Inoue YH
    2. Katsube H
    3. Hinami Y

    (2018) Drosophila models to investigate insulin action and mechanisms underlying human diabetes mellitus

    Advances in Experimental Medicine and Biology 1076:235–256.

    https://doi.org/10.1007/978-981-13-0529-0_13

    • PubMed
    • Google Scholar
21. 1. Ja WW
    2. Carvalho GB
    3. Mak EM
    4. de la Rosa NN
    5. Fang AY
    6. Liong JC
    7. Brummel T
    8. Benzer S

    (2007) Prandiology of Drosophila and the CAFE assay

    PNAS 104:8253–8256.

    https://doi.org/10.1073/pnas.0702726104

    • PubMed
    • Google Scholar
22. 1. Kitamoto T

    (2001) Conditional modification of behavior in Drosophila by targeted expression of a temperature-sensitive shibire allele in defined neurons

    Journal of Neurobiology 47:81–92.

    https://doi.org/10.1002/neu.1018

    • PubMed
    • Google Scholar
23. 1. Könner AC
    2. Hess S
    3. Tovar S
    4. Mesaros A
    5. Sánchez-Lasheras C
    6. Evers N
    7. Verhagen LAW
    8. Brönneke HS
    9. Kleinridders A
    10. Hampel B
    11. Kloppenburg P
    12. Brüning JC

    (2011) Role for insulin signaling in catecholaminergic neurons in control of energy homeostasis

    Cell Metabolism 13:720–728.

    https://doi.org/10.1016/j.cmet.2011.03.021

    • PubMed
    • Google Scholar
24. 1. Krashes MJ
    2. Keene AC
    3. Leung B
    4. Armstrong JD
    5. Waddell S

    (2007) Sequential use of mushroom body neuron subsets during Drosophila odor memory processing

    Neuron 53:103–115.

    https://doi.org/10.1016/j.neuron.2006.11.021

    • PubMed
    • Google Scholar
25. 1. Krashes MJ
    2. Waddell S

    (2008) Rapid consolidation to a radish and protein synthesis-dependent long-term memory after single-session appetitive olfactory conditioning in Drosophila

    The Journal of Neuroscience 28:3103–3113.

    https://doi.org/10.1523/JNEUROSCI.5333-07.2008

    • PubMed
    • Google Scholar
26. 1. Li Y
    2. Hoffmann J
    3. Li Y
    4. Stephano F
    5. Bruchhaus I
    6. Fink C
    7. Roeder T

    (2016) Octopamine controls starvation resistance, life span and metabolic traits in Drosophila

    Scientific Reports 6:35359.

    https://doi.org/10.1038/srep35359

    • Google Scholar
27. 1. Liu C
    2. Plaçais P-Y
    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
28. 1. Longo N
    2. Shuster RC
    3. Griffin LD
    4. Langley SD
    5. Elsas LJ

    (1992)

    Activation of insulin receptor signaling by a single amino acid substitution in the transmembrane domain

    The Journal of Biological Chemistry 267:12416–12419.

    • PubMed
    • Google Scholar
29. 1. Marella S
    2. Mann K
    3. Scott K

    (2012) Dopaminergic modulation of sucrose acceptance behavior in Drosophila

    Neuron 73:941–950.

    https://doi.org/10.1016/j.neuron.2011.12.032

    • PubMed
    • Google Scholar
30. 1. Mery F
    2. Kawecki TJ

    (2005) A cost of long-term memory in Drosophila

    Science 308:1148.

    https://doi.org/10.1126/science.1111331

    • PubMed
    • Google Scholar
31. 1. Monastirioti M
    2. Linn CE
    3. White K

    (1996) Characterization of Drosophila tyramine beta-hydroxylase gene and isolation of mutant flies lacking octopamine

    The Journal of Neuroscience 16:3900–3911.

    https://doi.org/10.1523/JNEUROSCI.16-12-03900.1996

    • PubMed
    • Google Scholar
32. 1. Naganos S
    2. Horiuchi J
    3. Saitoe M

    (2012) Mutations in the Drosophila insulin receptor substrate, CHICO, impair olfactory associative learning

    Neuroscience Research 73:49–55.

    https://doi.org/10.1016/j.neures.2012.02.001

    • PubMed
    • Google Scholar
33. 1. Nakai J
    2. Chikamoto N
    3. Fujimoto K
    4. Totani Y
    5. Hatakeyama D
    6. Dyakonova VE
    7. Ito E

    (2022) Insulin and memory in invertebrates

    Frontiers in Behavioral Neuroscience 16:882932.

    https://doi.org/10.3389/fnbeh.2022.882932

    • PubMed
    • Google Scholar
34. 1. Pedersen BK
    2. Febbraio MA

    (2012) Muscles, exercise and obesity: skeletal muscle as a secretory organ

    Nature Reviews. Endocrinology 8:457–465.

    https://doi.org/10.1038/nrendo.2012.49

    • PubMed
    • Google Scholar
35. 1. Plaçais P-Y
    2. de Tredern É
    3. Scheunemann L
    4. Trannoy S
    5. Goguel V
    6. Han K-A
    7. Isabel G
    8. Preat T

    (2017) Upregulated energy metabolism in the Drosophila mushroom body is the trigger for long-term memory

    Nature Communications 8:15510.

    https://doi.org/10.1038/ncomms15510

    • PubMed
    • Google Scholar
36. 1. Rai M
    2. Coleman Z
    3. Curley M
    4. Nityanandam A
    5. Platt A
    6. Robles-Murguia M
    7. Jiao J
    8. Finkelstein D
    9. Wang Y-D
    10. Xu B
    11. Fan Y
    12. Demontis F

    (2021) Proteasome stress in skeletal muscle mounts a long-range protective response that delays retinal and brain aging

    Cell Metabolism 33:1137–1154.

    https://doi.org/10.1016/j.cmet.2021.03.005

    • PubMed
    • Google Scholar
37. 1. Rajan A
    2. Perrimon N

    (2012) Drosophila cytokine unpaired 2 regulates physiological homeostasis by remotely controlling insulin secretion

    Cell 151:123–137.

    https://doi.org/10.1016/j.cell.2012.08.019

    • PubMed
    • Google Scholar
38. 1. Ranganayakulu G
    2. Elliott DA
    3. Harvey RP
    4. Olson EN

    (1998) Divergent roles for NK-2 class homeobox genes in cardiogenesis in flies and mice

    Development 125:3037–3048.

    https://doi.org/10.1242/dev.125.16.3037

    • PubMed
    • Google Scholar
39. 1. Ribeiro C
    2. Dickson BJ

    (2010) Sex peptide receptor and neuronal TOR/S6K signaling modulate nutrient balancing in Drosophila

    Current Biology 20:1000–1005.

    https://doi.org/10.1016/j.cub.2010.03.061

    • PubMed
    • Google Scholar
40. 1. Saltiel AR
    2. Kahn CR

    (2001) Insulin signalling and the regulation of glucose and lipid metabolism

    Nature 414:799–806.

    https://doi.org/10.1038/414799a

    • Google Scholar
41. 1. Scheiner R
    2. Steinbach A
    3. Claßen G
    4. Strudthoff N
    5. Scholz H

    (2014) Octopamine indirectly affects proboscis extension response habituation in Drosophila melanogaster by controlling sucrose responsiveness

    Journal of Insect Physiology 69:107–117.

    https://doi.org/10.1016/j.jinsphys.2014.03.011

    • PubMed
    • Google Scholar
42. 1. Schwaerzel M
    2. Monastirioti M
    3. Scholz H
    4. Friggi-Grelin F
    5. Birman S
    6. Heisenberg M

    (2003) Dopamine and octopamine differentiate between aversive and appetitive olfactory memories in Drosophila

    The Journal of Neuroscience 23:10495–10502.

    https://doi.org/10.1523/JNEUROSCI.23-33-10495.2003

    • PubMed
    • Google Scholar
43. 1. Semaniuk U
    2. Strilbytska O
    3. Malinovska K
    4. Storey KB
    5. Vaiserman A
    6. Lushchak V
    7. Lushchak O

    (2021) Factors that regulate expression patterns of insulin-like peptides and their association with physiological and metabolic traits in Drosophila

    Insect Biochemistry and Molecular Biology 135:103609.

    https://doi.org/10.1016/j.ibmb.2021.103609

    • PubMed
    • Google Scholar
44. 1. Shingleton AW
    2. Das J
    3. Vinicius L
    4. Stern DL

    (2005) The temporal requirements for insulin signaling during development in Drosophila

    PLOS Biology 3:e289.

    https://doi.org/10.1371/journal.pbio.0030289

    • PubMed
    • Google Scholar
45. 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
46. 1. Tully T
    2. Quinn WG

    (1985) Classical conditioning and retention in normal and mutant Drosophila melanogaster

    Journal of Comparative Physiology. A, Sensory, Neural, and Behavioral Physiology 157:263–277.

    https://doi.org/10.1007/BF01350033

    • PubMed
    • Google Scholar
47. 1. Vargas MA
    2. Luo N
    3. Yamaguchi A
    4. Kapahi P

    (2010) A role for S6 kinase and serotonin in postmating dietary switch and balance of nutrients in D. melanogaster

    Current Biology 20:1006–1011.

    https://doi.org/10.1016/j.cub.2010.04.009

    • PubMed
    • Google Scholar
48. 1. Wigglesworth VB

    (1949) The utilization of reserve substances in Drosophila during flight

    The Journal of Experimental Biology 26:150–163.

    https://doi.org/10.1242/jeb.26.2.150

    • PubMed
    • Google Scholar
49. 1. Wu Q
    2. Zhang Y
    3. Xu J
    4. Shen P

    (2005) Regulation of hunger-driven behaviors by neural ribosomal S6 kinase in Drosophila

    PNAS 102:13289–13294.

    https://doi.org/10.1073/pnas.0501914102

    • PubMed
    • Google Scholar
50. 1. Wu C-L
    2. Shih M-FM
    3. Lee P-T
    4. Chiang A-S

    (2013) An octopamine-mushroom body circuit modulates the formation of anesthesia-resistant memory in Drosophila

    Current Biology 23:2346–2354.

    https://doi.org/10.1016/j.cub.2013.09.056

    • PubMed
    • Google Scholar
51. 1. Yamada T
    2. Habara O
    3. Kubo H
    4. Nishimura T

    (2018) Fat body glycogen serves as a metabolic safeguard for the maintenance of sugar levels in Drosophila

    Development 145:dev158865.

    https://doi.org/10.1242/dev.158865

    • PubMed
    • Google Scholar
52. 1. Yamagata N
    2. Ichinose T
    3. Aso Y
    4. Plaçais P-Y
    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
53. 1. Youn H
    2. Kirkhart C
    3. Chia J
    4. Scott K

    (2018) A subset of octopaminergic neurons that promotes feeding initiation in Drosophila melanogaster

    PLOS ONE 13:e0198362.

    https://doi.org/10.1371/journal.pone.0198362

    • PubMed
    • Google Scholar
54. 1. Zars T
    2. Fischer M
    3. Schulz R
    4. Heisenberg M

    (2000) Localization of a short-term memory in Drosophila

    Science 288:672–675.

    https://doi.org/10.1126/science.288.5466.672

    • PubMed
    • Google Scholar

Author details

1. Michael Berger

   Department of Biology, Institute for Zoology, University Köln, Köln, Germany

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Writing - original draft

   Competing interests

   No competing interests declared

2. Michèle Fraatz

   Department of Biology, Institute for Zoology, University Köln, Köln, Germany

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

3. Katrin Auweiler

   Department of Biology, Institute for Zoology, University Köln, Köln, Germany

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0000-8598-4616

4. Katharina Dorn

   Department of Biology, Institute for Zoology, University Köln, Köln, Germany

   Contribution

   Formal analysis, Validation, Investigation

   Competing interests

   No competing interests declared

5. Tanna El Khadrawe

   Department of Biology, Institute for Zoology, University Köln, Köln, Germany

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Henrike Scholz

   Department of Biology, Institute for Zoology, University Köln, Köln, Germany

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   henrike.scholz@uni-koeln.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8619-5328

• 593

  views

• 60

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.