Described here is a protocol that enables the colorimetric quantification of the amount of food eaten within a defined interval of time by Drosophila melanogaster larvae exposed to diets of different macronutrient quality. These assays are conducted in the context of a neuronal thermogenetic screen.
Foraging and feeding behaviors allow animals to access sources of energy and nutrients essential for their development, health, and fitness. Investigating the neuronal regulation of these behaviors is essential for the understanding of the physiological and molecular mechanisms underlying nutritional homeostasis. The use of genetically tractable animal models such as worms, flies, and fish greatly facilitates these types of studies. In the last decade, the fruit fly Drosophila melanogaster has been used as a powerful animal model by neurobiologists investigating the neuronal control of feeding and foraging behaviors. While undoubtedly valuable, most studies examine adult flies. Here, we describe a protocol that takes advantage of the simpler larval nervous system to investigate neuronal substrates controlling feeding behaviors when larvae are exposed to diets differing in their protein and carbohydrates content. Our methods are based on a quantitative colorimetric no-choice feeding assay, performed in the context of a neuronal thermogenetic-activation screen. As a read-out, the amount of food eaten by larvae over a 1 h interval was used when exposed to one of the three dye-labeled diets that differ in their protein to carbohydrates (P:C) ratios. The efficacy of this protocol is demonstrated in the context of a neurogenetic screen in larval Drosophila, by identifying candidate neuronal populations regulating the amount of food eaten in diets of different macronutrient quality. We were also able to classify and group the genotypes tested into phenotypic classes. Besides a brief review of the currently available methods in the literature, the advantages and limitations of these methods are discussed and, also, some suggestions are provided about how this protocol might be adapted to other specific experiments.
All animals depend on a balanced diet to acquire the necessary amounts of nutrients for survival, growth, and reproduction1. The choice of what and how much to eat is influenced by a multitude of interacting factors related to the internal state of the animal, like the satiety level, and environmental conditions, such as food quality2,3,4,5. Protein and carbohydrates are two major macronutrients and its balanced intake is essential to sustain animals’ physiological processes. Therefore, the understanding of the neural mechanisms controlling feeding behaviors and sustaining a balanced intake of these macronutrients is extremely relevant. This is because life history traits like lifespan, fecundity, and metabolic health are directly affected by the levels of protein intake intake6,7,8,9,10.
The use of simpler more tractable organisms that exhibit evolutionarily conserved feeding habits with complex animals, including mammals, is essential to this type of studies. Importantly, these simpler animal models provide a good opportunity to dissect complex biological questions in a costly, ethically and technically more effective context. In the last decades, Drosophila, with its powerful genetic toolkit, intricate and stereotypical behavior and conserved architecture of peripheral and nutrient-sensing mechanisms with mammals, has been a fruitful model for behavioral neurobiologists11. Ultimately, the hope is that by understanding how food intake is regulated in this animal, with a simpler nervous system, we can then begin to untangle neuronal malfunctions underlying human eating disorders.
The study of neuronal substrates for feeding behaviors is deeply dependent on being able to simultaneously measure animals’ food intake while manipulating their neuronal activity. Due to the minimal quantities of food ingested, quantifying the amount of food eaten by flies is extremely challenging, and all methods currently available present significant limitations. Thus, the gold standard is to use a combination of complementary methodologies12. Adult flies have been historically favored as a genetic and behavioral model. Nevertheless, Drosophila larvae, also offer opportunities to investigate neuronal substrates encoding feeding behavior. The larval central nervous system (CNS), with around 12,000 neurons, is significantly less complex than that of the adult, which contains approximately 150,000 neurons. This lower complexity is not only numerical but also functional, since larval behaviors rely on simpler locomotive functions and sensory systems. Despite the apparent simplicity of their nervous systems, larvae still exhibit complete feeding behaviors, and some methods to quantify food ingestion in Drosophila larvae have been described5,13,14,15. By pairing with manipulations of neuronal activity, Drosophila larvae can constitute a highly tractable model for understanding the neural regulation of food intake.
Provided here is a detailed protocol to quantify food intake in larvae exposed to diets of different macronutrient quality. The diets, so-called macronutrient balancing diets, differed in the protein and carbohydrates contents, specifically with respect to the protein to carbohydrate (P:C) ratios: 1:1 (protein-rich diet), 1:4 (intermediate diet), and 1:16 (protein-poor diet), as shown in Figure 1A. Briefly, a quantitative no-choice feeding assay was established using these three isocaloric sucrose-yeast (SY)-based diets dyed with a blue food dye. Because yeast extract and sucrose were used as protein and carbohydrate sources, and both contain carbohydrates, variation in the P:C ratios was obtained by changing the balance of these two components, as previously described16 and as indicated in Figure 1B. A schematic overview of the protocol, showing the main experimental steps, is available in Figure 2.
This protocol was established with the aim of investigating the role of specific neuronal populations on the regulation of larval feeding levels in diets of different P:C ratios and in the context of a thermogenetic neuronal screen. A well-characterized neurogenetic tool was used from the Transient Receptor Potential (TRP) family: Drosophila Transient Receptor Potential channel (dTRPA1), which is a temperature and voltage-gated cation channel, allowing the firing of action potentials when ambient temperatures rise above 25 ˚C17. To express the dTRPA1 transgene, we took advantage of the Gal4 lines based on cis-regulatory regions from the Drosophila genome, established in the Rubin laboratory, in the context of the FlyLight project at Janelia Research Campus18,19.
Although the protocol, here described, has been established in the context of an activation screen, it can be easily adapted by the experimenter to other specific needs or interests, namely to perform a suppression screen using the temperature sensitive neuronal silencer ShibireTS20, in alternative to dTRPA1. This and other adaptions are discussed in the protocol and discussion sections.
1. Preparation of the sucrose-yeast (SY) diets
2. Genetic cross of parental lines
NOTE: Use the Gal4/UAS system21 to set up the genetic crosses. In this protocol, in order to activate neuronal function in specific neuronal populations, female virgins of the UAS dTRPA1 line17 were used and crossed to males from the Janelia Gal4 lines (Figure 2A). The genetic control used was the progeny of a cross between the dTRPA1 line and an “empty GAL4” line, which carries Gal4 in the vector used to generate the Rubin Gal4 collection but with no regulatory fragment present (attP2)22. To promote the neuronal suppression, a UAS line encoding ShibireTS20 can be used, instead of dTRPA1.
3. Preparation of third-instar larvae (L3)
4. Thermogenetic activation and no-choice feeding assay
NOTE: It is recommended to perform the feeding assays at approximately the same time of the day to minimize possible variations related to the circadian rhythms. Also, always run the control experiments (the progeny of the “empty Gal4” line crossed to UAS dTRPA1 and the “zero-dye food” larvae), in parallel with the genotypes of interest.
5. Food dye extraction
6. Colorimetric quantification of food consumption
Drosophila larvae regulate their protein intake at the cost of ingesting excess carbohydrates23 (schematic plot in Figure 2E). Actually, this prioritization of protein intake has been observed in many other animals and is called the protein leveraging24,25.
Taking advantage of this robust feeding behavioral response, a behavior-based screen was designed aiming to identify neuronal populations involved in macronutrient balancing. A no-choice feeding assay was established, which consisted of allowing groups of L3 (10 individuals per group) to feed ad libitum for 1 hour and under neuronal thermogenetic-activation conditions using dTRPA1, in three isocaloric (248 Cal/L) food-dyed diets containing specific P:C ratios (1:1, 1:4 and 1:16) (Figure 1 and Figure 2C). As a read-out, the mean amount of food eaten in the macronutrient diets of different P:C ratios was used. Taking advantage of the Gal4/UAS system21 and using some of the Janelia Gal4 lines from the FlyLight Project18,19, the expression of dTRPA1 was induced in specific neuronal populations.
With the methods described in this protocol, we were able to quantify the relative amount of macronutrients consumed, in terms of P:C ratios, for animals under thermogenetic activation of specific neuronal populations in the larval nervous system. This experimental approach demonstrated that activating distinct populations of neurons significantly affected macronutrient balancing in third-instar larvae (Figure 4, Table 1). The feeding pattern observed for the control line (attP2) demonstrates the effectiveness of the method by showing an expected compensatory increase of food intake by larvae tested in lower P:C ratio diets (grey dots and line in Figure 4). Moreover, a significant interaction between the genotypes and the diet was found, which means that the thermogenetic-activation of specific neuronal populations changes the way larvae regulate their food intake in response to the macronutrient quality of the diet.
The feeding patterns of the genotypes tested in the three macronutrient balancing diets (1:1, 1:4, 1:16) are shown by the colored dots and lines in Figure 4 and the statistical analysis are available in Table 1.
In the activation screen, in total, 36 Janelia Gal4 lines known to be sparsely expressed in the larval nervous system were tested. Using linear regression models, we determined which genotypes exhibited significantly different food intake with reference to the genetic control animals. These differences included either differences in the absolute amount of food eaten across all diets, or differences in the macronutrient balancing response (slope of the response to the different P:C ratios of the diets).
Across all three diets, R12E06 ate significantly more food than control animals. In addition, it overcompensated the increase in food intake on the intermediate and low protein diets, as indicated by a significant difference in the interaction term between food intake and P:C ratio of the diet (Table 1). R22H01 ate significantly more than controls but did not differ in the macronutrient balancing response (Table 1). R14B11, R19G11, R21B06, R29C02 and R48F09 larvae ate little amounts of food and lost the ability to compensate for the poor macronutrient quality of the diet available (as indicated by the significant interaction terms between food intake and P:C ratio of the diet, Table 1). Finally, R45D11 larvae ate significantly more in the protein-rich diet containing a P:C ratio of 1:1 than in the intermediate and in the protein-poor diets (1:4 and 1:16), which is the opposite of what one would expect on the low protein diets.
Therefore, our methods allowed us to classify the experimental larvae, from each genotype, into phenotypic classes related to the total amount of food eaten and ability to prioritize protein intake by overconsuming in the diets of low P:C ratio. Five phenotypic classes were established for the experimental animals (Figure 5): 1 – “Eat a lot” (more than the control animals) and overcompensate for protein dilution; 2 – “Eat a lot but compensate normally”; 3 – “Eat little (less than the control) but compensate”; 4 – “Eat little and do not compensate”; 5 – “Eat aberrantly” (more in protein-rich and intermediate diets than in the protein-poor diet). Additionally, for each of these phenotypic classes and genotypes, we show the GFP patterns in the central nervous systems of third-instar larvae. This information was obtained from the publicly available imaging data in the FlyLight Project online platform, where one can get access to the expression patterns of all the Rubin Gal4 lines of interest26.
Figure 1: The sucrose-yeast (SY) diets used in our protocol. (A) The blue dots represent the isocaloric (248 calories/L) macronutrient balancing diets used in the feeding assay, which differ in the protein to carbohydrate (P:C) ratios: 1:1, 1:4 and 1:16. The beige dot represents the diet used to rear the experimental third-instar larvae (L3), which contained a P:C ratio of 1:2 and a caloric density of 495 calories/L. (B) Detailed composition and nutritional information of the sucrose-yeast (SY) based diets. The components are the same for all the diets: agar, sucrose and yeast. The amount in grams of the components needed to prepare 1 L of diet is shown. Note that 1% (v/v) of blue dye must be added to the macronutrient balancing diets and to the L3 rearing diet nipagin and propionic acid solutions must be added to a final concentration (v/v) of 3% and 0.3%, respectively. Please click here to view a larger version of this figure.
Figure 2: Schematic representation of the main steps involved in our protocol (A) Genetic cross of parental lines taking advantage of the Gal4/UAS system. The cross between the Rubin Gal4 lines and the UAS line encoding dTRPA1, allows the thermogenetic activation of specific neuronal populations in the larval central nervous system. (B) Preparation of the experimental third-instar larvae (L3). The parental females were allowed to lay eggs for 3-4 h and the larval staging occurs at the permissive temperature (18 ˚C) for 9 days. Optional is the heat shock at 37 ˚C for 2 min before the feeding assay. (C) Thermogenetic activation of the neuronal function and no-choice feeding assay for 1 h at the non-permissive temperature (30 ˚C). Three groups of 10 experimental L3 from each genotype were allowed to feed in each one of the macronutrient balancing diets containing specific protein to carbohydrates (P:C) ratios (1:1, 1:4 and 1:16). (D) Food dye extraction. Mechanical lysis of larvae, using a tissue lyser, to extract the blue food dye. (E) Food intake quantification. Colorimetric quantification of the mean amount of food eaten per larva by quantifying food dye concentration in the larval extracts. The absorbance of the experimental samples, standards and “zero” was measured at 600 nm (blue), using a 96-well plate reader. Please click here to view a larger version of this figure.
Figure 3: Differences between second (L2) and third-instars Drosophila larvae (L3). The L2 and L3 can be easily distinguished by the observation of spiracles under a stereomicroscope. The anterior spiracles of L2 are club-like, while in L3 are branched. Other characteristics may help to distinguish the two instars but are subjective and less reliable. The posterior spiracles of L3 have a dark orange ring at their tip, which is lacking or weakly present in the L2. The trachea is thicker in L3 larvae. Illustration by Marisa Oliveira. Please click here to view a larger version of this figure.
Figure 4: Amount of food eaten per larva under neuronal thermogenetic-activation conditions in three macronutrient balancing diets containing specific protein to carbohydrates (P:C) ratios. Mean levels of amount of food eaten per larva (mL) in 3 macronutrient balancing diets containing the specific P:C ratios of 1:1, 1:4 and 1:16. Groups of 10 third-instar larvae, from each genotype, were allowed to feed during 1 hour, under neuronal thermogenetic-activation conditions, using dTRPA1, at 30 ˚C. The genotypes tested (larval progenies from the genetic crosses between the Rubin Gal4 lines and the UAS dTRPA1 line) are indicated by dots and lines of different colors. As a genetic control (indicated in grey), the larval progeny from a cross between the “empty Gal4” line (attP2) and UAS dTRPA1 were used. The names given to the genotypes, indicated in the legend, were related to the "Rubin GAL4" lines used. Please click here to view a larger version of this figure.
Figure 5: Grouping the lines tested in 5 main phenotypic classes. The phenotypic classes indicated by numbers were based on the combination of the phenotypes observed in terms of total amount of food eaten and ability to maintain the protein intake prioritization response: 1 – eat a lot (more than the control animals) and were able to compensate for protein dilution by overeating; 2 – eat a lot and were not able to compensate; 3 – eat little (less than the control) but compensate; 4 – eat little and were not able to compensate; and 5 – an extra phenotypic class, that were called “aberrant”, in which the larvae didn’t behave as expected in response to the macronutrient dilution of protein content in the diet, eating more in protein-rich and intermediate diets than in the protein-poor diet. For each genotype, the GFP expression pattern in the central nervous systems of third-instar larvae is shown. This imaging data of the Rubin Gal4 lines used in this assay was extracted from the publicly available FlyLight Project online platform26. Please click here to view a larger version of this figure.
Anova Table (Type II tests) | |||||
Response: Concentration/L3 | |||||
Sum Sq | Df | F value | Pr(>F) | ||
Food | 0.086832 | 1 | 113.5358 | < 2.2e-16 | *** |
Genotype | 0.078443 | 10 | 10.2567 | 9.762e-15 | *** |
Food : Genotype | 0.064038 | 10 | 8.3733 | 6.416e-12 | *** |
Residuals | 0.215673 | 282 | |||
Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | |||||
Summary Table (coefficients below are compared to the attP control genotype): | |||||
Estimate | Std. Error | t value | Pr(>|t|) | ||
(Intercept) | 0.064245 | 0.004316 | 14.886 | < 2e-16 | *** |
Food | -0.058117 | 0.007206 | -8.066 | 2.10e-14 | *** |
Genotype R12E06 | 0.040243 | 0.008961 | 4.491 | 1.03e-05 | *** |
Genotype R14B11 | -0.053347 | 0.014361 | -3.715 | 0.000245 | *** |
Genotype R19G11 | -0.044880 | 0.010788 | -4.160 | 4.23e-05 | *** |
Genotype R21B06 | -0.051912 | 0.009363 | -5.544 | 6.79e-08 | *** |
Genotype R22H01 | 0.017682 | 0.007296 | 2.423 | 0.016004 | * |
Genotype R29C02 | -0.043102 | 0.011113 | -3.879 | 0.000131 | *** |
Genotype R40D06 | -0.005341 | 0.009876 | -0.541 | 0.589102 | |
Genotype R45C03 | 0.004064 | 0.009876 | 0.412 | 0.680997 | |
Genotype R45D11 | -0.052579 | 0.009876 | -5.324 | 2.08e-07 | *** |
Genotype R48F09 | -0.044612 | 0.011362 | -3.926 | 0.000108 | *** |
Food : Genotype R12E06 | -0.037763 | 0.015440 | -2.446 | 0.015067 | * |
Food : Genotype R14B11 | 0.058054 | 0.027100 | 2.142 | 0.033031 | * |
Food : Genotype R19G11 | 0.051532 | 0.017726 | 2.907 | 0.003937 | ** |
Food : Genotype R21B06 | 0.054403 | 0.015689 | 3.467 | 0.000607 | *** |
Food : Genotype R22H01 | -0.020863 | 0.012377 | -1.686 | 0.092979 | . |
Food : Genotype R29C02 | 0.048996 | 0.018714 | 2.618 | 0.009317 | ** |
Food : Genotype R40D06 | 0.003804 | 0.016550 | 0.230 | 0.818371 | |
Food : Genotype R45C03 | 0.034117 | 0.016550 | 2.061 | 0.040177 | * |
Food : Genotype R45D11 | 0.090661 | 0.016550 | 5.478 | 9.53e-08 | *** |
Food : Genotype R48F09 | 0.051184 | 0.019045 | 2.688 | 0.007625 | ** |
Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 | |||||
Residual standard error: 0.02765 on 282 degrees of freedom | |||||
Multiple R-squared: 0.516, Adjusted R-squared: 0.4799 | |||||
F-statistic: 14.31 on 21 and 282 DF, p-value: < 2.2e-16 |
Table 1: ANOVA table for the effect of neuronal thermogenetic-activation and macronutrient quality of the diet available on the amount of food intake. A linear model was fitted in order to determine the genotypes exhibiting a feeding behaviour significantly different than the control animals.
Genotype | Associated Gene | Origin | BDSC Stock Number |
w[*] ; P{UAS-TrpA1(B).K}attP2 / TM6B, Tb[1] | Bloomington | 26264 | |
w[1118] ; P{GAL4.1Uw}attP2 | Janelia | 68384 | |
w[1118] ; P{GMR12E06-GAL4}attP2 | net (CG11450) | Janelia | NA |
w[1118] ; P{GMR14B11-GAL4}attP2 / TM3, Sb[1] | dnc (CG32498) | Janelia | 49255 |
w[1118] ; P{GMR19G11-GAL4}attP2 | CG33696 | Janelia | 48864 |
w[1118] ; P{GMR21B06-GAL4}attP2 | oa2 (CG6919) | Janelia | 49857 |
w[1118] ; P{GMR22H01-GAL4}attP2 | fru (CG14307) | Janelia | 49001 |
w[1118] ; P{GMR29C02-GAL4}attP2 | Ptp69D (CG10975) | Janelia | 48088 |
w[1118] ; P{GMR40D06-GAL4}attP2 | cnc (CG17894) | Janelia | 48616 |
w[1118] ; P{GMR45C03-GAL4}attP2 | kni (CG4717) | Janelia | 47936 |
w[1118] ; P{GMR45D11-GAL4}attP2 | pnt (CG17077) | Janelia | 49563 |
w[1118] ; P{GMR48F09-GAL4}attP2 | dpr8 (CG32600) | Janelia | 50377 |
Table 2: Drosophila lines used in this work. Detailed information of all the lines used: code name, genotype, associated gene, origin and the Bloomington Drosophila Stock Center (BDSC) number.
With this protocol, one could test the ability of larvae under thermogenetic-activation of specific neuronal populations to regulate the intake levels of protein and carbohydrates, two major macronutrients, when exposed to diets of different P:C composition. This method was tested in the context of a larval preliminary screening aiming to identify neuronal populations associated with the control of food intake across diets of different macronutrient quality. This work also contributes to demonstrating that Drosophila larvae are valuable animal models for investigating the neuronal basis of feeding behaviors associated to nutrient homeostasis.
In addition to the information provided as notes in the protocol, we would like to further discuss some important aspects. As in any behavioral assay, measures must be taken by the experimenter to minimize the variation associated to animal behavior. A very important aspect that one should keep in mind is related to the importance of obtaining developmentally synchronized animals. The use of early L3 larvae that are well synchronized in their developmental stage, will decrease the behavioral variations exhibited by the animals during the feeding assay27. Synchronizing larvae is achieved by short egg lays and by controlling the density of larvae in the cultures. Do not use longer periods of egg-laying than the ones we indicate in the protocol (3-4 hours). Also, controlling the larval density to a maximum number of 200 animals per plate will avoid developmental delays and eliminate additional variation in feeding behavior. Please, note that the first egg lay after mating, has to be discarded to maintain homogeneity and obtain better synchronized larval development. Females have fertilized eggs in oviduct and lay them in varying stages of development making it hard to maintain uniformity among larval collection. It is imperative that at least the first hour egg collection plate be discarded before the final collection. Please, take into consideration that the stress induced to the animals during larval handling can also negatively impact behavior. Try to be as gentle as possible, by using a soft and water-moistened brush. Finally, keep in mind that a high number of replicates generates a more reliable dataset.
As in any experimental protocol, our methods present some limitations. Using a colorimetric method to quantify food intake based on the accumulation of a food-dye in animals’ guts involves some precautions related to the duration of the assay. For adult flies it was demonstrated that there is a significant risk of reaching a steady state for dye accumulation, in which the rate of egestion equals the rate of intake, reducing the accuracy of the method28. Although there is no evidence of this happening in larvae, we decided to perform a feeding assay with a maximum duration of 60 min. This duration is convenient and compatible with high-throughput screen. Also, keeping the total duration of the protocol as short as possible allows the completion of all the steps in sections 4, 5 and 6 in one working day. If it is necessary to change the duration of the feeding assay, assay durations ranging from 60 to 120 minutes should allow an efficient quantification of food intake across genotypes, as previously demonstrated29. The sensitivity of food-dying methods is also relatively low when small amounts of food are consumed, which significantly reduces the resolution among genotypes exhibiting very low levels of food intake. We set up our feeding assays using a no-choice paradigm. Only one diet type is available to each experimental group of larvae, which doesn’t allow animals to independently regulate the levels of proteins and carbohydrates consumption. Furthermore, because we use chemically undefined diets, it is hard to keep control of nutrients concentrations that might directly affect the patterns of larval feeding. To overcome these issues, or to confirm and further dissect hits found on a preliminary screen, the experimenter might want to consider the possibility of establishing a precise and controlled experimental nutritional context, by using defined synthetic (holidic) mediums10 and setting up food choice assays as previously described30. While using a protocol involving thermogenetic neuronal modulation, it is important to consider that the necessary temperature shifts might directly affect animals’ behavioral outputs. A complementary use of optogenetic approaches would be interesting to control for temperature-induced false positives, but the use of optogenetics in the context of larval feeding assays is technically challenging, since feeding larvae spend most of the time burrowed in the food substrate.
Nevertheless, several strengths of our experimental approach can be enumerated. The simplicity and relatively high throughput of our method allow the quantification of food intake for several genotypes when exposed to different nutritional conditions. Feeding behaviors in the larval stage are more readily quantifiable than in adult flies, enabling the generation of better functional readouts. It is also less challenging to establish feeding assays resembling the natural environment in larvae than it is for adults, as it has been previously discussed31. Furthermore, when compared to other previously established methods to quantify feeding in larvae, namely the ones based on manual counting the number of mouth hook contractions during a certain period of time32, our colorimetric method enables genetic screening studies on larger scales. Some other methods are simply based on scoring the proportion of larvae with dyed-food in their guts, not allowing an accurate quantification of food intake levels33,34. Concerning the neurogenetic control of neuronal function, the fact that TRPA1 transgene is inactive at 18 ˚C ensures that neuronal activity is not affected throughout larval development. This ensures that the experimental neuronal activation will be performed exclusively during the feeding assay and not during the larval development. Additionally, we would like to mention, one more time, that our protocol can be easily adapted to specific needs and interests of the experimenter. For example, the suppression of the neuronal function, instead of activation, can be easily obtained by substituting the dTRPA1 for a UAS line encoding the temperature sensitive neuronal silencer ShibireTS20. Also, if the feeding levels exhibited by the experimental larvae are very low, making it hard to quantify food intake, it is possible to perform an extra step of 30 min larval starvation before the feeding assay (before the steps in section 4 of the protocol), as previously described15. This food-deprivation step can be particularly interesting if you are investigating modulators of hunger-driven behaviors. Finally, in previous studies, using quantitative colorimetric methods, it was shown that labeling food with blue-dye has no influence on feeding12. Nevertheless, we think that the use of complementary, more accurate and sensitive methods, like the radiolabeling of the food12, in more advanced stages of a study, aiming to confirm or further dissect hits found during preliminary stages would be a good complement of our method and should be considered by the experimenter. For all these reasons, we believe in the attractiveness of our methods to perform genetic screens (especially primary screens) aiming to identify neuronal populations involved in the assembling of neuronal circuits encoding feeding behaviors.
As a final note, we would like to mention the fact that thousands of larval Gal4 lines established in Janelia Research Campus are publicly available, at Bloomington Drosophila Stock Center and a large amount of information about larval26 and adult19 CNS expression patterns is also publicly accessible at the FlyLight Image Database (http://www.janelia.org/gal4-gen1). These resources make it possible to elaborate putative structure-function neuronal maps of the neurons regulating feeding behavior in Drosophila larvae. This is possible by integrating the phenotypic information generated in neuronal screens with the expression patterns of the drivers used. We believe our methods constitute a valid approach to generate preliminary neuronal maps for feeding behaviors associated to macronutrient balancing in the Drosophila brain.
The authors have nothing to disclose.
We would like to thank to Instituto Gulbenkian de Ciência (IGC) for providing us access to part of the experimental equipment described in this protocol. This work was supported by Portuguese Foundation for Science and Technology (FCT), LISBOA-01-0145-FEDER-007660, PTDC/NEU- NMC/2459/2014, IF/00697/2014 and La Caixa HR17-00595 to PMD and by an Australian Research Council Future Fellowship (FT170100259) to CKM.
1.5 mL microtubes | Sarstedt AG & Co. | 72.690.001 | |
10xPBS | Nytech | MB18201 | |
2.0 mL microtubes | Sarstedt AG & Co. | 72.695.500 | |
60 mm petri dishes | Greiner Bio-one, Austria | 628161 | |
96 well microplates | Santa Cruz Biotechnology | SC-204453 | |
Agar | Pró-vida, Portugal | ||
Bench cooler | Nalgene, USA | Labtop Cooler 5115-0032 | |
Blue food dye | Rayner, Billingshurst, UK | ||
Cell disruption media | Scientific Industries, Inc. | 888-850-6208 | (0.5 mm glass beads) |
Dish weight boats | Santa Cruz Biotechnology | SC-201606 | |
Embryo collection cage for 60 mm petri dishes | Flystuff, Scientific Laboratory Supplies, UK | FLY1212 (59-100) | |
Featherweight forceps | BioQuip Products, USA | 4750 | |
Fly food for stocks maintenance | 1 L food contains: 10 g Agar, 100 g Yeast Extract, 50 g Sucrose, 30 mL Nipagin, 3 mL propionic acid | ||
Forceps #5 | Dumont | 0108-5-PS | Standard tips, INOX, 11cm |
Incubator | LMS Ltd, UK | Series 2, Model 230 | For thermogenetic feeding assay (30∘C) |
Incubator | Percival Scientific, USA | DR36NL | To stage larvae (19∘C) |
Janelia lines | Janelia Research Campus | Detailed information in Table 2 | |
Macronutrient balancing diets | Composition and nutritional information in Figure 1 | ||
Methanol | VWR | CAS number: 67-56-1 | |
Nipagin (Methyl 4-hydroxybenzoate) | Sigma-Aldrich | H5501 | |
Nitrile gloves | VWR, USA | ||
Refrigerated centrifuge | Eppendorf, Germany | 5804 R / Serial number: 5805CI364293 | |
Rubin Gal4 ines | Janelia Research Campus | Stoks available at Bloomington Drosophila Stock Center | |
ShibireTS UAS line | Bloomington Drosophila Stock Center | BDSC number: 66600 | Provided by Carlos Ribeiro Group |
Soft brushes | For sorting anaesthetised fruit flies | ||
Spectrophotometer plate reader | Thermo Fisher Scientific | Multiskan Go 51119300 | |
Stereo microscope | Nikon | 1016625 | |
Sucrose | Sidul, Portugal | ||
Third-instar larvae (L3) rearing diet | Composition and nutritional information in Figure 1 | ||
Timer | |||
Tissue lyzer / bead beater | MP Biomedicals, USA | FastPrep-24 6004500 | |
TRPA1 UAS line | Bloomington Drosophila Stock Center | BDSC number: 26264 | Expresses TrpA1 under UAS control; may be used to activate neurons experimentally at 25 ∘C |
Water bath | Sheldon Manufacturing Inc., USA | W20M-2 / 03068308 / 9021195 | |
Yeast extract | Pró-vida, Portugal | 51% Protein, 15% Carbohydrate |