Factors that mediate ethanol preference in Drosophila melanogaster are not well understood. A major confound has been the use of diverse methods to estimate ethanol consumption. We measured fly consumptive ethanol preference on base diets varying in nutrients, taste and ethanol concentration. Both sexes showed an ethanol preference that was abolished on high nutrient concentration diets. Additionally, manipulating total food intake without altering the nutritive value of the base diet or the ethanol concentration was sufficient to evoke or eliminate ethanol preference. Absolute ethanol intake and food volume consumed were stronger predictors of ethanol preference than caloric intake or the dietary caloric content. Our findings suggest that the effect of the base diet on ethanol preference is largely mediated by total consumption associated with the delivery medium, which ultimately determines the level of ethanol intake. We speculate that a physiologically relevant threshold for ethanol intake is essential for preferential ethanol consumption.

Drosophila melanogaster is a powerful model for human behaviors and disorders, including alcohol use. Numerous reports have detailed factors that alter ethanol consumption, including sex (Park et al., 2018), mating status (Shohat-Ophir et al., 2012), previous exposure to ethanol (Devineni and Heberlein, 2009; Ja et al., 2007; Peru Y. Colón de Portugal et al., 2014) and genotype (Devineni et al., 2011; Sekhon et al., 2016). Despite progress in characterizing fly responses to ethanol, factors that mediate consumptive ethanol preference are less well understood. While some studies support the idea that ethanol preference in flies resembles alcohol use in humans (Devineni and Heberlein, 2009; Xu et al., 2012; Peru Y. Colón de Portugal et al., 2014), others have suggested that the caloric value of ethanol contributes to voluntary ethanol consumption (Pohl et al., 2012; Park et al., 2018).

The ongoing debate surrounding the effect of calories on ethanol preference stems in part from differences in the assays used to quantify feeding behavior. Previous studies of ethanol preference have employed diverse methods that vary in aspects such as the fed state of the animals prior to the experiment or the mode of food delivery (Park et al., 2018; Devineni and Heberlein, 2009; Xu et al., 2012; Devineni et al., 2011; Peru Y. Colón de Portugal et al., 2014; Ja et al., 2007; Pohl et al., 2012; Shohat-Ophir et al., 2012). A commonly used method is the capillary feeder (CAFE) assay, which facilitates precise, sensitive and high-resolution measurements of food consumption (Ja et al., 2007). However, the CAFE assay requires animals to feed on liquid diets from the tip of a glass capillary, which has led some to hypothesize that CAFE feeding induces undernutrition in animals and confounds previous ethanol preference studies (Park et al., 2018). A recent paper using a qPCR-based assay (BARCODE), in which flies feed on oligonucleotide-labeled agar-based food to determine relative intake, proposed that consumptive ethanol preference is sexually dimorphic – well-nourished males show no ethanol preference, whereas female preference is unaltered by hunger (Park et al., 2018). However, it is difficult to directly compare the results from CAFE assays – based on absolute consumption of liquid diets – and those from the BARCODE assay – based on relative consumption of solid food.

Likely as a result of differences in experimental paradigms, reports of ethanol preferences have been widely variable and context dependent, ranging from aversion or no preference (Pohl et al., 2012), to sex-specific (Park et al., 2018) or persistent preference (Devineni and Heberlein, 2009; Peru Y. Colón de Portugal et al., 2014; Xu et al., 2012). To identify a common factor that reliably predicts ethanol preference, we modified a radiolabeled feeding assay (Deshpande et al., 2014) to measure solid food consumption of flies offered a choice between different food sources. This method allows quantitative assessment of voluntary, preferential ethanol consumption on solid food that is easily accessible in standard fly vials – avoiding potential artifactual hunger from experimental paradigms (Devineni and Heberlein, 2009; Ja et al., 2007; Park et al., 2018; Peru Y. Colón de Portugal et al., 2014; Pohl et al., 2012; Shohat-Ophir et al., 2012). We report that the dietary medium is a major determinant of ethanol preference and that the effect is not sex specific. By manipulating total food consumption using non-nutritive tastants to maintain dietary caloric content, we show that ethanol preference can be eliminated or induced as the nutritive value of the base diet remains constant. Correlative analysis of preference for different concentrations of ethanol derived from various diets shows that absolute ethanol intake and total food consumption are stronger predictors of ethanol preference than caloric intake or dietary caloric content.

Our findings suggest that the effect of base diet on ethanol preference is largely mediated by total consumption associated with the delivery medium, which ultimately determines the level of ethanol intake. We speculate that there exists a physiologically relevant threshold for ethanol intake that is essential for ethanol preference. While these correlative analyses cannot definitively rule out any caloric contribution – from dietary content or consumption – to ethanol preference as previously proposed, the hypothesis that total ethanol intake determines ethanol preference may reconcile previous accounts of ethanol preference.

Fly stock and culture

Canton-S flies were reared on a standard cornmeal–sucrose medium [5.8% cornmeal, 1.2% sucrose, 3.1% active dry yeast and 0.7% agar (all w/v)], supplemented with 1% (v/v) propionic acid and 1% (v/v) methylparaben mixture [22.2% methylparaben (w/v) in ethanol] in an incubator with controlled temperature (25°C), humidity (60%) and light (12 h:12 h light:dark cycle) for the entire duration of the experiments. Adult flies were collected 0–2 days after eclosion and sexed 2 days later under CO2 anesthesia on standard medium (5 or 10 single-sex flies per vial) and transferred to fresh food vials every other day. All experiments were performed using flies that were 4–6 or 5–7 days old.

Two-choice solid food intake measurement

Total consumption of radiolabeled medium was measured as described in Deshpande et al. (2014), with modifications. All test diets were prepared 1 h prior to the experiment from heated (∼60°C) solutions of 0.5% Drosophila agar (Apex), sucrose (Sigma), Bacto tryptone (BD Biosciences), sucralose (Alfa Aesar) and/or papaverine (Adipogen). Indicated diets were then diluted with ddH2O or ethanol to the desired concentration (v/v) immediately before dispensing into vials. Each vial contained two ∼600 μl patches of food, one with ethanol and the other without, on opposite sides (Fig. 1A). Half of the vials contained radioactive label (1–2 μCi ml−1 [α-32P]dCTP; PerkinElmer) in the non-ethanol food and the other half had label in the ethanol-containing food. Once the medium had solidified, flies were transferred to the experimental vials around ZT 3. After 24 h, flies were collected in empty vials and killed by freezing at −80°C for scintillation counting. Flies from individual vials were submerged in scintillation fluid (ScintiVerse BD Cocktail, Fisher Scientific) and assayed in a multipurpose scintillation counter (LS 6500, Beckman Coulter). Pre-weighed aliquots of non-solidified media were used to calibrate feeding measurements. Each replicate is a measurement of 5 or 10 flies from a single vial.

Fig. 1.

Both sexes showadiet-dependentethanol preference. (A) Left: schematic diagram of the assay for consumptive ethanol preference. Flies had access to medium with (+EtOH) and without ethanol (−EtOH), and only one of the food patches was radiolabeled in each vial. PI, preference index. Right: absorption efficiency of the radiolabel (32P) mixed into the 5% sucrose diet was ≥96.7% (individual values are shown in bars) and was not substantially compromised by ethanol. (B,C) Both males and females display a preference for ethanol (5%) on diluted (2% tryptone+5% sucrose; B), but not concentrated (3×: 6% tryptone+15% sucrose; C), medium. N=15–19 per condition. Significance: base diet, P<0.001 for males and females; ethanol, P<0.001 for males and females; diet×ethanol, P<0.01 for males and P<0.05 for females. (D) Males display ethanol preference only on lower concentrations of sucrose: 0.17%, 0.86%, 5% and 15% sucrose are equivalent to 5, 25, 146 and 438 mmol l−1, respectively. N=10 per condition. Significance: sucrose concentration, P<0.001; ethanol, P<0.001; sucrose×ethanol, P<0.001. Measurements reflect 24 h consumption in Canton-S flies. Data shown are means±s.e.m. *P<0.05; **P<0.01; ***P<0.001 from two-way ANOVA followed by Tukey-adjusted post hoc comparison (raw consumption) or two-tailed one-sample Student's t-test (PI). For raw data and analysis, see Datasets 1 and 2.

Fig. 1.

Both sexes showadiet-dependentethanol preference. (A) Left: schematic diagram of the assay for consumptive ethanol preference. Flies had access to medium with (+EtOH) and without ethanol (−EtOH), and only one of the food patches was radiolabeled in each vial. PI, preference index. Right: absorption efficiency of the radiolabel (32P) mixed into the 5% sucrose diet was ≥96.7% (individual values are shown in bars) and was not substantially compromised by ethanol. (B,C) Both males and females display a preference for ethanol (5%) on diluted (2% tryptone+5% sucrose; B), but not concentrated (3×: 6% tryptone+15% sucrose; C), medium. N=15–19 per condition. Significance: base diet, P<0.001 for males and females; ethanol, P<0.001 for males and females; diet×ethanol, P<0.01 for males and P<0.05 for females. (D) Males display ethanol preference only on lower concentrations of sucrose: 0.17%, 0.86%, 5% and 15% sucrose are equivalent to 5, 25, 146 and 438 mmol l−1, respectively. N=10 per condition. Significance: sucrose concentration, P<0.001; ethanol, P<0.001; sucrose×ethanol, P<0.001. Measurements reflect 24 h consumption in Canton-S flies. Data shown are means±s.e.m. *P<0.05; **P<0.01; ***P<0.001 from two-way ANOVA followed by Tukey-adjusted post hoc comparison (raw consumption) or two-tailed one-sample Student's t-test (PI). For raw data and analysis, see Datasets 1 and 2.

Absorption efficiency measurement

Absorption efficiency was measured essentially as described in Wu et al. (2019). Male adults were provided with ∼0.25 ml of radiolabeled medium [5% sucrose+0.5% agar (w/v)] containing 0%, 5% or 10% (v/v) ethanol in a small plastic cap (5.5 mm in diameter) to maximize excreta collection. After 24 h, flies were removed, the cap containing solid food was discarded, and excreta from the walls of the tube were collected with 1 ml 1×PBST. 32P radioactivity in fly bodies (cpmfly) and excreta (cpmexcreta) were separately quantified, and absorption efficiency was calculated as:
(1)

Statistical analysis

Consumed amounts of food with or without ethanol were compared using Welch's t-test or two-way analysis of variance (ANOVA) followed by Tukey-adjusted post hoc comparisons of least-square means. Preference index (PI) was calculated as:
(2)
where is the mean consumption of food with () or without () ethanol. Standard deviation of PI was estimated by propagating the standard deviations of the consumption of either food, using the formula:
(3)
where δE+C is the combined standard deviation of the consumption of either food [δE+C=√(δE2C2)] and δ is the standard deviation of the consumption of food with (δE) or without (δC) ethanol. The calculated PI was compared with a hypothetical mean of 0 with one-sample t-test, using the formula:
(4)
where n is the sample size [n=(nE+nC)/2] and t is a Student t quantile with n−1 degrees of freedom. The calculated PI was compared with 0 with one-sample Student's t-test. To dissociate the likelihood of both null and alternative hypotheses, we used the Bayesian framework for performing correlation analyses between PI and the variables of interest. A Bayes factor (BF) between 1/3 and 1 was considered anecdotal evidence for the null hypothesis (H0: ρ=0), a BF between 3 and 10 was moderate evidence for the alternative hypothesis (H1: ρ≠0), and a BF between 10 and 30 was strong evidence for H1 as per convention (Lee and Wagenmakers, 2014; Jeffreys, 1961). Region of practical equivalence (ROPE) decision criterion was used for equivalence testing: reject H0 if the 95% high density interval (HDI) falls completely outside the ROPE; accept H0 ‘for practical purposes’ if the HDI falls fully inside the ROPE; remain ‘undecided’ otherwise (Kruschke, 2018). When there was more than anecdotal evidence for the existence of an association (BF<1/3 or BF>3), simple linear regression analyses were used to calculate the x-intercept after checking the fitted model for linearity, normality and homoscedasticity of the residuals. Caloric values of the base diets (without ethanol) were estimated assuming 3.87 kcal g−1 for sucrose and 4 kcal g−1 for tryptone. Statistical analyses were performed using R statistical software (http://www.R-project.org/) and the RStudio environment (http://www.rstudio.com/) with lsmeans (Lenth, 2016), lmSupport (https://CRAN.R-project.org/package=lmSupport), psycho (Makowski, 2018) and BayesFactor (https://CRAN.R-project.org/package=BayesFactor) packages, except the manual calculations of t scores for the one-sample Student's t-tests.

We utilized a radiolabeled feeding assay (Deshpande et al., 2014) to measure consumption when flies were presented with a choice between solid food with or without ethanol in a standard Drosophila vial (Fig. 1A). Flies had access to both diets, and the radiotracer was mixed into either food patch. Ethanol had a negligible effect on the near-complete absorption of the radiolabel, confirming that the radiolabel feeding assay can be used to accurately measure the consumption of food with or without ethanol (Fig. 1A). On a tryptone–sucrose (2% and 5% w/v, respectively) base diet, both males and females consumed greater amounts of food with 5% ethanol than without and showed a significant ethanol preference (Fig. 1B). Ethanol preference was dependent on the base diet in both sexes – a 3× concentrated medium eliminated consumptive preference for ethanol-spiked food (Fig. 1C). To further dissect the effect of nutrient concentration on ethanol preference, we next measured ethanol preference in males using a sucrose-concentration series. There was a significant sucrose concentration×ethanol interaction effect on consumption; flies ate more of the ethanol-containing than the ethanol-free food and showed significant ethanol preference on the lower concentrations of sucrose but not on the highest concentration tested (Fig. 1D).

Previous studies have concluded that the display of ethanol preference specific to lower concentration diets indicates that undernutrition underlies ethanol preference (Park et al., 2018). However, as flies adjust their feeding behavior to compensate for diet concentration, dilutions of a dietary medium do not necessarily produce a similar degree of change in the caloric intake – and consequent hunger state – of the animals (Fig. 1B–D, Table 1) (Deshpande et al., 2014; Carvalho et al., 2005; Ja et al., 2009). Moreover, absolute ethanol intake is inextricably tied to diet; if the concentration of ethanol mixed into the diet remains constant, increasing nutrient concentration may decrease absolute ethanol intake without altering caloric intake from food. Indeed, dietary dilution had a limited effect on caloric intake yet reversed ethanol preference (Fig. 1B–D, Table 1), suggesting that preferential ethanol consumption may be independent of caloric needs but closely related to total food consumption and thus gross ethanol intake.

Table 1.

Summary of feedingparameters on various media

Summary of feeding parameters on various media
Summary of feeding parameters on various media

To isolate the effect of total consumption – and absolute ethanol intake – on ethanol preference, we manipulated food intake without affecting dietary caloric value using non-nutritive tastants. On agar alone, flies preferred the unadulterated agar over that containing 5% ethanol, contradicting the idea that undernourishment drives ethanol preference (Fig. 2A). Adding sucralose to the agar, thereby sweetening the medium without adding nutritive value, increased overall consumption and induced ethanol preference (Fig. 2A). Conversely, although flies showed ethanol preference on a 1.71% sucrose base diet, adding increasing amounts of a bitter compound, papaverine, to the base diet decreased total consumption and eliminated ethanol preference (Fig. 2B).

Fig. 2.

Absolute ethanol intake predictsethanolpreference. (A,B) Manipulating total food consumption with the non-nutritive tastants sucralose (sweet; A) or papaverine (bitter; supplied with 1.71% sucrose, equivalent to 50 mmol l−1; B) alters ethanol preference. N=22 (0% sucralose) or 10 per diet. Significance: sucralose concentration, P<0.001; ethanol, P<0.01; sucralose×ethanol, P<0.001; papaverine concentration, P<0.001. Data shown are means±s.e.m. *P<0.05; **P<0.01; from two-way ANOVA followed by Tukey-adjusted post hoc comparison (raw consumption) or two-tailed one-sample Student's t-test (PI). (C) PI for ethanol mixed into various diets correlates with total food consumption and ethanol intake, but not with dietary caloric content. Each point represents PI on base diets of varying composition (N>10 per diet): gray, base diet with 5% ethanol; blue, base diet with 15% ethanol; red, non-caloric base diet with 5% ethanol. Results from Bayesian correlation analyses are summarized in each graph: BF, Bayesian factor; ρ, correlation estimate (median±median absolute deviation). Associations between PI and each of the parameters can be summarized as follows: dietary caloric content, anecdotal evidence for the absence of a negative association; caloric intake, anecdotal evidence for the absence of a positive association; total food consumption, moderate evidence for the existence of a positive association; ethanol intake, strong evidence for the existence of a positive association. Solid line and surrounding shaded area show linear regression and 95% confidence interval. Measurements reflect 24 h consumption in Canton-S males. For raw data and analysis, see Datasets 1 and 2.

Fig. 2.

Absolute ethanol intake predictsethanolpreference. (A,B) Manipulating total food consumption with the non-nutritive tastants sucralose (sweet; A) or papaverine (bitter; supplied with 1.71% sucrose, equivalent to 50 mmol l−1; B) alters ethanol preference. N=22 (0% sucralose) or 10 per diet. Significance: sucralose concentration, P<0.001; ethanol, P<0.01; sucralose×ethanol, P<0.001; papaverine concentration, P<0.001. Data shown are means±s.e.m. *P<0.05; **P<0.01; from two-way ANOVA followed by Tukey-adjusted post hoc comparison (raw consumption) or two-tailed one-sample Student's t-test (PI). (C) PI for ethanol mixed into various diets correlates with total food consumption and ethanol intake, but not with dietary caloric content. Each point represents PI on base diets of varying composition (N>10 per diet): gray, base diet with 5% ethanol; blue, base diet with 15% ethanol; red, non-caloric base diet with 5% ethanol. Results from Bayesian correlation analyses are summarized in each graph: BF, Bayesian factor; ρ, correlation estimate (median±median absolute deviation). Associations between PI and each of the parameters can be summarized as follows: dietary caloric content, anecdotal evidence for the absence of a negative association; caloric intake, anecdotal evidence for the absence of a positive association; total food consumption, moderate evidence for the existence of a positive association; ethanol intake, strong evidence for the existence of a positive association. Solid line and surrounding shaded area show linear regression and 95% confidence interval. Measurements reflect 24 h consumption in Canton-S males. For raw data and analysis, see Datasets 1 and 2.

To evaluate the relative contributions of the different dietary factors to ethanol preference, we compared the PI for different concentrations of ethanol observed on various base diets with the dietary caloric content and consumptive parameters associated with each diet (caloric intake, total food consumption and ethanol intake) (Table 1). Total food consumption and gross ethanol intake were the strongest predictors of PI; results from Bayesian correlational analyses indicated that greater amounts of overall food consumption and final ethanol intake were moderately and strongly, respectively, associated with a higher ethanol PI (Fig. 2C). Dietary caloric content and caloric intake (which is a better reflection of the internal nourishment state of the animals than the former) were both anecdotally unassociated with ethanol PI (Fig. 2C).

Although our correlational analysis cannot definitively rule out hunger as a contributor to ethanol preference, the absence of an association between dietary caloric content or intake and ethanol preference substantiates the idea that consumptive ethanol preference in flies is independent of caloric contributions. Previous work has shown that flies cannot derive substantial energy from ethanol yet display a preference for ethanol consumption (Peru Y. Colón de Portugal et al., 2014; Xu et al., 2012); such a preference has also been reported in honeybees, despite their inability to utilize it for energy (Mustard et al., 2019).

In our analysis, ethanol preference was more strongly associated with absolute ethanol intake than with total food consumption. However, it is difficult to ascertain the primary determinant of ethanol preference. Although it is tempting to attribute the relationship to the pharmacological effects of ethanol, it is possible that total consumption is the primary predictor of ethanol preference, while absolute ethanol intake is a collinear predictor. Flies may need to sample from both sources of food sufficiently to be able to differentiate between the ethanol-laced and control food patches. Testing of a wider range of ethanol concentrations on various base diets may help decouple the contribution of absolute ethanol intake versus food volume consumed.

It should be noted, however, that taste likely contributes to voluntary ethanol consumption. Flies find ethanol unpalatable (Devineni and Heberlein, 2009) and do not readily consume pure ethanol, just as humans use various mixers to mask the flavor of pure ethanol. Studies using other animals have similarly reported that rhesus monkeys will readily self-administer ethanol intravenously but not orally, and laboratory mice and rats that are not water or food deprived require specific induction protocols to start orally self-administering various concentrations of ethanol (Meisch, 1977). Although Devineni and Heberlein (2009) reported that Drosophilapoxn mutants lacking external chemosensilla show a preference for ethanol, poxn mutants have intact pharyngeal taste (Chen et al., 2018). Assessing ethanol preference in the absence of taste input using ‘taste-blind’ flies that lack both external and pharyngeal taste (Chen et al., 2019) might provide a better understanding of the behavioral response to the pharmacological effects of ethanol.

Human studies of ethanol drinking initiation distinguish between the experiences of the first drink and the first intoxication – which often do not coincide – on their effect on drinking habits in adulthood (Samson, 1987). We speculate that consumptive ethanol preference in Drosophila similarly depends on suprathreshold substance intake, as shown in studies of self-administered oral preference for drugs of abuse in rodents (Grim et al., 2018). This explanation is consistent with previous reports that ethanol preference increases with the duration and intensity of exposure (Devineni and Heberlein, 2009; Peru Y. Colón de Portugal et al., 2014) and that ethanol preference is attenuated or abolished when the CAFE vials are supplemented with an additional food source (Park et al., 2018) – which would effectively decrease overall consumption of the test diets and thus the mixed-in ethanol, making it less likely that animals will ingest enough ethanol to meet the threshold for establishing preference. It was previously shown that the internal ethanol concentration of non-starved flies that voluntarily consume ethanol is lower than that required to produce behavioral signs of intoxication such as hyperactivity and loss of postural control (Moore et al., 1998; Wolf et al., 2002; Devineni and Heberlein, 2009). It would be interesting to see how the internal ethanol concentration that produces voluntary consumption compares with those determined for behavioral intoxication and to identify the genes that selectively shift the threshold for consumptive preference, given the obvious relevance for clinical alcohol use disorder.

Given the pharmacological effects of ethanol, it is also plausible that there is a limited range of ethanol intake that is ‘desirable’. In such a case, PI, as calculated, may not be an appropriate readout of motivated ethanol consumption. When a high concentration of ethanol is mixed into one of the food sources, flies might consume more of the ethanol-free food after sufficient ethanol consumption to satisfy their caloric needs without overdosing on ethanol – resulting in a lower PI. Without determining a threshold and a target internal ethanol concentration for establishing voluntary ethanol consumption behavior, it is difficult to predict at which ethanol concentrations the preference indices truly reflect motivated consumption.

Contrary to a previous study (Park et al., 2018), we observed similar diet dependency of ethanol preference in both sexes. However, the sexes likely diverge in their total ethanol intake or internal concentration threshold for developing consumptive preference. Female flies metabolize ethanol more slowly than do males (Devineni and Heberlein, 2012), making it plausible that less ethanol intake is required for establishing ethanol preference in females.

Nonetheless, our results show that total food consumption and the accompanying net ethanol intake can predict the development of consumptive ethanol preference. It then follows that variables that affect feeding – including dietary properties (e.g. palatability, nutrition) and experiment duration – or ethanol accumulation (e.g. duration and intensity of ethanol exposure, metabolism) would impact consumptive ethanol preference. Our work adds to the body of evidence showing how changes in feeding behavior drive diverse phenotypes (Keebaugh et al., 2017; Keebaugh et al., 2018; Park et al., 2017; Yamada et al., 2017; Ja et al., 2009). Future studies, especially those focused on the psychopharmacological properties of ethanol, should test for the possibility that changes in total food intake contribute to addiction-like phenotypes.

We thank Dr Erin S. Keebaugh for providing helpful comments on the manuscript.

Author contributions

Conceptualization: S.J.P., W.W.J.; Methodology: S.J.P., W.W.J.; Formal analysis: S.J.P., W.W.J.; Investigation: S.J.P., W.W.J.; Data curation: S.J.P.; Writing - original draft: S.J.P., W.W.J.; Writing - review & editing: S.J.P., W.W.J.; Visualization: S.J.P.; Supervision: W.W.J.; Funding acquisition: W.W.J.

Funding

This work was funded by the National Institutes of Health (R56AG065986, to W.W.J.). S.J.P. received support from The Celia Lipton Farris and Victor W. Farris Foundation Graduate Student Fellowship. Deposited in PMC for release after 12 months.

Carvalho
,
G. B.
,
Kapahi
,
P.
and
Benzer
,
S
. (
2005
).
Compensatory ingestion upon dietary restriction in Drosophila melanogaster
.
Nat. Methods
2
,
813
-
815
.
Chen
,
Y. D.
,
Park
,
S. J.
,
Ja
,
W. W.
and
Dahanukar
,
A
. (
2018
).
Using Pox-neuro (Poxn) mutants in Drosophila gustation research: a double-edged sword
.
Front. Cell. Neurosci.
12
,
382
.
Chen
,
Y. D.
,
Park
,
S. J.
,
Joseph
,
R. M.
,
Ja
,
W. W.
and
Dahanukar
,
A. A
. (
2019
).
Combinatorial pharyngeal taste coding for feeding avoidance in adult Drosophila
.
Cell Rep
29
,
961
-
973.e4
.
Deshpande
,
S. A.
,
Carvalho
,
G. B.
,
Amador
,
A.
,
Phillips
,
A. M.
,
Hoxha
,
S.
,
Lizotte
,
K. J.
and
Ja
,
W. W
. (
2014
).
Quantifying Drosophila food intake: comparative analysis of current methodology
.
Nat. Methods
11
,
535
-
540
.
Devineni
,
A. V.
and
Heberlein
,
U
. (
2009
).
Preferential ethanol consumption in Drosophila models features of addiction
.
Curr. Biol.
19
,
2126
-
2132
.
Devineni
,
A. V.
and
Heberlein
,
U
. (
2012
).
Acute ethanol responses in Drosophila are sexually dimorphic
.
Proc. Natl. Acad. Sci. USA
109
,
21087
-
21092
.
Devineni
,
A. V.
,
Mcclure
,
K. D.
,
Guarnieri
,
D. J.
,
Corl
,
A. B.
,
Wolf
,
F. W.
,
Eddison
,
M.
and
Heberlein
,
U
. (
2011
).
The genetic relationships between ethanol preference, acute ethanol sensitivity and ethanol tolerance in Drosophila melanogaster
.
Fly
5
,
191
-
199
.
Grim
,
T. W.
,
Park
,
S. J.
,
Schmid
,
C. L.
,
Laprairie
,
R. B.
,
Cameron
,
M.
and
Bohn
,
L. M
. (
2018
).
The effect of quinine in two bottle choice procedures in C57BL6 mice: opioid preference, somatic withdrawal, and pharmacokinetic outcomes
.
Drug Alcohol Depend.
191
,
195
-
202
.
Ja
,
W. W.
,
Carvalho
,
G. B.
,
Mak
,
E. M.
,
De La Rosa
,
N. N.
,
Fang
,
A. Y.
,
Liong
,
J. C.
,
Brummel
,
T.
and
Benzer
,
S
. (
2007
).
Prandiology of Drosophila and the CAFE assay
.
Proc. Natl. Acad. Sci. USA
104
,
8253
-
8256
.
Ja
,
W. W.
,
Carvalho
,
G. B.
,
Zid
,
B. M.
,
Mak
,
E. M.
,
Brummel
,
T.
and
Benzer
,
S
. (
2009
).
Water- and nutrient-dependent effects of dietary restriction on Drosophila lifespan
.
Proc. Natl. Acad. Sci. USA
106
,
18633
-
18637
.
Jeffreys
,
H.
(
1961
).
Theory of Probability
.
Oxford
:
Clarendon Press
.
Keebaugh
,
E. S.
,
Park
,
J. H.
,
Su
,
C.
,
Yamada
,
R.
and
Ja
,
W. W
. (
2017
).
Nutrition influences caffeine-mediated sleep loss in Drosophila
.
Sleep
40
.
Keebaugh
,
E. S.
,
Yamada
,
R.
,
Obadia
,
B.
,
Ludington
,
W. B.
and
Ja
,
W. W
. (
2018
).
Microbial quantity impacts drosophila nutrition, development, and lifespan
. iScience
4
,
247
-
259
.
Kruschke
,
J. K
. (
2018
).
Rejecting or accepting parameter values in Bayesian estimation
.
Adv. Method. Pract. Psychol. Sci
.
1
,
270
-
280
.
Lee
,
M. D.
and
Wagenmakers
,
E. J.
(
2014
).
Bayesian Cognitive Modeling: A Practical Course
.
Cambridge University Press
.
Lenth
,
R. V
. (
2016
).
Least-square means: the R Package lsmeans
.
J. Stat. Softw.
69
.
Makowski
,
D
. (
2018
).
The psycho package: an efficient and publishing-oriented workflow for psychological science
.
J. Open Sci. Softw
.
3
,
470
.
Meisch
,
R. A
. (
1977
).
Ethanol self-administration: infrahuman studies
.
Adv. Behav. Pharmacol
.
1
,
35
-
84
.
Moore
,
M. S.
,
Dezazzo
,
J.
,
Luk
,
A. Y.
,
Tully
,
T.
,
Singh
,
C. M.
and
Heberlein
,
U
. (
1998
).
Ethanol intoxication in Drosophila: genetic and pharmacological evidence for regulation by the cAMP signaling pathway
.
Cell
93
,
997
-
1007
.
Mustard
,
J. A.
,
Oquita
,
R.
,
Garza
,
P.
and
Stoker
,
A
. (
2019
).
Honey bees (Apis mellifera) show a preference for the consumption of ethanol
.
Alcohol. Clin. Exp. Res
.
43
,
26
-
35
.
Park
,
J. H.
,
Carvalho
,
G. B.
,
Murphy
,
K. R.
,
Ehrlich
,
M. R.
and
Ja
,
W. W
. (
2017
).
Sucralose Suppresses Food Intake
.
Cell Metab.
,
25
,
484
-
485
.
Park
,
A.
,
Tran
,
T.
and
Atkinson
,
N. S
. (
2018
).
Monitoring food preference in Drosophila by oligonucleotide tagging
.
Proc. Natl. Acad. Sci. USA
115
,
9020
-
9025
.
Peru Y. Colón De Portugal
,
R. L.
,
Ojelade
,
S. A.
,
Penninti
,
P. S.
,
Dove
,
R. J.
,
Nye
,
M. J.
,
Acevedo
,
S. F.
,
Lopez
,
A.
,
Rodan
,
A. R.
and
Rothenfluh
,
A.
(
2014
).
Long-lasting, experience-dependent alcohol preference in Drosophila
.
Addict. Biol.
,
19
,
392
-
401
.
Pohl
,
J. B.
,
Baldwin
,
B. A.
,
Dinh
,
B. L.
,
Rahman
,
P.
,
Smerek
,
D.
,
Prado
,
F. J.
,
Sherazee
,
N.
and
Atkinson
,
N. S
. (
2012
).
Ethanol preference in Drosophila melanogaster is driven by its caloric value
.
Alcohol. Clin. Exp. Res
.
36
,
1903
-
1912
.
RStudio Team
(
2016
).
RStudio: Integrated Development Environment for R. Version 1.1.453 ed
.
Boston, MA
:
RStudio, Inc
.
Samson
,
H. H
. (
1987
).
Initiation of ethanol-maintained behavior: a comparison of animal models and their implication to human drinking
.
Adv. Behav. Pharmacol.
6
,
221
-
248
.
Sekhon
,
M. L.
,
Lamina
,
O.
,
Hogan
,
K. E.
and
Kliethermes
,
C. L
. (
2016
).
Common genes regulate food and ethanol intake in Drosophila
.
Alcohol
53
,
27
-
34
.
Shohat-Ophir
,
G.
,
Kaun
,
K. R.
,
Azanchi
,
R.
and
Heberlein
,
U
. (
2012
).
Sexual deprivation increases ethanol intake in Drosophila
.
Science
335
,
1351
-
1355
.
Wolf
,
F. W.
,
Rodan
,
A. R.
,
Tsai
,
L. T.-Y.
and
Heberlein
,
U
. (
2002
).
High-resolution analysis of ethanol-induced locomotor stimulation in Drosophila
.
J. Neurosci.
22
,
11035
-
11044
.
Wu
,
Q.
,
Yu
,
G.
,
Park
,
S. J.
,
Gao
,
Y.
,
Ja
,
W. W.
and
Yang
,
M
. (
2019
).
Excreta quantification (EX-Q) for longitudinal measurements of food intake in Drosophila
.
iScience
23
,
100776
.
Xu
,
S.
,
Chan
,
T.
,
Shah
,
V.
,
Zhang
,
S.
,
Pletcher
,
S. D.
and
Roman
,
G
. (
2012
).
The propensity for consuming ethanol in Drosophila requires rutabaga adenylyl cyclase expression within mushroom body neurons
.
Genes Brain Behav.
11
,
727
-
739
.
Yamada
,
R.
,
Deshpande
,
S. A.
,
Keebaugh
,
E. S.
,
Ehrlich
,
M. R.
,
Soto Obando
,
A.
and
Ja
,
W. W
. (
2017
).
Mifepristone reduces food palatability and affects Drosophila feeding and lifespan
.
J. Gerontol. A Biol. Sci. Med. Sci.
72
,
173
-
180
.

Competing interests

The authors declare no competing or financial interests.

Supplementary information