New types of behavioral inference from the FLIC system.

<p>(A) Flies spent 10% of their time in behaviors we categorized as tasting two foods prior to making their first meal choice. Fraction of time is calculated based on “total time spent tasting/time until the first meal”. (B) A greater fraction of tasting events were directed toward the food the flies choose to consume (mean Tasting PI = 0.35). A Tasting PI = 1 implies a fly tasted a single food before ultimately consuming that food. A Tasting PI = −1 implies that a fly tasted a single food before ultimately consuming the opposite food. (C) While a cumulative preference index (left panel) is effective at portraying overall feeding tendencies, time-dependent preference indices (right panel) reveal subtle differences in behavioral choices as the experiment progresses. Flies exhibited a strong preference toward 10% sucrose in the first 30 min, which was attenuated in later time periods then returned to a strong preference (N = 34; the size of the symbol is proportional to the sample size contributed to calculate PI in a given period). (D) Flies with increased feeding motivation (through longer periods of starvation) experienced their first meal earlier than control flies. Flies starved for increasing periods (0 hr, 24 hr, or 48 hr) exhibited reduced latencies until their first feeding event. Latency curves were found to be significantly different via log-rank test. (E) Flies with increased hunger (through longer periods of starvation) exhibited meals that were of significantly longer duration than control flies (One-way ANOVA followed by post-hoc test using a Bonferroni correction). (F) Taste input plays a role in motivation by decreasing latency to the first meal. Flies with loss of function in the trehalose receptor, <i>ΔGr5a</i>, were significantly delayed in their first meal of a liquid trehalose food compared with control animals (log-rank test). **P≤0.01; ***P≤0.001</p

Comparison between traditional food choice assays and the FLIC system.

<p>(A) The analog signals from feeding (left) and tasting (right) behaviors have distinct characteristics. (B) When presented with identical food in both food wells, male and female flies do not exhibit a preference, which rules out systematic bias in the FLIC system (open symbol, male; closed symbol, female; pooled paired randomization test, P = 0.97). (C) Flies exhibited strong preference in favor of 10% sucrose over 100 µM denatonium when measured using both two-dye and FLIC assays (Box charts represent mean, standard error of mean, and 10–90% quantile whiskers). (D) Flies demonstrated strong preference toward 10% sucrose over 1% sucrose when measured using both the CAFE and FLIC assays (Box charts represent mean, standard error of mean, and 10–90% quantile whiskers). (E) Estimates of food consumption using the CAFE and FLIC assays. Longer starvation resulted in increased food consumption as well as total feeding time (linear regression, P<1×10⁻⁵ for both assays). Changes in food volume in the capillary tubes was undetectable when fully fed flies were used, and only FLIC data are presented for that treatment. *P≤0.05; **P≤0.01; ***P≤0.001.</p

FLIC: High-Throughput, Continuous Analysis of Feeding Behaviors in <i>Drosophila</i>

<div><p>We present a complete hardware and software system for collecting and quantifying continuous measures of feeding behaviors in the fruit fly, <i>Drosophila melanogaster</i>. The FLIC (<u>F</u>ly <u>L</u>iquid-Food <u>I</u>nteraction <u>C</u>ounter) detects analog electronic signals as brief as 50 µs that occur when a fly makes physical contact with liquid food. Signal characteristics effectively distinguish between different types of behaviors, such as feeding and tasting events. The FLIC system performs as well or better than popular methods for simple assays, and it provides an unprecedented opportunity to study novel components of feeding behavior, such as time-dependent changes in food preference and individual levels of motivation and hunger. Furthermore, FLIC experiments can persist indefinitely without disturbance, and we highlight this ability by establishing a detailed picture of circadian feeding behaviors in the fly. We believe that the FLIC system will work hand-in-hand with modern molecular techniques to facilitate mechanistic studies of feeding behaviors in <i>Drosophila</i> using modern, high-throughput technologies.</p></div

Illustration of the FLIC system.

<p>(A) Cartoon of the <i>Drosophila</i> Feeding Monitor (DFM) from the top- and side-view along with a flowchart of data collection and processing. Analog signals from all DFMs are collected by the Master Control Unit (MCU), which relays the information to the PC where the signals are visualized and recorded by the real-time monitoring software. (B) Representative signals from each of two feeding wells within a single feeding arena taken from a 90 min subset of a 24-hour feeding measurement. Close-up signal patterns representative of two distinct classes of feeding behavior events are presented as insets. (C) Histograms representing the distribution of durations for individual feeding behavior events (an event is a set of contiguous signals above baseline) over a 24 hr measurement period. Each plot represents values from a single fly, and distributions for three flies are presented. N represents the number of behavior events observed. (D) Histograms representing the size of the intervals between successive feeding behaviors over a 24 hr measurement period. Each plot represents values from a single fly, and distributions for the same three flies as in panel C are presented. (E) Among-fly variability in the average feeding duration and average time between feeding events. Each point represents the average value over a 24 hr period (N = 21). (F) Event-time distribution that represents the fraction of the population that has experienced at least one feeding at a given elapsed time from a randomized point between 12pm-2pm (N = 21). It took roughly 197 min for 50% of the population to feed at least once during this time of the day.</p

Diet-induced shift in energy storage is not sufficient to induce sleep partitioning.

<p>(A,B) A metabolite panel was tested in control (<i>yw</i>) flies that had previously been fed 2.5% (LS) or 30% (HS) sucrose on a 2.5% yeast base food for 6 days during activity monitoring. There was a significant change in triglyceride (TAG) and a smaller change in protein levels with no significant effect on glycogen (Gly), glucose (Glu), or trehalose. (C) Flies carrying the imprecise P-element excision deletion <i>Lsd-2⁵¹</i> had reduced triglyceride∶protein levels relative to precise excision <i>Lsd-2^rev</i> controls (inset) but retained a significant sleep response to diet with no diet∶genotype interaction (two-way ANOVA). Error bars represent mean +/− SEM for each group. *** = p<0.001, ** = p<0.01,* = p<0.05 for all statistical calculations. Error bars represent mean +/− SEM for each group. *** p<0.001, ** p<0.01,* p<0.05 for all statistical calculations. Significance values for t-tests between dietary conditions are shown above each set of bars and significance values for two-way ANOVA are shown above the graph, where applicable.</p

Gustatory inputs mediate sleep partitioning in response to dietary sugar.

<p>For each indicated sugar, flies were tested on a medium with 2.5% or 30% of that sugar on a 2.5% yeast base. (A) Flies with a deletion in the <i>Gr64a-f</i> sweet-sensing cluster retained sensitivity to fructose (control food) but were insensitive to a glucose-containing food (test food). This effect was rescued by expression of a UAS-<i>Gr64abcd_GFP_f</i> construct with a <i>Gr5a</i>-GAL4 promoter in the <i>Gr64</i> deletion background. (B) Flies with a deletion in the <i>Gr5a</i> trehalose receptor retained sensitivity to sucrose (control food) but were insensitive to a trehalose-containing food (test food). This effect was rescued by expression of a UAS-<i>Gr5a</i> with a <i>Gr5a</i>-GAL4 promoter. Deletion of (C) the <i>Gr64</i> cluster or (D) <i>Gr5a</i> did not compromise the rapid shift in energy storage following dietary switch on either the control or test food. (E) Quinine supplementation of the food at the indicated concentrations (significance value from one-way ANOVA followed by Fisher LSD within LS group) attenuated the sleep response to LS diet in <i>yw</i> control flies. (F) Genetic stimulation of <i>Gr66a</i>-containing neurons using the <i>Vr1E600K</i> capsaicin receptor in the presence of capsaicin was sufficient to suppress the LS-induced sleep response (significant diet∶capsaicin interaction by two-way ANOVA). There was no effect of capsaicin feeding or diet∶capsaicin interaction in control flies containing either the Gr66a-GAL4 or UAS-Vr1E600K alone but these controls retained a significant response to diet. Error bars represent mean +/− SEM for each group. *** p<0.001, ** p<0.01,* p<0.05 for all statistical calculations. Significance values for t-tests between dietary conditions are shown above each set of bars and significance values for two-way ANOVA are shown above the graph, where applicable. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002668#pgen.1002668.s004" target="_blank">Figure S4</a> for additional supporting evidence.</p

Characterization of diet-induced changes to sleep architecture.

<p>(A) Video tracking analysis confirms the effect of sugar on sleep bout number was significant when the position of individual control (<i>yw</i>) flies was tracked with video monitoring (n = 15 per group). (B) The average distance traveled per hour was not significantly different between dietary conditions. (C) Position traces along the length of the food vial are plotted across time for two individuals per dietary condition.Arrows indicate the presence of sleep episodes. The food is oriented at the top and the cotton plug is at the bottom in this visualization (see graphic to the left of the plots) (D) The average time spent in a trip to the food and (E) the percent of sleep bouts resulting in an approach to the food within two minutes were not significantly different between dietary conditions. (F) Control flies were switched from 10% sucrose∶yeast fly medium to LS (2.5% sucrose) or HS (30% sucrose) in a 2.5% yeast base medium on day zero and sleep was measured for the subsequent 12 days. Flies were switched to fresh food on day 6. Each point represents the avergage +/− SEM for a 24 hour period.(G) Control flies were placed on a yeast-free amino acid mixture containing either 0%-OS(AA), 2.5%-LS(AA), or 30%-HS(AA) sucrose in order to compare complete sugar deprivation to the low and high sugar condition on a consistent sugar-free base medium. Error bars represent mean +/− SEM for each group. *** p<0.001, ** p<0.01,* p<0.05 for all statistical calculations. Significance values for t-tests between dietary conditions are shown above each set of bars. For panel C, significance values were calculated by one-way ANOVA followed by Fisher LSD for comparison between groups. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002668#pgen.1002668.s002" target="_blank">Figure S2</a> for additional supporting evidence.</p

Dietary composition modulates sleep architecture.

<p>(A) Control (<i>yw</i>) flies fed 5∶5% sucrose∶yeast (5% SY) displayed an increase in the number of sleep bouts (sleep partitioning) over a 5 day recording period relative to those on 20% SY food. Total sleep and average waking activity (beam crosses per minute) were unaffected. (B) The response to diet was present in both males and females. (C) The distribution (probability density function) of sleep bout lengths over the same 5 day recording period revealed that the 5% SY diet resulted in a reduced number of long sleep bouts in favor of a larger number of short and medium-length bouts (p<2×10–16, Kolmogorov-Smirnov test). (D) The carbohydrate (sucrose) component of the diet modulated sleep (p<0.001) with no significant effect of yeast or sucrose∶yeast interaction (two way ANOVA). Concentrations of sucrose (S) and yeast (Y) are shown as percentages by weight. (E) The effects of diet were robust in three control genotypes: <i>w¹¹¹⁸</i>, <i>yw</i>, Canton S (CS) when flies were fed on a base of 2.5% yeast with either low (LS, 2.5%) or high sucrose (HS, 30%). Error bars represent mean +/− SEM for each group. *** p<0.001, ** p<0.01,* p<0.05 for all statistical calculations. Significance values for t-tests between dietary conditions are shown above each set of bars and significance values for two-way ANOVA are shown above the graph, where applicable. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002668#pgen.1002668.s001" target="_blank">Figure S1</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002668#pgen.1002668.s006" target="_blank">Tables S1</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002668#pgen.1002668.s007" target="_blank">S2</a> for additional information.</p

Tissue-specific insulin signaling mediates female sexual attractiveness

<div><p>Individuals choose their mates so as to maximize reproductive success, and one important component of this choice is assessment of traits reflecting mate quality. Little is known about why specific traits are used for mate quality assessment nor about how they reflect it. We have previously shown that global manipulation of insulin signaling, a nutrient-sensing pathway governing investment in survival versus reproduction, affects female sexual attractiveness in the fruit fly, <i>Drosophila melanogaster</i>. Here we demonstrate that these effects on attractiveness derive from insulin signaling in the fat body and ovarian follicle cells, whose signals are integrated by pheromone-producing cells called oenocytes. Functional ovaries were required for global insulin signaling effects on attractiveness, and manipulations of insulin signaling specifically in late follicle cells recapitulated effects of global manipulations. Interestingly, modulation of insulin signaling in the fat body produced opposite effects on attractiveness, suggesting a competitive relationship with the ovary. Furthermore, all investigated tissue-specific insulin signaling manipulations that changed attractiveness also changed fecundity in the corresponding direction, pointing to insulin pathway activity as a reliable link between fecundity and attractiveness cues. The cues themselves, cuticular hydrocarbons, responded distinctly to fat body and follicle cell manipulations, indicating independent readouts of the pathway activity from these two tissues. Thus, here we describe a system in which female attractiveness results from an apparent connection between attractiveness cues and an organismal state of high fecundity, both of which are created by lowered insulin signaling in the fat body and increased insulin signaling in late follicle cells.</p></div

Insulin signaling in oenocytes does not affect attractiveness.

<p>An oenocyte-specific geneswitch Gal4 driver, <i>PromE800G</i>.<i>S</i>.<i>-Gal4</i>, was used to manipulate insulin signaling in these cells by causing RU486-dependent expression of insulin receptor (InR) for activation or of the phosphatase Pten for inhibition. The driver crossed to a standard laboratory strain (w^-) served as a control. Two-choice preference trials were then performed comparing females fed RU486 to those fed vehicle. P-values from Wilcoxon tests are reported. Box plot boundaries here and in other plots represent 99% confidence intervals around the mean.</p