High-Resolution Positional Tracking for Long-Term Analysis of Drosophila Sleep and Locomotion Using the “Tracker” Program

Drosophila melanogaster has been used for decades in the study of circadian behavior, and more recently has become a popular model for the study of sleep. The classic method for monitoring fly activity involves counting the number of infrared beam crosses in individual small glass tubes. Incident recording methods such as this can measure gross locomotor activity, but they are unable to provide details about where the fly is located in space and do not detect small movements (i.e. anything less than half the enclosure size), which could lead to an overestimation of sleep and an inaccurate report of the behavior of the fly. This is especially problematic if the fly is awake, but is not moving distances that span the enclosure. Similarly, locomotor deficiencies could be incorrectly classified as sleep phenotypes. To address these issues, we have developed a locomotor tracking technique (the “Tracker” program) that records the exact location of a fly in real time. This allows for the detection of very small movements at any location within the tube. In addition to circadian locomotor activity, we are able to collect other information, such as distance, speed, food proximity, place preference, and multiple additional parameters that relate to sleep structure. Direct comparisons of incident recording and our motion tracking application using wild type and locomotor-deficient (CASK-β null) flies show that the increased temporal resolution in the data from the Tracker program can greatly affect the interpretation of the state of the fly. This is especially evident when a particular condition or genotype has strong effects on the behavior, and can provide a wealth of information previously unavailable to the investigator. The interaction of sleep with other behaviors can also be assessed directly in many cases with this method

The Drosophila Neuropeptides PDF and sNPF Have Opposing Electrophysiological and Molecular Effects on Central Neurons

Neuropeptides have widespread effects on behavior, but how these molecules alter the activity of their target cells is poorly understood. We employed a new model system in Drosophila melanogaster to assess the electrophysiological and molecular effects of neuropeptides, recording in situ from larval motor neurons, which transgenically express a receptor of choice. We focused on two neuropeptides, pigment-dispersing factor (PDF) and small neuropeptide F (sNPF), which play important roles in sleep/rhythms and feeding/metabolism. PDF treatment depolarized motor neurons expressing the PDF receptor (PDFR), increasing excitability. sNPF treatment had the opposite effect, hyperpolarizing neurons expressing the sNPF receptor (sNPFR). Live optical imaging using a genetically encoded fluorescence resonance energy transfer (FRET)-based sensor for cyclic AMP (cAMP) showed that PDF induced a large increase in cAMP, whereas sNPF caused a small but significant decrease in cAMP. Coexpression of pertussis toxin or RNAi interference to disrupt the G-protein Gαo blocked the electrophysiological responses to sNPF, showing that sNPFR acts via Gαo signaling. Using a fluorescent sensor for intracellular calcium, we observed that sNPF-induced hyperpolarization blocked spontaneous waves of activity propagating along the ventral nerve cord, demonstrating that the electrical effects of sNPF can cause profound changes in natural network activity in the brain. This new model system provides a platform for mechanistic analysis of how neuropeptides can affect target cells at the electrical and molecular level, allowing for predictions of how they regulate brain circuits that control behaviors such as sleep and feeding.Fil: Pírez, Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires; Argentina. Brandeis University. Department of Biology; Estados UnidosFil: Vecsey, Christopher G.. Brandeis University. Department of Biology; Estados UnidosFil: Griffith, Leslie C.. Brandeis University. Department of Biology; Estados Unido

Astrocyte-Derived Adenosine and A1 Receptor Activity Contribute to Sleep Loss-Induced Deficits in Hippocampal Synaptic Plasticity and Memory in Mice

Sleep deprivation (SD) can have a negative impact on cognitive function, but the mechanism(s) by which SD modulates memory remains unclear. We have previously shown that astrocyte-derived adenosine is a candidate molecule involved in the cognitive deficits following a brief period of SD (Halassa et al., 2009). In this study, we examined whether genetic disruption of soluble N-ethylmaleimide-sensitive factor attached protein (SNARE)-dependent exocytosis in astrocytes (dnSNARE mice) or pharmacological blockade of A1 receptor signaling using an adenosine A1 receptor (A1R) antagonist, 8-cyclopentyl-1,3-dimethylxanthine (CPT), could prevent the negative effects of 6 h of SD on hippocampal late-phase long-term potentiation (L-LTP) and hippocampus-dependent spatial object recognition memory. We found that SD impaired L-LTP in wild-type mice but not in dnSNARE mice. Similarly, this deficit in L-LTP resulting from SD was prevented by a chronic infusion of CPT. Consistent with these results, we found that hippocampus-dependent memory deficits produced by SD were rescued in dnSNARE mice and CPT-treated mice. These data provide the first evidence that astrocytic ATP and adenosine A1R activity contribute to the effects of SD on hippocampal synaptic plasticity and hippocampus-dependent memory, and suggest a new therapeutic target to reverse the hippocampus-related cognitive deficits induced by sleep loss.United States. National Institutes of Health (P50 Grant AG017628)United States. National Institutes of Health (Training Grant HL07953)United States. National Institutes of Health (Grant R01 NS037585)United States. National Institutes of Health (Grant R01 NS043142

Control flies move much greater distances than <i>CASK-β</i> mutant flies and have a more dynamic locomotor pattern.

<p>Distance was averaged for each genotype and binned into minutes. (A) Female wild type control flies (Control, N = 29) had stereotypic locomotion across the day. (B) Female <i>CASK-β</i> mutant flies (<i>CASK-β</i>, N = 30) had a locomotor profile that was different from control. Nighttime movements were very low, with no morning light anticipation. (C) Speed plot for each genotype. (Green = control, Pink = <i>CASK-β</i>). The speed of the mutant fly followed closely with control only in the hours leading up to lights out. (D) All flies did not behave exactly the same all the time. Behavioral state was intrinsic to the individual. % Activity shows that control flies alternated their sleep/wake activity so that at least one fly was active all throughout the day. There was an overall sleep rhythm, but some individuals were awake while others were asleep. By contrast, there were long periods where all <i>CASK-β</i> mutant flies were not moving at night.</p

<i>CASK-β</i> mutant flies have different location preferences from controls when both asleep and awake.

<p>Location and Proportion of Occupancy plots show where the wild type control (Control, N = 29) and <i>CASK-β</i> mutant (<i>CASK-β</i>, N = 30) flies were when they were awake and asleep. (A, B) Female wild type control flies sleep near the food during siesta and at night and rarely venture to the half of the tube away from the food. (C) Wild type control flies show preference for being at the food when awake. The flies moved throughout the entire length of the tube, pausing at either end. This location preference coincides with peak activity times of day. (D) When awake, Control flies spent ∼24% of their time at the food. The rest of their waking time was dispersed throughout the tube. (E, F) Female <i>CASK-β</i> mutants sleep at different times and locations than the controls and do not have siesta. When the <i>CASK-β</i> mutants did sleep, they were dispersed throughout sections 4–9 of the tube. (G, H) When awake, the <i>CASK-β</i> mutants showed little location consolidation across the day as compared to control and spent the majority of their waking time at the food (∼22%). The rest of their time was spent dispersed throughout the rest of the tube, with some preference for the far end.</p

Data capture comparison for <i>CS</i> flies shows how restricting Tracker output conforms location data to DAM-like outputs.

<p>Sleep parameters from DAM and Tracker program analysis for <i>CS</i> wild type flies (N = 8). Data from 3–5 day old male flies were collected for three consecutive days in a 25<b>°</b>C incubator with 12-hr LD cycle. Same letters indicate no significant difference between methods using Tukey HSD (<i>P</i><0.05). NS = No Significant difference. Individual statistical tests were performed on each time comparison group (24, LP, DP). The order of letters represents the order of the analysis: DAM, Virtual Beam, Track 20%, Track 50%, Track 100% of the Fly Body Length (FBL). (A) Sleep profile for flies averaged for three days. (B, C, D, E, F) DAM data matches the Virtual Beam for all comparisons. Track 100% FBL matched DAM for all comparisons as well, but was also not significantly different from 50% FBL in many comparisons. 20% FBL was always significantly different from DAM. The 50% FBL showed significant differences from DAM in many comparisons, while still showing similarity, indicating that this resolution was most effective at capturing both DAM-insensitive and biologically meaningful locomotion.</p

List of ANOVA results for Figure 3.

<p>The ANOVA table shows that at least one factor was significantly different for each sleep parameter. Results from individual Tukey pairwise comparisons are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037250#pone-0037250-g003" target="_blank">Figure 3</a>. All analyses were significant at DF_(3,115), <i>P</i><0.05, DAM Control (N = 30), DAM <i>CASK-β</i> (N = 30), Track Control (N = 29), Track <i>CASK-β</i> (N = 30).</p