Data and code from: Blood-derived dietary protein promotes sleep in the mosquito Aedes aegypti
Data files
Aug 05, 2026 version files 395.38 KB
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README.md
8.02 KB
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Source_Data_for_Figure_1.xlsx
17.04 KB
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Source_Data_for_Figure_2.xlsx
201.90 KB
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Source_Data_for_Figure_3_figure_supplement_1.xlsx
24.02 KB
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Source_Data_for_Figure_3.xlsx
24.67 KB
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Source_Data_for_Figure_4_figure_supplement_1.xlsx
12.72 KB
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Source_Data_for_Figure_4.xlsx
16.91 KB
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Source_Data_for_Figure_5.xlsx
26.38 KB
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Source_Data_for_Figure_6.xlsx
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Supplementary_file_1_MosquitoSleepAnalysis_v3_1_python3_script.py
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Supplementary_file_2_Statistical_analysis_Spreadsheet.xlsx
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Abstract
Sleep is a ubiquitous, yet highly variable, behavior across species. The duration and timing of sleep are influenced by ecological demands and dietary context. In the mosquito Aedes aegypti, a blood-feeding insect with specialized nutritional requirements, the relationship between feeding and sleep remains poorly understood. Here, we investigated how blood-derived dietary protein influences sleep regulation. Using postural analysis, videography, and arousal-threshold assays, we established that immobility bouts of ≥10 minutes reliably define sleep in Ae. aegypti. Mosquitoes lacking the circadian clock gene cycle still maintained daily sleep rhythms but exhibited reduced sleep duration and heightened overall activity. Infrared activity monitoring revealed that blood-fed females showed a marked increase in sleep beginning immediately after feeding and persisting for several days, accompanied by reduced locomotor activity. Notably, this sleep elevation lasted well beyond the cessation of previously reported host-seeking phases, raising the possibility of distinct phases of opportunistic versus targeted host pursuit. To determine the dietary basis of this effect, we tested mosquitoes fed a bovine serum albumin (BSA)–based diet. BSA feeding alone was sufficient to mimic the sleep-promoting and activity-reducing effects of blood, suggesting dietary protein is a major nutritional regulator. Moreover, RNAi-mediated knockdown of the leucokinin receptor (Lkr), which has previously been associated with fluid homeostasis and feeding behavior, resulted in enhanced sleep and reduced activity, implicating mosquito LK signaling in the modulation of postprandial sleep. Together, these findings demonstrate that blood-derived proteins drive sustained increases in sleep and reductions in locomotor activity in Ae. aegypti. This work positions Ae. aegypti as a model for dissecting nutrient-specific regulation of sleep and highlights potential adaptive functions of protein-induced quiescence, such as energy conservation and predator avoidance. More broadly, it provides insight into how specialized diets shape the neural and behavioral architecture of sleep.
Dataset DOI: 10.5061/dryad.1g1jwsvcm
Description of the data and file structure
This data includes source data for each figure (submitted as .xlsx files, one file corresponding to one figure), supplementary file 1 (Python coding script for sleep analysis of mosquitoes, Python3, v3.14.2), and supplementary file 2 (Statistical analysis spreadsheet).
Files and variables
File: Source_Data_for_Figure_1.xlsx
Description: A custom-designed postural recording and a modified Drosophila Arousal Tracking (DART) system were applied to collect data for Figure 1.
Variables
- Time interval of postural changes (min); Normalized response; Averaged sleep duration (min/h); ABL (min)
File: Source_Data_for_Figure_2.xlsx
Description: A custom-built infrared activity monitor (LAM10, Trikinetics, Waltham, MA, USA), which detects movement via infrared beam breaks across 3-board stacks, was used to record sleep and locomotor activity of mosquitoes for continuously 6–8 days under controlled environmental conditions. Total sleep duration, mean activity level, bout number and sleep duration, and other sleep architecture parameters were computed per individual using custom Python scripts, which was submitted as Supplementary file 1.
Variables
- Activity profile (beam-cross count per hour); Activity (beam-cross count); Sleep profile (min/h); Day or night sleep duration (min/h, averaged by hours of daytime or nighttime); P(Wake); P(Doze)
File: Source_Data_for_Figure_3_figure_supplement_1.xlsx
Description: A custom-built infrared activity monitor (LAM10, Trikinetics, Waltham, MA, USA), which detects movement via infrared beam breaks across 3-board stacks, was used to record sleep and locomotor activity of mosquitoes for continuously 6–8 days under controlled environmental conditions. Total sleep duration, mean activity level, bout number and sleep duration, and other sleep architecture parameters were computed per individual using custom Python scripts, which was submitted as Supplementary file 1.
Variables
- Total sleep (min/day); Day or night sleep duration (min/h, averaged by hours of daytime or nighttime); Daily activity (beam-cross count/day); Activity profile (beam-cross count/hour)
File: Source_Data_for_Figure_4_figure_supplement_1.xlsx
Description: Video tracking was performed using EthoVision XT 15 (Noldus Information Technology Inc., VA, USA). Individual mosquitoes were transferred into separate wells of a 6-well plate immediately after feeding as previously described in Drosophila. Videos were recorded continuously for 24 h (one LD cycle) at 15 frames/s using a USB webcam (LifeCam Studio 1080p HD Webcam, Microsoft). Video acquisition was performed in VirtualDub (v1.10.4). To permit recording during the dark phase while maintaining uniform illumination, the built-in IR-cut filter of the camera was removed and replaced with an infrared long-pass filter (Edmund Optics Worldwide). After acquisition, videos were imported into EthoVision XT 15 to extract x-y coordinate data for each mosquito across the full 24 h recording period. The instantaneous velocity of each tracked subject was calculated and recorded for every frame. Position and velocity data were exported and further analyzed using a custom Perl script (v5.10.0) and Microsoft Excel macros. To classify behavioral state, a velocity threshold of 0.4 mm/s was used, with velocities above 0.4 mm/s defined as wake/activity and velocities below 0.4 mm/s defined as doze/sleep state.
Variables
- Velocity profile (mm/s); Wake or sleep bout distribution profile and counts (total count per day); P(Wake); P(Doze)
File: Source_Data_for_Figure_3.xlsx
Description: A custom-built infrared activity monitor (LAM10, Trikinetics, Waltham, MA, USA), which detects movement via infrared beam breaks across 3-board stacks, was used to record sleep and locomotor activity of mosquitoes for continuously 6–8 days under controlled environmental conditions. Total sleep duration, mean activity level, bout number and sleep duration, and other sleep architecture parameters were computed per individual using custom Python scripts, which was submitted as Supplementary file 1.
Variables
- Sleep profile (min/hour); Total sleep (min/day); P(Wake); P(Doze); Waking activity
File: Source_Data_for_Figure_4.xlsx
Description: Video tracking was performed using EthoVision XT 15 (Noldus Information Technology Inc., VA, USA). Individual mosquitoes were transferred into separate wells of a 6-well plate immediately after feeding as previously described in Drosophila. Videos were recorded continuously for 24 h (one LD cycle) at 15 frames/s using a USB webcam (LifeCam Studio 1080p HD Webcam, Microsoft). Video acquisition was performed in VirtualDub (v1.10.4). To permit recording during the dark phase while maintaining uniform illumination, the built-in IR-cut filter of the camera was removed and replaced with an infrared long-pass filter (Edmund Optics Worldwide). After acquisition, videos were imported into EthoVision XT 15 to extract x-y coordinate data for each mosquito across the full 24 h recording period. The instantaneous velocity of each tracked subject was calculated and recorded for every frame. Position and velocity data were exported and further analyzed using a custom Perl script (v5.10.0) and Microsoft Excel macros. To classify behavioral state, a velocity threshold of 0.4 mm/s was used, with velocities above 0.4 mm/s defined as wake/activity and velocities below 0.4 mm/s defined as doze/sleep state.
Variables
- Sleep profile (min/hour); Total sleep (hour/day); Distance (m/day)
File: Source_Data_for_Figure_5.xlsx
Description: A custom-built infrared activity monitor (LAM10, Trikinetics, Waltham, MA, USA), which detects movement via infrared beam breaks across 3-board stacks, was used to record sleep and locomotor activity of mosquitoes for continuously 6–8 days under controlled environmental conditions. Total sleep duration, mean activity level, bout number and sleep duration, and other sleep architecture parameters were computed per individual using custom Python scripts, which was submitted as Supplementary file 1.
Variables
- Total sleep (min/day); Sleep profile (min/hour); P(Wake); P(Doze); Waking activity
File: Source_Data_for_Figure_6.xlsx
Description: Custom-built infrared activity monitors (LAM10, Trikinetics, Waltham, MA, USA), which detect movement via infrared beam breaks across 3-board stacks, was used to record sleep and locomotor activity of mosquitoes for continuously 6–8 days under controlled environmental conditions. Total sleep duration, mean activity level, bout number and sleep duration, and other sleep architecture parameters were computed per individual using custom Python scripts, which was submitted as Supplementary file 1.
Variables
- Total sleep (min/day); Sleep profile (min/hour); P(Wake); P(Doze); Waking activity
File: Supplementary_file_2_Statistical_analysis_Spreadsheet.xlsx
Description: A summary of all statistical analyses applied in this paper.
Variables
- Comparison (two objects compared); N (sample size, Group1 vs Group2); Statistical test (the statistical analysis applied); Test statistic (value of the test); df (degrees of freedom); Exact p value (exact p value of comparison); Significance (asterisks indicate the level of significance); Effect size (magnitude of the difference, reported when meaningful)
File: Supplementary_file_1_MosquitoSleepAnalysis_v3_1_python3_script.py
Description: Custom Python script used for analysis of total sleep duration, mean activity level, bout number and sleep duration, and other sleep architecture parameters in mosquitoes.
Code/software
python3 (v3.14.2)
Our study examines how feeding, especially blood-derived dietary protein, regulates sleep in the mosquito Aedes aegypti. To define and quantify sleep in this species, we combined several complementary behavioral approaches. First, we used postural analysis and videography to characterize the transition into a sleep-like state, identifying distinct posture changes associated with quiescence. We then validated this behavioral definition using a modified Drosophila Arousal Tracking (DART) system, in which individually housed mosquitoes were exposed to vibrational stimuli of increasing intensity while being video-recorded. Because responsiveness decreased as immobility duration increased, we established that inactivity bouts of at least 10 minutes reliably represent sleep in Ae. aegypti. To measure sleep and locomotor activity over longer periods, we employed custom-designed Drosophila infrared activity monitors (DAM) that continuously recorded beam-break events from individual mosquitoes for 6–8 days. Sleep architecture, activity levels, and bout structure were extracted from these data using custom analysis scripts. In addition, EthoVision video tracking was used to monitor fine-scale locomotor behavior immediately after feeding, based on x-y movement and velocity thresholds. Together, these methods provided both high-resolution short-term behavioral tracking and long-term quantification of sleep and activity. Using these assays, sleep of sugar-fed, blood-fed, and bovine serum albumin (BSA)-fed females were compared.