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Behavior

High-Throughput Small Molecule Drug Screening For Age-Related Sleep Disorders Using Drosophila melanogaster

Published: October 20, 2023 doi: 10.3791/65787

Summary

Presented is a protocol for high-throughput drug screening to improve sleep by monitoring the sleep behavior of fruit flies in an elderly Drosophila model.

Abstract

Sleep, an essential component of health and overall well-being, often presents challenges for older individuals who frequently experience sleep disorders characterized by shortened sleep duration and fragmented patterns. These sleep disruptions also correlate with an increased risk of various illnesses in the elderly, including diabetes, cardiovascular diseases, and psychological disorders. Unfortunately, existing drugs for sleep disorders are associated with significant side effects such as cognitive impairment and addiction. Consequently, the development of new, safer, and more effective sleep disorder medications is urgently needed. However, the high cost and lengthy experimental duration of current drug screening methods remain limiting factors.

This protocol describes a cost-effective and high-throughput screening method that utilizes Drosophila melanogaster, a species with a highly conserved sleep regulation mechanism compared to mammals, making it an ideal model for studying sleep disorders in the elderly. By administering various small compounds to aged flies, we can assess their effects on sleep disorders. The sleep behaviors of these flies are recorded using an infrared monitoring device and analyzed with the open-source data package Sleep and Circadian Analysis MATLAB Program 2020 (SCAMP2020). This protocol offers a low-cost, reproducible, and efficient screening approach for sleep regulation. Fruit flies, due to their short life cycle, low husbandry cost, and ease of handling, serve as excellent subjects for this method. As an illustration, Reserpine, one of the tested drugs, demonstrated the ability to promote sleep duration in elderly flies, highlighting the effectiveness of this protocol.

Introduction

Sleep, one of the essential behaviors necessary for human survival, is characterized by two main states: rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep1. NREM sleep comprises three stages: N1 (the transition between wakefulness and sleep), N2 (light sleep), and N3 (deep sleep, slow wave sleep), representing the progression from wakefulness to deep sleep1. Sleep plays a crucial role in both physical and mental health2. However, aging reduces total sleep duration, sleep efficiency, slow-wave sleep percentage, and REM sleep percentage in adults3. Older individuals tend to spend more time in light sleep compared to slow-wave sleep, making them more sensitive to nocturnal awakenings. As the number of awakenings increases, average sleep time decreases, resulting in a fragmented sleep pattern in the elderly, which may be associated with excessive excitation of Hcrt neurons in mice4. Additionally, age-related declines in circadian mechanisms contribute to an earlier shift in sleep duration5,6. In combination with physical illness, psychological stress, environmental factors, and medication use, these factors make older adults more susceptible to sleep disorders, such as insomnia, REM sleep behavior disorder, narcolepsy, periodic leg movements, restless legs syndrome, and sleep-disordered breathing7,8.

Epidemiological studies have shown that sleep disorders are closely linked to chronic diseases in the elderly9, including depression10, cardiovascular disease11, and dementia12. Addressing sleep disorders plays a crucial role in improving and treating chronic diseases and enhancing the quality of life for older adults. Currently, patients primarily rely on drugs such as benzodiazepines, non-benzodiazepines, and melatonin receptor agonists to enhance sleep quality13. However, benzodiazepines can lead to downregulation of receptors and dependence after long-term use, causing severe withdrawal symptoms upon discontinuation14,15. Non-benzodiazepine drugs also carry risks, including dementia16, fractures17, and cancer18. The commonly used melatonin receptor agonist, ramelteon, reduces sleep latency but does not increase sleep duration and has hepatic function-related concerns due to extensive first-pass elimination19. Agomelatine, a melatonin receptor agonist and serotonin receptor antagonist, improves depression-related insomnia but also poses a risk of liver damage20. Consequently, there is an urgent need for safer drugs to treat or alleviate sleep disorders. However, current drug screening strategies, based on molecular and cellular experiments combined with automated systems and computer analysis, are expensive and time-consuming21. Structure-based drug design strategies, relying on receptor structure and properties, require a clear understanding of receptor three-dimensional structure and lack predictive capabilities for drug effects22.

In 2000, based on the sleep criteria proposed by Campbell and Tobler in 198423, researchers established simple animal models to study sleep24, including Drosophila melanogaster, which exhibited sleep-like states25,26. Despite anatomical differences between Drosophila and humans, many neurochemical components and signaling pathways regulating sleep in Drosophila are conserved in mammalian sleep, facilitating the study of human neurological diseases27,28. Drosophila is also extensively used in circadian rhythm studies, despite differences in core oscillators between flies and mammals29,30,31. Therefore, Drosophila serves as a valuable model organism for studying sleep behavior and conducting sleep-related drug screening.

This study proposes a cost-effective and simple phenotype-based approach for screening small-molecule drugs to treat sleep disorders using aged flies. Sleep regulation in Drosophila is highly conserved25, and the decline in sleep observed with age may be reversible through drug administration. Thus, this sleep phenotype-based screening method can intuitively reflect drug efficacy. We feed the flies with a mixture of the drug under investigation and food, monitor and record sleep behavior using the Drosophila Activity Monitor (DAM)32, and analyze the acquired data using the open-source SCAMP2020 data package in MATLAB (Figure1). Statistical analysis is performed using statistics and graphing software (see Table of Materials). As an example, we demonstrate the effectiveness of this protocol by presenting experimental data on Reserpine, a small-molecule inhibitor of the vesicular monoamine transporter reported to increase sleep33. This protocol provides a valuable approach to identify drugs for treating age-related sleep problems.

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Protocol

This protocol uses the 30-day-old w1118 flies from the Bloomington Drosophila Stock Center (BDSC_3605, see Table of Materials).

1. Preparation of the aged fruit flies

  1. Food preparation
    1. Prepare standard corn starch culture medium by mixing 50 g/L cornflakes, 110 g/L sugar, 5 g/L agar, and 25 g/L yeast. Heat the cornflakes and yeast with water to gelatinize, and then fully dissolve all the substances.
    2. When the medium cools to 50-60 °C, add 6 mL/L of propionic acid and promptly pack them into culture bottles.
  2. Fly rearing and preparation of aged flies
    1. Breed the fly strain w1118 in bottles containing a standard cornstarch culture medium and put bottles in a constant temperature incubator at 25° C, 68% relative humidity, 500-1000 lux lighting conditions, and a 12 h:12 h light: dark cycle.
    2. Transfer flies into a new bottle every 7 days according to the growth cycle of flies, keeping the age of individuals in the same bottle consistent.
    3. Collect the new batch of flies that hatch from the original bottle 3 days after transferring them and put them into a new bottle. Following the principle of changing the bottle every 7 days, they will be cultured until around 30 days old.

2. Preparation of medicinal food and glass tubes for monitoring

NOTE: The procedure for glass tube preparation follows the work of Jin et al. with modifications34.

  1. Cleaning and drying of glass tubes
    1. Place the glass tube (5 mm in diameter x 65 mm in length, see Table of Materials) into a large beaker, soak it, and boil it with double distilled water for 20 min. Repeat 3 times.
    2. Remove and bundle the glass tube, rinse the inside with double distilled water 3-5 times, and place it in an oven for drying.
  2. Preparation of simple culture medium (100 mL)
    1. Dissolve 1.5 g agar and 5 g sucrose in double distilled water, heat, and concentrate to 100 mL.
    2. Add 600 µL propionic acid when the medium cools to about 70 °C, preventing it from solidifying using a constant temperature water bath.
    3. Add approximately 4 mL of simple medium and Reserpine (see Table of Materials) into a 10 mL small beaker until the drug reaches 20 µM or 50 µM. Add dimethyl sulfoxide (DMSO) to the concentration of 0.2% in the negative control group.
  3. Preparation of the glass tubes containing medicine
    1. To facilitate the flow of the medium, carefully insert a suitable length of glass tube into a small beaker. The medium will naturally enter the glass tube due to atmospheric pressure.
    2. Pull out the glass tube when the culture medium is completely solidified and wipe the outer wall to obtain a monitoring glass tube with a culture medium containing drugs at one end.
    3. Heat the solid paraffin in a beaker until it melts at 70 °C, put the end of the glass tube close to the food into the paraffin liquid for about 5 mm, and quickly remove it. Wait for the paraffin to solidify to seal the food end of the glass tube.

3. Experimental design and fly treatment

  1. Design the experiment for the fly treatment following Table 1.

4. Drosophila assembly and sleep monitoring

NOTE: The procedure for Drosophila assembly follows the work of Jin et al.34 with modifications.

  1. Anesthetize flies with CO2 gas, put them into paraffin-sealed glass tubes (one per tube), and block the non-food end with an absorbent cotton ball to prevent flies from escaping and ensure air circulation.
  2. Load tubes onto the infrared monitor for monitoring them.
    1. Assemble the glass tubes containing flies onto an infrared monitor in the same direction, and record the monitor number and hole number corresponding to each drug.
    2. Adjust the alignment of each tube, and make the infrared rays pass vertically through the center of the fly's activity range.
    3. Place the monitor inside a 25 °C incubator located in the fly sleep darkroom, following the specified settings: 25 °C temperature, Zeitgeber 12 (ZT12) (equivalent to local time 08:00 p.m.), and ZT24 (equivalent to local time 08:00 a.m.). This setup ensures that the flies experience alternating periods of 12 h of light and darkness.
      NOTE: Try not to open the door until monitoring data collection is complete to maintain a stable environment in the incubator during monitoring.
    4. Start monitoring using the DAM2 system (see Table of Materials).
    5. Once the monitoring is complete, download the collected data in .txt format from the system.

5. Data processing

NOTE: The data processing using the DAM system, DAMFileScan107, and SCAMP was performed according to the instructions on their official websites (see Table of Materials).

  1. Import the above txt file into DAMFileScan107 software for scanning and divide it as needed to obtain sleep data.
    1. Set the starting time of segmentation data to 8:01 (1 min segmentation) or 8:00 (30 min segmentation) on the third morning after starting the monitors, and the termination time is 8:00 a.m. three days after the starting time (Figure 2A1).
      NOTE: Flies must adapt to the monitoring environment for at least one day. So, one can set the split data start time to 8 a.m. on the third day after the monitor begins.
    2. Split the data at intervals of 1 min and 30 min. Change the option "Bin Length" to 1 minute, change the option "Output File Type" to Channel files, rename, and output. The 30 min data segmentation method is the same as above (Figure 2A2-5).
      NOTE: When performing data segmentation at 1 min and 30 min intervals, the final renaming of the two files should be consistent; otherwise, it may be unreadable during subsequent Matlab processing. If necessary, the file name can be changed after output to facilitate differentiation.
  2. Data processing using SCAMP2020
    1. Open the program package SCAMP2020 in Matlab, and double-click on Vecsey Sleep and Circadian Analysis MATLAB Program (SCAMP) (Figure 2B).
    2. Add its subfolder "Vecsey SCAMP Scripts" to the path, find the file "scamp.m" in that folder, and run it. In the following pop-up window, select the process 1 min and 30 min folders in sequence (Figure 2C,D).
    3. Select a monitor, click on Load individual See Plots to preview (Figure 3A1), and check the image that appears. Uncheck the corresponding channel of dead flies (Figure 3A2, Figure 3B).
    4. Repeat the above steps to check all monitors.
    5. Rename each channel in each monitor based on the corresponding drug to be tested (Figure 3A3), select all monitors, and click on ANALYZE Selected Data for analysis (Figure 3A4).
    6. Default to the selected option, click on Analyze for Chosen Bin, check Export Data, and finally click on GRAPH 30 min Data Types for All Days for Selected Groups and EXPORT All Data to output the results (Figure 3C).
  3. Select the file named s30 from the CSV file, find the corresponding mean value and standard error data for each monitor, back up it to Excel for modification and adjustment, and paste it into GraphPad Prism (see Table of Materials) to draw a sleep status diagram (Figure 4A,B).
  4. Find the file named "stdur" and calculate the average values of daytime, nighttime, and total sleep for each fly within three days (Figure 4A,C). Paste the data into the Prism software to complete the difference test and draw a graph.

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Representative Results

Reserpine is a small-molecule inhibitor of the vesicular monoamine transporter (VMAT), which inhibits the reuptake of monoamines into presynaptic vesicles, leading to increased sleep33. The sleep-promoting effects of Reserpine were examined in 30-day-old flies, with the control group being fed solely with the solvent dimethyl sulfoxide (DMSO). In the Reserpine group, older flies exhibited significantly increased sleep during both the day and night compared to the DMSO group. Figure 5A,E illustrate the sleep patterns of the Reserpine and DMSO flies over three consecutive days, while Figure 5B-D and Figure 5F-H show the results of the differential test on the sleep data. To eliminate the possibility of the drug acting exclusively on one sex, the experiments were repeated using male flies. Different concentrations of Reserpine, 20 µM, and 50 µM, were administered, demonstrating a positive correlation between Reserpine concentration and the promotion of sleep.

Figure 1
Figure 1: Small molecular drug screening for age-related sleep disorders experimental process. Elderly flies were placed in a small glass tube with food containing the drugs to be tested. Sleep patterns were continuously monitored for three days using the DAM System. The acquired data were imported into a computer for processing, visualization, and analysis, leading to conclusions. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Scanning and division of data. (A) Data selection and scanning, followed by sequential temporal segmentation. (B) Location of the "Vecsey Sleep and Circadian Analysis MATLAB Program (SCAMP)" folder. (C) Addition of the subfolder "Vecsey SCAMP Scripts" to the path. (D) Location of the file "scamp.m". Please click here to view a larger version of this figure.

Figure 3
Figure 3: Selection and processing of sleep data. (A) Preview of fly sleeping conditions, unchecking the channel for dead flies, and grouping and analyzing selected data. (B) Preview of Drosophila sleep, where a uniform blue rectangle indicates active sleep, while a certain moment of a uniform blue rectangle suggests the fly is dead. Dead flies are marked with red rectangles. (C) Analysis and output of selected data. Please click here to view a larger version of this figure.

Figure 4
Figure 4: Results of sleep data analysis. (A) Selection of the s30 and stdur files from the CSV file. (B) The average value and standard error of the mean (SEM) of sleep for each group in "s30.csv". (C) Values of daytime (Bin1, Bin3, Bin5), nighttime (Bin2, Bin4, Bin6), and total sleep for each fly within three days in "stdur.csv". Please click here to view a larger version of this figure.

Figure 5
Figure 5: Sleep conditions of aged flies treated with Reserpine. (A) Schematic representation of sleep time within 3 days in aged females fed 0.2% DMSO, 20 µM Reserpine, and 50 µM Reserpine. (B-D) Quantitative analysis of the average daytime, nighttime, and total sleep time within 3 days with or without drugs. The results demonstrate a significant increase in sleep time in aged females fed Reserpine. N = 8 for each group, One-way ANOVA, **p < 0.01, ***p < 0.001. (E) Schematic representation of sleep time within 3 days in aged males fed 0.2% DMSO, 20 µM Reserpine, and 50 µM Reserpine. (F-H) Quantitative analysis of the average daytime, nighttime, and total sleep time within 3 days with or without drugs. The results indicate that sleep time increased in males fed Reserpine. n = 16 for each group, One-way ANOVA, *p < 0.05, **p < 0.01. Please click here to view a larger version of this figure.

Figure 6
Figure 6: Comparison of sleep duration between young and old flies. (A) Schematic diagram illustrating the monitoring of sleep duration over 3 days in young and old males. (B-D) Quantitative analysis of the average daytime, nighttime, and total sleep time over 3 days in young and old males revealed no significant difference. n = 32 for each group, unpaired t-test, n.s., not significant. (E) Schematic monitoring of sleep duration over 3 days in young and old females. (F-H) Quantitative analysis of the average daytime, nighttime, and total sleep time over 3 days in young and old females demonstrated a significant decrease in daytime, nighttime, and total sleep time in old females compared to young females. n = 32 for each group, unpaired t-test, ****p < 0.0001. Please click here to view a larger version of this figure.

Group Study group Treatment Age and sex of flies Numbers of flies
Equation 1 Normal controls 4 mL simple culture medium with 0.2% DMSO for 4 days 30 days males/females 16 flies per group
Equation 2 Low-dose drug test  Equation 6 4 mL simple culture medium with 20 μM reserpine for 4 days 30 days females 16 flies per group
Equation 3 High-dose drug test  Equation 6 4 mL simple culture medium with 50 μM reserpine for 4 days 30 days females 16 flies per group
Equation 4 Low-dose drug test  Equation 7 4 mL simple culture medium with 20 μM reserpine for 4 days 30 days males 16 flies per group
Equation 5 High-dose drug test  Equation 7 4 mL simple culture medium with 50 μM reserpine for 4 days 30 days males 16 flies per group

Table 1: Experimental design for the fly treatment.

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Discussion

The described method is suitable for rapidly screening small and medium-sized sleep drugs. Currently, most mainstream high-throughput drug screening methods are based on biochemical and cellular levels. For example, the structure and properties of the receptor are examined to search for specific ligands that can bind to it22. Another approach involves analyzing the binding mode and strength of molecular fragments of selected drugs using Nuclear Magnetic Resonance (NMR) with mass spectrometry35. However, these methods often have a relatively high screening error rate, and the drugs selected through them often show no effect in animal or clinical experiments. The efficacy of drugs in the body is influenced by various factors, such as drug absorption, distribution, metabolism, and excretion, leading to a high rate of false screening. In contrast, although our proposed method has a smaller screening scale compared to high-throughput methods, it offers a more straightforward and cost-effective approach by directly observing drug effects on phenotypes. This demonstrates the potential of using the Drosophila model for effective drug screening and identification of drug targets.

Drosophila possesses a conserved sleep regulation mechanism and exhibits sleep disturbances associated with aging. We observed that the sleep duration of 30-day-old female flies was significantly shorter than that of 7-day-old flies, while the sleep duration of 30-day-old male flies did not differ significantly from that of 7-day-old flies (Figure 6). Consequently, 30-day-old female flies were selected for the current experiments. The screening process in multiple rounds was conducted to minimize accidental factor interference. The drug concentration in the first round was set at 20 µM to avoid toxic side effects that could lead to fly mortality. In the second screening round, the drug concentration was increased to 50 µM to assess the effects of the drug at different concentrations. Drugs selected from the second round were administered to male flies at both 20 µM and 50 µM to evaluate sex differences in drug effects. This allowed one to screen for drugs that consistently demonstrated sleep-related effects. For instance, Reserpine has previously been shown to increase sleep in adult flies aged 4-6 days31. We successfully replicated this result in our model using older flies, where older females showed a significant increase in sleep after being administered Reserpine (Figure 5).

DMSO was used to dissolve the drugs, but its potential toxicity should be considered. Previous studies have shown that concentrations of 0.1% to 0.25% DMSO in the culture medium do not harm rat hair cells within 24 h, while concentrations of 0.5% to 6% significantly increase cell death36. Similarly, it has been found that DMSO concentrations of 0.1% or less do not affect the expression of key drug metabolism-related enzymes or transporters in human hepatocytes. Still, higher concentrations can induce changes in expression37. However, it should be noted that 0.1% DMSO has been found to significantly affect the lifespan of female flies but not males38. Additionally, intraperitoneal administration of 15% and 20% DMSO has been shown to interfere with sleep in rats39. To mitigate the potential toxicity of DMSO, we kept its concentration below 0.2%.

Currently, there are two main methods used to characterize the behavior of Drosophila. One method is based on video analysis, which provides a wealth of behavioral parameters, including fly position, speed, and subtle movements of body parts. The other method is based on infrared beam fracture, such as the DAM system.40. However, it is important to note that certain video analysis tools like PySolo are designed for studying multiple single-resident flies, limiting the number of flies that can be placed under a camera41. Other tools like C-trax42 and JAABA43 can perform population tracking but are computationally expensive and time-consuming. For high-throughput screening, capturing the overall sleep duration of flies is usually sufficient, and precise movement parameters are not necessary. Therefore, the widely used and highly scalable method based on infrared beam fracture is preferred. However, this method also has its limitations. For instance, if flies only move at one end of the tube without interrupting the infrared beam, the system may mistakenly record it as sleep, leading to an overestimation of sleep44. Additionally, it is important to carefully test the motility of the fly strain before using it in screening to avoid unintended influences.

Here are some helpful tips for a successful setup: (1) To prevent food from sticking to the glass tube when removing it from the small beaker after solidification, one can try inserting the glass tube vertically into the bottom of the small beaker before the food solidifies. Gently pulling the glass tube back and forth, tapping the bottom of the beaker to allow air to enter, slowly rotating the beaker to remove all the food and the glass tube, and then carefully wiping off any remaining food on the outer wall of the glass tube can be effective. (2) When sealing the food end of the glass tube with paraffin film, it is recommended to use a water bath to slowly heat the film until the paraffin melts. This approach helps avoid the problem of the medicinal food splashing violently at high temperatures and contaminating the paraffin film. Alternatively, one can use small plastic caps for sealing, but ensure that air may enter during sealing, causing the food to push up overall. (3) It is worth considering that some potent sleep-promoting drugs may initially lead to the incorrect judgment of tested flies as dead. To overcome this issue, it is recommended to set a concentration gradient, allowing exploration of the optimal drug concentration and repeating the experiment. (4) Take into account that the odor of the drug may influence the amount of food consumed by the flies and their intake of the drug, potentially affecting the accuracy of the experimental results. Therefore, it can be beneficial to extend the duration of the experiment appropriately, ensuring that flies have ample time to consume as much drug as possible and enhancing the accumulation effect of the drug. (5) For data processing, while many universities and institutes have access to Matlab for public use, there are lower-cost alternatives available for individuals or research institutions that have not yet purchased the program. One recommended option is ShinyR-DAM v3.1 «Refresh»45.

In conclusion, we have developed a step-by-step procedure for screening drugs to treat sleep disorders. Using an older fly model exhibiting a phenotype of shorter sleep duration, the efficacy of Reserpine in increasing sleep duration in older female flies is validated. This method offers a new approach to drug screening with significant application potential and serves as a foundation for further drug research. While drug effects are assessed based on phenotypes, the underlying mechanism of drug action remains unknown. Further studies will be conducted to investigate the pathology of sleep disorders and the molecular regulation of sleep, thereby shedding light on the pharmacological mechanisms involved. Although the circadian machinery in Drosophila bears similarities to human oscillators, differences in sleep control mechanisms between humans and flies should not be overlooked. This protocol provides a basic framework for drug screening for sleep disorders. However, future research will determine whether any of the screened drugs can be utilized for clinical treatment, as well as elucidate their mechanisms of action.

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Disclosures

The authors declare no competing interests.

Acknowledgments

We thank Prof. Junhai Han's lab members for their discussion and comments. This work was supported by the National Natural Science Foundation of China 32170970 to Y.T and the "Cyanine Blue Project" of Jiangsu Province to Z.C.Z.

Materials

Name Company Catalog Number Comments
Ager BIOFROXX 8211KG001
Artificial Climate Box PRANDT PRX-1000A official website:https://www.nbplt17.com/PLTXBS-Products-20643427/
DAM2 Drosophila Activity Monitor TriKineics DAM2 official website:https://www.trikinetics.com/
DAM2system TriKineics version:v3.03 official website:https://www.trikinetics.com/
DAMFileScan TriKineics version:1.0.7.0 official website:https://www.trikinetics.com/
Dimethyl Sulfoxide SIGMA 276855
Drosophila Activity Monitoring Incubator Tritech Research DT2-CIRC-TK official website:https://www.tritechresearch.com/DT2-CIRC-TK.html
Drosophila Bottles Biologix 51-17720 official website:http://biologixgroup.com/goods.php?id=48
Drosophila: w1118 Bloomington Drosophila Stock Center  BDSC_3605
Excel Microsoft version:Excel 2016 official website:https://www.microsoftstore.com.cn/software/office/excel
Glass tubes TriKinetics PPT5x65 official website:https://www.trikinetics.com/
MATLABR2022b MathWorks version:9.13.0.2049777 official website:https://ww2.mathworks.cn/products/matlab.html
Prism GraphPad Version:Prism 8.0.1 official website:https://www.graphpad.com/features
Reserpine MACKLIN R817202-1g
Saccharose SIGMA 1245GR500
SCAMP Vecsey Lab N/A official website:https://academics.skidmore.edu/blogs/cvecsey/

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References

  1. Le Bon, O. Relationships between REM and NREM in the NREM-REM sleep cycle: a review on competing concepts. Sleep Medicine. 70, 6-16 (2020).
  2. Krueger, J. M., Frank, M. G., Wisor, J. P., Roy, S. Sleep function: Toward elucidating an enigma. Sleep Medicine Reviews. 28, 46-54 (2016).
  3. Ohayon, M. M., Carskadon, M. A., Guilleminault, C., Vitiello, M. V. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep. 27 (7), 1255-1273 (2004).
  4. Li, S. B., et al. Hyperexcitable arousal circuits drive sleep instability during aging. Science. 375 (6583), eabh3021 (2022).
  5. Rodriguez, J. C., Dzierzewski, J. M., Alessi, C. A. Sleep problems in the elderly. Medical Clinics of North America. 99 (2), 431-439 (2015).
  6. Gulia, K. K., Kumar, V. M. Sleep disorders in the elderly: a growing challenge. Psychogeriatrics. 18 (3), 155-165 (2018).
  7. Wolkove, N., Elkholy, O., Baltzan, M., Palayew, M. Sleep and aging: 1. Sleep disorders commonly found in older people. Canadian Medical Association Journal. 176 (9), 1299-1304 (2007).
  8. Suzuki, K., Miyamoto, M., Hirata, K. Sleep disorders in the elderly: Diagnosis and management. Journal of General and Family Medicine. 18 (2), 61-71 (2017).
  9. Foley, D. J., et al. Sleep complaints among elderly persons - an epidemiologic-study of 3 communities. Sleep. 18 (6), 425-432 (1995).
  10. Yu, D. S. Insomnia Severity Index: psychometric properties with Chinese community-dwelling older people. Journal of Advanced Nursing. 66 (10), 2350-2359 (2010).
  11. Hoevenaar-Blom, M. P., Spijkerman, A. M., Kromhout, D., van den Berg, J. F., Verschuren, W. M. Sleep duration and sleep quality in relation to 12-year cardiovascular disease incidence: the MORGEN study. Sleep. 34 (11), 1487-1492 (2011).
  12. Rebok, G. W., Rovner, B. W., Folstein, M. F. Sleep disturbance and Alzheimer's disease: relationship to behavioral problems. Aging (Milano). 3 (2), 193-196 (1991).
  13. Schroeck, J. L., et al. Review of safety and efficacy of sleep medicines in older adults. Clinical Therapeutics. 38 (11), 2340-2372 (2016).
  14. Pericic, D., Strac, D. S., Jembrek, M. J., Vlainic, J. Allosteric uncoupling and up-regulation of benzodiazepine and GABA recognition sites following chronic diazepam treatment of HEK 293 cells stably transfected with alpha1beta2gamma2S subunits of GABA (A) receptors. Naunyn-Schmiedeberg's Archives of Pharmacology. 375 (3), 177-187 (2007).
  15. Lader, M. History of benzodiazepine dependence. Journal of Substance Abuse Treatment. 8 (1-2), 53-59 (1991).
  16. Chen, P. L., Lee, W. J., Sun, W. Z., Oyang, Y. J., Fuh, J. L. Risk of dementia in patients with insomnia and long-term use of hypnotics: a population-based retrospective cohort study. Plos One. 7 (11), e49113 (2012).
  17. Kang, D. Y., et al. Zolpidem use and risk of fracture in elderly insomnia patients. Journal of Preventive Medicine and Public Health. 45 (4), 219-226 (2012).
  18. Kao, C. H., et al. Relationship of zolpidem and cancer risk: a Taiwanese population-based cohort study. Mayo Clinic Protocols. 87 (5), 430-436 (2012).
  19. Sateia, M. J., Kirby-Long, P., Taylor, J. L. Efficacy and clinical safety of ramelteon: an evidence-based review. Sleep Medicine Reviews. 12 (4), 319-332 (2008).
  20. Friedrich, M. E., et al. Drug-induced liver injury during antidepressant treatment: results of amsp, a drug surveillance program. The International Journal of Neuropsychopharmacology. 19 (4), pyv126 (2016).
  21. Entzeroth, M., Flotow, H., Condron, P. Overview of high-throughput screening. Current Protocols in Pharmacology. Chapter 9, (2009).
  22. Ferreira, L. G., Dos Santos, R. N., Oliva, G., Andricopulo, A. D. Molecular docking and structure-based drug design strategies. Molecules. 20 (7), 13384-13421 (2015).
  23. Campbell, S. S., Tobler, I. Animal sleep - a review of sleep duration across phylogeny. Neuroscience and Biobehavioral Reviews. 8 (3), 269-300 (1984).
  24. Hendricks, J. C., Sehgal, A., Pack, A. I. The need for a simple animal model to understand sleep. Progress in Neurobiology. 61 (4), 339-351 (2000).
  25. Hendricks, J. C., et al. Rest in Drosophila is a sleep-like state. Neuron. 25 (1), 129-138 (2000).
  26. Shaw, P. J., Cirelli, C., Greenspan, R. J., Tononi, G. Correlates of sleep and waking in Drosophila melanogaster. Science. 287 (5459), 1834-1837 (2000).
  27. Ly, S., Pack, A. I., Naidoo, N. The neurobiological basis of sleep: Insights from Drosophila. Neuroscience & Biobehavioral Reviews. 87, 67-86 (2018).
  28. Jeibmann, A., Paulus, W. Drosophila melanogaster as a model organism of brain diseases. International Journal of Molecular Sciences. 10 (2), 407-440 (2009).
  29. Morse, D., Sassone-Corsi, P. Time after time: inputs to and outputs from the mammalian circadian oscillators. Trends in Neuroscience. 25 (12), 632-637 (2002).
  30. De Nobrega, A. K., Lyons, L. C. Drosophila: an emergent model for delineating interactions between the circadian clock and drugs of abuse. Neural Plasticity. 2017, 4723836 (2017).
  31. Reppert, S. M., Weaver, D. R. Coordination of circadian timing in mammals. Nature. 418 (6901), 935-941 (2002).
  32. Koudounas, S., Green, E. W., Clancy, D. Reliability and variability of sleep and activity as biomarkers of ageing in Drosophila. Biogerontology. 13 (5), 489-499 (2012).
  33. Nall, A. H., Sehgal, A. Small-molecule screen in adult Drosophila identifies VMAT as a regulator of sleep. Journal of Neuroscience. 33 (19), 8534-8464 (2013).
  34. Jin, X., Gu, P., Han, J. Protocol for Drosophila sleep deprivation using single-chip board. STAR Protocols. 2 (4), 100827 (2021).
  35. Kashyap, A., Singh, P. K., Silakari, O. Counting on fragment based drug design approach for drug discovery. Current Topics in Medicinal Chemistry. 18 (27), 2284-2293 (2018).
  36. Qi, W., Ding, D., Salvi, R. J. Cytotoxic effects of dimethyl sulphoxide (DMSO) on cochlear organotypic cultures. Hearing Research. 236 (1-2), 52-60 (2008).
  37. Nishimura, M., Ueda, N., Naito, S. Effects of dimethyl sulfoxide on the gene induction of cytochrome P450 isoforms, UGT-dependent glucuronosyl transferase isoforms, and ABCB1 in primary culture of human hepatocytes. Biological and Pharmaceutical Bulletin. 26 (7), 1052-1056 (2003).
  38. Solovev, I. A., Shaposhnikov, M. V., Moskalev, A. A. Chronobiotics KL001 and KS15 extend lifespan and modify circadian rhythms of Drosophila melanogaster. Clocks Sleep. 3 (3), 429-441 (2021).
  39. Cavas, M., Beltran, D., Navarro, J. F. Behavioural effects of dimethyl sulfoxide (DMSO): changes in sleep architecture in rats. Toxicology Letters. 157 (3), 221-232 (2005).
  40. Pfeiffenberger, C., Lear, B. C., Keegan, K. P., Allada, R. Locomotor activity level monitoring using the Drosophila Activity Monitoring (DAM) System. Cold Spring Harbor Protocols. 2010 (11), 5518 (2010).
  41. Gilestro, G. F. Video tracking and analysis of sleep in Drosophila melanogaster. Nature Protocols. 7 (5), 995-1007 (2012).
  42. Branson, K., Robie, A. A., Bender, J., Perona, P., Dickinson, M. H. High-throughput ethomics in large groups of Drosophila. Nature Methods. 6 (6), 451-457 (2009).
  43. Kabra, M., Robie, A. A., Rivera-Alba, M., Branson, S., Branson, K. JAABA: interactive machine learning for automatic annotation of animal behavior. Nature Methods. 10 (1), 64-67 (2013).
  44. Donelson, N. C., et al. High-resolution positional tracking for long-term analysis of Drosophila sleep and locomotion using the "tracker" program. Plos One. 7 (5), e37250 (2012).
  45. Cichewicz, K., Hirsh, J. ShinyR-DAM: a program analyzing Drosophila activity, sleep and circadian rhythms. Communications Biology. 1, 25 (2018).

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High-throughput Small Molecule Drug Screening Age-related Sleep Disorders Drosophila Melanogaster Sleep Duration Fragmented Patterns Elderly Illnesses Diabetes Cardiovascular Diseases Psychological Disorders Existing Drugs Side Effects Cognitive Impairment Addiction Safer Medications Effective Sleep Disorder Medications Cost-effective Screening Method Sleep Regulation Mechanism Model Organism Infrared Monitoring Device Sleep And Circadian Analysis MATLAB Program 2020 (SCAMP2020) Low-cost Screening Protocol
High-Throughput Small Molecule Drug Screening For Age-Related Sleep Disorders Using <em>Drosophila melanogaster</em>
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Zhang, Z., Wang, Y., Zhao, J., Han,More

Zhang, Z., Wang, Y., Zhao, J., Han, S., Zhang, Z. C., Tian, Y. High-Throughput Small Molecule Drug Screening For Age-Related Sleep Disorders Using Drosophila melanogaster. J. Vis. Exp. (200), e65787, doi:10.3791/65787 (2023).

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