Dataset Information

Screening of sleep assisting drug candidates with a Drosophila model.

ABSTRACT: Lately, Drosophila has been favored as a model in sleep and circadian rhythm research due to its conserved mechanism and easily manageable operation. These studies have revealed the sophisticated parameters in whole-day sleep profiles of Drosophila, drawing connections between Drosophila sleep and human sleep. In this study, we tested several sleep deprivation protocols (mechanical shakes and light interruptions) on Drosophila and delineated their influences on Drosophila sleep. We applied a daytime light-deprivation protocol (DD) mimicking jet-lag to screen drugs that alleviate sleep deprivation. Characteristically, classical sleep-aid compounds exhibited different forms of influence: phenobarbital and pentobarbital modified total sleep time, while melatonin only shortened the latency to sleep. Such results construct the basis for further research on sleep benefits in other treatments in Drosophila. We screened seven herb extracts, and found very diverse results regarding their effect on sleep regulation. For instance, Panax notoginseng and Withania somnifera extracts displayed potent influence on total sleep time, while Melissa officinalis increased the number of sleep episodes. By comparing these treatments, we were able to rank drug potency in different aspects of sleep regulation. Notably, we also confirmed the presence of sleep difficulties in a Drosophila Alzheimer's disease (AD) model with an overexpression of human Abeta, and recognized clear differences between the portfolios of drug screening effects in AD flies and in the control group. Overall, potential drug candidates and receipts for sleep problems can be identified separately for normal and AD Drosophila populations, outlining Drosophila's potential in drug screening tests in other populations if combined with the use of other genetic disease tools.

SUBMITTER: Wang YY

PROVIDER: S-EPMC7390450 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Screening of sleep assisting drug candidates with a Drosophila model.

Wang Yan-Ying YY Ma Wei-Wei WW Peng I-Feng IF

PloS one 20200729 7

Lately, Drosophila has been favored as a model in sleep and circadian rhythm research due to its conserved mechanism and easily manageable operation. These studies have revealed the sophisticated parameters in whole-day sleep profiles of Drosophila, drawing connections between Drosophila sleep and human sleep. In this study, we tested several sleep deprivation protocols (mechanical shakes and light interruptions) on Drosophila and delineated their influences on Drosophila sleep. We applied a day ...[more]

PMID: 32726319

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Project description:Purpose: Most prostate cancers (PCs) initially respond to androgen deprivation therapy (ADT), but eventually many PC patients develop castration resistant PC (CRPC). Currently, available drugs that have been approved for the treatment of CRPC patients are limited. Computational drug repositioning methods using public databases represent a promising and efficient tool for discovering new uses for existing drugs. The purpose of the present study is to predict drug candidates that can treat CRPC using a computational method that integrates publicly available gene expression data of tumors from CRPC patients, drug-induced gene expression data and drug response activity data. Methods: Gene expression data from tumoral and normal or benign prostate tissue samples in CRPC patients were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in CRPC were determined with a meta-signature analysis by a metaDE R package. Additionally, drug activity data were downloaded from the ChEMBL database. Furthermore, the drug-induced gene expression data were downloaded from the LINCS database. The reversal relationship between the CRPC and drug gene expression signatures as the Reverse Gene Expression Scores (RGES) were computed. Drug candidates to treat CRPC were predicted using summarized scores (sRGES). Additionally, synergic effects of drug combinations were predicted with a Target Inhibition interaction using the Minimization and Maximization Averaging (TIMMA) algorithm. Results: The drug candidates of sorafenib, olaparib, elesclomol, tanespimycin, and ponatinib were predicted to be active for the treatment of CRPC. Meanwhile, CRPC-related genes, in this case MYL9, E2F2, APOE, and ZFP36, were identified as having gene expression data that can be reversed by these drugs. Additionally, lenalidomide in combination with pazopanib was predicted to be most potent for CRPC. Conclusion: These findings support the use of a computational reversal gene expression approach to identify new drug and drug combination candidates that can be used to treat CRPC.

Dataset Information

Screening of sleep assisting drug candidates with a Drosophila model.

Publications

Screening of sleep assisting drug candidates with a Drosophila model.

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