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• W2952187573 endingPage "e0207027" @default.
• W2952187573 startingPage "e0207027" @default.
• W2952187573 abstract "Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United States. Besides analgesic effects, chronic use of opioid can also cause tolerance, dependence, and even addiction. Effective treatment of opioid addiction remains a big challenge today. Studies on addictive effects of opioids focus on striatum, a main component in the brain responsible for drug dependence and addiction. Some transcription regulators have been associated with opioid addiction, but relationship between analgesic effects of opioids and dependence behaviors mediated by them at the molecular level has not been thoroughly investigated. In this paper, we developed a new computational strategy that identifies novel targets and potential therapeutic molecular compounds for opioid dependence and addiction. We employed several statistical and machine learning techniques and identified differentially expressed genes over time which were associated with dependence-related behaviors after exposure to either morphine or heroin, as well as potential transcription regulators that regulate these genes, using time course gene expression data from mouse striatum. Moreover, our findings revealed that some of these dependence-associated genes and transcription regulators are known to play key roles in opioid-mediated analgesia and tolerance, suggesting that an intricate relationship between opioid-induce pain-related pathways and dependence may develop at an early stage during opioid exposure. Finally, we determined small compounds that can potentially target the dependence-associated genes and transcription regulators. These compounds may facilitate development of effective therapy for opioid dependence and addiction. We also built a database (http://daportals.org) for all opioid-induced dependence-associated genes and transcription regulators that we discovered, as well as the small compounds that target those genes and transcription regulators." @default.
• W2952187573 created "2019-06-27" @default.
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• W2952187573 date "2018-11-07" @default.
• W2952187573 modified "2023-09-26" @default.
• W2952187573 title "A computational strategy for finding novel targets and therapeutic compounds for opioid dependence" @default.
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• W2952187573 doi "https://doi.org/10.1371/journal.pone.0207027" @default.
• W2952187573 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6221321" @default.
• W2952187573 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30403753" @default.
• W2952187573 hasPublicationYear "2018" @default.
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