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A computational strategy for finding novel targets and therapeutic compounds for opioid dependence
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 addi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6221321/ https://www.ncbi.nlm.nih.gov/pubmed/30403753 http://dx.doi.org/10.1371/journal.pone.0207027 |
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author | Wu, Xiaojun Xie, Siwei Wang, Lirong Fan, Peihao Ge, Songwei Xie, Xiang-Qun Wu, Wei |
author_facet | Wu, Xiaojun Xie, Siwei Wang, Lirong Fan, Peihao Ge, Songwei Xie, Xiang-Qun Wu, Wei |
author_sort | Wu, Xiaojun |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6221321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-62213212018-11-19 A computational strategy for finding novel targets and therapeutic compounds for opioid dependence Wu, Xiaojun Xie, Siwei Wang, Lirong Fan, Peihao Ge, Songwei Xie, Xiang-Qun Wu, Wei PLoS One Research Article 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. Public Library of Science 2018-11-07 /pmc/articles/PMC6221321/ /pubmed/30403753 http://dx.doi.org/10.1371/journal.pone.0207027 Text en © 2018 Wu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wu, Xiaojun Xie, Siwei Wang, Lirong Fan, Peihao Ge, Songwei Xie, Xiang-Qun Wu, Wei A computational strategy for finding novel targets and therapeutic compounds for opioid dependence |
title | A computational strategy for finding novel targets and therapeutic compounds for opioid dependence |
title_full | A computational strategy for finding novel targets and therapeutic compounds for opioid dependence |
title_fullStr | A computational strategy for finding novel targets and therapeutic compounds for opioid dependence |
title_full_unstemmed | A computational strategy for finding novel targets and therapeutic compounds for opioid dependence |
title_short | A computational strategy for finding novel targets and therapeutic compounds for opioid dependence |
title_sort | computational strategy for finding novel targets and therapeutic compounds for opioid dependence |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6221321/ https://www.ncbi.nlm.nih.gov/pubmed/30403753 http://dx.doi.org/10.1371/journal.pone.0207027 |
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