The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Single-nucleotide polymorphism in the human mu opioid receptor gene alters β-endorphin binding and activity: Possible implications for opiate addiction
Open accessAbstract
Opioid drugs play important roles in the clinical management of pain, as well as in the development and treatment of drug abuse. The mu opioid receptor is the primary site of action for the most commonly used opioids, including morphine, heroin, fentanyl, and methadone. By sequencing DNA from 113 former heroin addicts in methadone maintenance and 39 individuals with no history of drug or alcohol abuse or dependence, we have identified five different single-nucleotide polymorphisms (SNPs) in the coding region of the mu opioid receptor gene. The most prevalent SNP is a nucleotide substitution at position 118 (A118G), predicting an amino acid change at a putative N-glycosylation site. This SNP displays an allelic frequency of approximately 10% in our study population. Significant differences in allele distribution were observed among ethnic groups studied. The variant receptor resulting from the A118G SNP did not show altered binding affinities for most opioid peptides and alkaloids tested. However, the A118G variant receptor binds β-endorphin, an endogenous opioid that activates the mu opioid receptor, approximately three times more tightly than the most common allelic form of the receptor. Furthermore, β-endorphin is approximately three times more potent at the A118G variant receptor than at the most common allelic form in agonist-induced activation of G protein-coupled potassium channels. These results show that SNPs in the mu opioid receptor gene can alter binding and signal transduction in the resulting receptor and may have implications for normal physiology, therapeutics, and vulnerability to develop or protection from diverse diseases including the addictive diseases.
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Cites
Statistical Aspects of the Analysis of Data From Retrospective Studies of Disease
(1959) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/22.4.719 · Cited by 14,689
Kreek, M., Ragunath, J., Plevy, S., Hamer, D., Schneider, B., & Hartman, N. (1984) · Neuropeptides · doi:10.1016/0143-4179(84)90081-7 · Cited by 97
Self-citation
Cited by
Molecular mechanisms of opioid receptor-dependent signaling and behavior
Al-Hasani, R., & Bruchas, M. R. (2011) · Anesthesiology · doi:10.1097/aln.0b013e318238bba6 · Cited by 1,067
Kreek, M. J., Nielsen, D. A., Butelman, E. R., & LaForge, K. S. (2005) · Nature Neuroscience · doi:10.1038/nn1583 · Cited by 1,024
Self-citationdepue, R. A., & morrone-strupinsky, J. V. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05000063 · Cited by 958
Regulation of μ-opioid receptors: desensitization, phosphorylation, internalization, and tolerance
Williams, J. T., Ingram, S. L., Henderson, G., Chavkin, C., von Zastrow, M., Schulz, S., Koch, T., Evans, C. J., & Christie, M. J. (2013) · Pharmacological Reviews · doi:10.1124/pr.112.005942 · Cited by 802
Eap, C. B., Buclin, T., & Baumann, P. (2002) · Clinical Pharmacokinetics · doi:10.2165/00003088-200241140-00003 · Cited by 599
Metadata
- Authors
- Bond, C., LaForge, K. S., Tian, M., Melia, D., Zhang, S., Borg, L., Gong, J., Schluger, J., Strong, J. A., Leal, S. M., Tischfield, J. A., Kreek, M. J., & Yu, L.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 1998-08-04
- Volume
- 95
- Issue
- 16
- Pages
- 9608-9613
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2012743573
- Cited by
- 1,074
- References
- 2
- FWCI
- 8.09
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Single-nucleotide polymorphism in the human mu opioid receptor gene alters β-endorphin binding and activity: Possible implications for opiate addiction. https://app.cochat.ai/papers/10.1073/pnas.95.16.9608
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- Bond, C., LaForge, K. S., Tian, M., Melia, D., Zhang, S., Borg, L., Gong, J., Schluger, J., Strong, J. A., Leal, S. M., Tischfield, J. A., Kreek, M. J., & Yu, L. (1998). Single-nucleotide polymorphism in the human mu opioid receptor gene alters β-endorphin binding and activity: Possible implications for opiate addiction. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.95.16.9608
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