Last updated: 2025-08-09
Checks: 2 0
Knit directory: mu-paper-dataviz/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 7fb9e4f. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rproj.user/542A54EA/
Unstaged changes:
Modified: .Rproj.user/shared/notebooks/paths
Modified: README.md
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/index.Rmd) and HTML
(docs/index.html) files. If you’ve configured a remote Git
repository (see ?wflow_git_remote), click on the hyperlinks
in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7fb9e4f | sdhutchins | 2025-08-09 | Light tweaks. |
| html | 36b9ab2 | S. Hutchins | 2018-09-28 | Build site. |
| html | 5d39156 | S. Hutchins | 2018-09-28 | Added code and analysis for bar charts. |
| html | fbd5b51 | S. Hutchins | 2018-09-25 | Build site. |
| Rmd | d5a2bf7 | S. Hutchins | 2018-09-25 | Added correct github url |
| html | d4b0350 | S. Hutchins | 2018-09-25 | Build site. |
| html | 1921828 | S. Hutchins | 2018-09-25 | Build site. |
| html | 342ec6b | S. Hutchins | 2018-09-25 | Build site. |
| Rmd | 8e4901f | S. Hutchins | 2018-09-24 | Start workflowr project. |
Interest has emerged in biased agonists at the mu opioid receptor (MOR) as a possible means for maintaining potent analgesia with reduced side effect profiles. While approaches measuring in vitro biased agonism are used in the development of these compounds, their therapeutic utility will ultimately be determined by in vivo functional effects. Nonhuman primates (NHPs) are the most translational model for evaluating the behavioral effects of candidate medications, but biased signaling of these drugs at NHP MOR receptors has been unstudied.
The goal of the current work was to characterize MOR ligand bias in rhesus macaques, focusing on agonists that have previously been reported to show different patterns of biased agonism in rodents and humans. Downstream signaling pathways that responded to MOR activation were identified using a luciferase reporter array. Concentration-response curves for specific pathways (cAMP, NF-ĸB, MAPK/JNK) were generated using six agonists previously reported to differ in terms of signaling bias at rodent and human MORs.
Using DAMGO as a reference ligand, relative cAMP, NF-ĸB and MAPK/JNK signaling by morphine, endomorphin-1, and TRV130 were found to be comparable between species. Further, the bias patterns across ligands for NF-ĸB and MAPK/JNK were largely similar between species. There was a high degree of concordance between rhesus macaque and human MOR receptor signaling bias for all agonists tested, further demonstrating their utility for future translational behavioral studies.
This site contains data visualizations and analysis related to the mu opioid receptor biased agonism study in rhesus macaques. The research focuses on characterizing signaling bias patterns across different agonists and comparing these patterns between species to validate the translational utility of nonhuman primate models.