Chronic tianeptine induces tolerance in analgesia and hyperlocomotion via mu-opioid receptor activation in mice
Bibliographic record
Abstract
Introduction: Tianeptine is approved in some countries to treat depression and anxiety. In addition to its activity on serotonin and glutamate neurotransmission, tianeptine has been proven to be a mu-opioid receptor (MOR) agonist, but only a few preclinical studies have characterized the opioid-like behavioral effects of tianeptine. Methods: In this study, we tested tianeptine activity on G protein activation using the [S35] GTPγS binding assay in brain tissue from MOR+/+ and MOR-/- mice. Then, to determine whether tianeptine behavioral responses are MOR-dependent, we characterized the analgesic, locomotor, and rewarding responses of tianeptine in MOR+/+ and MOR-/- mice using tail immersion, hot plate, locomotor, and conditioned place preference tests. Results: Using the [S35] GTPγS binding assay, we found that tianeptine signaling is mediated by MOR in the brain with properties similar to those of DAMGO (a classic MOR agonist). Furthermore, we found that the MOR is necessary for tianeptine's analgesic (tail immersion and hot plate), locomotor, and rewarding (conditioned place preference) effects. Indeed, these behavioral effects could only be measured in MOR+/+ mice but not in MOR-/- mice. Additionally, chronic administration of tianeptine induced tolerance to its analgesic and hyperlocomotor effects. Discussion: These findings suggest that tianeptine's opioid-like effects require MOR and that chronic use could lead to tolerance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".