Rapid antidepressant effect of single-bout exercise is mediated by adiponectin-induced APPL1 nucleus translocation in anterior cingulate cortex
Bibliographic record
Abstract
Emerging evidence suggests that a single bout of physical exercise can induce rapid antidepressant effects, yet the underlying neural mechanisms remain largely unknown. We established a mouse model designed to mimic the mood-elevating effects of acute exercise observed in humans. We found that this rapid antidepressant response in exercised mice correlated with increased brain levels of adiponectin, an adipocyte-secreted hormone. Using whole-brain c-Fos mapping, immunofluorescence staining of glutamatergic neurons using the marker CaMKII, and in vivo calcium imaging, we identified ACC glutamatergic neurons that were rapidly activated by single-bout physical exercise. Chemogenetic manipulation of these neurons modulated the rapid antidepressant effects of exercise. Genetic manipulations demonstrated that the global knockout of adiponectin or the selective deletion of its receptor, AdipoR1, in ACC-glutamatergic neurons abolished both neural activation and the rapid antidepressant response. Mechanistically, acute exercise upregulated adiponectin, activating AdipoR1 and promoting nuclear translocation of APPL1 in ACC-glutamatergic neurons. This molecular cascade enhanced the epigenetic regulation of synaptic protein expression and spinogenesis, culminating in a rapid antidepressant response. These findings not only elucidate a novel role for APPL1 nuclear translocation in mediating the antidepressant effects of acute exercise but also identify AdipoR1 as a potential therapeutic target for developing rapid-acting antidepressant interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".