Bmal1 haploinsufficiency impairs fear memory and modulates neuroinflammation via the 5-HT2C receptor
Bibliographic record
Abstract
Background BMAL1, a key regulator of circadian rhythms, plays a multifaceted role in brain function. However, the complex interplay between BMAL1, memory, neuroinflammation, and neurotransmitter regulation remains poorly understood. To investigate these interactions, we conducted a study using BMAL1-haplodeficient mice (BMAL1+/−). Methods We exposed BMAL1+/− mice to behavioral assessments including cued fear conditioning, new objection recognition (NOR) test, and Y-maze test to evaluate BMAL1+/− haplodeficiency impact on memory. Furthermore, biochemical changes were analyzed through western blotting, and ELISA to explore further the mechanism of BMAL1+/− in memory, and neuroinflammation. Results We found that BMAL1 haploinsufficiency led to deficits in cued fear learning and memory, while spatial memory and object recognition remained intact. Further analysis revealed dysregulated neurotransmitter levels and alterations in neurotransmitter-related proteins in the prefrontal cortex of BMAL1+/− mice. Pharmacological interventions targeting dopamine uptake or the 5-HT2C receptor demonstrated that inhibiting the 5-HT2C receptor could rescue fear learning and memory impairments in BMAL1+/− mice. Additionally, we observed downregulation of the inflammasome and neuroinflammation pathways in BMAL1+/− mice, which is validated by inflammation mediator lipopolysaccharide (LPS) administration. Conclusion These findings highlight that BMAL1 haploinsufficiency leads to deficits in fear learning and memory, which are linked to alterations in neurotransmitters and receptors, particularly the 5-HT2C receptor. Targeting the 5-HT2C receptor may offer a potential therapeutic strategy for mitigating cognitive impairments associated with BMAL1 dysfunction.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".