MicroRNA-132 regulates quinolinic acid production in the brain during LPS-induced neuroinflammation
Bibliographic record
Abstract
Background The kynurenine pathway (KP) plays a major role in neuroinflammation by converting the amino acid tryptophan into a variety of neuroactive products, including neurotoxic quinolinic acid (QUIN). The gene expression regulatory role of microRNAs in neuroinflammation is well documented; however, their impact on KP in the brain remains unexplored. Methods In this study, we investigated whether the neuroimmune miR-132/212 cluster regulates one or more members of the KP during lipopolysaccharide (LPS)-induced neuroinflammation in vivo in mice and in vitro in BV-2 microglial cells. Results In wildtype mice, we demonstrated that a subtoxic dose of LPS triggers a significant neuroinflammatory response with upregulation of KP enzymes, particularly kynurenine 3-monooxygenase (KMO), a key enzyme in QUIN synthesis, leading to elevated brain levels of this neurotoxic metabolite. Interestingly, KMO expression and activity remained elevated in miR-132/212 knockout mice after post-inflammation resolution. In vitro experiments using BV-2 microglial cells showed that miR-132 overexpression led to downregulation of KMO expression and enzyme activity and reduced QUIN levels without altering the microglial activation status. Conclusion Collectively, these findings suggest that the miR-132/212 cluster functions as a novel modulator of KP metabolism during LPS-induced inflammation, and acts as a potential therapeutic target for controlling neurotoxic QUIN accumulation in neuroinflammatory conditions.
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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 | 0.000 |
| 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".