Uric Acid Mitigates Cognitive Deficits via TFEB-Mediated Microglial Autophagy in Mice Models of Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Clinical trials have demonstrated the potential neuroprotective effects of uric acid in Alzheimer's disease. However, the specific mechanism underlying the neuroprotective effect of uric acid remains unclear. In the present study, we investigated the neuroprotective effect and underlying mechanism of uric acid in Alzheimer's disease mouse models. Various behavioral tests including elevated plus maze, Barnes maze, and Morris water maze was conducted to evaluate the impact of uric acid on cognitive function in β-amyloid microinjection and APP23/PS45 double transgenic mice models of Alzheimer's disease. Immunohistochemical staining was employed to visualize pathological changes in the brains of Alzheimer's disease mice. Western blotting and immunofluorescence techniques were used to assess levels of autophagy-related proteins and transcription factor EB -related signaling pathways. BV2 cells were used to investigate the association between uric acid and microglial autophagy. We reported that uric acid treatment significantly alleviated memory decline and β-amyloid pathology in β-amyloid -induced Alzheimer's disease model mice and APP23/PS45 double transgenic Alzheimer's disease model mice. Furthermore, uric acid activated microglia and upregulated the autophagy-related proteins such as LC3II/I ratio, Beclin-1 and LAMP1 in the hippocampus of Alzheimer's disease model mice. Similarly, uric acid protected BV2 cells from β-amyloid toxicity by upregulating the expressions of Beclin-1, LAMP1 and the LC3II/I ratio, whereas genetic inhibition of transcription factor EB completely abolished these protective effects. Our results indicate that uric acid may serve as a novel activator of transcription factor EB to induce microglia autophagy and facilitate β-amyloid degradation, thereby improving cognitive function in Alzheimer's disease model mice. Therefore, these findings suggest that uric acid may be a novel therapeutic agent for Alzheimer's disease treatment.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".