Amelioration of hyperuricemia by Lactobacillus acidophilus F02 with uric acid-lowering ability via modulation of NLRP3 inflammasome and gut microbiota homeostasis
Bibliographic record
Abstract
Hyperuricemia is a metabolic disease caused by disturbances in purine metabolism and imbalances in the formation and excretion of uric acid. It is closely related to the gut microbiota, which is considered a therapeutic target. Some lactic acid bacteria with a uric acid lowering effect have the potential to ameliorate hyperuricemia in the host. However, the mechanism of action remains unclear. Here, we screened a strain of Lactobacillus acidophilus F02 that efficiently degrades uric acid precursors (inosine and guanosine) and further investigated its underlying mechanism on the alleviation of hyperuricemia in high fructose-adenine induced mice. Results showed that L. acidophilus F02 administration reduced serum uric acid levels in hyperuricemia mice by inhibiting xanthine oxidase and adenosine deaminase activity, while protecting hepatorenal injury and systemic inflammation by regulating the relative expression of NLRP3 inflammasome. Most importantly, it restored gut microbial homeostasis and the abundance of bacterial taxa (i.e., a balance in the relative abundance of Firmicutes and Actinobacteria and an increase in the abundance of Bacteroides, Ruminococcus and Lactobacillus). Furthermore, the ability of L. acidophilus F02 to lower uric acid and suppress NLRP3 inflammasome expression was confirmed in the HK-2 cell model, as well as the multiple immunomodulatory effects on RAW264.7 cells. Taken together, we speculate that fructose-adenine induced hyperuricemia triggers hepatorenal injury and gut microbiota dysbiosis, which is ameliorated by L. acidophilus F02. Collectively, the results suggest that lowering uric acid synthesis, increasing uric acid excretion and reducing NLRP3-related IL-1β levels are effective strategies to ameliorate hyperuricemia. Such information could inform future studies on the ecology of L. acidophilus and guide the use of this species for dietary applications in the food industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".