IL‐1β functionally attenuates ABCG2 and PDZK1 expression in HK‐2 cells partially through NF‐ĸB activation
Bibliographic record
Abstract
Long-standing untreated hyperuricemia could lead to gout. Several recent studies have demonstrated a significant decrease of serum urate during acute gout attack, which is an aseptic inflammation process focusing on IL-1β. However, how IL-1β, by itself, alters the expression and the functional activity of urate transporters in renal tubular epithelial cells is still unclear. Herein, we revealed that IL-1β could attenuate the mRNA and protein levels of ABCG2, a major urate efflux pump, in HK-2 cells by real-time PCR and Western-blot assays. Moreover, using an ABCG2 specific inhibitor and a new sensitive and specific detection system, it was found that IL-1β also reduced the ABCG2 transporter activities. Incubation with specific inhibitors of the NF-κB pathway partly dampened the inhibitory effect of IL-1β on ABCG2, indicating that IL-1β reduced the ABCG2 expression partially through the NF-ĸB pathway. Furthermore, the decreased expression of PDZK1 induced by IL-1β, which is dependent on the NF-κB pathway, could account for the imbalance between the functions and expressions of ABCG2 on this status. These findings demonstrated a new role for IL-1β, whereby it leads to the inhibition of ABCG2 in renal tubular epithelial cells; this new role probably does not encompass its involvement in the process of renal urate excretion mediated by inflammation. Therefore, other regulation mechanisms of urate reabsorption in renal tubular epithelial cells deserve to be examined in further studies.
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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 | 0.000 | 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) | 0.002 | 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".