Influence of IL-6 on MDR and MRP-mediated multidrug resistance in human hepatoma cells
Bibliographic record
Abstract
The objective of this study was to examine effects of interleukin-6 (IL-6) on the expression and activity of the drug resistance transporters (MDR1 and MRP) in human hepatoma cell lines. Expression and activity of MDR1 and MRP transporters were examined in IL-6-treated and control HuH 7 and HepG2 cells using semi-quantitative RT-PCR analysis and by rhodamine 123 and 5-carboxyfluorescin efflux assays. Results from RT-PCR demonstrated expression of MRP3, MRP6, and MDR1 in HuH 7 cells and expression of MRP1, MRP2, MRP3, MRP6, and MDR1 in HepG2 cells. Compared with controls, treatment of HuH 7 cells with IL-6 (10 ng/mL, 24 h) resulted in a 1.8-fold increase in MRP-mediated efflux of 5-CF with a corresponding 1.5-fold induction of MRP3 mRNA levels (p < 0.05). Similarly, in HepG2 cells, a 2-fold increase in MRP functional activity and a 1.8-fold induction of MRP1 mRNA levels were seen in the IL-6 treated cells (p < 0.05). Treatment of cells with IL-6 was also found to cause significant reductions in the expression and activity of MDR1 in HuH 7 cells, but not in HepG2 cells. Our data suggest that IL-6 induces MRP expression and activity in human hepatoma cell lines. Suppressive effects of IL-6 on MDR1 expression and activity were also observed in HuH 7 cells. This underscores the importance of examining the regulation of multiple drug resistance proteins as these proteins may have opposing regulatory mechanisms in malignant cells.
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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) | 0.002 | 0.001 |
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".