Modulation of P‐glycoprotein function by sphingosine kinase‐1 in brain endothelial cells
Bibliographic record
Abstract
P-glycoprotein (P-gp), an ABC-transporter highly expressed in brain capillaries, protects the brain by extruding xenobiotics. However, its overexpression has also been associated with the multidrug resistance phenotype in tumors. Here, we have investigated the regulation of P-gp transport activity by sphingosine kinase 1 (SphK-1) in brain endothelial cells. We first demonstrated that SphK-1 is overexpressed in endothelial cells (EC) isolated from rat brain tumors compared with EC from normal brain. We also provide evidence that the overexpression of SphK-1 in the cerebral EC line RBE4 leads to the up-regulation of P-gp, both at the gene and protein levels, and that this modulation depends on the catalytic activity of SphK-1. Moreover, we determined the effect of sphingosine-1-phosphate (S1P), the product of SphK-1, on P-gp function. S1P strongly stimulates P-gp transport activity, without modulating its expression. Finally, we found that the S1P-mediated stimulation of P-gp activity is mediated by S1P-1 and S1P-3 receptors at the RBE4 cell surface. Altogether, these results indicate that SphK-1 and its product S1P are involved in the control of P-gp activity in RBE4 cells. Since SphK-1 is overexpressed in EC from brain tumors, these data also suggest that this kinase and its product could contribute to the acquisition and the maintenance of the multidrug resistance phenotype in brain tumor-derived endothelial 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 | 0.001 |
| 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 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".