Function of Multidrug Resistance Transporters is Disrupted by Infection Mimics in Human Brain Endothelial Cells
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Bibliographic record
Abstract
P-glycoprotein (P-gp/ABCB1) and breast cancer resistance protein (BCRP/ABCG2) modulate the distribution of drugs and toxins across the blood-brain barrier (BBB). Animal studies reported that infection-induced disruption of these transporters in the developing BBB impairs fetal brain protection. However, the impact of infection mimics on P-gp/BCRP function in human brain endothelium is less well understood. We hypothesized that Toll-like receptor ligands mimicking bacterial and viral infection would modify the expression and function of P-gp and BCRP in human brain endothelial cells (BECs). Human cerebral microvascular endothelial cells (hCMEC/D3) were challenged with bacterial [Lipopolysaccharide (LPS)] and viral-mimics [polyinosinic:polycytidylic acid (PolyI:C) or single-stranded RNA (ssRNA)], or pro-inflammatory cytokines interleukin (IL)-6, tumor necrosis factor (TNF)-α and interferon gamma (IFN)-ɣ. P-gp and BCRP function was assessed after 4 or 24 h, using Calcein-AM and Chlorin-6 assays, respectively. Western blot and qPCR quantified P-gp/ABCB1 and BCRP/ABCG2 expression following treatments. Infection mimics are potent modulators of drug transporters in human BECs in vitro. LPS and PolyI:C increased, while ssRNA exposure reduced P-gp activity. In contrast, LPS and PolyI:C decreased, while ssRNA increased BCRP activity (P < .05). There was little correlation between drug transporter function, gene expression and total protein level. Altered plasma membrane BCRP may suggest modified intracellular trafficking induced by infection in human BECs. Bacterial and viral infection mimics modify P-gp and BCRP transport function in human BECs, in vitro. This knowledge may contribute and have important implications for human brain protection and possible altered biodistribution of drugs and xenobiotics in the brain following exposure to TLR agonists.
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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 | 0.000 | 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) | 0.001 | 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 it