Function of Multidrug Resistance Transporters is Disrupted by Infection Mimics in Human Brain Endothelial Cells
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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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) | 0.001 | 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 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".