Regulation of P‐glycoprotein by human immunodeficiency virus‐1 in primary cultures of human fetal astrocytes
Bibliographic record
Abstract
P-glycoprotein (P-gp), a drug efflux pump, is known to alter the bioavailability of antiretroviral drugs at several sites, including the brain. We have previously shown that human immunodeficiency virus-1 (HIV-1) glycoprotein 120 (gp120) induces proinflammatory cytokine secretion and decreases P-gp functional expression in rat astrocytes, a cellular reservoir of HIV-1. However, whether P-gp is regulated in a similar way in human astrocytes is unknown. This study investigates the regulation of P-gp in an in vitro model of gp120-triggered human fetal astrocytes (HFAs). In this system, elevated levels of interleukin-6 (IL-6), IL-1β, and tumor necrosis factor-α were detected in culture supernatants. Pretreatment with CCR5 neutralizing antibody attenuated cytokine secretion, suggesting that gp120-CCR5 interaction mediated cytokine production. Treatment with gp120 (R5-tropic) resulted in reduced P-gp expression (64%) and function as determined by increased (1.6-fold) cellular accumulation of [(3) H]digoxin, a P-gp substrate. Exposure to R5 or R5/X4-tropic viral isolates led to a downregulation in P-gp expression (75% or 90%, respectively), and treatment with IL-6 also showed lower P-gp expression (50%). Moreover, IL-6 neutralizing antibody blocked gp120-mediated P-gp downregulation, suggesting that IL-6 is a key modulator of P-gp. Gp120- or IL-6-mediated downregulation of P-gp was attenuated by SN50 (a nuclear factor-κB [NF-κB] inhibitor), suggesting involvement of NF-κB signaling in P-gp regulation. Our results suggest that, similarly to the case with rodent astrocytes, pathophysiological stressors associated with brain HIV-1 infection have a downregulatory effect on P-gp functional expression in human astrocytes, which may ultimately result in altered antiretroviral drug accumulation within brain parenchyma.
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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) | 0.001 | 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".