Linking TLR-7 Signaling to Downregulation of Placental P-Glycoprotein: Implications for Fetal Drug Exposure
Bibliographic record
Abstract
Background/Objectives: Activation of the Toll-like receptor 7 (TLR-7) plays an important role in the pathogenesis of many autoimmune diseases and viral infections. Although we have previously observed inflammation-mediated dysregulation of placental transporters, the role of TLR-7 has not been examined. Using the TLR-7 agonist, imiquimod (IMQ), we evaluated transporter expression in IMQ-treated pregnant rats and ex vivo in cultured rat placental explants. Methods: We administered 5 mg/kg (IP) of IMQ to pregnant Sprague Dawley rats on gestational day (GD) 14. The expression levels of inflammatory biomarkers and transporters were measured in maternal and fetal tissues by qRT-PCR and immunodetection methods, and effects on the placental proteome were assessed using LC/MS/MS. The involvement of TLR-7 was confirmed in rat placental explants. Results: IMQ administration resulted in Irf7 induction and increased levels of IL-6, Tnf-α, and type-I/II interferon pathways in maternal liver and placenta, which is consistent with TLR-7 activation. Proteomic profiling revealed IMQ-mediated activation of pathways involved in immune response, vesicle trafficking, and oxidative stress. Significantly decreased placental, hepatic, and renal protein expression of P-glycoprotein (PGP) was seen in the IMQ group. Likewise, TLR-7 activation using single-stranded RNA resulted in an induction of inflammatory biomarkers and downregulation of PGP in rat placental explants. Conclusions: We demonstrated that the activation of TLR-7 signaling during pregnancy reduces the expression of PGP in placenta and maternal tissues. Further studies are warranted, as decreased protein expression could result in decreased activity and altered fetal exposure to its substrates.
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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) | 0.003 | 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".