Endotoxin Modulates the Expression of Renal Drug Transporters in HIV-1 Transgenic Rats
Bibliographic record
Abstract
PURPUSE: Bacterial co-infections and low grade endotoxemia are common in HIV patients. Inflammation due to endotoxin or HIV may influence the expression and activity of transporters. Kidney transporters influence renal drug clearances including many antiretroviral agents. Our objective was to study the effect of endotoxin and HIV on the renal expression of drug transporters in an HIV-transgenic (HIV-Tg) rat model. These rats develop immune dysfunction and AIDS-associated conditions like humans. METHODS: Endotoxin or saline was administered intraperitoneally to HIV-Tg or wild type (WT) littermates and kidneys were collected 18 hours later. Expression of transporters and cytokines were measured by qRT-PCR and Western blots. Serum cytokine levels were measured by ELISA. RESULTS: Endotoxin induced serum levels of IL-6, TNF-α and IFN-γ in both HIV-Tg and WT animals. The basal mRNA expression of Oct2, Oct3, Octn1, Mate1, Urat1 and Ent1was significantly lower (33-60%) and the expression of Ent2 and Pept2 was significantly higher (33-45%) in HIV-Tg as compared to WT. While endotoxin significantly downregulated the mRNA expression of Mdra1 and Pept2 in both HIV and WT groups (69-78%), it imposed a significant reduction on the mRNA expression of Oct2, Oct3, Octn1, Mate1, Oat2, urat1, and Ent1 (54-83%) only in the WT group. Endotoxin significantly increased the mRNA expression of Pept1 (140%) in both WT and HIV groups. CONCLUSIONS: HIV and endotoxin each imposed alterations in the expression of many clinically important renal drug transporters although co-infection did not augment this effect. Viral and/or bacterial infections may impact the renal clearance of drug substrates in patients and could potentially be a source of drug-disease interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".