Toll-like receptor-4 regulation of hepatic<i>Cyp3a11</i>metabolism in a mouse model of LPS-induced CNS inflammation
Bibliographic record
Abstract
Central nervous system (CNS) infection and inflammation severely reduce the capacity of cytochrome P-450 metabolism in the liver. We developed a mouse model to examine the effects of CNS inflammation on hepatic cytochrome P-450 metabolism. FVB, C57BL/6, and C3H/HeouJ mice were given Escherichia coli LPS (2.5 microg) by intracerebroventricular (ICV) injection. The CNS inflammatory response was confirmed by the elevation of TNF-alpha and/or IL-1beta proteins in the brain. In all mouse strains, LPS produced a 60-70% loss in hepatic Cyp3a11 expression and activity compared with saline-injected controls. Adrenalectomy did not prevent the loss in Cyp3a11 expression or activity, thereby precluding the involvement of the hypothalamic-adrenal-pituitary axis. Endotoxin was detectable (1-10 ng/ml) in serum between 15 and 120 min after ICV dosing of 2.5 microg LPS. Peripheral administration of 2.5 microg LPS by intraperitoneal injection produced similar serum endotoxin levels and a similar loss (60%) in Cyp3a11 expression and activity in the liver. The loss of Cyp3a11 in response to centrally or peripherally administered LPS could not be evoked in Toll-like receptor-4 (TLR4)-mutant (C3H/HeJ) mice, indicating that TLR4 signaling pathways are directly involved in the enzyme loss. In summary, we conclude that LPS is transferred from the brain to the circulation in significant quantities in a model of CNS infection or inflammation. Subsequently, LPS that has reached the circulation stimulates a TLR4-dependent mechanism in the periphery, evoking a reduction in Cyp3a11 expression and metabolism in the liver.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".