Time-Dependent Effects of Localized Inflammation on Peripheral Clock Gene Expression in Rats
Bibliographic record
Abstract
Many aspects of the immune system, including circulating cytokine levels as well as counts and function of various immune cell types, present circadian rhythms. Notably, the mortality rate of animals subjected to high doses of lipopolysaccharide is dependent on the time of treatment. In addition, the severity of symptoms of various inflammatory conditions follows a daily rhythmic pattern. The mechanisms behind the crosstalk between the circadian and immune systems remain elusive. Here we demonstrate that localized inflammation induced by turpentine oil (TURP) causes a time-dependent induction of interleukin (IL)-6 and has time-, gene- and tissue-specific effects on clock gene expression. More precisely, TURP blunts the peak of Per1 and Per2 expression in the liver while in other tissues, the expression nadir is elevated. In contrast, Rev-erbα expression remains relatively unaffected by TURP treatment. Co-treatment with the anti-inflammatory agent IL-1 receptor antagonist (IL-1Ra) did not alter the response of Per2 to TURP treatment in liver, despite the reduced induction of fever and IL-6 serum levels. This indicates that the TURP-mediated changes of Per2 in the liver might be due to factors other than systemic IL-6 and fever. Accordingly, IL-6 treatment had no effect on clock gene expression in HepG2 liver carcinoma cells. Altogether, we show that localized inflammation causes significant time-dependent changes in peripheral circadian clock gene expression, via a mechanism likely involving mediators independent from IL-6 and fever.
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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) | 0.001 | 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 | 0.000 | 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".