Reciprocal Antagonism Between Estrogen Receptor and NF-κB Activity In Vivo
Bibliographic record
Abstract
The functional interaction, or "cross-talk," between estrogen receptor (ER) and the proinflammatory transcription factor nuclear factor (NF)-kappaB demonstrated in vitro has been suggested to play a role in estrogen prevention of cardiovascular disease. Here, we demonstrate that this reciprocal cross-talk occurs in vivo. Ovariectomized C57BL/6 mice fed an atherogenic diet had increased hepatic levels of active NF-kappaB and numerous inflammatory genes, including MHC invariant chain (Ii), vascular cell adhesion molecule-1, tumor necrosis factor-alpha, and RANTES. Treatment with 17alpha-ethinylestradiol (EE) strongly blocked induction of these genes but had no effect on their basal expression levels. ER was required for this activity, because the antagonist ICI 182,780 completely blocked the inhibitory activity of EE. Gene activation by EE was not required for inhibition of inflammatory gene expression, because both the phytoestrogen genistein and low doses of EE were effective in blocking inflammatory gene induction without inducing marker genes such as intestinal trefoil factor (ITF) or myo-inositol-1-phosphate synthase (IPS). The in vivo transcriptional interference was reciprocal, with EE induction of ITF and IPS greatly reduced in animals fed the atherogenic diet versus chow-fed controls. This interference was specific to the liver, because diet had no effect on uterine weight increases produced by EE. Transfection experiments confirmed that the extent of inhibition of ER-mediated transcription by inflammatory stimuli correlated with the extent of NF-kappaB activation. These results suggest that the cross-talk between ER and NF-kappaB does occur in vivo and may indeed contribute significantly to the cardioprotective effects of estrogen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".