Reducing Cholesterol in Macrophage Activates NF-kB through Mitochondria, Resulting in Epigenomic Reprogramming to Dampen Inflammation
Bibliographic record
Abstract
Cholesterol plays an important role in macrophage functions including their immune response 1 . Recently, NF- k B was shown to reprogram the epigenome in macrophages 2 . Here, we show that NF-kB pathway is activated in resting macrophages when cholesterol is reduced by statin or methyl-β-cyclodextrin (MCD). Activated NF- k B increases the expression of histone-modifying enzymes, such as demethylase JMJD3. We provide evidence that the epigenome in these macrophages is reprogrammed, likely driven by NF- k B and histone modifications 2 . We also show that cholesterol reduction in macrophages results in suppression of mitochondria respiration. Specifically, cholesterol levels in the inner membrane of the mitochondria is reduced, which impairs the efficiency of ATP synthase (complex V). Consequently, protons accumulate in the intermembrane space to active NF- k B and JMJD3, thereby modifying the epigenome. When subsequently challenged by the inflammatory stimulus lipopolysaccharide (LPS), cholesterol-reduced macrophages generate responses that are less pro-inflammatory and more homeostatic, which should favour inflammation resolution. Taken together, we describe a mechanism by which the level of mitochondrial cholesterol in resting macrophages regulates the epigenome through NF-kB, thereby preparing macrophage for future immune activation.
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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 | 0.001 |
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".