Cholesterol accumulation impairs HIF-1α-dependent immunometabolic reprogramming of LPS-stimulated macrophages by upregulating the NRF2 pathway
Bibliographic record
Abstract
Lipid accumulation in macrophages (Mφs) is a hallmark of atherosclerosis. Yet, how lipid loading modulates Mφ inflammatory responses remains unclear. We endeavored to gain mechanistic insights into how pre-loading with free cholesterol modulates Mφ metabolism upon LPS-induced TLR4 signaling. We found that activities of prolyl hydroxylases (PHDs) and factor inhibiting HIF (FIH) are higher in cholesterol loaded Mφs post-LPS stimulation, resulting in impaired HIF-1α stability, transactivation capacity and glycolysis. In RAW264.7 cells expressing mutated HIF-1α proteins resistant to PHDs and FIH activities, cholesterol loading failed to suppress HIF-1α function. Cholesterol accumulation induced oxidative stress that enhanced NRF2 protein stability and triggered a NRF2-mediated antioxidative response prior to and in conjunction with LPS stimulation. LPS stimulation increased NRF2 mRNA and protein expression, but it did not enhance NRF2 protein stability further. NRF2 deficiency in Mφs alleviated the inhibitory effects of cholesterol loading on HIF-1α function. Mutated KEAP1 proteins defective in redox sensing expressed in RAW264.7 cells partially reversed the effects of cholesterol loading on NRF2 activation. Collectively, we showed that cholesterol accumulation in Mφs induces oxidative stress and NRF2 stabilization, which when combined with LPS-induced NRF2 expression leads to enhanced NRF2-mediated transcription that ultimately impairs HIF-1α-dependent glycolytic and inflammatory responses.
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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) | 0.001 | 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".