Intracellular accumulation of free cholesterol in macrophages triggers a PARP1 response to DNA damage and PARP1 impairs lipopolysaccharide-induced inflammatory response
Bibliographic record
Abstract
Abstract The formation of macrophage (Mφ) foam cells is a hallmark of atherosclerosis, yet how the process of lipid loading can modulate Mφ inflammatory responses by rewiring their intracellular metabolic circuits is not well understood. Our previous studies have shown that the accumulation of oxidized LDL (oxLDL) or free cholesterol in Mφs impaired their inflammatory response by suppressing HIF-1α-mediated glycolysis and upregulating NRF2 antioxidative response. However, it remains unclear if other metabolic processes are also contributory. In this study, we found that the accumulation of free cholesterol, but not oxLDL, in primary murine thioglycolate-elicited peritoneal Mφs (PMφs) enhanced a PARP1-dependent response associated with repair of DNA damage, which was characterized by poly ADP-ribosylation of proteins, phosphorylation of histone 2A.X and consumption of NAD + . Both oxLDL and cholesterol enhanced the PARP1 response after LPS stimulation. Treatment of PMφs with mitoTEMPO, a specific mitochondrial reactive oxygen species (mtROS) scavenger, alleviated mtROS during cholesterol loading, blocked the PARP1 response and partially restored LPS-induced inflammatory gene expression. In contrast to inhibition of PARP1 enzymatic activity, knockdown of PARP1 expression in RAW264.7 Mφs with siRNA elevated LPS-induced inflammatory gene expression. Overall, our study suggests that cholesterol accumulation triggers a PARP1 response to DNA damage in Mφs and that PARP1 inhibits LPS-mediated inflammation through a non-enzymatic function.
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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 | 0.000 | 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) | 0.002 | 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".