Loss of MLKL Decreases Necrotic Core but Increases Macrophage Lipid Accumulation In Atherosclerosis
Bibliographic record
Abstract
ABSTRACT Objectives During the advancement of atherosclerosis, the cellularity of the plaque is governed by the influx of monocyte-derived macrophages and their turnover via apoptotic and non-apoptotic forms of cell death. Previous reports have demonstrated that programmed necrosis, or necroptosis, of macrophages within the plaque contribute to necrotic core formation. Knockdown or inhibition of the necrosome components RIPK1 and RIPK3 slow the progression of atherosclerosis, and activation of the terminal step of necroptosis, MLKL, has been demonstrated in advanced human atherosclerotic plaques. However, whether MLKL directly contributes to lesion development and necrotic core formation has not been investigated. Approaches and Results MLKL expression was knocked down in atherogenic Apoe - knockout mice via subcutaneous administration of antisense oligonucleotides (ASO). During advanced atherogenesis, Mlkl knockdown potently reduced cell death in the plaque, with a significant reduction in the necrotic core. However, total lesion area in the aortic sinus remained unchanged. Furthermore, treatment with the MLKL ASO unexpectedly reduced circulating cholesterol levels compared to control ASO, while staining for lipids within the plaque was significantly increased. Peritoneal macrophages transfected with the MLKL ASO showed increased lipid loading upon incubation with modified cholesterol-rich lipoproteins. In lipid-loaded macrophages, MLKL co-localized with Rab7, a marker of the late endosome. Conclusions These studies confirm the requirement for MLKL as the executioner of necroptosis, and as such a significant contributor to the necrotic core during atherogenesis. We also identified a previously unknown role for MLKL in interacting with endosomal trafficking components to regulate lipid uptake in macrophages during atherogenesis.
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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) | 0.002 | 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".