Abstract 3: Targeting Macrophage Necroptosis for Therapeutic and Diagnostic Interventions to Treat Atherosclerosis
Bibliographic record
Abstract
Background: Atherosclerosis results from maladaptive inflammation driven primarily by macrophages, whose recruitment and proliferation drive plaque progression. In advanced plaques, macrophage death contributes centrally to the formation of plaque necrosis, which underlies the instability that promotes plaque rupture and myocardial infarction. As such, targeting macrophage cell death pathways may offer promise for the stabilization of vulnerable plaques. Necroptosis is a recently discovered pathway of programmed cell necrosis regulated by RIP3 and MLKL kinases that in contrast to apoptosis, induces a pro-inflammatory state. We hypothesize that atherogenic ligands within the plaque promote macrophage necroptosis and this process underlies necrotic core formation and drives atherosclerotic plaque instability. Results: In humans with unstable carotid atherosclerosis, expression of RIP3 and MLKL is increased and MLKL phosphorylation, a key step in the commitment to necroptosis, is detected in advanced atheromas. Investigation of the molecular mechanisms underlying plaque necroptosis showed that macrophages treated with oxidized LDL have increased expression of necroptotic genes RIP3 and MLKL through ROS-dependent activation of the promoter region and increased RIP3 and MLKL phosphorylation. Combined treatment with oxLDL and DAMPs (damage associated molecular patterns) amplified macrophage necroptotic cell death, indicating that additional inflammatory stimuli present in the lesion could act synergistically to promote necroptosis. Using a radiotracer developed with the necroptosis inhibitor Nec-1, we show that 123 I-Nec1 localizes specifically to atherosclerotic plaques in Apoe-/- mice, and its uptake is tightly correlated to lesion areas by ex vivo nuclear imaging. Furthermore, treatment of Apoe-/- mice with established atherosclerosis with Nec-1 reduced lesion size and markers of plaque instability, including necrotic core formation. Conclusions: Our findings offer molecular insight into the mechanisms of macrophage cell death that drive necrotic core formation in atherosclerosis and suggest that this pathway can be used as both a diagnostic and therapeutic tool for the treatment of unstable atherosclerosis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".