Abstract 352: Resolvin D1 Enhances the Clearance of Necroptotic Cells
Bibliographic record
Abstract
Inflammation-resolution is a protective response that is mediated by specialized pro-resolving mediators (SPMs). The clearance of dead cells or efferocytosis is a critical cellular program of inflammation-resolution. Impaired efferocytosis can lead to tissue damage in prevalent human diseases, like atherosclerosis. Therefore understanding mechanisms associated with swift clearance of dead cells is of utmost clinical importance. Recently, the accumulation of necroptotic cells (NCs) has been observed in human plaques and we postulate that this is due to defective clearance of NCs. Here we present evidence that NCs are inefficiently taken up by macrophages because they have increased surface expression of a well-known “don’t eat me” signal called CD47. High levels of CD47 on NCs stimulated RhoA-pMLC signaling in macrophages that promoted “nibbling”, rather than whole-cell engulfment of NCs. Anti-CD47 blocking antibodies or RhoA inhibition limited RhoA-p-MLC signaling and promoted whole-cell NC engulfment in vitro . Treatment with anti-CD47 blocking antibodies to Ldlr -/- mice with established atherosclerosis led to decreased necrotic cores, less accumulation of plaque NCs and increased lesional SPMs, including Resolvin D1 (RvD1) compared with anti-IgG controls. Next, we observed that RvD1 acts in a feed-forward manner to further enhance the clearance of NCs. Mechanistically, RvD1 promoted whole-cell engulfment of NCs by decreasing RhoA signaling and activating CDC42. RvD1 specifically targeted NCs for engulfment by facilitating the release of the well-known “eat me signal” called calreticulin from macrophages in a CDC42 dependent manner. Lastly, RvD1 enhanced the clearance of NCs in advanced murine plaques. Together, these results suggest new molecules and signaling associated with the clearance of NCs, provide a new paradigm for the regulation of inflammation-resolution, and offer a potential treatment strategy for diseases that are associated with the accumulation of NCs.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".