Phosphatidylethanolamine is a phagocytic ligand implicated in the binding and removal of apoptotic and microbial extracellular vesicles
Bibliographic record
Abstract
Abstract The efficient recognition and removal of apoptotic cells by phagocytes is critical to prevent secondary necrosis and maintain tissue homeostasis. Such detection involves receptors and bridging molecules that recognize lipids −normally restricted to the inner leaflet of healthy cells− which become exposed on the surface of dead cells and the vesicles they produce. A majority of studies focus on phosphatidylserine (PS) for which there are well-established receptors that either bind to the lipid directly or indirectly via intermediary proteins. Phosphatidylethanolamine (PE) is even more prevalent than PS in the inner leaflet of mammalian cells and also becomes exposed by the action of scramblases during cell death, though little is known about the effects of PE once scrambled. Here, we report that PE can itself serve as a phagocytic ligand for macrophages by engaging CD300 family receptors. CD300a and CD300b specifically modulated PE-mediated uptake, and this process involved ITAM-containing adaptors and the spleen tyrosine kinase (Syk). For bacteria, which contain PE but largely lack PS in their membranes, we report that PE engagement enabled the binding and uptake of spheroplasts and extracellular vesicles (EVs) that were unsheathed by the cell wall. The inflammatory responses of macrophages to PE particles containing LPS was also curtailed by CD300a expression. Based on these observations, we posit that the direct recognition of PE facilitates mechanisms of clearance that stand to have a broad impact on the immune response.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".