Macrophages disseminate pathogen associated molecular patterns through the direct extracellular release of the soluble content of their phagolysosomes
Bibliographic record
Abstract
Recognition of pathogen-or-damage-associated molecular patterns is critical to inflammation. However, most pathogen-or-damage-associated molecular patterns exist within intact microbes/cells and are typically part of non-diffusible, stable macromolecules that are not optimally immunostimulatory or available for immune detection. Partial digestion of microbes/cells following phagocytosis potentially generates new diffusible pathogen-or-damage-associated molecular patterns, however, our current understanding of phagosomal biology would have these molecules sequestered and destroyed within phagolysosomes. Here, we show the controlled release of partially-digested, soluble material from phagolysosomes of macrophages through transient, iterative fusion-fission events between mature phagolysosomes and the plasma membrane, a process we term eructophagy. Eructophagy is most active in proinflammatory macrophages and further induced by toll like receptor engagement. Eructophagy is mediated by genes encoding proteins required for autophagy and can activate vicinal cells by release of phagolysosomally-processed, partially-digested pathogen associated molecular patterns. We propose that eructophagy allows macrophages to amplify local inflammation through the processing and dissemination of pathogen-or-damage-associated molecular patterns.
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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 | 0.000 |
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".