Focal cholesterol depletion during Fcγ receptor-mediated phagocytosis contributes to Lyn kinase regulation
Bibliographic record
Abstract
Abstract Cholesterol-rich nanodomains, historically referred to as lipid rafts, have previously been reported to be critical for proper Fcγ Receptor and Lyn kinase signaling during phagocytosis. Throughout the initial stages of phagocytosis, the nascent phagosome is actively remodeled by localized lipid metabolism and exocytosis. However, to date, little is known about the dynamics of cholesterol during this stage of particle engulfment. Using a genetically-encoded biosensor for cholesterol, we find that cholesterol is depleted from the nascent phagosome prior to sealing. Additionally, protein markers of both cholesterol-rich and cholesterol-poor nanodomains also clear from the site of phagocytosis arguing against the selective depletion of specific membrane domains. Consistent with previous studies we find that exocytosis contributes to the remodeling of the nascent phagosome. The displacement of cholesterol from the forming phagosome was paralleled by Lyn kinase helping to explain the reduction of phosphotyrosine signal in the nascent phagosome. This diminution of cholesterol and Lyn from the base of the cup may aid in the processivity of the phagocytic signal during pseudopod extension, and provide an unappreciated mechanism by which Lyn kinase signaling is regulated during phagocytosis. Summary Statement: Localized exocytosis dilutes cholesterol from the phagocytic cup leading to the displacement of Lyn kinase and an attenuation of signaling.
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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".