Differential kinase requirements in human and mouse Fc-gamma receptor phagocytosis and endocytosis
Bibliographic record
Abstract
Fc gamma receptors (FcgammaRs) contribute to the internalization of large and small immune complexes through phagocytosis and endocytosis, respectively. The molecular processes underlying these internalization mechanisms differ dramatically and have distinct outcomes in immune clearance and modulation of cell function. However, it is unclear how the same receptors (FcgammaR) binding to identical ligands (IgG) can elicit such distinct responses. We and others have shown that Syk kinase, Src-related tyrosine kinases (SRTKs) and phosphatidyl inositol 3-kinases (PI3K) play important roles in FcgammaR phagocytosis. Herein, we demonstrate that these kinases are not required for FcgammaR endocytosis. Endocytosis of heat-aggregated IgG (HA-IgG) by COS-1 cells stably transfected with FcgammaRIIA or chimeric FcgammaRI-gamma-gamma (EC-TM-CYT) was not significantly altered by PP2, piceatannol, or wortmannin. In contrast, phagocytosis of large opsonized particles (IgG-sensitized sheep erythrocytes, EA) was markedly reduced by these inhibitors. These results were confirmed in primary mouse bone marrow-derived macrophages and freshly isolated human monocytes. Levels of receptor phosphorylation were similar when FcgammaRIIA was cross-linked using HA-IgG or EA. However, inhibition of FcgammaR phosphorylation prevented only FcgammaR phagocytosis. Finally, biochemical analyses of PI3K(p85)-Syk binding indicated that direct interactions between native Syk and PI3K proteins are differentially regulated during FcgammaR phagocytosis and endocytosis. Overall, our results indicate that FcgammaR endocytosis and phagocytosis differ dramatically in their requirement for Syk, SRTKs, and PI3K, pointing to striking differences in their signal transduction mechanisms. We propose a competitive inhibition-based model in which PI3K and c-Cbl play contrasting roles in the induction of phagocytosis or endocytosis signaling cascades.
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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".