Functional Association of CD9 with the Fcγ Receptors in Macrophages
Bibliographic record
Abstract
CD9, a member of the tetraspan family of proteins, is highly expressed on macrophages. Although a clear function for the molecule has yet to be described, we have found that the anti-CD9 mAb activates mouse macrophages. The rat anti-CD9 mAb, KMC8.8, but not the F(ab')(2), induced tyrosine phosphorylation of proteins including syk and cbl and induced cell aggregation in the mouse macrophage cell line, J774, suggesting that co-cross-linking of CD9 and Fc gamma R was required for the signal. Co-cross-linking of CD9-Fc gamma R with KMC8.8 on macrophages from three different FcR-deficient mice, FcR gamma-chain(-/-), Fc gamma RIIB(-/-), and Fc gamma RIII(-/-), revealed that Fc gamma RIII is specific and crucial for syk phosphorylation. Although both KMC8.8 and the anti-Fc gamma RIIB/III mAb, 2.4G2, evoked similar phosphorylation patterns, only KMC8.8 induced cell aggregation. Additionally, KMC8.8 treatment led to reduce levels of TNF-alpha production and p42/44 extracellular signal-related kinase phosphorylation relative to 2.4G2 stimulation. Immunofluorescence staining showed that co-cross-linking of CD9-Fc gamma R with KMC8.8 induced filopodium extension before cell aggregation, which was followed by simultaneous colocalization of CD9, Fc gamma RIIB/III, Mac-1, ICAM-1, and F-actin at the cell-cell adhesion site. Moreover, KMC8.8 treatment of Fc gamma R-deficient macrophages revealed that the colocalization of CD9, Fc gamma RIII, Mac-1, and F-actin requires co-cross-linking of CD9-Fc gamma RIII, whereas co-cross-linking of CD9-Fc gamma RIIB induced the colocalization of only CD9 and Fc gamma RIIB. Our results demonstrate that co-cross-linking of CD9 and Fc gamma Rs activates macrophages; therefore, CD9 may collaborate with FcRs functioning in infection and inflammation on macrophages.
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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 | 0.001 |
| 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".