CD44 Antibody Inhibition of Macrophage Phagocytosis Targets Fcγ Receptor– and Complement Receptor 3–Dependent Mechanisms
Bibliographic record
Abstract
Targeting CD44, a major leukocyte adhesion molecule, using specific Abs has been shown beneficial in several models of autoimmune and inflammatory diseases. The mechanisms contributing to the anti-inflammatory effects of CD44 Abs, however, remain poorly understood. Phagocytosis is a key component of immune system function and can play a pivotal role in autoimmune states where CD44 Abs have shown to be effective. In this study, we show that the well-known anti-inflammatory CD44 Ab IM7 can inhibit murine macrophage phagocytosis of RBCs. We assessed three selected macrophage phagocytic receptor systems: Fcγ receptors (FcγRs), complement receptor 3 (CR3), and dectin-1. Treatment of macrophages with IM7 resulted in significant inhibition of FcγR-mediated phagocytosis of IgG-opsonized RBCs. The inhibition of FcγR-mediated phagocytosis was at an early stage in the phagocytic process involving both inhibition of the binding of the target RBC to the macrophages and postbinding events. This CD44 Ab also inhibited CR3-mediated phagocytosis of C3bi-opsonized RBCs, but it did not affect the phagocytosis of zymosan particles, known to be mediated by the C-type lectin dectin-1. Other CD44 Abs known to have less broad anti-inflammatory activity, including KM114, KM81, and KM201, did not inhibit FcγR-mediated phagocytosis of RBCs. Taken together, these findings demonstrate selective inhibition of FcγR and CR3-mediated phagocytosis by IM7 and suggest that this broadly anti-inflammatory CD44 Ab inhibits these selected macrophage phagocytic pathways. The understanding of the immune-regulatory effects of CD44 Abs is important in the development and optimization of therapeutic strategies for the potential treatment of autoimmune conditions.
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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 | 0.000 |
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".