Anti-inflammatory activity of CD44 antibodies in murine immune thrombocytopenia is mediated by Fcγ receptor inhibition
Bibliographic record
Abstract
Monoclonal immunoglobulin G (IgG) antibodies to CD44 (anti-CD44) are anti-inflammatory in numerous murine autoimmune models, but the mechanisms are poorly understood. Anti-CD44 anti-inflammatory activity shows complete therapeutic concordance with IV immunoglobulin (IVIg) in treating autoimmune disease models, making anti-CD44 a potential IVIg alternative. In murine immune thrombocytopenia (ITP), there is no mechanistic explanation for anti-CD44 activity, although anti-CD44 ameliorates disease similarly to IVIg. Here, we demonstrate a novel anti-inflammatory mechanism of anti-CD44 that explains disease amelioration by anti-CD44 in murine ITP. Macrophages treated with anti-CD44 in vitro had dramatically suppressed phagocytosis through FcγRs in 2 separate systems of IgG-opsonized platelets and erythrocytes. Phagocytosis inhibition by anti-CD44 was mediated by blockade of the FcγR IgG binding site without changing surface FcγR expression. Anti-CD44 of different subclasses revealed that FcγR blockade was specific to receptors that could be engaged by the respective anti-CD44 subclass, and Fc-deactivated anti-CD44 variants lost all FcγR-inhibiting activity. In vivo, anti-CD44 functioned analogously in the murine passive ITP model and protected mice from ITP when thrombocytopenia was induced through an FcγR that could be engaged by the CD44 antibody's subclass. Consistent with FcγR blockade, Fc-deactivated variants of anti-CD44 were completely unable to ameliorate ITP. Together, anti-CD44 inhibits macrophage FcγR function and ameliorates ITP consistent with an FcγR blockade mechanism. Anti-CD44 is a potential IVIg alternative and may be of particular benefit in ITP because of the significant role that FcγRs play in human ITP pathophysiology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".