Recombinant Factor VIII Fc Inhibits B Cell Activation via Engagement of the FcγRIIB Receptor
Bibliographic record
Abstract
The development of neutralizing antibodies (inhibitors) against factor VIII (FVIII) is a major complication of hemophilia A treatment. The sole clinical therapy to restore FVIII tolerance in patients with inhibitors remains immune tolerance induction (ITI) which is expensive, difficult to administer and not always successful. Although not fully understood, the mechanism of ITI is thought to rely on inhibition of FVIII-specific B cells (1). Its efficacy might therefore be improved through more aggressive B cell suppression. FcγRIIB is an inhibitory Fc receptor that down-regulates B cell signaling when cross-linked with the B cell receptor (BCR). We sought to investigate if recombinant FVIII Fc (rFVIIIFc), an Fc fusion molecule composed of FVIII and the Fc region of immunoglobulin G1 (IgG1) (2), is able to inhibit B cell activation more readily than FVIII. rFVIIIFc was able to bind FVIII-exposed and naïve B cells from hemophilia A mice as well as a FVIII-specific murine B cell hybridoma line (413 cells). An anti-FcγRIIB antibody and FVIII inhibited binding, suggesting that rFVIIIFc is able to interact with both FcγRIIB and the BCR. Furthermore, incubation of B cells from FVIII-exposed mice and 413 cells with rFVIIIFc resulted in increased phosphorylation of SH-2 containing inositol 5-phosphatase (SHIP) when compared to FVIII. B cells from FVIII-exposed hemophilia A mice also exhibited decreased extracellular signal-regulated kinase (ERK) phosphorylation when exposed to rFVIIIFc. These differences were absent in B cells from naïve, non-FVIII exposed hemophilic mice suggesting an antigen-dependent effect. Finally, rFVIIIFc was able to inhibit B cell calcium flux induced by anti-Ig F(ab)2. Our results therefore indicate that rFVIIIFc is able to crosslink FcγRIIB and the BCR of FVIII-specific B cells, causing inhibitory signaling in these cells.
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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".