rIgG1 Fc Hexamer Inhibits Antibody-Mediated Autoimmune Disease via Effects on Complement and FcγRs
Bibliographic record
Abstract
Activation of Fc receptors and complement by immune complexes is a common important pathogenic trigger in many autoimmune diseases and so blockade of these innate immune pathways may be an attractive target for treatment of immune complex-mediated pathomechanisms. High-dose IVIG is used to treat autoimmune and inflammatory diseases, and several studies demonstrate that the therapeutic effects of IVIG can be recapitulated with the Fc portion. Further, recent data indicate that recombinant multimerized Fc molecules exhibit potent anti-inflammatory properties. In this study, we investigated the biochemical and biological properties of an rFc hexamer (termed Fc-μTP-L309C) generated by fusion of the IgM μ-tailpiece to the C terminus of human IgG1 Fc. Fc-μTP-L309C bound FcγRs with high avidity and inhibited FcγR-mediated effector functions (Ab-dependent cell-mediated cytotoxicity, phagocytosis, respiratory burst) in vitro. In addition, Fc-μTP-L309C prevented full activation of the classical complement pathway by blocking C2 cleavage, avoiding generation of inflammatory downstream products (C5a or sC5b-9). In vivo, Fc-μTP-L309C suppressed inflammatory arthritis in mice when given therapeutically at approximately a 10-fold lower dose than IVIG, which was associated with reduced inflammatory cytokine production and complement activation. Likewise, administration of Fc-μTP-L309C restored platelet counts in a mouse model of immune thrombocytopenia. Our data demonstrate a potent anti-inflammatory effect of Fc-μTP-L309C in vitro and in vivo, likely mediated by blockade of FcγRs and its unique inhibition of complement activation.
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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".