Engineering multivalent Fc display for FcγR blockade
Bibliographic record
Abstract
ABSTRACT Autoimmune diseases, driven by Fcγ receptor (FcγR) activation through autoantibody immune complexes (IC), present a complex therapeutic challenge of achieving pharmacological blockade of FcγR without triggering receptor activation. The assembly of ICs into polydisperse, higher-order structures is required for FcγR activation. However, engineered multimeric, monodisperse Fc assemblies have been reported to prevent FcγR activation, suggesting that Fc spatial organization determines FcγR activation. In this study, we engineered a functional single-chain Fc domain protein (scFc) for unidirectional, multivalent presentation by virus-like particles (VLPs), used as a display platform. We found that the multivalent display of scFc on the VLPs elicited distinct cellular responses compared with monovalent scFc, highlighting the importance of the structural context of scFc on its function. scFc-VLPs had minimal impact on the nanoscale spatial organization of FcγR at the cell membrane and caused limited receptor activation and internalization. In contrast, the monovalent scFc acted as an FcγR agonist, inducing receptor clustering, activation, and internalization. Increasing scFc valency in scFc-VLPs was associated with increased binding to monocytes, reaching a plateau at high valencies. Notably, the ability of scFc-VLPs to block IC-mediated phagocytosis in vitro increased with scFc valency. In a murine model of passive immune thrombocytopenia (ITP), a high valency scFc-VLP variant with a desirable immunogenicity profile induced attenuation of thrombocytopenia. Here we show that multivalent presentation of an engineered scFc on a display platform can be tailored to promote suppression of IC-mediated phagocytosis while preventing FcγR activation. This work introduces a new paradigm that can contribute to the development of therapies for autoimmune diseases.
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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) | 0.000 | 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".