Immunity against a therapeutic xenoprotein/Fc construct delivered by gene transfer is reduced through binding to the inhibitory receptor FcγRIIb
Bibliographic record
Abstract
BACKGROUND: Therapeutic xenoproteins are immunogenic and can induce neutralizing antibodies. When delivered by intramuscular injection of a plasmid vector, this mimics classical DNA vaccination. To demonstrate this, we chose Exendin-4 (Ex4), which is a glucagon-like peptide-1 mimetic xenoprotein in clinical use for treating type 2 diabetes. We constructed an Ex4 and mouse immunoglobulin (Ig)G1-Fc fusion fragment (Ex4/Fc), and hypothesized that it would have minimal immunogenicity as a result of its capacity to bind the inhibitory Fc receptor FcγRIIb expressed by B lymphocytes. METHODS: Plasmid vectors encoding Ex4/Fc constructs, with wild-type or mutant Fc, were injected intramuscularly into mice, and local electroporation was applied to enhance gene transfer. Gene transfer was performed in both wild-type and FcγRIIb knockout mice. Antibody production was detected in serum by an enzyme-linked immunosorbent assay. RESULTS: Recombinant Ex4/Fc bound only to B cells expressing FcγRIIb. This binding was dependent on a motif in the Fc region, which we mutated to abolish binding (Ex4/Fcmut). Ex4 antibody was detected in mice treated with Ex4, as well as Ex4/Fcmut, but not in those treated with Ex4/Fc. Thus, wild-type Fc was associated with reduced immunogenicity. To confirm this was related to the presence of inhibitory Fc receptors, we also performed experiments in FcγRIIb-null mice. Mice lacking this receptor produced antibodies against all Ex4 constructs, including the wild-type Fc (Ex4/Fc). CONCLUSIONS: The present study shows that inhibitory FcγRIIb receptors interacting with the wild-type IgG1-Fc reduce immunity against Ex4/Fc, suggesting an approach for reducing the immunogenicity of therapeutic proteins in the context of gene therapy.
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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) | 0.001 | 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) | 0.002 | 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".