Impact of IgG1 N-glycosylation on their interaction with Fc gamma receptors
Bibliographic record
Abstract
The effector functions of the IgGs are modulated by the N-glycosylation of their Fc region. Particularly, the absence of core fucosylation is known to increase the affinity of IgG1s for the Fcγ receptor IIIa expressed by immune cells, in turn translating in an improvement in the antibody-dependent cellular cytotoxicity. However, the impact of galactosylation and sialylation is still debated in the literature. In this study, we have investigated the influence of high and low levels of core fucosylation, terminal galactosylation and terminal α2,6-sialylation of the Fc N-glycans of trastuzumab on its affinity for the FcγRIIIa. A large panel of antibody glycoforms (i.e., highly α2,6-sialylated or galactosylated IgG1s, with high or low levels of core fucosylation) were generated and characterized, while their interactions with the FcγRs were analysed by a robust surface plasmon resonance-based assay as well as in a cell-based reporter bioassay. Overall, IgG1 glycoforms with reduced fucosylation display a stronger affinity for the FcγRIIIa. In addition, fucosylation, and the presence of terminal galactose and sialic acids are shown to increase the affinity for the FcγRIIIa as compared to the agalactosylated forms. These observations perfectly translate in the response observed in our reporter bioassay.
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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".