Impact of N-glycosylation on Fcγ receptor / IgG interactions: unravelling differences with an enhanced surface plasmon resonance biosensor assay based on coiled-coil interactions
Bibliographic record
Abstract
The N-glycosylation profile of immunoglobulin G (IgG) is considered a critical quality attribute due to its impact on IgG-Fc gamma receptor (FcγR) interactions, which subsequently affect antibody-dependent cell-based immune responses. In this study, we investigated the impact of the FcγR capture method, as well as FcγR N-glycosylation, on the kinetics of interaction with various glycoforms of trastuzumab (TZM) in a surface plasmon resonance (SPR) biosensor assay. More specifically, we developed a novel strategy based on coiled-coil interactions for the stable and oriented capture of coil-tagged FcγRs at the biosensor surface. Coil-tagged FcγR capture outperformed all other capture strategies applied to the SPR study of IgG-FcγR interactions, as the robustness and reproducibility of the assay and the shelf life of the biosensor chip were excellent (> 1,000 IgG injections with the same biosensor surface). Coil-tagged FcγRs displaying different N-glycosylation profiles were generated either by different expression systems, in vitro glycoengineering or by size-exclusion chromatography, and roughly characterized by lectin blotting. Of salient interest, the overlay of their kinetics of interaction with several TZM glycoforms revealed key differences on both association and dissociation kinetics, confirming a complex influence of the FcγR N-glycosylation and its inherent heterogeneity upon receptor interaction with mAbs. This work is thus an important step towards better understanding of the impact of glycosylation upon binding of IgGs, either natural or engineered, to their receptors.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".