Decoding human-macaque interspecies differences in Fc-effector functions: The structural basis for CD16-dependent effector function in Rhesus macaques
Bibliographic record
Abstract
Fc mediated effector functions of antibodies play important roles in immunotherapies and vaccine efficacy but assessing those functions in animal models can be challenging due to species differences. Rhesus macaques, Macaca mulatta (Mm) share approximately 93% sequence identity with humans but display important differences in their adaptive immune system that complicates their use in validating therapeutics and vaccines that rely on Fc effector functions. In contrast to humans, macaques only have one low affinity FcγRIII receptor, CD16, which shares a polymorphism at position 158 with human FcγRIIIa with Ile158 and Val158 variants. Here we describe structure-function relationships of the Ile/Val158 polymorphism in Mm FcγRIII. Our data indicate that the affinity of the allelic variants of Mm FcγRIII for the macaque IgG subclasses vary greatly with changes in glycan composition both on the Fc and the receptor. However, unlike the human Phe/Val158 polymorphism in FcγRIIIa, the higher affinity variant corresponds to the larger, more hydrophobic side chain, Ile, even though it is not directly involved in the binding interface. Instead, this side chain appears to modulate glycan-glycan interactions at the Fc/FcγRIII interface. Furthermore, changes in glycan composition on the receptor have a greater effect for the Val158 variant such that with oligomannose type glycans and with glycans only on Asn45 and Asn162, Val158 becomes the variant with higher affinity to Fc. These results have implications not only for the better interpretation of nonhuman primate studies but also for studies performed with human effector cells carrying different FcγRIIIa alleles.
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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 | 0.001 |
| 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 | 0.000 |
| 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".