Glycomic Analysis Identifies Pre-Vaccination Markers of Response to Influenza Vaccine, Implicating the Complement Pathway
Bibliographic record
Abstract
ABSTRACT Response to vaccination can vary significantly from person to person. A key to improving vaccine design and vaccination strategy is to understand the mechanism behind this variation. The role of glycosylation, a critical modulator of immunity, is unknown in determining vaccine responses. To gain insight into the association between glycosylation and vaccine-induced antibody levels we profiled the pre- and post-vaccination serum protein glycomes of 160 Caucasian adults receiving the FLUZONE™ influenza vaccine during the 2019-2020 influenza season. Using lectin microarrays, we observed that pre-vaccination levels of Lewis A antigen (Le a ) are significantly higher in people who did not mount significant antibody responses, when compared to responders. Glycoproteomic analysis showed that Le a -bearing proteins are enriched in complement activation pathways, suggesting a potential role of glycosylation in tuning the activities of complement proteins, which may be implicated in mounting vaccine responses. We also observed post-vaccination increases in sialyl Lewis X antigen (sLe x ) and decreases in high mannose glycans among high responders, which were not observed in non-responders. This data suggests that the immune system may actively modulate glycosylation as part of its effort to establish effective protection post-vaccination.
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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) | 0.002 | 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".