Glycomic Profiling Unveils Association Between Serum Glycosylation and Antibody Responses to Influenza Vaccines
Bibliographic record
Abstract
Despite being the most powerful approach to tackle influenza infections, current influenza vaccines have a mixed record of protection across the population. In the 2019‐2020 flu season, flu vaccines in the U.S. were ~ 40% effective. High‐risk populations, such as the obese and elderly, tend to have weaker responses to influenza vaccines, the molecular mechanisms behind which remain poorly understood. Glycans play critical roles in the human immune system. In this study, we examined serum glycosylation of 160 Caucasian adults pre‐ and post‐vaccination with the 2019 Fluzone® Quadrivalent Vaccine using an expanded version of our lectin microarray technology that included antibodies against innate immune lectins and select sera glycoproteins. Glycosylation profiles were examined in light of post‐vaccination antibody responses. Results showed that obese participants who exhibited poor antibody responses (non‐responders) to the vaccines had higher levels of pre‐vaccination innate immune lectins and high mannose glycans, suggesting a potential role of pre‐existing, lectin‐mediated immunity in the determination of vaccination outcomes. We observed widespread shifts in glycosylation in all participants associated with vaccination, possibly a result of antibody production. Non‐responders and responders showed strikingly similar degrees of glycosylation changes. However, we observed higher levels of β1,6‐branched N‐glycans in responders, indicating a correlation of this specific glycan epitope with effective antibody responses. Our findings reveal the association of serum glycosylation with antibody responses to vaccination for influenza and may point to new directions in the development of better vaccines.
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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".