Signaling differences in peripheral blood mononuclear cells of high and low vaccine responders prior to, and following, vaccination in piglets
Bibliographic record
Abstract
Individual variability in responses to vaccination can result in vaccinated subjects failing to develop a protective immune response. Vaccine non-responders can remain susceptible to infection and may compromise efforts to achieve herd immunity. Biomarkers of vaccine unresponsiveness could aid vaccine research and development as well as strategically improve vaccine administration programs. We previously vaccinated piglets (n = 117) against a commercial Mycoplasma hyopneumoniae vaccine (RespiSure-One) and observed in low vaccine responder piglets, as defined by serum IgG antibody titers, differential phosphorylation of peptides involved in pro-inflammatory cytokine signaling within peripheral blood mononuclear cells (PBMCs) prior to vaccination, elevated plasma interferon-gamma concentrations, and lower birth weight compared to high vaccine responder piglets. In the current study, we use kinome analysis to investigate signaling events within PBMCs collected from the same high and low vaccine responders at 2 and 6 days post-vaccination. Furthermore, we evaluate the use of inflammatory plasma cytokines, birthweight, and signaling events as biomarkers of vaccine unresponsiveness in a validation cohort of high and low vaccine responders. Differential phosphorylation events (FDR < 0.05) within PBMCs are established between high and low responders at the time of vaccination and at six days post-vaccination. A subset of these phosphorylation events were determined to be consistently differentially phosphorylated (p < 0.05) in the validation cohort of high and low vaccine responders. In contrast, there were no differences in birth weight (p > 0.5) and plasma IFNγ concentrations at the time of vaccination (p > 0.6) between high and low responders within the validation cohort. The results in this study suggest, at least within this study population, phosphorylation biomarkers are more robust predictors of vaccine responsiveness than other physiological markers.
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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".