mRNA Vaccine-Induced SARS-CoV-2 Spike-Specific IFN-γ and IL-2 T-cell Responses are Predictive of Serological Neutralization and are Transiently Enhanced by Pre-Existing Cross-Reactive Immunity
Bibliographic record
Abstract
The contributions of SARS-CoV-2-specific T-cells to vaccine efficacy and durability are unclear. We investigated the relationships between mRNA vaccine-induced spike-specific IFN-gamma and IL-2 T-cell responses, anti-spike/RBD IgG/IgA antibodies, and live virus neutralizing capacity in long-term-care-home staff doubly vaccinated with BNT162b2 or mRNA-1273. The impacts of pre-existing cross-reactive T-cell immunity to SARS-CoV-2 on cellular and humoral responses to vaccination were additionally assessed. Mathematical modelling of the kinetics of spike-specific IFN-gamma and IL-2 T-cell responses over 6-months post-second dose was bifurcated into recipients who exhibited gradual increases (54% and 42%, respectively) with doubling times of 173 days, or decreases (46% and 58%, respectively) with half-lives of 115 days. Differences in kinetics did not correlate with any clinical phenotypes, although increases were proposed to be due to subclinical viral exposures. Serological anti-spike/RBD IgG/IgA antibody levels had otherwise decayed in all participants with half-lives of 55, 53, 76, and 59 days, respectively. Spike-specific T-cell responses induced at 2-6 weeks correlated with live viral neutralization at 6-months post-second dose, especially in hybrid immune individuals. Participants with pre-existing cross-reactive T-cell immunity to SARS-CoV-2 exhibited greater spike-specific T-cell responses, reduced anti-RBD IgA antibody levels, and a trending increase in neutralization at 2-6 weeks post-second dose. Non-spike-specific T-cells predominantly targeted SARS-CoV-2 non-structural protein at 6-months post-second dose in cross-reactive participants. mRNA vaccination was lastly shown to induce off-target T-cell responses against unrelated antigens. In summary, vaccine-induced spike-specific T-cell immunity appeared to influence serological neutralizing capacity, with only a modest effect induced by pre-existing cross-reactivity.
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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".