Staggered immunization with mRNA vaccines encoding SARS-CoV-2 polymerase or spike antigens broadens the T cell epitope repertoire
Bibliographic record
Abstract
Combining a T cell-targeting mRNA vaccine encoding the conserved SARS-CoV-2 RNA-dependent RNA polymerase, RdRp, with a Spike-encoding mRNA vaccine may offer an additional pathway toward COVID-19 protection. Here, we show that a nucleoside-modified RdRp mRNA vaccine raises robust and durable CD8+ T cell responses in mice. Immunization drives a CD8+ T cell response enriched toward a specific RdRp epitope. Unexpectedly, coadministration of mRNA vaccines encoding RdRp or the Spike Receptor Binding Domain (RBD) dampens RBD-specific immune responses. Contralateral administration reduces the suppression of RBD-specific T cell responses while type I interferon signaling blockade restores RBD-specific antibodies. A staggered immunization strategy maintains both RBD vaccine-mediated antibody and T cell responses as well as protection against lethal SARS-CoV-2 challenge in human ACE2 transgenic mice. In HLA-A2.1 transgenic mice, the RdRp vaccine elicits CD8+ T cell responses against HLA-A*02:01-restricted epitopes recognized by human donor T cells. These results highlight RdRp as a candidate antigen for COVID-19 vaccines. The findings also offer insights into crafting effective multivalent mRNA vaccines to broaden CD8+ T cell responses against SARS-CoV-2 and potentially other viruses with pandemic potential.
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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 | 0.001 |
| 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".