Lipid-nanoparticle-encapsulated mRNA vaccines induce protective memory CD8 T cells against a lethal viral infection
Bibliographic record
Abstract
It is well established that memory CD8 T cells protect susceptible strains of mice from mousepox, a lethal viral disease caused by ectromelia virus (ECTV), the murine counterpart to human variola virus. While mRNA vaccines induce protective antibody (Ab) responses, it is unknown whether they also induce protective memory CD8 T cells. We now show that immunization with different doses of unmodified or N(1)-methylpseudouridine-modified mRNA (modified mRNA) in lipid nanoparticles (LNP) encoding the ECTV gene EVM158 induced similarly strong CD8 T cell responses to the epitope TSYKFESV, albeit unmodified mRNA-LNP had adverse effects at the inoculation site. A single immunization with 10 μg modified mRNA-LNP protected most susceptible mice from mousepox, and booster vaccination increased the memory CD8 T cell pool, providing full protection. Moreover, modified mRNA-LNP encoding TSYKFESV appended to green fluorescent protein (GFP) protected against wild-type ECTV infection while lymphocytic choriomeningitis virus glycoprotein (GP) modified mRNA-LNP protected against ECTV expressing GP epitopes. Thus, modified mRNA-LNP can be used to create protective CD8 T cell-based vaccines against viral infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".