An <i>Il12</i> mRNA-LNP adjuvant enhances mRNA vaccine–induced CD8 T cell responses
Bibliographic record
Abstract
Optimizing vaccine design to induce CD8 T cell responses has been challenging, but lipid nanoparticle (LNP)–encapsulated mRNA vaccines effectively generate CD8 T cell memory. Interleukin-12 (IL-12) supports CD8 T cell expansion and acquisition of effector function, but the role of IL-12 in the generation of CD8 T responses to mRNA vaccination is unclear. Here, we determine that endogenous IL-12 is not required for CD8 T cell responses to mRNA-LNP vaccination. We assessed the adjuvant activity of an mRNA-LNP encapsulating a codon-optimized mRNA that encodes both subunits of IL-12 (LNP–IL-12). Coadministration of LNP–IL-12 with ovalbumin (OVA) mRNA-LNPs enhanced CD8 T cell expansion and effector function and expanded circulating, effector, and tissue-resident memory CD8 T cells. LNP–IL-12 increased CD8 T cell responses against SARS-CoV-2 and influenza virus antigens and improved protection against Listeria monocytogenes –OVA and B16F0-OVA melanoma. Thus, modification of mRNA-LNP formulations to include a cytokine mRNA provides a strategy to enhance CD8 T cell–mediated protection.
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".