Endogenous antigen processing promotes mRNA vaccine CD4 <sup>+</sup> T cell responses
Bibliographic record
Abstract
ABSTRACT Lipid nanoparticle (LNP)-encapsulated nucleoside-modified mRNA vaccines elicit robust CD4 + T cell responses, which are essential for antiviral immunity 1–3 . While peptides presented to CD4 + T cells via major histocompatibility complex class II (MHC II) are traditionally thought to be derived from extracellular sources that are processed by antigen presenting cells (APCs) through the classical exogenous pathway 4,5 , the precise mechanisms of mRNA-LNP vaccine-specific CD4 + T cell priming remain unknown. Here, we investigated the role of alternative, endogenous antigen presentation pathways 6,7 in inducing CD4 + T cell responses to mRNA-LNP vaccines. APCs treated with mRNA-LNP vaccines were consistently superior in activating T cells under conditions of endogenous, rather than exogenous, presentation. Immunization with an mRNA-LNP vaccine that excludes antigen expression in APCs resulted in lower antigen-specific CD4 + T cell, T follicular helper cell, and antibody responses than mice receiving control vaccine. In contrast, depletion of vaccine antigen from exogenous sources such as muscle cells resulted in little to no reduction in antigen-specific CD4 + T cells. Our findings demonstrate that direct presentation of endogenous antigen on MHC II is crucial to mRNA-LNP vaccine-induced immune responses and adds to a growing body of literature that redefines the paradigm of MHC II-restricted antigen processing and presentation.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".