Formulation, inflammation, and RNA sensing impact the immunogenicity of self-amplifying RNA vaccines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To be effective, RNA vaccines require both in situ translation and the induction of an immune response to recruit cells to the site of immunization. These factors can pull in opposite directions with the inflammation reducing expression of the vaccine antigen. We investigated how formulation affects the acute systemic cytokine response to a self-amplifying RNA (saRNA) vaccine. We compared a cationic polymer (pABOL), a lipid emulsion (nanostructured lipid carrier, NLC), and three lipid nanoparticles (LNP). After immunization, we measured serum cytokines and compared the response to induced antibodies against influenza virus. Formulations that induced a greater cytokine response induced a greater antibody response, with a significant correlation between IP-10, MCP-1, KC, and antigen-specific antibody titers. We then investigated how innate immune sensing and signaling impacted the adaptive immune response to vaccination with LNP-formulated saRNA. Mice that lacked MAVS and are unable to signal through RIG-I-like receptors had an altered cytokine response to saRNA vaccination and had significantly greater antibody responses than wild-type mice. This indicates that the inflammation induced by formulated saRNA vaccines is not solely deleterious in the induction of antibody responses and that targeting specific aspects of RNA vaccine sensing might improve the quality of the response. To be effective, RNA vaccines require both in situ translation and the induction of an immune response to recruit cells to the site of immunization. These factors can pull in opposite directions with the inflammation reducing expression of the vaccine antigen. We investigated how formulation affects the acute systemic cytokine response to a self-amplifying RNA (saRNA) vaccine. We compared a cationic polymer (pABOL), a lipid emulsion (nanostructured lipid carrier, NLC), and three lipid nanoparticles (LNP). After immunization, we measured serum cytokines and compared the response to induced antibodies against influenza virus. Formulations that induced a greater cytokine response induced a greater antibody response, with a significant correlation between IP-10, MCP-1, KC, and antigen-specific antibody titers. We then investigated how innate immune sensing and signaling impacted the adaptive immune response to vaccination with LNP-formulated saRNA. Mice that lacked MAVS and are unable to signal through RIG-I-like receptors had an altered cytokine response to saRNA vaccination and had significantly greater antibody responses than wild-type mice. This indicates that the inflammation induced by formulated saRNA vaccines is not solely deleterious in the induction of antibody responses and that targeting specific aspects of RNA vaccine sensing might improve the quality of the response.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it