Controlling intracellular processing to enhance spherical nucleic acid immune stimulation
Bibliographic record
Abstract
To mount a robust and durable immune response, the antigen in a vaccine must be processed efficiently in the appropriate intracellular compartment. Here, we report an approach for optimizing the antigen processing pathway and efficiency by using the modular design of spherical nucleic acid (SNA) nanostructures. We utilized the substrate specificity of endoplasmic reticulum aminopeptidase1 (ERAP1), a protease known to generate major histocompatibility complex class I (MHC I) epitopes in the endoplasmic reticulum, to design two ERAP1-responsive peptide linkers (EPLs). The peptide linkers append the peptide antigen onto the SNAs to bias the processing pathway toward ERAP1 and to vary the antigen processing efficiency. The two EPLs varied ERAP1 antigen processing efficiency by 10-fold. Subsequently, the EPLs increased colocalization of the antigen with ERAP1 by up to ca. 58% when compared to an SNA that did not employ this linker. The EPL that drove more efficient cleavage, augmented antigen surface presentation by 30%, ex vivo CD8 + T cell proliferation by fivefold, and in vivo generation of proinflammatory and effector memory CD8 + T cells by 5% and 18%, respectively. Furthermore, the more efficiently processed EPL-containing SNA resulted in a 2.5-fold more effective inhibition of E.G7-OVA lymphoma tumors in vivo. Taken together, these findings underscore the importance of the deliberate and rational design of vaccine structures to spatially bias antigen processing and elevate its processing efficiency to augment immune stimulation.
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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 | 0.000 |
| 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".