SARS-CoV-2 mutations affect proteasome processing to alter CD8 <sup>+</sup> T cell responses
Bibliographic record
Abstract
Abstract Viral CD8 + epitopes are generated by the cellular turnover of viral proteins, predominantly by the proteasome. Mutations located within viral epitopes can result in escape from memory T cells but the contribution of mutations in flanking regions of epitopes in SARS-CoV-2 has not been investigated. Focusing on two of the most dominant SARS-CoV-2 nucleoprotein CD8 + epitopes, we identified mutations in epitope flanking regions and investigated the contribution of these mutations to antigen processing and T cell activation using SARS-CoV-2 nucleoprotein transduced B cell lines and in vitro proteasomal processing of peptides. We found that decreased NP 9-17 -B*27:05 CD8 + T cell responses to the NP-Q7K mutation correlated with lower epitope surface expression, likely due to a lack of efficient epitope production by the proteasome, suggesting immune escape caused by this mutation. In contrast, NP-P6L and NP-D103N/Y mutations flanking the NP 9-17 -B*27:05 and NP 105-113 -B*07:02 epitopes, respectively, increased CD8 + T cell responses associated with enhanced epitope production by the proteasome. Our results provide evidence that SARS-CoV-2 mutations outside the epitope could have a significant impact on antigen processing and presentation, thereby contributing to escape from immunodominant T cell responses. Alternatively, mutations could enhance antigen processing and efficacy of T cell recognition, opening new avenues for improving future vaccine designs. One Sentence Summary Natural mutations in the flanking regions of known immunodominant SARS-CoV-2 nucleoprotein epitopes can decrease CD8 + T cell responses leading to partial escape.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".