Profiling MHC-I and MHC-II canonical and out-of-frame epitopes from SARS-CoV-2 and seasonal human coronavirus infected human cells
Bibliographic record
Abstract
Abstract Understanding the targets of adaptive immunity to SARS-CoV-2 is essential for vaccine development and interpretation of coronavirus disease 2019 (COVID-19) pathogenesis. SARS-CoV-2 and other human coronaviruses share substantial sequence homology raising the possibility that the previous exposure to seasonal “common cold” human coronaviruses could impact the T cell response upon SARS-CoV-2 infection. Detailed investigation of the antigenic peptides presented in SARS-CoV-2 and other common coronavirus infections would be beneficial to understand the impact of preexisting T cell mediated immunity on SARS-CoV-2 infection. The repertoire of naturally processed and presented viral peptides by MHC II upon SARS-CoV-2 and seasonal human coronaviruses infections remains largely uncharacterized. Here, we report the MHC I and II immunopeptidome of cells infected with SARS-CoV-2 or hCoV-OC43, one of the seasonal coronaviruses. We identified 11 MHC I peptides and 13 MHC II peptides of SARS-CoV-2 infected cells including both canonical and out-of-frame spike epitopes, 27 MHC I and 91 MHC II peptides from membrane, nucleocapsid, spike and hemagglutinin-esterase from OC43 infected cells using mass spectrometry. We validated some peptides using HLA peptide binding assays, and these peptides were shown to recall T cell responses in donors with presumed history of common cold viral infection. Some of these peptides share substantial homology with common cold coronaviruses, indicating possibility of conserved T cell epitopes eliciting the protective immunity in COVID-19. The identification of naturally presented peptides and their striking homology with other coronavirus could aid in the peptide selection for the vaccine development.
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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".