Integrated Immunopeptidomics and Proteomics Study of SARS-CoV-2–Infected Calu-3 Cells Reveals Dynamic Changes in Allele-specific HLA Abundance and Antigen Presentation
Bibliographic record
Abstract
We present an integrated immunopeptidomics and proteomics study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection to comprehensively decipher the changes in host cells in response to viral infection. Immunopeptidomics analysis identified viral antigens presented by host cells through both class I and class II MHC system for recognition by the adaptive immune system. The host proteome changes were characterized by quantitative proteomics and glycoproteomics and from these data, the activation of toll-like receptor 3-interferon pathway was identified. Glycosylation analysis of human leukocyte antigen (HLA) proteins from the elution and flow-through of immunoprecipitation revealed that SARS-CoV-2 infection changed the glycosylation pattern of certain HLA alleles with different HLA alleles, showing distinct dynamic changes in relative abundance. The difference in the glycosylation and abundance of HLA alleles changed the number of strong binding antigens each allele presented, suggesting the impact of SARS-CoV-2 infection on antigen presentation is allele-specific. These results could be further exploited to explain the imbalanced response from innate and adaptive immune system in coronavirus disease 2019 cases, which would be helpful for the development of therapeutics and vaccine for coronavirus disease 2019 and preparation for future pandemic.
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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) | 0.001 | 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".