Systematic Examination of Antigen-Specific Recall T Cell Responses to SARS-CoV-2 versus Influenza Virus Reveals a Distinct Inflammatory Profile
Bibliographic record
Abstract
Abstract There is a pressing need for an in-depth understanding of immunity to SARS-CoV-2. In this study, we investigated human T cell recall responses to fully glycosylated spike trimer, recombinant N protein, as well as to S, N, M, and E peptide pools in the early convalescent phase and compared them with influenza-specific memory responses from the same donors. All subjects showed SARS-CoV-2–specific T cell responses to at least one Ag. Both SARS-CoV-2–specific and influenza-specific CD4+ T cell responses were predominantly of the central memory phenotype; however SARS-CoV-2–specific CD4+ T cells exhibited a lower IFN-γ to TNF ratio compared with influenza-specific memory responses from the same donors, independent of disease severity. SARS-CoV-2–specific T cells were less multifunctional than influenza-specific T cells, particularly in severe cases, potentially suggesting exhaustion. Most SARS-CoV-2–convalescent subjects also produced IFN-γ in response to seasonal OC43 S protein. We observed granzyme B+/IFN-γ+, CD4+, and CD8+ proliferative responses to peptide pools in most individuals, with CD4+ T cell responses predominating over CD8+ T cell responses. Peripheral T follicular helper (pTfh) responses to S or N strongly correlated with serum neutralization assays as well as receptor binding domain–specific IgA; however, the frequency of pTfh responses to SARS-CoV-2 was lower than the frequency of pTfh responses to influenza virus. Overall, T cell responses to SARS-CoV-2 are robust; however, CD4+ Th1 responses predominate over CD8+ T cell responses, have a more inflammatory profile, and have a weaker pTfh response than the response to influenza virus within the same donors, potentially contributing to COVID-19 disease.
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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) | 0.002 | 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".