Systematic examination of antigen-specific recall T cell responses to SARS-CoV-2 versus influenza virus reveals 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. 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. Work is in progress to assess long-term T cell immunity 6 months to 1 year after SARS-CoV-2 infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".