Long-persisting SARS-CoV-2 spike-specific CD4+ T cells associated with mild disease and increased cytotoxicity post COVID-19
Bibliographic record
Abstract
Abstract The recent COVID-19 pandemic left behind the lingering question as whether new variants of concern might cause further waves of infection. Thus, it is important to investigate the long-term protection gained via vaccination or exposure to the SARS-CoV-2 virus. Here we compare the evolution of memory T-cell responses following primary infection with subsequent antigen exposures. Single-cell TCR analysis of three dominant SARS-CoV-2 spike-specific CD4+ T-cell responses identifies the dominant public TCRα clonotypes pairing with diverse TCRβ clonotypes that associated with mild disease at primary infection. These clonotypes are found at higher frequencies in pre-pandemic repertoires compared to other epitope-specific clonotypes. Longitudinal transcriptomics and TCR analysis, combined with functional evaluation, reveals that the clonotypes persisting 3–4 years post initial infection exhibit distinct functionality compared to those that were lost. Furthermore, spike-specific CD4+ T cells at this time point show decreased Th1 signatures and enhanced GZMA-driven cytotoxic transcriptomic profiles that were independent of TCR clonotype and associated with viral suppression. In summary, we identify common public TCRs used by immunodominant spike-specific memory CD4+ T-cells, associated with mild disease outcome, which likely play important protective roles to subsequent viral infection events.
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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".