Distinct CD8 <sup>+</sup> T-cell types Associated with COVID-19 Severity in Unvaccinated HLA-A2 <sup>+</sup> Patients
Bibliographic record
Abstract
Abstract Although emerging data have revealed the critical role of memory CD8 + T cells in preventing and controlling SARS-CoV-2 infection, virus-specific CD8 + T-cell responses against SARS-CoV-2 and its memory and innate-like subsets in unvaccinated COVID-19 patients with various disease manifestations in an HLA-restricted fashion remain to be understood. Here, we show the strong association of protective cellular immunity with mild COVID-19 and unique cell types against SARS-CoV-2 virus in an HLA-A2 restricted manner. ELISpot assays reveal that SARS-CoV-2-specific CD8 + T-cell responses in mild COVID-19 patients are significantly higher than in severe patients, whereas neutralizing antibody responses against SARS-CoV-2 virus significantly correlate with disease severity. Single-cell analyses of HLA-A2-restricted CD8 + T cells, which recognize highly conserved immunodominant SARS-CoV-2-specific epitopes, demonstrate divergent profiles in unvaccinated patients with mild versus severe disease. CD8 + T-cell types including cytotoxic KLRB1 + CD8αα cells with innate-like T-cell signatures, IFNG hi ID3 hi memory cells and IL7R + proliferative stem cell-like memory cells are preferentially observed in mild COVID-19, whereas distinct terminally-differentiated T-cell subsets are predominantly detected in severe COVID-19: highly activated FASL hi T-cell subsets and early-terminated or dysfunctional IL4R + GATA3 + stem cell-like memory T-cell subset. In conclusion, our findings suggest that unique and contrasting SARS-CoV-2-specific CD8 + T-cell profiles may dictate COVID-19 severity. Abstract Figure Graphical abstract. SARS-CoV-2 epitope-specific CD8 + T-cell subtypes associated with mild or severe COVID-19 patients. Potent memory CD8 + T-cell subtypes with gene signature in mild COVID-19 patients (upper) and dysfunctional CD8 + T-cell subtypes with gene signature in severe COVID-19 patients (lower).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".