The Quality of SARS-CoV-2–Specific T Cell Functions Differs in Patients with Mild/Moderate versus Severe Disease, and T Cells Expressing Coinhibitory Receptors Are Highly Activated
Bibliographic record
Abstract
Abstract Understanding the function of SARS-CoV-2 Ag-specific T cells is crucial for the monitoring of antiviral immunity and vaccine design. Currently, both impaired and robust T cell immunity is described in COVID-19 patients. In this study, we explored and compared the effector functions of SARS-CoV-2–reactive T cells expressing coinhibitory receptors and examine the immunogenicity of SARS-CoV-2 S, M, and N peptide pools in regard to specific effector T cell responses, Th1/Th2/Th17, in COVID-19 patients. Analyzing a cohort of 108 COVID-19 patients with mild, moderate, and severe disease, we observed that coinhibitory receptors (e.g., PD-1, CTLA-4, TIM-3, VISTA, CD39, CD160, 2B4, TIGIT, Gal-9, and NKG2A) were upregulated on both CD4+ and CD8+ T cells. Importantly, the expression of coinhibitory receptors on T cells recognizing SARS-CoV-2 peptide pools (M/N/S) was associated with increased frequencies of cytokine-producing T cells. Thus, our data refute the concept of pathological T cell exhaustion in COVID-19 patients. Despite interindividual variations in the T cell response to viral peptide pools, a Th2 phenotype was associated with asymptomatic and milder disease, whereas a robust Th17 was associated with severe disease, which may potentiate the hyperinflammatory response in patients admitted to the Intensive Care Unit. Our data demonstrate that T cells may either play a protective or detrimental role in COVID-19 patients. This finding could have important implications for immune correlates of protection, diagnostic, and prophylaxis with respect to COVID-19 management.
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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 | 0.001 |
| 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".