Decreased progenitor TCF1 + T-cells correlate with COVID-19 disease severity
Bibliographic record
Abstract
COVID-19, caused by SARS-CoV-2, can lead to a severe inflammatory disease characterized by significant lymphopenia. However, the underlying cause for the depletion of T-cells in COVID-19 patients remains incompletely understood. In this study, we assessed the presence of different T-cell subsets in the progression of COVID-19 from mild to severe disease, with a focus on TCF1 expressing progenitor T-cells that are needed to replenish peripheral T-cells during infection. Our results showed a preferential decline in TCF1+ progenitor CD4 and CD8+ T-cells with disease severity. This decline was seen in various TCF1+ subsets including naive, memory and effector-memory cells, and surprisingly, was accompanied by a loss in cell division as seen by a marked decline in Ki67 expression. In addition, TCF1+ T-cells showed a reduction in pro-survival regulator, BcL2, and the appearance of a new population of TCF1 negative caspase-3 expressing cells in peripheral blood from patients with severe disease. The decline in TCF1+ T-cells was also seen in a subgroup of severe patients with vitamin D deficiency. Lastly, we found that sera from severe patients inhibited TCF1 transcription ex vivo which was attenuated by a blocking antibody against the cytokine, interleukin-12 (IL12). Collectively, our findings underscore the potential significance of TCF1+ progenitor T-cells in accounting for the loss of immunity in severe COVID-19 and outline an array of markers that could be used to identify disease progression.
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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 | 0.001 | 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".