Dok-1 and Dok-2 Regulate the Formation of Memory CD8+ T Cells
Bibliographic record
Abstract
Abstract Diverse signals received by CD8+ T cells are integrated to achieve the required magnitude of cell expansion and the appropriate balance of effector/memory CD8+ T cell generation. Notably, the strength and nature of TCR signaling influence the differentiation and functional capacity of effector and memory CD8+ T cells. Dok-1 and Dok-2, the two members of the Dok family expressed in T cells, negatively regulate TCR signaling in vitro. However, the role of Dok proteins in modulating T cell function in vivo has not yet studied. We studied the function of Dok-1 and Dok-2 proteins in the regulation of the CD8+ T cell response to vaccinia virus infection. Comparison of responses to vaccinia virus expressing OVA peptide SIINFEKL by wild-type and Dok-1/2−/− CD8+ OT-I cells showed that the absence of Dok-1 and Dok-2 slightly reduced the magnitude of virus-specific effector CD8+ T cell expansion. This was not due to reduced proliferation or enhanced apoptosis of effector CD8+ T cells. Dok-1/2–deficient effector CD8+ T cells showed increased cell surface TCR expression following virus infection in vivo and increased expression of granzyme B and TNF upon stimulation with peptide Ag ex vivo. Finally, Dok-1/2–deficient effector CD8+ T had a severe defect in survival that resulted in impaired generation of memory CD8+ T cells. These results reveal the critical involvement of Dok-1 and Dok-2 in a negative-feedback loop that prevents overactivation of CD8+ T cells and promotes memory formation.
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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) | 0.001 | 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".