JNK2 negatively regulates CD8<sup>+</sup> T cell effector function and anti‐tumor immune response
Bibliographic record
Abstract
JNK1 and JNK2 have distinct effects on activation, differentiation and function of CD8+ T cells. Our early studies demonstrated that JNK1 is required for CD8+ T cell-mediated tumor immune surveillance. However, the role of JNK2 in CD8+ T cell response and effector functions, especially in anti-tumor immune response, is unknown. To define the role of JNK2 in antigen-specific immune response, we have investigated CD8+ T cells from OT-1 CD8+ transgenic mice in response to either high- or low-affinity peptides. JNK2-/- CD8+ T cells proliferated better in response to both peptides, with more cell division and less cell death. In addition, JNK2-/- CD8+ T cells produced higher levels of IFN-gamma, which is associated with increased expression of T-bet and Eomesodermin (Eomes). Moreover, JNK2-/- CD8+ T cells expresses high levels of granzyme B and show increased CTL activity. Finally, the enhanced expansion and effector function of JNK2-/- CD8+ T cells was further evidenced by their capacity to delay tumor growth in vivo. In summary, our results demonstrate that JNK2 negatively regulates antigen-specific CD8+ T cell expansion and effector function, and thus selectively blocking JNK2 in CD8+ T cells may potentially enhance anti-tumor immune response.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".