Non-Redundant Activity of GSK-3α and GSK-3β in T Cell-Mediated Tumour Rejection
Bibliographic record
Abstract
Glycogen synthase kinase-3 (GSK-3α/β) has previously been identified as an upstream regulator of PD-1 gene expression in CD8+ T cells and GSK-3 inhibition is as effective as anti-PD-1 in the control of tumor growth. While GSK-3 has two co-expressed isoforms, GSK-3α and GSK-3β, their relative roles in regulating T cell activity are unclear. Here, using conditional gene targeting of each isoform, we demonstrate that both isoforms contribute to T cell function to different degrees. GSK-3β-/- mice were able to limit tumor growth to the same degree as GSK-3α/β-/- mice pointing to a dominate role for GSK-3α in tumor rejection. Interestingly, the loss of either GSK-3α or β increased expression of the transcription factor Tbet, but only the loss of GSK-3β reduced PD-1 expression, yet depletion of both isoforms led to maximum reduction of PD-1 expression. In terms of tumor infiltrating T cells (TILs), the loss of GSK-3α or β promoted an increase in interferon (IFN)-g expressing CD8+ TILs, while the individual loss of either isoform increased the presence of granzyme B (GZMB) expressing CD8+ TILs. These findings indicate that GSK-3 α or β isoforms have differential effects on PD-1, IFNg and GZMB expression, while operating in synergy to reduce PD-1 expression and promote the infiltration of tumors with CD4 and CD8 T cells. Overall, our data suggests a complex interplay of the isoforms in the control of tumour immunity.
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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) | 0.001 | 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".