Roles of GSK-3α and GSK-3β in T cell-mediated tumour rejection 3299
Bibliographic record
Abstract
Abstract Description Glycogen synthase kinase-3 (GSK-3) has been shown as an upstream regulator of programmed cell death protein 1 (PD-1) and Lymphocyte activation gene-3 (LAG-3) gene expression in CD8 T cells and its inhibition can suppress tumour growth. However, the relative roles of the individual isoforms of GSK-3: a and b, in the modulation of PD-1 and protective immunity against cancer are yet to be elucidated. Here, we have generated individual a and b isoform-specific conditional KO (cKO) mice, resulting in mice with T cells devoid of either isoform. In vivo tumour models, have shown both isoforms to contribute to T cell function to different degrees. Interestingly, the loss of either isoform increased expression of the transcription factor Tbet, however, optimal reduction of PD-1 expression required the depletion of both isoforms. GSK-3b cKO mice were able to limit solid tumour growth to the same degree as GSK-3a/b cKO mice, pointing to a dominate role for GSK-3b in regulating tumour growth. However, depletion of the alpha isoform alone offered higher protection against intracranial tumours, which surpassed the impact of knocking out either the beta isoform alone or both isoforms. Both isoforms were found to have differential effects on IFNg and GZMB expression, while operating in synergy to reduce PD-1 expression and promote the infiltration of tumours with CD4 and CD8 T cells. Overall, our data suggests a complex interplay between the two isoforms in the control of tumour immunity. Funding Sources Supported by MRC MR/V033336/1 Topic Categories Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
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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 | 0.001 | 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".