Role of the Notch-TRIB2 axis in CD8+ T cell differentiation
Bibliographic record
Abstract
Abstract Following activation, naïve CD8+ T cells expand and differentiate into two effector subpopulations: short-lived effector cells (SLECs) and memory precursor effector cells (MPECs). After the effector response has taken place, SLECs undergo apoptosis, while MPECs mature into memory T cells that protect against reinfection. Notch, a highly conserved pathway, was shown to control SLEC differentiation. Our transcriptomic analysis has revealed that Notch signalling controls the transcription of a large number of genes in activated CD8+ T cells. Among them is Trib2, coding for the pseudokinase Tribbles homolog 2. TRIB2 was reported to activate AKT in some tumour cell lines while Notch-deficient CD8+ T cells show decreased AKT activation, suggesting that Notch transcriptional induction of Trib2 might be important to properly activate AKT and promote SLEC differentiation. Therefore, we hypothesize that induction of TRIB2 expression by Notch signalling promotes SLEC differentiation through AKT activation. To test this, we overexpressed TRIB2 in Notch1–2 deficient CD8+ T cells using retroviral transduction and followed their differentiation after their adoptive transfer into Listeria infected mice. Our results show that overexpression of TRIB2 partially restores SLEC generation in absence of Notch. In conclusion, we uncovered that TRIB2 promotes SLEC differentiation through the Notch pathway. These findings provide a better understanding of the molecular events controlling the differentiation of SLECs, a cell type essential for the elimination of infected cells and cancer.
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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 | 0.000 |
| 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".