Notch signalling shapes the CD8 T cell response following <i>Listeria</i> infection (P1416)
Bibliographic record
Abstract
Abstract Following an infection, naïve CD8 T cells expand and differentiate into effectors able to eliminate the pathogen. At the peak of the response, two populations of effectors are distinguishable: short-lived effector cells (SLECs) meant to die by apoptosis and memory precursor effector cells (MPECs) destined to survive as memory cells that will confer long-term protection. Thus, following activation, the CD8 T cell faces a binary cell fate decision. We postulate that the Notch signalling pathway, known for its role in cellular differentiation and binary cell fate choice, acts as a key player in the MPEC/SLEC choice. To elucidate the role of Notch signalling in CD8 T cell response, we infected mice lacking or not expression of Notch1 and Notch2 in mature CD8 T cells with Listeria monocytogenes expressing OVA. Notch deficiency led to the generation of more OVA-specific effector CD8 T cells but less of these effectors had a SLEC phenotype (CD127loKLRG1hi) at the peak of the response. Surprisingly, Notch did not impair CD8 T cell memory generation following infection. Thus, Notch signalling influences the expansion and the acquisition of a SLEC phenotype following Listeria infection but not the generation of memory cells. Understanding the molecular pathways leading to memory generation is crucial as this knowledge will ultimately contribute to new vaccine development.
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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) | 0.001 | 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".