Duration and Strength of Extracellular Signal-Regulated Kinase Signals Are Altered During Positive Versus Negative Thymocyte Selection
Bibliographic record
Abstract
During thymocyte development, high-affinity/avidity TCR engagement leads to the induction of negative selection and apoptosis, while lower TCR affinity-avidity interactions lead to positive selection and survival. To elucidate how these extracellular interactions are translated into intracellular signals that distinguish between positive and negative selection, we developed a culture system in which naive double-positive thymocytes were either induced to differentiate along the CD8(+) lineage pathway or were triggered for clonal deletion. Using this system, we show that sustained low level activation of extracellular signal-regulated kinases (ERKs) promotes positive selection, whereas strong but transient ERK activation is coupled with negatively selecting stimuli. Importantly, similar ERK activation profiles were demonstrated during positive selection for strong agonist ligands presented at low concentrations or weak agonist ligands. This is consistent with the affinity/avidity model and a role for strong or weak agonists during positive selection. Surprisingly, the addition of a pharmacological inhibitor which blocks ERK activation prevented the induction of negative selection. These data suggest that the duration and strength of the TCR signal is involved in discriminating between positive and negative selection.
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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".