Weak agonist self‐peptides promote selection and tuning of virus‐specific T cells
Bibliographic record
Abstract
Recent progress has begun to define the interactions and signaling pathways that are triggered during positive selection. To identify and further examine self-peptides that can mediate positive selection, we searched a protein-database to find peptides that have minimal homology with the viral peptide (p33) that activates a defined P14 transgenic TCR. We identified four peptides that could bind the restriction element H-2D(b) and induce proliferation of P14 transgenic splenocytes at high concentration. Two of the four peptides (DBM and RPP) were able to positively select the virus-specific TCR in fetal thymic organ culture but were unable to induce clonal deletion. Reverse-phase HPLC and mass spectrometry demonstrated that these peptides were presented by H-2D(b) molecules on thymic epithelial cell lines. We also examined whether the selecting ligands altered T cell responsiveness in vitro. DBM-selected T cells lost their ability to respond to the positively selecting ligand DBM, whereas RPP-selected T cells only retained their ability to respond to high concentrations of RPP. These results demonstrate that self-peptides that mediate positive selection can differentially "tune" the activation threshold of T cells and alter the functional repertoire of T cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".