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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".