Transcriptional Analysis of Clonal Deletion In Vivo
Bibliographic record
Abstract
Engagement of the TCR on CD4(+)CD8(+) thymocytes initiates either a program of survival and differentiation (positive selection) or death (clonal deletion), which is dictated in large part by the affinity of the TCR for self-peptide-MHC complexes. Although much is known about the factors involved in positive selection, little is understood about the molecular mechanism leading to clonal deletion. To gain further insight into this process, we used a highly physiological TCR transgenic mouse model to compare gene expression changes under conditions of nonselection, positive selection, and negative selection. We identified 388 genes that were differentially regulated in negative selection compared with either nonselection or positive selection. These regulated genes fall into many functional categories including cell surface and intracellular signal transduction, survival and apoptosis, transcription and translation, and adhesion and migration. Additionally, we have compared our transcriptional profile to profiles of negative selection in other model systems in an effort to identify those genes with a higher probability of being functionally relevant. These included three up-regulated genes, bim, nur77, and ian1, and one down-regulated gene, lip1. Collectively, these data provide a framework for understanding the molecular basis of clonal deletion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".