At the crossroads: diverse roles of early thymocyte transcriptional regulators
Bibliographic record
Abstract
Transcriptional regulation of T-cell development involves successive interactions between complexes of transcriptional regulators and their binding sites within the regulatory regions of each gene. The regulatory modules that control expression of T-lineage genes frequently include binding sites for a core set of regulators that set the T-cell-specific background for signal-dependent control, including GATA-3, Notch/CSL, c-myb, TCF-1, Ikaros, HEB/E2A, Ets, and Runx factors. Additional regulators in early thymocytes include PU.1, Id-2, SCL, Spi-B, Erg, Gfi-1, and Gli. Many of these factors are involved in simultaneous regulation of non-T-lineage genes, T-lineage genes, and genes involved in cell cycle control, apoptosis, or survival. Potential and known interactions between early thymic transcription factors such as GATA-3, SCL, PU.1, Erg, and Spi-B are explored. Regulatory modules involved in the expression of several critical T-lineage genes are described, and models are presented for shifting occupancy of the DNA-binding sites in the regulatory modules of pre-Talpha, T-cell receptor beta (TCRbeta), recombinase activating genes 1 and 2 (Rag-1/2), and CD4 during T-cell development. Finally, evidence is presented that c-kit, Erg, Hes-1, and HEBAlt are expressed differently in Rag-2(-/-) thymocytes versus normal early thymocytes, which provide insight into potential regulatory interactions that occur during normal T-cell 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".