Interplay of transcription factors in T-cell differentiation and function: the role of Runx
Bibliographic record
Abstract
Over the past years, increasing numbers of distinct subsets have been discovered and identified for a T lymphocytes' entity. Differentiation and function of each T cell subset are controlled by a specific master transcription factor. Importantly, Runt-related transcription factors, particularly Runx1 and Runx3, interplay with these master regulators in various aspects of T cells' immunity. In this review article, we first explain roles of Th-Pok and Runx3 in differentiation of CD4 versus CD8 single positive cells, and later focus on cross-regulation of Th-Pok and Runx3 and their relationship with other factors such as TCR strength. Next, we provide evidences for the direct interplay of Runx1/3 with T-bet and GATA3 during Th1 versus Th2 commitment to activate or silence transcription of signature cytokine genes, IFNγ and IL4. Lastly, we explain feed-forward relationship between Runx1 and Foxp3 and discuss roles of Runx1 in regulatory T cells' suppressive activity. This review highlights an essential importance of Runx molecules in controlling various T cell subsets' differentiation and functions through molecular interplay with the master transcription factors in terms of protein-protein interaction as well as regulation of gene expression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".