Contextual regulation of T follicular helper cell expansion and differentiation into T regulatory type 1 cells by multiple transcription factors
Bibliographic record
Abstract
Differentiation of naive T cells into T helper (TH), including T follicular helper (TFH), cell subsets is regulated by subset-specific transcription factors (TFs). We examine the role of 20 TFs binding to motifs enriched in open chromatin regions of TFH cells in the TFH-to-T regulatory type 1 (TR1) cell conversion following repetitive antigen exposure. Certain TFH TFs, such as TCF-1 and TOX-2, contribute to TFH formation and function but are dispensable for TFH expansion and TR1 transdifferentiation. Eleven others, including activating TF 6 (ATF-6), BATF, BCL-6, class E basic-helix-loop-helix protein 40 (BHLHE40), BLIMP-1, ELK-4, IRF4, c-MAF, signal transducer and activator of transcription (STAT)-3, STAT-4, and T-BET, play context-specific roles at different stages of the pathway. Most of these TFs simultaneously promote and suppress different cell fate transitions along the TFH-TR1 axis and regulate subset-specific genes in a stage-specific manner. Thus, TFs with distinct TH cell reprogramming properties in naive T cells drive the acquisition of different gene expression programs along the multicellular TFH-TR1 axis, depending on each cell's transcriptional state.
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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 | 0.000 |
| 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".