TCF-1 controls Treg functions that regulate inflammation, CD8 T-cell cytotoxicity, and severity of colon cancer
Bibliographic record
Abstract
Abstract The transcription factor TCF-1 is essential for the development and function of T regulatory (Treg) cells, however its function is poorly understood. Here, we show that TCF-1 primarily suppresses transcription of genes that are co-bound by Foxp3. Single-cell RNA-seq analysis identified effector- and central-memory Treg-cells with differential expression of Klf2 and memory and activation markers. TCF-1 deficiency did not change the core Treg transcriptional signature, but promoted alternative signaling pathways whereby Treg-cells became activated and gained gut-homing and TH17 characteristics. TCF-1-deficient Treg-cells strongly suppressed T-cell proliferation and cytotoxicity, but were compromised in controlling CD4+ T-cell polarization and inflammation. In mice with polyposis, Treg cell-specific TCF-1 deficiency promoted tumor growth. Consistently, tumor-infiltrating Treg cells of colorectal cancer patients showed lower TCF-1 expression and increased TH17 expression signatures compared to adjacent normal tissue and circulating T-cells. Thus, Treg cell-specific TCF-1 expression differentially regulates TH17-mediated inflammation and T-cell cytotoxicity, and can determine colorectal cancer outcome. Supported by NIH R01 AI 108682 (FG & KK), NIH RO1 AI 147652 (FG), NIH R35GM138283 (MK), and Praespero Innovation Award Alberta, Canada (FG & KK)
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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".