TCF7L1 and TCF7 differentially regulate specific mouse ES cell genes in response to GSK-3 inhibition
Bibliographic record
Abstract
The genome-wide chromatin occupancy of the TCF/LEF factors and its modulation by Wnt pathway activation remain poorly defined. Here, we describe mouse ES cell (mESC) lines expressing a single copy knock-in of the 3xFLAG epitope at the N-terminus of TCF7L1 and TCF7, the two most-highly expressed TCF/LEF factors in mESCs. TCF7L1 protein levels, detected by immunoblotting with a FLAG antibody, were much higher than TCF7 in mESCs maintained in standard serum- and LIF-supplemented medium, even in the presence of the GSK-3 inhibitor, CHIR99021 (CHIR). We used FLAG antibody-mediated ChIP-seq to determine TCF7 and TCF7L1 chromatin occupancy in mESCs cultured in standard medium with or without CHIR for 14 hours. TCF7 and TCF7L1 displayed very few overlapping ChIP peaks across the genome, with TCF7L1 binding significantly more genes than TCF7 in both culture conditions. Despite a reduction in total TCF7L1 protein after CHIR treatment, the TCF7L1 ChIP peak profiles were not uniformly attenuated. Our data demonstrate that TCF7L1 chromatin occupancy upon short-term CHIR treatment is modulated in a target-specific manner. Our findings also suggest that Wnt target genes in mESCs are not regulated by TCF/LEF switching, and TCF7L1, although often called a constitutive repressor, may serve as a transcriptional activator of certain target genes in CHIR-treated mESCs. Highlights ChIP and cytometry data suggest that TCF7L1 does not directly regulate mESC Nanog expression. TCF7L1 remains associated with β-catenin in the presence of CHIR99021. TCF7 and TCF7L1 display different chromatin occupancies in mESCs. TCF7L1 binding at specific genomic sites is variably altered by CHIR99021.
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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) | 0.001 | 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".