Endogenous BioID elucidates TCF7L1 interactome modulation upon GSK-3 inhibition in mouse ESCs
Bibliographic record
Abstract
Modulation of Wnt target gene expression via the TCF/LEFs remains poorly understood. We employ proximity-based biotin labeling (BioID) to examine GSK-3 inhibitor effects on the TCF7L1 interactome in mouse ESCs. We generated ESC lines with biotin ligase BirA* fused to TCF7L1 by knocking it into the endogenous TCF7L1 locus or by inserting a doxinducible BirA*-TCF7L1 transgene into the Rosa26 locus. Induction yielded BirA*-TCF7L1 levels 3-fold higher than in the endogenous system, but substantial overlap in biotinylated proteins with high peptide counts were detected by each method. Known TCF7L1 interactors TLE3/4 and β-catenin, and numerous proteins not previously associated with TCF7L1, were identified in both systems. Despite reduced BirA*-TCF7L1 levels, the number of hits identified with both BioID approaches increased after GSK-3 inhibition. We elucidate the network of TCF7L1 proximal proteins regulated by GSK-3 inhibition, validate the utility of endogenous BioID, and provide mechanistic insights into TCF7L1 target gene regulation. Highlights BirA*-TCF7L1 at single-copy physiological levels generates robust BioID data CHIR99021 reduces TCF7L1 levels but increases detectable TCF7L1-proximal proteins The TCF7L1 interactome of largely epigenetic/transcription factors fluctuates with GSK-3 inhibition JMJD1C, SALL4 and BRG1/SMARCA4 are validated as TCF7L-interacting proteins
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".