Functional characterization of LEF1 isoforms in mouse embryonic stem cells
Bibliographic record
Abstract
The expression of Wnt/β-catenin signaling target genes is regulated by nuclear translocation of β-catenin, mainly in complex with T-cell factor (TCF)/lymphoid enhancer binding factor (LEF) factors. LEF1, a member of the TCF/LEF family of transcription factors, holds significant importance in both normal developmental processes and various pathological conditions such as cancer. Our objective was to characterize the function of two LEF1 isoforms, namely full-length LEF1 and ΔN-LEF1, via detecting the proteins they interact with by using TurboID-based proximity labeling. Additionally, we aimed to map their binding profiles throughout the genome by using the CUT&RUN technique. This investigation was conducted in the context of differentiating mouse embryonic stem cells under two conditions: the presence of a GSK-3 inhibitor known as CHIR99021 (CHIR), which serves to mimic Wnt pathway activation, and the absence of Wnt pathway activation. In our TurboID data, we detected both well-established LEF1 interactors such as Wnt enhanceosome components and new isoform-specific interactors, such as NuRD complex proteins and the transcription factor, ZSCAN10, in the presence and absence of CHIR, respectively. Notably, the enrichment of RNA-binding proteins, chromatin modifiers, and numerous novel transient interactors such as ubiquitin ligases, deubiquitinases, kinases and phosphatases with FL-LEF1 in the presence of CHIR underscores the impact of Wnt/β-catenin signaling on modulating LEF1 function in an isoform-specific manner. The CUT&RUN data discovered the binding of LEF1 isoforms to the Lef1 gene and intriguingly revealed an unexpected interaction with Zscan10. Our findings reveal new insights into isoform-specific interactions and the underlying mechanisms through which LEF1 isoforms function in regulating gene expression and warrant further studies characterizing the function of other TCF/LEF isoforms in a similar fashion.
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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 | 0.001 | 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 | 0.001 |
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".