Tbx20 dose-dependently regulates transcription factor networks required for mouse heart and motoneuron development
Bibliographic record
Abstract
To elucidate the function of the T-box transcription factor Tbx20 in mammalian development, we generated a graded loss-of-function series by transgenic RNA interference in entirely embryonic stem cell-derived mouse embryos. Complete Tbx20 knockdown resulted in defects in heart formation, including hypoplasia of the outflow tract and right ventricle, which derive from the anterior heart field (AHF), and decreased expression of Nkx2-5 and Mef2c, transcription factors required for AHF formation. A mild knockdown led to persistent truncus arteriosus (unseptated outflow tract) and hypoplastic right ventricle, entities similar to human congenital heart defects, and demonstrated a critical requirement for Tbx20 in valve formation. Finally, an intermediate knockdown revealed a role for Tbx20 in motoneuron development, specifically in the regulation of the transcription factors Isl2 and Hb9, which are important for terminal differentiation of motoneurons. Tbx20 could activate promoters/enhancers of several genes in cultured cells, including the Mef2c AHF enhancer and the Nkx2-5 cardiac enhancer. The Mef2c AHF enhancer relies on Isl1- and Gata-binding sites. We identified a similar Isl1 binding site in the Nkx2-5 AHF enhancer, which in transgenic mouse embryos was essential for activity in a large part of the heart, including the outflow tract. Tbx20 synergized with Isl1 and Gata4 to activate both the Mef2c and Nkx2-5 enhancers, thus providing a unifying mechanism for gene activation by Tbx20 in the AHF. We conclude that Tbx20 is positioned at a critical node in transcription factor networks required for heart and motoneuron development where it dose-dependently regulates gene expression.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".