Divergent transcriptional activities determine limb identity
Bibliographic record
Abstract
Limbs develop using a common genetic programme despite widely differing morphologies. This programme is modulated by limb-restricted regulators such as hindlimb (HL) transcription factors Pitx1 and Tbx4 and the forelimb (FL) Tbx5. Both Tbx factors have been implicated in limb patterning and growth, but their relative activities and underlying mechanisms remain unclear. In this paper, we show that Tbx4 and Tbx5 harbour conserved and divergent transcriptional regulatory domains that account for their roles in limb development. In particular, both factors share an activator domain and the ability to stimulate limb growth. However, we find that Tbx4 is the primary effector of HL identity for both skeletal and muscle development; this activity relies on a repressor domain that is inactivated by a human TBX4 small-patella syndrome mutation. We propose that limb identity is largely achieved by default in FL, whereas a specific repressor activity unique to Tbx4 determines HL identity. The embryonic development of forelimbs and hindlimbs is regulated by transcription factors, including Pitx1, Tbx4 and Tbx5. In this study, the contributions of Tbx4 and Tbx5 to limb identity are further delineated, resulting in the demonstration of a role for Tbx4 in hindlimb skeletal and muscle patterning.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".