Clustering of Tissue-Specific Sub-TADs Accompanies the Regulation of HoxA Genes in Developing Limbs
Bibliographic record
Abstract
HoxA genes exhibit central roles during development and causal mutations have been found in several human syndromes including limb malformation. Despite their importance, information on how these genes are regulated is lacking. Here, we report on the first identification of bona fide transcriptional enhancers controlling HoxA genes in developing limbs and show that these enhancers are grouped into distinct topological domains at the sub-megabase scale (sub-TADs). We provide evidence that target genes and regulatory elements physically interact with each other through contacts between sub-TADs rather than by the formation of discreet "DNA loops". Interestingly, there is no obvious relationship between the functional domains of the enhancers within the limb and how they are partitioned among the topological domains, suggesting that sub-TAD formation does not rely on enhancer activity. Moreover, we show that suppressing the transcriptional activity of enhancers does not abrogate their contacts with HoxA genes. Based on these data, we propose a model whereby chromatin architecture defines the functional landscapes of enhancers. From an evolutionary standpoint, our data points to the convergent evolution of HoxA and HoxD regulation in the fin-to-limb transition, one of the major morphological innovations in vertebrates.
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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 | 0.000 |
| 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".