An intronic enhancer of Bmp6 underlies evolved tooth gain in sticklebacks
Bibliographic record
Abstract
Threespine stickleback fish offer a powerful system to dissect the genetic basis of morphological evolution in nature. Marine sticklebacks have repeatedly invaded and adapted to numerous freshwater environments throughout the Northern hemisphere. In response to new diets in freshwater habitats, changes in craniofacial morphology, including heritable increases in tooth number, have evolved in derived freshwater populations. Using a combination of quantitative genetics and genome resequencing, here we fine-mapped a quantitative trait locus (QTL) regulating evolved tooth gain to a cluster of ten QTL-associated single nucleotide variants, all within intron four of Bone Morphogenetic Protein 6 (Bmp6). Transgenic reporter assays revealed this intronic region contains a tooth enhancer. We induced mutations in Bmp6, revealing required roles for survival, growth, and tooth patterning. Transcriptional profiling of Bmp6 mutant dental tissues identified significant downregulation of a set of genes whose orthologs were previously shown to be expressed in quiescent mouse hair stem cells. Collectively these data support a model where mutations within a Bmp6 intronic tooth enhancer contribute to evolved tooth gain, and suggest that ancient shared genetic circuitry regulates the regeneration of diverse vertebrate epithelial appendages including mammalian hair and fish teeth.
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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".