Evolution of stickleback spines through independent cis-regulatory changes at HOXDB
Bibliographic record
Abstract
Understanding the mechanisms leading to new traits or additional features in organisms is a fundamental goal of evolutionary biology. We show that HOXDB regulatory changes have been used repeatedly in different fish genera to alter the length and number of the prominent dorsal spines used to classify stickleback species. In Gasterosteus aculeatus (typically 'three-spine sticklebacks'), a variant HOXDB allele is genetically linked to shortening an existing spine and adding an additional spine. In Apeltes quadracus (typically 'four-spine sticklebacks'), a variant HOXDB allele is associated with lengthening a spine and adding an additional spine in natural populations. The variant alleles alter the same non-coding enhancer region in the HOXDB locus but do so by diverse mechanisms, including single-nucleotide polymorphisms, deletions and transposable element insertions. The independent regulatory changes are linked to anterior expansion or contraction of HOXDB expression. We propose that associated changes in spine lengths and numbers are partial identity transformations in a repeating skeletal series that forms major defensive structures in fish. Our findings support the long-standing hypothesis that natural Hox gene variation underlies key patterning changes in wild populations and illustrate how different mutational mechanisms affecting the same region may produce opposite gene expression changes with similar phenotypic outcomes.
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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".