Interspecies transcriptome analyses identify genes that control the development and evolution of limb skeletal proportion
Bibliographic record
Abstract
Abstract Despite the great diversity of vertebrate limb proportion and our deep understanding of the genetic mechanisms that drive skeletal elongation, little is known about how individual bones reach different lengths in any species. Here, we directly compare the transcriptomes of homologous growth cartilages of the mouse ( Mus musculus ) and bipedal jerboa ( Jaculus jaculus ), which has extremely long metatarsals of the feet and ‘mouse-like’ arms. When we intersected gene expression differences in metatarsals of the two species with expression differences in forearms, we found that about 10% of all orthologous genes are associated with disproportionate elongation of jerboa feet. Among these, Shox2 , has gained expression in jerboa metatarsals where it is not expressed in other vertebrates that have been assessed. This transcription factor is necessary for proximal limb elongation, and we show that it is sufficient to increase mouse distal limb length. Unexpectedly, we also found evidence that jerboa foot elongation occurs in part by releasing latent growth potential that is repressed in mouse feet. In jerboa metatarsals, we observed higher expression of Crabp1 , an antagonist of growth inhibitory retinoic acid, lower expression of Gdf10 , an inhibitory TGFβ ligand, and lower expression of Mab21L2 , a BMP signaling inhibitor that we show is sufficient to reduce limb bone elongation. By intersecting our data with prior expression analyses in other systems, we identify mechanisms that may both establish limb proportion during development and diversify proportion during evolution. The genes we identified here therefore provide a framework to understand the modular genetic control of skeletal growth and the remarkable malleability of vertebrate limb proportion.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".