Comparative Genomics of the World's Smallest Mammals Reveals Links to Echolocation, Metabolism, and Body Size Plasticity
Bibliographic record
Abstract
Originating 30 million years ago, shrews (Soricidae) have diversified into around 400 species worldwide. Shrews display a wide array of adaptations, with some species having developed distinctive traits such as echolocation, underwater diving, and venomous saliva. Accordingly, these tiny insectivores are ideal to study the genomic mechanisms of evolution and adaptation. We conducted a comparative genomic analysis of four shrew species and 16 other mammals to identify genomic variations unique to shrews. Using two existing shrew genomes and two de novo assemblies for the maritime (Sorex maritimensis) and smoky (Sorex fumeus) shrews, we identified mutations in conserved regions of the genomes, also known as accelerated regions, gene families that underwent significant expansion, and positively selected genes. Our analyses unveiled shrew-specific genomic variants in genes associated with the nervous, metabolic, and auditory systems, which can be linked to unique traits in shrews. Notably, genes suggested to be under convergent evolution in echolocating mammals exhibited accelerated regions in shrews, and pathways linked to putative body size plasticity were detected. These findings provide insight into the evolutionary mechanisms shaping shrew species, shedding light on their adaptation and divergence over time.
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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".