Sex-Linked Mammalian Sperm Proteins Evolve Faster Than Autosomal Ones
Bibliographic record
Abstract
X-linked genes can evolve slower or faster depending on whether most recessive, or at least partially recessive alleles are deleterious or beneficial due to their hemizygous expression in males. Molecular studies of X chromosome divergence have provided conflicting evidence for both a higher and lower rate of nucleotide substitution at both synonymous and nonsynonymous sites, depending on the nucleotide sites sampled. Using human and mouse orthologous genes, we tested the hypothesis that genes encoding male-specific sperm proteins are evolving faster on the X chromosome compared with autosomes. X-linked sperm proteins have an average nonsynonymous mutation rate almost twice as high as sperm genes found on autosomes, unlike other tissue-specific genes, where no significant difference in the nonsynonymous mutation rate between the X chromosome and autosomes was found. However, no difference was found in the average synonymous mutation rate of X-linked versus autosomal sperm proteins, which along with corresponding higher values of Ka/Ks in X-linked sperm proteins suggest that differences in selective forces and not mutation rates are the underlying cause of higher X-linked mammalian sperm protein divergence.
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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".