Gender-specific selection on codon usage in plant genomes
Bibliographic record
Abstract
BACKGROUND: Currently, there is little data available regarding the role of gender-specific gene expression on synonymous codon usage (translational selection) in most organisms, and particularly plants. Using gender-specific EST libraries (with > 4000 ESTs) from Zea mays and Triticum aestivum, we assessed whether gender-specific gene expression per se and gender-specific gene expression level are associated with selection on codon usage. RESULTS: We found clear evidence of a greater bias in codon usage for genes expressed in female than in male organs and gametes, based on the variation in GC content at third codon positions and the frequency of species-preferred codons. This finding holds true for both highly and for lowly expressed genes. In addition, we found that highly expressed genes have greater codon bias than lowly expressed genes for both female- and male-specific genes. Moreover, in both species, genes with female-specific expression show a greater usage of species-specific preferred codons for each of the 18 amino acids having synonymous codons. A supplemental analysis of Brassica napus suggests that bias in codon usage could also be higher in genes expressed in male gametophytic tissues than in heterogeneous (flower) tissues. CONCLUSION: This study reports gender-specific bias in codon usage in plants. The findings reported here, based on the analysis of 1,497,876 codons, are not caused either by differences in the biological functions of the genes or by differences in protein lengths, nor are they likely attributable to mutational bias. The data are best explained by gender-specific translational selection. Plausible explanations for these findings and the relevance to these and other organisms are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".