Selective Constraints on Codon Usage of Nuclear Genes from Arabidopsis thaliana
Bibliographic record
Abstract
Highly expressed nuclear genes from Arabidopsis thaliana show an increased frequency of codons that match abundant tRNAs, and it has been suggested that this reflects a selective pressure to increase translation efficiency. Here we explore the possibility that the difference in codon usage between highly expressed genes and other Arabidopsis genes is not the result of selection but, rather, arises from mutation biases. Specifically, we explore the possibility that an influence of transcription level on mutational properties coupled with a context dependency of mutations, both of which have been observed in various organisms, contribute to variation in codon-usage bias across genes. Using noncoding sites immediately flanking both high- and low-expression-coding sequences to infer context-dependent composition biases, we analyze codon-usage bias across genes. The data show that mutation bias cannot explain codon usage of high-expression genes in Arabidopsis and, surprisingly, also indicate that even low-expression genes are under selective constraints. In addition, the data indicate that the general preference for certain codons is context dependent; the composition of the 3' nucleotide, that is, the first position of the next codon, is correlated with what codon is found at an increased frequency in highly expressed genes. This context dependency indicates that selective pressure on codon usage is more complex than previously thought. Overall, the study supports previous suggestions that selection plays a significant role in determining codon usage of nuclear genes in A. thaliana.
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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 | 0.000 | 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) | 0.000 | 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".