Evolutionary Rates and Expression Level in Chlamydomonas
Bibliographic record
Abstract
In many biological systems, especially bacteria and unicellular eukaryotes, rates of synonymous and nonsynonymous nucleotide divergence are negatively correlated with the level of gene expression, a phenomenon that has been attributed to natural selection. Surprisingly, this relationship has not been examined in many important groups, including the unicellular model organism Chlamydomonas reinhardtii. Prior to this study, comparative data on protein-coding sequences from C. reinhardtii and its close noninterfertile relative C. incerta were very limited. We compiled and analyzed protein-coding sequences for 67 nuclear genes from these taxa; the sequences were mostly obtained from the C. reinhardtii EST database and our C. incerta EST data. Compositional and synonymous codon usage biases varied among genes within each species but were highly correlated between the orthologous genes of the two species. Relative rates of synonymous and nonsynonymous substitution across genes varied widely and showed a strong negative correlation with the level of gene expression estimated by the codon adaptation index. Our comparative analysis of substitution rates in introns of lowly and highly expressed genes suggests that natural selection has a larger contribution than mutation to the observed correlation between evolutionary rates and gene expression level in Chlamydomonas.
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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 | 0.001 |
| 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) | 0.000 | 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".