The Correlation of Evolutionary Rate with Pathway Position in Plant Terpenoid Biosynthesis
Bibliographic record
Abstract
Genes are expected to face stronger selective constraint and to evolve more slowly if they encode enzymes upstream as opposed to downstream in metabolic pathways, because upstream genes are more pleiotropic, being required for a wider range of end products. However, few clear examples of this trend in evolutionary rate variation exist. We examined whether genes involved in plant terpenoid biosynthesis exhibit such a pattern, using data for 40 genes from five fully sequenced angiosperms, Oryza, Vitis, Arabidopsis, Populus, and Ricinus. Our results show that d(N)/d(S) does in fact correlate with pathway position along pathways converting glucose to the terpenoid phytohormones abscissic acid, gibberellic acid (GA), and brassinosteroids. Upstream versus downstream rate variation is particularly strong in the GA pathway. In contrast, we found no or little apparent variation in d(N)/d(S) with gene copy number. We also introduce a new measure of pathway position, the Pathway Pleiotropy Index (PPI), which counts groups of enzymes between pathway branch points. We found that this measure is superior to pathway position in explaining variation in d(N)/d(S) along each pathway. Although at least 8 of the 40 genes showed evidence of positive selection, correlations of d(N)/d(S) with PPI remain significant when these genes are removed. Therefore, our results are consistent with the prediction that selective constraint is progressively relaxed along metabolic pathways.
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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 | 0.001 | 0.002 |
| 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".