The evolution of multi-gene families and metabolic pathways in the evening primroses (Oenothera: Onagraceae): a comparative transcriptomics approach
Bibliographic record
Abstract
The plant genus Oenothera has played an important role in the study of genome evolution and plant defense and reproduction. Here, we built on the 1kp transcriptomic dataset and developed a molecular resource of 63 transcriptomes and present a large-scale comparative study across 29 Oenothera species. We produced 2.3 million transcripts and 25.4 Mb of total length assembly per individual. We used this transcriptome resource to examine genome-wide evolutionary patterns and functional diversification by searching for orthologous genes and performed gene family evolution analysis. We found wide heterogeneity in gene family evolution across the genus, with section Oenothera exhibiting the most pronounced evolutionary changes. Overall, more significant expansions occurred than contractions. We also analyzed the molecular evolution of phenolic metabolism by retrieving proteins annotated for phenolic enzymatic complexes. We identified 1,568 phenolic genes arranged into 83 multigene families that varied widely across the genus. All taxa experienced rapid phenolic evolution involving 33 gene families, which exhibited large expansions, gaining about 2-fold more genes than they lost. Upstream enzymes phenylalanine ammonia-lyase (PAL) and 4-coumaroyl: CoA ligase (4CL) accounted for most of the significant expansions and contractions. Our results suggest that adaptive responses to environmental stress coupled with non-adaptive evolutionary forces have contributed to Oenothera diversification and rapid gene family evolution.
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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 | 0.001 | 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".