Evolution of scent genes in roses
Bibliographic record
Abstract
<i>Rosa</i> is a complex taxon with more than 150 intertwined species. Only few have been domesticated by humans since Antiquity, reaching today more than 30,000 cultivars. One of the major traits that have been selected is scent. However, rose scent is a complex trait comprised of dozens of volatile molecules. Some of these molecules originate from a specific and uniquely evolved biosynthetic pathway in the genus Rosa, which arose from acquisition of the duplication and neofunctionalization of genes to become involved in the production of scent compounds. Examples include <i>NUDX1</i>, a gene involved in geraniol biosynthesis, specifically in roses (Magnard et al., 2015). We have shown that multiple trans- and cis-duplications of <i>NUDX1</i> during the evolution of Rosaceae and Rosa, have led to the specialization of the paralog <i>NUDX1-1a</i> toward geraniol production (Conart et al., 2022). This paralog is not present in the more ancient wild roses making them unsuitable for crosses to obtain fragrant roses. Previously we showed that some hybrid cultivars with <i>R</i>. <i>wichurana</i> as one parent, have a different specialization: <i>NUDX1-1a</i> is inactive, while <i>NUDX1-2c</i>, another paralog, is active and involved in (E,E)-farnesol production (Sun et al., 2020). Furthermore, some genes well-known to be involved in scent production encode enzymes that are functional in vitro, but are not always highly expressed in planta. Examples include <i>LIS</i> and <i>PAAS</i> genes, respectively involved in linalool and 2-phenylethanol biosynthesis (Magnard et al., 2018; Roccia et al., 2019). Taken together, these results indicate different evolutionary scenarios in different rose species. A better understanding of the genes and alleles involved in the production of fragrant molecules is thus needed to help the selection of new, scented rose cultivars. This paper focuses on the <i>NUDX1 </i>gene evolution as an example of what knowledge of a gene family can bring to the breeding of roses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".