Virus‐induced gene silencing identifies <i><scp>C</scp>atharanthus roseus</i> 7‐deoxyloganic acid‐7‐hydroxylase, a step in iridoid and monoterpene indole alkaloid biosynthesis
Bibliographic record
Abstract
Iridoids are a major group of biologically active molecules that are present in thousands of plant species, and one versatile iridoid, secologanin, is a precursor for the assembly of thousands of monoterpenoid indole alkaloids (MIAs) as well as a number of quinoline alkaloids. This study uses bioinformatics to screen large databases of annotated transcripts from various MIA-producing plant species to select candidate genes that may be involved in iridoid biosynthesis. Virus-induced gene silencing of the selected genes combined with metabolite analyses of silenced plants was then used to identify the 7-deoxyloganic acid 7-hydroxylase (CrDL7H) that is involved in the 3rd to last step in secologanin biosynthesis. Silencing of CrDL7H reduced secologanin levels by at least 70%, and increased the levels of 7-deoxyloganic acid to over 4 mg g(-1) fresh leaf weight compared to control plants in which this iridoid is not detected. Functional expression of this CrDL7H in yeast confirmed its biochemical activity, and substrate specificity studies showed its preference for 7-deoxyloganic acid over other closely related substrates. Together, these results suggest that hydroxylation precedes carboxy-O-methylation in the secologanin pathway in Catharanthus roseus.
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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 | 0.000 | 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) | 0.001 | 0.001 |
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".