Gene Discovery of Modular Diterpene Metabolism in Nonmodel Systems
Bibliographic record
Abstract
Plants produce over 10,000 different diterpenes of specialized (secondary) metabolism, and fewer diterpenes of general (primary) metabolism. Specialized diterpenes may have functions in ecological interactions of plants with other organisms and also benefit humanity as pharmaceuticals, fragrances, resins, and other industrial bioproducts. Examples of high-value diterpenes are taxol and forskolin pharmaceuticals or ambroxide fragrances. Yields and purity of diterpenes obtained from natural sources or by chemical synthesis are often insufficient for large-volume or high-end applications. Improvement of agricultural or biotechnological diterpene production requires knowledge of biosynthetic genes and enzymes. However, specialized diterpene pathways are extremely diverse across the plant kingdom, and most specialized diterpenes are taxonomically restricted to a few plant species, genera, or families. Consequently, there is no single reference system to guide gene discovery and rapid annotation of specialized diterpene pathways. Functional diversification of genes and plasticity of enzyme functions of these pathways further complicate correct annotation. To address this challenge, we used a set of 10 different plant species to develop a general strategy for diterpene gene discovery in nonmodel systems. The approach combines metabolite-guided transcriptome resources, custom diterpene synthase (diTPS) and cytochrome P450 reference gene databases, phylogenies, and, as shown for select diTPSs, single and coupled enzyme assays using microbial and plant expression systems. In the 10 species, we identified 46 new diTPS candidates and over 400 putatively terpenoid-related P450s in a resource of nearly 1 million predicted transcripts of diterpene-accumulating tissues. Phylogenetic patterns of lineage-specific blooms of genes guided functional characterization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".