Discovery, Biosynthesis and Stress-Related Accumulation of Dolabradiene-Derived Defenses in Maize
Bibliographic record
Abstract
Terpenoids are a major component of maize (Zea mays) chemical defenses that mediate responses to herbivores, pathogens, and other environmental challenges. Here, we describe the biosynthesis and elicited production of a class of maize diterpenoids, named dolabralexins. Dolabralexin biosynthesis involves the sequential activity of two diterpene synthases, ENT-COPALYL DIPHOSPHATE SYNTHASE (ZmAN2) and KAURENE SYNTHASE-LIKE4 (ZmKSL4). Together, ZmAN2 and ZmKSL4 form the diterpene hydrocarbon dolabradiene. In addition, we biochemically characterized a cytochrome P450 monooxygenase, ZmCYP71Z16, which catalyzes the oxygenation of dolabradiene to yield the epoxides 15,16-epoxydolabrene (epoxydolabrene) and 3β-hydroxy-15,16-epoxydolabrene (epoxydolabranol). The absence of dolabradiene and epoxydolabranol in Zman2 mutants under elicited conditions confirmed the in vivo biosynthetic requirement of ZmAN2. Combined mass spectrometry and NMR experiments demonstrated that much of the epoxydolabranol is further converted into 3β,15,16-trihydroxydolabrene (trihydroxydolabrene). Metabolite profiling of field-grown maize root tissues indicated that dolabralexin biosynthesis is widespread across common maize cultivars, with trihydroxydolabrene as the predominant diterpenoid. Oxidative stress induced dolabralexin accumulation and transcript expression of ZmAN2 and ZmKSL4 in root tissues, and metabolite and transcript accumulation were up-regulated in response to elicitation with the fungal pathogens Fusarium verticillioides and Fusarium graminearum. Consistently, epoxydolabranol significantly inhibited the growth of both pathogens in vitro at 10 µg mL−1, while trihydroxydolabrene-mediated inhibition was specific to F. verticillioides. These findings suggest that dolabralexins have defense-related roles in maize stress interactions and expand the known chemical space of diterpenoid defenses as genetic targets for understanding and ultimately improving maize resilience.
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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) | 0.000 | 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 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".