Dedicated farnesyl diphosphate synthases circumvent isoprenoid‐derived growth‐defense tradeoffs in <i>Zea mays</i>
Bibliographic record
Abstract
Zea mays (maize) makes phytoalexins such as sesquiterpenoid zealexins, to combat invading pathogens. Zealexins are produced from farnesyl diphosphate in microgram per gram fresh weight quantities. As farnesyl diphosphate is also a precursor for many compounds essential for plant growth, the question arises as to how Z. mays produces high levels of zealexins without negatively affecting vital plant systems. To examine if specific pools of farnesyl diphosphate are made for zealexin synthesis we made CRISPR/Cas9 knockouts of each of the three farnesyl diphosphate synthases (FPS) in Z. mays and examined the resultant impacts on different farnesyl diphosphate-derived metabolites. We found that FPS3 (GRMZM2G098569) produced most of the farnesyl diphosphate for zealexins, while FPS1 (GRMZM2G168681) made most of the farnesyl diphosphate for the vital respiratory co-factor ubiquinone. Indeed, fps1 mutants had strong developmental phenotypes such as reduced stature and development of chlorosis. The replication and evolution of the fps gene family in Z. mays enabled it to produce dedicated FPSs for developmentally related ubiquinone production (FPS1) or defense-related zealexin production (FPS3). This partitioning of farnesyl diphosphate production between growth and defense could contribute to the ability of Z. mays to produce high levels of phytoalexins without negatively impacting its growth.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".