Fungal derived cytokinins are necessary for normal <i>Ustilago maydis</i> infection of maize
Bibliographic record
Abstract
Phytohormones derived from fungi play a key role in regulating plant–pathogen interactions; however, deciphering the separate contributions of pathogen and plant during infection has been difficult. Here, the Ustilago maydis–Zea mays pathosystem was used to investigate this chemical exchange. Ustilago maydis , the causative agent of maize smut, produces cytokinins ( CK ), which are a group of phytohormones responsible for directing plant development. The characteristic symptom of smut disease is the formation of tumours composed of plant and fungal tissue. Isopentenyltransferase ( IPT ) catalyses the rate‐limiting step in CK biosynthesis, and U. maydis strains in which the sole tRNA ‐ipt gene was deleted no longer produced CK s. These deletion strains elicited fewer, smaller tumours than the pathogenic strain SG 200. High performance liquid chromatography‐electrospray ionization tandem mass spectrometry ( HPLC ‐ ESI MS / MS ) was used to detect and quantify phytohormone levels in infected tissue. This revealed that key hormone changes in SG 200 infections were not present in infections by deletion strains, suggesting that CK production by U. maydis is required for the altered phytohormone profile in infected tissue relative to uninfected tissue. Separate analyses indicated that U. maydis tRNA ‐ipt mutants might be altered in their ability to metabolize CK s taken up from the environment. Mining the U. maydis genome identified genes encoding putative CK signalling and biosynthesis proteins.
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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 | 0.000 |
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".