Phytohormone profiling reveals fungal signatures and strong manipulation of infection cycle in the <i>Gymnosporangium juniper-virginianae</i> dual-host plant system
Bibliographic record
Abstract
The aim of this work was to examine the function of phytohormones in the pathogenesis of cedar-apple rust, a fungal disease caused by Gymnosporangium juniper-virginianae Schwein. on Eastern red cedar (Juniperus virginiana L.) and crabapple trees (Malus spp. Mill.). Control cedar branchlets, gall tissues, fungal telial horns, as well as healthy and infected apple leaves were collected throughout fungal and plant development and used for profiling endogenous cytokinins (CK) and abscisic acid (ABA) by high performance liquid chromatography – electrospray ionization – tandem mass spectrometry. Phytohormone composition implicates cytokinin involvement in the development of rust infection. Moreover, increased levels of total CKs, as well as the unique profiles of sporulating galls on cedar trees, telial horns, and infected apple leaves, dominated by cis-Zeatin type CK, suggest that the fungus can synthesize hormones to facilitate the infection process. Distribution of ABA in the fungal and plant tissues indicates an important function of this stress hormone in regulating rapid changes in osmotic pressure during teliospore production by the galls. This study of the cedar-apple rust disease cycle is the first elucidation of phytohormones profiling between a pathogenic fungus and the attacked plant in a dual-host infection system.
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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".