Unrelated Fungal Rust Candidate Effectors Act on Overlapping Plant Functions
Bibliographic record
Abstract
Rust fungi cause epidemics that threaten the production of important plant species, such as wheat and soy. Melampsora larici-populina (Mlp) causes the poplar rust and encodes at least 1184 candidate effectors (CEs) whose functions are poorly known. In this study, we sequenced the transcriptome and used mass spectrometry to analyze the metabolome of Arabidopsis plants constitutively expressing 14 Mlp CEs and of a control line to discover alterations leading to plant susceptibility. We found 2299 deregulated genes across the experiment. Genes involved in pattern-triggered immunity, such as FRK1, PR1, RBOHD, and WRKY33, as well as AUX/IAA genes were down-regulated. We further observed that 680 metabolites were deregulated in at least one CE-expressing transgenic line, with “highly unsaturated and phenolic compounds” and “peptides” enriched among down- and up-regulated metabolites. Interestingly, transgenic lines expressing unrelated CEs had correlated patterns of gene and metabolite deregulation, while expression of CEs belonging to the same family deregulated different genes and metabolites. Thus, our results uncouple effector sequence similarity and function. This supports that effector functional investigation in the context of their virulence activity and effect on plant susceptibility requires the investigation of the individual effector and precludes generalization based on sequence similarity.
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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) | 0.001 | 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".