Infection assays in <i>Arabidopsis</i> reveal candidate effectors from the poplar rust fungus that promote susceptibility to bacteria and oomycete pathogens
Bibliographic record
Abstract
Fungi of the Pucciniales order cause rust diseases which, altogether, affect thousands of plant species worldwide and pose a major threat to several crops. How rust effectors-virulence proteins delivered into infected tissues to modulate host functions-contribute to pathogen virulence remains poorly understood. Melampsora larici-populina is a devastating and widespread rust pathogen of poplar, and its genome encodes 1184 identified small secreted proteins that could potentially act as effectors. Here, following specific criteria, we selected 16 candidate effector proteins and characterized their virulence activities and subcellular localizations in the leaf cells of Arabidopsis thaliana. Infection assays using bacterial (Pseudomonas syringae) and oomycete (Hyaloperonospora arabidopsidis) pathogens revealed subsets of candidate effectors that enhanced or decreased pathogen leaf colonization. Confocal imaging of green fluorescent protein-tagged candidate effectors constitutively expressed in stable transgenic plants revealed that some protein fusions specifically accumulate in nuclei, chloroplasts, plasmodesmata and punctate cytosolic structures. Altogether, our analysis suggests that rust fungal candidate effectors target distinct cellular components in host cells to promote parasitic 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 | 0.000 | 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".