Candidate Effector Proteins of the Rust Pathogen <i>Melampsora larici-populina</i> Target Diverse Plant Cell Compartments
Why this work is in the frame
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Bibliographic record
Abstract
Rust fungi are devastating crop pathogens that deliver effector proteins into infected tissues to modulate plant functions and promote parasitic growth. The genome of the poplar leaf rust fungus Melampsora larici-populina revealed a large catalog of secreted proteins, some of which have been considered candidate effectors. Unraveling how these proteins function in host cells is a key to understanding pathogenicity mechanisms and developing resistant plants. In this study, we used an effectoromics pipeline to select, clone, and express 20 candidate effectors in Nicotiana benthamiana leaf cells to determine their subcellular localization and identify the plant proteins they interact with. Confocal microscopy revealed that six candidate effectors target the nucleus, nucleoli, chloroplasts, mitochondria, and discrete cellular bodies. We also used coimmunoprecipitation (coIP) and mass spectrometry to identify 606 N. benthamiana proteins that associate with the candidate effectors. Five candidate effectors specifically associated with a small set of plant proteins that may represent biologically relevant interactors. We confirmed the interaction between the candidate effector MLP124017 and TOPLESS-related protein 4 from poplar by in planta coIP. Altogether, our data enable us to validate effector proteins from M. larici-populina and reveal that these proteins may target multiple compartments and processes in plant cells. It also shows that N. benthamiana can be a powerful heterologous system to study effectors of obligate biotrophic pathogens.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it