The Fungal Effector Mlp37347 Alters Plasmodesmata Fluxes and Enhances Susceptibility to Pathogen
Bibliographic record
Abstract
Melampsora larici-populina (Mlp) is a devastating pathogen of poplar trees, causing the defoliating poplar leaf rust disease. Genomic studies have revealed that Mlp possesses a repertoire of 1184 small secreted proteins (SSPs), some of them being characterized as candidate effectors. However, how they promote virulence is still unclear. This study investigates the candidate effector Mlp37347’s role during infection. We developed a stable Arabidopsis transgenic line expressing Mlp37347 tagged with the green fluorescent protein (GFP). We found that the effector accumulated exclusively at plasmodesmata (PD). Moreover, the presence of the effector at plasmodesmata favors enhanced plasmodesmatal flux and reduced callose deposition. Transcriptome profiling and a gene ontology (GO) analysis of transgenic Arabidopsis plants expressing the effector revealed that the genes involved in glucan catabolic processes are up-regulated. This effector has previously been shown to interact with glutamate decarboxylase 1 (GAD1), and in silico docking analysis supported the strong binding between Mlp37347 and GAD1 in this study. In infection assays, the effector promoted Hyalonoperospora arabidopsidis growth but not bacterial growth. Our investigation suggests that the effector Mlp37347 targets PD in host cells and promotes 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".