A Comprehensive Analysis of Genes Encoding Small Secreted Proteins Identifies Candidate Effectors in <i>Melampsora larici-populina</i> (Poplar Leaf Rust)
Bibliographic record
Abstract
The obligate biotrophic rust fungus Melampsora larici-populina is the most devastating and widespread pathogen of poplars. Studies over recent years have identified various small secreted proteins (SSP) from plant biotrophic filamentous pathogens and have highlighted their role as effectors in host-pathogen interactions. The recent analysis of the M. larici-populina genome sequence has revealed the presence of 1,184 SSP-encoding genes in this rust fungus. In the present study, the expression and evolutionary dynamics of these SSP were investigated to pinpoint the arsenal of putative effectors that could be involved in the interaction between the rust fungus and poplar. Similarity with effectors previously described in Melampsora spp., richness in cysteines, and organization in large families were extensively detailed and discussed. Positive selection analyses conducted over clusters of paralogous genes revealed fast-evolving candidate effectors. Transcript profiling of selected M. laricipopulina SSP showed a timely coordinated expression during leaf infection, and the accumulation of four candidate effectors in distinct rust infection structures was demonstrated by immunolocalization. This integrated and multifaceted approach helps to prioritize candidate effector genes for functional studies.
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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".