Comparative analysis of secreted protein evolution using expressed sequence tags from four poplar leaf rusts (Melampsora spp.)
Bibliographic record
Abstract
BACKGROUND: Obligate biotrophs such as rust fungi are believed to establish long-term relationships by modulating plant defenses through a plethora of effector proteins, whose most recognizable feature is the presence of a signal peptide for secretion. Since the phenotypes of these effectors extend to host cells, their genes are expected to be under accelerated evolution stimulated by host-pathogen coevolutionary arms races. Recently, whole genome sequence data has allowed the prediction of secretomes, facilitating the identification of putative effectors. RESULTS: We generated cDNA libraries from four poplar leaf rust pathogens (Melampsora spp.) and used computational approaches to identify and annotate putative secreted proteins with the aim of uncovering new knowledge about the nature and evolution of the rust secretome. While more than half of the predicted secretome members encoded lineage-specific proteins, similarities with experimentally characterized fungal effectors were also identified. A SAGE analysis indicated a strong stage-specific regulation of transcripts encoding secreted proteins. The average sequence identity of putative secreted proteins to their closest orthologs in the wheat stem rust Puccinia graminis f. sp. tritici was dramatically reduced compared with non-secreted ones. A comparative genomics approach based on homologous gene groups unravelled positive selection in putative members of the secretome. CONCLUSION: We uncovered robust evidence that different evolutionary constraints are acting on the rust secretome when compared to the rest of the genome. These results are consistent with the view that these genes are more likely to exhibit an effector activity and be involved in coevolutionary arms races with host factors.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".