Proteome analysis of wheat leaf rust fungus, <i>Puccinia triticina</i> , infection structures enriched for haustoria
Bibliographic record
Abstract
Puccinia triticina (Pt) is a representative of several cereal-infecting rust fungal pathogens of major economic importance world wide. Upon entry through leaf stomata, these fungi establish intracellular haustoria, crucial feeding structures. We report the first proteome of infection structures from parasitized wheat leaves, enriched for haustoria through filtration and sucrose density centrifugation. 2-D PAGE MS/MS and gel-based LC-MS (GeLC-MS) were used to separate proteins. Generated spectra were compared with a partial proteome predicted from a preliminary Pt genome and generated ESTs, to a comprehensive genome-predicted protein complement from the related wheat stem rust fungus, Puccinia graminis f. sp. tritici (Pgt) and to various plant resources. We identified over 260 fungal proteins, 16 of which matched peptides from Pgt. Based on bioinformatic analyses and/or the presence of a signal peptide, at least 50 proteins were predicted to be secreted. Among those, six have effector protein signatures, some are related and the respective genes of several seem to belong to clusters. Many ribosomal structural proteins, proteins involved in energy, general metabolism and transport were detected. Measuring gene expression over several life cycle stages of ten representative candidates using quantitative RT-PCR, all were shown to be strongly upregulated and four expressed solely upon infection.
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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".