The proteome of the phytopathogenic fungus <b><i>Sclerotinia sclerotiorum</i></b>
Bibliographic record
Abstract
In order to gain a more thorough understanding of the phytopathogenic fungus, Sclerotinia sclerotiorum, we initiated a proteome-level study of the fungal mycelia and secretome. To our knowledge, this is the first comprehensive proteome-level study of this fungus. Extracted mycelial proteins and secreted proteins collected from liquid culture were separated using 2-DE and annotated following ESI-q-TOF MS/MS. Fifty-two secreted proteins were reproducibly present in three biological replicates and 18 of them were identified by MS/MS while over 200 mycelial proteins were reproducibly present in three independent extractions and approximately half of them were identified. Many of the annotated secreted proteins were cell wall degrading enzymes that had been previously identified as pathogenicity or virulence factors of S. sclerotiorum; however, the contribution to the virulence of S. sclerotiorum of one of the identified proteins, alpha-L-arabinofuranosidase, is yet to be analyzed. Furthermore, previous comprehensive EST studies did not detect the presence of the alpha-L-arabinofuranosidase transcript, which demonstrates the merit of performing proteome-level research. All of the secreted and mycelial proteins identified were functionally classified, and the known and proposed roles in disease initiation or progression for many of them are discussed.
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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".