Gene expression profiling of candidate virulence factors in the laminated root rot pathogen Phellinus sulphurascens
Bibliographic record
Abstract
BACKGROUND: Phellinus sulphurascens is a fungal pathogen that causes laminar root rot in conifers, one of the most damaging root diseases in western North America. Despite its importance as a forest pathogen, this fungus is still poorly studied at the genomic level. An understanding of the molecular events involved in establishment of the disease should help to develop new methods for control of this disease. RESULTS: We generated over 4600 expressed sequence tags from two cDNA libraries constructed using either mycelia grown on cellophane sheets and exposed to Douglas-fir roots or tissues from P. sulphurascens-infected Douglas-fir roots. A total of 890 unique genes were identified from the two libraries, and functional classification of 636 of these genes was possible using the Functional Catalogue (FunCat) annotation scheme. cDNAs were identified that encoded 79 potential virulence factors, including numerous genes implicated in virulence in a variety of phytopathogenic fungi. Many of these putative virulence factors were also among 82 genes identified as encoding putatively secreted proteins. The expression patterns of 86 selected fungal genes over 7 days of infection of Douglas-fir were examined using real-time PCR, and those significantly up-regulated included rhamnogalacturonan acetylesterase, 1,4-benzoquinone reductase, a cyclophilin, a glucoamylase, 3 hydrophobins, a lipase, a serine carboxypeptidase, a putative Ran-binding protein, and two unknown putatively secreted proteins called 1 J04 and 2 J12. Significantly down-regulated genes included a manganese-superoxide dismutase, two metalloproteases, and an unknown putatively secreted protein called Ps0058. CONCLUSIONS: This first collection of Phellinus sulphurascens EST sequences and its annotation provide an important resource for future research aimed at understanding key virulence factors of this forest pathogen. We examined the expression patterns of numerous fungal genes with potential roles in virulence, and found a collection of functionally diverse genes that are significantly up- or down-regulated during infection of Douglas-fir seedling roots by P. sulphurascens.
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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".