The genome and transcriptome of the pine saprophyte Ophiostoma piceae, and a comparison with the bark beetle-associated pine pathogen Grosmannia clavigera
Bibliographic record
Abstract
BACKGROUND: Ophiostoma piceae is a wood-staining fungus that grows in the sapwood of conifer logs and lumber. We sequenced its genome and analyzed its transcriptomes under a range of growth conditions. A comparison with the genome and transcriptomes of the mountain pine beetle-associated pathogen Grosmannia clavigera highlights differences between a pathogen that colonizes and kills living pine trees and a saprophyte that colonizes wood and the inner bark of dead trees. RESULTS: We assembled a 33 Mbp genome in 45 scaffolds, and predicted approximately 8,884 genes. The genome size and gene content were similar to those of other ascomycetes. Despite having similar ecological niches, O. piceae and G. clavigera showed no large-scale synteny. We identified O. piceae genes involved in the biosynthesis of melanin, which causes wood discoloration and reduces the commercial value of wood products. We also identified genes and pathways involved in growth on simple carbon sources and in sapwood, O. piceae's natural substrate. Like the pathogen, the saprophyte is able to tolerate terpenes, which are a major class of pine tree defense compounds; unlike the pathogen, it cannot utilize monoterpenes as a carbon source. CONCLUSIONS: This work makes available the second annotated genome of a softwood ophiostomatoid fungus, and suggests that O. piceae's tolerance to terpenes may be due in part to these chemicals being removed from the cells by an ABC transporter that is highly induced by terpenes. The data generated will provide the research community with resources for work on host-vector-fungus interactions for wood-inhabiting, beetle-associated saprophytes and pathogens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".