Genome and transcriptome analyses of the mountain pine beetle-fungal symbiont <i>Grosmannia clavigera</i> , a lodgepole pine pathogen
Bibliographic record
Abstract
In western North America, the current outbreak of the mountain pine beetle (MPB) and its microbial associates has destroyed wide areas of lodgepole pine forest, including more than 16 million hectares in British Columbia. Grosmannia clavigera (Gc), a critical component of the outbreak, is a symbiont of the MPB and a pathogen of pine trees. To better understand the interactions between Gc, MPB, and lodgepole pine hosts, we sequenced the ∼30-Mb Gc genome and assembled it into 18 supercontigs. We predict 8,314 protein-coding genes, and support the gene models with proteome, expressed sequence tag, and RNA-seq data. We establish that Gc is heterothallic, and report evidence for repeat-induced point mutation. We report insights, from genome and transcriptome analyses, into how Gc tolerates conifer-defense chemicals, including oleoresin terpenoids, as they colonize a host tree. RNA-seq data indicate that terpenoids induce a substantial antimicrobial stress in Gc, and suggest that the fungus may detoxify these chemicals by using them as a carbon source. Terpenoid treatment strongly activated a ∼100-kb region of the Gc genome that contains a set of genes that may be important for detoxification of these host-defense chemicals. This work is a major step toward understanding the biological interactions between the tripartite MPB/fungus/forest system.
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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 | 0.001 |
| 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".