Transcriptomic Responses of the Softwood-Degrading White-Rot Fungus Phanerochaete carnosa during Growth on Coniferous and Deciduous Wood
Bibliographic record
Abstract
To identify enzymes that could be developed to reduce the recalcitrance of softwood resources, the transcriptomes of the softwood-degrading white-rot fungus Phanerochaete carnosa were evaluated after growth on lodgepole pine, white spruce, balsam fir, and sugar maple and compared to the transcriptome of P. carnosa after growth on liquid nutrient medium. One hundred fifty-two million paired-end reads were obtained, and 63% of these reads were mapped to 10,257 gene models from P. carnosa. Five-hundred thirty-three of these genes had transcripts that were at least four times more abundant during growth on at least one wood medium than on nutrient medium. The 30 transcripts that were on average over 100 times more abundant during growth on wood than on nutrient medium included 6 manganese peroxidases, 5 cellulases, 2 hemicellulases, a lignin peroxidase, glyoxal oxidase, and a P450 monooxygenase. Notably, among the genes encoding putative cellulases, one encoding a glycosyl hydrolase family 61 protein had the highest relative transcript abundance during growth on wood. Overall, transcripts predicted to encode lignin-degrading activities were more abundant than those predicted to encode carbohydrate-active enzymes. Transcripts predicted to encode three MnPs represented the most highly abundant transcripts in wood-grown cultivations compared to nutrient medium cultivations. Gene set enrichment analyses did not distinguish transcriptomes resulting from softwood and hardwood cultivations, suggesting that similar sets of enzyme activities are elicited by P. carnosa grown on different wood substrates, albeit to different expression levels.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".