Enhanced degradation of softwood versus hardwood by the white-rot fungus Pycnoporus coccineus
Bibliographic record
Abstract
BACKGROUND: White-rot basidiomycete fungi are potent degraders of plant biomass, with the ability to mineralize all lignocellulose components. Recent comparative genomics studies showed that these fungi use a wide diversity of enzymes for wood degradation. Deeper functional analyses are however necessary to understand the enzymatic mechanisms leading to lignocellulose breakdown. The Polyporale fungus Pycnoporus coccineus BRFM310 grows well on both coniferous and deciduous wood. In the present study, we analyzed the early response of the fungus to softwood (pine) and hardwood (aspen) feedstocks and tested the effect of the secreted enzymes on lignocellulose deconstruction. RESULTS: Transcriptomic and proteomic analyses revealed that P. coccineus grown separately on pine and aspen displayed similar sets of transcripts and enzymes implicated in lignin and polysaccharide degradation. In particular, the expression of lignin-targeting oxidoreductases, such as manganese peroxidases, increased upon cultivation on both woods. The sets of enzymes secreted during growth on both pine and aspen were more efficient in saccharide release from pine than from aspen, and characterization of the residual solids revealed polysaccharide conversion on both pine and aspen fiber surfaces. CONCLUSION: The combined analysis of soluble sugars and solid residues showed the suitability of P. coccineus secreted enzymes for softwood degradation. Analyses of solubilized products and residual surface chemistries of enzyme-treated wood samples pointed to differences in fiber penetration by different P. coccineus secretomes. Accordingly, beyond the variety of CAZymes identified in P. coccineus genome, transcriptome and secretome, we discuss several parameters such as the abundance of manganese peroxidases and the potential role of cytochrome P450s and pectin degradation on the efficacy of fungi for softwood conversion.
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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) | 0.001 | 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) | 0.001 | 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".