Genome, transcriptome, and secretome analysis of wood decay fungus <i>Postia placenta</i> supports unique mechanisms of lignocellulose conversion
Bibliographic record
Abstract
Brown-rot fungi such as Postia placenta are common inhabitants of forest ecosystems and are also largely responsible for the destructive decay of wooden structures. Rapid depolymerization of cellulose is a distinguishing feature of brown-rot, but the biochemical mechanisms and underlying genetics are poorly understood. Systematic examination of the P. placenta genome, transcriptome, and secretome revealed unique extracellular enzyme systems, including an unusual repertoire of extracellular glycoside hydrolases. Genes encoding exocellobiohydrolases and cellulose-binding domains, typical of cellulolytic microbes, are absent in this efficient cellulose-degrading fungus. When P. placenta was grown in medium containing cellulose as sole carbon source, transcripts corresponding to many hemicellulases and to a single putative beta-1-4 endoglucanase were expressed at high levels relative to glucose-grown cultures. These transcript profiles were confirmed by direct identification of peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Also up-regulated during growth on cellulose medium were putative iron reductases, quinone reductase, and structurally divergent oxidases potentially involved in extracellular generation of Fe(II) and H(2)O(2). These observations are consistent with a biodegradative role for Fenton chemistry in which Fe(II) and H(2)O(2) react to form hydroxyl radicals, highly reactive oxidants capable of depolymerizing cellulose. The P. placenta genome resources provide unparalleled opportunities for investigating such unusual mechanisms of cellulose conversion. More broadly, the genome offers insight into the diversification of lignocellulose degrading mechanisms in fungi. Comparisons with the closely related white-rot fungus Phanerochaete chrysosporium support an evolutionary shift from white-rot to brown-rot during which the capacity for efficient depolymerization of lignin was lost.
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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 | 0.001 |
| 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".