Significant Alteration of Gene Expression in Wood Decay Fungi Postia placenta and Phanerochaete chrysosporium by Plant Species
Bibliographic record
Abstract
Identification of specific genes and enzymes involved in conversion of lignocellulosics from an expanding number of potential feedstocks is of growing interest to bioenergy process development. The basidiomycetous wood decay fungi Phanerochaete chrysosporium and Postia placenta are promising in this regard because they are able to utilize a wide range of simple and complex carbon compounds. However, systematic comparative studies with different woody substrates have not been reported. To address this issue, we examined gene expression of these fungi colonizing aspen (Populus grandidentata) and pine (Pinus strobus). Transcript levels of genes encoding extracellular glycoside hydrolases, thought to be important for hydrolytic cleavage of hemicelluloses and cellulose, showed little difference for P. placenta colonizing pine versus aspen as the sole carbon source. However, 164 genes exhibited significant differences in transcript accumulation for these substrates. Among these, 15 cytochrome P450s were upregulated in pine relative to aspen. Of 72 P. placenta extracellular proteins identified unambiguously by mass spectrometry, 52 were detected while colonizing both substrates and 10 were identified in pine but not aspen cultures. Most of the 178 P. chrysosporium glycoside hydrolase genes showed similar transcript levels on both substrates, but 13 accumulated >2-fold higher levels on aspen than on pine. Of 118 confidently identified proteins, 31 were identified in both substrates and 57 were identified in pine but not aspen cultures. Thus, P. placenta and P. chrysosporium gene expression patterns are influenced substantially by wood species. Such adaptations to the carbon source may also reflect fundamental differences in the mechanisms by which these fungi attack plant cell walls.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".