Gene Expression Patterns of Wood Decay Fungi Postia placenta and Phanerochaete chrysosporium Are Influenced by Wood Substrate Composition during Degradation
Bibliographic record
Abstract
UNLABELLED: Identification of the specific genes and enzymes involved in the fungal degradation of lignocellulosic biomass derived from feedstocks with various compositions is essential to the development of improved bioenergy processes. In order to elucidate the effect of substrate composition on gene expression in wood-rotting fungi, we employed microarrays based on the annotated genomes of the brown- and white-rot fungi, Rhodonia placenta (formerly Postia placenta) and Phanerochaete chrysosporium, respectively. We monitored the expression of genes involved in the enzymatic deconstruction of the cell walls of three 4-year-old Populus trichocarpa (poplar) trees of genotypes with distinct cell wall chemistries, selected from a population of several hundred trees grown in a common garden. The woody substrates were incubated with wood decay fungi for 10, 20, and 30 days. An analysis of transcript abundance in all pairwise comparisons highlighted 64 and 84 differentially expressed genes (>2-fold, P < 0.05) in P. chrysosporium and P. placenta, respectively. Cross-fungal comparisons also revealed an array of highly differentially expressed genes (>4-fold, P < 0.01) across different substrates and time points. These results clearly demonstrate that gene expression profiles of P. chrysosporium and P. placenta are influenced by wood substrate composition and the duration of incubation. Many of the significantly expressed genes encode "proteins of unknown function," and determining their role in lignocellulose degradation presents opportunities and challenges for future research. IMPORTANCE: This study describes the variation in expression patterns of two wood-degrading fungi (brown- and white-rot fungi) during colonization and incubation on three different naturally occurring poplar substrates of differing chemical compositions, over time. The results clearly show that the two fungi respond differentially to their substrates and that several known and, more interestingly, currently unknown genes are highly misregulated in response to various substrate compositions. These findings highlight the need to characterize several unknown proteins for catalytic function but also as potential candidate proteins to improve the efficiency of enzymatic cocktails to degrade lignocellulosic substrates in industrial applications, such as in a biochemically based bioenergy platform.
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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".