Comparative Proteomic Analysis of Flammulina filiformis Reveals Substrate-Specific Enzymatic Strategies for Lignocellulose Degradation
Bibliographic record
Abstract
Flammulina filiformis, one of the most delicious and commercially important mushrooms, demonstrates remarkable adaptability to diverse agricultural wastes. However, it is unclear how different substrates affect the degradation of lignocellulosic biomass and the production of lignocellulolytic enzymes in F. filiformis. In this study, label-free comparative proteomic analysis of F. filiformis cultivated on sugarcane bagasse, cotton seed shells, corn cobs, and glucose substrates was conducted to identify degradation mechanism across various substrates. Label-free quantitative proteomics identified 1104 proteins. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of protein expression differences were predominantly enriched in energy metabolism and carbohydrate metabolic pathways. Detailed characterization of carbohydrate-active enzymes among the identified proteins revealed glucanase (GH7, A0A067NSK0) as the key enzyme. F. filiformis secreted higher levels of cellulases and hemicellulases on sugarcane bagasse substrate. In the cotton seed shells substrate, multiple cellulases functioned collaboratively, while in the corn cobs substrate, glucanase predominated among the cellulases. These findings reveal the enzymatic strategies and metabolic flexibility of F. filiformis in lignocellulose utilization, providing novel insights for metabolic engineering applications in biotechnology. The study establishes a theoretical foundation for optimizing biomass conversion and developing innovative substrates using targeted enzyme systems.
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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 | 0.001 | 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) | 0.000 | 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 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".