Systematic metadata analysis of brown rot fungi gene expression data reveals the genes involved in Fenton’s reaction and wood decay process
Bibliographic record
Abstract
Brown-rot fungi are rapid holocellulose degraders and are the most predominant degraders of coniferous wood products in North America. Brown-rot fungi degrades wood by both enzymatic (plant biomass degrading carbohydrate active enzymes-CAZymes) and non-enzymatic systems (Fenton's reaction) mechanisms. Identifying the genes and molecular mechanisms involved in Fenton's reaction would significantly improve our understanding about brown-rot decay. Our present study identifies the common gene expression patterns involved in brown rot decay by performing metadata analysis of fungal transcriptome datasets. We have also analyzed and compared the genome-wide annotations (InterPro and CAZymes) of the selected brown rot fungi. Genes encoding for various oxidoreductases, iron homeostasis, and metabolic enzymes involved in Fenton's mechanism were found to be significantly expressed among all the brown-rot fungal datasets. Interestingly, a higher number of hemicellulases encoding genes were differentially expressed among all the datasets, while a fewer number of cellulases and peroxidases were expressed (especially haem peroxidase and chloroperoxidase). Apart from these lignocellulose degrading enzymes genes encoding for aldo/keto reductases, 2-nitro dioxygenase, aromatic-ring dioxygenase, dienelactone hydrolase, alcohol dehydrogenase, major facilitator superfamily, cytochrome-P450 monoxygenase, cytochrome b5, and short-chain dehydrogenase were common and differentially up regulated among all the analyzed brown-rot fungal datasets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".