Systematic metadata analysis of brown rot fungi gene expression data reveals the genes involved in Fenton’s reaction and wood decay process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it