Molecular Networks of <i>Postia placenta</i> Involved in Degradation of Lignocellulosic Biomass Revealed from Metadata Analysis of Open Access Gene Expression Data
Bibliographic record
Abstract
To understand the common gene expression patterns employed by P. placenta during lignocellulose degradation, we have retrieved genome wide transcriptome datasets from NCBI GEO database and analyzed using customized analysis pipeline.We have retrieved the top differentially expressed genes and compared the common significant genes among two different growth conditions.Genes encoding for cellulolytic (GH1, GH3, GH5, GH12, GH16, GH45) and hemicellulolytic (GH10, GH27, GH31, GH35, GH47, GH51, GH55, GH78, GH95) glycoside hydrolase classes were commonly up regulated among all the datasets.Fenton's reaction enzymes (iron homeostasis, reduction, hydrogen peroxide generation) were significantly expressed among all the datasets under lignocellulolytic conditions.Due to the evolutionary loss of genes coding for various lignocellulolytic enzymes (including several cellulases), P. placenta employs hemicellulolytic glycoside hydrolases and Fenton's reactions for the rapid depolymerization of plant cell wall components.Different classes of enzymes involved in aromatic compound degradation, stress responsive and detoxification mechanisms (cytochrome P450 monoxygenases) were found highly expressed in complex plant biomass substrates.We have reported the genome wide expression patterns of genes coding for information, storage and processing (KOG), tentative and predicted molecular networks involved in cellulose, hemicellulose degradation and list of significant protein-ID's commonly expressed among different lignocellulolytic growth conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".