Molecular Networks of <i>Postia placenta</i> Involved in Degradation of Lignocellulosic Biomass Revealed from Metadata Analysis of Open Access Gene Expression Data
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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