Genomic and transcriptomic insights into the cellulose-degrading mechanism of Bacillus subtilis DC-11 and its novel cellulose catabolic pathway
Bibliographic record
Abstract
Abstract The accumulation of silkworm excrement poses a significant environmental challenge, contributing to pollution and resource squandering. Unraveling the novel mechanism governing bacterial cellulose degradation represents a vital avenue for augmenting cellulose conversion efficiency. This study investigated the cultivation of Bacillus subtilis DC-11 with different carbon sources, utilizing transcriptome sequencing to identify metabolic pathways and differentially expressed genes (DEGs) closely related to cellulose degradation. Transcriptome analysis revealed 3,917 DEGs between the carboxymethyl cellulose sodium (CMC-Na) treatment group and the glucose-supplemented (GLU) control group. Compared to the control group, the CMC-Na treatment group exhibited upregulation of 942 genes, while 1,996 genes were downregulated. KEGG pathway analysis of DEGs indicated the involvement of that carbohydrate metabolism and phosphotransferase system (PTS) pathways in response to cellulose degradation. Real-time quantitative PCR validation confirmed that the expressions of key genes, namely ytoP, bglH, gmuD, licH, licC, ywbA, licA, gmuA, and gmuB, associated with the PTS pathway were consistent with the transcriptomics data. These results suggest that B. subtilis DC-11 degrades cellulose via carbohydrate metabolism and PTS pathways. This study offers new insights into the cellulose metabolism pathway of B. subtilis DC-11, providing both a theoretical basis and innovative strategies for the efficient degradation of cellulose.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".