Taxonomic composition and carbohydrate-active enzyme content in microbial enrichments from pulp mill anaerobic granules after cultivation on lignocellulosic substrates
Bibliographic record
Abstract
Metagenomes of lignocellulose-degrading microbial communities are reservoirs of carbohydrate-active enzymes relevant to biomass processing. Whereas several metagenomes of natural digestive systems have been sequenced, the current study analyses metagenomes originating from an industrial anaerobic digester that processes effluent from a cellulose pulp mill. Both 16S ribosomal DNA and metagenome sequences were obtained following anaerobic cultivation of the digester inoculum on cellulose and pretreated (steam exploded) poplar wood chips. The community composition and profile of predicted carbohydrate-active enzymes were then analyzed in detail. Recognized lignocellulose degraders were abundant in the resulting cultures, including populations belonging to Clostridiales and Bacteroidales orders. Poorly defined taxonomic lineages previously identified in other lignocellulose-degrading communities were also detected, including the uncultivated Firmicutes lineage OPB54 which represented nearly 10% of the cellulose-fed enrichment even though it was not detected in the bioreactor inoculum. In total, 3580 genes encoding carbohydrate-active enzymes were identified through metagenome sequencing. Similar to earlier enrichments of animal digestive systems, the profile encoded by the bioreactor inoculum following enrichment on pretreated wood was distinguished from the cellulose counterpart by a higher occurrence of enzymes predicted to act on pectin. The majority (> 93%) of carbohydrate-active enzymes predicted to act on plant polysaccharides were identified in the metagenome assembled genomes, permitting taxonomic assignment. The taxonomic assignment revealed that only a small selection of organisms directly participates in plant polysaccharide deconstruction and supports the rest of the community.
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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 | 0.000 |
| 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) | 0.000 | 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 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".