Recovering glycoside hydrolase genes from active tundra cellulolytic bacteria
Bibliographic record
Abstract
Bacteria responsible for cellulose hydrolysis in situ are poorly understood, largely because of the relatively recent development of cultivation-independent methods for their detection and characterization. This study combined DNA stable-isotope probing (DNA-SIP) and metagenomics for identifying active bacterial communities that assimilated carbon from glucose and cellulose in Arctic tundra microcosms. Following DNA-SIP, bacterial fingerprint analysis of gradient fractions confirmed isotopic enrichment. Sequenced fingerprint bands and clone library analysis of 16S rRNA genes identified active bacterial taxa associated with cellulose-associated labelled DNA, including Bacteroidetes (Sphingobacteriales), Betaproteobacteria (Burkholderiales), Alphaproteobacteria (Caulobacteraceae), and Chloroflexi (Anaerolineaceae). We also compared glycoside hydrolase metagenomic profiles from bulk soil and heavy DNA recovered from DNA-SIP incubations. Active populations consuming [(13)C]glucose and [(13)C]cellulose were distinct, based on ordinations of light and heavy DNA. Metagenomic analysis demonstrated a ∼3-fold increase in the relative abundance of glycoside hydrolases in DNA-SIP libraries over bulk-soil libraries. The data also indicate that multiple displacement amplification introduced bias into the resulting metagenomic analysis. This research identified DNA-SIP incubation conditions for glucose and cellulose that were suitable for Arctic tundra soil and confirmed that DNA-SIP enrichment can increase target gene frequencies in metagenomic libraries.
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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 | 0.001 | 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".