Multisubstrate Isotope Labeling and Metagenomic Analysis of Active Soil Bacterial Communities
Bibliographic record
Abstract
Soil microbial diversity represents the largest global reservoir of novel microorganisms and enzymes. In this study, we coupled functional metagenomics and DNA stable-isotope probing (DNA-SIP) using multiple plant-derived carbon substrates and diverse soils to characterize active soil bacterial communities and their glycoside hydrolase genes, which have value for industrial applications. We incubated samples from three disparate Canadian soils (tundra, temperate rainforest, and agricultural) with five native carbon ((12)C) or stable-isotope-labeled ((13)C) carbohydrates (glucose, cellobiose, xylose, arabinose, and cellulose). Indicator species analysis revealed high specificity and fidelity for many uncultured and unclassified bacterial taxa in the heavy DNA for all soils and substrates. Among characterized taxa, Actinomycetales (Salinibacterium), Rhizobiales (Devosia), Rhodospirillales (Telmatospirillum), and Caulobacterales (Phenylobacterium and Asticcacaulis) were bacterial indicator species for the heavy substrates and soils tested. Both Actinomycetales and Caulobacterales (Phenylobacterium) were associated with metabolism of cellulose, and Alphaproteobacteria were associated with the metabolism of arabinose; members of the order Rhizobiales were strongly associated with the metabolism of xylose. Annotated metagenomic data suggested diverse glycoside hydrolase gene representation within the pooled heavy DNA. By screening 2,876 cloned fragments derived from the (13)C-labeled DNA isolated from soils incubated with cellulose, we demonstrate the power of combining DNA-SIP, multiple-displacement amplification (MDA), and functional metagenomics by efficiently isolating multiple clones with activity on carboxymethyl cellulose and fluorogenic proxy substrates for carbohydrate-active enzymes. Importance: The ability to identify genes based on function, instead of sequence homology, allows the discovery of genes that would not be identified through sequence alone. This is arguably the most powerful application of metagenomics for the recovery of novel genes and a natural partner of the stable-isotope-probing approach for targeting active-yet-uncultured microorganisms. We expanded on previous efforts to combine stable-isotope probing and metagenomics, enriching microorganisms from multiple soils that were active in degrading plant-derived carbohydrates, followed by construction of a cellulose-based metagenomic library and recovery of glycoside hydrolases through functional metagenomics. The major advance of our study was the discovery of active-yet-uncultivated soil microorganisms and enrichment of their glycoside hydrolases. We recovered positive cosmid clones in a higher frequency than would be expected with direct metagenomic analysis of soil DNA. This study has generated an invaluable metagenomic resource that future research will exploit for genetic and enzymatic potential.
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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 | 0.000 | 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".