Metagenomics-Guided Survey, Isolation, and Characterization of Uranium Resistant Microbiota from the Savannah River Site, USA
Bibliographic record
Abstract
Despite the recent advancements in culturomics, isolation of the majority of environmental microbiota performing critical ecosystem services, such as bioremediation of contaminants, remains elusive. Towards this end, we conducted a metagenomics-guided comparative assessment of soil microbial diversity and functions present in uraniferous soils relative to those that grew in diffusion chambers (DC) or microbial traps (MT), followed by isolation of uranium (U) resistant microbiota. Shotgun metagenomic analysis performed on the soils used to establish the DC/MT chambers revealed Proteobacterial phyla and Burkholderia genus to be the most abundant among bacteria. The chamber-associated growth conditions further increased their abundances relative to the soils. Ascomycota was the most abundant fungal phylum in the chambers relative to the soils, with Penicillium as the most dominant genus. Metagenomics-based taxonomic findings completely mirrored the taxonomic composition of the retrieved isolates such that the U-resistant bacteria and fungi mainly belonged to Burkholderia and Penicillium species, thus confirming that the chambers facilitated proliferation and subsequent isolation of specific microbiota with environmentally relevant functions. Furthermore, shotgun metagenomic analysis also revealed that the gene classes for carbohydrate metabolism, virulence, and respiration predominated with functions related to stress response, membrane transport, and metabolism of aromatic compounds were also identified, albeit at lower levels. Of major note was the successful isolation of a potentially novel Penicillium species using the MT approach, as evidenced by whole genome sequence analysis and comparative genomic analysis, thus enhancing our overall understanding on the uranium cycling microbiota within the tested uraniferous soils.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".