Illuminating Microbial Dark Matter in Meromictic Sakinaw Lake
Bibliographic record
Abstract
Despite recent advances in metagenomic and single-cell genomic sequencing to investigate uncultivated microbial diversity and metabolic potential, fundamental questions related to population structure, interactions, and biogeochemical roles of candidate divisions remain. Numerous molecular surveys suggest that stratified ecosystems manifesting anoxic, sulfidic, and/or methane-rich conditions are enriched in these enigmatic microbes. Here we describe diversity, abundance, and cooccurrence patterns of uncultivated microbial communities inhabiting the permanently stratified waters of meromictic Sakinaw Lake, British Columbia, Canada, using 454 sequencing of the small-subunit rRNA gene with three-domain resolution. Operational taxonomic units (OTUs) were affiliated with 64 phyla, including more than 25 candidate divisions. Pronounced trends in community structure were observed for all three domains with eukaryotic sequences vanishing almost completely below the mixolimnion, followed by a rapid and sustained increase in methanogen-affiliated (∼10%) and unassigned (∼60%) archaeal sequences as well as bacterial OTUs affiliated with Chloroflexi (∼22%) and candidate divisions (∼28%). Network analysis revealed highly correlated, depth-dependent cooccurrence patterns between Chloroflexi, candidate divisions WWE1, OP9/JS1, OP8, and OD1, methanogens, and unassigned archaeal OTUs indicating niche partitioning and putative syntrophic growth modes. Indeed, pathway reconstruction using recently published Sakinaw Lake single-cell genomes affiliated with OP9/JS1 and OP8 revealed complete coverage of the Wood-Ljungdahl pathway with potential to drive syntrophic acetate oxidation to hydrogen and carbon dioxide under methanogenic conditions. Taken together, these observations point to previously unrecognized syntrophic networks in meromictic lake ecosystems with the potential to inform design and operation of anaerobic methanogenic bioreactors.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".