Meta-omics analyses of the diversity and metabolism of the uncultivated CL500-3 clade of Planctomycetes in seasonally ice-covered northern lakes
Bibliographic record
Abstract
Owing to cold water temperature and low light penetration through ice, microbial communities were thought to be dormant during winter in seasonally ice-covered lakes. Recent observations of under-ice blooms have challenged this view and raised questions about how microbes contribute to nutrient cycling throughout the year. A previous study demonstrated that Planctomycetes bacteria were commonly abundant under the ice. Therefore, in this thesis, I investigated the diversity and metabolic traits of Planctomycetes in three distinct Canadian lakes over a three-year time series using a combination of 16S rRNA gene sequencing, metagenomic and metatranscriptomic analyses. Winter Planctomycetes assemblages were comprised in large part of the uncultivated CL500-3 clade, which consisted of five subclades (CL500-3a to CL500-3e), each exhibiting a seasonal and biogeographical distribution. Six CL500-3 genomes were reconstructed from Quebec lake metagenomic data and a large fraction of genes were detected in the metatranscriptomic datasets, supporting the notion that CL500-3 subclades were metabolically active throughout the year. CL500-3 genomes were enriched in amino acid degradation pathways and depleted in carbohydrate degrading enzymes compared to other Planctomycetes. The abundance and expression of amino acid degradation pathways suggests that they serve as a source of nitrogen, as well as carbon substrates that feed into the citric acid cycle. Additionally, expression of phosphate-selective porins and chemotactic genes indicates the ability to adapt and respond to cellular and environmental changes. In total, these findings shed light on the metabolism of the widely distributed but poorly characterized CL500-3 clade of Planctomycetes and increase our understanding of the diversity and metabolic processes occurring under-ice in northern lakes ecosystems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".