Genome‐resolved metagenomics reveals diverse taxa and metabolic complexity in Antarctic lake microbial structures
Bibliographic record
Abstract
Lake Untersee, a lake in Antarctica that is perennially covered with ice, is home to unique microbial structures that are not lithified. We have evaluated the structure of the community and its metabolic potential across the pigmented upper layers and the sediment-enriched deeper layers in these pinnacle and cone-shaped microbial structures using metagenomics. These microbial structures are inhabited by distinct communities. The upper layers of the cone-shaped structures have a higher abundance of the cyanobacterial MAG Microcoleus, while the pinnacle-shaped structures have a higher abundance of Elainellacea MAG. This suggests that cyanobacteria influence the morphologies of the mats. We identified stark contrasts in the composition of the community and its metabolic potential between the upper and lower layers of the mat. The upper layers of the mat, which receive light, have an increased abundance of photosynthetic pathways. In contrast, the lower layer has an increased abundance of heterotrophic pathways. Our results also showed that Lake Untersee is the first Antarctic lake with a substantial presence of ammonia-oxidizing Nitrospiracea and amoA genes. The genomic capacity for recycling biological molecules was prevalent across metagenome-assembled genomes (MAGs) that cover 19 phyla. This highlights the importance of nutrient scavenging in ultra-oligotrophic environments. Overall, our study provides new insights into the formation of microbial structures and the potential metabolic complexity of Antarctic laminated microbial mats. These mats are important environments for biodiversity that drives biogeochemical cycling in polar deserts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".