Shotgun metagenomics reveals distinct functional diversity and metabolic capabilities between 12 000-year-old permafrost and active layers on Muot da Barba Peider (Swiss Alps)
Bibliographic record
Abstract
sub-zero temperatures, their energetic strategies and their metabolic versatility in using soil organic materials determine their growth and functionality upon thawing. Hence, functional characterization of the permafrost microbiome, particularly in the underexplored mid-latitudinal alpine regions, is a crucial first step in predicting its responses to the changing climate, and the consequences for soil-climate feedbacks. In this study, for the first time, the functional potential and metabolic capabilities of a temperate mountain permafrost microbiome from central Europe has been analysed using shotgun metagenomics. Permafrost and active layers from the summit of Muot da Barba Peider (MBP) [Swiss Alps, 2979 m above sea level (a.s.l.)] revealed a strikingly high functional diversity in the permafrost (north-facing soils at a depth of 160 cm). Permafrost metagenomes were enriched in stress-response genes (e.g. cold-shock genes, chaperones), as well as in genes involved in cell defence and competition (e.g. antiviral proteins, antibiotics, motility, nutrient-uptake ABC transporters), compared with active-layer metagenomes. Permafrost also showed a higher potential for the synthesis of carbohydrate-active enzymes, and an overrepresentation of genes involved in fermentation, carbon fixation, denitrification and nitrogen reduction reactions. Collectively, these findings demonstrate the potential capabilities of permafrost microorganisms to thrive in cold and oligotrophic conditions, and highlight their metabolic versatility in carbon and nitrogen cycling. Our study provides a first insight into the high functional gene diversity of the central European mountain permafrost microbiome. Our findings extend our understanding of the microbial ecology of permafrost and represent a baseline for future investigations comparing the functional profiles of permafrost microbial communities at different latitudes.
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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 | 0.001 | 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) | 0.001 | 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".