Characterization of active microbial ecosystems in icy astrobiology analog cryo-environments.
Bibliographic record
Abstract
Ice environments, characterized by subzero temperatures, low nutrient levels, and low water activity, present unique challenges to microbial life. Studying these microbes helps us understand the cold limits of life on Earth and extraterrestrial bodies. Lava tube ice caves, such as those at Lava Beds National Monument near California's Medicine Lake Volcano, are analogs to Martian lava tubes that may host microbial ecosystems. Similarly, glaciers and ice caps, like those on Jupiter and Saturn's moons, have thick ice crusts and could support microbial life. Despite being some of the best-studied ice masses in the world, the microbial ecology of the Devon Island ice cap, White Glacier in the Canadian High Arctic, and Johnsons Glacier in Antarctica remains largely unexplored. This study uses both culture-dependent and independent methods to investigate the microbial ecosystems in these four environments.In Lava Beds National Monument, 16S rRNA amplicon sequencing identified ice samples primarily consisting of Actinomycetota, Pseudomonadota, Bacteroidota, Bacillota, and Chloroflexota and culture experiments and metabolic activity assays identified a viable and metabolically active microbial community at subzero temperatures. Furthermore, these communities were found to be more closely related to other cryoenvironments than caves suggesting that ice and cold temperatures exert stronger selection pressures on microbial communities than caves.In the Devon Island ice cap, flow cytometry coupled with live/dead staining identified an ultra-low biomass microbial ecosystem containing 3.62 x 10^4 cells/mL with a live fraction of 0.8%. Viable cells were also isolated and found to grow at subzero temperatures, high salinity (> 6%), and low pH (< pH 5), including one isolate capable of growth at -5°C, in 15% NaCl, and pH 3. Metagenomic and metatranscriptomic sequences belonging to diverse metabolic marker genes, including those related to thiosulfate oxidation, aerobic carbon monoxide oxidation, aerobic respiration, fumarate reduction, nitrate and nitrite reduction, nitrogen fixation, oxygenic photosynthesis and carbon fixation, indicating a diverse ecosystem composed of lithoautotrophs, photoautotrophs and heterotrophs. Transcripts involved in cold adaptation were also abundant, including cold shock proteins, transcription and translation factors, and membrane and peptidoglycan-altering proteins.The microbial ecosystems of White Glacier and Johnsons Glacier were compared, and their taxonomy was found to be significantly different from each other but shared an overlap in functional potential. Metatranscriptome sequencing of White Glacier revealed an active microbial ecosystem dominated by Cyanobacteria performing oxygenic photosynthesis and carbon fixation. In addition, lithoautotrophic metabolisms including carbon fixation via the 3-hydroxyproprionate cycle, anoxygenic photosynthesis, sulfide oxidation, and nitrate reduction/denitrification support a heterotrophic community performing aerobic respiration and aerobic carbon monoxide oxidation. Metagenome-assembled genomes were also found to be active including Cyanobacteriota, and novel phyla Armatimonadota, Eremiobacterota, and Gemmatimonadota. Similarly to the Devon Island ice cap, total biomass within White Glacier was 4.75 x 10^4 cells/ml with a live fraction of 0.5%. The majority of cultured isolates were capable of growth at subzero temperatures, high salinity (> 6%) and low pH (< pH 5). This study determined that ice environments contain diverse, active microbial ecosystems which are well adapted to life in ice, providing insights into how microbial life is sustained in some of Earth’s most extreme environments and how it might survive on Mars or the icy moons
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 | 0.001 | 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".