A shared core microbiome in soda lakes separated by large distances
Bibliographic record
Abstract
Abstract In alkaline soda lakes, high concentrations of dissolved carbonates establish an environment favouring productive phototrophic microbial mat communities. Here we show how different species of microbial phototrophs and autotrophs contribute to this exceptional productivity. Four years of amplicon and shotgun DNA sequencing data from microbial mats from four different lakes indicated the presence of over 2,000 different species of Bacteria and Eukaryotes. Metagenome-assembled-genomes were obtained for a core microbiome of <100 abundant bacteria, which was shared among lakes and accounted for half of the extracted DNA throughout the four year sampling period. Most of the associated species were related to similar microbes previously detected in sediments of Central Asian alkaline soda lakes, showing that common selection principles drive community assembly from a globally distributed reservoir of alkaliphile biodiversity. Dispersal events between the two distant lake systems were shown to be extremely rare, with dispersal rates a function of abundance in microbial mats, but not sediments. Detection of more than 7,000 expressed proteins showed how phototrophic populations allocated resources to specific processes and occupied complementary niches. Carbon fixation only proceeded by the Calvin-Benson-Bassham cycle, detected in Cyanobacteria, Alphaproteobacteria, and, suprisingly, Gemmatomonadetes. Our study not only provides new fundamental insight into soda lake ecology, but also provides a template, guiding future efforts to engineer robust and productive biotechnology for carbon dioxide conversion. Importance Alkaline soda lakes are among the most productive ecosystems worldwide, despite their high pH. This high productivity leads to growth of thick “mats” of filamentous cyanobacteria. Here, we show that such mats have very high biodiversity, but at the same time contain a core, shared set of only approximately 100 different bacteria that perform key functions, such as photosynthesis. This “core microbiome” occurs both in Canadian and Central Asian soda lakes, >8,000 km apart. We present evidence for (very rare) dispersion of some core microbiome members from Canadian mats to Central Asian soda lake sediments. The close similarity between distant microbial communities indicates that these communities share common design principles, that reproducibly lead to a high and robust productivity. We unravel a few examples of such principles and speculate that these might be applied to create robust biotechnology for carbon dioxide conversion, to mitigate of global climate change.
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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) | 0.000 | 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 | 0.001 |
| 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".