Several independent adaptations of archaea to hypersaline environments
Bibliographic record
Abstract
Abstract Several archaeal lineages thrive in high, saturating salt concentrations. These extremely halophilic archaea, including Halobacteria, Nanohaloarchaeota, Methanonatronarchaeia, and Haloplasmatales, must maintain osmotic equilibrium with their environment. For this, they use a ‘salt-in’ strategy, which involves pumping molar concentrations of potassium into the cells, which, in turn, has led to extensive proteome-wide modifications to prevent protein aggregation. However, the evolutionary history underlying these adaptations remains poorly understood. In particular, the number of times that these dramatic proteome-sweeping changes occurred is unclear due to the conflicting phylogenetic positions found for several of these lineages. Here, we present a resolved phylogeny of extremely halophilic archaea obtained using improved taxon sampling and state-of-the-art phylogenetic approaches designed to cope with the strong compositional biases of their proteomes. We describe two new uncultured lineages, Afararchaeaceae and Asboarchaeaceae, which break the long branches at the base of Haloarchaea and Nanohaloarchaeota, respectively. Our extensive phylogenomic analyses show that at least four independent adaptations to extreme halophily occurred during archaeal evolution. Finally, gene-tree/species-tree reconciliation suggests that gene duplication and horizontal gene transfer played an important role in this process, for example, by spreading key genes (such as those encoding potassium transporters) across the various extremely halophilic lineages.
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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 | 0.001 | 0.001 |
| 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".