Pressure adaptation is linked to thermal adaptation in salt‐saturated marine habitats
Bibliographic record
Abstract
The present study provides a deeper view of protein functionality as a function of temperature, salt and pressure in deep-sea habitats. A set of eight different enzymes from five distinct deep-sea (3040-4908 m depth), moderately warm (14.0-16.5°C) biotopes, characterized by a wide range of salinities (39-348 practical salinity units), were investigated for this purpose. An enzyme from a 'superficial' marine hydrothermal habitat (65°C) was isolated and characterized for comparative purposes. We report here the first experimental evidence suggesting that in salt-saturated deep-sea habitats, the adaptation to high pressure is linked to high thermal resistance (P value = 0.0036). Salinity might therefore increase the temperature window for enzyme activity, and possibly microbial growth, in deep-sea habitats. As an example, Lake Medee, the largest hypersaline deep-sea anoxic lake of the Eastern Mediterranean Sea, where the water temperature is never higher than 16°C, was shown to contain halopiezophilic-like enzymes that are most active at 70°C and with denaturing temperatures of 71.4°C. The determination of the crystal structures of five proteins revealed unknown molecular mechanisms involved in protein adaptation to poly-extremes as well as distinct active site architectures and substrate preferences relative to other structurally characterized enzymes.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".