Hydrophobic substances induce water stress in microbial cells
Bibliographic record
Abstract
Ubiquitous noxious hydrophobic substances, such as hydrocarbons, pesticides and diverse industrial chemicals, stress biological systems and thereby affect their ability to mediate biosphere functions like element and energy cycling vital to biosphere health. Such chemically diverse compounds may have distinct toxic activities for cellular systems; they may also share a common mechanism of stress induction mediated by their hydrophobicity. We hypothesized that the stressful effects of, and cellular adaptations to, hydrophobic stressors operate at the level of water : macromolecule interactions. Here, we present evidence that: (i) hydrocarbons reduce structural interactions within and between cellular macromolecules, (ii) organic compatible solutes - metabolites that protect against osmotic and chaotrope-induced stresses - ameliorate this effect, (iii) toxic hydrophobic substances induce a potent form of water stress in macromolecular and cellular systems, and (iv) the stress mechanism of, and cellular responses to, hydrophobic substances are remarkably similar to those associated with chaotrope-induced water stress. These findings suggest that it may be possible to devise new interventions for microbial processes in both natural environments and industrial reactors to expand microbial tolerance of hydrophobic substances, and hence the biotic windows for such processes.
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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 | 0.000 | 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) | 0.001 | 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".