Pressure-Sensitive and Osmolyte-Modulated Liquid–Liquid Phase Separation of Eye-Lens γ-Crystallins
Bibliographic record
Abstract
Biomolecular condensates can be functional (e.g., as membrane-less organelles) or dysfunctional (e.g., as precursors to pathological protein aggregates). A major physical underpinning of biomolecular condensates is liquid-liquid phase separation (LLPS) of proteins and nucleic acids. Here we investigate the effects of temperature and pressure on the LLPS of the eye-lens protein γ-crystallin using UV/vis and IR absorption, fluorescence spectroscopy, and light microscopy to characterize the mesoscopic phase states. Quite unexpectedly, the LLPS of γ-crystallin is much more sensitive to pressure than folded states of globular proteins. At low temperatures, the phase-separated droplets of γ-crystallin dissolve into a homogeneous solution at as low as ∼0.1 kbar whereas proteins typically unfold above ∼3 kbar. This observation suggests, in general, that organisms thriving under high-pressure conditions in the deep sea, with pressure of up to 1 kbar, have to cope with this pressure sensitivity of biomolecular condensates to avoid detrimental impacts to their physiology. Interestingly, our experiments demonstrate that trimethylamine- N-oxide, an osmolyte upregulated in deep-sea fish, significantly enhances the stability of the condensed protein droplets, pointing to a previously unrecognized aspect of the adaptive advantage of increased concentrations of osmolytes in deep-sea organisms. As the birth place of life on earth could have been the deep sea, studies of pressure effects on LLPS as presented here are relevant to the possible formation of protocells under prebiotic conditions. A physical framework to conceptualize our observations and further ramifications of biomolecular LLPS at low temperatures and high hydrostatic pressures is discussed.
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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".