Biomolecular condensates undergo a generic shear-mediated liquid-to-solid transition
Bibliographic record
Abstract
A wide range of systems containing proteins have been shown to undergo liquid-liquid phase separation (LLPS) forming membraneless compartments, such as processing bodies 1 , germ granules 2 , stress granules 3 and Cajal bodies 4 . The condensates resulting from this phase transition control essential cell functions, including mRNA regulation, cytoplasm structuring, cell signalling and embryogenesis 1–4 . RNA-binding Fused in Sarcoma (FUS) protein is one of the most studied systems in this context, due to its important role in neurodegenerative diseases 5–7 . It has recently been discovered that FUS condensates can undergo an irreversible phase transition which results in fibrous aggregate formation 6 . Gelation of protein condensates is generally associated with pathology. One case where liquid-to-solid transition (LST) of liquid-liquid phase separated proteins is functional, however, is that of silk spinning 8,9 , which is largely driven by shear, but it is not known what factors control the pathological gelation of functional condensates. Here we show that four proteins and one peptide system not related to silk, and with no function associated with fibre formation, have a strong propensity to undergo LST when exposed to even low levels of mechanical shear comparable to those found inside a living cell, once present in their liquid-liquid phase separated forms. Using microfluidics to control the application of mechanical shear, we generated fibres from single protein condensates and characterized their structures and material properties as a function of shear stress. Our results inform on the molecular grammar underlying protein LST and highlight generic backbone-backbone hydrogen bonding constraints as a determining factor in governing this transition. Taken together, these observations suggest that the shear plays an important role in the irreversible phase transition of liquid-liquid phase separated droplets, shed light on the role of physical factors in driving this transition in protein aggregation related diseases, and open a new route towards artificial shear responsive biomaterials.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".