DNA Partitioning Modulates Liquid-to-Solid Transitions and the Internal Microstructure of FUS Condensates
Bibliographic record
Abstract
Abstract Protein liquid-liquid phase separation has recently been recognized as an essential process involved in cellular functions, including transcription, translation, and DNA damage repair. However, a further liquid-to-solid transition (LST) of condensates due to mutation or external stimuli can result in aggregation, sometimes pathological. The unique ability of protein condensates to concentrate and sequester biomolecules is at the heart of the regulatory mechanism, controlling the dynamics and function of the condensates. While the recruitment of essential biomolecules, such as RNA has been studied to have the impact to the condensate formation, how DNA partition can affect the phase behaviour and dynamics of protein condensates is not fully elucidated. In this study, we investigate both the short-term and long-term kinetics of double-stranded DNA partitioning into preformed fresh and aged FUS protein condensates. Confocal imaging shows that DNA partition follows the core-shell diffusion pattern within the condensates. LST slows down and reduces FUS condensates’ ability to recruit DNA but stabilizes the DNA-FUS condensate complex due to the heterogeneous solid network formation. Using the optical technique of Spatial Dynamic Mapping (SDM), we find that DNA partition promotes coalescence and alters the characteristics of the condensates. The partition made the condensates more dynamic in the short term (within minutes) but accelerated LST in long-term incubation (within hours), ultimately leading to an irreversible porous core-shell structure of FUS condensates. Our findings reveal the kinetics of DNA partition during aging and its impact on LST, underlining the modulation of condensate properties by molecule sequestration, shedding light on possible regulation of disease-related LST of biomolecular condensates.
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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".