Effects of Cosolvents and Crowding Agents on the Stability and Phase Transition Kinetics of the SynGAP/PSD-95 Condensate Model of Postsynaptic Densities
Bibliographic record
Abstract
The SynGAP/PSD-95 binary protein system serves as a simple mimicry of postsynaptic densities (PSDs), which are protein assemblies based largely on liquid–liquid phase separation (LLPS), that are located underneath the plasma membrane of excitatory synapses. Surprisingly, the LLPS of the SynGAP/PSD-95 system is much more pressure sensitive than typical folded states of proteins or nucleic acids. It was found that phase-separated SynGAP/PSD-95 droplets dissolve into a homogeneous solution at a pressure of tens to hundred bar. Since organisms in the deep sea are exposed to pressures of up to ∼1000 bar, this observation suggests that deep-sea organisms must counteract the high pressure sensitivity of PSDs to avoid neurological impairment. We demonstrate here that the compatible osmolyte trimethylamine- N -oxide (TMAO) as well as macromolecular crowding agents at moderate concentrations can mitigate the deleterious effect of pressure on SynGAP/PSD-95 droplet stability, extending stable LLPS up to almost a kbar level. Moreover, the formation of SynGAP/PSD-95 droplets is a very rapid process that can be switched on and off in seconds. In contrast with the marked effects of the cosolutes on droplet stability, at the cosolutes’ respective biologically relevant concentrations, their impact on the phase transformation kinetics is rather small. Only a high TMAO concentration results in a significant kinetic retardation of LLPS. Taken together, these findings offer new biophysical insights into the neurological effects of hydrostatic pressure. In particular, our results indicate how pressure-induced neurological disorders might be alleviated by upregulating certain cosolutes in the cellular milieu.
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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".