Microtubule dynamic instability is sensitive to specific biological viscogens <i>in vitro</i>
Bibliographic record
Abstract
Abstract Cytoplasm is a viscous, crowded, and heterogeneous environment, and its local viscosity and degree of macromolecular crowding have significant effects on cellular reaction rates. Increasing viscosity slows down diffusion and protein conformational changes, while increasing macromolecular crowding speeds up reactions. As a model system for cellular reactions, microtubule dynamics are slowed down in vivo when cytoplasm concentration is increased by osmotic shifts, indicating a dominant role for viscosity in microtubule reaction pathways. In the cell, viscosity is determined by diverse species of “biological viscogens”, including glycerol, trehalose, intermediate metabolites, proteins, polymers, organelles, and condensates. Here we show in vitro that microtubule dynamic instability is sensitive to specific viscogen species, particularly glycerol. We found that increasing viscosity with glycerol or trehalose slowed microtubule growth, slowed microtubule shrinkage, and increased microtubule lifetimes, similar to the “freezing” observed previously in vivo . Increasing viscosity with a globular protein, bovine serum albumin, increased microtubule growth rates, as its viscous effects may be balanced against its macromolecular crowding effects. At matched viscosities, glycerol had an outsized effect on microtubule lifetimes, rescues, and nucleation compared to other viscogens. Increasing viscosity did not, however, increase the intensity of EB3-GFP comets, indicating that GTP hydrolysis is unaffected by buffer conditions. We propose that glycerol exerts its distinct effect on microtubule dynamic instability by stabilizing the microtubule lattice after phosphate release. Effects of specific viscogens may modulate many cellular reaction rates within local environments of cytoplasm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".