Thermal hysteresis in microtubule assembly/disassembly dynamics: The aging-induced degradation of tubulin dimers
Bibliographic record
Abstract
The assembly/disassembly of biological macromolecules plays an important role in their biological functionalities. Although the dynamics of tubulin polymers and their super-assembly into microtubule structures is critical for many cellular processes, details of their cyclical polymerization/depolymerization are not fully understood. Here, we use a specially designed light scattering technique to continuously examine the effects of temperature cycling on the process of microtubule assembly/disassembly. We observe a thermal hysteresis loop during tubulin assembly/disassembly, consistently with earlier reports on the coexistence of tubulin and microtubules as a phase transition. In a cyclical process, the structural hysteresis has a kinetic component that depends on the rate of temperature change but also an intrinsic thermodynamic component that depends on the protein topology, possibly related to irreversible processes. Analyzing the evolution of such thermal hysteresis loops over successive cycles, we found that the assembly/disassembly ceases after some time, which is indicative of protein aging leading to its inability to self-assemble after a finite number of temperature cycles. The emergence of assembly-incompetent tubulin could have major consequences for human pathologies related to microtubules, including aging, neurodegenerative diseases and cancer.
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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".