Identification of microtubule growth deceleration and its regulation by conserved and novel proteins
Bibliographic record
Abstract
Microtubules (MTs) are cytoskeletal polymers that participate in diverse cellular functions, including cell division, intracellular trafficking, and templating of cilia and flagella. MTs undergo dynamic instability, alternating between growth and shortening via catastrophe and rescue events. The rates and frequencies of MT dynamic parameters appear to be characteristic for a given cell type. We recently reported that all MT dynamic parameters vary throughout differentiation of a smooth muscle cell type in intact Caenorhabditis elegans. Here we describe local differences in MT dynamics and a novel MT behavior: an abrupt change in growth rate (deceleration) of single MTs occurring in the cell periphery of these cells. MT deceleration occurs where there is a decrease in local soluble tubulin concentration at the cell periphery. This local regulation of tubulin concentration and MT deceleration are dependent on two novel homologues of human cylicin. These novel ORFs, which we name cylc-1 and -2, share sequence homology with stathmins and encode small, very basic proteins containing several KKD/E repeats. The TOG domain-containing protein ZYG-9(TOGp) is responsible for the faster polymerization rate within the cell body. Thus we have defined two contributors to the molecular regulation for this novel MT behavior.
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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) | 0.000 | 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".