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Record W4402401207 · doi:10.1101/2024.09.09.612121

A fluid droplet harvests the force generated by shrinking microtubules in living cells

2024· preprint· en· W4402401207 on OpenAlexaff
Katherine Morelli, Sandro M. Meier, Angela Zhao, Madhurima Choudhury, M Willis, Yves Barral, Jackie Vogel

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsMcGill University Health CentreMcGill University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsMicrotubuleResizingMechanicsCell biologyNanotechnologyBiophysicsPhysicsMaterials scienceBiologyBusinessInternational trade

Abstract

fetched live from OpenAlex

Summary The energy-consuming dynamic instability of microtubules generates significant forces which are thought to be harnessed to move large cargos in cells. However, identification of mechanisms which can capture the force released during microtubule depolymerization to move large loads has been elusive. In this work we show that a biomolecular condensate provides an elegant solution to this problem. Using live cell super-resolution microscopy, we directly observe that budding yeast +TIP bodies are nanoscale droplets with classic fluid-like behaviors which accumulate type V myosin (Myo2) at their surfaces. We find that conserved self-oligomerization interfaces in the protein Kar9 tune the biophysical properties of the viscoelastic +TIP body and its ability to e]iciently move the mitotic spindle. Our findings introduce a paradigm for how forces generated by microtubule dynamics are harnessed in cells and open a frontier of research on nanoscale biomolecular condensates in their native environment.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.201
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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