MétaCan
Menu
Back to cohort
Record W3014157397 · doi:10.1016/j.celrep.2020.03.041

Precise Tuning of Cortical Contractility Regulates Cell Shape during Cytokinesis

2020· article· en· W3014157397 on OpenAlexfundno aff
Nilay Taneja, Matthew R. Bersi, Sophie M. Baillargeon, Aidan M. Fenix, James A. Cooper, Ryoma Ohi, Vivian Gama, W. David Merryman, Dylan T. Burnette

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteMcMaster Institute for Research on Aging, McMaster University
KeywordsCytokinesisContractilityCell biologyCellCortical neuronsNeuroscienceBiologyChemistryCell divisionEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

The mechanical properties of the actin cortex regulate shape changes during cell division, cell migration, and tissue morphogenesis. We show that modulation of myosin II (MII) filament composition allows tuning of surface tension at the cortex to maintain cell shape during cytokinesis. Our results reveal that MIIA generates cortex tension, while MIIB acts as a stabilizing motor and its inclusion in MII hetero-filaments reduces cortex tension. Tension generation by MIIA drives faster cleavage furrow ingression and bleb formation. We also show distinct roles for the motor and tail domains of MIIB in maintaining cytokinetic fidelity. Maintenance of cortical stability by the motor domain of MIIB safeguards against shape instability-induced chromosome missegregation, while its tail domain mediates cortical localization at the terminal stages of cytokinesis to mediate cell abscission. Because most non-muscle contractile systems are cortical, this tuning mechanism will likely be applicable to numerous processes driven by myosin-II contractility.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.042
GPT teacher head0.279
Teacher spread0.237 · 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 teacher head, 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

Citations61
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCell ReportsSame topicPhotoreceptor and optogenetics researchFrench-language works237,207