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Record W4288034204 · doi:10.1242/jcs.259993

Proteomic analysis of the actin cortex in interphase and mitosis

2022· article· en· W4288034204 on OpenAlexafffund
Neža Vadnjal, Sami Nourreddine, Geneviève Lavoie, Murielle P. Serres, Philippe P. Roux, Ewa K. Paluch

Bibliographic record

VenueJournal of Cell Science · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersMedical Research CouncilCanadian Institutes of Health Research
KeywordsCell cortexInterphaseMitosisBiologyCell biologyCortex (anatomy)CytoskeletonActinMorphogenesisNeuroscienceCellGeneticsGene

Abstract

fetched live from OpenAlex

Many animal cell shape changes are driven by gradients in the contractile tension of the actomyosin cortex, a thin cytoskeletal network supporting the plasma membrane. Elucidating cortical tension control is thus essential for understanding cell morphogenesis. Increasing evidence shows that alongside myosin activity, actin network organisation and composition are key to cortex tension regulation. However, owing to a poor understanding of how cortex composition changes when tension changes, which cortical components are important remains unclear. In this article, we compared cortices from cells with low and high cortex tensions. We purified cortex-enriched fractions from cells in interphase and mitosis, as mitosis is characterised by high cortical tension. Mass spectrometry analysis identified 922 proteins consistently represented in both interphase and mitotic cortices. Focusing on actin-related proteins narrowed down the list to 238 candidate regulators of the mitotic cortical tension increase. Among these candidates, we found that there is a role for septins in mitotic cell rounding control. Overall, our study provides a comprehensive dataset of candidate cortex regulators, paving the way for systematic investigations of the regulation of cell surface mechanics. This article has an associated First Person interview with the first author of the paper.

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)0.000
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 score0.010
Threshold uncertainty score0.093

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.241
Teacher spread0.236 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

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