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Record W4382931810 · doi:10.1073/pnas.2301285120

Nuclear compression regulates YAP spatiotemporal fluctuations in living cells

2023· article· en· W4382931810 on OpenAlexafffund
Newsha Koushki, Ajinkya Ghagre, Luv Kishore Srivastava, Clayton Molter, Allen J. Ehrlicher

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMechanotransductionLaminNuclear laminaCell biologyCytoskeletonActinNuclear transportMyosinNuclear localization sequenceContractilityCell nucleusBiologyNuclear proteinChemistryCellNucleusTranscription factorGenetics

Abstract

fetched live from OpenAlex

Yes-associated protein (YAP) is a key mechanotransduction protein in diverse physiological and pathological processes; however, a ubiquitous YAP activity regulatory mechanism in living cells has remained elusive. Here, we show that YAP nuclear translocation is highly dynamic during cell movement and is driven by nuclear compression arising from cell contractile work. We resolve the mechanistic role of cytoskeletal contractility in nuclear compression by manipulation of nuclear mechanics. Disrupting the linker of nucleoskeleton and cytoskeleton complex reduces nuclear compression for a given contractility and correspondingly decreases YAP localization. Conversely, decreasing nuclear stiffness via silencing of lamin A/C increases nuclear compression and YAP nuclear localization. Finally, using osmotic pressure, we demonstrated that nuclear compression even without active myosin or filamentous actin regulates YAP localization. The relationship between nuclear compression and YAP localization captures a universal mechanism for YAP regulation with broad implications in health and biology.

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)0.001
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 score0.036
Threshold uncertainty score0.145

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.294
Teacher spread0.266 · 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

Citations86
Published2023
Admission routes2
Has abstractyes

Explore more

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