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Record W2586566142 · doi:10.1038/ncomms14360

FAK signalling controls insulin sensitivity through regulation of adipocyte survival

2017· article· en· W2586566142 on OpenAlexafffund
Cynthia T. Luk, Sally Yu Shi, Erica P. Cai, Tharini Sivasubramaniyam, Mansa Krishnamurthy, Jara J. Brunt, Stephanie A. Schroer, Daniel A. Winer, Minna Woo

Bibliographic record

VenueNature Communications · 2017
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchUniversity of California, San FranciscoBanting and Best Diabetes Centre, University of TorontoNovo NordiskCanadian Liver Foundation
KeywordsAdipocyteAdipose tissueInternal medicineEndocrinologyInsulin resistanceFocal adhesionContext (archaeology)Insulin receptorBiologyInsulinSignal transductionCell biologyMedicine

Abstract

fetched live from OpenAlex

Focal adhesion kinase (FAK) plays a central role in integrin signalling, which regulates growth and survival of tumours. Here we show that FAK protein levels are increased in adipose tissue of insulin-resistant obese mice and humans. Disruption of adipocyte FAK in mice or in 3T3 L1 cells decreases adipocyte survival. Adipocyte-specific FAK knockout mice display impaired adipose tissue expansion and insulin resistance on prolonged metabolic stress from a high-fat diet or when crossed on an obese db/db or ob/ob genetic background. Treatment of these mice with a PPARγ agonist does not restore adiposity or improve insulin sensitivity. In contrast, inhibition of apoptosis, either genetically or pharmacologically, attenuates adipocyte death, restores normal adiposity and improves insulin sensitivity. Together, these results demonstrate that FAK is required for adipocyte survival and maintenance of insulin sensitivity, particularly in the context of adipose tissue expansion as a result of caloric excess.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.382
Teacher spread0.319 · 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

Citations72
Published2017
Admission routes2
Has abstractyes

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