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Record W2098916371 · doi:10.1091/mbc.e13-08-0451

miR-218 regulates focal adhesion kinase–dependent TGFβ signaling in fibroblasts

2014· article· en· W2098916371 on OpenAlexafffund
Feng Guo, David E. Carter, Andrew Leask

Bibliographic record

VenueMolecular Biology of the Cell · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOral and gingival health research
Canadian institutionsRobarts Clinical TrialsWestern University
FundersCanadian Institutes of Health Research
KeywordsMyofibroblastFocal adhesionExtracellular matrixCell biologyTransforming growth factorBiologyConnective tissueActinFibroblastGene knockdownSignal transductionFibrosisPathologyCell cultureMedicine

Abstract

fetched live from OpenAlex

Scarring, which occurs in essentially all adult tissue, is characterized by the excessive production and remodeling of extracellular matrix by α-smooth muscle actin (SMA)-expressing myofibroblasts located within connective tissue. Excessive scarring can cause organ failure and death. Oral gingivae do not scar. Compared to dermal fibroblasts, gingival fibroblasts are less responsive to transforming growth factor β (TGFβ) due to the reduced expression, due to the reduced expression and activity of focal adhesion kinase (FAK) by this cell type. Here we show that, compared with dermal fibroblasts, gingival fibroblasts show reduced expression of miR-218. Introduction of pre-miR-218 into gingival fibroblasts elevates FAK expression and, via a FAK/src-dependent mechanism, results in the ability of TGFβ to induce α-SMA. The deubiquitinase cezanne is a direct target of miR-218 and has increased expression in gingival fibroblasts compared with dermal fibroblasts. Knockdown of cezanne in gingival fibroblasts increases FAK expression and causes TGFβ to induce α-smooth muscle actin (α-SMA). These results suggest that miR-218 regulates the ability of TGFβ to induce myofibroblast differentiation in fibroblasts via cezanne/FAK.

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 score0.010
Threshold uncertainty score0.358

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 integrity0.0000.001
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 head0.035
GPT teacher head0.397
Teacher spread0.362 · 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

Citations38
Published2014
Admission routes2
Has abstractyes

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