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Record W2413611580 · doi:10.1016/j.celrep.2016.05.051

SET9-Mediated Regulation of TGF-β Signaling Links Protein Methylation to Pulmonary Fibrosis

2016· article· en· W2413611580 on OpenAlexaff
Maximilianos Elkouris, Haroula Kontaki, Athanasios Stavropoulos, Anastasia Antonoglou, Kostas C. Nikolaou, Martina Samiotaki, Eszter Szántai, Dimitra Saviolaki, Peter J. Brown, Paschalis Sideras, George Panayotou, Iannis Talianidis

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
Fundersnot available
KeywordsPulmonary fibrosisMethyltransferaseExtracellular matrixCell biologySignal transductionMethylationTransforming growth factorCancer researchFibrosisUbiquitinUbiquitin ligaseBiologyChemistryMedicineGeneInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

TGF-β signaling regulates a variety of cellular processes, including proliferation, apoptosis, differentiation, immune responses, and fibrogenesis. Here, we describe a lysine methylation-mediated mechanism that controls the pro-fibrogenic activity of TGF-β. We find that the methyltransferase Set9 potentiates TGF-β signaling by targeting Smad7, an inhibitory downstream effector. Smad7 methylation promotes interaction with the E3 ligase Arkadia and, thus, ubiquitination-dependent degradation. Depletion or pharmacological inhibition of Set9 results in elevated Smad7 protein levels and inhibits TGF-β-dependent expression of genes encoding extracellular matrix components. The inhibitory effect of Set9 on TGF-β-mediated extracellular matrix production is further demonstrated in mouse models of pulmonary fibrosis. Lung fibrosis induced by bleomycin or Ad-TGF-β treatment was highly compromised in Set9-deficient mice. These results uncover a complex regulatory interplay among multiple Smad7 modifications and highlight the possibility that protein methyltransferases may represent promising therapeutic targets for treating lung fibrosis.

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 score0.009
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations73
Published2016
Admission routes1
Has abstractyes

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