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Sulfated Glycosaminoglycans Control the Extracellular Trafficking and the Activity of the Metalloprotease Inhibitor TIMP-3

2014· article· en· W2094665308 on OpenAlexfundno aff
Linda Troeberg, Christopher Lazenbatt, Md. Ferdous Anower-E-Khuda, Craig Freeman, Oleg Federov, Hiroko Habuchi, Osami Habuchi, Koji Kimata, Hideaki Nagase

Bibliographic record

VenueChemistry & Biology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Institutes of Health ResearchVersus ArthritisWellcome Trust
KeywordsADAMTSSulfationThrombospondinExtracellular matrixGlycosaminoglycanMatrix metalloproteinaseMetalloproteinaseBiochemistryCell biologyChondroitin sulfateChemistryHeparan sulfateGlycanThrombospondinsTissue inhibitor of metalloproteinaseDisintegrinBiologyGlycoprotein

Abstract

fetched live from OpenAlex

Tissue inhibitor of metalloproteinase 3 (TIMP-3) is an important regulator of extracellular matrix (ECM) turnover. TIMP-3 binds to sulfated ECM glycosaminoglycans or is endocytosed by cells via low-density lipoprotein receptor-related protein 1 (LRP-1). Here, we report that heparan sulfate (HS) and chondroitin sulfate E (CSE) selectively regulate postsecretory trafficking of TIMP-3 by inhibiting its binding to LRP-1. HS and CSE also increased TIMP-3 affinity for glycan-binding metalloproteinases, such as adamalysin-like metalloproteinase with thrombospondin motifs 5 (ADAMTS-5), by reducing the dissociation rate constants. The sulfation pattern was crucial for these activities because monosulfated or truncated heparin had a reduced ability to bind to TIMP-3 and increase its affinity for ADAMTS-5. Therefore, sulfation of ECM glycans regulates the levels and inhibitory activity of TIMP-3 and modulates ECM turnover, and small mimicries of sulfated glycans may protect the tissue from the excess destruction seen in diseases such as osteoarthritis, cancer, and atherosclerosis.

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 score0.011
Threshold uncertainty score0.429

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 studies0.0000.001
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 head0.200
Teacher spread0.195 · 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

Citations76
Published2014
Admission routes1
Has abstractyes

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