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Record W2259131273 · doi:10.1161/atvb.34.suppl_1.582

Abstract 582: Inflammatory Cytokines Reduce Thrombin Activatable Fibrinolysis Inhibitor Protein Levels in HepG2 Cells via TTP-Mediated Destabilization of TAFI mRNA

2014· article· en· W2259131273 on OpenAlexaff
Dragana Novakovic, Michael B. Boffa

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2014
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTristetraprolinMessenger RNAFibrinolysisInflammationUntranslated regionProinflammatory cytokineCytokineThrombinTumor necrosis factor alphaChemistryMolecular biologyBiologyImmunologyGeneMedicineBiochemistryInternal medicinePlatelet

Abstract

fetched live from OpenAlex

Thrombin activatable fibrinolysis inhibitor (TAFI) is the zymogen form of a basic carboxypeptidase (TAFIa) with both anti-fibrinolytic and anti-inflammatory properties. The role of TAFI in inflammatory disease is multifaceted and involves both recognition of specific pro-inflammatory substrates by TAFIa as well as regulation of TAFI gene expression by inflammatory mediators. In this study we addressed the hypothesis that decreased TAFI levels observed in inflammation are due to destabilization of its mRNA via the A/U-rich responsive element binding protein tristetraprolin (TTP). TAFI protein levels were measured in conditioned medium of HepG2 cells with recently developed assay specific for TAFIa. Treatment of cells with pro-inflammatory cytokines TNFα, IL-6 in combination with IL-1β, or with bacterial lipopolysaccharide (LPS) decreased TAFI protein levels by approximately 2-fold over 24 to 48 hours of treatment. Conversely, treatment of HepG2 cells with the anti-inflammatory cytokine IL-10 increased TAFI protein levels by 2-fold at both 24 and 48 hour time points. We employed luciferase reporter gene studies using human TAFI promoter constructs and found no change in promoter activity with these treatments. We then hypothesized that changes in mRNA stability may be involved through binding of TTP to the TAFI 3’-UTR, as we recently characterized the role of TTP in mediating TAFI mRNA stability. Using constructs expressing β-globin fusion mRNAs containing the TAFI 3’-UTR, we found that TNFα, IL-6, IL-1β, and LPS reduce TAFI mRNA half-life by 30%. This effect appears to be dependent on TTP binding, since these cytokines did not alter the stability of a fusion transcript lacking the TTP binding site. IL-10 caused an increase in fusion transcript stability by 38%, an effect that was also observed when the TTP binding site was mutated. Using reporter constructs expressing fusion mRNA’s containing the luciferase coding region and the TAFI 3’-UTR, we found that translation rate remained unaffected with both pro- and anti-inflammatory treatments. In conclusion, destabilization of TAFI mRNA appears to be the main mechanism behind decreased TAFI protein levels observed in the presence of pro-inflammatory mediators.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.032
GPT teacher head0.266
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes1
Has abstractyes

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