Abstract 582: Inflammatory Cytokines Reduce Thrombin Activatable Fibrinolysis Inhibitor Protein Levels in HepG2 Cells via TTP-Mediated Destabilization of TAFI mRNA
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".