Pro-inflammatory cytokines reduce human TAFI expression via tristetraprolin-mediated mRNA destabilisation and decreased binding of HuR
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 modulation both of specific inflammatory mediators as well as of the behaviour of inflammatory cells. Moreover, as suggested by in vitro studies, inflammatory mediators are capable of regulating the expression of CPB2, the gene encoding TAFI. In this study we addressed the hypothesis that decreased TAFI levels observed in inflammation are due to post-transcriptional mechanisms. Treatment of human HepG2 cells with pro-inflammatory cytokines TNFα, IL-6 in combination with IL-1β, or with bacterial lipopolysaccharide (LPS) decreased TAFI protein levels by approximately two-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 two-fold at both time points. We found that the mechanistic basis for this modulation of TAFI levels involves binding of tristetraprolin (TTP) to the CPB2 3'-UTR, which mediates CPB2 mRNA destabilisation. In this report we also identified that HuR, another ARE-binding protein but one that stabilises transcripts, is capable of binding the CBP2 3'UTR. We found that pro-inflammatory mediators reduce the occupancy of HuR on the CPB2 3'-UTR and that the mutation of the TTP binding site in this context abolishes this effect, although TTP and HuR appear to contact discrete binding sites. Interestingly, all of the mediators tested appear to increase TAFI protein expression in THP-1 macrophages, likewise through effects on CPB2 mRNA stability.
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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) | 0.002 | 0.000 |
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.
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".