Acute Phase Mediators Modulate Thrombin-activable Fibrinolysis Inhibitor (TAFI) Gene Expression in HepG2 Cells
Bibliographic record
Abstract
Thrombin-activable fibrinolysis inhibitor (TAFI) has recently been identified as a positive acute phase protein in mice, an observation that may have important implications for the interaction of the coagulation, fibrinolytic, and inflammatory systems. Activated TAFI (TAFIa) inhibits fibrinolysis by removing the carboxyl-terminal lysines from partially degraded fibrin that are important for maximally efficient plasminogen activation. In addition, TAFIa has been shown to be capable of removing the carboxyl-terminal arginine residues from the anaphylatoxins and bradykinin, thus implying a role for the TAFI pathway in the vascular responses to inflammation. In the current study, we investigated the ability of acute phase mediators to modulate human TAFI gene expression in cultured human hepatoma (HepG2) cells. Surprisingly, we found that treatment of HepG2 cells with a combination of interleukin (IL)-1 and IL-6 suppressed endogenous TAFI mRNA abundance in HepG2 cells (~60% decrease), while treatment with IL-1 or IL-6 alone had no effect. Treatment with IL-1 and/or IL-6 had no effect on TAFI promoter activity as measured using a luciferase reporter plasmid containing the human TAFI 5'-flanking region, whereas treatment with IL-1 and IL-6 in combination, but not alone, decreased the stability of the endogenous TAFI mRNA. Treatment with the synthetic glucocorticoid dexamethasone resulted in a 2-fold increase of both TAFI mRNA levels and promoter activity. We identified a functional glucocorticoid response element (GRE) in the human TAFI promoter between nucleotides 92 and 78. The GRE was capable of binding the glucocorticoid receptor, as assessed by gel mobility shift assays, and mutation of this element markedly decreased the ability of the TAFI promoter to be activated by dexamethasone.
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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 | 0.001 |
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".