<i>Trans</i> Fatty Acids Suppress TNF‐α‐Induced Inflammatory Gene Expression in Endothelial (HUVEC) and Hepatocellular Carcinoma (HepG2) Cells
Bibliographic record
Abstract
Trans fatty acids (TFA) intake has been linked to cardiovascular diseases and liver diseases; yet the effect of TFA on inflammation remains controversial. Accordingly, the objective of this paper was to determine the in vitro effects of TFA on inflammatory gene expression. Human umbilical vein endothelial cells (HUVEC) and human hepatocellular carcinoma (HepG2) cells were treated for 24 h with either trans-vaccenic acid (tVA), trans-palmitoleic acid (tPA) or elaidic acid (EA) at concentrations of 5-150 µM, or with a mixture of tVA and tPA (150/50 µM). All TFA were highly incorporated into cell membranes, as determined by gas chromatography, representing 15-20% of total fatty acids in HUVEC and 3-8% in HepG2 cells. Incorporation of EA, a common industrial TFA, increased the ratio of the stearoyl-CoA desaturase (SCD-1), a key enzyme involved in fatty acid metabolism. Ruminant TFA, including tVA, tPA and the mixture of tVA and tPA, significantly reduced the TNF-α-induced gene expression of TNF, VCAM-1 and SOD2 in HUVEC, as well as TNF and IL-8 in HepG2 cells. EA also decreased inflammatory gene expression in HUVEC, but not in HepG2 cells. The inhibition of peroxisome proliferator-activated receptor (PPAR)-γ did not influence the effects of TFA on gene expression. Overall, physiological and supraphysiological concentrations of TFA, especially tVA and tPA, prevented inflammatory gene expression in vitro. This effect is independent of PPAR-γ activation and may be due to an alteration of fatty acid metabolism in cell membranes caused by the high incorporation of TFA.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".