Fatty Acid Desaturase 3 (FADS3) Is a Specific ∆13-Desaturase of Ruminant trans-Vaccenic Acid

Lithium Chloride Promotes Endogenous Synthesis of CLA in Bovine Mammary Epithelial Cells

Although conjugated linoleic acid (CLA) can promote human health, its content in milk is insufficient to have a significant impact. The majority of the CLA in milk is produced endogenously by the mammary gland. However, research on improving its content through nutrient-induced endogenous synthesis is relatively scarce. Previous research found that the key enzyme, stearoyl-CoA desaturase (SCD) for the synthesis of CLA, can be expressed more actively in bovine mammary epithelial cells (MAC-T) when lithium chloride (LiCl) is present. This study investigated whether LiCl can encourage CLA synthesis in MAC-T cells. The results showed that LiCl effectively increased SCD and proteasome α5 subunit (PSMA5) protein expression in MAC-T cells as well as the content of CLA and its endogenous synthesis index. LiCl enhanced the expression of proliferator-activated receptor-γ (PPARγ), sterol regulatory element-binding protein 1 (SREBP1), and its downstream enzymes acetyl CoA carboxylase (ACC), fatty acid synthase (FASN), lipoprotein lipase (LPL), and Perilipin 2 (PLIN2). The addition of LiCl significantly enhanced p-GSK-3β, β-catenin, p-β-catenin protein expression, hypoxia-inducible factor-1α (HIF-1α), and downregulation factor genes for mRNA expression (P < 0.05). These findings highlight that LiCl can increase the expression of SCD and PSMA5 by activating the transcription of HIF-1α, Wnt/β-catenin, and the SREBP1 signaling pathways to promote the conversion of trans-vaccenic acid (TVA) to the endogenous synthesis of CLA. This data suggests that the exogenous addition of nutrients can increase CLA content in milk through pertinent signaling pathways.

Development of a lipid metabolism-related gene model to predict prognosis in patients with pancreatic cancer

BACKGROUND

Pancreatic cancer is a highly heterogeneous disease, making prognosis prediction challenging. Altered energy metabolism to satisfy uncontrolled proliferation and metastasis has become one of the most important markers of tumors. However, the specific regulatory mechanism and its effect on prognosis have not been fully elucidated.

AIM

To construct a prognostic polygene signature of differentially expressed genes (DEGs) related to lipid metabolism.

METHODS

First, 9 tissue samples from patients with pancreatic cancer were collected and divided into a cancer group and a para-cancer group. All patient samples were subjected to metabolomics analysis based on liquid tandem chromatography quadrupole time of flight mass spectrometry. Then, mRNA expression profiles and corresponding clinical data of pancreatic cancer were downloaded from a public database. Least absolute shrinkage and selection operator Cox regression analysis was used to construct a multigene model for The Cancer Genome Atlas.

RESULTS

Principal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) based on lipid metabolomics analysis showed a clear distribution in different regions. A Euclidean distance matrix was used to calculate the quantitative value of differential metabolites. The permutation test of the OPLS-DA model for tumor tissue and paracancerous tissue indicated that the established model was consistent with the actual condition based on sample data. A bar plot showed significantly higher levels of the lipid metabolites phosphatidylcholine (PC), phosphatidyl ethanolamine (PE), phosphatidylethanol(PEtOH), phosphatidylmethanol (PMeOH), phosphatidylserine (PS) and diacylglyceryl trimethylhomoserine (DGTS) in tumor tissues than in paracancerous tissues. According to bubble plots, PC, PE, PEtOH, PMeOH, PS and DGTS were significantly higher in tumor tissues than in paracancerous tissues. In total, 12.3% (25/197) of genes related to lipid metabolism were differentially expressed between tumor tissues and adjacent paracancerous tissues. Six DEGs correlated with overall survival in univariate Cox regression analysis (P < 0.05), and a 4-gene signature model was developed to divide patients into two risk groups, with patients in the high-risk group having significantly lower overall survival than those in the low-risk group (P < 0.05). ROC curve analysis confirmed the predictive power of the model.

CONCLUSION

This novel model comprising 4 lipid metabolism-related genes might assist clinicians in the prognostic evaluation of patients with pancreatic cancer.