Simultaneous Quantitative Determination of Different Ceramide and Diacylglycerol Species in Cultured Cells by Using Liquid Chromatography–Electrospray Tandem Mass Spectrometry

A sensitive, reproducible reverse-phased high performance liquid chromatography electrospray tandem mass spectrometry (HPLC-ESI-MS/MS) method with simple sample preparation was developed for the simultaneous determination of a wide range of ceramides, diacylglycerols (DAGs) in cultured cells. Chromatographic separation of the compounds was achieved in a 14-minute run using a C8 column with a gradient elution by methanol and 10 mM ammonium acetate buffer as mobile phase at a flow rate of 0.5 ml/min. Various ceramides, DAGs were detected with a triple quadrupol system in multiple reaction monitoring mode, which is based on a soft positive electrospray ionization. The usual sample preparation process was shortened by the application of pure methanol for the extraction instead of the widely used methanol/chloroform mixture. C17:0 ceramide which does not occur in the cell samples, was used as an internal standard. The sample preparation process was optimized and the methodology was tested on a human hepatocarcinoma cell culture. Our results clearly showed accumulation of some ceramides and DAGs in the cells treated with BSA-conjugated palmitate for 8 hours. Since both ceramides and DAGs are important lipid intermediates and signal messengers, alteration in their cellular levels have major impact on cell functions, and thus our novel analytic method can be widely used in lipotoxicity research. The presented technique can be further developed to measure other intermediates of ceramide synthesis and other derivatives of DAGs as well

Cellular toxicity of dietary trans fatty acids and its correlation with ceramide and diglyceride accumulation

High fatty acid (FA) levels are deleterious to pancreatic β-cells, largely due to the accumulation of biosynthetic lipid intermediates, such as ceramides and diglycerides, which induce ER stress and apoptosis. Toxicity of palmitate (16:0) and oleate (18:1 cis-Δ9) has been widely investigated, while very little data is available on the cell damages caused by elaidate (18:1 trans-Δ9) and vaccenate (18:1 trans-Δ11), although the potential health effects of these dietary trans fatty acids (TFAs) received great publicity. We compared the effects of these four FAs on cell viability, apoptosis, ER stress, JNK phosphorylation and autophagy as well as on ceramide and diglyceride contents in RINm5F insulinoma cells. Similarly to oleate and unlike palmitate, TFAs reduced cell viability only at higher concentration, and they had mild effects on ER stress, apoptosis and autophagy. Palmitate increased ceramide and diglyceride levels far more than any of the unsaturated fatty acids; however, incorporation of TFAs in ceramides and diglycerides was strikingly more pronounced than that of oleate. This indicates a correlation between the accumulation of lipid intermediates and the severity of cell damage. Our findings reveal important metabolic characteristics of TFAs that might underlie a long term toxicity and hence deserve further investigation

Comprehensive Classification and Regression Modeling of Wine Samples Using 1H NMR Spectra

Recently, 1H NMR (nuclear magnetic resonance) spectroscopy was presented as a viable option for the quality assurance of foods and beverages, such as wine products. Here, a complex chemometric analysis of red and white wine samples was carried out based on their 1H NMR spectra. Extreme gradient boosting (XGBoost) machine learning algorithm was applied for the wine variety classification with an iterative double cross-validation loop, developed during the present work. In the case of red wines, Cabernet Franc, Merlot and Blue Frankish samples were successfully classified. Three very common white wine varieties were selected and classified: Chardonnay, Sauvignon Blanc and Riesling. The models were robust and were validated against overfitting with iterative randomization tests. Moreover, four novel partial least-squares (PLS) regression models were constructed to predict the major quantitative parameters of the wines: density, total alcohol, total sugar and total SO2 concentrations. All the models performed successfully, with R2 values above 0.80 in almost every case, providing additional information about the wine samples for the quality control of the products. 1H NMR spectra combined with chemometric modeling can be a good and reliable candidate for the replacement of the time-consuming traditional standards, not just in wine analysis, but also in other aspects of food science