Strawberry and Drupe Fruit Wines Antioxidant Activity and Protective Effect Against Induced Oxidative Stress in Rat Synaptosomes

Abstract

The aim of this study was to investigate the antioxidant capacity of fruit wines and their protective effects against hydrogen peroxide-induced oxidative stress in rat synaptosomes in vitro. The wines were produced from strawberries and drupe fruits (i.e., plum, sweet cherry, peach, and apricot) through microvinification with a pure S. cerevisiae yeast culture. Fruit wines were produced with and without added sugar before the start of fermentation, whereas subvariants with and without pits were only applied to drupe fruit wines. First, synaptosomes were treated with the wines, while oxidative stress was induced with H₂O₂. Subsequently, the activities of antioxidant enzymes (superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx)) and the content of malondialdehyde (MDA), an indicator of membrane injury, were determined. In addition, the Briggs-Rauscher reaction (BR) was used to evaluate the inhibition capacity against free radicals. All investigated fruit wines increased the activity of the studied antioxidant enzymes and decreased MDA content compared to the corresponding controls (synaptosomes treated with H₂O₂). After synaptosomal treatment with plum wine, the highest activities were observed for SOD (5.57 U/mg protein) and GPx (0.015 U/mg protein). Strawberry wine induced the highest CAT activity (0.047 U/mg protein) and showed the best ability to reduce lipid peroxidation, yielding the lowest MDA level (2.68 nmol/mg). Strawberry, plum, and sweet cherry wines were identified as samples with higher antioxidant activity in both principal component analysis (PCA) and hierarchical cluster analysis (HCA). Finally, plum wine exhibited the highest inhibitory activity in the BR reaction (397 s). The results suggest that fruit wines could be considered potential functional food due to their protective effects against oxidative stress.

Keywords: Briggs–Rauscher (BR) reaction; catalase (CAT); glutathione peroxidase (GPx); hierarchical cluster analysis (HCA); malondialdehyde (MDA); principal component analysis (PCA); superoxide dismutase (SOD).

Figure 1

Boxplot of superoxide dismutase—SOD (U/mg protein) values (mean ± SD) measured in different types of fruit wines produced with or without pits (+pit; −pit) and with or without added sugar (+sugar; −sugar). a–e: Different lowercase letters indicate the significant difference between groups, which was confirmed by the ANOVA post hoc Tukey test (p < 0.05).

Figure 2

Boxplot of catalase—CAT (U/mg protein) values (mean ± SD) measured in different types of fruit wines produced with or without pit (+pit; −pit) and with or without added sugar (+sugar; −sugar). a–g: Different lowercase letters indicate the significant difference between the groups, confirmed by the ANOVA post hoc Tukey test (p < 0.05).

Figure 3

Boxplot of glutathione peroxidase—GPx values (U/mg protein) (mean ± SD) measured in different types of fruit wines produced with or without pits (+pit; −pit) and with or without added sugar (+sugar; −sugar). a–d: Different lowercase letters indicate the significant difference between groups, confirmed by the ANOVA post hoc Tukey test (p < 0.05).

Figure 4

Boxplot of malondialdehyde—MDA values (nmol/mg protein) (mean ± SD) measured in different types of fruit wines produced with or without pits (+pit; −pit) and with or without added sugar (+sugar; −sugar). a–g: Different lowercase letters indicate the significant difference between groups confirmed by the ANOVA post hoc Tukey test (p < 0.05).

Figure 5

Graphical representation of the correlation heat map between the oxidative stress parameters (SOD, CAT, GPx, and MDA) and the antioxidant properties (FRAP, TPC, and DPPH). Red indicates positive and blue represents negative correlation coefficients. All correlation coefficients are statistically significant at the level of 0.05. SOD—superoxide dismutase, CAT—catalase, GPx—glutathione peroxidase, MDA—malondialdehyde, FRAP—ferric reducing ability of plasma, TPC—total phenolic content, DPPH—2,2-diphenyl-1-picrylhydrazyl.

Figure 6

Biplot from the principal component analysis. The arrows represent the loadings of the variables they represent, and the dots represent the values of the principal components for the different types of fruit wines shown along the first two components. The types of fruit wine are represented by different colors. Square and circular shapes represent the presence or absence of sugar. Light and dark edges of the dots represent the presence or absence of pits. SOD—superoxide dismutase, CAT—catalase, GPx—glutathione peroxidase, MDA—malondialdehyde, FRAP—ferric reducing ability of plasma, TPC—total phenolic content, DPPH—2,2-diphenyl-1-picrylhydrazyl.

Figure 7

Hierarchical cluster analysis with the correlation heat map showing the relationship between oxidative stress parameters and antioxidant properties (x-axis) and the different types of fruit wines produced with or without pits and with or without added sugar (y-axis). SOD—superoxide dismutase, CAT—catalase, GPx—glutathione peroxidase, MDA—malondialdehyde, FRAP—ferric reducing ability of plasma, TPC—total phenolic content, DPPH—2,2-diphenyl-1-picrylhydrazyl.

Figure 8

The Briggs–Rauscher reaction was followed potentiometrically; (a) the basic BR oscillogram obtained without the addition of wine; (b) the BR dynamics caused by the addition of wine (inhibition time and the appearance of new oscillation packets).

Figure 9

Boxplot of inhibition time (s) values (mean ± SD) measured for different types of fruit wines produced with or without pits and with or without added sugar. a–n: Different lowercase letters indicate the significant difference between the groups, which was confirmed by ANOVA post hoc Tukey test (p < 0.05).