Foods and Raw Materials (Oct 2020)

Toxicity of apple juice and its components in the model plant system

  • Samoylov Artem V. ,
  • Suraeva Natal’ya M. ,
  • Zaytseva Mariya V. ,
  • Rachkova Vera P. ,
  • Kurbanova Madinat N. ,
  • Belozerov Georgy А.

DOI
https://doi.org/10.21603/2308-4057-2020-2-321-328
Journal volume & issue
Vol. 8, no. 2
pp. 321 – 328

Abstract

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Introduction. In view of the ongoing research into the negative effects of fruit juice on human health, we aimed to study the subchronic toxicity of apple juice, a model mixture based on its components, and ethanol on biomass growth, cellular oxidative enzymes, and chromosomal abnormalities in Allium cepa roots. Study objects and methods. Our objects of study included clarified apple juice and its components such as fructose, glucose, sucrose, D-sorbitol, and malic acid. After treating Allium cepa roots with apple juice and a model mixture in different concentrations, we analyzed their toxic effects on biomass growth, malondialdehyde levels, as well as the nature and frequency of proliferative and cytogenetic disorders in the plant tissues. Results and discussion. The incubation in an aqueous solution of apple juice at a concentration of 1:5 inhibited the growth in root mass by 50% compared to the control (water). The mitotic index of cells decreased with higher concentrations of juice, reaching zero at a 1:5 dilution. The fructose and model solutions in the same concentrations appeared less toxic in relation to cell mitosis and root mass growth. Although malondialdehyde levels increased in the onion roots treated with juice and model solutions, they were twice as low as in the control due to the juice’s antioxidant activity. Adding 1% ethanol to the 1:2 diluted juice abolished the effect of acute toxicity on root growth and reduced malondialdehyde levels by 30%. Conclusion. The study revealed a complex of interdependent biomarkers of apple juice responsible for its subchronic toxicity in Allium cepa roots. These data can be used to create biological response models based on the approaches of systems biology and bioinformatics.

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