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Changes in Growth, Ionic Status, Metabolites Content and Antioxidant Activity of Two Ferns Exposed to Shade, Full Sunlight, and Salinity.

Anna PietrakPiotr SalachnaŁukasz Łopusiewicz
Published in: International journal of molecular sciences (2022)
The interactions between ferns and the environment have been frequently researched. However, detailed data on how ferns respond to specific stresses and a combination of stress factors during cultivation are lacking. This study assessed the effects of salinity and full sunlight and the combination of both stresses on the growth and selected metabolic parameters of two hardy ferns ( Athyrium nipponicum cv. Red Beauty and Dryopteris erythrosora ) under production conditions. Hardy ferns are highly interesting ornamental plants that can serve as a potential source of antioxidants for the pharmaceutical, cosmetic, and food industries. The results showed that in both ferns, salinity and salinity combined with full sunlight lowered the dry weight of the aerial part and potassium/sodium and calcium/potassium ratio compared with control plants. Salinity, full sunlight, and multi-stress did not affect the total polyphenol content in both ferns but increased the total free amino acids and flavonoids in D. erythrosora . In A. nipponicum cv. Red Beauty, all stressors decreased the total free amino acids content and the antioxidant activities determined by ABTS, DPPH, FRAP, and reducing power assays. By contrast, plants of D. erythrosora grown under full sunlight are characterized by higher antioxidant activities determined by DPPH, FRAP, and reducing power assays. Overall, a greater adaptive potential to abiotic stresses was found in D. erythrosora than in A. nipponicum cv. Red Beauty. Our findings shed some light on the physiological mechanisms responsible for sensitivity/tolerance to salinity, full sunlight, and combined stresses in hardy ferns.
Keyphrases
  • microbial community
  • amino acid
  • oxidative stress
  • high throughput
  • magnetic resonance
  • body mass index
  • human health
  • risk assessment
  • anti inflammatory
  • weight loss
  • machine learning
  • weight gain
  • transcription factor