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Stability and Chemical Conversion of the Purified Reference Material of Gymnodimine-A under Different Temperature and pH Conditions.

Guixiang WangJiangbing QiuAifeng LiYing JiZhixuan TangPhilipp Hess
Published in: Toxins (2022)
Gymnodimines (GYMs) are a group of fast-acting phycotoxins and their toxicological effects on human beings are still unclear due to the lack of sufficiently well-characterized large quantities of purified toxins for toxicology studies. In this study, a certified reference material (CRM) of GYM-A was prepared from the dinoflagellate Karenia selliformis , followed by multi-step chromatography separation and purification. Subsequently, the stability of GYM-A in methanolic media was evaluated at different temperature (-20, 4, and 20 °C) and pH (3, 5, and 7) conditions for 8 months, and the conversion products of GYM-A were explored by liquid chromatography-high resolution mass spectrometry (LC-HRMS). The results show that the stability of GYM-A decreased with increasing temperature and pH values. The GYM-A was stable during storage at -20 °C regardless of pH, but it decreased rapidly (81.8% ± 9.3%) at 20 °C in pH 7 solution after 8 months. Moreover, the concentrations of GYM-A did not significantly change at all temperatures in solutions with pH 3 ( p > 0.05). It is recommended that GYM-A should be stored at low temperature (≤-20 °C) and pH (≤3) conditions for long-term storage in aqueous methanolic media. In addition, two conversion products of GYM-A, tentatively named as GYM-K ( m / z 540) and GYM-L ( m / z 524), were identified in the samples stored at high levels of pH and temperature. Based on the LC-HRMS data, the hypothetical chemical structures of both converting derivatives were proposed. A useful strategy for long-term storage of GYM-A CRM in aqueous methanolic media was suggested and two hypothesized conversion products of GYM-A were discovered in this study.
Keyphrases
  • liquid chromatography
  • high resolution mass spectrometry
  • mass spectrometry
  • tandem mass spectrometry
  • ultra high performance liquid chromatography
  • endothelial cells
  • high resolution
  • ionic liquid
  • machine learning