Frontiers in Chemistry (Apr 2022)

Comprehending Cardiac Dysfunction by Oxidative Stress: Untargeted Metabolomics of In Vitro Samples

  • Alan Gonçalves Amaral,
  • Isabela Aparecida Moretto,
  • Flávia da Silva Zandonadi,
  • Hans Rolando Zamora-Obando,
  • Isabela Rocha,
  • Alessandra Sussulini,
  • Alessandra Sussulini,
  • André Alexandre de Thomaz,
  • Regina Vincenzi Oliveira,
  • Aline Mara dos Santos,
  • Ana Valéria Colnaghi Simionato,
  • Ana Valéria Colnaghi Simionato

DOI
https://doi.org/10.3389/fchem.2022.836478
Journal volume & issue
Vol. 10

Abstract

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Cardiovascular diseases (CVDs) are noncommunicable diseases known for their complex etiology and high mortality rate. Oxidative stress (OS), a condition in which the release of free radical exceeds endogenous antioxidant capacity, is pivotal in CVC, such as myocardial infarction, ischemia/reperfusion, and heart failure. Due to the lack of information about the implications of OS on cardiovascular conditions, several methodologies have been applied to investigate the causes and consequences, and to find new ways of diagnosis and treatment as well. In the present study, cardiac dysfunction was evaluated by analyzing cells’ alterations with untargeted metabolomics, after simulation of an oxidative stress condition using hydrogen peroxide (H2O2) in H9c2 myocytes. Optimizations of H2O2 concentration, cell exposure, and cell recovery times were performed through MTT assays. Intracellular metabolites were analyzed right after the oxidative stress (oxidative stress group) and after 48 h of cell recovery (recovery group) by ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) in positive and negative ESI ionization mode. Significant alterations were found in pathways such as “alanine, aspartate and glutamate metabolism”, “glycolysis”, and “glutathione metabolism”, mostly with increased metabolites (upregulated). Furthermore, our results indicated that the LC-MS method is effective for studying metabolism in cardiomyocytes and generated excellent fit (R2Y > 0.987) and predictability (Q2 > 0.84) values.

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