Untargeted Metabolomics Studies of H9c2 Cardiac Cells Submitted to Oxidative Stress, ß-Adrenergic Stimulation and Doxorubicin Treatment: Investigation of Cardiac Biomarkers.

One of the biggest challenges in the search for more effective treatments for diseases is understanding their etiology. Cardiovascular diseases (CVD) are an important example of this, given the high number of deaths annually. Oxidative stress (the imbalance between oxidant and antioxidant species in biological system) is one of the factors responsible for CVD occurrence, demanding extensive investigation. Excess of reactive oxygen species (ROS) are primarily responsible for this condition, and clinical and scientific literature have reported a significant increase in ROS when therapeutic drugs, such as doxorubicin and isoproterenol, are administered. In this context, the aim of this study is the investigation of potential biomarkers that might be associated with oxidative stress in cardiomyocytes. For this purpose, H9c2 cardiomyocytes were submitted to oxidative stress conditions by treatment with doxorubicin (DOX), isoproterenol (ISO) and hydrogen peroxide (PER). Metabolomics analyses of the cell extract and the supernatant obtained from the culture medium were then evaluated by CE-ESI(+)-TOF-MS. Following signal processing, statistical analyses, and molecular features annotations, the results indicate changes in the aspartate, serine, pantothenic acid, glycerophosphocholine and glutathione metabolism in the cell extract.

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

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.

Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin's lymphoma urinary metabolite markers.

Non-Hodgkin's lymphoma (NHL) is a cancer of the lymphatic system where the lymphoid and hematopoietic tissues are infiltrated by malignant neoplasms of B, T, and natural killer lymphocytes. Effective and less invasive methods for NHL screening are urgently needed. Herein, we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to investigate metabolic changes in non-volatile derivatized compounds from urine samples of NHL patients (N = 15) and compare them to healthy controls (N = 34). Uni- and multivariate data analysis showed 18 endogenous metabolites, including amino acids and their metabolites, sugars, small organic acids, and vitamins, as statistically significant for group differentiation. A receiver operating characteristic curve (ROC) generated from a support vector machine (SVM) algorithm-based model achieved 0.998 of predictive accuracy, displaying the potential and relevance of GC-MS-detected urinary non-volatile compounds for predictive purposes. Furthermore, a specific panel of key metabolites was also evaluated, showing similar results. All in all, our results indicate that this robust GC-MS method is an effective screening tool for NHL diagnosis and it is able to highlight different pathways of the disease. Graphical Abstract.