Exploration of the Amino Acid Metabolic Signature in Anthracycline-induced Cardiotoxicity Using an Optimized Targeted Metabolomics Approach Based on UPLC-MS/MS

Background: Although anthracyclines improve the long-term survival rate of patients with cancer, severe and irreversible myocardial damage limits their clinical application. Amino acids (AAs) play critical roles in protein synthesis, energy generation, and metabolism, as well as maintenance of the normal structure of cardiomyocytes. Conversely, AA metabolism in cardiomyocytes can be altered under pathological conditions. Therefore, exploring the AA metabolic signature in anthracycline-induced cardiotoxicity (AIC) is important for identifying novel mechanisms. Methods: We established mouse and cellular models of Adriamycin (ADR)-induced cardiac injury. Using a targeted AA metabolomics approach based on ultra-performance lipid chromatography–tandem mass spectrometry (UPLC-MS/MS), we quantified more than 120 AA metabolites through derivatization-assisted sensitivity enhancement with 5-aminoisoquinolyl- N -hydroxysuccinimidyl carbamate (5-AIQC). The AA metabolic signatures in the sera of AIC mice and supernatant samples of ADR-treated H9c2 cardiomyocytes were analyzed. Results: The levels of 14 AA metabolites were altered in ADR-treated mice ( p < 0.05). l-2-aminoadipic acid (2-AA) was one of the most suppressed metabolites in AIC. Pre-treatment with 2-AA failed to alter ADR-induced cardiac function impairment, but it exacerbated the ADR-induced decrease of left ventricular anterior wall thickness, indicating that 2-AA might contribute to AIC. Via bioinformatics analysis, we identified nine differential AA metabolites in mice, namely l-glutamic acid, l-lysine, l-serine, l-tryptophan, l-methionine, l-histidine, l-asparagine, l-tyrosine, and O -phosphorylethanolamine, and five differential AA metabolites in ADR-treated H9c2 cardiomyocytes, specifically l-tyrosine, l-alanine, l-glutamine, l-serine, and l-glutamic acid. Three AAs with increased levels (l-glutamate, l-serine, and l-tyrosine) overlapped in the two models, suggesting a possible mechanism of AA metabolic impairment during AIC. The metabolic pathways perturbed by AIC involved aminoacyl-tRNA biosynthesis and alanine, aspartate, and glutamate metabolism. Conclusions: These data indicate that a targeted AA metabolomics approach based on UPLC-MS/MS can be used to explore the AA metabolic signature and identify novel mechanisms of AIC, which may provide new clues for the prevention and treatment of this condition in the early clinical stage.