A framework for large-scale dynamic metabolome drug profiling in mammalian cells: a case study analysis of the anti-cancer drug dichloroacetate

Abstract

Metabolic profiling of cell line collections have become an invaluable tool to study disease etiology, drug modes of action and personalized medicine. However, large-scale in vitro dynamic metabolic profiling is limited by time-consuming sampling and complex measurement procedures. By adapting an MS-based metabolomics workflow for high-throughput profiling of diverse adherent mammalian cells, we establish a technique for the rapid measurement and analysis of drug-induced dynamic changes in intracellular metabolites. This methodology is scalable to large compound libraries and is here applied to study the mechanism underlying the toxic effect of dichloroacetate in ovarian cancer cell lines. System-level analysis of the metabolic responses revealed a key and unexpected role of CoA imbalance in dichloroacetate toxicity. The herein proposed strategy for large-scale drug metabolic profiling is complementary to other molecular profiling techniques, opening new scientific and drug-discovery opportunities.