Rapid emergence of antibiotic resistance in Mycobacterium tuberculosis is threatening to make one of humankind's deadliest infectious diseases incurable. The lack of effective platforms for antituberculosis drug discovery are responsible for the low discovery rate of novel antimicrobials and demand new strategies for early drug discovery. Classical antibiotic discovery campaigns limit the search for new antimicrobial modes of action (MoA ) exclusively to single compounds capable of inhibiting in-vitro microbial growth. Furthermore, the lack of efficient, rapid and systematic methods to investigate the MoA of discovered growth-inhibitors has often misguided the selection of most promising leads. As a direct consequence, this approach also frequently resulted in re-discovering known molecules, reducing the possibility to identify radically new antimicrobial targets. To address this problem we have developed and validated a rapid and systematic metabolic fingerprinting method to classify the MoA of bioactive compounds. This approach is based on non- targeted metabolomics and enables rapid measurements at up to 100x higher throughput compared to classical omics technologies such as proteomics, transcriptomics, or genome sequencing. The metabolome-based screening approach combined with genome-scale metabolic modeling can extract multiple quantitative signatures indicative of functional properties of MoAs in large compound libraries at early stages in drug discovery. The goal of this project is to apply this new platform directly to virulent Mycobacterium tuberculosis using a novel library of ~1200 anti-tuberculosis and natural compounds with unknown MoAs. Our approach will identify new compounds with promising and novel antimicrobial properties and could radically change the way we search for new strategies to kill M. tuberculosis. The newly identified MoAs can be used to rationally design combinatorial therapies to fight the emergence of resistance pathogens.
The current drought of novel antibiotics and the rapid emergence of antibiotic resistance in Mycobacterium tuberculosis is threatening to make one of humankind?s deadliest infectious diseases incurable. This threat calls for new and innovative drug discovery paradigms. To tackle this challenge, we propose a novel systematic and rapid metabolomics approach to predict the mode of action of antimicrobial compounds. In contrast to existing methods based on phenotypic drug profiling, we here exploit the intracellular response of about 1000 metabolites as a truly multi-parametric high- throughput readout of the cellular response. By applying our methodology to ~1200 anti- tuberculosis compounds directly on the pathogen Mycobacterium tuberculosis, we will identify potential relevant and radically new strategies to fight tuberculosis infection. This methodology can be applied at early drug discovery stages and innovate/accelerate the selection and optimization of promising anti-tuberculosis leads. !
