Abstract

Generating Molecular Hypotheses from Dynamic Metabolomics Data

Uwe Sauer, Professor, ETH Zurich

The omics revolution has vastly expanded our ability to monitor intracellular molecular events. This technological advance has typically not been matched with conceptual advances in our ability to interpret and understand such data or to systematically generate hypotheses from them. In particular the rapidly developing metabolomics technologies generate data that are even harder to interpret mechanistically than transcript and protein abundances because the direct connection to the genome is lost. This is particularly problematic for metabolomics data in a cellular context, where multivariate statistic data interpretation alone is typically not sufficient. Here I will discuss two unpublished examples to illustrate recent advances in generating hypotheses on molecular functions from metabolomics data. In the first, host-pathogen interaction example we identify active metabolic pathways in non-growing, hypoxic Mycobacteria that are a model for latent tuberculosis. In the second example, we infer regulatory metabolite-protein interactions from dynamic metabolomics data that enable E. coli to coordinate metabolic rearrangements during the transition from stationary phase to growth. By combining, real-time metabolome profiling with modeling and inhibitor experiments, we demonstrated that allosteric metabolite regulation, without transcription, governs the rapid utilization of energetically costly metabolites that accumulated unexpectedly during starvation.