Dynamic metabolome profiling uncovers potential TOR signaling genes

Abstract

AbstractAlthough the genetic code of the yeastSaccharomyces cerevisiaewas sequenced 25 years ago, the characterization of the roles of genes within it is far from complete. The lack of a complete mapping of functions to genes hampers systematic understanding of the biology of the cell. The advent of high-throughput metabolomics offers a unique approach to uncovering gene function with an attractive combination of cost, robustness, and breadth of applicability. Here we used flow-injection time-of-flight mass spectrometry (FIA-MS) to dynamically profile the metabolome of 164 loss-of-function mutants in TOR and receptor or receptor-like genes under a time-course of rapamycin treatment, generating a dataset with over 7,000 metabolomics measurements. In order to provide a resource to the broader community, those data are made available for browsing through an interactive data visualization app hosted athttps://rapamycin-yeast-metabolome.herokuapp.com/. We demonstrate that dynamic metabolite responses to rapamycin are more informative than steady state responses when recovering known regulators of TOR signaling, as well as identifying new ones. Deletion of a subset of the novel genes causes phenotypes and proteome responses to rapamycin that further implicate them in TOR signaling. We found that one of these genes,CFF1, was connected to the regulation of pyrimidine biosynthesis through URA10. These results demonstrate the efficacy of the approach for flagging novel potential TOR signaling-related genes and highlights the utility of dynamic perturbations when using functional metabolomics to deliver biological insight.