Linking genetic regulation and the metabolic state

Abstract

Genome sequencing and the subsequent development of high-throughput probing of cellular states have dramatically increased our ability to understand cellular compensation to perturbation. As such, integrating system-wide measurements (e.g. gene expression) with networks of protein-protein interactions and transcription factor binding has been proven as an effective means to help elucidate insights into cellular behavior. This very cellular behavior, however, is most closely linked to the metabolites and metabolic interactions occurring within the cell. Despite this fact, metabolic measurements are often given a secondary role in efforts to unravel the multi-tiered regulatory response of cells to perturbations. To begin to address this gap, we first report on the development the application of a novel derivatization method for metabolome analysis of yeast, coupled to data-mining software that achieve comparable throughput, effort, and cost compared with DNA arrays. Our sample workup method enables simultaneous metabolite measurements with coverage throughout central carbon metabolism and amino acid biosynthesis, using a standard Gas Chromatography Mass Spectrometry (GC-MS) platform optimized for this purpose.

(cont.) As an implementation proof-of-concept, we assayed metabolite levels across two different yeast strains and two different environmental conditions with the aim of metabolic pathway reconstruction. In doing so, we demonstrate that differential metabolite level data distinguish among sample types, such as those found in typical metabolic fingerprinting or footprinting techniques. More importantly, we demonstrate that this differential metabolite level data provides further insight into specific metabolic pathways. However, the data analysis of this GC-MS metabolomic profiling data relied upon reference libraries of metabolite mass spectra to structurally identify and track metabolites. In general, techniques to enumerate and track unidentified metabolites are non-systematic and require manual curation, thus requiring a novel method for computational mining of the spectral data for automated, exhaustive analysis. Accordingly, we developed a method and software implementation that can systematically detect components that are conserved across samples without the need for a reference library or manual curation. We validate this approach by correctly identifying the components in a known mixture and the discriminating components in a spiked mixture.

(cont.) Combining these robust capabilities to characterize metabolic state along with methods of measuring transcriptional states and protein interactions, we constructed a global network-based model of yeast amino acid biosynthesis containing 154 molecules, 37 rates, and 250 interactions to link genetic regulation and metabolic state. To interrogate this model, we created a battery of five genetic perturbations to the transcriptional regulators of amino acid biosynthesis and measured transcript levels, biomass 13C-labeling, and metabolite levels in batch culture. With this data, we designed a more detailed experiment to quantify 5764 mRNAs, 54 metabolites, and 83 experimental 13C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. While mRNA expression alone was insufficient to directly predict metabolic responses, this correlation improved through incorporating a network-based model of amino-acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux by transcriptional regulation and metabolite-enzyme interaction density as a key biosynthetic control determinant.