Global transcription machinery engineering (gTME) is an approach for reprogramming gene transcription to elicit cellular phenotypes important for technological applications. Here we show the application of gTME to Saccharomyces cerevisiae for improved glucose/ethanol tolerance, a key trait for many biofuels programs. Mutagenesis of the transcription factor Spt15p and selection led to dominant mutations that conferred increased tolerance and more efficient glucose conversion to ethanol. The desired phenotype results from the combined effect of three separate mutations in the SPT15 gene [serine substituted for phenylalanine (Phe(177)Ser) and, similarly, Tyr(195)His, and Lys(218)Arg]. Thus, gTME can provide a route to complex phenotypes that are not readily accessible by traditional methods.

No abstract

Analysis of metabolomic profiling data from gas chromatography-mass spectrometry (GC/MS) measurements usually relies upon reference libraries of metabolite mass spectra to structurally identify and track metabolites. In general, techniques to enumerate and track unidentified metabolites are nonsystematic and require manual curation. We present a method and software implementation, freely available at http://spectconnect.mit.edu, 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. Finally, we demonstrate an application of this approach with a brief analysis of the Escherichia coli metabolome. By systematically cataloguing conserved metabolite peaks prior to data analysis methods, our approach broadens the scope of metabolomics and facilitates biomarker discovery.

Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of proteinprotein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental 13 C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, 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 (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional 13 C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.amino acid stress response ͉ fluxomics ͉ gcn4 ͉ systems biology M etabolic fluxes are informative indicators of cellular physiology and in vivo homeostasis. Fluxes are the modelindependent rates of metabolite interconversion that emerge through the interplay of genes, proteins, and metabolites at multiple regulatory levels. Protein-protein interactions (1) and transcription factor binding (2, 3) can be used to predict a cellular behavior at the gene and protein level (4, 5). However, complex metabolite-enzyme interactions (6), translational regulation (7), and posttranscriptional mechanisms prevent direct linkage of transcriptional state and metabolic phenotype. Previous in silico attempts to address this gap included metabolic network and regulatory on/off switch models for growth and viability prediction (8, 9). In addition, experimental studies of posttranscriptional control have measured mRNA expression in conjunction with either 13 C-based flux (10) or metabolite level measurements (11). Such studies have proven i...

The lack of comparable metabolic state assays severely limits understanding the metabolic changes caused by genetic or environmental perturbations. The present study reports 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 throughout central carbon metabolism and amino acid biosynthesis, using a standard GC-MS platform that was optimized for this purpose. As an implementation proof-of-concept, we assayed metabolite levels in two yeast strains and two different environmental conditions in the context of metabolic pathway reconstruction. We demonstrate that these differential metabolite level data distinguish among sample types, such as typical metabolic fingerprinting or footprinting. More importantly, we demonstrate that this differential metabolite level data provides insight into specific metabolic pathways and lays the groundwork for integrated transcription-metabolism studies of yeasts.