Metabolomic approach for improving ethanol stress tolerance in Saccharomyces cerevisiae.

The budding yeast Saccharomyces cerevisiae is widely used for brewing and ethanol production. The ethanol sensitivity of yeast cells is still a serious problem during ethanol fermentation, and a variety of genetic approaches (e.g., random mutant screening under selective pressure of ethanol) have been developed to improve ethanol tolerance. In this study, we developed a strategy for improving ethanol tolerance of yeast cells based on metabolomics as a high-resolution quantitative phenotypic analysis. We performed gas chromatography-mass spectrometry analysis to identify and quantify 36 compounds on 14 mutant strains including knockout strains for transcription factor and metabolic enzyme genes. A strong relation between metabolome of these mutants and their ethanol tolerance was observed. Data mining of the metabolomic analysis showed that several compounds (such as trehalose, valine, inositol and proline) contributed highly to ethanol tolerance. Our approach successfully detected well-known ethanol stress related metabolites such as trehalose and proline thus, to further prove our strategy, we focused on valine and inositol as the most promising target metabolites in our study. Our results show that simultaneous deletion of LEU4 and LEU9 (leading to accumulation of valine) or INM1 and INM2 (leading to reduction of inositol) significantly enhanced ethanol tolerance. This study shows the potential of the metabolomic approach to identify target genes for strain improvement of S. cerevisiae with higher ethanol tolerance.

Molar-based targeted metabolic profiling of cyanobacterial strains with potential for biological production.

Recently, cyanobacteria have become one of the most attractive hosts for biochemical production due to its high proliferative ability and ease of genetic manipulation. Several researches aimed at biological production using modified cyanobacteria have been reported previously. However, to improve the yield of bioproducts, a thorough understanding of the intercellular metabolism of cyanobacteria is necessary. Metabolic profiling techniques have proven to be powerful tools for monitoring cellular metabolism of various organisms and can be applied to elucidate the details of cyanobacterial metabolism. In this study, we constructed a metabolic profiling method for cyanobacteria using 13C-labeled cell extracts as internal standards. Using this method, absolute concentrations of 84 metabolites were successfully determined in three cyanobacterial strains which are commonly used as background strains for metabolic engineering. By comparing the differences in basic metabolic potentials of the three cyanobacterial strains, we found a well-correlated relationship between intracellular energy state and growth in cyanobacteria. By integrating our results with the previously reported biological production pathways in cyanobacteria, we found putative limiting step of carbon flux. The information obtained from this study will not only help gain insights in cyanobacterial physiology but also serve as a foundation for future metabolic engineering studies using cyanobacteria.

MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies.

Based on theoretically calculated comprehensive lipid libraries, in lipidomics as many as 1000 multiple reaction monitoring (MRM) transitions can be monitored for each single run. On the other hand, lipid analysis from each MRM chromatogram requires tremendous manual efforts to identify and quantify lipid species. Isotopic peaks differing by up to a few atomic masses further complicate analysis. To accelerate the identification and quantification process we developed novel software, MRM-DIFF, for the differential analysis of large-scale MRM assays. It supports a correlation optimized warping (COW) algorithm to align MRM chromatograms and utilizes quality control (QC) sample datasets to automatically adjust the alignment parameters. Moreover, user-defined reference libraries that include the molecular formula, retention time, and MRM transition can be used to identify target lipids and to correct peak abundances by considering isotopic peaks. Here, we demonstrate the software pipeline and introduce key points for MRM-based lipidomics research to reduce the mis-identification and overestimation of lipid profiles. The MRM-DIFF program, example data set and the tutorials are downloadable at the "Standalone software" section of the PRIMe (Platform for RIKEN Metabolomics, http://prime.psc.riken.jp/) database website.