Downloaded from www.microbiologyresearch.org by IP: 54.226.235.197 On: Sat, 19 Nov 2016 15:49:15 TCA cycle activity in Saccharomyces cerevisiae is a function of the environmentally determined specific growth and glucose uptake rates Lars M. Blank and Uwe Sauer Correspondence Lars M. Blank blank@biotech.biol.ethz.ch Institute of Biotechnology, ETH Zu ¨ rich, Zu ¨ rich, Switzerland Received 16 October 2003 Revised 19 December 2003 Accepted 22 December 2003 Metabolic responses of Saccharomyces cerevisiae to different physical and chemical environmental conditions were investigated in glucose batch culture by GC-MS-detected mass isotopomer distributions in proteinogenic amino acids from 13 C-labelling experiments. For this purpose, GC-MS-based metabolic flux ratio analysis was extended from bacteria to the compartmentalized metabolism of S. cerevisiae. Generally, S. cerevisiae was shown to have low catabolic fluxes through the pentose phosphate pathway and the tricarboxylic acid (TCA) cycle. Notably, respiratory TCA cycle fluxes exhibited a strong correlation with the maximum specific growth rate that was attained under different environmental conditions, including a wide range of pH, osmolarity, decoupler and salt concentrations, but not temperature. At pH values of 4?0 to 6?0 with near-maximum growth rates, the TCA cycle operated as a bifurcated pathway to fulfil exclusively biosynthetic functions. Increasing or decreasing the pH beyond this physiologically optimal range, however, reduced growth and glucose uptake rates but increased the ‘cyclic’ respiratory mode of TCA cycle operation for catabolism. Thus, the results indicate that glucose repression of the TCA cycle is regulated by the rates of growth or glucose uptake, or signals derived from these. While sensing of extracellular glucose concentrations has a general influence on the in vivo TCA cycle activity, the growth-rate-dependent increase in respiratory TCA cycle activity was independent of glucose sensing. INTRODUCTION Genome-wide mRNA responses of the baker’s yeast Saccharomyces cerevisiae to changes in pH, osmolarity or temperature revealed differential expression of more than 1000 transcripts during adaptation (Causton et al., 2001; Gasch et al., 2000). Since transcriptome or proteome changes do not directly reveal cellular phenotypes, one would like to connect these inventory data with the appar- ent cellular physiology (Bailey, 1999). One such approach is metabolic flux analysis, which estimates material flow through biochemical reaction networks, and thus provides a direct link to the physiological phenotype (Hellerstein, 2003). Different approaches for metabolic flux analysis based on 13 C-labelling experiments have been developed, allowing precise quantification of central carbon metabolism (Sauer, 2004; Wiechert, 2001). Recent applications include the bacteria Bacillus subtilis (Dauner et al., 2001; Zamboni & Sauer, 2003), Corynebacterium glutamicum (Klapa et al., 2003; Petersen et al., 2000; Wittmann & Heinzle, 2002) and Escherichia coli (Emmerling et al., 2002; Fischer & Sauer, 2003b; Jiao et al., 2003; Sauer et al., 2004). Although conceptually more difficult, flux analysis has also been applied successfully to compartmentalized microbes such as S. cerevisiae (Christensen et al., 2002; Dos Santos et al., 2003), Saccharomyces kluyveri (Mo ¨ller et al., 2002), Kluyvero- myces marxianus (Wittmann et al., 2002b) and Penicillium chrysogenum (Van Winden et al., 2003). Often, metabolic flux analysis combines 13 C-labelling data with quantitative physiology data to obtain a best-fit flux solution. A somewhat different methodology is metabolic flux ratio (METAFoR) analysis, which quantifies the relative contribution of con- verging pathways or reactions to a given intracellular meta- bolite (Fischer & Sauer, 2003a). Without data fitting, this biochemical approach relies exclusively on 13 C data and has been used successfully with NMR data in the yeasts S. cerevisiae (Maaheimo et al., 2001) and Pichia stipitis (Fiaux et al., 2003). Here, we extend METAFoR analysis by GC-MS from E. coli (Fischer & Sauer, 2003a) to the compartmentalized S. cerevisiae metabolism. The particular focus of this study was to investigate the impact of different environmental conditions such as pH, osmolarity and temperature on the central carbon metabolism of S. cerevisiae during growth on glucose. Abbreviations: MDV, mass distribution vector; METAFoR, metabolic flux ratio; PP, pentose phosphate; TCA, tricarboxylic acid (for other abbreviations see the legend of Fig. 1). 0002-6845 G 2004 SGM Printed in Great Britain 1085 Microbiology (2004), 150, 1085–1093 DOI 10.1099/mic.0.26845-0