Global Transcription and Metabolic Flux Analysis of Escherichia coli in Glucose-Limited Fed-Batch Cultivations▿ †

A time series of whole-genome transcription profiling of Escherichia coli K-12 W3110 was performed during a carbon-limited fed-batch process. The application of a constant feed rate led to the identification of a dynamic sequence of diverse carbon limitation responses (e.g., the hunger response) and at the same time provided a global view of how cellular and extracellular resources are used: the synthesis of high-affinity transporters guarantees maximal glucose influx, thereby preserving the phosphoenolpyruvate pool, and energy-dependent chemotaxis is reduced in order to provide a more economic “work mode.” σS-mediated stress and starvation responses were both found to be of only minor relevance. Thus, the experimental setup provided access to the hunger response and enabled the differentiation of the hunger response from the general starvation response. Our previous topological model of the global regulation of the E. coli central carbon metabolism through the crp, cra, and relA/spoT modulons is supported by correlating transcript levels and metabolic fluxes and can now be extended. The substrate is extensively oxidized in the tricarboxylic acid (TCA) cycle to enhance energy generation. However, the general rate of oxidative decarboxylation within the pentose phosphate pathway and the TCA cycle is restricted to a minimum. Fine regulation of the carbon flux through these pathways supplies sufficient precursors for biosyntheses. The pools of at least three precursors are probably regulated through activation of the (phosphoenolpyruvate-)glyoxylate shunt. The present work shows that detailed understanding of the genetic regulation of bacterial metabolism provides useful insights for manipulating the carbon flux in technical production processes

Timantinkaltaisen hiilipinnoitteen mekaaninen käyttäytyminen mikrosysteemeissä

The field of systems biology is often held back by difficulties in obtaining comprehensive, high-quality, quantitative data sets. In this paper, we undertook an interlaboratory effort to generate such a data set for a very large number of cellular components in the yeast Saccharomyces cerevisiae, a widely used model organism that is also used in the production of fuels, chemicals, food ingredients and pharmaceuticals. With the current focus on biofuels and sustainability, there is much interest in harnessing this species as a general cell factory. In this study, we characterized two yeast strains, under two standard growth conditions. We ensured the high quality of the experimental data by evaluating a wide range of sampling and analytical techniques. Here we show significant differences in the maximum specific growth rate and biomass yield between the two strains. On the basis of the integrated analysis of the high-throughput data, we hypothesize that differences in phenotype are due to differences in protein metabolism