A universal trade-off between growth and lag in fluctuating environments.

The rate of cell growth is crucial for bacterial fitness and drives the allocation of bacterial resources, affecting, for example, the expression levels of proteins dedicated to metabolism and biosynthesis1,2. It is unclear, however, what ultimately determines growth rates in different environmental conditions. Moreover, increasing evidence suggests that other objectives are also important3-7, such as the rate of physiological adaptation to changing environments8,9. A common challenge for cells is that these objectives cannot be independently optimized, and maximizing one often reduces another. Many such trade-offs have indeed been hypothesized on the basis of qualitative correlative studies8-11. Here we report a trade-off between steady-state growth rate and physiological adaptability in Escherichia coli, observed when a growing culture is abruptly shifted from a preferred carbon source such as glucose to fermentation products such as acetate. These metabolic transitions, common for enteric bacteria, are often accompanied by multi-hour lags before growth resumes. Metabolomic analysis reveals that long lags result from the depletion of key metabolites that follows the sudden reversal in the central carbon flux owing to the imposed nutrient shifts. A model of sequential flux limitation not only explains the observed trade-off between growth and adaptability, but also allows quantitative predictions regarding the universal occurrence of such tradeoffs, based on the opposing enzyme requirements of glycolysis versus gluconeogenesis. We validate these predictions experimentally for many different nutrient shifts in E. coli, as well as for other respiro-fermentative microorganisms, including Bacillus subtilis and Saccharomyces cerevisiae.

Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies.

Measuring the dynamics of E. coli ribosome biogenesis using pulse-labeling and quantitative mass spectrometry.

The ribosome is an essential organelle responsible for cellular protein synthesis. Until recently, the study of ribosome assembly has been largely limited to in vitro assays, with few attempts to reconcile these results with the more complex ribosome biogenesis process inside the living cell. Here, we characterize the ribosome synthesis and assembly pathway for each of the E. coli ribosomal protein (r-protein) in vivo using a stable isotope pulse-labeling timecourse. Isotope incorporation into assembled ribosomes was measured by quantitative mass spectrometry (qMS) and fit using steady-state flux models. Most r-proteins exhibit precursor pools ranging in size from 0% to 7% of completed ribosomes, and the sizes of these individual r-protein pools correlate well with the order of r-protein binding in vitro. Additionally, we observe anomalously large precursor pools for specific r-proteins with known extra-ribosomal functions, as well as three r-proteins that apparently turnover during steady-state growth. Taken together, this highly precise, time-dependent proteomic qMS approach should prove useful in future studies of ribosome biogenesis and could be easily extended to explore other complex biological processes in a cellular context.