Measuring precise concentrations of proteins can provide insights into biological processes. Here, we use efficient protein extraction and sample fractionation and state-of-the-art quantitative mass spectrometry techniques to generate a comprehensive, condition-dependent protein abundance map of Escherichia coli. We measure cellular protein concentrations for 55% of predicted E. coli genes (>2300 proteins) under 22 different experimental conditions and identify methylation and N-terminal protein acetylations previously not known to be prevalent in bacteria. We uncover system-wide proteome allocation, expression regulation, and post-translational adaptations. These data provide a valuable resource for the systems biology and broader E. coli research communities.

Metabolite-protein interactions control a variety of cellular processes, thereby playing a major role in maintaining cellular homeostasis. Metabolites comprise the largest fraction of molecules in cells, but our knowledge of the metabolite-protein interactome lags behind our understanding of protein-protein or protein-DNA interactomes. Here, we present a chemoproteomic workflow for the systematic identification of metabolite-protein interactions directly in their native environment. The approach identified a network of known and novel interactions and binding sites in Escherichia coli, and we demonstrated the functional relevance of a number of newly identified interactions. Our data enabled identification of new enzyme-substrate relationships and cases of metabolite-induced remodeling of protein complexes. Our metabolite-protein interactome consists of 1,678 interactions and 7,345 putative binding sites. Our data reveal functional and structural principles of chemical communication, shed light on the prevalence and mechanisms of enzyme promiscuity, and enable extraction of quantitative parameters of metabolite binding on a proteome-wide scale.

Beyond fuelling cellular activities with building blocks and energy, metabolism also integrates environmental conditions into intracellular signals. The underlying regulatory network is complex and multifaceted: it ranges from slow interactions, such as changing gene expression, to rapid ones, such as the modulation of protein activity via post-translational modification or the allosteric binding of small molecules. In this Review, we outline the coordination of common metabolic tasks, including nutrient uptake, central metabolism, the generation of energy, the supply of amino acids and protein synthesis. Increasingly, a set of key metabolites is recognized to control individual regulatory circuits, which carry out specific functions of information input and regulatory output. Such a modular view of microbial metabolism facilitates an intuitive understanding of the molecular mechanisms that underlie cellular decision making.

Hundreds of molecular-level changes within central metabolism allow a cell to adapt to the changing environment. A primary challenge in cell physiology is to identify which of these molecular-level changes are active regulatory events. Here, we introduce pseudo-transition analysis, an approach that uses multiple steady-state observations of (13)C-resolved fluxes, metabolites, and transcripts to infer which regulatory events drive metabolic adaptations following environmental transitions. Pseudo-transition analysis recapitulates known biology and identifies an unexpectedly sparse, transition-dependent regulatory landscape: typically a handful of regulatory events drive adaptation between carbon sources, with transcription mainly regulating TCA cycle flux and reactants regulating EMP pathway flux. We verify these observations using time-resolved measurements of the diauxic shift, demonstrating that some dynamic transitions can be approximated as monotonic shifts between steady-state extremes. Overall, we show that pseudo-transition analysis can explore the vast regulatory landscape of dynamic transitions using relatively few steady-state data, thereby guiding time-consuming, hypothesis-driven molecular validations.

Recent data suggest that the majority of proteins bind specific metabolites and that such interactions are relevant to metabolic and gene regulation. However, there are no methods to systematically identify functional allosteric protein-metabolite interactions. Here we present an experimental and computational approach for using dynamic metabolite data to discover allosteric regulation that is relevant in vivo. By switching the culture conditions of Escherichia coli every 30 s between medium containing either pyruvate or (13)C-labeled fructose or glucose, we measured the reversal of flux through glycolysis pathways and observed rapid changes in metabolite concentration. We fit these data to a kinetic model of glycolysis and systematically tested the consequences of 126 putative allosteric interactions on metabolite dynamics. We identified allosteric interactions that govern the reversible switch between gluconeogenesis and glycolysis, including one by which pyruvate activates fructose-1,6-bisphosphatase. Thus, from large sets of putative allosteric interactions, our approach can identify the most likely ones and provide hypotheses about their function.

Regulation of metabolic operation in response to extracellular cues is crucial for cells' survival. Next to the canonical nutrient sensors, which measure the concentration of nutrients, recently intracellular "metabolic flux" was proposed as a novel impetus for metabolic regulation. According to this concept, cells would have molecular systems ("flux sensors") in place that regulate metabolism as a function of the actually occurring metabolic fluxes. Although this resembles an appealing concept, we have not had any experimental evidence for the existence of flux sensors and also we have not known how these flux sensors would work in detail. Here, we show experimental evidence that supports the hypothesis that Escherichia coli is indeed able to measure its glycolytic flux and uses this signal for metabolic regulation. Combining experiment and theory, we show how this flux-sensing function could emerge from an aggregate of several molecular mechanisms: First, the system of reactions of lower glycolysis and the feedforward activation of fructose-1,6-bisphosphate on pyruvate kinase translate flux information into the concentration of the metabolite fructose-1,6-bisphosphate. The interaction of this "flux-signaling metabolite" with the transcription factor Cra then leads to flux-dependent regulation. By responding to glycolytic flux, rather than to the concentration of individual carbon sources, the cell may minimize sensing and regulatory expenses. R egulation of metabolic operation is crucial for cells' survival. The canonical view is that this regulation occurs in response to extracellular cues, where, for instance, nutrient-specific transmembrane or intracellular receptors sense the presence of a nutrient and transfer respective commands to the regulatory machinery (1-5).Recently, however, a novel impetus for metabolic regulation was proposed: cells could regulate their metabolism as a function of the actually occurring intracellular metabolic fluxes (6, 7). According to this concept, changes in extracellular nutrient abundances would first-in a rather passive manner-result in changes in intracellular metabolic fluxes. In a second instance, the metabolic fluxes would be sensed by molecular systems ("flux sensors"), which in turn would transmit the sensed "flux signal" to the regulatory machinery that consequently would adjust metabolic operation (6). On the basis of a detailed mathematical model of Escherichia coli's central metabolism and its regulation, Kotte et al. suggested that this organism would have such flux sensors in place that establish a correlation between a metabolic flux and the concentration of certain so-called flux-signaling metabolites, which in turn affect the activity of transcription factors and thus would allow for transcriptional regulation in a flux-dependent manner.Using intracellular flux to regulate metabolism is an appealing concept as it would omit the need for nutrient-specific sensors for many different nutrients, and would allow the integration of multiple nutrient inputs directl...

To counteract oxidative stress and reactive oxygen species (ROS), bacteria evolved various mechanisms, primarily reducing ROS through antioxidant systems that utilize cofactor NADPH. Cells must stabilize NADPH levels by increasing flux through replenishing metabolic pathways like pentose phosphate (PP) pathway. Here, we investigate the mechanism enabling the rapid increase in NADPH supply by exposing Escherichia coli to hydrogen peroxide and quantifying the immediate metabolite dynamics. To systematically infer active regulatory interactions governing this response, we evaluated ensembles of kinetic models of glycolysis and PP pathway, each with different regulation mechanisms. Besides the known inactivation of glyceraldehyde 3-phosphate dehydrogenase by ROS, we reveal the important allosteric inhibition of the first PP pathway enzyme by NADPH. This NADPH feedback inhibition maintains a below maximum-capacity PP pathway flux under non-stress conditions. Relieving this inhibition instantly increases PP pathway flux upon oxidative stress. We demonstrate that reducing cells' capacity to rapidly reroute their flux through the PP pathway increases their oxidative stress sensitivity.

An experimental-computational approach is applied to dissect the contribution of specific transcription factor-mediated versus global growth-dependent regulation to bacterial gene expression, and obtain a quantitative understanding of dynamic adaptations in arginine biosynthesis of E. coli.