Systematic identification of allosteric protein-metabolite interactions that control enzyme activity in vivo

Abstract

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.

The Pyruvate-Tricarboxylic Acid Cycle Node

“…Obviously, the carbon fluxome of Y. pseudotuberculosis is largely under post-transcriptional control. Similarly, the primary metabolism of other bacteria is mainly controlled by post-transcriptional mechanisms (44, 67, 68), which typically involve direct regulation of flux at the enzyme level by allosteric control (69). Such a strategy seems crucial for the ability of pathogenic bacteria, such as Y. pseudotuberculosis, to cope with the rapidly changing environments that they face during their infection cycle.…”

“…4 and Table 4). Additionally, pyruvate is a key signal in bacteria to program core metabolism (55,69); thus, the secretion might contribute to the fine adjustment of the intracellular level of this metabolite. Taken together, these findings indicate that pyruvate metabolism and export are likely used to control the biological fitness and virulence of Yersinia.…”

High coverage metabolomics analysis reveals phage-specific alterations to Pseudomonas aeruginosa physiology during infection

“…Metabolite extraction and analysis Bacteria, grown in 75 ml of minimal medium Pseudomonas, were infected with phages at OD 600 = 0.3 (1.25 × 10 8 CFU ml -1 ) and sampled for metabolite profiling by fast filtration (Link et al, 2013) at different time points during phage infection (Supplementary Figure S1). At each time point, the biomass quantity was measured by following the OD 600 and the sampling volumes were adjusted to a volume corresponding to a biomass of a 1 ml culture at OD 600 = 1.0 (approximately 4 × 10 8 CFU).…”

Allosteric Activation of Escherichia coli Glucosamine-6-Phosphate Deaminase (NagB) In Vivo Justified by Intracellular Amino Sugar Metabolite Concentrations

“…(B) Fraction of NagB Ec in the R conformer (R ) and fractional saturation of the enzyme with substrate (y S ) or allosteric activator (y A ) during growth on GlcN and GlcNAc. R was calculated with the parameters given in panel A using the following state equation: 6 } (from reference 52, where S and A are the concentrations of substrate [GlcN6P] and allosteric activator [GlcNAc6P] and K S and K A are their microscopic dissociation constants) and the measured metabolite concentrations ( Fig. 3; see Table S2 in the supplemental material).…”

“…In the case of glycolysis, the function of a feed-forward loop is to sense flux through the pathway (4), and feed-forward regulation in general speeds up the response time to extrinsic signals (5). Several of the enzymes of glycolysis and gluconeogenesis have been shown to respond allosterically to changes in concentrations of the metabolites of glycolysis and thus allow a rapid response to changes in metabolic flux (6,7). NagB enzymes have been purified and studied kinetically from a variety of sources: bacteria (2,3,8), fungi (9), protozoa (10), and mammals (11,12).…”