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Proteins can bind target molecules through either induced fit or conformational selection pathways. In the conformational selection model, a protein samples a scarcely populated high-energy state that resembles a target-bound conformation. In enzymatic catalysis, such high-energy states have been identified as crucial entities for activity and the dynamic interconversion between ground states and high-energy states can constitute the rate-limiting step for catalytic turnover. The transient nature of these states has precluded direct observation of their properties. Here, we present a molecular description of a high-energy enzyme state in a conformational selection pathway by an experimental strategy centered on NMR spectroscopy, protein engineering, and X-ray crystallography. Through the introduction of a disulfide bond, we succeeded in arresting the enzyme adenylate kinase in a closed high-energy conformation that is on-pathway for catalysis. A 1.9-Å X-ray structure of the arrested enzyme in complex with a transition state analog shows that catalytic sidechains are properly aligned for catalysis. We discovered that the structural sampling of the substrate free enzyme corresponds to the complete amplitude that is associated with formation of the closed and catalytically active state. In addition, we found that the trapped high-energy state displayed improved ligand binding affinity, compared with the wild-type enzyme, demonstrating that substrate binding to the high-energy state is not occluded by steric hindrance. Finally, we show that quenching of fast time scale motions observed upon ligand binding to adenylate kinase is dominated by enzyme–substrate interactions and not by intramolecular interactions resulting from the conformational change.

Enzymatic substrate selectivity is critical for the precise control of metabolic pathways. In cases where chemically related substrates are present inside cells, robust mechanisms of substrate selectivity are required. Here, we report the mechanism utilized for catalytic ATP versus GTP selectivity during adenylate kinase (Adk) -mediated phosphorylation of AMP. Using NMR spectroscopy we found that while Adk adopts a catalytically competent and closed structural state in complex with ATP, the enzyme is arrested in a catalytically inhibited and open state in complex with GTP. X-ray crystallography experiments revealed that the interaction interfaces supporting ATP and GTP recognition, in part, are mediated by coinciding residues. The mechanism provides an atomic view on how the cellular GTP pool is protected from Adk turnover, which is important because GTP has many specialized cellular functions. In further support of this mechanism, a structure–function analysis enabled by synthesis of ATP analogs suggests that a hydrogen bond between the adenine moiety and the backbone of the enzyme is vital for ATP selectivity. The importance of the hydrogen bond for substrate selectivity is likely general given the conservation of its location and orientation across the family of eukaryotic protein kinases.

Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in—and even dominate—the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence. Using kefir as a natural model microbial ecosystem, the authors apply metabolomics, transcriptomics and large-scale mapping of inter-species interactions to study the drivers of stable coexistence of species in space and time.

An emerging paradigm in enzymology is that transient high-energy structural states play crucial roles in enzymatic reaction cycles. Generally, these high-energy or ‘invisible’ states cannot be studied directly at atomic resolution using existing structural and spectroscopic techniques owing to their low populations or short residence times. Here we report the direct NMR-based detection of the molecular topology and conformational dynamics of a catalytically indispensable high-energy state of an adenylate kinase variant. On the basis of matching energy barriers for conformational dynamics and catalytic turnover, it was found that the enzyme’s catalytic activity is governed by its dynamic interconversion between the high-energy state and a ground state structure that was determined by X-ray crystallography. Our results show that it is possible to rationally tune enzymes’ conformational dynamics and hence their catalytic power—a key aspect in rational design of enzymes catalysing novel reactions. Adenylate kinase (AdK) plays a key role in cellular energy homeostasis by catalysing the reversible magnesium-dependent formation of ADP from AMP and ATP. Here the authors present a detailed analysis of adenylate kinase’s conformational dynamics and characterize a high-energy state of AdK indispensable for catalysis.

It has long been known that bacteria coordinate their physiology with their nutrient environment, yet our current understanding offers little intuition for how bacteria respond to the second-to-minute scale fluctuations in nutrient concentration characteristic of many microbial habitats. To investigate the effects of rapid nutrient fluctuations on bacterial growth, we couple custom microfluidics with single-cell microscopy to quantify the growth rate of E. coli experiencing 30 s to 60 min nutrient fluctuations. Compared to steady environments of equal average concentration, fluctuating environments reduce growth rate by up to 50%. However, measured reductions in growth rate are only 38% of the growth loss predicted from single nutrient shifts. This enhancement derives from the distinct growth response of cells grown in environments that fluctuate rather than shift once. We report an unexpected physiology adapted for growth in nutrient fluctuations and implicate nutrient timescale as a critical environmental parameter beyond nutrient identity and concentration. Here the authors use microfluidics and single-cell microscopy to quantify the growth dynamics of individual E. coli cells exposed to nutrient fluctuations with periods as short as 30 seconds, finding that nutrient fluctuations reduce growth rates up to 50% compared to a steady nutrient delivery of equal average concentration, implying that temporal variability is an important parameter in bacterial growth.

Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance. Synopsis Bacterial metabolism constrains the evolution of antibiotic resistance. A modeling approach is developed to interpret the functionality of metabolic rewiring in resistance‐evolving E. coli growing on glycolytic or gluconeogenic carbon sources from metabolomics data. Large‐scale untargeted metabolome profiling reveals metabolic adaptations in 190 evolved antibiotic‐resistant E. coli populations, in part as compensation for consequences of the primary resistance mechanisms. Carbon and energy metabolism strongly constrain the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. A novel constraint‐based modeling approach, together with genome re‐sequencing of resistant populations, identifies condition‐dependent compensatory mechanisms. Graphical Abstract Bacterial metabolism constrains the evolution of antibiotic resistance. A modeling approach is developed to interpret the functionality of metabolic rewiring in resistance‐evolving E. coli growing on glycolytic or gluconeogenic carbon sources from metabolomics data.

It is presently unclear how much individual community members contribute to the overall metabolic output of a gut microbiota. To address this question, we used the honey bee, which harbors a relatively simple and remarkably conserved gut microbiota with striking parallels to the mammalian system and importance for bee health. Using untargeted metabolomics, we profiled metabolic changes in gnotobiotic bees that were colonized with the complete microbiota reconstituted from cultured strains. We then determined the contribution of individual community members in mono-colonized bees and recapitulated our findings using in vitro cultures. Our results show that the honey bee gut microbiota utilizes a wide range of pollen-derived substrates, including flavonoids and outer pollen wall components, suggesting a key role for degradation of recalcitrant secondary plant metabolites and pollen digestion. In turn, multiple species were responsible for the accumulation of organic acids and aromatic compound degradation intermediates. Moreover, a specific gut symbiont, Bifidobacterium asteroides, stimulated the production of host hormones known to impact bee development. While we found evidence for cross-feeding interactions, approximately 80% of the identified metabolic changes were also observed in mono-colonized bees, with Lactobacilli being responsible for the largest share of the metabolic output. These results show that, despite prolonged evolutionary associations, honey bee gut bacteria can independently establish and metabolize a wide range of compounds in the gut. Our study reveals diverse bacterial functions that are likely to contribute to bee health and provide fundamental insights into how metabolic activities are partitioned within gut communities.

Postnatal colonization of the body with microbes is assumed to be the main stimulus to postnatal immune development. By transiently colonizing pregnant female mice, we show that the maternal microbiota shapes the immune system of the offspring. Gestational colonization increases intestinal group 3 innate lymphoid cells and F4/80⁺CD11c⁺ mononuclear cells in the pups. Maternal colonization reprograms intestinal transcriptional profiles of the offspring, including increased expression of genes encoding epithelial antibacterial peptides and metabolism of microbial molecules. Some of these effects are dependent on maternal antibodies that potentially retain microbial molecules and transmit them to the offspring during pregnancy and in milk. Pups born to mothers transiently colonized in pregnancy are better able to avoid inflammatory responses to microbial molecules and penetration of intestinal microbes.

With a method involving the rapid injection of living cells into a mass spectrometer, researchers demonstrate the ability to monitor metabolite profiles in real time over the course of several hours. Metabolic systems are often the first networks to respond to environmental changes, and the ability to monitor metabolite dynamics is key for understanding these cellular responses. Because monitoring metabolome changes is experimentally tedious and demanding, dynamic data on time scales from seconds to hours are scarce. Here we describe real-time metabolome profiling by direct injection of living bacteria, yeast or mammalian cells into a high-resolution mass spectrometer, which enables automated monitoring of about 300 compounds in 15–30-s cycles over several hours. We observed accumulation of energetically costly biomass metabolites in Escherichia coli in carbon starvation–induced stationary phase, as well as the rapid use of these metabolites upon growth resumption. By combining real-time metabolome profiling with modeling and inhibitor experiments, we obtained evidence for switch-like feedback inhibition in amino acid biosynthesis and for control of substrate availability through the preferential use of the metabolically cheaper one-step salvaging pathway over costly ten-step de novo purine biosynthesis during growth resumption.

How microbes dynamically coordinate uptake and simultaneous utilization of nutrients in complex nutritional ecosystems is still an open question. Here, we develop a constraint-based modeling approach that exploits non-targeted exo-metabolomics data to unravel adaptive decision-making processes in dynamic nutritional environments. We thereby investigate metabolic adaptation of Escherichia coli to continuously changing conditions during batch growth in complex medium. Unexpectedly, model-based analysis of time resolved exo-metabolome data revealed that fastest growth coincides with preferred catabolism of amino acids, which, in turn, reduces glucose uptake and increases acetate overflow. We show that high intracellular levels of the amino acid degradation metabolites pyruvate and oxaloacetate can directly inhibit the phosphotransferase system (PTS), and reveal their functional role in mediating regulatory decisions for uptake and catabolism of alternative carbon sources. Overall, the proposed methodology expands the spectrum of possible applications of flux balance analysis to decipher metabolic adaptation mechanisms in naturally occurring habitats and diverse organisms. Bacteria must adapt their metabolism in the face of dynamically changing nutrient availability. Here, using their constraint-based modeling approach the authors analyze E. coli exometabolome data during growth in complex medium, revealing temporal coordination of glucose and amino acid catabolism.