Battery lifetime prognosis is a key requirement for successful market introduction of electric and hybrid vehicles. This work aims at the development of a lifetime prediction approach based on an ...aging model for lithium-ion batteries. A multivariable analysis of a detailed series of accelerated lifetime experiments representing typical operating conditions in hybrid electric vehicle is presented. The impact of temperature and state of charge on impedance rise and capacity loss is quantified. The investigations are based on a high-power NMC/graphite lithium-ion battery with good cycle lifetime. The resulting mathematical functions are physically motivated by the occurring aging effects and are used for the parameterization of a semi-empirical aging model. An impedance-based electric-thermal model is coupled to the aging model to simulate the dynamic interaction between aging of the battery and the thermal as well as electric behavior. Based on these models different drive cycles and management strategies can be analyzed with regard to their impact on lifetime. It is an important tool for vehicle designers and for the implementation of business models. A key contribution of the paper is the parameterization of the aging model by experimental data, while aging simulation in the literature usually lacks a robust empirical foundation.
► Extended accelerated aging tests on lithium-ion batteries. ► Semi-empirical aging model based on extended calendar aging data. ► Impedance-based electro-thermal model coupled to aging model. ► Lifetime prediction under real application condition possible concerning capacity fade.
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.
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.
When microbes lack the nutrients necessary for growth, they enter stationary phase. In cases when energy sources are still present in the environment, they must decide whether to continue to use ...their metabolic program to harvest the available energy. Here we characterized the metabolic response to a variety of types of nutrient starvation in Escherichia coli and Bacillus subtilis. We found that E. coli exhibits a range of phenotypes, with the lowest metabolic rates under nitrogen starvation and highest rates under magnesium starvation. In contrast, the phenotype of B. subtilis was dominated by its decision to form metabolically inactive endospores. While its metabolic rates under most conditions were thus lower than those of E. coli, when sporulation was suppressed by a genetic perturbation or an unnatural starvation condition, the situation was reversed. To further probe stationary-phase metabolism, we used quantitative metabolomics to investigate possible small-molecule signals that may regulate the metabolic rate of E. coli and initiate sporulation in B. subtilis. We hypothesize a role for phosphoenolpyruvate (PEP) in regulating E. coli glucose uptake and for the redox cofactors NAD(H) and NADP(H) in initiation of sporulation. Our work is directly relevant to synthetic biology and metabolic engineering, where active metabolism during stationary phase, which uncouples production from growth, remains an elusive goal.
Microbes have shown a remarkable ability in evading the killing actions of antimicrobial agents, such that treatment of bacterial infections represents once more an urgent global challenge. ...Understanding the initial bacterial response to antimicrobials may reveal intrinsic tolerance mechanisms to antibiotics and suggest alternative and less conventional therapeutic strategies. Here, we used mass spectrometry-based metabolomics to monitor the immediate metabolic response of Escherichia coli to a variety of antibiotic perturbations. We show that rapid metabolic changes can reflect drug mechanisms of action and reveal the active role of metabolism in mediating the first stress response to antimicrobials. We uncovered a role for ammonium imbalance in aggravating chloramphenicol toxicity and the essential function of deoxythymidine 5′-diphosphate (dTDP)-rhamnose synthesis for the immediate transcriptional upregulation of GyrA in response to quinolone antibiotics. Our results suggest bacterial metabolism as an attractive target to interfere with the early bacterial response to antibiotic treatments and reduce the probability for survival and eventual evolution of antibiotic resistance.
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•Charting the metabolome response of E. coli to antibiotic treatment•Functional role of the rapid metabolic response in coping with antibiotic stress•The role for ammonium imbalance in aggravating chloramphenicol toxicity•dTDP-rhamnose regulates GyrA transcription in response to quinolone antibiotics
Zampieri et al. monitor short-term metabolic changes in Escherichia coli after exposure to antibiotics. A core set of metabolites exhibits a unique rapid response to antibiotic with common or radically different modes of action. By interfering with such cellular response, the authors reveal the functional role of metabolism in mediating the first immediate response to antibiotics.
Multidimensional Optimality of Microbial Metabolism Schuetz, Robert; Zamboni, Nicola; Zampieri, Mattia ...
Science (American Association for the Advancement of Science),
05/2012, Letnik:
336, Številka:
6081
Journal Article
Recenzirano
Although the network topology of metabolism is well known, understanding the principles that govern the distribution of fluxes through metabolism lags behind. Experimentally, these fluxes can be ...measured by ¹³C-flux analysis, and there has been a long-standing interest in understanding this functional network operation from an evolutionary perspective. On the basis of ¹³C-determined fluxes from nine bacteria and multi-objective optimization theory, we show that metabolism operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Consistent with flux data from evolved Escherichia coli, we propose that flux states evolve under the trade-off between two principles: optimality under one given condition and minimal adjustment between conditions. These principles form the forces by which evolution shapes metabolic fluxes in microorganisms' environmental context.
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•Protein–metabolite interactions are mapped using protein-centric and metabolite-centric approaches.•Proteome-wide monitoring of ligand-induced effects enables HTP mapping in intact ...cells.•New modeling approaches infer in vivo functionality from omics data.
New mapping approaches have greatly expanded our view on the cellular landscape of protein–metabolite interactions. These methods either identify proteins interacting with a selected metabolite or vice versa. By reviewing recent developments, we found that comprehensive mapping of the protein–metabolite interaction space can be achieved eventually using existing methods, amongst which proteomics techniques to assess cell wide protein property changes in response to metabolite treatment currently offer the highest potential. Since we expect major advances in mapping protein–metabolite interactions in the near future, the challenge shifts to the identification of interaction functionality, for which currently only few specialized methods are available.
Lithium-ion battery packs in hybrid and pure electric vehicles are always equipped with a battery management system (BMS). The BMS consists of hardware and software for battery management including, ...among others, algorithms determining battery states. The continuous determination of battery states during operation is called battery monitoring. In this paper, the methods for monitoring of the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications. To this end, more than 350 sources including scientific and technical literature are studied and the respective approaches are classified in various groups.
Highlights ► Overlapping regulatory mechanisms control metabolic fluxes. ► Post-translational modifications are abundant regulators of microbial metabolism. ► Input–output regulatory functions are ...most advanced for transcriptional networks. ► Metabolic feedback into regulatory networks governs many cellular decisions. ► Kinetic models are crucial to turn data into understanding of metabolic control.
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.
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