Xiao-Jiang Feng

Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy

Viruses rely on the metabolic network of their cellular hosts to provide energy and building blocks for viral replication. We developed a flux measurement approach based on liquid chromatography–tandem mass spectrometry to quantify changes in metabolic activity induced by human cytomegalovirus (HCMV). This approach reliably elucidated fluxes in cultured mammalian cells by monitoring metabolome labeling kinetics after feeding cells 13C-labeled forms of glucose and glutamine. Infection with HCMV markedly upregulated flux through much of the central carbon metabolism, including glycolysis. Particularly notable increases occurred in flux through the tricarboxylic acid cycle and its efflux to the fatty acid biosynthesis pathway. Pharmacological inhibition of fatty acid biosynthesis suppressed the replication of both HCMV and influenza A, another enveloped virus. These results show that fatty acid synthesis is essential for the replication of two divergent enveloped viruses and that systems-level metabolic flux profiling can identify metabolic targets for antiviral therapy.

Quiescent Fibroblasts Exhibit High Metabolic Activity

Metabolomics technology reveals that fibroblast that have exited the proliferative cell cycle nevertheless utilize glucose throughout central carbon metabolism and rely on the pentose phosphate pathway for viability.

Metabolomics-driven quantitative analysis of ammonia assimilation in E. coli.

Despite extensive study of individual enzymes and their organization into pathways, the means by which enzyme networks control metabolite concentrations and fluxes in cells remains incompletely understood. Here, we examine the integrated regulation of central nitrogen metabolism in Escherichia coli through metabolomics and ordinary‐differential‐equation‐based modeling. Metabolome changes triggered by modulating extracellular ammonium centered around two key intermediates in nitrogen assimilation, α‐ketoglutarate and glutamine. Many other compounds retained concentration homeostasis, indicating isolation of concentration changes within a subset of the metabolome closely linked to the nutrient perturbation. In contrast to the view that saturated enzymes are insensitive to substrate concentration, competition for the active sites of saturated enzymes was found to be a key determinant of enzyme fluxes. Combined with covalent modification reactions controlling glutamine synthetase activity, such active‐site competition was sufficient to explain and predict the complex dynamic response patterns of central nitrogen metabolites.

Systems-Level Metabolic Flux Profiling Elucidates a Complete, Bifurcated Tricarboxylic Acid Cycle in Clostridium acetobutylicum

Obligatory anaerobic bacteria are major contributors to the overall metabolism of soil and the human gut. The metabolic pathways of these bacteria remain, however, poorly understood. Using isotope tracers, mass spectrometry, and quantitative flux modeling, here we directly map the metabolic pathways of Clostridium acetobutylicum, a soil bacterium whose major fermentation products include the biofuels butanol and hydrogen. While genome annotation suggests the absence of most tricarboxylic acid (TCA) cycle enzymes, our results demonstrate that this bacterium has a complete, albeit bifurcated, TCA cycle; oxaloacetate flows to succinate both through citrate/alpha-ketoglutarate and via malate/fumarate. Our investigations also yielded insights into the pathways utilized for glucose catabolism and amino acid biosynthesis and revealed that the organisms one-carbon metabolism is distinct from that of model microbes, involving reversible pyruvate decarboxylation and the use of pyruvate as the one-carbon donor for biosynthetic reactions. This study represents the first in vivo characterization of the TCA cycle and central metabolism of C. acetobutylicum. Our results establish a role for the full TCA cycle in an obligatory anaerobic organism and demonstrate the importance of complementing genome annotation with isotope tracer studies for determining the metabolic pathways of diverse microbes.

Toward closed-loop optimization of deep brain stimulation for Parkinson's disease: concepts and lessons from a computational model

Deep brain stimulation (DBS) of the subthalamic nucleus with periodic, high-frequency pulse trains is an increasingly standard therapy for advanced Parkinsons disease. Here, we propose that a closed-loop global optimization algorithm may identify novel DBS waveforms that could be more effective than their high-frequency counterparts. We use results from a computational model of the Parkinsonian basal ganglia to illustrate general issues relevant to eventual clinical or experimental tests of such an algorithm. Specifically, while the relationship between DBS characteristics and performance is highly complex, global search methods appear able to identify novel and effective waveforms with convergence rates that are acceptably fast to merit further investigation in laboratory or clinical settings.

Metabolome Remodeling during the Acidogenic-Solventogenic Transition in Clostridium acetobutylicum

ABSTRACT The fermentation carried out by the biofuel producer Clostridium acetobutylicum is characterized by two distinct phases. Acidogenesis occurs during exponential growth and involves the rapid production of acids (acetate and butyrate). Solventogenesis initiates as cell growth slows down and involves the production of solvents (butanol, acetone, and ethanol). Using metabolomics, isotope tracers, and quantitative flux modeling, we have mapped the metabolic changes associated with the acidogenic-solventogenic transition. We observed a remarkably ordered series of metabolite concentration changes, involving almost all of the 114 measured metabolites, as the fermentation progresses from acidogenesis to solventogenesis. The intracellular levels of highly abundant amino acids and upper glycolytic intermediates decrease sharply during this transition. NAD(P)H and nucleotide triphosphates levels also decrease during solventogenesis, while low-energy nucleotides accumulate. These changes in metabolite concentrations are accompanied by large changes in intracellular metabolic fluxes. During solventogenesis, carbon flux into amino acids, as well as flux from pyruvate (the last metabolite in glycolysis) into oxaloacetate, decreases by more than 10-fold. This redirects carbon into acetyl coenzyme A, which cascades into solventogenesis. In addition, the electron-consuming reductive tricarboxylic acid (TCA) cycle is shutdown, while the electron-producing oxidative (clockwise) right side of the TCA cycle remains active. Thus, the solventogenic transition involves global remodeling of metabolism to redirect resources (carbon and reducing power) from biomass production into solvent production.

Optimal Identification of Biochemical Reaction Networks

Advances in biotechnology and computer science are providing the possibility to construct mathematical models for complex biological networks and systematically understand their properties. Traditional network identification approaches, however, cannot accurately recover the model parameters from the noisy laboratory measurements. This article introduces the concept of optimal identification (OI), which utilizes a global inversion algorithm to extract the full distribution of parameters consistent with the laboratory data. In addition, OI integrates suitable computational algorithms with experimental capabilities in a closed loop fashion to maximally reduce the breadth of the extracted parameter distribution. The closed loop OI procedure seeks out the optimal set of control chemical fluxes and data observations that actively filter out experimental noise and enhance the sensitivity to the desired parameters. In this fashion, the highest quality network parameters can be attained from inverting the tailored laboratory data. The operation of OI is illustrated by identifying a simulated tRNA proofreading mechanism, in which OI provides superior solutions for all the rate constants compared with suboptimal and nonoptimal methods.

Ultrasensitive regulation of anapleurosis via allosteric activation of PEP carboxylase.

Anapleurosis is the filling of the TCA cycle with four-carbon units. The common substrate for both anapleurosis and glucose phosphorylation in bacteria is the terminal glycolytic metabolite, phosphoenolpyruvate (PEP). Here we show that E. coli quickly and almost completely turns off PEP consumption upon glucose removal. The resulting build-up of PEP is used to quickly import glucose if it becomes re-available. The switch-like termination of anapleurosis results from depletion of fructose-1,6-bisphosphate (FBP), an ultrasensitive allosteric activator of PEP carboxylase. E. coli expressing an FBP-insensitive point mutant of PEP carboxylase grow normally on steady glucose. However, they fail to build-up PEP upon glucose removal, grow poorly on oscillating glucose, and suffer from futile cycling at the PEP node on gluconeogenic substrates. Thus, bacterial central carbon metabolism is intrinsically programmed with ultrasensitive allosteric regulation to enable rapid adaptation to changing environmental conditions.

Enhancing the Efficiency of Directed Evolution in Focused Enzyme Libraries by the Adaptive Substituent Reordering Algorithm

Directed evolution is a broadly successful strategy for protein engineering in the quest to enhance the stereoselectivity, activity, and thermostability of enzymes. To increase the efficiency of directed evolution based on iterative saturation mutagenesis, the adaptive substituent reordering algorithm (ASRA) is introduced here as an alternative to traditional quantitative structure-activity relationship (QSAR) methods for identifying potential protein mutants with desired properties from minimal sampling of focused libraries. The operation of ASRA depends on identifying the underlying regularity of the protein property landscape, allowing it to make predictions without explicit knowledge of the structure-property relationships. In a proof-of-principle study, ASRA identified all or most of the best enantioselective mutants among the synthesized epoxide hydrolase from Aspergillus niger, in the absence of peptide seeds with high E-values. ASRA even revealed a laboratory error from irregularities of the reordered E-value landscape alone.

Diverse metabolic model parameters generate similar methionine cycle dynamics

Parameter estimation constitutes a major challenge in dynamic modeling of metabolic networks. Here we examine, via computational simulations, the influence of system nonlinearity and the nature of available data on the distribution and predictive capability of identified model parameters. Simulated methionine cycle metabolite concentration data (both with and without corresponding flux data) was inverted to identify model parameters consistent with it. Thousands of diverse parameter families were found to be consistent with the data to within moderate error, with most of the parameter values spanning over 1000-fold ranges irrespective of whether flux data was included. Due to strong correlations within the extracted parameter families, model predictions were generally reliable despite the broad ranges found for individual parameters. Inclusion of flux data, by strengthening these correlations, resulted in substantially more reliable flux predictions. These findings suggest that, despite the difficulty of extracting biochemically accurate model parameters from system level data, such data may nevertheless prove adequate for driving the development of predictive dynamic metabolic models.