Beyond growth rate 0.6: Corynebacterium glutamicum cultivated in highly diluted environments

Fast growth of industrial microorganisms, such as Corynebacterium glutamicum, is a direct amplifier for the productivity of any growth coupled or decoupled production process. Recently, it has been shown that C. glutamicum when grown in a novel picoliter bioreactor (PLBR) exhibits a 50% higher growth rate compared to a 1 L batch cultivation [Grünberger et al. (2012) Lab Chip]. We here compare growth of C. glutamicum with glucose as substrate at different scales covering batch cultivations in the liter range down to single cell cultivations in the picoliter range. The maximum growth rate of standard batch cultures as estimated from different biomass quantification methods is ${\hat {\mu }} = 0.42\pm 0.03\,{\rm h}^{- 1} $ even for microtiter scale cultivations. In contrast, growth in a microfluidic perfusion system enabling analysis of single cells reproducibly reveals a higher growth rate of ${\hat {\mu }} = 0.62\pm 0.02\,{\rm h}^{- 1} $ . When in the same perfusion system cell‐free supernatant from exponentially grown shake flask cultures is used the growth rate of single cells is reduced to ${\hat {\mu }} = 0.47\pm 0.02\,{\rm h}^{- 1} $ . Likewise, when fresh medium is additionally supplied with 5 mM acetate, a growth rate of ${\hat {\mu }} = 0.51\pm 0.01\,{\rm h}^{- 1} $ is determined. These results prove that higher growth rates of C. glutamicum than known from typical batch cultivations are possible, and that growth is definitely impaired by very low concentrations of byproducts such as acetate. Biotechnol. Bioeng. 2013; 110: 220–228. © 2012 Wiley Periodicals, Inc.     

Visual exploration of isotope labeling networks in 3D

Isotope labeling networks (ILNs) are graphs explaining the flow of isotope labeled molecules in a metabolic network. Moreover, they are the structural backbone of metabolic flux analysis (MFA) by isotopic tracers which has been established as a standard experimental tool in fluxomics. To configure an isotope labeling experiment (ILE) for MFA, the structure of the corresponding ILN must be understood to a certain extent even by a practitioner. Graph algorithms help to analyze the network structure but produce rather abstract results. Here, the major obstruction is the high dimension of these networks and the large number of network components which, consequently, are hard to figure out manually. At the interface between theory and experiment, the three-dimensional interactive visualization tool CumoVis has been developed for exploring the network structure in a step by step manner. Navigation and orientation within ILNs are supported by exploiting the natural 3D structure of an underlying metabolite network with stacked labeled particles on top of each metabolite node. Network exploration is facilitated by rotating, zooming, forward and backward path tracing and, most important, network component reduction. All features of CumoVis are explained with an educational example and a realistic network describing carbon flow in the citric acid cycle.     

13C tracer experiments and metabolite balancing for metabolic flux analysis: comparing two approaches

Conventional metabolic flux analysis uses the information gained from determination of measurable fluxes and a steady-state assumption for intracellular metabolites to calculate the metabolic fluxes in a given metabolic network. The determination of intracellular fluxes depends heavily on the correctness of the assumed stoichiometry including the presence of all reactions with a noticeable impact on the model metabolite balances. Determination of fluxes in complex metabolic networks often requires the inclusion of NADH and NADPH balances, which are subject to controversial debate. Transhydrogenation reactions that transfer reduction equivalents from NADH to NADPH or vice versa can usually not be included in the stoichiometric model, because they result in singularities in the stoichiometric matrix. However, it is the NADPH balance that, to a large extent, determines the calculated flux through the pentose phosphate pathway. Hence, wrong assumptions on the presence or activity of transhydrogenation reactions will result in wrong estimations of the intracellular flux distribution. Using 13C tracer experiments and NMR analysis, flux analysis can be performed on the basis of only well established stoichiometric equations and measurements of the labeling state of intracellular metabolites. Neither NADH/NADPH balancing nor assumptions on energy yields need to be included to determine the intracellular fluxes. Because metabolite balancing methods and the use of 13C labeling measurements are two different approaches to the determination of intracellular fluxes, both methods can be used to verify each other or to discuss the origin and significance of deviations in the results. Flux analysis based entirely on metabolite balancing and flux analysis, including labeling information, have been performed independently for a wild-type strain of Aspergillus oryzae producing alpha-amylase. Two different nitrogen sources, NH4+ and NO3-, have been used to investigate the influence of the NADPH requirements on the intracellular flux distribution. The two different approaches to the calculation of fluxes are compared and deviations in the results are discussed. Copyright 1998 John Wiley & Sons, Inc.     

Evolutionary engineering of Corynebacterium glutamicum

A unique feature of biotechnology is that we can harness the power of evolution to improve process performance. Rational engineering of microbial strains has led to the establishment of a variety of successful bioprocesses, but it is hampered by the overwhelming complexity of biological systems. Evolutionary engineering represents a straightforward approach for fitness‐linked phenotypes (e.g., growth or stress tolerance) and is successfully applied to select for strains with improved properties for particular industrial applications. In recent years, synthetic evolution strategies have enabled selection for increased small molecule production by linking metabolic productivity to growth as a selectable trait. This review summarizes the evolutionary engineering strategies performed with the industrial platform organism Corynebacterium glutamicum. An increasing number of recent studies highlight the potential of adaptive laboratory evolution (ALE) to improve growth or stress resistance, implement the utilization of alternative carbon sources, or improve small molecule production. Advances in next‐generation sequencing and automation technologies will foster the application of ALE strategies to streamline microbial strains for bioproduction and enhance our understanding of biological systems.     

Experimental design principles for isotopically instationary 13C labeling experiments

13C metabolic flux analysis (MFA) is a well-established tool in Metabolic Engineering that found numerous applications in recent years. However, one strong limitation of the current method is the requirement of an-at least approximate-isotopic stationary state at sampling time. This requirement leads to a principle lower limit for the duration of a 13C labeling experiment. A new methodological development is based on repeated sampling during the instationary transient of the 13C labeling dynamics. The statistical and computational treatment of such instationary experiments is a completely new terrain. The computational effort is very high because large differential equations have to be solved and, moreover, the intracellular pool sizes play a significant role. For this reason, the present contribution works out principles and strategies for the experimental design of instationary experiments based on a simple example network. Hereby, the potential of isotopically instationary experiments is investigated in detail. Various statistical results on instationary flux identifiability are presented and possible pitfalls of experimental design are discussed. Finally, a framework for almost optimal experimental design of isotopically instationary experiments is proposed which provides a practical guideline for the analysis of large-scale networks.     

The thermodynamic meaning of metabolic exchange fluxes

Metabolic flux analysis (MFA) deals with the experimental determination of steady-state fluxes in metabolic networks. An important feature of the ¹³C MFA method is its capability to generate information on both directions of bidirectional reaction steps given by exchange fluxes. The biological interpretation of these exchange fluxes and their relation to thermodynamic properties of the respective reaction steps has never been systematically investigated. As a central result, it is shown here that for a general class of enzyme reaction mechanisms the quotients of net and exchange fluxes measured by ¹³C MFA are coupled to Gibbs energies of the reaction steps. To establish this relation the concept of apparent flux ratios of enzymatic isotope-labeling networks is introduced and some computing rules for these flux ratios are given. Application of these rules reveals a conceptional pitfall of ¹³C MFA, which is the inherent dependency of measured exchange fluxes on the chosen tracer atom. However, it is shown that this effect can be neglected for typical biochemical reaction steps under physiological conditions. In this situation, the central result can be formulated as a two-sided inequality relating fluxes, pool sizes, and standard Gibbs energies. This relation has far-reaching consequences for metabolic flux analysis, quantitative metabolomics, and network thermodynamics.     

Optical sensors for monitoring dynamic changes of intracellular metabolite levels in mammalian cells

Knowledge of the in vivo levels, distribution and flux of ions and metabolites is crucial to our understanding of physiology in both healthy and diseased states. The quantitative analysis of the dynamics of ions and metabolites with subcellular resolution in vivo poses a major challenge for the analysis of metabolic processes. Genetically encoded F?rster resonance energy transfer (FRET) sensors can be used for real-time in vivo detection of metabolites. FRET sensor proteins, for example, for glucose, can be targeted genetically to any cellular compartment, or even to subdomains (e.g., a membrane surface), by adding signal sequences or fusing the sensors to specific proteins. The sensors can be used for analyses in individual mammalian cells in culture, in tissue slices and in intact organisms. Applications include gene discovery, high-throughput drug screens or systematic analysis of regulatory networks affecting uptake, efflux and metabolism. Quantitative analyses obtained with the help of FRET sensors for glucose or other ions and metabolites provide valuable data for modeling of flux. Here we provide a detailed protocol for monitoring glucose levels in the cytosol of mammalian cell cultures through the use of FRET glucose sensors; moreover, the protocol can be used for other ions and metabolites and for analyses in other organisms, as has been successfully demonstrated in bacteria, yeast and even intact plants. The whole procedure typically takes ～4 d including seeding and transfection of mammalian cells; the FRET-based analysis of transfected cells takes ～5 h.