Comparative phenotypic analysis of CHO clones and culture media for lactate shift.

We explored the effects of media and clonal variation on the lactate shift which can be considered as one of the desirable features in CHO cell culture. Various culture profiles with the specific growth and antibody production rates under three different media conditions in two CHO producing clones were evaluated by resorting to multivariate statistical analysis. In most cases, glutamine depletion coincided with lactate consumption, suggesting that glutaminolysis rather than glycolysis was the preferred pathway for the pyruvate supply toward lactate production. With respect to the lactate shift, high performing medium showed higher glutamate uptake, higher aspartate secretion and lower serine uptake compared to other media conditions. In addition, clone itself exhibited the desired lactate consumption more consistently accompanying with distinguishing phenotype. The clone exhibiting lactate shift produced lesser lactate in exponential phase but two-fold higher non-toxic alanine, thus leading to better culture environment. Thus, we understand the balanced selection of clone and media composition enables cells to utilize the metabolic pathways for the desired lactate shift.

Concurrent isotope-assisted metabolic flux analysis and transcriptome profiling reveal responses of poplar cells to altered nitrogen and carbon supply.

Reduced nitrogen is indispensable to plants. However, its limited availability in soil combined with the energetic and environmental impacts of nitrogen fertilizers motivates research into molecular mechanisms toward improving plant nitrogen use efficiency (NUE). We performed a systems-level investigation of this problem by employing multiple 'omics methodologies on cell suspensions of hybrid poplar (Populus tremula × Populus alba). Acclimation and growth of the cell suspensions in four nutrient regimes ranging from abundant to deficient supplies of carbon and nitrogen revealed that cell growth under low-nitrogen levels was associated with substantially higher NUE. To investigate the underlying metabolic and molecular mechanisms, we concurrently performed steady-state 13 C metabolic flux analysis with multiple isotope labels and transcriptomic profiling with cDNA microarrays. The 13 C flux analysis revealed that the absolute flux through the oxidative pentose phosphate pathway (oxPPP) was substantially lower (~threefold) under low-nitrogen conditions. Additionally, the flux partitioning ratio between the tricarboxylic acid cycle and anaplerotic pathways varied from 84%:16% under abundant carbon and nitrogen to 55%:45% under deficient carbon and nitrogen. Gene expression data, together with the flux results, suggested a plastidic localization of the oxPPP as well as transcriptional regulation of certain metabolic branchpoints, including those between glycolysis and the oxPPP. The transcriptome data also indicated that NUE-improving mechanisms may involve a redirection of excess carbon to aromatic metabolic pathways and extensive downregulation of potentially redundant genes (in these heterotrophic cells) that encode photosynthetic and light-harvesting proteins, suggesting the recruitment of these proteins as nitrogen sinks in nitrogen-abundant conditions.

Mannose metabolism in recombinant CHO cells and its effect on IgG glycosylation.

Understanding the causes of high-mannose (HM) glycosylation of recombinant IgG in CHO cells would facilitate the production of therapeutics. CHO cells grown with mannose as the major carbon source demonstrated a dramatic increase in total HM glycosylation in recombinant IgG, with no effect on cell growth, viability, or titer. Quantitative metabolomics and (13) C flux analysis were used to explore the mechanism for increased HM glycosylation and understand the metabolism of mannose in CHO cells. It was demonstrated that mannose was a good carbon source for CHO cell growth and IgG production, readily entering both glycolysis and the TCA Cycle. Previous mechanisms for increased HM glycosylation during antibody production have been attributed to changes in pH, osmolality, increased specific productivity, and nutrient limitation. The results from this study propose a novel mechanism where an increased carbon flux in the GDP-mannose synthetic pathway increased the intracellular concentration of mannose-containing metabolites. The abnormally high concentration of mannose and mannose-metabolites were shown to inhibit α-mannosidase activity and it was proposed that this inhibition in the ER and Golgi caused the production of IgG with increased high-mannose glycosylation. Biotechnol. Bioeng. 2016;113: 1468-1480. © 2016 Wiley Periodicals, Inc.

Elucidating the role of copper in CHO cell energy metabolism using (13)C metabolic flux analysis.

(13)C-metabolic flux analysis was used to understand copper deficiency-related restructuring of energy metabolism, which leads to excessive lactate production in recombinant protein-producing CHO cells. Stationary-phase labeling experiments with U-(13)C glucose were conducted on CHO cells grown under high and limiting copper in 3 L fed-batch bioreactors. The resultant labeling patterns of soluble metabolites were measured by GC-MS and used to estimate metabolic fluxes in the central carbon metabolism pathways using OpenFlux. Fluxes were evaluated 300 times from stoichiometrically feasible random guess values and their confidence intervals calculated by Monte Carlo simulations. Results from metabolic flux analysis exhibited significant carbon redistribution throughout the metabolic network in cells under Cu deficiency. Specifically, glycolytic fluxes increased (25%-79% relative to glucose uptake) whereas fluxes through the TCA and pentose phosphate pathway (PPP) were lower (15%-23% and 74%, respectively) compared with the Cu-containing condition. Furthermore, under Cu deficiency, 33% of the flux entering TCA via the pyruvate node was redirected to lactate and malate production. Based on these results, we hypothesize that Cu deficiency disrupts the electron transport chain causing ATP deficiency, redox imbalance, and oxidative stress, which in turn drive copper-deficient CHO cells to produce energy via aerobic glycolysis, which is associated with excessive lactate production, rather than the more efficient route of oxidative phosphorylation.

Flux and reflux: metabolite reflux in plant suspension cells and its implications for isotope-assisted metabolic flux analysis.

Isotope-assisted metabolic flux analysis (MFA) is a powerful methodology to quantify intracellular fluxes via isotope labeling experiments (ILEs). In batch cultures, which are often convenient, inexpensive or inevitable especially for eukaryotic systems, MFA is complicated by the presence of the initially present biomass. This unlabeled biomass may either mix with the newly synthesized labeled biomass or reflux into the metabolic network, thus masking the true labeling patterns in the newly synthesized biomass. Here, we report a detailed investigation of such metabolite reflux in cell suspensions of the tree poplar. In ILEs supplying 28% or 98% U-(13)C glucose as the sole organic carbon source, biomass components exhibited lower (13)C enrichments than the supplied glucose as well as anomalous isotopomers not explainable by simple mixing of the initial and newly synthesized biomass. These anomalous labeling patterns were most prominent in a 98% U-(13)C glucose ILE. By comparing the performance of light- and dark-grown cells as well as by analyzing the isotope labeling patterns in aspartic and glutamic acids, we eliminated photosynthetic or anaplerotic fixation of extracellular (12)CO2 as explanations for the anomalous labeling patterns. We further investigated four different metabolic models for interpreting the labeling patterns and evaluating fluxes: (i) a carbon source (glucose) dilution model, (ii) an isotopomer correction model with uniform dilution for all amino acids, (iii) an isotopomer correction model with variable dilution for different amino acids, and (iv) a comprehensive metabolite reflux model. Of these, the metabolite reflux model provided a substantially better fit for the observed labeling patterns (sum of squared residues: 538) than the other three models whose sum of squared residues were (i) 4626, (ii) 4983, and (iii) 1748, respectively. We compared fluxes determined using the metabolite reflux model to those determined using an independent methodology involving an excessively long ILE to wash out initial biomass and a minimal reflux model. This comparison showed identical or similar distributions for a majority of fluxes, thus validating our comprehensive reflux model. In summary, we have demonstrated the need for quantifying interactions between initially present biomass and newly synthesized biomass in batch ILEs, especially through the use of ≈100% U-(13)C carbon sources. Our ILEs reveal a high amount of metabolite reflux in poplar cell suspensions, which is well explained by a comprehensive metabolite reflux model.

Mathematical modeling of isotope labeling experiments for metabolic flux analysis.

Isotope labeling experiments (ILEs) offer a powerful methodology to perform metabolic flux analysis. However, the task of interpreting data from these experiments to evaluate flux values requires significant mathematical modeling skills. Toward this, this chapter provides background information and examples to enable the reader to (1) model metabolic networks, (2) simulate ILEs, and (3) understand the optimization and statistical methods commonly used for flux evaluation. A compartmentalized model of plant glycolysis and pentose phosphate pathway illustrates the reconstruction of a typical metabolic network, whereas a simpler example network illustrates the underlying metabolite and isotopomer balancing techniques. We also discuss the salient features of commonly used flux estimation software 13CFLUX2, Metran, NMR2Flux+, FiatFlux, and OpenFLUX. Furthermore, we briefly discuss methods to improve flux estimates. A graphical checklist at the end of the chapter provides a reader a quick reference to the mathematical modeling concepts and resources.

Nuclear magnetic resonance methods for metabolic fluxomics.

Fluxomics, through its core methodology of metabolic flux analysis (MFA), enables quantification of carbon traffic through cellular biochemical pathways. Isotope labeling experiments aid MFA by providing information on intracellular fluxes, especially through parallel and cyclic pathways. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are two complementary methods to measure abundances of isotopomers generated in these experiments. 2-D [(13)C, (1)H] heteronuclear correlation NMR spectra can detect (13)C isotopes coupled to protons and thus noninvasively separate molecules and atoms with a specific isotopic content from a mixture of molecular species. Furthermore, the fine structures of the peaks in these spectra can reveal scalar couplings between chemically bonded carbon atoms in the sample, from which isotopomer abundances can be quantified. This chapter introduces methods for NMR sample preparation and spectral acquisition of 2-D [(13)C, (1)H] correlation maps, followed by a detailed presentation of methods to process the spectra and quantify isotopomer abundances. We explain the use of the software NMRViewJ for spectral visualization and processing, as well as MATLAB scripts developed by us for peak extraction, deconvolution of overlapping peaklets, and isotopomer abundance quantification. Finally, we discuss the applications of NMR-derived isotopomer data toward quantitatively understanding metabolic pathways.

Designer labels for plant metabolism: statistical design of isotope labeling experiments for improved quantification of flux in complex plant metabolic networks.

Metabolic fluxes are powerful indicators of cell physiology and can be estimated by isotope-assisted metabolic flux analysis (MFA). The complexity of the compartmented metabolic networks of plants has constrained the application of isotope-assisted MFA to them, principally because of poor identifiability of fluxes from the measured isotope labeling patterns. However, flux identifiability can be significantly improved by a priori design of isotope labeling experiments (ILEs). This computational design involves evaluating the effect of different isotope label and isotopomer measurement combinations on flux identifiability, and thereby identifying optimal labels and measurements toward evaluating the fluxes of interest with the highest confidence. This article reports ILE designs for two major, compartmented plant metabolic pathways - the pentose phosphate pathway (PPP) and γ-aminobutyric acid (GABA) shunt. Together, these pathways represent common motifs in plant metabolism including duplication of pathways in different subcellular compartments, reversible reactions and cyclic carbon flow. To compare various ILE designs, we employed statistical A- and D-optimality criteria. Our computations showed that 1,2-(13)C Glc is a powerful and robust label for the plant PPPs, given currently popular isotopomer measurement techniques (single quadrupole mass spectrometry [MS] and 2-D nuclear magnetic resonance [NMR]). Further analysis revealed that this label can estimate several PPP fluxes better than the popular label 1-(13)C Glc. Furthermore, the concurrent measurement of the isotopomers of hexose and pentose moieties synthesized exclusively in the cytosol or the plastid compartments (measurable through intracellular glucose or sucrose, starch, RNA ribose and histidine) considerably improves the identifiability of PPP fluxes in the individual compartments. Additionally, MS-derived isotopomer measurements outperform NMR-derived measurements in identifying PPP fluxes. The potency of 1,2-(13)C Glc can be improved substantially by combining it with other labels (e.g. 3-(13)C Glc, 1-(13)C Glc and U-(13)C Glc) in parallel ILEs. For the GABA shunt, we calculated that 100% 2-(13)C Ala and 100% U-(13)C Gln constitute the best labels. We anticipate that the ILE designs presented in this article can enhance the quality of flux estimates in these two complex plant pathways. In the future, these ILE designs can be further improved by leveraging recent analytical and computational developments in isotope-assisted MFA.