Biologically consistent annotation of CHO cell culture metabolomics data

Metabolomics represents the effort to understand the role of metabolites in a biological system. Unfortunately, unambiguous metabolite identification represents a major bottleneck in liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics. A widely used approach is to search spectral (MS/MS) libraries in reference databases for matching metabolites; however, this approach is limited by incomplete coverage. An alternative approach is to match detected features to candidate chemical structures based on their mass and computationally predicted fragmentation pattern. Both approaches often return too many possible matches; moreover, the results from different annotation tools rarely agree. This presentation describes a novel annotation tool that combines search results from several MS/MS libraries and computational fragmentation tools, and evaluates these results based on the content of a metabolic model. This captures the relevant biological context to determine the most likely identity for a given LC-MS data feature. This workflow, termed Biologically Consistent Annotation (BioCAn), improves on other publicly available annotation tools, achieving superior accuracy and sensitivity, while reducing the false discovery rate. The utility of this tool for investigating metabolic inefficiencies in cell culture processes is demonstrated by identifying novel CHO cell metabolites associated with enhanced or reduced cell growth and monoclonal antibody production. The function of these metabolites was evaluated in shake flask and controlled bioreactor experiments

Metabolomics approach for increasing CHO cell specific productivity

Chinese hamster ovary cells are the most commonly used expression system in the production of monoclonal antibody therapeutic drugs. The biomanufacturing industry has made significant advances in increasing protein titers of these cell cultures by over 100-fold since the 1980s to gram-per-liter ranges, and much of this progress has been made via increasing cell density and viability. However, even next generation processes are approaching the limits of how high cell densities can be reached with available technologies. On the other hand, the specific productivity (qP) of the cell lines, though much higher now than at the advent of biologics production, has not been improved to the same degree, and advances on this front are needed to attain higher titers in shorter times. In this work, a library of twelve cell lines, having a wide range of qPs but all derived from the same parental cell line and expressing one of two different antibodies, was investigated using an untargeted metabolomics approach. Spent medium samples were collected from each fed-batch culture at two time points. BioCAn (Biologically Consistent Annotation), a recently developed automated annotation tool, was used to determine the most likely identities of features detected in LC-MS data from these cell lines. A correlation analysis was then performed to find annotated features that were significantly associated with either cell growth (37 features), qP (32 features), or both (56 features). Interestingly, all features associated with cell growth showed a negative correlation, while all features associated with qP showed a positive correlation. To investigate whether metabolites positively correlated with qP reflect endogenous metabolic activity beneficial for productivity, several metabolites were added to the culture medium at varying concentrations. We found that supplementing the medium with one or more select metabolites could improve qP without negatively impacting cell growth. We next evaluated whether these metabolites could be used as biomarkers to identify clones with potential for high productivity, as current screening methods can falsely eliminate clones due to sub-optimal culture media or process conditions. Together, these studies demonstrate opportunities for using untargeted metabolomics to achieve higher titer in biologics production processes. Further, the identification of biomarkers has potential to shorten cell line development timelines, which is on the critical path to biologics manufacturing

Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design

<p>Abstract</p> <p>Background</p> <p>Microbial hosts offer a number of unique advantages when used as production systems for both native and heterologous small-molecules. These advantages include high selectivity and benign environmental impact; however, a principal drawback is low yield and/or productivity, which limits economic viability. Therefore a major challenge in developing a microbial production system is to maximize formation of a specific product while sustaining cell growth. Tools to rationally reconfigure microbial metabolism for these potentially conflicting objectives remain limited. Exhaustively exploring combinations of genetic modifications is both experimentally and computationally inefficient, and can become intractable when multiple gene deletions or insertions need to be considered. Alternatively, the search for desirable gene modifications may be solved heuristically as an evolutionary optimization problem. In this study, we combine a genetic algorithm and elementary mode analysis to develop an optimization framework for evolving metabolic networks with energetically favorable pathways for production of both biomass and a compound of interest.</p> <p>Results</p> <p>Utilization of thermodynamically-weighted elementary modes for flux reconstruction of <it>E. coli </it>central metabolism revealed two clusters of EMs with respect to their Δ<it>G</it>_<it>p</it>°. For proof of principle testing, the algorithm was applied to ethanol and lycopene production in <it>E. coli</it>. The algorithm was used to optimize product formation, biomass formation, and product and biomass formation simultaneously. Predicted knockouts often matched those that have previously been implemented experimentally for improved product formation. The performance of a multi-objective genetic algorithm showed that it is better to couple the two objectives in a single objective genetic algorithm.</p> <p>Conclusion</p> <p>A computationally tractable framework is presented for the redesign of metabolic networks for maximal product formation combining elementary mode analysis (a form of convex analysis), pathway thermodynamics, and a genetic algorithm to optimize the production of two industrially-relevant products, ethanol and lycopene, from <it>E. coli</it>. The designed algorithm can be applied to any small-scale model of cellular metabolism theoretically utilizing any substrate and applied towards the production of any product.</p

Understanding and overcoming process insults through application of ‘omics technologies

Modern industrial process development, at both small and large corporations, usually consists of applying a well-characterized and established cell culture platform. Despite the high productivity available from these process platforms, difficult challenges remain, including with respect to the ability of the process to endure insults or disruptions. We previously demonstrated that overfeeding resulted in an undesirable increase in lactate production late in fed batch culture, which decreased productivity[i]. Here we report on metabolic flux analysis performed utilizing this process and isotopically labeling with multiple tracers (glucose and glutamate) delivered at five distinct time points of the cell culture process. Notably, we identified unexpected behavior within the tricarboxylic acid (TCA) cycle. The corresponding labeling data indicated a significant redistribution of the fluxes in and around the TCA cycle. Understanding the intracellular changes occurring when cells are challenged with a process insult, such as overfeeding, should lead to enhanced process development. Consequently metabolic flux analysis is only the first step in improving the process. We have identified two medium supplements which each independently permit the cell culture to endure overfeeding and result in maintaining or increasing titer despite the process insult. The overfed process and the supplemented processes were utilized to evaluate changes in the cellular metabolism with an untargeted metabolomics approach. Novel findings from the untargeted metabolomics approach when combined with metabolic flux analysis give a complete picture of the cellular metabolism as both reaction rates and relative concentrations are known over the full process duration. With this knowledge in hand, the platform process can evolve to routinely overcome process insults such as overfeeding

N-acetylglucosamine 6-Phosphate Deacetylase (nagA) Is Required for N-acetyl Glucosamine Assimilation in Gluconacetobacter xylinus

Metabolic pathways for amino sugars (N-acetylglucosamine; GlcNAc and glucosamine; Gln) are essential and remain largely conserved in all three kingdoms of life, i.e., microbes, plants and animals. Upon uptake, in the cytoplasm these amino sugars undergo phosphorylation by phosphokinases and subsequently deacetylation by the enzyme N-acetylglucosamine 6-phosphate deacetylase (nagA) to yield glucosamine-6-phosphate and acetate, the first committed step for both GlcNAc assimilation and amino-sugar-nucleotides biosynthesis. Here we report the cloning of a DNA fragment encoding a partial nagA gene and its implications with regard to amino sugar metabolism in the cellulose producing bacterium Glucoacetobacter xylinus (formally known as Acetobacter xylinum). For this purpose, nagA was disrupted by inserting tetracycline resistant gene (nagA::tetr; named as ΔnagA) via homologous recombination. When compared to glucose fed conditions, the UDP-GlcNAc synthesis and bacterial growth (due to lack of GlcNAc utilization) was completely inhibited in nagA mutants. Interestingly, that inhibition occured without compromising cellulose production efficiency and its molecular composition under GlcNAc fed conditions. We conclude that nagA plays an essential role for GlcNAc assimilation by G. xylinus thus is required for the growth and survival for the bacterium in presence of GlcNAc as carbon source. Additionally, G. xylinus appears to possess the same molecular machinery for UDP-GlcNAc biosynthesis from GlcNAc precursors as other related bacterial species

A chalcone synthase-like bacterial protein catalyzes heterocyclic c-ring cleavage of naringenin to alter bioactivity against nuclear receptors in colonic epithelial cells

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Metabolic Flux Analysis of Mitochondrial Uncoupling in 3T3-L1 Adipocytes

BACKGROUND:Increasing energy expenditure at the cellular level offers an attractive option to limit adiposity and improve whole body energy balance. In vivo and in vitro observations have correlated mitochondrial uncoupling protein-1 (UCP1) expression with reduced white adipose tissue triglyceride (TG) content. The metabolic basis for this correlation remains unclear. METHODOLOGY/PRINCIPAL FINDINGS:This study tested the hypothesis that mitochondrial uncoupling requires the cell to compensate for the decreased oxidation phosphorylation efficiency by up-regulating lactate production, thus redirecting carbon flux away from TG synthesis. Metabolic flux analysis was used to characterize the effects of non-lethal, long-term mitochondrial uncoupling (up to 18 days) on the pathways of intermediary metabolism in differentiating 3T3-L1 adipocytes. Uncoupling was induced by forced expression of UCP1 and chemical (FCCP) treatment. Chemical uncoupling significantly decreased TG content by ca. 35%. A reduction in the ATP level suggested diminished oxidative phosphorylation efficiency in the uncoupled adipocytes. Flux analysis estimated significant up-regulation of glycolysis and down-regulation of fatty acid synthesis, with chemical uncoupling exerting quantitatively larger effects. CONCLUSIONS/SIGNIFICANCE:The results of this study support our hypothesis regarding uncoupling-induced redirection of carbon flux into glycolysis and lactate production, and suggest mitochondrial proton translocation as a potential target for controlling adipocyte lipid metabolism

Metabolic engineering analysis of post-burn hepatic hypermetabolism

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2002.Includes bibliographical references (p. 166-184).Metabolic engineering refers to the directed improvement of product formation or cellular properties through the modification of specific biochemical reactions or introduction of new ones with the use of recombinant DNA technology. It has been used to investigate and modify intermediary metabolism in a variety of microbial organisms of biotechnological interest. An emerging area of application for metabolic engineering is medicine, in particular the study of metabolic disorders, where analysis and manipulation of metabolic pathways have obvious relevance. Central to metabolic engineering is the notion that metabolism results from the concerted and coordinated activities of biochemical pathways connected through shared intermediates in the form of common reactants, products, and catalysts. According to this "metabolic network" concept, an enhanced understanding of metabolism and cellular function is obtained by considering the component biochemical reactions together, rather than individually. In this light, this thesis work was motivated by the idea that the application of metabolic engineering analysis to biological systems relevant to human disease has the potential to provide valuable insight into the biochemical underpinnings behind metabolic disorders. In the present dissertation, this idea was explored by investigating a metabolic disorder known clinically as hypermetabolism that is associated with the systemic inflammatory response to severe injury. At the whole body level, hypermetabolism is characterized by elevated resting energy expenditure and increased turnover of proteins, fatty acids, and carbohydrates.(cont.) If this state persists over a period of days to weeks, the patient is predisposed to muscle wasting, progressive organ dysfunction, multiple organ failure, and ultimately death. Unfortunately, existing nutritional therapies are inadequate for preventing the onset of persistent hypermetabolism, because many of the mechanistic details of this process are poorly understood. An important player in the hypermetabolic response to injury is the liver, which responsible for synthesizing healing factors from muscle protein derived amino acids, converting carbohydrate and lipid fuel resources to useful energy substrates, and eliminating waste products generated by these processes. In order to better understand the biochemical underpinnings behind injury derived hypermetabolism in the liver, the following specific aims were addressed: 1) to develop and validate tissue and organ models of injury for the liver; 2) to delineate activity changes in the major metabolic pathways in the liver during the developmental period of hypermetabolism; and 3) to build diagnostic tools for detecting and grading the injury derived metabolic abnormalities in the liver. A particularly useful metabolic engineering tool is metabolic flux analysis (MFA), which refers to a methodology whereby intracellular reaction fluxes are estimated using a stoichiometric model for the major intracellular reactions and applying mass balances around intracellular metabolites. A powerful feature of this methodology is its ability to consider cellular biochemistry in terms of a network of reactions ...by Kyongbum Lee.Ph.D