(13)C metabolic flux analysis of recombinant expression hosts

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

Silk fibroin production in Escherichia coli is limited by a positive feedback loop between metabolic burden and toxicity stress

To investigate the metabolic elasticity and production bottlenecks for recombinant silk proteins in Escherichia coli, we performed a comprehensive characterization of one elastin-like peptide strain (ELP) and two silk protein strains (A5 4mer, A5 16mer). Our approach included ¹³C metabolic flux analysis, genome-scale modeling, transcription analysis, and ¹³C-assisted media optimization experiments. Three engineered strains maintained their central flux network during growth, while measurable metabolic flux redistributions (such as the Entner-Doudoroff pathway) were detected. Under metabolic burdens, the reduced TCA fluxes forced the engineered strain to rely more on substrate-level phosphorylation for ATP production, which increased acetate overflow. Acetate (as low as 10 mM) in the media was highly toxic to silk-producing strains, which reduced 4mer production by 43% and 16mer by 84%, respectively. Due to the high toxicity of large-size silk proteins, 16mer's productivity was limited, particularly in the minimal medium. Therefore, metabolic burden, overflow acetate, and toxicity of silk proteins may form a vicious positive feedback loop that fractures the metabolic network. Three solutions could be applied: 1) addition of building block supplements (i.e., eight key amino acids: His, Ile, Phe, Pro, Tyr, Lys, Met, Glu) to reduce metabolic burden; 2) disengagement of growth and production; and 3) use of non-glucose based substrate to reduce acetate overflow. Other reported strategies were also discussed in light of decoupling this positive feedback loop.

Attenuation of glutamine synthetase selection marker improves product titer and reduces glutamine overflow in Chinese hamster ovary cells

The glutamine synthetase (GS) expression system is commonly used to ensure stable transgene integration and amplification in CHO host lines. Transfected cell populations are typically grown in the presence of the GS inhibitor, methionine sulfoximine (MSX), to further select for increased transgene copy number. However, high levels of GS activity produce excess glutamine. We hypothesized that attenuating the GS promoter while keeping the strong IgG promoter on the GS-IgG expression vector would result in a more efficient cellular metabolic phenotype. Herein, we characterized CHO cell lines expressing GS from either an attenuated promoter or an SV40 promoter and selected with/without MSX. CHO cells with the attenuated GS promoter had higher IgG specific productivity and lower glutamine production compared to cells with SV40-driven GS expression. Selection with MSX increased both specific productivity and glutamine production, regardless of GS promoter strength. ¹³ C metabolic flux analysis (MFA) was performed to further assess metabolic differences between these cell lines. Interestingly, central carbon metabolism was unaltered by the attenuated GS promoter while the fate of glutamate and glutamine varied depending on promoter strength and selection conditions. This study highlights the ability to optimize the GS expression system to improve IgG production and reduce wasteful glutamine overflow, without significantly altering central metabolism. Additionally, a detailed supplementary analysis of two "lactate runaway" reactors provides insight into the poorly understood phenomenon of excess lactate production by some CHO cell cultures. This article is protected by copyright. All rights reserved.

Adapting isotopic tracer and metabolic flux analysis approaches to study C1 metabolism

Single-carbon (C1, or one-carbon) substrates are promising feedstocks for sustainable biofuel and biochemical production. Crucial to the goal of engineering C1-utilizing strains for improved production is a quantitative understanding of the organization, regulation and rates of the reactions that underpin C1 metabolism. ¹³C Metabolic flux analysis (MFA) is a well-established platform for interrogating these questions with multi-carbon substrates, and uses the differential labeling of metabolites that results from feeding a substrate with position-specific incorporation of ¹³C in order to infer quantitative fluxes and pathway topology. Adapting isotopic tracer approaches to C1 metabolism, where position-specific substrate labeling is impossible, requires additional experimental considerations. Here we review recent studies that have developed isotopic tracer approaches to overcome the challenge of uniform metabolite labeling and provide quantitative insight into C1 metabolism.

In Vivo and In Vitro Quantification of Glucose Kinetics: From Bedside to Bench

Like other substrates, plasma glucose is in a dynamic state of constant turnover (i.e., rates of glucose appearance [Ra glucose] into and disappearance [Rd glucose] from the plasma) while staying within a narrow range of normal concentrations, a physiological priority. Persistent imbalance of glucose turnover leads to elevations (i.e., hyperglycemia, Ra>Rd) or falls (i.e., hypoglycemia, Ra<Rd) in the pool size, leading to clinical conditions such as diabetes. Endogenous Ra glucose is divided into hepatic glucose production via glycogenolysis and gluconeogenesis (GNG) and renal GNG. On the other hand, Rd glucose, the summed rate of glucose uptake by tissues/organs, involves various intracellular metabolic pathways including glycolysis, the tricarboxylic acid (TCA) cycle, and oxidation at varying rates depending on the metabolic status. Despite the dynamic nature of glucose metabolism, metabolic studies typically rely on measurements of static, snapshot information such as the abundance of mRNAs and proteins and (in)activation of implicated signaling networks without determining actual flux rates. In this review, we will discuss the importance of obtaining kinetic information, basic principles of stable isotope tracer methodology, calculations of in vivo glucose kinetics, and assessments of metabolic flux in experimental models in vivo and in vitro.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.

Recent Advances in the Microbial Synthesis of Hemoglobin

Hemoglobin is a cofactor-containing protein with heme that plays important roles in transporting and storing oxygen. Hemoglobins have been widely applied as acellular oxygen carriers, bioavailable iron-supplying agents, and food-grade coloring and flavoring agents. To meet increasing demands and overcome the drawbacks of chemical extraction, the biosynthesis of hemoglobin has become an attractive alternative. Several hemoglobins have recently been synthesized by various microorganisms through metabolic engineering and synthetic biology. In this review, we summarize the novel strategies that have been used to biosynthesize hemoglobin. These strategies can also serve as references for producing other heme-binding proteins.

Evaluation of freely available software tools for untargeted quantification of 13C isotopic enrichment in cellular metabolome from HR-LC/MS data

¹³C Metabolic Flux Analysis (¹³C-MFA) involves the quantification of isotopic enrichment in cellular metabolites and fitting the resultant data to the metabolic network model of the organism. Coverage and resolution of the resultant flux map depends on the total number of metabolites and fragments in which ¹³C enrichment can be quantified accurately. Experimental techniques for tracking ¹³C enrichment are evolving rapidly and large volumes of data are now routinely generated through the use of Liquid Chromatography coupled with High-Resolution Mass Spectrometry (HR-LC/MS). Therefore, the current manuscript is focused on the challenges in high-throughput analyses of such large datasets. Current ¹³C-MFA studies often have to rely on the targeted quantification of a small subset of metabolites, thereby leaving a large fraction of the data unexplored. A number of public domain software tools have been reported in recent years for the untargeted quantitation of isotopic enrichment. However, the suitability of their application across diverse datasets has not been investigated. Here, we test the software tools X¹³CMS, DynaMet, geoRge, and HiResTEC with three diverse datasets. The tools provided a global, untargeted view of ¹³C enrichment in metabolites in all three datasets and a much-needed automation in data analysis. Some inconsistencies were observed in results obtained from the different tools, which could be partially ascribed to the lack of baseline separation and potential mass conflicts. After removing the false positives manually, isotopic enrichment could be quantified reliably in a large repertoire of metabolites. Of the software tools explored, geoRge and HiResTEC consistently performed well for the untargeted analysis of all datasets tested.

Simultaneous detection of nicotinamide adenine nucleotides and adenylate pool to quantify redox and energy states in mAb-producing CHO cells by capillary electrophoresis

Chinese hamster ovary (CHO) cells are predominant in the production of therapeutic proteins to treat various diseases. Characterization and investigation of CHO cell metabolism in a quick and simple way could boost process and cell line development. Therefore, a method to simultaneously detect seven redox- and energy-related metabolites in CHO cells by capillary electrophoresis has been developed. An on-line focusing technique was applied to improve the peak shape and resolution by using a 50 μm × 44 cm uncoated fused silica capillary. Key parameters and their interactions were investigated by design of experiments (DoE) and optimized conditions were determined by desirability function as follows: 24 °C, 95 mM, and pH 9.4 of BGE. The method was validated to ensure sensitivity, linearity, and reproducibility. The limits of detection (LODs) ranged from 0.050 to 0.688 mg/L for seven metabolites, and correlation coefficients of linearity were all greater than 0.996. The relative standard deviations (RSD) of migration time and peak area were smaller than 0.872% and 5.5%, respectively, except for NADPH, and the recoveries were between 97.5 and 101.2%. The method was successfully applied to analyze the extracts from CHO cells under two different culture conditions. Graphical abstract.

Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production

Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, ¹³C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.

Identifying model error in metabolic flux analysis - a generalized least squares approach

BACKGROUND

The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit.

RESULTS

In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range).

CONCLUSIONS

The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.

13C metabolic flux analysis of microbial and mammalian systems is enhanced with GC-MS measurements of glycogen and RNA labeling

¹³C metabolic flux analysis (¹³C-MFA) is a widely used tool for quantitative analysis of microbial and mammalian metabolism. Until now, ¹³C-MFA was based mainly on measurements of isotopic labeling of amino acids derived from hydrolyzed biomass proteins and isotopic labeling of extracted intracellular metabolites. Here, we demonstrate that isotopic labeling of glycogen and RNA, measured with gas chromatography-mass spectrometry (GC-MS), provides valuable additional information for ¹³C-MFA. Specifically, we demonstrate that isotopic labeling of glucose moiety of glycogen and ribose moiety of RNA greatly enhances resolution of metabolic fluxes in the upper part of metabolism; importantly, these measurements allow precise quantification of net and exchange fluxes in the pentose phosphate pathway. To demonstrate the practical importance of these measurements for ¹³C-MFA, we have used Escherichia coli as a model microbial system and CHO cells as a model mammalian system. Additionally, we have applied this approach to determine metabolic fluxes of glucose and xylose co-utilization in the E. coli ΔptsG mutant. The convenience of measuring glycogen and RNA, which are stable and abundant in microbial and mammalian cells, offers the following key advantages: reduced sample size, no quenching required, no extractions required, and GC-MS can be used instead of more costly LC-MS/MS techniques. Overall, the presented approach for ¹³C-MFA will have widespread applicability in metabolic engineering and biomedical research.

Unsupervised Identification of Isotope-Labeled Peptides

In vivo isotopic labeling coupled with high-resolution proteomics is used to investigate primary metabolism in techniques such as stable isotope probing (protein-SIP) and peptide-based metabolic flux analysis (PMFA). Isotopic enrichment of carbon substrates and intracellular metabolism determine the distribution of isotopes within amino acids. The resulting amino acid mass distributions (AMDs) are convoluted into peptide mass distributions (PMDs) during protein synthesis. With no a priori knowledge on metabolic fluxes, the PMDs are therefore unknown. This complicates labeled peptide identification because prior knowledge on PMDs is used in all available peptide identification software. An automated framework for the identification and quantification of PMDs for nonuniformly labeled samples is therefore lacking. To unlock the potential of peptide labeling experiments for high-throughput flux analysis and other complex labeling experiments, an unsupervised peptide identification and quantification method was developed that uses discrete deconvolution of mass distributions of identified peptides to inform on the mass distributions of otherwise unidentifiable peptides. Uniformly (13)C-labeled Escherichia coli protein was used to test the developed feature reconstruction and deconvolution algorithms. The peptide identification was validated by comparing MS(2)-identified peptides to peptides identified from PMDs using unlabeled E. coli protein. Nonuniformly labeled Glycine max protein was used to demonstrate the technology on a representative sample suitable for flux analysis. Overall, automatic peptide identification and quantification were comparable or superior to manual extraction, enabling proteomics-based technology for high-throughput flux analysis studies.

High-throughput evaluation of synthetic metabolic pathways

A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.

Metabolic modeling of clostridia: current developments and applications

Anaerobic Clostridium spp. is an important bioproduction microbial genus that can produce solvents and utilize a broad spectrum of substrates including cellulose and syngas. Genome-scale metabolic (GSM) models are increasingly being put forth for various clostridial strains to explore their respective metabolic capabilities and suitability for various bioconversions. In this study, we have selected representative GSM models for six different clostridia (Clostridium acetobutylicum, C. beijerinckii, C. butyricum, C. cellulolyticum, C. ljungdahlii and C. thermocellum) and performed a detailed model comparison contrasting their metabolic repertoire. We also discuss various applications of these GSM models to guide metabolic engineering interventions as well as assessing cellular physiology.

13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many ¹³C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms