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WIREs Syst Biol Med
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Applications of stable isotope‐based metabolomics and fluxomics toward synthetic biology of cyanobacteria

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Abstract Unique features of cyanobacteria (e.g., photosynthesis and nitrogen fixation) make them potential candidates for production of biofuels and other value‐added biochemicals. As prokaryotes, they can be readily engineered using synthetic and systems biology tools. Metabolic engineering of cyanobacteria for the synthesis of desired compounds requires in‐depth knowledge of central carbon and nitrogen metabolism, pathway fluxes, and their regulation. Metabolomics and fluxomics offer the comprehensive analysis of metabolism by directly characterizing the biochemical activities of cells. This information is acquired by measuring the abundance of key metabolites and their rates of interconversion, which can be achieved by labeling cells with stable isotopes, quantifying metabolite pool sizes and isotope incorporation by gas chromatography/liquid chromatography‐mass spectrometry GC/LC‐MS or nuclear magnetic resonance (NMR), and mathematical modeling to estimate in vivo metabolic fluxes. Herein, we review progress that has been made to adapt metabolomics and fluxomics tools to examine model cyanobacterial species. We summarize the application of metabolic flux analysis (MFA) strategies to identify metabolic bottlenecks that can be targeted to boost cell growth, improve stress tolerance, or enhance biochemical production in cyanobacteria. Despite the advances in metabolomics, fluxomics, and other synthetic and systems biology tools during the past years, further efforts are required to increase our understanding of cyanobacterial metabolism in order to create efficient photosynthetic hosts for the production of value‐added compounds. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Biological Mechanisms > Metabolism Analytical and Computational Methods > Analytical Methods
A schematic workflow of cyanobacterial metabolomics and fluxomics analysis. Exponentially growing cells in a culture (flask or bioreactor injected with 13C tracer (fluxomics) or without tracers (metabolomics)) are sampled at different time points and immediately mixed with prechilled quenching solution and placed in an ice bath. Subsequently, cells are harvested by centrifugation, flash frozen in liquid nitrogen, and stored at −80°C until metabolite extraction. Metabolites are extracted by a suitable solvent mixture (i.e., chloroform/methanol). Extracts are thoroughly dried, or vacuum evaporated to remove the extraction solvent. Dried samples are converted to either tert‐butyldimethylsilyl / trimethylsilyl derivatives for gas chromatography/mass spectrometry (GC‐MS) analysis or directly (without derivatization) resuspended in an appropriate solvent for LC‐MS or desorption electrospray ionization—imaging mass spectrometry (DESI‐IMS) analysis
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A typical workflow for analytical and computational aspects of 13C‐MFA (metabolic flux analysis) in cyanobacteria. First, an isotopically labeled substrate is fed to cell cultures to enrich intracellular metabolites while maintaining metabolic steady‐state conditions. Heterotrophic cultures can be labeled with 13C‐glucose or 13C‐glycerol, while autotrophic cultures require administration of 13CO2 or 13C‐bicarbonate. Next, culture samples are collected to assess isotope labeling of metabolites over time and to quantify absolute rates of substrate uptake, cell growth, and product formation. Heterotrophic cultures can be analyzed with steady‐state MFA, while autotrophic cultures require isotopically nonstationary metabolic flux analysis (INST‐MFA) to regress fluxes from dynamic labeling measurements. Finally, the best‐fit flux solution is obtained by minimizing the sum‐of‐squared residuals between experimental and model‐simulated measurements, and statistical tests are used to assess the goodness‐of‐fit and to determine the uncertainties associated with each flux estimate. The solution is often visualized as a flux map that depicts the distribution of carbon flow within the network
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Analytical and Computational Methods > Analytical Methods
Biological Mechanisms > Metabolism
Laboratory Methods and Technologies > Metabolomics

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