13C-Tracer and Gas Chromatography-Mass Spectrometry Analyses Reveal Metabolic Flux Distribution in the Oleaginous Microalga Chlorella protothecoides

The green alga Chlorella protothecoides has received considerable attention because it accumulates neutral triacylglycerols, commonly regarded as an ideal feedstock for biodiesel production. In order to gain a better understanding of its metabolism, tracer experiments with [U-¹³C]/[1-¹³C]glucose were performed with heterotrophic growth of C. protothecoides for identifying the metabolic network topology and estimating intracellular fluxes. Gas chromatography-mass spectrometry analysis tracked the labeling patterns of protein-bound amino acids, revealing a metabolic network consisting of the glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle with inactive glyoxylate shunt. Evidence of phosphoenolpyruvate carboxylase, phosphoenolpyruvate carboxykinase, and malic enzyme activity was also obtained. It was demonstrated that the relative activity of the pentose phosphate pathway to glycolysis under nitrogen-limited environment increased, reflecting excess NADPH requirements for lipid biosynthesis. Although the growth rate and cellular oil content were significantly altered in response to nitrogen limitation, global flux distribution of C. protothecoides remained stable, exhibiting the rigidity of central carbon metabolism. In conclusion, quantitative knowledge on the metabolic flux distribution of oleaginous alga obtained in this study may be of value in designing strategies for metabolic engineering of desirable bioproducts.Due to the energy shortage, rising petroleum prices, and the environmental impact of fossil energy-based industries, oleaginous microalgae are receiving increasing attention as a potential feedstock for providing a large source of renewable fuel materials. Biofuel from microalgae (the third-generation biofuel) offers obvious environmental benefits because the process can be coupled with photosynthetic carbon dioxide mitigation (Wang et al., 2008; Brune et al., 2009). Besides this, other eco-friendly behaviors of microalgae also garner interest, including that some algal species can remove nitrogen oxide from combustion gases, grow well in brackish habitats, use far less water than traditional oilseed crops, and double their numbers within 1 d, resulting in increased biomass yield (Li et al., 2008).The economical and environmental benefits of microalgal biofuel prompted research and development of algal strains that synthesize a high proportion of oil. The green alga Chlorella protothecoides is one of the best oil-producing species ever reported (Rosenberg et al., 2008). Under heterotrophic culture conditions, it rapidly transforms carbohydrates into triacylglycerols (more than 50% of dry cell weight), which can be further transesterified with alcohol (Miao and Wu, 2006; Xu et al., 2006; Xiong et al., 2008). These processes open a promising and highly efficient pathway for biodiesel production. A crucial point affecting biodiesel refinery is the oil productivity of heterotrophic Chlorella, which primarily relies on carbon flow from sugar to oil. Since the backbone of biomass components and by-products (e.g. proteins, CO₂) besides triacylglycerols are also derived from carbon substrate, the final yield of oil is determined by the intracellular distribution of carbon flux. In our previous work (Miao and Wu, 2006), carbon flux targeting into lipid synthesis was severely affected by the nutrient environment of the medium. Oil accumulation tends to occur under carbon-sufficient but nitrogen-limited conditions (Rosenberg et al., 2008), reflecting a unique way in which metabolic networks of Chlorella respond to environmental perturbations. However, until now, in-depth knowledge of the metabolic network in green alga was limited by the lack of quantitative data. Thus, it is urgently required to gain metabolic information on microalgae to better understand the intracellular distribution of carbon fluxes in response to environmental stimuli.On the basis of ¹³C-labeling experiments, metabolic flux analysis (MFA) emerged as an integrated experimental/computational tool to identify the biochemical network of active reactions and to provide quantitative insight into the in vivo distribution of molecular fluxes throughout central carbon metabolism (Zamboni et al., 2009). The general principle of this cutting-edge methodology is based on ¹³C-tracer study, which can distinguish fluxes through different pathways when these fluxes lead to different positional isotopic enrichments. These labeling patterns are imprinted in metabolic intermediates (e.g. protein-bound amino acids) and can be analyzed by gas chromatography-mass spectrometry (GC-MS) or NMR spectroscopy. Fluxomic information then can be quantified from the isotope data by mathematical modeling. For instance, an alternative approach, named metabolic flux ratio analysis (Fischer and Sauer, 2003), is to utilize algebraic equations for determining strictly local ratios of converging fluxes. Absolute intracellular fluxes may be further assessed by implementing such flux partitioning ratios to a linear equation system, which results from material balances (e.g. ¹³C constraint flux analysis; Fischer et al., 2004). Following these procedures, ¹³C MFA has the ability to resolve parallel, cyclic, and reversible fluxes, making it a powerful technique not only for quantifying metabolic fluxes but also for identifying novel or unexpected metabolic pathways. In recent years, the successful application of the ¹³C-flux method for determining the in vivo reaction velocities in model microorganisms, such as Escherichia coli (Yang et al., 2003), Bacillus subtilis (Fischer and Sauer, 2005), Corynebacterium glutamicum (Hoon Yang et al., 2006), and Saccharomyces cerevisiae (Blank et al., 2005), has been widely reported. Moreover, the ¹³C-flux method also demonstrated its value in tracking metabolic profiles in plant and animal cells. For example, recent research on heterotrophic cell suspension cultures of Arabidopsis (Arabidopsis thaliana) highlighted the stability of the flux distribution under different oxygenation conditions (Williams et al., 2008), while work on breast tumor cells revealed widespread changes to central metabolism upon cellular transformation (Yang et al., 2008). Nevertheless, to our knowledge, this approach has yet to be applied to eukaryotic alga grown heterotrophically.In recent decades, heterotrophic fermentation of photosynthetic microorganisms was raised as an important strategy to improve the efficiency and reduce the cost of alga-based biorefinery. Particularly, the heterotrophic growth of Chlorella is of wide concern for commercial production of high-value carotenoid (Sansawa and Endo, 2004), lutein (Shi et al., 2002), astaxanthin (Del Campo et al., 2004), and even biofuels (Xiong et al., 2008). Nevertheless, compared with other photosynthetic organisms, Chlorella grown on organic substrates is less understood due to the smaller data set of accurate genomic and biochemical information than is typically available for model plants. Here, we demonstrate that by using well-designed ¹³C-tracer experiments and highly sensitive isotopomer analysis, quantitative metabolic knowledge in not fully characterized species can be obtained. We adopted GC-MS to analyze the labeling patterns of the amino acids in biomass hydrolysates of Chlorella grown in a chemically defined medium with different carbon-nitrogen (C/N) ratios. Two-dimensional ¹H-¹³C NMR spectroscopy was further utilized to confirm flux ratios in key nodes of the Chlorella metabolic network. By integrating these labeling measurement data with metabolite balancing, the intracellular flux distributions in Chlorella were thus quantitated. This article is, to our knowledge, the first ¹³C MFA study for identifying and quantifying the intracellular metabolic fluxes in oleaginous alga.