Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris - PubMed (original) (raw)
Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris
Cristal Zuñiga et al. Plant Physiol. 2018 Jan.
Abstract
Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, _i_CZ946, thus increasing our knowledgebase by 10% for this model green alga.
© 2018 American Society of Plant Biologists. All Rights Reserved.
Figures
Figure 1.
Overview and workflow diagram of data analysis. A, Experimental and predicted growth rates under photoautotrophic growth. B, Flux distributions and gene expression data sets were independently normalized. N_r,j_ represents the flux distribution normalized value of reaction r at j time point. N_g,j_ is the normalized value for the g gene at time j. The normalized values ranged between zero and one in each data set. C, Iteratively fitted regression models (linear, quadratic, cubic, exponential, and two-term exponential regression model). D, Statistically identical models were extracted using a homogeneity test of coefficients to define the agreement between the predicted flux and expression data. C1 and C2 correspond to the two coefficients being compared, and SEC1 and SEC2 to the
se
of coefficients calculated by least-squares fitting method. The number of fitted coefficients depends of the regression model (i.e. linear has two fitted coefficients [p1 and p2] and two-term exponential model has four). G_1..n_ represents the number of genes associated with specific reaction.
Figure 2.
Overview of experimental data breakdown. A, Growth course biomass composition (proteins, nucleotides, lipids, and carbohydrates) under photoautotrophy. B, Biomass composition under heterotrophy. C, Carbohydrate composition under photoautotrophy. D, Lipid distribution under heterotrophy. Experimental and predicted growth rates for C. vulgaris in heterotrophy (McConnell and Antoniewicz, 2016; Zuñiga et al., 2016).
Figure 3.
Comparison of experimental and estimated protein composition. Experimentally determined protein composition for C. vulgaris for photoautotrophic or heterotrophic growth in comparison to estimated protein composition based on genome annotation. Arg, Cys, Gln, and Trp were discarded because measurements over time were not found.
Figure 4.
Nitrogen uptake and intracellular storage. Circles represent experimental measurements (Zuñiga et al., 2016; A and B) and squares the predicted uptake rates (B and C). Every letter represents an experimental point in which all biomass compounds were collected. A, Experimental nitrogen concentration in the supernatant under photoautotrophy (green solid line) and heterotrophy (red solid line). Dashed lines represent the nitrate concentration over time (top x axis). B, Predicted uptake rate of NO3 using the C. vulgaris genome-scale model _i_CZ843. Shaded areas show the experimental nitrate uptake rate under photoautotrophy (green) and heterotrophy (red). D, Predicted nitrogen consumption rates from the intracellular pool. The shaded areas are equivalent to the volumetric pool concentration and indicate nitrogen depletion from the culture medium. The first point of the definite integral was point b.
Figure 5.
Active reactions and expression data match. A, Compartmental distribution of the active reactions and genes under photoautotrophy. B, Distribution of the reactions and genes along the main subsystems in the metabolism. C, Differential expression fits versus predicted flux distribution fits. D, Pie charts of reactions with significant correspondence between differential expression and flux distribution. Color match between B and D.
Figure 6.
Reactions with no agreement between flux and expression trends. More than 40% of reactions without agreement due to lack of knowledge at the metabolic level and subcellular localization are present in compartments different from the cytoplasm.
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