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Title: Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

Abstract

The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate withmore » the addition of tryptophan and methionine.« less

Authors:
 [1];  [2]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [3]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [4];  [3]; ORCiD logo [2];  [1]
  1. Univ. of California, San Diego, La Jolla, CA (United States)
  2. Johns Hopkins Univ., Baltimore, MD (United States)
  3. Univ. of Delaware, Newark, DE (United States)
  4. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1395087
Report Number(s):
NREL/JA-5100-66824
Journal ID: ISSN 0032-0889
Grant/Contract Number:
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Plant Physiology (Bethesda)
Additional Journal Information:
Journal Name: Plant Physiology (Bethesda); Journal Volume: 172; Journal Issue: 1; Journal ID: ISSN 0032-0889
Publisher:
American Society of Plant Biologists
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; 59 BASIC BIOLOGICAL SCIENCES; Chlorella vulgaris; genome-scale; reconstruction; validation; application

Citation Formats

Zuniga, Cristal, Li, Chien -Ting, Huelsman, Tyler, Levering, Jennifer, Zielinski, Daniel C., McConnell, Brian O., Long, Christopher P., Knoshaug, Eric P., Guarnieri, Michael T., Antoniewicz, Maciek R., Betenbaugh, Michael J., and Zengler, Karsten. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions. United States: N. p., 2016. Web. doi:10.1104/pp.16.00593.
Zuniga, Cristal, Li, Chien -Ting, Huelsman, Tyler, Levering, Jennifer, Zielinski, Daniel C., McConnell, Brian O., Long, Christopher P., Knoshaug, Eric P., Guarnieri, Michael T., Antoniewicz, Maciek R., Betenbaugh, Michael J., & Zengler, Karsten. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions. United States. doi:10.1104/pp.16.00593.
Zuniga, Cristal, Li, Chien -Ting, Huelsman, Tyler, Levering, Jennifer, Zielinski, Daniel C., McConnell, Brian O., Long, Christopher P., Knoshaug, Eric P., Guarnieri, Michael T., Antoniewicz, Maciek R., Betenbaugh, Michael J., and Zengler, Karsten. 2016. "Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions". United States. doi:10.1104/pp.16.00593. https://www.osti.gov/servlets/purl/1395087.
@article{osti_1395087,
title = {Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions},
author = {Zuniga, Cristal and Li, Chien -Ting and Huelsman, Tyler and Levering, Jennifer and Zielinski, Daniel C. and McConnell, Brian O. and Long, Christopher P. and Knoshaug, Eric P. and Guarnieri, Michael T. and Antoniewicz, Maciek R. and Betenbaugh, Michael J. and Zengler, Karsten},
abstractNote = {The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.},
doi = {10.1104/pp.16.00593},
journal = {Plant Physiology (Bethesda)},
number = 1,
volume = 172,
place = {United States},
year = 2016,
month = 7
}

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  • Among sugars, glucose and mannose were found to be the most suitable substrates for mixotrophic growth, uptake of galactose and its influence on growth was negligible, and sucrose and fructose occupied intermediary positions. The optimum temperature for sugar uptake was 30 degrees C both under light and in darkness. Enhancement in the photosynthetic oxygen-evolution rate, based on the utilization of substrates, was foremost in the presence of glucose, followed by mannose, sucrose, and fructose. Industrial by-products such as sugarcane molasses also were utilized to increase the algal growth under mixotrophic conditions. A maximum yield in biomass was obtained subsequent tomore » the combined supply of sugarcane molasses with carbon dioxide to indoor as well as outdoor mixotrophic cultures. Doubling the carbon dioxide supply alone above a certain level, under autotrophic and mixotrophic outdoor conditions, did not produce a pronounced increase in the algal growth rate. The results on autotrophic and mixotrophic growth variations are discussed in this article. (Refs. 36).« less
  • The growth of the green alga Chlorella pyrenoidosa was inhibited by terpene alcohols and the terpene aldehyde citral. The strongest activity was shown by citral. Nerol, geraniol, and citronellol also showed pronounced activity. Strong inhibition was linked to acyclic terpenes containing a primary alcohol or aldehyde function. Inhibition appeared to be taking place through the vapor phase rather than by diffusion through the agar medium from the terpene-treated paper disks used in the system. Inhibition through agar diffusion was shown by certain aged samples of terpene hydrocarbons but not by recently purchased samples.
  • The exchange of /sup 18/O between CO/sub 2/ and H/sub 2/O in stirred suspensions of Chlorella vulgaris (UTEX 263) was measured using a membrane inlet to a mass spectrometer. The depletion of /sup 18/O from CO/sub 2/ in the fluid outside the cells provides a method to study CO/sub 2/ and HCO/sub 3//sup -/ kinetics in suspensions of algae that contain carbonic anhydrase since /sup 18/O loss to H/sub 2/O is catalyzed inside the cells but not in the external fluid. Low-CO/sub 2/ cells of Chlorella vulgaris (grown with air) were added to a solution containing /sup 18/O enriched CO/submore » 2/ and HCO/sub 3//sup -/ with 2 to 15 millimolar total inorganic carbon. The observed depletion of /sup 18/O from CO/sub 2/ was biphasic and the resulting /sup 18/O content of CO/sub 2/ was much less than the /sup 18/O content of HCO/sub 3//sup -/ in the external solution. Analysis of the slopes showed that the Fick's law rate constant for entry of HCO/sub 3//sup -/ into the cell was experimentally indistinguishable from zero (bicarbonate impermeable) with an upper limit of 3 x 10/sup -4/ s/sup -1/ due to experimental errors. The Fick's law rate constant for entry of CO/sub 2/ to the sites of intracellular carbonic anhydrase was large, 0.013 per second, but not as great as calculated for no membrane barrier to CO/sub 2/ flux (6 per second). The experimental value may be explained by a nonhomogeneous distribution of carbonic anhydrase in the cell (such as membrane-bound enzyme) or by a membrane barrier to CO/sub 2/ entry into the cell or both. The CO/sub 2/ hydration activity inside the cells was 160 times the uncatalyzed CO/sub 2/ hydration rate.« less
  • As a general rule an increase in carbohydrates occurs during the light phase of the cell cycle and that of protein during the dark phase, although variations were found in these components under autotrophic and mixotrophic growth conditions. The results are based on the quantitative determination of carbohydrates as trimethylsilyl (TMS) derivatives and amino acids as N-trifluoroacetyl-n-butyl (TAB) esters in algal cells cultured in light and dark periods by gas-liquid chromatogrpahy (GLC). Cells harvested during the dark period contained more amino acids as compared to similar cultures harvested during the light phase. In light, the production of amino acids ofmore » the aspartate family increased in cells cultivated with glucose and carbon dioxide. With glucose as solar carbon source, the carbohydrate content was higher in the dark than in the light period. Under continuous light conditions, in the presence of carbon dioxide, there was a decrease in the synthesis of amino acids, and supplementation with glucose or carbon dioxide led to a decrease in the carbohydrate content also. Gas-liquid chromatography analysis of the extract of the purified cell walls showed that they are made up of 0.076% carbohydrates and 0.28% amino acids on the dry weight (DW) basis of whole cells. The results on the metabolism of cells, under autotrophic and mixotrophic conditions are discussed in this article. (Refs. 17).« less
  • Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated withmore » microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparisonto prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model under conditions in which one or more nutrients were limiting. The fitted Monod kinetic model was also applied at the Darcy scale; that is, to simulate average reaction processes at the scale of the entire pore-scale model domain. As we expected, even under excess nutrient conditions for which the Monod and genome-scale models predicted equal reaction rates at the pore scale, the Monod model over-predicted the rates of biomass growth and iron and acetate utilization when applied at the Darcy scale. This discrepancy is caused by an inherent assumption of perfect mixing over the Darcy-scale domain, which is clearly violated in the pore-scale models. These results help to explain the need to modify the flux constraint parameters in order to match observations in previous applications of the genome-scale model at larger scales. These results also motivate further investigation of quantitative multi-scale relationships between fundamental behavior at the pore scale (where genome-scale models are appropriately applied) and observed behavior at larger scales (where predictions of reactive transport phenomena are needed).« less