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Title: Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris

Abstract

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. 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 fluxes trends and gene expression trends was found for 65% of multi-subunit 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 acids metabolism. Moreover, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization,more » specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.« less

Authors:
 [1];  [1];  [2];  [3];  [4];  [1]
  1. Univ. of California, San Diego, CA (United States). Dept. of Physics. Dept. of Pediatrics
  2. Univ. of Delaware, Newark, DE (United States). Dept. of Chemical and Biomolecular Engineering
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States). National Bioenergy Center
  4. Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Chemical and Biomolecular Engineering
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); National Science Foundation (NSF)
OSTI Identifier:
1399847
Report Number(s):
NREL/JA-5100-70303
Journal ID: ISSN 0032-0889
Grant/Contract Number:
AC36-08GO28308; SC0012658; 1332344
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Plant Physiology (Bethesda)
Additional Journal Information:
Journal Name: Plant Physiology (Bethesda); Journal ID: ISSN 0032-0889
Publisher:
American Society of Plant Biologists
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; biomass; composition; genome-scale

Citation Formats

Zuniga, Cristal, Levering, Jennifer, Antoniewicz, Maciek R., Guarnieri, Michael T., Betenbaugh, Michael J., and Zengler, Karsten. Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris. United States: N. p., 2017. Web. doi:10.1104/pp.17.00605.
Zuniga, Cristal, Levering, Jennifer, Antoniewicz, Maciek R., Guarnieri, Michael T., Betenbaugh, Michael J., & Zengler, Karsten. Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris. United States. doi:10.1104/pp.17.00605.
Zuniga, Cristal, Levering, Jennifer, Antoniewicz, Maciek R., Guarnieri, Michael T., Betenbaugh, Michael J., and Zengler, Karsten. 2017. "Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris". United States. doi:10.1104/pp.17.00605.
@article{osti_1399847,
title = {Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris},
author = {Zuniga, Cristal and Levering, Jennifer and Antoniewicz, Maciek R. and Guarnieri, Michael T. and Betenbaugh, Michael J. and Zengler, Karsten},
abstractNote = {Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. 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 fluxes trends and gene expression trends was found for 65% of multi-subunit 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 acids metabolism. Moreover, 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, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.},
doi = {10.1104/pp.17.00605},
journal = {Plant Physiology (Bethesda)},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 9
}

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  • 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 organismmore » 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.« less
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