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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. Sat . "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 = {Sat Jul 02 00:00:00 EDT 2016},
month = {Sat Jul 02 00:00:00 EDT 2016}
}

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