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

Eric Knoshaug, Michael Guarnieri, Cristal Zuniga, Chien-Ting Li, Tyler Huelsman, Jennifer Levering, Daniel Zielinski, Brian McConnell, Christopher Long, Maciek Antoniewicz, Michael Betenbaugh, Karsten Zengler

Research output: Contribution to journalArticlepeer-review

83 Scopus Citations

Abstract

The green microalga 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. Furthermore, 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.

Original languageAmerican English
Pages (from-to)589-602
Number of pages14
JournalPlant Physiology
Volume172
Issue number1
DOIs
StatePublished - Sep 2016

Bibliographical note

Publisher Copyright:
© 2016 American Society of Plant Biologists. All rights reserved.

NREL Publication Number

  • NREL/JA-5100-66824

Keywords

  • application
  • Chlorella vulgaris
  • genome-scale
  • reconstruction
  • validation

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