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A refined genome‐scale reconstruction of Chlamydomonas metabolism provides a platform for systems‐level analyses
Author(s) -
Imam Saheed,
Schäuble Sascha,
Valenzuela Jacob,
López García de Lomana Adrián,
Carter Warren,
Price Nathan D.,
Baliga Nitin S.
Publication year - 2015
Publication title -
the plant journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.058
H-Index - 269
eISSN - 1365-313X
pISSN - 0960-7412
DOI - 10.1111/tpj.13059
Subject(s) - chlamydomonas reinhardtii , chemostat , biology , flux balance analysis , chlamydomonas , systems biology , computational biology , genome , metabolic pathway , metabolism , gene , biochemistry , genetics , mutant , bacteria
Summary Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems‐level understanding of their metabolism, which can be fundamentally achieved through large‐scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome‐scale metabolic model for the widely‐studied microalga, Chlamydomonas reinhardtii . The model, i C re1355, represents a major advance over previous models, both in content and predictive power. i C re1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting for 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat‐grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high‐resolution time series transcriptomics data using i C re1355 to uncover dynamic pathway‐level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth‐associated pathways to increased light intensity. Thus, i C re1355 represents an experimentally validated genome‐scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae.