A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses
Abstract
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, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 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 iCre1355 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, themore »
- Authors:
-
- Institute for Systems Biology, Seattle, WA (United States)
- Institute for Systems Biology, Seattle, WA (United States); Friedrich-Schiller-Univ. Jena, Jena (Germany)
- Institute for Systems Biology, Seattle, WA (United States); Univ. of Washington, Seattle, WA (United States)
- Institute for Systems Biology, Seattle, WA (United States); Univ. of Washington, Seattle, WA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1378660
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- The Plant Journal
- Additional Journal Information:
- Journal Volume: 84; Journal Issue: 6; Journal ID: ISSN 0960-7412
- Publisher:
- Society for Experimental Biology
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; metabolic modeling; Chlamydomonas reinhardtii; constraint-based analysis; flux balance analysis; data integration; systems biology; biomass; lipid accumulation; photosynthesis
Citation Formats
Imam, Saheed, Schäuble, Sascha, Valenzuela, Jacob, Lopez Garcia de Lomana, Adrian, Carter, Warren, Price, Nathan D., and Baliga, Nitin S. A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses. United States: N. p., 2015.
Web. doi:10.1111/tpj.13059.
Imam, Saheed, Schäuble, Sascha, Valenzuela, Jacob, Lopez Garcia de Lomana, Adrian, Carter, Warren, Price, Nathan D., & Baliga, Nitin S. A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses. United States. https://doi.org/10.1111/tpj.13059
Imam, Saheed, Schäuble, Sascha, Valenzuela, Jacob, Lopez Garcia de Lomana, Adrian, Carter, Warren, Price, Nathan D., and Baliga, Nitin S. Tue .
"A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses". United States. https://doi.org/10.1111/tpj.13059. https://www.osti.gov/servlets/purl/1378660.
@article{osti_1378660,
title = {A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses},
author = {Imam, Saheed and Schäuble, Sascha and Valenzuela, Jacob and Lopez Garcia de Lomana, Adrian and Carter, Warren and Price, Nathan D. and Baliga, Nitin S.},
abstractNote = {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, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 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 iCre1355 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, iCre1355 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.},
doi = {10.1111/tpj.13059},
journal = {The Plant Journal},
number = 6,
volume = 84,
place = {United States},
year = {Tue Oct 20 00:00:00 EDT 2015},
month = {Tue Oct 20 00:00:00 EDT 2015}
}
Web of Science
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