Genome-wide network analysis of metabolism in Chlamydomonas reinhardtii
Roger L. Chang1, Ani Manichaikul2, Balaji Santhanam3, Lila Ghamsari3, Erik F.Y. Hom4, Kourosh Salehi-Ashtiani3, and Jason A. Papin2
1) Department of Bioengineering, University of California, San Diego, CA, USA
2) Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
3) Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA
4) Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
We report the first genome-scale reconstruction of C. reinhardtii, accounting for all pathways and metabolic functions indicated by the latest release of the genome (JGI v4.0) combined with our in-house generated functional annotation (please see abstract by Ghamsari et al. for details). The reconstruction accounts for 978 genes, associated with 1671 reactions, and includes 1029 unique metabolites. Our reconstruction accounts for multiple wavelengths of light and includes considerable expansion of fatty acid metabolism over previous reconstructions. Further, the metabolic network reconstruction presented here provides a greater level of compartmentalization than existing reconstructions of C. reinhardtii, with the inclusion of the lumen as a distinct component of the chloroplast for photosynthetic functionality, and the eyespot used to guide the flagella in phototaxis. We present simulations under a variety of growth conditions and physiological validation of in silico gene knockout against known mutant data for a variety of phenotypes. We further present detailed simulations demonstrating how photon absorption and different wavelengths of light affect downstream metabolic processes, elucidating the benefits of sunlight versus artificial light conditions. Our well-validated and comprehensive genome-scale reconstruction of C. reinhardtii metabolism provides a valuable quantitative and predictive resource for metabolic engineering toward improved production of biofuels and other commercial targets. Supported by DOE grant DE-FG02-07ER64496.
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