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Front Bioeng Biotechnol. 2014 Dec 10;2:68. doi: 10.3389/fbioe.2014.00068. eCollection 2014.

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling.

Author information

1
Division of Science and Math, New York University Abu Dhabi , Abu Dhabi , UAE ; Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute , Abu Dhabi , UAE.
2
Division of Science and Math, New York University Abu Dhabi , Abu Dhabi , UAE ; Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute , Abu Dhabi , UAE ; Engineering Division, Biofinery , Manhattan, KS , USA.

Abstract

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

KEYWORDS:

Chlamydomonas reinhardtii; flux balance analysis; metabolic network refinement; microalgae; phenotype microarray

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