Metabolic Reconstruction of Marine Microalgae, Dunaliella Tertiolecta, Based on OMICs Data

Metabolic Reconstruction of Marine Microalgae, Dunaliella tertiolecta,
Based on OMICs Data

Seong-Joo Hong¹, Na Rae Yim¹, Yong Sung Park¹, Byung-Kwan Cho², Hookeun Lee^3,4, Hyung-Kyoon Choi⁵,and Choul-Gyun Lee^1*

¹ National Marine Bioenergy Research Center & Department of Biological Engineering, Inha University, Incheon 402-751, Korea

² Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea

³ Institute of Pharmaceutical Research, College of Pharmacy, Gachon University, Incheon 406-840 Korea

⁴Gachon Medical Research Institute, Gil Medical Center, Incheon 405-760, Korea

⁵College of Pharmacy, Chung-Ang University, Seoul 156-756, Korea

^*Corresponding author's Tel: 82-32-872-7518, E-mail: leecg@inha.ac.kr

Most microalgae can change the regulations on their metabolic pathways to accumulate lipids or valuable products in response to stressful conditions. Transcriptomics and proteomics, metabolomics studies on fast growing microalga, Dunaliella tertiolecta, have been performed to investigate the regulation of fatty acid or starch synthesis pathways. The first step for reconstructing in silico model is to construct framework using transcriptomic and proteomic data of D. tertiolecta and determine their biochemical reactions. After that, network gaps and missing metabolic pathways are determined by metabolomic data or physiological evidences. The accuracy of metabolic network can be analyzed by mathematical methods such as flux-balance analysis. In silico model for D. Tertiolecta was built with 1837 biochemical reactions and 1499 metabolites in 6 compartments. The resulting model consists of central metabolism, lipid metabolism, amino acid metabolism and so on. Specific three metabolic pathways of D. tertiolecta were added to the in silico model through the global metabolome analysis. To enhance fatty acids content, two target enzymes were determined by comparison of the in silico models of D. tertiolecta and oleagenous microalga, N. Gaditana. With inhibition of two target enzymes by chemical inhibitors, TFA content in D. tertiolecta increased up to 60% at day 3.5 compared to the control. Genome-scale metabolic network would provide insights into photosynthetic metabolism and contribute to control metabolic pathways in eukaryotic microalgal cell metabolism for enhancement of target metabolite production.

Acknowledgment

This research was supported by a grant from Marine Biotechnology Program Funded by Ministry of Oceans and Fisheries, Korea. This research was also supported by the Basic Core Technology Development Program for the Oceans and the Polar Regions of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2013M1A5A1055492).

References

1. R. L. Chang et al., Mol. Syst. Biol., 7(518), 1-13 (2011).

2. R. Radakovits and R. E. Jinkerson et el., Nat. Commun., 3, 686 (2012).

3. R. E. Jinkerson et el., Bioengineered, 4(1), 37-43 (2013).