Genome-Scale Model for a Marine Diatom with Distinct Evolutionary Footprint

 Diatoms are eukaryotic, photosynthetic organisms that are believed to have descended from a secondary endosymbiotic event between a red algae and a eukaryotic cell. Due to their specific evolutionary footprint they are taxonomically and functionally distinct from green and red algae as well as vascular plants. The diverse origin of diatom genes introduces novel metabolic capabilities, which have not yet been explored at a systems level. We reconstructed the metabolic network of the marine diatom Phaeodactylum tricornutum, the second diatom for which a whole genome sequence has been published (Bowler et al., 2008). During the reconstruction process we faced several challenges. The first one was the poor genome annotation. Its genome is about 27.4 megabases in size and predicted to contain more than 10,000 genes with only a small portion having a functional annotation. Using all reviewed proteins with evidence at transcript or protein level we were able to improve P. tricornutum's genome annotation based on homology. Additionally, due to their endosymbiotic origin, diatoms possess a complex chloroplast structure, which complicates the prediction of subcellular protein localization. Based on previous work (Sunaga et al., 2015) we implemented a pipeline which exploits a series of bioinformatic tools to predict subcellular localization of proteins. Third, due to the phylogenetic distance between target and template organisms, the draft reconstruction based on homology to Chlamydomonas reinhardtii (Chang et al., 2011) and Synechocystis sp. PCC6803 (Knoop et al., 2013; Nogales et al., 2012) required intensive manual curation. Using several resources, such as organism-specific literature, experimental data and pathway databases we manually curated each pathway. The reconstructed metabolic network accounts for more than 1,000 genes associated with approximately 4,300 reactions and about 2,000 metabolites distributed across six compartments, namely cytoplasm, extracellular space, chloroplast including thylakoid lumen, mitochondria and peroxisome. The model was evaluated and tested against well-known metabolic capabilities of P. tricornutum such as growth on different carbon and nitrogen sources. The model is applied to gain insight into light-driven lipid metabolism in P. tricornutum and to predict strategies to optimize P. tricornutum's metabolism for next-generation biofuel production.

References

Bowler, C., Allen, A.E., Badger, J.H., et al., 2008. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 456, 239–44.

Chang, R., Ghamsari, L., Manichaikul, A., Hom, E., Balaji, S., Fu, W., Shen, Y., Hao, T., Palsson, B., Salehi-Ashtiani, K., Papin, J., 2011. Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Mol. Syst. Biol. 7, 518.

Knoop, H., Gründel, M., Zilliges, Y., Lehmann, R., Hoffmann, S., Lockau, W., Steuer, R., 2013. Flux balance analysis of cyanobacterial metabolism: The metabolic network of Synechocystis sp. PCC 6803. PLoS Comput. Biol. 9, e1003081.

Nogales, J., Gudmundsson, S., Knight, E.M., Palsson, B.O., Thiele, I., 2012. Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis. Proc. Natl. Acad. Sci. 109, 2678–2683.

Sunaga, Y., Maeda, Y., Yabuuchi, T., Muto, M., Yoshino, T., Tanaka, T., 2015. Chloroplast-targeting protein expression in the oleaginous diatom Fistulifera solaris JPCC DA0580 toward metabolic engineering. J. Biosci. Bioeng. 119, 28–34.