(472e) Development of a Genome-Scale Metabolic Model for Auxenochlorella Protothecoides to Enable Rational Engineering

With increasing energy demands across the globe and the need to mitigate climate change, it is imperative that we develop renewable and/or carbon negative fuels rapidly. Algae have the potential to serve as a source of sustainable fuels and chemicals, but more focused efforts to engineer metabolism is needed. One approach to aid in rational engineering is the use of genome-scale metabolic models. Here, we will describe the development and use of a genome scale metabolic of an oleaginous green alga, Auxenochlorella protothecoides (A. pro.) to predict increased carbon fluxes toward biofuel precursors because of the organism’s ability to accumulate up to 60% dry weight as triacylglycerols using photosynthesis [1]. After reconstructing an initial draft network using RAPS, an automated algorithm developed by our lab to generate first draft metabolic networks of algae [2], we manually curated the model to introduce two essential reactions driven by DXP reductoisomerase and Uroporphyrinogen III synthase. Overall, our model includes 2820 metabolites in 3357 reactions and 11 compartments, where 65 genes were determined to be essential to growth. Further, we will present how autotrophic, heterotrophic, and mixotrophic growth regimes affect the macromolecule content and biomass composition of A. pro., as well as how these growth conditions affect central carbon metabolism as predicted by flux balance analysis simulations and ¹³C-MFA studies.

References

1. Matsuka and E. Hase, “The Role of Respiration and Photosynthesis in the Chloroplast Regeneration in the ‘Glucose-bleached’ Cells of Chlorella Protothecoides,” Plant and Cell Physiology, vol. 7, no. 1, pp. 149–162, Mar. 1966, doi: 10.1093/oxfordjournals.pcp.a079161.
2. J. Metcalf, A. Nagygyor, and N. R. Boyle, “Rapid Annotation of Photosynthetic Systems (RAPS): automated algorithm to generate genome-scale metabolic networks from algal genomes,” Algal Research, vol. 50, p. 101967, Sep. 2020, doi: 10.1016/j.algal.2020.101967.