Genome-scale metabolic model of oleaginous yeast Papiliotrema laurentii
Journal article, 2022

Oleaginous yeasts are promising candidates as single cell oil (SCO) platforms to meet energy demand due to their high growth rate, easy scaling and the possibility of being cultivated in culture media based on lignocellulosic biomass. The oleaginous yeast Papiliotrema laurentii UFV-1 is able to reach high lipid contents in short periods, besides its composition of fatty acids is suitable in terms of quality standards required for biodiesel production. However, little is known about the regulation of its metabolism. We present here the first genome-scale metabolic reconstruction of P. laurentii, papla-GEM. The reconstruction was based on homology to another oleaginous yeast and the model was subjected to intensive manual curation throughout the reconstruction stages to find out the metabolic specificities of this yeast. The final model includes 796 genes, 2465 reactions and 2127 metabolites, and its biomass equation is based on direct measurements of all major biomass components. The validation step was performed using experimental data obtained in this work, and simulation results evaluated the growth and lipid accumulation physiology of P. laurentii. Therefore, the papla-GEM will lead to a better understanding of the metabolic capabilities of P. laurentii and thus will be useful in systems metabolic engineering approaches.

Single cell oil

Papiliotrema laurentii

Yeast

Metabolism

Genome-scale model

Author

Rafaela Zandonade Ventorim

Federal University of Viçosa

Maurício Alexander de Moura Ferreira

Federal University of Viçosa

Eduardo Luís Menezes de Almeida

Federal University of Viçosa

Eduard Kerkhoven

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Wendel Batista da Silveira

Federal University of Viçosa

Biochemical Engineering Journal

1369-703X (ISSN) 1873295x (eISSN)

Vol. 180 108353

Subject Categories

Chemical Process Engineering

Bioenergy

Bioinformatics and Systems Biology

DOI

10.1016/j.bej.2022.108353

More information

Latest update

2/10/2022