Genome-scale metabolic modeling reveals metabolic trade-offs associated with lipid production in Rhodotorula toruloides

PLoS Comput Biol. 2023 Apr 26;19(4):e1011009. doi: 10.1371/journal.pcbi.1011009. eCollection 2023 Apr.

Abstract

Rhodotorula toruloides is a non-conventional, oleaginous yeast able to naturally accumulate high amounts of microbial lipids. Constraint-based modeling of R. toruloides has been mainly focused on the comparison of experimentally measured and model predicted growth rates, while the intracellular flux patterns have been analyzed on a rather general level. Hence, the intrinsic metabolic properties of R. toruloides that make lipid synthesis possible are not thoroughly understood. At the same time, the lack of diverse physiological data sets has often been the bottleneck to predict accurate fluxes. In this study, we collected detailed physiology data sets of R. toruloides while growing on glucose, xylose, and acetate as the sole carbon source in chemically defined medium. Regardless of the carbon source, the growth was divided into two phases from which proteomic and lipidomic data were collected. Complemental physiological parameters were collected in these two phases and altogether implemented into metabolic models. Simulated intracellular flux patterns demonstrated the role of phosphoketolase in the generation of acetyl-CoA, one of the main precursors during lipid biosynthesis, while the role of ATP citrate lyase was not confirmed. Metabolic modeling on xylose as a carbon substrate was greatly improved by the detection of chirality of D-arabinitol, which together with D-ribulose were involved in an alternative xylose assimilation pathway. Further, flux patterns pointed to metabolic trade-offs associated with NADPH allocation between nitrogen assimilation and lipid biosynthetic pathways, which was linked to large-scale differences in protein and lipid content. This work includes the first extensive multi-condition analysis of R. toruloides using enzyme-constrained models and quantitative proteomics. Further, more precise kcat values should extend the application of the newly developed enzyme-constrained models that are publicly available for future studies.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carbon
  • Lipids
  • Proteomics*
  • Xylose*

Substances

  • Xylose
  • Carbon
  • Lipids

Supplementary concepts

  • Rhodotorula toruloides

Grants and funding

This work was supported by the Estonian Research Council grants PUT1488P, PRG1101 (AR, MJP, NB, IB, PJL) and PRG1198 (ET) and NordForsk grant 103506 (AR, NB, PJL). MJP would additionally like to acknowledge Coordination for the Improvement of Higher Education Personnel (Capes), São Paulo Research Foundation (FAPESP, grant 2016/10636-8). EJK acknowledges funding by the Novo Nordisk Foundation (grant NNF10CC1016517), and the Research Council for Environment, Agricultural Sciences, and Spatial Planning (Formas) (grant 2018-00597). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.