Genome-scale metabolic modeling reveals metabolic trade-offs associated with lipid production in Rhodotorula toruloides
Artikel i vetenskaplig tidskrift, 2023

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 k(cat) values should extend the application of the newly developed enzyme-constrained models that are publicly available for future studies. Author summaryTransition towards a biobased, circular economy to reduce the industrial dependence on fossil-based resources requires new technologies. One of the options is to convert available biomass feedstocks into valuable chemicals using microbes as biocatalysts. Rhodotorula toruloides is a nonpathogenic, nonconventional yeast that has recently emerged as one of the most promising yeasts for sustainable production of chemicals and fuels due to its natural ability to synthesize large amounts of lipids. However, its unique metabolic properties are not yet fully understood. We have computationally predicted metabolic fluxes in R. toruloides while growing in economically viable growth conditions inducing lipid accumulation and analyzed them together with absolute proteome quantification. Our holistic approach has highlighted metabolic pathways important for lipid biosynthesis and revealed metabolic trade-offs associated with NADPH allocation during lipogenesis. In addition, our work highlighted the necessity for accurate computational approaches in characterizing enzymatic kinetic properties that would improve the metabolic studies of R. toruloides.

Författare

Alina J. Rekena

Tallinns tekniska universitet (TalTech)

Marina Pinheiro

Universidade Estadual de Campinas

Nemailla Bonturi

Tallinns tekniska universitet (TalTech)

Isma Belouah

Tallinns tekniska universitet (TalTech)

Eliise Tammekivi

Tartu Ülikool

Koit Herodes

Tartu Ülikool

Eduard Kerkhoven

Chalmers, Life sciences, Systembiologi

Petri-Jaan Lahtvee

Tallinns tekniska universitet (TalTech)

PLoS Computational Biology

1553-734X (ISSN) 1553-7358 (eISSN)

Vol. 19 4 e1011009

Optimera mikrobiell tillverkning av itakonat för hållbar plast och tvättmedel

Formas (2018-00597), 2019-01-01 -- 2021-12-31.

Ämneskategorier

Biokemi och molekylärbiologi

Mikrobiologi

Bioinformatik och systembiologi

Genetik

DOI

10.1371/journal.pcbi.1011009

PubMed

37099621

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Senast uppdaterat

2023-06-12