Enzyme-constrained genome-scale model of Yarrowia lipolytica predicts growth-phase specific metabolic engineering targets
File(s)
Published version
Author(s)
De Biaggi, Juliano Sabedotti
Park, Young-Kyoung
Kerkhoven, Eduard J
Ledesma-Amaro, Rodrigo
Lahtvee, Petri-Jaan
Type
Journal Article
Abstract
The oleaginous yeast Yarrowia lipolytica has been gaining increasing importance as an industrial biotech platform, supported by several available metabolic engineering tools. Genome-scale models (GEMs) are relevant to the iterative improvement of this yeast, and their predictive ability is enhanced by enzymatic activity constraints (ecGEMs). Although the newest tools for ecGEM reconstruction use deep learning to expand the coverage of these constraints, this approach has not yet been applied to Y. lipolytica models. This paper describes the reconstruction of an ecGEM of Y. lipolytica (eciYali5-GEM) and its application in predicting metabolic engineering targets for enhanced lipid and carotenoid production, respectively, in this yeast. To achieve this, we constrained a manually curated Y. lipolytica model with physiological flux and mass-spectrometry proteomics data collected from distinct growth phases of two engineered strains (producing lipids and carotenoids, respectively) and their parental strain. We found that the enzymatic constraints enable the prediction of growth-phase-specific metabolic engineering targets, a feature not displayed by regular GEMs. Combining these targets with other ecGEM-based insights, we propose two strategies for further metabolic engineering, including the use of inducible promoters for precise, growth-phase-specific expression of targets such as phytoene dehydrogenase for carotenoid production. These targets included genes previously validated elsewhere, as well as novel genes awaiting experimental validation. This model, which is publicly available, can be similarly adapted and used by different metabolic engineering efforts, making it a versatile tool for the development of Y. lipolytica as a microbial cell factory.
Key points
• eciYali5-GEM reconstruction used in silico and in vitro proteomics data.
• Enzyme constraints enriched Y. lipolytica ecGEM predictions.
• eciYali5-GEM improves metabolic engineering rational design.
Key points
• eciYali5-GEM reconstruction used in silico and in vitro proteomics data.
• Enzyme constraints enriched Y. lipolytica ecGEM predictions.
• eciYali5-GEM improves metabolic engineering rational design.
Date Issued
2026-03-25
Date Acceptance
2026-03-08
Citation
Applied Microbiology and Biotechnology, 2026, 110
ISSN
0175-7598
Publisher
Springer
Journal / Book Title
Applied Microbiology and Biotechnology
Volume
110
Copyright Statement
© The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41876849
PII: 10.1007/s00253-026-13791-4
Subjects
Yarrowia lipolytica
Enzyme-constrained models
FSEOF
Flux control coefficients
GECKO
Systems biology
Yarrowia
Metabolic Engineering
Carotenoids
Genome, Fungal
Proteomics
Metabolic Networks and Pathways
Publication Status
Published
Coverage Spatial
Germany
Article Number
123
Date Publish Online
2026-03-24