Peroxisomal Compartmentalization of the Methylerythritol-4-phosphate Pathway Alleviates Cellular Stress and Enhances Geraniol Production in Saccharomyces cerevisiae.
Jerome R Lon, Xuemei Zhao, Gulkiz Mamatrixat, Zhoukang Zhuang, Zhehao Jin, Tao Yu, Jufang Wang, Hongting Tang
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引用次数: 0
Abstract
Terpenoids are a diverse class of compounds with significant application potential. While prokaryotic bacteria synthesize terpenoids via the methylerythritol-4-phosphate (MEP) pathway, fungi utilize the mevalonate (MVA) pathway. The MVA pathway has been widely employed for efficient terpenoid production in bacteria such as Escherichia coli, but the MEP pathway performs poorly for biosynthesis in yeast. In this study, we constructed a compartmentalized MEP pathway to enhance monoterpenoid production in Saccharomyces cerevisiae. By introducing a geraniol synthase, we initially achieved the production of geraniol from glucose. Further effective incorporation of a cytosolic MEP pathway with nine enzymes increased geraniol production by 174.5%. However, this also significantly inhibited cell growth. Overexpression analysis revealed that flavodoxin and flavodoxin reductase were major contributors to growth inhibition, which could also be a factor limiting the application of the MEP pathway. To address these issues, we employed peroxisomal compartmentalization to isolate the MEP pathway from cytosolic metabolism. This strategy alleviated growth inhibition and improved geraniol production by 93.18% compared to that of cytosolic expression. Through additional metabolic engineering, we optimized peroxisomal geraniol production, achieving a yield of 30.64 mg/L. Our findings demonstrate the potential of compartmentalized MEP pathway expression as a viable approach for enhancing terpenoid biosynthesis in yeast, offering valuable insights for future metabolic engineering efforts.
期刊介绍:
The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism.
Topics may include, but are not limited to:
Design and optimization of genetic systems
Genetic circuit design and their principles for their organization into programs
Computational methods to aid the design of genetic systems
Experimental methods to quantify genetic parts, circuits, and metabolic fluxes
Genetic parts libraries: their creation, analysis, and ontological representation
Protein engineering including computational design
Metabolic engineering and cellular manufacturing, including biomass conversion
Natural product access, engineering, and production
Creative and innovative applications of cellular programming
Medical applications, tissue engineering, and the programming of therapeutic cells
Minimal cell design and construction
Genomics and genome replacement strategies
Viral engineering
Automated and robotic assembly platforms for synthetic biology
DNA synthesis methodologies
Metagenomics and synthetic metagenomic analysis
Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction
Gene optimization
Methods for genome-scale measurements of transcription and metabolomics
Systems biology and methods to integrate multiple data sources
in vitro and cell-free synthetic biology and molecular programming
Nucleic acid engineering.