bioRxivMicrobiology
Exceptional Solvent Tolerance in Yarrowia lipolytica Is Enhanced by Sterols
Abstract
Microbial biocatalysis in organic solvents such as ionic liquids (ILs) is attractive for making fuels and chemicals from complex substrates including lignocellulosic biomass. However, low IL concentrations of 0.5-1.0 % (v/v) can drastically inhibit microbial activity. In this study, we engineered an exceptionally robust oleaginous yeast Yarrowia lipolytica, YlCW001, by adaptive laboratory evolution (ALE). The mutant YlWC001 shows robust growth in up to 18% (v/v) 1-ethyl-3-methylimidazolium acetate ([EMIM][OAc]), which makes it the most IL-tolerant microorganism published to our knowledge. Remarkably, YlCW001 exhibits broad tolerance in most commonly used hydrophilic ILs beyond [EMIM][OAc]. Scanning electron microscopy revealed that ILs significantly damage cell wall and/or membrane of wildtype Y. lipolytica with observed cavities, dents, and wrinkles while YlCW001 maintains healthy morphology even in high concentrations of ILs up to 18% (v/v). By performing comprehensive metabolomics, lipidomics, and transcriptomics to elucidate this unique phenotype, we discovered that both wildtype Y. lipolytica and YlCW001 reconfigured membrane composition (e.g., glycerophospholipids and sterols) and cell wall structure (e.g., chitin) under IL-stressful environments. By probing the steroid pathway at transcriptomic, enzymatic, and metabolic levels, we validated that sterols (i.e., ergosterol) are a key component of the cell membrane that enables Y. lipolytica to resist IL-responsive membrane damage and hence tolerate high IL concentrations. This study provides a better understanding of exceptional robustness of Y. lipolytica that can be potentially harnessed as a microbial manufacturing platform for production of fuels and chemicals in organic solvents.
- organism: yeast
- assay: metabolomics
- assay: proteomics
- assay: metagenomics
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
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