Background: The development of robust microbes with tolerance to the combined lignocellulose-derived inhibitors is critical for the efficient cellulosic ethanol production. However, the lack of understanding on the inhibition mechanism limited the rational engineering of tolerant strain. Here, through the metabolomic analysis of an adaptation process of Saccharomyces cerevisiae to representative inhibitors, i.e., furfural, acetic acid and phenol (FAP), we figured out the new candidates for improving inhibitor tolerance.
Results: After metabolomic analysis, proline and myo-inositol were identified as the potential metabolites responsible for strain tolerance to inhibitors. The deletion of genes involved in proline or myo-inositol synthesis weakened strain tolerance against FAP stress. On the contrary, the addition of proline or myo-inositol in medium exerted a protective effect on cell growth under FAP stress. Furthermore, the enhancement of proline or myo-inositol synthesis by overexpressing key gene PRO1 or INO1 conferred yeast strain significantly increased FAP tolerance. All the recombinant strains finished the fermentation within 60 h under FAP stress, while the control strain was still in the lag phase. Meanwhile, it was found that the intracellular level of reactive oxygen species (ROS) under FAP condition was decreased with the increase of proline content, suggesting the function of proline as a ROS scavenger to protect strains from inhibitor damage.
Conclusion: Increasing proline and myo-inositol were uncovered as the new determinants for improving strain tolerance to FAP under the guidance of metabolomics. Meanwhile, this study displayed the powerful application of metabolomics to develop rational strategies to increase stress tolerance and provided valuable insights into the design of recombinant microbes for the complex traits.
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| Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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| Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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| Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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| Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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| Site | Modification | Modifier | Source | Reference |
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| Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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| Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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| Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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| Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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| Evidence ID | Analyze ID | File | Description |
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