Molecular and cellular processes that are responsible for industrially relevant phenotypes of fermenting microorganisms are a central focus of biotechnological research. Such research intends to generate insights and solutions for fermentation-based industries with regards to issues such as improving product yield or the quality of the final fermentation product. For logistical reasons, and to ensure data reproducibility, such research is mostly carried out in defined or synthetic media and in small-scale fermentation vessels. Two questions are frequently raised regarding the applicability of this approach to solve problems experienced in industrial fermentations: (1) Is synthetic medium a sufficiently accurate approximation of the generally more complex natural (and frequently highly variable) substrates that are employed in most fermentation-based industries, and (2) can results obtained in small-scale laboratory fermentations be extrapolated to large-scale industrial environments? Here, we address the second question through a comparative transcriptomic approach by assessing the response of an industrial wine yeast strain fermenting a natural grape juice in small-scale laboratory and large-scale industrial conditions. In yeast, transcriptome analysis is arguably the best available tool to holistically assess the physiological state of a population and its response to changing environmental conditions. The data suggest that scale does indeed impact on some environmental parameters such as oxygen availability. However, the data show that small-scale fermentations nevertheless accurately reflect general molecular processes and adaptations during large-scale fermentation and that extrapolation of laboratory datasets to real industrial processes can be justified.
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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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