Understanding the adaptive laboratory evolution of multiple stress-resistant yeast strains by genome-scale modeling

Cetin H., Çakar Z. P., Ulgen K. O.

YEAST, vol.39, no.8, pp.449-465, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 39 Issue: 8
  • Publication Date: 2022
  • Doi Number: 10.1002/yea.3806
  • Journal Name: YEAST
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, Chemical Abstracts Core, EMBASE, Food Science & Technology Abstracts, MEDLINE, Veterinary Science Database
  • Page Numbers: pp.449-465
  • Keywords: differential expression analysis, evolutionary engineering, flux balance analysis, genome-scale metabolic modeling, Saccharomyces cerevisiae, systems biology, SACCHAROMYCES-CEREVISIAE, GLYCERALDEHYDE-3-PHOSPHATE DEHYDROGENASE, GENE, RECONSTRUCTION, EXPRESSION, BIOSYNTHESIS, LOCALIZATION, MAINTENANCE, INTEGRATION, IMPROVEMENT
  • Istanbul Technical University Affiliated: Yes


Stress responses triggered by external exposures in adaptive laboratory evolution studies alter the ordinary behavior of cells, and the identification of the differences between the starting and the evolved strains would provide ideal strategies to obtain the desired strains. Metabolic networks are one of the most useful tools to analyze data for this purpose. This study integrates differential expression profiles of multiple Saccharomyces cerevisiae strains that have evolved in eight different stress conditions (ethanol, caffeine, coniferyl aldehyde, iron, nickel, phenylethanol, and silver) and enzyme kinetics into a genome-scale metabolic model of yeast, following a new enhanced method. Flux balance analysis, flux variability analysis, robustness, phenotype phase plane, minimization of metabolic adjustment, survivability, sensitivity analyses, and random sampling are conducted to identify the most common and divergent points within strains. Results were examined both individually and comparatively, and the target reactions, metabolites, and enzymes were identified. Our results showed that the models reconstructed by our methodology were able to simulate experimental conditions where efficient protein allocation was the main goal for survival under stressful conditions, and most of the metabolic changes in the adaptation process mainly arose from the differences in the metabolic reactions of energy maintenance (through coenzyme-A and FAD utilization), cell division (folate requirement of DNA synthesis), and cell wall formation (through sterol and ergosterol biosynthesis).