Abstract
Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.
Original language | English |
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Article number | e10980 |
Journal | Molecular Systems Biology |
Volume | 18 |
Issue number | 10 |
DOIs | |
Publication status | Published - 1 Oct 2022 |
MoE publication type | A1 Journal article-refereed |
Funding
This work was sponsored by the ERASysAPP project WINESYS (the German Ministry of Education and Research grant no. 031A605; the Research Council of Norway (Norges Forskningsråd) grant no. 245160) and by the Ministry of Science, Innovation and Universities, Spain (España, Ministerio de Ciencia e Innovación (MCIN)) (Project CoolWine, PCI2018-092962), under the call ERA-NET ERA COBIOTECH. PJ acknowledges funding from the Academy of Finland, decision numbers 310514, 314125, and 329930. KRP received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 866028).
Keywords
- adaptive evolution
- genome-scale metabolic model
- predictive evolution
- Saccharomyces cerevisiae
- wine aroma
- Phenotype
- Saccharomyces cerevisiae/metabolism
- Genomics
- Proteomics
- Genome