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Predictive evolution of metabolic phenotypes using model-designed environments.

Paula Jouhten1,2,3, Dimitrios Konstantinidis1, Filipa Pereira1

  • 1European Molecular Biology Laboratory, Heidelberg, Germany.

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|October 6, 2022
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Summary
This summary is machine-generated.

We designed nutrient environments using metabolic models to enhance metabolite secretion in microbes, overcoming growth trade-offs. This predictive evolution strategy successfully increased aroma compound secretion in yeast.

Keywords:
Saccharomyces cerevisiaeadaptive evolutiongenome-scale metabolic modelpredictive evolutionwine aroma

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Area of Science:

  • Synthetic Biology
  • Metabolic Engineering
  • Evolutionary Biology

Background:

  • Adaptive evolution is effective for selecting beneficial phenotypes like stress tolerance.
  • Desired traits such as metabolite secretion are often inaccessible due to growth-secretion trade-offs.

Purpose of the Study:

  • To design nutrient environments for selecting lineages with enhanced metabolite secretion.
  • To overcome the growth-secretion trade-off by correlating growth with a secondary 'tacking' trait.

Main Methods:

  • Utilized genome-scale metabolic models to design selection environments.
  • Employed adaptive laboratory evolution (ALE) with Saccharomyces cerevisiae.
  • Validated predictions using genomic, transcriptomic, and proteomic analyses.

Main Results:

  • Successfully evolved yeast lineages with enhanced secretion of aroma compounds.
  • Demonstrated model-predicted flux rerouting towards secretion.
  • Showcased the effectiveness of model-designed environments for predictive evolution.

Conclusions:

  • Model-designed selection environments offer a novel approach for predictive evolution.
  • This strategy overcomes limitations of traditional adaptive selection for complex traits.
  • Opens new avenues for engineering microbial production systems.