PT  - JOURNAL ARTICLE
AU  - Paula Jouhten
AU  - Dimitrios Konstantinidis
AU  - Filipa Pereira
AU  - Sergej Andrejev
AU  - Kristina Grkovska
AU  - Payam Ghiachi
AU  - Gemma Beltran
AU  - Eivind Almaas
AU  - Albert Mas
AU  - Jonas Warringer
AU  - Ramon Gonzalez
AU  - Pilar Morales
AU  - Kiran R. Patil
TI  - Predictive evolution of metabolic phenotypes using model-designed selection niches
AID  - 10.1101/2021.05.14.443989
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.14.443989
4099  - http://biorxiv.org/content/early/2021/05/16/2021.05.14.443989.short
4100  - http://biorxiv.org/content/early/2021/05/16/2021.05.14.443989.full
AB  - Traits lacking fitness benefit cannot be directly selected for under Darwinian evolution. Thus, features such as metabolite secretion are currently inaccessible to adaptive laboratory evolution. Here, we utilize environment-dependency of trait correlations to enable Darwinian selection of fitness-neutral or costly traits. We use metabolic models to design selection niches and to identify surrogate traits that are genetically correlated with cell fitness in the selection niche but coupled to the desired trait in the target niche. Adaptive evolution in the selection niche and subsequent return to the target niche is thereby predicted to enhance the desired trait. We experimentally validate the theory by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds in wine fermentation. Genomic, transcriptomic, and proteomic changes in the evolved strains confirmed the predicted flux re-routing to aroma biosynthesis. The use of model-designed selection niches facilitates the predictive evolution of fitness-costly traits for ecological and biotechnological applications.Competing Interest StatementPJ and KRP are co-inventors of a pending patent application of EvolveX algorithm.