Model‐guided development of an evolutionarily stable yeast chassis

Abstract

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories.

Predictive evolution of metabolic phenotypes using model‐designed environments

“…Instead of enzyme inhibitors, possible metabolic gene deletions/mutants can be used to expand the coverage. EvolveX thus extends the design of growth-product coupling (Burgard et al, 2003;Jantama et al, 2008;Brochado & Patil, 2013;Jensen et al, 2019;Pereira et al, 2021) from genotypedependent trait-fitness dependences to also considering environment-dependence of the trait selection.…”

“…The genome‐scale metabolic models can also be used for predicting metabolic gene deletions that couple a desired production trait to growth (Burgard et al , 2003 ; Patil et al , 2005 ). After such model‐guided genome editing adaptive laboratory evolution has successfully been used to improve the growth‐coupled production rates (Burgard et al , 2003 ; Jantama et al , 2008 ; Brochado & Patil, 2013 ; Jensen et al , 2019 ; Pereira et al , 2021 ). We use these genome‐scale metabolic models to predict environment‐dependence of the coupling between metabolic traits, and that between metabolic traits and the cell fitness.…”

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

“…While most algorithms predicted knockout targets that improved product production such as squalene (Paramasivan, Kumar HN and Mutturi 2019 ), l -phenylacetylcarbinol (Iranmanesh, Asadollahi and Biria 2020 ) and dicarboxylic acid (Pereira et al . 2021 ) in S. cerevisiae and lipids in Y. lipolytica (Kim et al . 2019 ), a few also predicted testable upregulation candidates.…”

In vivo, in vitro and in silico: an open space for the development of microbe‐based applications of synthetic biology

“…For example, the details of the complex metabolism of actinomycetes are now clarified, allowing metabolic engineering in these organisms (Palazzotto et al ., 2019). Furthermore, these applications are not restricted to bacteria: Flux balance analysis guided the evolution of a yeast chassis (Pereira et al ., 2021).…”