4.6 Article

Model-guided development of an evolutionarily stable yeast chassis

期刊

MOLECULAR SYSTEMS BIOLOGY
卷 17, 期 7, 页码 -

出版社

WILEY
DOI: 10.15252/msb.202110253

关键词

adaptive laboratory evolution; chassis cell; metabolic engineering; multi-objective optimization; systems biology

资金

  1. FCT/MCTES (Portugal) [ERA-IB-2/0003/2013]
  2. BMBF (Germany) [ERA-IB-2/0003/2013, 031A343A]
  3. Portuguese Foundation for Science and Technology (FCT) [PD/BD/52336/2013]
  4. FCT [UID/BIO/04469/2013, POCI-01-0145-FEDER-006684, RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462)]
  5. Projekt DEAL
  6. Fundação para a Ciência e a Tecnologia [ERA-IB-2/0003/2013, PD/BD/52336/2013] Funding Source: FCT

向作者/读者索取更多资源

First-principle metabolic modelling was used to design Saccharomyces cerevisiae chassis strains for dicarboxylic acid production, which were then tested experimentally. Adaptive laboratory evolution improved production, and multi-omics analysis revealed flux bypass in some cases. Flux balance analysis confirmed model predictions.
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.

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