Computational biology predicts metabolic engineering targets for increased production of 102 valuable chemicals in yeast

Development of efficient cell factories that can compete with traditional chemical production processes is complex and generally driven by case-specific strategies, based on the product and microbial host of interest. Despite major advancements in the field of metabolic modelling in recent years, prediction of genetic modifications for increased production remains challenging. Here we present a computational pipeline that leverages the concept of protein limitations in metabolism for prediction of optimal combinations of gene engineering targets for enhanced chemical bioproduction. We used our pipeline for prediction of engineering targets for 102 different chemicals using Saccharomyces cerevisiae as a host. Furthermore, we identified sets of gene targets predicted for groups of multiple chemicals, suggesting the possibility of rational model-driven design of platform strains for diversified chemical production. One sentence summary Novel strain design algorithm ecFactory on top of enzyme-constrained models provides unprecedented chances for rational strain design and development.

[1]  Benjamín J. Sánchez,et al.  Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae , 2022, Proceedings of the National Academy of Sciences of the United States of America.

[2]  J. Nielsen,et al.  Yeast has evolved to minimize protein resource cost for synthesizing amino acids , 2022, Proceedings of the National Academy of Sciences.

[3]  J. Keasling,et al.  Engineering yeast metabolism for the discovery and production of polyamines and polyamine analogues , 2021, Nature Catalysis.

[4]  Benjamín J. Sánchez,et al.  Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0 , 2021, Nature Communications.

[5]  J. Nicaud,et al.  Yarrowia lipolytica chassis strains engineered to produce aromatic amino acids via the shikimate pathway , 2020, Microbial biotechnology.

[6]  J. Morrissey,et al.  Rational engineering of Kluyveromyces marxianus to create a chassis for the production of aromatic products , 2020, bioRxiv.

[7]  V. Martin,et al.  A yeast platform for high-level synthesis of tetrahydroisoquinoline alkaloids , 2020, Nature Communications.

[8]  Weifeng Liu,et al.  Metabolic engineering Saccharomyces cerevisiae for de novo production of the sesquiterpenoid (+)-nootkatone , 2020, Microbial Cell Factories.

[9]  Bei Gao,et al.  Metabolic engineering of Saccharomyces cerevisiae to overproduce squalene. , 2020, Journal of agricultural and food chemistry.

[10]  J. Keasling,et al.  Programmable polyketide biosynthesis platform for production of aromatic compounds in yeast , 2020, Synthetic and systems biotechnology.

[11]  J. Pronk,et al.  Connecting central carbon and aromatic amino acid metabolisms to improve de novo 2-phenylethanol production in Saccharomyces cerevisiae. , 2019, Metabolic engineering.

[12]  Shuang Li,et al.  High production of valencene in Saccharomyces cerevisiae through metabolic engineering , 2019, Microbial Cell Factories.

[13]  C. Smolke,et al.  Engineering a microbial biosynthesis platform for de novo production of tropane alkaloids , 2019, Nature Communications.

[14]  Florian David,et al.  Reprogramming Yeast Metabolism from Alcoholic Fermentation to Lipogenesis , 2018, Cell.

[15]  J. Nielsen,et al.  Expression of cocoa genes in Saccharomyces cerevisiae improves cocoa butter production , 2018, Microbial Cell Factories.

[16]  C. Maranas,et al.  Multilevel engineering of the upstream module of aromatic amino acid biosynthesis in Saccharomyces cerevisiae for high production of polymer and drug precursors. , 2017, Metabolic engineering.

[17]  Ying Wang,et al.  Manipulation of GES and ERG20 for geraniol overproduction in Saccharomyces cerevisiae. , 2017, Metabolic engineering.

[18]  C. Maranas,et al.  A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains , 2016, Nature Communications.

[19]  M. Win,et al.  Engineering a microbial platform for de novo biosynthesis of diverse methylxanthines. , 2016, Metabolic engineering.

[20]  V. Siewers,et al.  Production of fatty acid-derived oleochemicals and biofuels by synthetic yeast cell factories , 2016, Nature Communications.

[21]  J. Keasling,et al.  Engineering Cellular Metabolism , 2016, Cell.

[22]  Avlant Nilsson,et al.  Metabolic Trade-offs in Yeast are Caused by F1F0-ATP synthase , 2016, Scientific Reports.

[23]  Jens Nielsen,et al.  Impact of synthetic biology and metabolic engineering on industrial production of fine chemicals. , 2015, Biotechnology advances.

[24]  Genlin Zhang,et al.  Refactoring β‐amyrin synthesis in Saccharomyces cerevisiae , 2015 .

[25]  L. Tang,et al.  Three-pathway combination for glutathione biosynthesis in Saccharomyces cerevisiae , 2015, Microbial Cell Factories.

[26]  B. Jiang,et al.  Modular pathway rewiring of Saccharomyces cerevisiae enables high-level production of L-ornithine , 2015, Nature Communications.

[27]  J. Nielsen,et al.  Yeast cell factories on the horizon , 2015, Science.

[28]  C. Smolke,et al.  Complete biosynthesis of opioids in yeast , 2015, Science.

[29]  J. Nielsen,et al.  Production of β-ionone by combined expression of carotenogenic and plant CCD1 genes in Saccharomyces cerevisiae , 2015, Microbial Cell Factories.

[30]  Costas D. Maranas,et al.  k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design , 2014, PLoS Comput. Biol..

[31]  Y. Choi,et al.  Microbial production of short-chain alkanes , 2013, Nature.

[32]  J. Nielsen,et al.  Industrial Systems Biology of Saccharomyces cerevisiae Enables Novel Succinic Acid Cell Factory , 2013, PloS one.

[33]  Q. Wang,et al.  Production of pyruvate in Saccharomyces cerevisiae through adaptive evolution and rational cofactor metabolic engineering , 2012 .

[34]  Tomer Shlomi,et al.  Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters , 2012, PLoS Comput. Biol..

[35]  Wei Gao,et al.  Modular pathway engineering of diterpenoid synthases and the mevalonic acid pathway for miltiradiene production. , 2012, Journal of the American Chemical Society.

[36]  Jay D. Keasling,et al.  Production of amorphadiene in yeast, and its conversion to dihydroartemisinic acid, precursor to the antimalarial agent artemisinin , 2012, Proceedings of the National Academy of Sciences.

[37]  M. Oh,et al.  Production of 2,3-butanediol in Saccharomyces cerevisiae by in silico aided metabolic engineering , 2011, Microbial Cell Factories.

[38]  Alexander Vainstein,et al.  Harnessing yeast subcellular compartments for the production of plant terpenoids. , 2011, Metabolic engineering.

[39]  Yi Zhou,et al.  Catabolic efficiency of aerobic glycolysis: The Warburg effect revisited , 2010, BMC Systems Biology.

[40]  Costas D. Maranas,et al.  OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions , 2010, PLoS Comput. Biol..

[41]  Sang Yup Lee,et al.  In Silico Identification of Gene Amplification Targets for Improvement of Lycopene Production , 2010, Applied and Environmental Microbiology.

[42]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[43]  Jack T. Pronk,et al.  Malic Acid Production by Saccharomyces cerevisiae : Engineering of Pyruvate Carboxylation , Oxaloacetate Reduction , and Malate Export † , 2007 .

[44]  Ziv Bar-Joseph,et al.  Impact of the solvent capacity constraint on E. coli metabolism , 2008, BMC Systems Biology.

[45]  S. Lee,et al.  Metabolic engineering of Escherichia coli for the production of l-valine based on transcriptome analysis and in silico gene knockout simulation , 2007, Proceedings of the National Academy of Sciences.

[46]  Timothy S. Ham,et al.  Production of the antimalarial drug precursor artemisinic acid in engineered yeast , 2006, Nature.

[47]  F. Blattner,et al.  In silico design and adaptive evolution of Escherichia coli for production of lactic acid. , 2005, Biotechnology and bioengineering.

[48]  G. Stephanopoulos,et al.  Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. , 2005, Metabolic engineering.

[49]  A. Burgard,et al.  Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization , 2003, Biotechnology and bioengineering.

[50]  G. Church,et al.  Analysis of optimality in natural and perturbed metabolic networks , 2002 .

[51]  B. Palsson,et al.  Network analysis of intermediary metabolism using linear optimization. I. Development of mathematical formalism. , 1992, Journal of theoretical biology.

[52]  E. Boles,et al.  Engineering of hydroxymandelate synthases and the aromatic amino acid pathway enables de novo biosynthesis of mandelic and 4-hydroxymandelic acid with Saccharomyces cerevisiae. , 2018, Metabolic engineering.

[53]  J. Nielsen,et al.  Production of farnesene and santalene by Saccharomyces cerevisiae using fed-batch cultivations with RQ-controlled feed. , 2016, Biotechnology and bioengineering.

[54]  Kathleen A. Curran,et al.  Metabolic engineering of muconic acid production in Saccharomyces cerevisiae. , 2013, Metabolic engineering.

[55]  Bernhard Ø. Palsson,et al.  Systems Biology: METABOLISM , 2011 .

[56]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .