Coupling feedback genetic circuits with growth phenotype for dynamic population control and intelligent bioproduction.

Metabolic engineering entails target modification of cell metabolism to maximize cell's production potential. Due to the complexity of cell metabolism, feedback genetic circuits have emerged as basic tools to combat metabolic heterogeneity, enhance microbial cooperation as well as boost cell's productivity. This is generally achieved by applying social reward-punishment rules to eliminate cheaters and reward winners in a mixed cell population. With metabolite-responsive transcriptional factors to rewire gene expression, metabolic engineers are well-positioned to integrate feedback genetic circuits with growth fitness and achieve dynamic population control. Towards this goal, we argue that feedback genetic circuits and microbial interactions will be a golden mine for future metabolic engineering. We will summarize the design principles of engineering burden-driven feedback control to combat metabolic stress, implementing population quality control to eliminate cheater cell, applying product addiction to reward productive cell, as well as layering dual dynamic regulation to decouple cell growth from product formation. Collectively, these strategies will be useful to improve community-level cellular performance. Encoding such decision-marking functions and reprogramming cellular logics at population level will enable metabolic engineers to deliver robust cell factories and pave the way for intelligent bioproduction. We envision that various cellular regulation mechanisms and genetic/metabolic circuits could be exploited to achieve self-adaptive or autonomous metabolic function for diverse biotechnological and medical applications. Applying these design rules may offer us a genetic solution beyond bioprocess engineering strategies to further improve the cost-competitiveness of industrial fermentation.

[1]  Sang Yup Lee,et al.  Systems metabolic engineering for chemicals and materials. , 2011, Trends in biotechnology.

[2]  Qipeng Yuan,et al.  Microbial biosynthesis of the anticoagulant precursor 4-hydroxycoumarin , 2013, Nature Communications.

[3]  Gregory Stephanopoulos,et al.  Engineering lipid overproduction in the oleaginous yeast Yarrowia lipolytica. , 2015, Metabolic engineering.

[4]  K. Zengler,et al.  Elucidation of complexity and prediction of interactions in microbial communities , 2017, Microbial biotechnology.

[5]  Kechun Zhang,et al.  Engineered biosynthesis of medium-chain esters in Escherichia coli. , 2015, Metabolic engineering.

[6]  Morten Otto Alexander Sommer,et al.  Synthetic addiction extends the productive life time of engineered Escherichia coli populations , 2018, Proceedings of the National Academy of Sciences.

[7]  Wolfgang Wiechert,et al.  Homogenizing bacterial cell factories: Analysis and engineering of phenotypic heterogeneity. , 2017, Metabolic engineering.

[8]  Jay D Keasling,et al.  An Orthogonal and pH-Tunable Sensor-Selector for Muconic Acid Biosynthesis in Yeast. , 2018, ACS synthetic biology.

[9]  M. Lynch,et al.  The bioenergetic costs of a gene , 2015, Proceedings of the National Academy of Sciences.

[10]  Tae Seok Moon,et al.  Production of Glucaric Acid from a Synthetic Pathway in Recombinant Escherichia coli , 2009, Applied and Environmental Microbiology.

[11]  Martin Fussenegger,et al.  Engineering synergy in biotechnology. , 2014, Nature chemical biology.

[12]  Fuzhong Zhang,et al.  Exploiting nongenetic cell-to-cell variation for enhanced biosynthesis. , 2016, Nature chemical biology.

[13]  Kristala L. J. Prather,et al.  Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit , 2017, Nature Biotechnology.

[14]  Paul J. Choi,et al.  Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells , 2010, Science.

[15]  G. Stephanopoulos,et al.  Improving fatty acids production by engineering dynamic pathway regulation and metabolic control , 2014, Proceedings of the National Academy of Sciences.

[16]  Kristala L. J. Prather,et al.  Layered dynamic regulation for improving metabolic pathway productivity in Escherichia coli , 2018, Proceedings of the National Academy of Sciences.

[17]  Maria Papagianni,et al.  Recent advances in engineering the central carbon metabolism of industrially important bacteria , 2012, Microbial Cell Factories.

[18]  G. Stan,et al.  Burden-driven feedback control of gene expression , 2017, Nature Methods.

[19]  Gregory Stephanopoulos,et al.  Synthetic biology and metabolic engineering. , 2012, ACS synthetic biology.

[20]  V. Hatzimanikatis,et al.  Manipulating redox and ATP balancing for improved production of succinate in E. coli. , 2011, Metabolic engineering.

[21]  Frank Bernhard,et al.  The autoregulatory role of EsaR, a quorum‐sensing regulator in Pantoea stewartii ssp. stewartii: evidence for a repressor function , 2002, Molecular microbiology.

[22]  J. Keasling,et al.  Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids , 2012, Nature Biotechnology.

[23]  Fuzhong Zhang,et al.  Engineering Microbial Metabolite Dynamics and Heterogeneity , 2017, Biotechnology journal.

[24]  Irene M. Brockman,et al.  Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites. , 2014, Metabolic engineering.

[25]  Shreya Patel,et al.  Book Review: Recent Advances in Yeast Metabolic Engineering , 2017, Front. Bioeng. Biotechnol..

[26]  G. Jung,et al.  Synthetic auxotrophs for stable and tunable maintenance of plasmid copy number. , 2018, Metabolic engineering.

[27]  X. Xing,et al.  Maltose Utilization as a Novel Selection Strategy for Continuous Evolution of Microbes with Enhanced Metabolite Production. , 2017, ACS synthetic biology.

[28]  G. Stephanopoulos,et al.  Metabolic engineering: past and future. , 2013, Annual review of chemical and biomolecular engineering.

[29]  Jay D. Keasling,et al.  A model for improving microbial biofuel production using a synthetic feedback loop , 2010, Systems and Synthetic Biology.

[30]  Ron Milo,et al.  Spanning high-dimensional expression space using ribosome-binding site combinatorics , 2013, Nucleic acids research.

[31]  Kechun Zhang,et al.  Scalable production of mechanically tunable block polymers from sugar , 2014, Proceedings of the National Academy of Sciences.

[32]  G. Stan,et al.  Quantifying cellular capacity identifies gene expression designs with reduced burden , 2015, Nature Methods.

[33]  B. Glick.  Metabolic load and heterologous gene expression. , 1995, Biotechnology advances.

[34]  Jay D. Keasling,et al.  Engineering Static and Dynamic Control of Synthetic Pathways , 2010, Cell.

[35]  Kelly M. Thayer,et al.  Combining metabolic and protein engineering of a terpenoid biosynthetic pathway for overproduction and selectivity control , 2010, Proceedings of the National Academy of Sciences.

[36]  G. Stephanopoulos,et al.  Engineering the push and pull of lipid biosynthesis in oleaginous yeast Yarrowia lipolytica for biofuel production. , 2013, Metabolic engineering.

[37]  Patrick C Cirino,et al.  Design and application of a mevalonate-responsive regulatory protein. , 2011, Angewandte Chemie.

[38]  M. Isalan,et al.  Commentary: Synthetic Addiction Extends the Productive Life Time of Engineered Escherichia coli Populations , 2018, Front. Bioeng. Biotechnol..

[39]  Andreas Porse,et al.  Diverse genetic error modes constrain large-scale bio-based production , 2018, Nature Communications.

[40]  James C. Liao,et al.  ATP drives direct photosynthetic production of 1-butanol in cyanobacteria , 2012, Proceedings of the National Academy of Sciences.

[41]  C. Smolke,et al.  A microbial biomanufacturing platform for natural and semi-synthetic opiates , 2014, Nature chemical biology.

[42]  Yinjie J. Tang,et al.  Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications. , 2016, Trends in biotechnology.

[43]  Christopher A. Voigt,et al.  Genetic circuit performance under conditions relevant for industrial bioreactors. , 2012, ACS synthetic biology.