Dynamic control in metabolic engineering: Theories, tools, and applications

Abstract

Metabolic engineering has allowed the production of a diverse number of valuable chemicals using microbial organisms. Many biological challenges for improving bio-production exist which limit performance and slow the commercialization of metabolically engineered systems. Dynamic metabolic engineering is a rapidly developing field that seeks to address these challenges through the design of genetically encoded metabolic control systems which allow cells to autonomously adjust their flux in response to their external and internal metabolic state. This review first discusses theoretical works which provide mechanistic insights and design choices for dynamic control systems including two-stage, continuous, and population behavior control strategies. Next, we summarize molecular mechanisms for various sensors and actuators which enable dynamic metabolic control in microbial systems. Finally, important applications of dynamic control to the production of several metabolite products are highlighted, including fatty acids, aromatics, and terpene compounds. Altogether, this review provides a comprehensive overview of the progress, advances, and prospects in the design of dynamic control systems for improved titer, rate, and yield metrics in metabolic engineering.

Keywords: Biosensors; Dynamic metabolic control; Dynamic metabolic engineering; Genetic circuits; Synthetic biology.

Fig. 1.. Overview of theoretical benefits of dynamic metabolic control.

(A) Comparison of one-stage control with two-stage dynamic control strategy. Lower graphs show theoretical biomass and product formation over time for each strategy (one-stage control, green line; two-stage control, blue line), with yellow and blue shaded regions marking the growth and production phases respectively (Gadkar et al., 2005). (B) Comparison of open loop strategy (green) with continuous dynamic control strategies (NML, blue; NLML, purple) for accelerating metabolic response to input (Liu and Zhang, 2018). Blue oval shapes represent metabolite-responsive transcription factors. Graph shows metabolite concentration over time for each architecture with the desired metabolite concentration in red. (C) Comparison of open loop control (green) with a continuous dynamic control (blue) for reducing parametric sensitivity (Dunlop et al., 2010). Graph shows final titers for each architecture for a range of parameter values. A hypothetical acceptable production titer (red line) is shown. (D) Stochastic gene expression and stochastic enzyme kinetics cause cell-to-cell variation in enzyme and metabolite levels. Lower graph shows parameter values for three distinct metabolite distribution regimes: one unimodal and two bimodal regimes (Tonn et al., 2019). Side graphs show single-cell distribution for enzyme (green) and metabolite (red) concentrations corresponding to each regime. (E) Comparison of static control (green) with population behavior control (blue) for selecting high-performing individual cells. Graph shows distribution of single-cell metabolite concentrations for each strategy (Xiao et al., 2016).

Fig. 2.. Overview of sensing mechanisms used in dynamic metabolic control.

(A) A chemical inducer binds to a repressor that undergoes a conformational change and releases itself from binding to DNA, thus allowing RNA polymerase to promote gene expression (Soma et al., 2014). (B) As cell density increases, more AHL molecules are released into the extracellular environment until the concentration is high enough to trigger a quorum response (Gupta et al., 2017). (C) Thermolabile repressor cI857 changes conformation and DNA binding activity in response to temperature (Zhou et al., 2012). (D) Light responsive transcription factors are created by fusing light-sensitive protein domains with DNA binding domains (Milias-Argeitis et al., 2016). (E) The P_gas promoter is able to detect and respond to low pH levels (Yin et al., 2017). (F) FadR is a repressor transcription factor that binds to acyl-CoA thus acting as a sensor for acyl-CoA (Zhang et al., 2012). (G) The L-Trp biosensor is based on a metabolite-responsive signal peptide TnaC (Fang et al., 2016). Without L-Trp, the operon undergoes the Rho-dependent transcriptional termination. L-Trp can prevent the release of the ribosome at the tnaC stop codon, blocking Rho from binding to the mRNA, so that RNA polymerase can continue to transcribe the downstream GOI.

Fig. 3.. Overview of actuator mechanisms in dynamic metabolic control.

(A) MRTF-based transcriptional actuation (Mannan et al., 2017). (B) CRISPRi-based transcriptional actuation (Wu et al., 2020). (C) Antisense RNA-based post-transcriptional actuation (Yang et al., 2018). asRNA, antisense RNA. (D) RNA-interference-based post-transcriptional actuation. In S. cerevisiae, RNA hairpins are cleaved by a Dicer protein to create fragments, which interact with the Argonaute protein to destroy target mRNAs, thus preventing translation (Williams et al., 2015). dsRNA, double-stranded RNA; siRNA, small interfering RNA. (E) A post-translation actuation mechanism based on the TevP proteinase (Gao et al., 2019).

Fig. 4.. Representative pathways with engineered metabolic regulation.

(A) Glucaric acid production in E. coli (Doong et al., 2018). AHL, N-acyl homoserine lactones; PFK-1, phospho-fructokinase-1; G6P, glucose-6-ph osphate; F6P, fructose-6-phosphate; MI, myo-inositol; MIOX, myo-inositol oxygenase. (B) 4-HIL production in C. glutamicum (Zhang et al., 2018). α-KG, α-ketoglutarate; SUCC, succinyl-CoA; SUC, succinate; OAA, oxaloacetate; Ile, L-isoleucine; 4-HIL, 4-hydroxyiso leucine; OdhA, α-ketoglutarate dehydrogenase complex E1 subunit. (C) MA production in E. coli (Yang et al., 2018). E4P, erythrose 4-phosphate; PEP, phosphoenolpyruvate; DAHP, 3-deoxy-d-ara-bino-heptulosonate-7-phosphate; MA, muconic acid; AAA, aromatic amino acid; EntC, isochorismate synthase; PchB, isochorismate pyruvate lyase; PykF, pyruvate kinase I; PykA, pyruvate kinase II; Ppc, phosphoenolpyruvate carboxylase. (D) Naringenin production in Y. lipolytica (Lv et al., 2020). LEU2, 3-isopropylmalate dehydrogenase; FAS1, fatty acid synthetases Fas1p; FAS2, fatty acid synthetases Fas2p; FabD, malonyl-CoA-ACP transacylase. β-IPM, 3-isopropylmalate; KIC, α-ketoi-socarpoate (E) Monoterpene production in S. cerevisiae (Peng et al., 2018). DMAPP, dimethylallyl pyrophosphate; IPP, isopentenyl pyrophosphate; GPP, geranyl pyrophosphate; FPP, farnesol pyrophosphate; Erg20p, FPP synthase. (F) Xylonate production in E. coli (Gao et al., 2020). PPP, pentose phosphate pathway; XylA, xylose isomerase; CcxylB, xylose dehydrogenase from Caulobacter crescentus; Dashed line, multiple enzymatic steps; blue line, engineered regulatory interaction; Circle, the signal molecule in the dynamic control system. Pathways and metabolites in bold are essential to the host.