Elsevier

Metabolic Engineering

Volume 67, September 2021, Pages 41-52
Metabolic Engineering

Development of a growth coupled and multi-layered dynamic regulation network balancing malonyl-CoA node to enhance (2S)-naringenin biosynthesis in Escherichia coli

https://doi.org/10.1016/j.ymben.2021.05.007Get rights and content

Highlights

  • Three-layered dynamic regulation network for the production of (2S)-naringenin

  • Acyl carrier protein was used as a novel critical node for malonyl-CoA balancing

  • Biosensor-based directed evolution to optimize dynamic regulation network

  • Growth coupled dynamic regulation network increases cellular production

Abstract

Metabolic heterogeneity and dynamic changes in metabolic fluxes are two inherent characteristics of microbial fermentation that limit the precise control of metabolisms, often leading to impaired cell growth and low productivity. Dynamic metabolic engineering addresses these challenges through the design of multi-layered and multi-genetic dynamic regulation network (DRN) that allow a single cell to autonomously adjust metabolic flux in response to its growth and metabolite accumulation conditions. Here, we developed a growth coupled NCOMB (Naringenin-Coumaric acid-Malonyl-CoA-Balanced) DRN with systematic optimization of (2S)-naringenin and p-coumaric acid-responsive regulation pathways for real-time control of intracellular supply of malonyl-CoA. In this scenario, the acyl carrier protein was used as a novel critical node for fine-tuning malonyl-CoA consumption instead of direct repression of fatty acid synthase commonly employed in previous studies. To do so, we first engineered a multi-layered DRN enabling single cells to concurrently regulate acpH, acpS, acpT, acs, and ACC in malonyl-CoA catabolic and anabolic pathways. Next, the NCOMB DRN was optimized to enhance the synergies between different dynamic regulation layers via a biosensor-based directed evolution strategy. Finally, a high producer obtained from NCOMB DRN approach yielded a 8.7-fold improvement in (2S)-naringenin production (523.7 ± 51.8 mg/L) with a concomitant 20% increase in cell growth compared to the base strain using static strain engineering approach, thus demonstrating the high efficiency of this system for improving pathway production.

Introduction

Microbial production of value-added biochemicals often requires the development of a robust, efficient, and high-performance strain. To this end, static metabolic engineering strategies including multi-module optimization (Wu et al., 2013), promoter and RBS engineering (Zhang et al., 2015), and CRISPR/Cas9-based genome engineering (Ran et al., 2013) are commonly employed for rewiring metabolic flux to maximize production. Despite significant successes in the literature and industry using these static strain engineering paradigms, it remains difficult to balance the trade-offs between growth and production. To address this imbalance within static strain engineering approaches, researchers often control the changes in fermentation conditions including dynamic control of environmental changes such as incubation temperature, osmolarity, and inducer and nutrient availability (Schmidt, 2005; Singh et al., 2017). As an alternative, genetic approach with the use of transcription factor-based biosensors provides the potential for enabling real-time monitoring of cell growth and/or metabolite production and dynamically regulating pathway-relevant gene expression in response to intracellular signals (Moser et al., 2018).

Using the principles of dynamic regulation, a series of control systems (typically enabled by biosensors) fine-tune the cells to synthesize a target compound at maximum production levels (Shen et al., 2019). These control systems can be dynamically regulated at either the environmental or metabolic level. Generally, biosensors that could respond to universal, environmental induction factors such as temperature (Zheng et al., 2019), dissolved oxygen (Moser et al., 2018), cell density (Tian et al., 2020), and light (Zhao et al., 2019) enable construction of environment-responding dynamic regulation networks (DRNs). While these biosensors can dynamically modulate gene expression of all cells in response to the global environment changes, namely “population regulation”, the differences between intracellular states of single cells are often overlooked. More specifically, fermentation conditions such as cell density represent the overall state of the cell population. In reality, a culture contains a mixture of both young and aged cells with concomitant high- and low-performance cells in all growth phases, which is so called metabolic heterogeneity (Xiao et al., 2016). As a result, precisely regulating metabolic flux based on the intracellular state can help improve overall productivity within a fermentation. To this end, it is important to identify biosensors that can directly monitor the intracellular metabolic state of single cells and thus individually regulate gene expression within single cells in response to a critical metabolite level (Shen et al., 2019).

Generally, spatial distribution states of inducers directly influence the biosensor responsive mode. Environmental induction and extracellular metabolites responsive biosensors could confer a “population regulation” phenomenon which regulates and synchronizes cellular gene expression at a singular, special induction point without considering differences between single-cell states under different conditions. In contrast, intracellular metabolites responsive biosensors could fine-tune the target gene expression of individual cells by reflecting on the specific, single-cell metabolite accumulation levels, thus leading to a more optimized ensemble of cells. Recent examples that aim to dynamically regulate the metabolic flux of single cells have been used to balance malonyl-CoA as it only exists intracellularly. For example, by coupling the glucose-responsive promoter (PHXT1) with a malonyl-CoA sensitive transcriptional repressor (FapR), David et al. constructed a DRN to drive the flux from the malonyl-CoA pool toward 3-hydroxypropionic acid formation, leading to a 10-fold increase in titer (David et al., 2016). Additionally, Xu et al. developed an oscillator to dynamically balance malonyl-CoA pool through controlling the expression of acetyl-CoA carboxylase (ACC) and fatty acid synthase (Xu et al., 2014). In their report, the best producer displayed a 2.1-fold improvement in fatty acids titer (3.86 g/L). While successful in improving product titers, metabolite-responsive DRNs only monitor the fluctuating level of a metabolite without coupling with the growth profile of single cells, usually at the expense of growth.

Currently, quorum sensing (QS)-based dynamic regulation is the most popular strategy for coupling cellular growth and metabolites production (Ge et al., 2020; Tian et al., 2020). However, the transmembrane transport of QS signal molecules N-acyl homoserine lactones (AHLs) is freely diffusible and accompanied by active efflux, leading to the same intra- and extracellular concentrations (Minagawa et al., 2012; Pearson et al., 1999). Hence, QS-based dynamic regulation is a typical “population regulation” strategy that overlooked the differences between single cells. Moreover, simply synchronizing cells based on growth state overlooks the differences in intracellular metabolite fluxes across individual cells. To date, growth coupled single-cell level dynamic regulation is still rarely explored. In particular, such a design often requires the dynamic control of several critical metabolic nodes simultaneously to control metabolic flux distribution. By taking cell growth, product synthesis, and transient changes in intermediate metabolite accumulation states into account, these more complex dynamic control systems multi-layered and sophisticated DRNs. However, it is difficult to modulate the regulation manner (up-regulation or down-regulation) and expression level of various genes between different DRN layers.

To address this challenge, the work present here demonstrates an optimized layered dynamic regulation device that implements a growth coupled single cell sensing and regulating DRNs cascade to improve the production of the flavonoid (2S)-naringenin. Specifically, this DRN cascade focuses on dynamically regulating the expression of 5 genes (acpH, acpS, acpT, acs, and ACC) closely related to supply of malonyl-CoA, a crucial metabolite used as a carbon-chain elongation unit for important compounds such as flavonoids (Palmer et al., 2020; Zhou et al., 2019, 2020abib_Zhou_et_al_2020abib_Zhou_et_al_2019), antibiotics (Bretschneider et al., 2011), and fatty acids (Milke and Marienhagen; Xu et al., 2014). Generally, the availability of intracellular malonyl-CoA is limited and reduces from 0.23 to 0.01 nmol/(mg dry wt) when cells transit from exponential to stationary stage (Takamura and Nomura, 1988). Traditional approaches to increase malonyl-CoA content usually represses the fatty acid biosynthetic pathway (Yang et al., 2015). However, excessive repression of this pathway leads to impaired cell growth. Additionally, oversupply of malonyl-CoA causes an undesired malonylation of the proteome, thus establishing a further carbon burden in engineered E. coli (Xu et al., 2018). As a result, malonyl-CoA must be in an exquisite balance within cells. In this study, we designed and optimized a metabolite-responsive NCOMB (Naringenin-Coumaric acid-Malonyl-CoA-Balanced) DRN which can real-time monitor the intracellular concentration of (2S)-naringenin and p-coumaric acid to dynamically enhance the production of malonyl-CoA at all growth phases. The designed DRN includes three layers: Layer I that functionally produces (2S)-naringenin; Layer II that dynamically amplifies the metabolic flux toward the (2S)-naringenin synthetic pathway by coupling cell growth; Layer III that dynamically responds to p-coumaric acid and enhances the production of malonyl-CoA during late stages of fermentation (Fig. 1). In doing so, we successfully demonstrate the ability of NCOMB DRN to achieve single cell dynamic regulation and a balance of (2S)-naringenin production with cell growth based on the level of intracellular metabolite, which could further facilitate the application of DRN in the complicated metabolic network.

Section snippets

Comparison of intra- and extra-cellular concentrations of (2S)-naringenin and p-coumaric acid

To evaluate the efficacy of a biosensor-based control approach, we measured the spatiotemporal distribution of (2S)-naringenin and p-coumaric acid. In doing so, we measured the intracellular and extracellular content of (2S)-naringenin and p-coumaric acid using the previously constructed (2S)-naringenin producer (9G3) (Zhou et al., 2019). The results indicated that the intracellular concentration of (2S)-naringenin and p-coumaric acid is much higher than extracellular content in both the log

Discussion

Dynamic regulation of metabolic pathways marked with precise control of enzyme expression levels as well as optimal cell growth has received attention in the last few years (Dinh and Prather, 2019). In this study, we focus on optimizing and developing an NCOMB DRN to dynamically regulate the biosynthesis and conversion of malonyl-CoA at a single cell level to direct it toward (2S)-naringenin production. In our three-part scheme, the constitutively expressed (2S)-naringenin synthetic pathway

Strains, medium, and culture conditions

E. coli JM109 and E. coli BL21 were used for plasmids construction and (2S)-naringenin production, respectively. LB medium was used to harvest cells for plasmid construction or fluorescence measurement. For the NCOMB DRN optimization process, (2S)-naringenin fermentation was performed at 30 °C with 220 rpm using MOPS minimal medium (supplemented with 10 g/L D-glucose, 1 g/L peptone, 3 mM L-tyrosine, and 4 g/L NH4Cl). Temperature-shift fermentation was conducted as described in our previous

CRediT authorship contribution statement

Shenghu Zhou: Conceptualization, Methodology, Investigation, Visualization, Writing – original draft. Shuo-Fu Yuan: Investigation, Formal analysis, Validation, Writing – original draft. Priya H. Nair: Investigation. Hal S. Alper: Conceptualization, Resources, Writing – review & editing. Yu Deng: Conceptualization, Supervision, Resources, Writing – review & editing. Jingwen Zhou: Conceptualization, Supervision, Resources, Funding acquisition, Writing – review & editing.

Declaration of competing interest

The authors declare no competing interests.

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2019YFA0904800), the National Science Fund for Excellent Young Scholars (21822806), the National Natural Science Foundation of China (31900066, 21877053, 31770097), the Air Force Office of Scientific Research under Award No. FA9550-14-1-0089, the National First-class Discipline Program of Light Industry Technology and Engineering (LITE2018-24), the Fundamental Research Funds for the Central Universities (

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    Shenghu Zhou and Shuo-Fu Yuan contributed equally.

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