Elsevier

Metabolic Engineering

Volume 67, September 2021, Pages 186-197
Metabolic Engineering

Combinational quorum sensing devices for dynamic control in cross-feeding cocultivation

https://doi.org/10.1016/j.ymben.2021.07.002Get rights and content

Highlights

  • Dynamic regulation combining QS-SLC and QS-MTS proposed for cocultivation.

  • Glucose split ratio characterizes trade-off between cell growth and production.

  • Optimality of QS-SLC and QS-MTS schemes assessed.

  • Combinational QS devices used for balancing species and salidroside titer improvement.

Abstract

Quorum sensing (QS) offers cell density dependent dynamic regulations in cell culture through devices such as synchronized lysis circuit (SLC) and metabolic toggle switch (MTS). However, there is still a lack of studies on cocultivation with a combination of different QS-based devices. Taking the production of isopropanol and salidroside as case studies, we have mathematically modeled a comprehensive set of QS-regulated cocultivation schemes and constructed experimental combinations of QS devices, respectively, to evaluate their feasibility and optimality for regulating growth competition and corporative production. Glucose split ratio is proposed for the analysis of competition between cell growth and targeted production. Results show that the combination of different QS devices across multiple members offers a new tool with the potential to effectively coordinate synthetic microbial consortia for achieving high product titer in cross-feeding cocultivation. It is also evident that the performance of such systems is significantly affected by dynamic characteristics of chosen QS devices, carbon source control and the operational settings. This study offers insights for future applications of combinational QS devices in synthetic microbial consortia.

Introduction

The development of metabolic engineering has enabled the biosynthesis of a wide range of valuable chemicals through utilizing microorganisms as microbial cell factories (Beri et al., 2020; Liang et al., 2015; Yang et al., 2020). Traditionally, this process was conducted by assembly of the target biosynthetic pathway in a single strain (Yao et al., 2013). In recent years, coculture-based engineering approaches (Arora et al., 2020; Minty et al., 2013; Stephens et al., 2019; Zhang and Wang, 2016) have emerged, where a complex synthetic metabolic pathway is divided into modules expressed in separate strains thus forming a synthetic microbial consortium (Lindemann et al., 2016) that effectively produces high-value metabolites (Honjo et al., 2019; Tsoi et al., 2019; Wang et al., 2020; Zhou et al., 2015).

The functioning of a synthetic microbial consortium often requires dedicated co-ordination and regulation (Di and Yang, 2019). Recently, Quorum sensing (QS) (Stephens and Bentley, 2020) has been applied in cocultivations to synchronize microbial communities (Du et al., 2020; Kong et al., 2018; Kylilis et al., 2018; Scott et al., 2017; Wu et al., 2021) and reduce the competition between cell growth and targeted production (Gupta et al., 2017; Soma and Hanai, 2015; Wu et al., 2020a). Specific QS signals such as acylated homoserine lactones (AHLs) (Papenfort and Bassler, 2016) accumulate in cell populations and induce the activation of various QS-based synthetic devices (Wu et al., 2020b), such as QS-based synchronized lysis circuit (QS-SLC) and QS-based metabolic toggle switch (QS-MTS). Combined with specific killing proteins such as CcdB (You et al., 2004) or φX174 E (Din et al., 2016), different QS-SLCs were constructed to control the cell density of a single strain. Based on the bi-directional communication through lux and las QS systems, Balagadde et al. (2008) developed two QS-SLCs for simulating a synthetic Escherichia coli predator–prey ecosystem. Scott et al. (2017) combined lux QS-SLC with rpa QS-SLC to control population densities of competitive microbes of Salmonella typhimurium strains. To realize one-step fermentation for vitamin C, Wang et al. (2019) applied a lux QS-SLC to control the lysis of Gluconobacter oxydans for L-sorbose production in a three-species consortium. Recently, to avoid QS crosstalk, an QS-based inducible syncronized lysis circuit (iSLC) was constructed orthogonally based on p-coumaric acid and was verified at the community level (Miano et al., 2020).

On the other hand, QS-MTS has been successfully employed in dynamic metabolic engineering to reduce the competition between cell growth and targeted production to increase bioprocess productivity and yield of products such as isopropanol (IPA) (Soma and Hanai, 2015), poly-β-hydroxybutyrate (Gu et al., 2020), 1,4-butanediol (Liu and Lu, 2015), myo-inositol (Gupta et al., 2017), D-glucaric acid (Doong et al., 2018), salicylic acid (Dinh and Prather, 2019), and naringenin (Dinh et al., 2020). In particular, Soma et al. (Soma and Hanai, 2015) constructed a synthetic QS-MTS for the mono-cultivation of the E. coli strain to achieve a dynamic switch of the metabolic flux between TCA cycle and IPA synthesis pathway to increase the IPA titer. Gupta et al. (2017) and Doong et al. (2018) introduced esa QS-MTS to identify the optimal point of switch to direct most of the glucose to the target pathway to increase titers of myo-inositol and D-glucaric acid. For the production of naringenin and salicylic acid, Dinh et al. (Dinh and Prather, 2019) integrated lux or esa QS systems and CRISPRi to accomplish the metabolic flux control in engineered E. coli. Recently, Wu et al. (2020a) built a global resource allocation device combining the sequence-dependent endoribonuclease MazF with QS-based circuits to develop a pathway-independent and full-autonomous global resource allocation strategy in one strain, and it was verified by the titer improvement of medium chain fatty acids.

Among the targeted products, IPA is a renewable cellulosic biofuel (Walther and François, 2016) which is regarded as a potential alternative for petroleum-based transport fuels. Glycosides, on the other hand, represent a type of highly important natural products relevant to various pharmacological, food, and nutraceuticals activities (Liang et al., 2015). Salidroside, a typical glycoside, has been extensively used for treating or preventing cerebral ischemia, fatigue, hypoxia and neurodegenerative diseases (Panossian et al., 2010). To tackle the metabolic burden, products including IPA and salidroside can be heterologously produced in a synthetic microbial coculture. With the help of a lux QS-SLC, Honjo et al. (2019) constructed a coculture to improve the isopropanol (IPA) titer with an engineered microbial community composed of an E. coli strain producing beta-glucosidase (BGL) enzyme and glucose (GLU strain) and the other E. coli strain producing IPA (IPA strain). Previously, to improve the titer of salidroside, we have constructed a syntrophic E. coli coculture with the aglycone (AG) strain and the glycoside (GD) strain for the first time producing tyrosol and salidroside, respectively (Liu et al., 2018). We found that when individually optimized strains are physically mixed and co-cultivated, the coculture might be imbalanced due to the competitive growth and metabolic stress. Selective utilization of carbon source such as the glucose and xylose could be applied for reducing the cell-cell competition (Freilich et al., 2011; Liu et al., 2018; Saini et al., 2017; Zhang et al., 2015), but it requires the dynamic and automatic regulation for the cell growth to realize the cell growth balance and sustained stability.

While QS-based circuits such as QS-SLC and QS-MTS have demonstrated their potential in synchronizing and improving the target metabolism of various strains and microbial communities, they have largely been used separately so far, which may limit their scope of application in cocultivation. Besides, existing studies have often focused on the demonstration of feasibility and potential function of a novel scheme; optimal design and selection of QS systems in connection with engineering objectives such as coexistence, titer improvement, and batch fermentation duration are yet to be further explored particularly for cocultivations.

In this work, we aim to address the above deficiencies through a combination of theoretical analysis and experimental investigation (a workflow diagram can be found in Fig. S1). We have first used existing data on IPA production and carried out in silico assessment of co-cultivation designs that incorporate both QS-SLC and QS-MTS implemented through a range of combinations of four QS systems (lux, rpa, tra, las). The aim is to shed light on the feasibility and optimality of such systems which simultaneously involve cell growth competition and cooperative IPA production. A process characteristic termed “glucose split ratio” is proposed to analyze the competition between cell growth and targeted production. The impact of the initial settings such as the initial seeding ratio in cocultivation has also been evaluated on selected cases. Furthermore, for a previously unstable cross-feeding system producing salidroside, we have experimentally constructed the combinations of QS-SLC, QS-MTS, and other control modules and manipulated the initial operational settings for the first time to regulate the cell growth competition, realize the growth balance of strains, and improve the production titer. Overall, this study is to shed light on the potential significance of optimal selection and combination of QS devices in the engineering of synthetic microbial communities for producing valuable products.

Section snippets

Mathematical modelling development for IPA case

The complete models for GLU or IPA individual strain mono-cultivation are given in Sections 1.2 and 1.3 of supplementary material; the model development for cocultivations of GLU and IPA strain is shown in Sections 1.4 to 1.6 of supplementary material; here a brief summary is provided. Four QS systems commonly used in recent studies (lux QS (Miller and Bassler, 2001), las QS (Sandoz et al., 2007), rpa QS (Scott et al., 2017)) and tra QS (Vannini et al., 2002) were investigated in this work. The

IPA production

Presenting the mathematical modelling results of the IPA production case, this section begins with the GLU strain monoculture with a focus on the comparison between the model-predicted performance of four different QS systems implementing the lysis control (Section 3.1.1). A similar comparison is subsequently presented to the IPA strain monoculture equipped with different implementations of QS-MTS (Section 3.1.2). Finally, GLU-IPA co-cultivation regulated with a variety of QS schemes is

Discussion

The successful microbial production of a valuable product often depends on the effective channeling of key resources to the desirable metabolic pathways, which holds for both monoculture- and cocultivation-based designs. For cocultivation, additional challenges may arise from the need for maintaining balanced growth of the members of a synthetic community. In this work, we have investigated, through mathematical modelling and experimentally, a novel ecosystem-based fermentation strategy with

Conclusions

The continued development and applications of QS-based genetic networks are expected to increasingly enable the optimal regulation of synthetic microbial consortia in ecosystem-based fermentation to achieve their goals of synthesis of valuable products. Taking the production of IPA and salidroside as two examples, which each involve both QS-regulated lysis and QS-based metabolic toggle switch (MTS), we have revealed several insights potentially important for future design with respect to the

Code availability

MATLAB Version R2016a were used to develop and solve the mathematical models. The codes are available from the corresponding author upon request.

Declaration of competing interest

The authors declare no competing financial interests.

Acknowledgements

The present study was supported by grants from National Key Research and Development Program of China (No. 2017YFD0201400), the Funds for Creative Research Groups of China (21621004), and National Key Research and Development Program of China (No. 2020YFA0907900).

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