Biosensor-driven, model-based optimization of the orthogonally expressed naringenin biosynthesis pathway

Metabolic engineering of Acinetobacter baylyi ADP1 for naringenin production

Naringenin, a flavanone and a precursor for a variety of flavonoids, has potential applications in the health and pharmaceutical sectors. The biological production of naringenin using genetically engineered microbes is considered as a promising strategy. The naringenin synthesis pathway involving chalcone synthase (CHS) and chalcone isomerase (CHI) relies on the efficient supply of key substrates, malonyl-CoA and p-coumaroyl-CoA. In this research, we utilized a soil bacterium, Acinetobacter baylyi ADP1, which exhibits several characteristics that make it a suitable candidate for naringenin biosynthesis; the strain naturally tolerates and can uptake and metabolize p-coumaric acid, a primary compound in alkaline-pretreated lignin and a precursor for naringenin production. A. baylyi ADP1 also produces intracellular lipids, such as wax esters, thereby being able to provide malonyl-CoA for naringenin biosynthesis. Moreover, the genomic engineering of this strain is notably straightforward. In the course of the construction of a naringenin-producing strain, the p-coumarate catabolism was eliminated by a single gene knockout (ΔhcaA) and various combinations of plant-derived CHS and CHI were evaluated. The best performance was obtained by a novel combination of genes encoding for a CHS from Hypericum androsaemum and a CHI from Medicago sativa, that enabled the production of 17.9 mg/L naringenin in batch cultivations from p-coumarate. Furthermore, the implementation of a fed-batch system led to a 3.7-fold increase (66.4 mg/L) in naringenin production. These findings underscore the potential of A. baylyi ADP1 as a host for naringenin biosynthesis as well as advancement of lignin-based bioproduction.

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Application of modern synthetic biology technology in aromatic amino acids and derived compounds biosynthesis

Aromatic amino acids (AAA) and derived compounds have enormous commercial value with extensive applications in the food, chemical and pharmaceutical fields. Microbial production of AAA and derived compounds is a promising prospect for its environmental friendliness and sustainability. However, low yield and production efficiency remain major challenges for realizing industrial production. With the advancement of synthetic biology, microbial production of AAA and derived compounds has been significantly facilitated. In this review, a comprehensive overview on the current progresses, challenges and corresponding solutions for AAA and derived compounds biosynthesis is provided. The most cutting-edge developments of synthetic biology technology in AAA and derived compounds biosynthesis, including CRISPR-based system, genetically encoded biosensors and synthetic genetic circuits, were highlighted. Finally, future prospects of modern strategies conducive to the biosynthesis of AAA and derived compounds are discussed. This review offers guidance on constructing microbial cell factory for aromatic compound using synthetic biology technology.

Pathway Evolution Through a Bottlenecking-Debottlenecking Strategy and Machine Learning-Aided Flux Balancing

The evolution of pathway enzymes enhances the biosynthesis of high-value chemicals, crucial for pharmaceutical, and agrochemical applications. However, unpredictable evolutionary landscapes of pathway genes often hinder successful evolution. Here, the presence of complex epistasis is identifued within the representative naringenin biosynthetic pathway enzymes, hampering straightforward directed evolution. Subsequently, a biofoundry-assisted strategy is developed for pathway bottlenecking and debottlenecking, enabling the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory in six weeks. This study then utilizes a machine learning model, ProEnsemble, to further balance the pathway by optimizing the transcription of individual genes. The broad applicability of this strategy is demonstrated by constructing an Escherichia coli chassis with evolved and balanced pathway genes, resulting in 3.65 g L-1 naringenin. The optimized naringenin chassis also demonstrates enhanced production of other flavonoids. This approach can be readily adapted for any given number of enzymes in the specific metabolic pathway, paving the way for automated chassis construction in contemporary biofoundries.

YaliCMulti and YaliHMulti: Stable, efficient multi-copy integration tools for engineering Yarrowia lipolytica

Yarrowia lipolytica is widely used in biotechnology to produce recombinant proteins, food ingredients and diverse natural products. However, unstable expression of plasmids, difficult and time-consuming integration of single and low-copy-number plasmids hampers the construction of efficient production pathways and application to industrial production. Here, by exploiting sequence diversity in the long terminal repeats (LTRs) of retrotransposons and ribosomal DNA (rDNA) sequences, a set of vectors and methods that can recycle multiple and high-copy-number plasmids was developed that can achieve stable integration of long-pathway genes in Y. lipolytica. By combining these sequences, amino acids and antibiotic tags with the Cre-LoxP system, a series of multi-copy site integration recyclable vectors were constructed and assessed using the green fluorescent protein (HrGFP) reporter system. Furthermore, by combining the consensus sequence with the vector backbone of a rapidly degrading selective marker and a weak promoter, multiple integrated high-copy-number vectors were obtained and high levels of stable HrGFP expression were achieved. To validate the universality of the tools, simple integration of essential biosynthesis modules was explored, and 7.3 g/L of L-ergothioneine and 8.3 g/L of (2S)-naringenin were achieved in a 5 L fermenter, the highest titres reported to date for Y. lipolytica. These novel multi-copy genome integration strategies provide convenient and effective tools for further metabolic engineering of Y. lipolytica.

Using design of experiments to guide genetic optimization of engineered metabolic pathways

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

ONE-SENTENCE SUMMARY

This is a review of literature related to applying Design of Experiments for genetic optimization.

Genetically Encoded Biosensor Engineering for Application in Directed Evolution

Although rational genetic engineering is nowadays the favored method for microbial strain improvement, building up mutant libraries based on directed evolution for improvement is still in many cases the better option. In this regard, the demand for precise and efficient screening methods for mutants with high performance has stimulated the development of biosensor-based high-throughput screening strategies. Genetically encoded biosensors provide powerful tools to couple the desired phenotype to a detectable signal, such as fluorescence and growth rate. Herein, we review recent advances in engineering several classes of biosensors and their applications in directed evolution. Furthermore, we compare and discuss the screening advantages and limitations of two-component biosensors, transcription-factor-based biosensors, and RNA-based biosensors. Engineering these biosensors has focused mainly on modifying the expression level or structure of the biosensor components to optimize the dynamic range, specificity, and detection range. Finally, the applications of biosensors in the evolution of proteins, metabolic pathways, and genome-scale metabolic networks are described. This review provides potential guidance in the design of biosensors and their applications in improving the bioproduction of microbial cell factories through directed evolution.

Metabolic engineering strategies for naringenin production enhancement in Streptomyces albidoflavus J1074

BACKGROUND

Naringenin is an industrially relevant compound due to its multiple pharmaceutical properties as well as its central role in flavonoid biosynthesis.

RESULTS

On our way to develop Streptomyces albidoflavus J1074 as a microbial cell factory for naringenin production, we have significantly increased the yields of this flavanone by combining various metabolic engineering strategies, fermentation strategies and genome editing approaches in a stepwise manner. Specifically, we have screened different cultivation media to identify the optimal production conditions and have investigated how the additive feeding of naringenin precursors influences the production. Furthermore, we have employed genome editing strategies to remove biosynthetic gene clusters (BGCs) associated with pathways that might compete with naringenin biosynthesis for malonyl-CoA precursors. Moreover, we have expressed MatBC, coding for a malonate transporter and an enzyme responsible for the conversion of malonate into malonyl-CoA, respectively, and have duplicated the naringenin BGC, further contributing to the production improvement. By combining all of these strategies, we were able to achieve a remarkable 375-fold increase (from 0.06 mg/L to 22.47 mg/L) in naringenin titers.

CONCLUSION

This work demonstrates the influence that fermentation conditions have over the final yield of a bioactive compound of interest and highlights various bottlenecks that affect production. Once such bottlenecks are identified, different strategies can be applied to overcome them, although the efficiencies of such strategies may vary and are difficult to predict.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Strengthening microbial cell factories for efficient production of bioactive molecules

High demand of bioactive molecules (food additives, antibiotics, plant growth enhancers, cosmetics, pigments and other commercial products) is the prime need for the betterment of human life where the applicability of the synthetic chemical product is on the saturation due to associated toxicity and ornamentations. It has been noticed that the discovery and productivity of such molecules in natural scenarios are limited due to low cellular yields as well as less optimized conventional methods. In this respect, microbial cell factories timely fulfilling the requirement of synthesizing bioactive molecules by improving production yield and screening more promising structural homologues of the native molecule. Where the robustness of the microbial host can be potentially achieved by taking advantage of cell engineering approaches such as tuning functional and adjustable factors, metabolic balancing, adapting cellular transcription machinery, applying high throughput OMICs tools, stability of genotype/phenotype, organelle optimizations, genome editing (CRISPER/Cas mediated system) and also by developing accurate model systems via machine-learning tools. In this article, we provide an overview from traditional to recent trends and the application of newly developed technologies, for strengthening the systemic approaches and providing future directions for enhancing the robustness of microbial cell factories to speed up the production of biomolecules for commercial purposes.

Unlocking the bacterial domain for industrial biotechnology applications using universal parts and tools

Synthetic biology can play a major role in the development of sustainable industrial biotechnology processes. However, the development of economically viable production processes is currently hampered by the limited availability of host organisms that can be engineered for a specific production process. To date, standard hosts such as Escherichia coli and Saccharomyces cerevisiae are often used as starting points for process development since parts and tools allowing their engineering are readily available. However, their suboptimal metabolic background or impaired performance at industrial scale for a desired production process, can result in increased costs associated with process development and/or disappointing production titres. Building a universal and portable gene expression system allowing genetic engineering of hosts across the bacterial domain would unlock the bacterial domain for industrial biotechnology applications in a highly standardized manner and doing so, render industrial biotechnology processes more competitive compared to the current polluting chemical processes. This review gives an overview of a selection of bacterial hosts highly interesting for industrial biotechnology based on both their metabolic and process optimization properties. Moreover, the requirements and progress made so far to enable universal, standardized, and portable gene expression across the bacterial domain is discussed.