Improving Metabolic Pathway Efficiency by Statistical Model-Based Multivariate Regulatory Metabolic Engineering

In silico analysis of design of experiment methods for metabolic pathway optimization

Microbial cell factories allow the production of chemicals presenting an alternative to traditional fossil fuel-dependent production. However, finding the optimal expression of production pathway genes is crucial for the development of efficient production strains. Unlike sequential experimentation, combinatorial optimization captures the relationships between pathway genes and production, albeit at the cost of conducting multiple experiments. Fractional factorial designs followed by linear modeling and statistical analysis reduce the experimental workload while maximizing the information gained during experimentation. Although tools to perform and analyze these designs are available, guidelines for selecting appropriate factorial designs for pathway optimization are missing. In this study, we leverage a kinetic model of a seven-genes pathway to simulate the performance of a full factorial strain library. We compare this approach to resolution V, IV, III, and Plackett Burman (PB) designs. Additionally, we evaluate the performance of these designs as training sets for a random forest algorithm aimed at identifying best-producing strains. Evaluating the robustness of these designs to noise and missing data, traits inherent to biological datasets, we find that while resolution V designs capture most information present in full factorial data, they necessitate the construction of a large number of strains. On the other hand, resolution III and PB designs fall short in identifying optimal strains and miss relevant information. Besides, given the small number of experiments required for the optimization of a pathway with seven genes, linear models outperform random forest. Consequently, we propose the use of resolution IV designs followed by linear modeling in Design-Build-Test-Learn (DBTL) cycles targeting the screening of multiple factors. These designs enable the identification of optimal strains and provide valuable guidance for subsequent optimization cycles.

Enhancing Pseudomonas cell growth for the production of medium-chain-length polyhydroxyalkanoates from Antarctic krill shell waste

Antarctic krill shell waste (AKSW), a byproduct of Antarctic krill processing, has substantial quantity but low utilization. Utilizing microbial-based cell factories, with Pseudomonas putida as a promising candidate, offers an ecofriendly and sustainable approach to producing valuable bioproducts from renewable sources. However, the high fluoride content in AKSW impedes the cell growth of P. putida. This study aims to investigate the transcriptional response of P. putida to fluoride stress from AKSW and subsequently conduct genetic modification of the strain based on insights gained from transcriptomic analysis. Notably, the engineered strain KT+16840+03100 exhibited a remarkable 33.7-fold increase in cell growth, capable of fermenting AKSW for medium-chain-length-polyhydroxyalkanoates (mcl-PHA) biosynthesis, achieving a 40.3-fold increase in mcl-PHA yield compared to the control strain. This research advances our understanding of how P. putida responds to fluoride stress from AKSW and provides engineered strains that serve as excellent platforms for producing mcl-PHA through AKSW.

Synthetic co-culture in an interconnected two-compartment bioreactor system: violacein production with recombinant E. coli strains

The concept of modular synthetic co-cultures holds considerable potential for biomanufacturing, primarily to reduce the metabolic burden of individual strains by sharing tasks among consortium members. However, current consortia often show unilateral relationships solely, without stabilizing feedback control mechanisms, and are grown in a shared cultivation setting. Such 'one pot' approaches hardly install optimum growth and production conditions for the individual partners. Hence, novel mutualistic, self-coordinating consortia are needed that are cultured under optimal growth and production conditions for each member. The heterologous production of the antibiotic violacein (VIO) in the mutually interacting E. coli-E. coli consortium serves as an example of this new principle. Interdependencies for growth control were implemented via auxotrophies for L-tryptophan and anthranilate (ANT) that were satisfied by the respective partner. Furthermore, VIO production was installed in the ANT auxotrophic strain. VIO production, however, requires low temperatures of 20-30 °C which conflicts with the optimum growth temperature of E. coli at 37 °C. Consequently, a two-compartment, two-temperature level setup was used, retaining the mutual interaction of the cells via the filter membrane-based exchange of medium. This configuration also provided the flexibility to perform individualized batch and fed-batch strategies for each co-culture member. We achieved maximum biomass-specific productivities of around 6 mg (g h)-1 at 25 °C which holds great promise for future applications.

Engineering is evolution: a perspective on design processes to engineer biology

Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems.

Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae

Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organism's genotype and its environment. This study employs statistical design of experiments to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted in a 168-fold variation in pCA titre. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bioprocess design efforts to unlock the full potential of microbial cell factories.

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.

Enabling pathway design by multiplex experimentation and machine learning

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.

Control points for design of taxonomic composition in synthetic human gut communities

Microbial communities offer vast potential across numerous sectors but remain challenging to systematically control. We develop a two-stage approach to guide the taxonomic composition of synthetic microbiomes by precisely manipulating media components and initial species abundances. By combining high-throughput experiments and computational modeling, we demonstrate the ability to predict and design the diversity of a 10-member synthetic human gut community. We reveal that critical environmental factors governing monoculture growth can be leveraged to steer microbial communities to desired states. Furthermore, systematically varied initial abundances drive variation in community assembly and enable inference of pairwise inter-species interactions via a dynamic ecological model. These interactions are overall consistent with conditioned media experiments, demonstrating that specific perturbations to a high-richness community can provide rich information for building dynamic ecological models. This model is subsequently used to design low-richness communities that display low or high temporal taxonomic variability over an extended period. A record of this paper's transparent peer review process is included in the supplemental information.

Research progress of engineering microbial cell factories for pigment production

Pigments are widely used in people's daily life, such as food additives, cosmetics, pharmaceuticals, textiles, etc. In recent years, the natural pigments produced by microorganisms have attracted increased attention because these processes cannot be affected by seasons like the plant extraction methods, and can also avoid the environmental pollution problems caused by chemical synthesis. Synthetic biology and metabolic engineering have been used to construct and optimize metabolic pathways for production of natural pigments in cellular factories. Building microbial cell factories for synthesis of natural pigments has many advantages, including well-defined genetic background of the strains, high-density and rapid culture of cells, etc. Until now, the technical means about engineering microbial cell factories for pigment production and metabolic regulation processes have not been systematically analyzed and summarized. Therefore, the studies about construction, modification and regulation of synthetic pathways for microbial synthesis of pigments in recent years have been reviewed, aiming to provide an up-to-date summary of engineering strategies for microbial synthesis of natural pigments including carotenoids, melanins, riboflavins, azomycetes and quinones. This review should provide new ideas for further improving microbial production of natural pigments in the future.

Rational search of genetic design space for a heterologous terpene metabolic pathway in Streptomyces

Modern tools in DNA synthesis and assembly give genetic engineers control over the nucleotide-level design of complex, multi-gene systems. Systematic approaches to explore genetic design space and optimize the performance of genetic constructs are lacking. Here we explore the application of a five-level Plackett-Burman fractional factorial design to improve the titer of a heterologous terpene biosynthetic pathway in Streptomyces. A library of 125 engineered gene clusters encoding the production of diterpenoid ent-atiserenoic acid (eAA) via the methylerythritol phosphate pathway was constructed and introduced into Streptomyces albidoflavus J1047 for heterologous expression. The eAA production titer varied within the library by over two orders of magnitude and host strains showed unexpected and reproducible colony morphology phenotypes. Analysis of Plackett-Burman design identified expression of dxs, the gene encoding the first and the flux-controlling enzyme, having the strongest impact on eAA titer, but with a counter-intuitive negative correlation between dxs expression and eAA production. Finally, simulation modeling was performed to determine how several plausible sources of experimental error/noise and non-linearity impact the utility of Plackett-Burman analyses.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Investigating the Impact of Bank Housing Credit Risk Control Strategy by Blockchain Technology on the Household Consumption Plan

It is essential to solve the problem of information asymmetry in bank housing credit management, thus reducing bank operating costs and credit risk. Therefore, the present work constructs a bank housing credit blockchain alliance system based on the technical characteristics of blockchain to strengthen bank housing credit risk control. First, according to blockchain's elements and system architecture, the application of blockchain to bank housing credit business is analyzed. On this basis, the bank housing credit risk control strategy is proposed. Finally, the mathematical model is used to investigate residents' household consumption to explore the impact of housing credit on the household consumption plan of residents. The results show that when the educational level of the head of household is illiterate or semi-illiterate, the coefficients of participation credit (PC), nonparticipation credit (NPC), and total samples (TSs) of total household consumption are -0.064, 0.067, and 0.174, respectively; when the educational level of the head of household is primary school or junior middle school, the coefficients of PC, NPC, and TS of total household consumption are -0.026, 0.017, and 0.105, respectively; when the educational level of the head of household is high school or above, the coefficients of PC, NPC, and TS of total household consumption are 0.084, 0.073, and 0.064, respectively. The results indicate that the education level of the head of household plays a crucial role in the family consumption plan. Those with a high educational level will compromise on the best consumption decision after constantly weighing the consumption motivation and consumption thinking after obtaining the credit funds. The content reported here is vitally significant in guiding households to clarify the proportion of credit fund distribution and understand their consumption tendency.

The Derived Components of Gnaphalium hypoleucum DC. Reduce Quorum Sensing of Chromobacterium violaceum

Gnaphalium hypoleucum DC. was first recorded in the Chinese National Pharmacopoeia “Yi Plant Medicine”. There is no detailed report on its main components’ activity in suppressing the quorum sensing activity (QS) of bacteria. Our study aimed to screen the main components in extracts of G. hypoleucum DC. in order to measure their effects on bacterial QS activity and to explore specific quorum sensing mechanisms that are affected by G. hypoleucum DC. extracts. Crude extracts of G. hypoleucum DC. contained significant amounts of two compounds shown to inhibit bacterial QS activity, namely apigenin and luteolin. Apigenin and luteolin in crude extracts of G. hypoleucum DC. showed substantial inhibition of pigment formation, biofilm production, and motility in Chromobacterium violaceum ATCC 12472 compared to the effects of other phytochemicals from G. hypoleucum DC. Apigenin and luteolin exhibited a strong QS inhibitory effect on C. violaceum, interfering with the violacein pigment biosynthesis by downregulating the vioB, vioC, and vioD genes. In the presence of signal molecules, the QS effect is prevented, and the selected compounds can still inhibit the production of the characteristic purple pigment in C. violaceum. Based on qualitative and quantitative research using genomics and bioinformatics, we concluded that apigenin and luteolin in crude extracts of G. hypoleucum DC can interfere with the generation of QS in C. violaceum by downregulating the vioB, vioC, and vioD genes. Indeed, G. hypoleucum DC. is used for the treatment of bacterial infections, and this research provides new ideas and potential alternative uses for medicinal plants.

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

Background

The rapidly expanding synthetic biology toolbox allows engineers to develop smarter strategies to tackle the optimization of complex biosynthetic pathways. In such a strategy, multi-gene pathways are subdivided in several modules which are each dynamically controlled to fine-tune their expression in response to a changing cellular environment. To fine-tune separate modules without interference between modules or from the host regulatory machinery, a sigma factor (σ) toolbox was developed in previous work for tunable orthogonal gene expression. Here, this toolbox is implemented in E. coli to orthogonally express and fine-tune a pathway for the heterologous biosynthesis of the industrially relevant plant metabolite, naringenin. To optimize the production of this pathway, a practical workflow is still imperative to balance all steps of the pathway. This is tackled here by the biosensor-driven screening, subsequent genotyping of combinatorially engineered libraries and finally the training of three different computer models to predict the optimal pathway configuration.

Results

The efficiency and knowledge gained through this workflow is demonstrated here by improving the naringenin production titer by 32% with respect to a random pathway library screen. Our best strain was cultured in a batch bioreactor experiment and was able to produce 286 mg/L naringenin from glycerol in approximately 26 h. This is the highest reported naringenin production titer in E. coli without the supplementation of pathway precursors to the medium or any precursor pathway engineering. In addition, valuable pathway configuration preferences were identified in the statistical learning process, such as specific enzyme variant preferences and significant correlations between promoter strength at specific steps in the pathway and titer.

Conclusions

An efficient strategy, powered by orthogonal expression, was applied to successfully optimize a biosynthetic pathway for microbial production of flavonoids in E. coli up to high, competitive levels. Within this strategy, statistical learning techniques were combined with combinatorial pathway optimization techniques and an in vivo high-throughput screening method to efficiently determine the optimal operon configuration of the pathway. This “pathway architecture designer” workflow can be applied for the fast and efficient development of new microbial cell factories for different types of molecules of interest while also providing additional insights into the underlying pathway characteristics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01775-8.

PASIV: A Pooled Approach-Based Workflow to Overcome Toxicity-Induced Design of Experiments Failures and Inefficiencies

We present here a newly developed workflow—which we have called PASIV—designed to provide a solution to a practical problem with design of experiments (DoE) methodology: i.e., what can be done if the scoping phase of the DoE cycle is severely hampered by burden and toxicity issues (caused by either the metabolite or an intermediary), making it unreliable or impossible to proceed to the screening phase? PASIV—standing for pooled approach, screening, identification, and visualization—was designed so the (viable) region of interest can be made to appear through an interplay between biology and software. This was achieved by combining multiplex construction in a pooled approach (one-pot reaction) with a viability assay and with a range of bioinformatics tools (including a novel construct matching tool). PASIV was tested on the exemplar of the lycopene pathway—under stressful constitutive expression—yielding a region of interest with comparatively stronger producers.

Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology

We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).

Key reaction components affect the kinetics and performance robustness of cell-free protein synthesis reactions

Cell-free protein synthesis (CFPS) reactions have grown in popularity with particular interest in applications such as gene construct prototyping, biosensor technologies and the production of proteins with novel chemistry. Work has frequently focussed on optimising CFPS protocols for improving protein yield, reducing cost, or developing streamlined production protocols. Here we describe a statistical Design of Experiments analysis of 20 components of a popular CFPS reaction buffer. We simultaneously identify factors and factor interactions that impact on protein yield, rate of reaction, lag time and reaction longevity. This systematic experimental approach enables the creation of a statistical model capturing multiple behaviours of CFPS reactions in response to components and their interactions. We show that a novel reaction buffer outperforms the reference reaction by 400% and importantly reduces failures in CFPS across batches of cell lysates, strains of E. coli, and in the synthesis of different proteins. Detailed and quantitative understanding of how reaction components affect kinetic responses and robustness is imperative for future deployment of cell-free technologies.

Microbial assimilation of lignin-derived aromatic compounds and conversion to value-added products

Lignin is an abundant and sustainable source of aromatic compounds that can be converted to value-added products. However, lignin is underutilized, since depolymerization produces a complex mixture of aromatic compounds that is difficult to convert to a single product. Microbial conversion of mixed aromatic substrates provides a potential solution to this conversion challenge. Recent advances have expanded the range of lignin-derived aromatic substrates that can be assimilated and demonstrated efficient conversion via central metabolism to new potential products. The development of additional non-model microbial hosts and genetic tools for these hosts have accelerated engineering efforts. However, yields with real depolymerized lignin are still low, and additional work will be required to achieve viable conversion processes.

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence-spaces is a labour-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a rate-limiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13-fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

A DNA Inversion System in Eukaryotes Established via Laboratory Evolution

DNA inversion is a type of site-specific recombination system that plays an important role in the generation of genetic diversity and phenotypic adaptation by programmed rearrangements in bacteria. However, no such inversion system exhibiting a strong directionality bias has been identified or developed in eukaryotes yet. Here, using directed evolution of Rci recombinase, a tyrosine recombinase from a bacterial DNA inversion system, we identified a mutant Rci8 with a ratio of inversion/deletion up to ∼4320 in yeast. Based on Rci8 recombinase and sfxa101 sites, we have established a DNA inversion system in yeast and mammalian cells, enabling specificity for DNA inversions between inverted sites over deletions between directly repeated sites. Our results validated that the reversible DNA inversion system can act as an on/off transcriptional switch. Moreover, we demonstrate that the inversion system can also work on linear chromosomes. The eukaryotic DNA inversion system would provide a new tool for fields of genetic circuits, cellular barcoding, and synthetic genomes.

The evolving art of creating genetic diversity: From directed evolution to synthetic biology

The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.

Active and machine learning-based approaches to rapidly enhance microbial chemical production

In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)-requiring many experimental datasets for their parameterization-while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.

Rapid Genome Modification in Serratia marcescens Through Red Homologous Recombination

Despite the great potential of Serratia marcescens in industrial applications, lack of powerful genetic modification tools limits understanding of the regulatory networks of the useful metabolites and therefore restricts their mass production. To meet the urgent demand, we established a genome-editing strategy for S. marcescens based on Red recombineering in this study. Without host modification in advance, nucA and pigA were substituted by PCR-amplified resistance genes. No long homologous arms were required at the two sides of resistance genes. Using this procedure, the fragment at the S. marcescens as large as 20 kb was easily deleted. Then we constructed a counter-selection gene kil constructed under the control of inducible P_BAD operon, which demonstrates obvious lethality to S. marcescens. Subsequently, Gm^R-kil double selection cassette was inserted into the CDS of pigA gene. Using single-stranded DNA-mediated recombination, this insertion mutation was efficiently repaired through kil counter-selection. A powerful genetic modification platform based on Red recombineering system was successfully established for S. marcescens. Multiple types of modification and multiple recombination strategies can all be performed easily in this species. We hope this study will be useful for the theoretical research and the research of metabolic engineering in S. marcescens.

Synthetic biology-inspired strategies and tools for engineering of microbial natural product biosynthetic pathways

Microbial-derived natural products (NPs) and their derivative products are of great importance and used widely in many fields, especially in pharmaceutical industries. However, there is an immediate need to establish innovative approaches, strategies, and techniques to discover new NPs with novel or enhanced biological properties, due to the less productivity and higher cost on traditional drug discovery pipelines from natural bioresources. Revealing of untapped microbial cryptic biosynthetic gene clusters (BGCs) using DNA sequencing technology and bioinformatics tools makes genome mining possible for NP discovery from microorganisms. Meanwhile, new approaches and strategies in the area of synthetic biology offer great potentials for generation of new NPs by engineering or creating synthetic systems with improved and desired functions. Development of approaches, strategies and tools in synthetic biology can facilitate not only exploration and enhancement in supply, and also in the structural diversification of NPs. Here, we discussed recent advances in synthetic biology-inspired strategies, including bioinformatics and genetic engineering tools and approaches for identification, cloning, editing/refactoring of candidate biosynthetic pathways, construction of heterologous expression hosts, fitness optimization between target pathways and hosts and detection of NP production.

Statistical Design of Experiments for Synthetic Biology

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

A Golden-Gate Based Cloning Toolkit to Build Violacein Pathway Libraries in Yarrowia lipolytica

Violacein is a naturally occurring anticancer therapeutic compound with deep purple color. In this work, we harnessed the modular and combinatorial feature of a Golden Gate assembly method to construct a library of violacein producing strains in the oleaginous yeast Yarrowia lipolytica, where each gene in the violacein pathway was controlled by three different promoters with varying transcriptional strength. After optimizing the linker sequence and the Golden Gate reaction, we achieved high transformation efficiency and obtained a panel of representative Y. lipolytica recombinant strains. By evaluating the gene expression profile of 21 yeast strains, we obtained three colorful compounds in the violacein pathway: green (proviolacein), purple (violacein), and pink (deoxyviolacein). Our results indicated that strong expression of VioB, VioC, and VioD favors violacein production with minimal byproduct deoxyvioalcein in Y. lipolytica, and high deoxyviolacein production was found strongly associated with the weak expression of VioD. By further optimizing the carbon to nitrogen ratio and cultivation pH, the maximum violacein reached 70.04 mg/L with 5.28 mg/L of deoxyviolacein in shake flasks. Taken together, the development of Golden Gate cloning protocols to build combinatorial pathway libraries, and the optimization of culture conditions set a new stage for accessing the violacein pathway intermediates and engineering violacein production in Y. lipolytica. This work further expands the toolbox to engineering Y. lipolytica as an industrially relevant host for plant or marine natural product biosynthesis.

Engineering Yarrowia lipolytica for Production of Fatty Alcohols with YaliBrick Vectors

Biosynthesis of fatty alcohol holds great promise as substitute to replace petroleum-derived fatty alcohols to mitigate environmental concerns and reduce earth's carbon footprint. In this protocol, we detail the procedures of how to use the YaliBrick gene assembly platform to achieve modular assembly of fatty alcohol pathway in Y. lipolytica. To limit fatty alcohol oxidation, we will also describe the hydroxyurea-based protocols for the efficient disruption of POX1 gene, encoding the fatty acyl coenzyme A in Y. lipolytica, with the homologous arm about 500 bp. We envision that this chapter would improve our ability to engineer microbial cell factories for oleochemical and fatty alcohol production in oleaginous yeast species.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

A roadmap to engineering antiviral natural products synthesis in microbes

Natural products continue to be the inspirations for us to discover and acquire new drugs. The seemingly unstoppable viruses have kept records high to threaten human health and well-being. The diversity and complexity of natural products (NPs) offer remarkable efficacy and specificity to target viral infection steps and serve as excellent source for antiviral agents. The discovery and production of antiviral NPs remain challenging due to low abundance in their native hosts. Reconstruction of NP biosynthetic pathways in microbes is a promising solution to overcome this limitation. In this review, we surveyed 23 most prominent NPs (from more than 200 antiviral NP candidates) with distinct antiviral mode of actions and summarized the recent metabolic engineering effort to produce these compounds in various microbial hosts. We envision that the scalable and low-cost production of novel antiviral NPs, enabled by metabolic engineering, may light the hope to control and eradicate the deadliest viruses that plague our society and humanity.

Exploiting Single Domain Antibodies as Regulatory Parts to Modulate Monoterpenoid Production in E. coli

Synthetic biology and metabolic engineering offer potentially green and attractive routes to the production of high value compounds. The provision of high-quality parts and pathways is crucial in enabling the biosynthesis of chemicals using synthetic biology. While a number of regulatory parts that provide control at the transcriptional and translational level have been developed, relatively few exist at the protein level. Single domain antibodies (sdAb) such as camelid heavy chain variable fragments (V_HH) possess binding characteristics which could be exploited for their development and use as novel parts for regulating metabolic pathways at the protein level in microbial cell factories. Here, a platform for the use of V_HH as tools in Escherichia coli is developed and subsequently used to modulate linalool production in E. coli. The coproduction of a Design of Experiments (DoE) optimized pBbE8k His₆-V_HHCyDisCo system alongside a heterologous linalool production pathway facilitated the identification of anti-bLinS V_HH that functioned as modulators of bLinS. This resulted in altered product profiles and significant variation in the titers of linalool, geraniol, nerolidol, and indole obtained. The ability to alter the production levels of high value terpenoids, such as linalool, in a tunable manner at the protein level could represent a significant step forward for the development of improved microbial cell factories. This study serves as a proof of principle indicating that V_HH can be used to modulate enzyme activity in engineered pathways within E. coli. Given their almost limitless binding potential, we posit that single domain antibodies could emerge as powerful regulatory parts in synthetic biology applications.

Effect of kitasamycin and nitrofurantoin at subinhibitory concentrations on quorum sensing regulated traits of Chromobacterium violaceum

Quorum sensing (QS) is a mechanism of intercellular communication in bacteria that received substantial attention as alternate strategy for combating bacterial resistance and the development of new anti-infective agents. The present investigation reports on the assessment of using subinhibitory concentrations of antibiotics for the inhibition of QS-regulated phenotypes in Chromobacterium violaceum. Primarily, the minimum inhibitory concentrations of a series of antibiotics were determined by a microdilution method. Subsequently, the inhibitory effects of selected antibiotics on QS-regulated traits, namely violacein and chitinase production, biofilm formation and motility were evaluated using C. violaceum CV026 and C. violaceum ATCC 12472. Results revealed that kitasamycin and nitrofurantoin exhibited the highest quorum sensing inhibitory (QSI) activity. The amount of violacein produced by C. violaceum was significantly reduced in the presence of either kitasamycin or nitrofurantoin. Moreover, the chitinolytic activity, biofilm formation, and motility were also impaired in kitasamycin or nitrofurantoin-treated cultures. We further confirmed QSI effects at the molecular level using molecular docking and real-time quantitative polymerase chain reaction (RT-qPCR). Results of molecular docking suggested that both antibiotics can interact with CviR transcriptional regulator of C. violaceum. Furthermore, RT-qPCR revealed the suppressive effect of kitasamycin and nitrofurantoin on five genes under the control of the CviI/CviR system: cviI, cviR, vioB, vioC, and vioD. Giving that kitasamycin and nitrofurantoin are being safely used for decades, this study emphasizes their potential application as antivirulence agents to disarm resistant bacterial strains, making their removal an easier task for the immune system or for another antibacterial agent.

Increasing metabolic pathway flux by using machine learning models

Machine learning is transforming many industries through self-improving models that are fueled by big data and high computing power. The field of metabolic engineering, which uses cellular biochemical network to manufacture useful small molecules, has also witnessed the first wave of machine learning applications in the past five years, covering reaction route design, enzyme selection, pathway engineering and process optimization. This review focuses on pathway engineering, and uses a few recent studies to illustrate (1) how machine learning models can be useful in overcoming an evident rate-limiting step, and (2) how the models may be used to exhaustively search - or guide optimization algorithms to search - a large design space when the cellular regulation of the reaction network is more convoluted.

Cell-free synthesis system-assisted pathway bottleneck diagnosis and engineering in Bacillus subtilis

Metabolic engineering is a key technology for cell factories construction by rewiring cellular resources to achieve efficient production of target chemicals. However, the existence of bottlenecks in synthetic pathway can seriously affect production efficiency, which is also one of the core issues for metabolic engineers to solve. Therefore, developing an approach for diagnosing potential metabolic bottlenecks in a faster and simpler manner is of great significance to accelerate cell factories construction. The cell-free reaction system based on cell lysates can transfer metabolic reactions from in vivo to in vitro, providing a flexible access to directly change protein and metabolite variables, thus provides a potential solution for rapid identification of bottlenecks. Here, bottleneck diagnosis of the N-acetylneuraminic acid (NeuAc) biosynthesis pathway in industrially important chassis microorganism Bacillus subtilis was performed using cell-free synthesis system. Specifically, a highly efficient B. subtilis cell-free system for NeuAc de novo synthesis was firstly constructed, which had a 305-fold NeuAc synthesis rate than that in vivo and enabled fast pathway dynamics analysis. Next, through the addition of all potential key intermediates in combination with substrate glucose respectively, it was found that insufficient phosphoenolpyruvate supply was one of the NeuAc pathway bottlenecks. Rational in vivo metabolic engineering of NeuAc-producing B. subtilis was further performed to eliminate the bottleneck. By down-regulating the expression level of pyruvate kinase throughout the growth phase or only in the stationary phase using inhibitory N-terminal coding sequences (NCSs) and growth-dependent regulatory NCSs respectively, the maximal NeuAc titer increased 2.0-fold. Our study provides a rapid method for bottleneck diagnosis, which may help to accelerate the cycle of design, build, test and learn cycle for metabolic engineering.

Applying Statistical Design of Experiments To Understanding the Effect of Growth Medium Components on Cupriavidus necator H16 Growth

Chemically defined media (CDM) for cultivation of C. necator vary in components and compositions. This lack of consensus makes it difficult to optimize new processes for the bacterium. This study employed statistical design of experiments (DOE) to understand how basic components of defined media affect C. necator growth. Our growth model predicts that C. necator can be cultivated to high cell density with components held at low concentrations, arguing that CDM for large-scale cultivation of the bacterium for industrial purposes will be economically competitive. Although existing CDM for the bacterium are without amino acids, addition of a few amino acids to growth medium shortened lag phase of growth. The interactions highlighted by our growth model show how factors can interact with each other during a process to positively or negatively affect process output. This approach is efficient, relying on few well-structured experimental runs to gain maximum information on a biological process, growth.

Cupriavidus necator H16 is gaining significant attention as a microbial chassis for range of biotechnological applications. While the bacterium is a major producer of bioplastics, its lithoautotrophic and versatile metabolic capabilities make the bacterium a promising microbial chassis for biofuels and chemicals using renewable resources. It remains necessary to develop appropriate experimental resources to permit controlled bioengineering and system optimization of this microbe. In this study, we employed statistical design of experiments to gain understanding of the impact of components of defined media on C. necator growth and built a model that can predict the bacterium’s cell density based on medium components. This highlighted medium components, and interaction between components, having the most effect on growth: fructose, amino acids, trace elements, CaCl₂, and Na₂HPO₄ contributed significantly to growth (t values of <−1.65 or >1.65); copper and histidine were found to interact and must be balanced for robust growth. Our model was experimentally validated and found to correlate well (r² = 0.85). Model validation at large culture scales showed correlations between our model-predicted growth ranks and experimentally determined ranks at 100 ml in shake flasks (ρ = 0.87) and 1 liter in a bioreactor (ρ = 0.90). Our approach provides valuable and quantifiable insights on the impact of medium components on cell growth and can be applied to model other C. necator responses that are crucial for its deployment as a microbial chassis. This approach can be extended to other nonmodel microbes of medical and industrial biotechnological importance.

IMPORTANCE Chemically defined media (CDM) for cultivation of C. necator vary in components and compositions. This lack of consensus makes it difficult to optimize new processes for the bacterium. This study employed statistical design of experiments (DOE) to understand how basic components of defined media affect C. necator growth. Our growth model predicts that C. necator can be cultivated to high cell density with components held at low concentrations, arguing that CDM for large-scale cultivation of the bacterium for industrial purposes will be economically competitive. Although existing CDM for the bacterium are without amino acids, addition of a few amino acids to growth medium shortened lag phase of growth. The interactions highlighted by our growth model show how factors can interact with each other during a process to positively or negatively affect process output. This approach is efficient, relying on few well-structured experimental runs to gain maximum information on a biological process, growth.

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

Modular engineering for microbial production of carotenoids

There is an increasing demand for carotenoids due to their applications in the food, flavor, pharmaceutical and feed industries, however, the extraction and synthesis of these compounds can be expensive and technically challenging. Microbial production of carotenoids provides an attractive alternative to the negative environmental impacts and cost of chemical synthesis or direct extraction from plants. Metabolic engineering and synthetic biology approaches have been widely utilized to reconstruct and optimize pathways for carotenoid overproduction in microorganisms. This review summarizes the current advances in microbial engineering for carotenoid production and divides the carotenoid biosynthesis building blocks into four distinct metabolic modules: 1) central carbon metabolism, 2) cofactor metabolism, 3) isoprene supplement metabolism and 4) carotenoid biosynthesis. These four modules focus on redirecting carbon flux and optimizing cofactor supplements for isoprene precursors needed for carotenoid synthesis. Future perspectives are also discussed to provide insights into microbial engineering principles for overproduction of carotenoids.

Recent Advances in Metabolically Engineered Microorganisms for the Production of Aromatic Chemicals Derived From Aromatic Amino Acids

Aromatic compounds derived from aromatic amino acids are an important class of diverse chemicals with a wide range of industrial and commercial applications. They are currently produced via petrochemical processes, which are not sustainable and eco-friendly. In the past decades, significant progress has been made in the construction of microbial cell factories capable of effectively converting renewable carbon sources into value-added aromatics. Here, we systematically and comprehensively review the recent advancements in metabolic engineering and synthetic biology in the microbial production of aromatic amino acid derivatives, stilbenes, and benzylisoquinoline alkaloids. The future outlook concerning the engineering of microbial cell factories for the production of aromatic compounds is also discussed.

Large scale active-learning-guided exploration for in vitro protein production optimization

Lysate-based cell-free systems have become a major platform to study gene expression but batch-to-batch variation makes protein production difficult to predict. Here we describe an active learning approach to explore a combinatorial space of ~4,000,000 cell-free buffer compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provide a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.

Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling

Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (∼4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.

Maximizing Biocatalytic Cyclohexane Hydroxylation by Modulating Cytochrome P450 Monooxygenase Expression in P. taiwanensis VLB120

Cytochrome P450 monooxygenases (Cyps) effectively catalyze the regiospecific oxyfunctionalization of inert C-H bonds under mild conditions. Due to their cofactor dependency and instability in isolated form, oxygenases are preferably applied in living microbial cells with Pseudomonas strains constituting potent host organisms for Cyps. This study presents a holistic genetic engineering approach, considering gene dosage, transcriptional, and translational levels, to engineer an effective Cyp-based whole-cell biocatalyst, building on recombinant Pseudomonas taiwanensis VLB120 for cyclohexane hydroxylation. A lac-based regulation system turned out to be favorable in terms of orthogonality to the host regulatory network and enabled a remarkable specific whole-cell activity of 34 U g_CDW ^-1. The evaluation of different ribosomal binding sites (RBSs) revealed that a moderate translation rate was favorable in terms of the specific activity. An increase in gene dosage did only slightly elevate the hydroxylation activity, but severely impaired growth and resulted in a large fraction of inactive Cyp. Finally, the introduction of a terminator reduced leakiness. The optimized strain P. taiwanensis VLB120 pSEVA_Cyp allowed for a hydroxylation activity of 55 U g_CDW ^-1. Applying 5 mM cyclohexane, molar conversion and biomass-specific yields of 82.5% and 2.46 mmol_cyclohexanol g_biomass ^-1 were achieved, respectively. The strain now serves as a platform to design in vivo cascades and bioprocesses for the production of polymer building blocks such as ε-caprolactone.

Strategies for the Biosynthesis of Pharmaceuticals and Nutraceuticals in Microbes from Renewable Feedstock

BACKGROUNDS

Abundant and renewable biomaterials serve as ideal substrates for the sustainable production of various chemicals, including natural products (e.g., pharmaceuticals and nutraceuticals). For decades, researchers have been focusing on how to engineer microorganisms and developing effective fermentation processes to overproduce these molecules from biomaterials. Despite many laboratory achievements, it remains a challenge to transform some of these into successful industrial applications.

RESULTS

Here, we review recent progress in strategies and applications in metabolic engineering for the production of natural products. Modular engineering methods, such as a multidimensional heuristic process markedly improve efficiencies in the optimization of long and complex biosynthetic pathways. Dynamic pathway regulation realizes autonomous adjustment and can redirect metabolic carbon fluxes to avoid the accumulation of toxic intermediate metabolites. Microbial co-cultivation bolsters the identification and overproduction of natural products by introducing competition or cooperation of different species. Efflux engineering is applied to reduce product toxicity or to overcome storage limitation and thus improves product titers and productivities.

CONCLUSION

Without dispute, many of the innovative methods and strategies developed are gradually catalyzing this transformation from the laboratory into the industry in the biosynthesis of natural products. Sometimes, it is necessary to combine two or more strategies to acquire additive or synergistic benefits. As such, we foresee a bright future of the biosynthesis of pharmaceuticals and nutraceuticals in microbes from renewable biomaterials.

A Combinatorial Approach to Optimize the Production of Curcuminoids From Tyrosine in Escherichia coli

Curcuminoids are well-known for their therapeutic properties. However, their extraction from natural sources is environmentally unfriendly, expensive and limited by seasonal variability, highlighting the need for alternative production processes. We propose an optimized artificial biosynthetic pathway to produce curcuminoids, including curcumin, in Escherichia coli. This pathway involves six enzymes, tyrosine ammonia lyase (TAL), 4-coumarate 3-hydroxylase (C3H), caffeic acid O-methyltransferase (COMT), 4-coumarate-CoA ligase (4CL), diketide-CoA synthase (DCS), and curcumin synthase (CURS1). Curcuminoids pathway was divided in two modules, the first module included TAL, C3H and COMT and the second one 4CL, DCS and CURS1. Optimizing the first module of the pathway, from tyrosine to ferulic acid, enabled obtaining the highest ferulic acid titer reported so far (1325.1 μM). Afterward, ferulic acid was used as substrate to optimize the second module of the pathway. We achieved the highest concentration of curcumin ever reported (1529.5 μM), corresponding to a 59.4% increase. Subsequently, curcumin and other curcuminoids were produced from tyrosine (using the whole pathway) in mono-culture. The production increased comparing to a previously reported pathway that used a caffeoyl-CoA O-methyltransferase enzyme (to convert caffeoyl-CoA to feruloyl-CoA) instead of COMT (to convert caffeic to ferulic acid). Additionally, the potential of a co-culture approach was evaluated to further improve curcuminoids production by reducing cells metabolic burden. We used one E. coli strain able to convert tyrosine to ferulic acid and another able to convert the hydroxycinnamic acids produced by the first one to curcuminoids. The co-culture strategies tested led to 6.6 times increase of total curcuminoids (125.8 μM) when compared to the mono-culture system. The curcuminoids production achieved in this study corresponds to a 6817% improvement. In addition, by using an inoculation ratio of 2:1, although total curcuminoids production decreased, curcumin production was enhanced and reached 43.2 μM, corresponding to an improvement of 160% comparing to mono-culture system. To our knowledge, these values correspond to the highest titers of curcuminoids obtained to date. These results demonstrate the enormous potential of modular co-culture engineering to produce curcumin, and other curcuminoids, from tyrosine.

Towards a fully automated algorithm driven platform for biosystems design

Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.

Existing efforts have been focused on one of the elements in the automation of the design, build, test, and learn (DBTL) cycle for biosystems design. Here, the authors integrate a robotic system with machine learning algorithms to fully automate the DBTL cycle and apply it in optimizing the lycopene biosynthetic pathway.

Emerging molecular biology tools and strategies for engineering natural product biosynthesis

Natural products and their related derivatives play a significant role in drug discovery and have been the inspiration for the design of numerous synthetic bioactive compounds. With recent advances in molecular biology, numerous engineering tools and strategies were established to accelerate natural product synthesis in both academic and industrial settings. However, many obstacles in natural product biosynthesis still exist. For example, the native pathways are not appropriate for research or production; the key enzymes do not have enough activity; the native hosts are not suitable for high-level production. Emerging molecular biology tools and strategies have been developed to not only improve natural product titers but also generate novel bioactive compounds. In this review, we will discuss these emerging molecular biology tools and strategies at three main levels: enzyme level, pathway level, and genome level, and highlight their applications in natural product discovery and development.

Stimulus response-based fine-tuning of polyhydroxyalkanoate pathway in Halomonas

Optimization of intracellular biosynthesis process involving regulation of multiple gene expressions is dependent on the efficient and accurate expression of each expression unit independently. However, challenges of analyzing intermediate products seriously hinder the application of high throughput assays. This study aimed to develop an engineering approach for unsterile production of poly(3-hydroxybutyrate-co-4-hydroxybutyrate) or (P3HB4HB) by recombinant Halomonas bluephagenesis (H. bluephagenesis) constructed via coupling the design of GFP-mediated transcriptional mapping and high-resolution control of gene expressions (HRCGE), which consists of two inducible systems with high- and low-dynamic ranges employed to search the exquisite transcription level of each expression module in the presence of γ-butyrolactone, the intermediate for 4-hydroxybutyrate (4HB) synthesis. It has been successful to generate a recombinant H. bluephagenesis, namely TD68-194, able to produce over 36 g/L P3HB4HB consisting of 16 mol% 4HB during a 7-L lab-scale fed-batch growth process, of which cell dry weight and PHA content reached up to 48.22 g/L and 74.67%, respectively, in 36 h cultivation. HRCGE has been found useful for metabolic pathway construction.