Growth-coupled selection of synthetic modules to accelerate cell factory development

Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories

Metabolic engineering principles have long been applied to explore the metabolic networks of complex microbial cell factories under a variety of environmental constraints for effective deployment of the microorganisms in the optimal production of biochemicals like biofuels, polymers, amino acids, recombinant proteins. One of the methodologies used for analyzing microbial metabolic networks is the Flux Balance Analysis (FBA), which employs applications of optimization techniques for forecasting biomass growth and metabolic flux distribution of industrially important products under specified environmental conditions. The in silico flux simulations are instrumental for designing the production-specific microbial cell factories. However, FBA has some inherent limitations. The present review emphasizes how the incorporation of additional kinetic, thermodynamic, expression and regulatory constraints and integration of omics data into the classical FBA platform improve the prediction accuracy of FBA. A programmed comparison of the simulated data with the experimental observations is presented for supporting the claim. The review further accounts for the successful implementation of classical FBA in biotechnological applications and identifies areas in which classical FBA fails to make correct predictions. The analysis of the predictive capabilities of the different FBA strategies presented here is expected to help researchers in finding new avenues in engineering highly efficient microbial metabolic pathways and identify the key metabolic bottlenecks during the process. Based on the appropriate metabolic network design, fermentation engineers will be able to effectively design the bioreactors and optimize large-scale biochemical production through suitable pathway modifications.

Evolution-assisted engineering of E. coli enables growth on formic acid at ambient CO2 via the Serine Threonine Cycle

Atmospheric CO2 poses a major threat to life on Earth by causing global warming and climate change. On the other hand, it can be considered as a resource that is scalable enough to establish a circular carbon economy. Accordingly, technologies to capture and convert CO2 into reduced one-carbon (C1) compounds (e.g. formic acid) are developing and improving fast. Driven by the idea of creating sustainable bioproduction platforms, natural and synthetic C1-utilization pathways are engineered into industrially relevant microbes. The realization of synthetic C1-assimilation cycles in living organisms is a promising but challenging endeavour. Here, we engineer the Serine Threonine Cycle, a synthetic C1-assimilation cycle in Escherichia coli to achieve growth on formic acid. Our stepwise engineering approach in tailored selection strains combined with adaptive laboratory evolution experiments enabled formatotrophic growth of the organism. Whole genome sequencing and reverse engineering allowed us to determine the key mutations linked to pathway activity. The Serine Threonine Cycle strains created in this work use formic acid as a carbon and energy source and can grow at ambient CO2 cultivation conditions. This work sets an example for the engineering of complex C1-assimilation cycles in heterotrophic microbes.

Disentangling the Regulatory Response of Agrobacterium tumefaciens CHLDO to Glyphosate for Engineering Whole-Cell Phosphonate Biosensors

Phosphonates (PHTs), organic compounds with a stable C-P bond, are widely distributed in nature. Glyphosate (GP), a synthetic PHT, is extensively used in agriculture and has been linked to various human health issues and environmental damage. Given the prevalence of GP, developing cost-effective, on-site methods for GP detection is key for assessing pollution and reducing exposure risks. We adopted Agrobacterium tumefaciens CHLDO, a natural GP degrader, as a host and the source of genetic parts for constructing PHT biosensors. In this bacterial species, the phn gene cluster, encoding the C-P lyase pathway, is regulated by the PhnF transcriptional repressor. We selected the phnG promoter, which displays a dose-dependent response to GP, to build a set of whole-cell biosensors. Through stepwise genetic optimization of the transcriptional cascade, we created a whole-cell biosensor capable of detecting GP in the 0.25-50 μM range in various samples, including soil and water.

Fundamentals and Exceptions of the LysR-type Transcriptional Regulators

LysR-type transcriptional regulators (LTTRs) are emerging as a promising group of macromolecules for the field of biosensors. As the largest family of bacterial transcription factors, the LTTRs represent a vast and mostly untapped repertoire of sensor proteins. To fully harness these regulators for transcription factor-based biosensor development, it is crucial to understand their underlying mechanisms and functionalities. In the first part, this Review discusses the established model and features of LTTRs. As dual-function regulators, these inducible transcription factors exude precise control over their regulatory targets. In the second part of this Review, an overview is given of the exceptions to the "classic" LTTR model. While a general regulatory mechanism has helped elucidate the intricate regulation performed by LTTRs, it is essential to recognize the variations within the family. By combining this knowledge, characterization of new regulators can be done more efficiently and accurately, accelerating the expansion of transcriptional sensors for biosensor development. Unlocking the pool of LTTRs would significantly expand the currently limited range of detectable molecules and regulatory functions available for the implementation of novel synthetic genetic circuitry.

A versatile microbial platform as a tunable whole-cell chemical sensor

Biosensors are used to detect and quantify chemicals produced in industrial microbiology with high specificity, sensitivity, and portability. Most biosensors, however, are limited by the need for transcription factors engineered to recognize specific molecules. In this study, we overcome the limitations typically associated with traditional biosensors by engineering Pseudomonas putida for whole-cell sensing of a variety of chemicals. Our approach integrates fluorescent reporters with synthetic auxotrophies within central metabolism that can be complemented by target analytes in growth-coupled setups. This platform enables the detection of a wide array of structurally diverse chemicals under various conditions, including co-cultures of producer cell factories and sensor strains. We also demonstrate the applicability of this versatile biosensor platform for monitoring complex biochemical processes, including plastic degradation by either purified hydrolytic enzymes or engineered bacteria. This microbial system provides a rapid, sensitive, and readily adaptable tool for monitoring cell factory performance and for environmental analyzes.

Strangers in a foreign land: 'Yeastizing' plant enzymes

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

A hierarchy of metabolite exchanges in metabolic models of microbial species and communities

The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells' metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology.

Biosensor-Assisted Engineering for Diverse Microbial Cellular Physiologies

Recent advancements in biosensor technology have revolutionized the field of microbial engineering, enabling efficient and precise optimization of strains for the production of valuable chemicals. This review comprehensively explores the innovative integration of biosensors to enhance microbial cell factories, with a particular emphasis on the crucial role of high-throughput biosensor-assisted screening. Biosensor-assisted approaches have enabled the identification of novel transporters, the elucidation of underlying transport mechanisms, and the fine-tuning of metabolic pathways for enhanced production. Furthermore, this review illustrates the utilization of biosensors for manipulating cellular behaviors, including interactions with environmental factors, and the reduction of nongenetic cell-to-cell variations. This review highlights the indispensable role of biosensors in advancing the field of microbial engineering through the modulation and exploitation of diverse cellular physiological processes.

Engineering of a Substrate Affinity Reduced S-Adenosyl-methionine Synthetase as a Novel Biosensor for Growth-Coupling Selection of L-Methionine Overproducers

Biosensors are powerful tools for monitoring specific metabolites or controlling metabolic flux towards the products in a single cell, which play important roles in microbial cell factory construction. Despite their potential role in metabolic flux monitoring, the development of biosensors for small molecules is still limited. Reported biosensors often exhibit bottlenecks of poor specificity and a narrow dynamic range. Moreover, fine-tuning the substrate binding affinity of a crucial enzyme can decrease its catalytic activity, which ultimately results in the repression of the corresponding essential metabolite biosynthesis and impairs cell growth. However, increasing intracellular substrate concentration can elevate the availability of the essential metabolite and may lead to restore cellular growth. Herein, a new strategy was proposed for constructing whole-cell biosensors based on enzyme encoded by essential gene that offer inherent specificity and universality. Specifically, S-adenosyl-methionine synthetase (MetK) in E. coli was chosen as the crucial enzyme, and a series of MetK variants were identified that were sensitive to L-methionine concentration. This occurrence enabled the engineered cell to sense L-methionine and exhibit L-methionine dose-dependent cell growth. To improve the biosensor's dynamic range, an S-adenosyl-methionine catabolic enzyme was overexpressed to reduce the intracellular availability of S-adenosyl-methionine. The resulting whole-cell biosensor effectively coupled the intracellular concentration of L-methionine with growth and was successfully applied to select strains with enhanced L-methionine biosynthesis from random mutagenesis libraries. Overall, our study presents a universal strategy for designing and constructing growth-coupled biosensors based on crucial enzyme, which can be applied to select strains overproducing high value-added metabolites in cellular metabolism.

Design, construction and optimization of formaldehyde growth biosensors with broad application in biotechnology

Formaldehyde is a key metabolite in natural and synthetic one-carbon metabolism. To facilitate the engineering of formaldehyde-producing enzymes, the development of sensitive, user-friendly, and cost-effective detection methods is required. In this study, we engineered Escherichia coli to serve as a cellular biosensor capable of detecting a broad range of formaldehyde concentrations. Using both natural and promiscuous formaldehyde assimilation enzymes, we designed three distinct E. coli growth biosensor strains that depend on formaldehyde for cell growth. These strains were engineered to be auxotrophic for one or several essential metabolites that could be produced through formaldehyde assimilation. The respective assimilating enzyme was expressed from the genome to compensate the auxotrophy in the presence of formaldehyde. We first predicted the formaldehyde dependency of the biosensors by flux balance analysis and then analysed it experimentally. Subsequent to strain engineering, we enhanced the formaldehyde sensitivity of two biosensors either through adaptive laboratory evolution or modifications at metabolic branch points. The final set of biosensors demonstrated the ability to detect formaldehyde concentrations ranging approximately from 30 μM to 13 mM. We demonstrated the application of the biosensors by assaying the in vivo activity of different methanol dehydrogenases in the most sensitive strain. The fully genomic nature of the biosensors allows them to be deployed as "plug-and-play" devices for high-throughput screenings of extensive enzyme libraries. The formaldehyde growth biosensors developed in this study hold significant promise for advancing the field of enzyme engineering, thereby supporting the establishment of a sustainable one-carbon bioeconomy.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

A model-driven approach to upcycling recalcitrant feedstocks in Pseudomonas putida by decoupling PHA production from nutrient limitation

Bacterial polyhydroxyalkanoates (PHAs) have emerged as promising eco-friendly alternatives to petroleum-based plastics since they are synthesized from renewable resources and offer exceptional properties. However, their production is limited to the stationary growth phase under nutrient-limited conditions, requiring customized strategies and costly two-phase bioprocesses. In this study, we tackle these challenges by employing a model-driven approach to reroute carbon flux and remove regulatory constraints using synthetic biology. We construct a collection of Pseudomonas putida-overproducing strains at the expense of plastics and lignin-related compounds using growth-coupling approaches. PHA production was successfully achieved during growth phase, resulting in the production of up to 46% PHA/cell dry weight while maintaining a balanced carbon-to-nitrogen ratio. Our strains are additionally validated under an upcycling scenario using enzymatically hydrolyzed polyethylene terephthalate as a feedstock. These findings have the potential to revolutionize PHA production and address the global plastic crisis by overcoming the complexities of traditional PHA production bioprocesses.

Pseudomonas putida as a platform for medium-chain length α,ω-diol production: Opportunities and challenges

Medium-chain-length α,ω-diols (mcl-diols) play an important role in polymer production, traditionally depending on energy-intensive chemical processes. Microbial cell factories offer an alternative, but conventional strains like Escherichia coli and Saccharomyces cerevisiae face challenges in mcl-diol production due to the toxicity of intermediates such as alcohols and acids. Metabolic engineering and synthetic biology enable the engineering of non-model strains for such purposes with P. putida emerging as a promising microbial platform. This study reviews the advancement in diol production using P. putida and proposes a four-module approach for the sustainable production of diols. Despite progress, challenges persist, and this study discusses current obstacles and future opportunities for leveraging P. putida as a microbial cell factory for mcl-diol production. Furthermore, this study highlights the potential of using P. putida as an efficient chassis for diol synthesis.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

Engineering and evolution of the complete Reductive Glycine Pathway in Saccharomyces cerevisiae for formate and CO2 assimilation

Using captured CO2 and C1-feedstocks like formate and methanol derived from electrochemical activation of CO2 are key solutions for transforming industrial processes towards a circular carbon economy. Engineering formate and CO2-based growth in the biotechnologically relevant yeast Saccharomyces cerevisiae could boost the emergence of a formate-mediated circular bio-economy. This study adopts a growth-coupled selection scheme for modular implementation of the Reductive Glycine Pathway (RGP) and subsequent Adaptive Laboratory Evolution (ALE) to enable formate and CO2 assimilation for biomass formation in yeast. We first constructed a serine biosensor strain and then implemented the serine synthesis module of the RGP into yeast, establishing glycine and serine synthesis from formate and CO2. ALE improved the RGP-dependent growth by 8-fold. 13C-labeling experiments reveal glycine, serine, and pyruvate synthesis via the RGP, demonstrating the complete pathway activity. Further, we re-established formate and CO2-dependent growth in non-evolved biosensor strains via reverse-engineering a mutation in GDH1 identified from ALE. This mutation led to significantly more 13C-formate assimilation than in WT without any selection or overexpression of the RGP. Overall, we demonstrated the activity of the complete RGP, showing evidence for carbon transfer from formate to pyruvate coupled with CO2 assimilation.

Self-assembly systems to troubleshoot metabolic engineering challenges

Enzyme self-assembly is a technology in which enzyme units can aggregate into ordered macromolecules, assisted by scaffolds. In metabolic engineering, self-assembly strategies have been explored for aggregating multiple enzymes in the same pathway to improve sequential catalytic efficiency, which in turn enables high-level production. The performance of the scaffolds is critical to the formation of an efficient and stable assembly system. This review comprehensively analyzes these scaffolds by exploring how they assemble, and it illustrates how to apply self-assembly strategies for different modules in metabolic engineering. Functional modifications to scaffolds will further promote efficient strategies for production.

Microbial Secondary Metabolites via Fermentation Approaches for Dietary Supplementation Formulations

Food supplementation formulations refer to products that are designed to provide additional nutrients to the diet. Vitamins, dietary fibers, minerals and other functional compounds (such as antioxidants) are concentrated in dietary supplements. Specific amounts of dietary compounds are given to the body through food supplements, and these include as well so-called non-essential compounds such as secondary plant bioactive components or microbial natural products in addition to nutrients in the narrower sense. A significant social challenge represents how to moderately use the natural resources in light of the growing world population. In terms of economic production of (especially natural) bioactive molecules, ways of white biotechnology production with various microorganisms have recently been intensively explored. In the current review other relevant dietary supplements and natural substances (e.g., vitamins, amino acids, antioxidants) used in production of dietary supplements formulations and their microbial natural production via fermentative biotechnological approaches are briefly reviewed. Biotechnology plays a crucial role in optimizing fermentation conditions to maximize the yield and quality of the target compounds. Advantages of microbial production include the ability to use renewable feedstocks, high production yields, and the potential for cost-effective large-scale production. Additionally, it can be more environmentally friendly compared to chemical synthesis, as it reduces the reliance on petrochemicals and minimizes waste generation. Educating consumers about the benefits, safety, and production methods of microbial products in general is crucial. Providing clear and accurate information about the science behind microbial production can help address any concerns or misconceptions consumers may have.

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

Dispersive phase microscopy incorporated with droplet-based microfluidics for biofactory-on-a-chip

Biomolecular imaging of intracellular structures of a single cell and subsequent screening of the cells are of high demand in metabolic engineering to develop strains with the desired phenotype. However, the capability of current methods is limited to population-scale identification of cell phenotyping. To address this challenge, we propose to utilize dispersive phase microscopy incorporated with a droplet-based microfluidic system that combines droplet volume-on-demand generation, biomolecular imaging, and droplet-on-demand sorting, to achieve high-throughput screening of cells with an identified phenotype. Particularly, cells are encapsulated in homogeneous environments with microfluidic droplet formation, and the biomolecule-induced dispersive phase can be investigated to indicate the biomass of a specific metabolite in a single cell. The retrieved biomass information consequently guides the on-chip droplet sorting unit to screen cells with the desired phenotype. To demonstrate the proof of concept, we showcase the method by promoting the evolution of the Haematococcus lacustris strain toward a high production of natural antioxidant astaxanthin. The validation of the proposed system with on-chip single-cell imaging and droplet manipulation functionalities reveals the high-throughput single-cell phenotyping and selection potential that applies to many other biofactory scenarios, such as biofuel production, critical quality attribute control in cell therapy, etc.

High-throughput screening of microbial strains in large-scale microfluidic droplets

The transformation of engineered microbial cells is a pivotal link in green biomanufacturing. Its distinctive research application involves genetic modification of microbial chassis to impart targeted traits and functions for effective synthesis of the desired products. Microfluidics, as an emerging complementary solution, focuses on controlling and manipulating fluid in channels at the microscopic scale. One of its subcategories is droplet-based microfluidics (DMF), which can generate discrete droplets using immiscible multiphase fluids at kHz frequencies. To date, droplet microfluidics has been successfully applied to a variety of microbes, including bacteria, yeast, and filamentous fungi, and the detection of massive metabolites of strain products, such as polypeptides, enzymes, and lipids, has been realized. In summary, we firmly believe that droplet microfluidics has evolved into a powerful technology that will pave the way for high-throughput screening of engineered microbial strains in the green biomanufacturing industry.

Growth-coupled enzyme engineering through manipulation of redox cofactor regeneration

Enzymes need to be efficient, robust, and highly specific for their effective use in commercial bioproduction. These properties can be introduced using various enzyme engineering techniques, with random mutagenesis and directed evolution (DE) often being chosen when there is a lack of structural information -or mechanistic understanding- of the enzyme. The screening or selection step of DE is the limiting part of this process, since it must ideally be (ultra)-high throughput, specifically target the catalytic activity of the enzyme and have an accurately quantifiable metric for said activity. Growth-coupling selection strategies involve coupling a desired enzyme activity to cellular metabolism and therefore growth, where growth (rate) becomes the output metric. Redox cofactors (NAD⁺/NADH and NADP⁺/NADPH) have recently been identified as promising target molecules for growth coupling, owing to their essentiality for cellular metabolism and ubiquitous nature. Redox cofactor oxidation or reduction can be disrupted through metabolic engineering and the use of specific culturing conditions, rendering the cell inviable unless a 'rescue' reaction complements the imposed metabolic deficiency. Using this principle, enzyme variants displaying improved cofactor oxidation or reduction rates can be selected for through an increased growth rate of the cell. In recent years, several E. coli strains have been developed that are deficient in the oxidation or reduction of NAD⁺/NADH and NADP⁺/NADPH pairs, and of non-canonical redox cofactor pairs NMN⁺/NMNH and NCD⁺/NCDH, which provides researchers with a versatile toolbox of enzyme engineering platforms. A range of redox cofactor dependent enzymes have since been engineered using a variety of these strains, demonstrating the power of using this growth-coupling technique for enzyme engineering. This review aims to summarize the metabolic engineering involved in creating strains auxotrophic for the reduced or oxidized state of redox cofactors, and the resulting successes in using them for enzyme engineering. Perspectives on the unique features and potential future applications of this technique are also presented.

Paving the way for synthetic C1 - Metabolism in Pseudomonas putida through the reductive glycine pathway

One-carbon (C1) compounds such as methanol, formate, and CO₂ are alternative, sustainable microbial feedstocks for the biobased production of chemicals and fuels. In this study, we engineered the carbon metabolism of the industrially important bacterium Pseudomonas putida to modularly assimilate these three substrates through the reductive glycine pathway. First, we demonstrated the functionality of the C1-assimilation module by coupling the growth of auxotrophic strains to formate assimilation. Next, we extended the module in the auxotrophic strains from formate to methanol-dependent growth using both NAD and PQQ-dependent methanol dehydrogenases. Finally, we demonstrated, for the first time, engineered CO₂-dependent formation of part of the biomass through CO₂ reduction to formate by the native formate dehydrogenase, which required short-term evolution to rebalance the cellular NADH/NAD ⁺ ratio. This research paves the way to further engineer P. putida towards full growth on formate, methanol, and CO₂ as sole feedstocks, thereby substantially expanding its potential as a sustainable and versatile cell factory.

Transcription factor-based biosensors for screening and dynamic regulation

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Challenges and opportunities in bringing nonbiological atoms to life with synthetic metabolism

The relatively narrow spectrum of chemical elements within the microbial 'biochemical palate' limits the reach of biotechnology, because several added-value compounds can only be produced with traditional organic chemistry. Synthetic biology offers enabling tools to tackle this issue by facilitating 'biologization' of non-canonical chemical atoms. The interplay between xenobiology and synthetic metabolism multiplies routes for incorporating nonbiological atoms into engineered microbes. In this review, we survey natural assimilation routes for elements beyond the essential biology atoms [i.e., carbon (C), hydrogen (H), nitrogen (N), oxygen (O), phosphorus (P), and sulfur (S)], discussing how these mechanisms could be repurposed for biotechnology. Furthermore, we propose a computational framework to identify chemical elements amenable to biologization, ranking reactions suitable to build synthetic metabolism. When combined and deployed in robust microbial hosts, these approaches will offer sustainable alternatives for smart chemical production.

Synthetic metabolism for in vitro acetone biosynthesis driven by ATP regeneration

In vitro ketone production continues to be a challenge due to the biochemical features of the enzymes involved-even when some of them have been extensively characterized (e.g. thiolase from Clostridium acetobutylicum), the assembly of synthetic enzyme cascades still face significant limitations (including issues with protein aggregation and multimerization). Here, we designed and assembled a self-sustaining enzyme cascade with acetone yields close to the theoretical maximum using acetate as the only carbon input. The efficiency of this system was further boosted by coupling the enzymatic sequence to a two-step ATP-regeneration system that enables continuous, cost-effective acetone biosynthesis. Furthermore, simple methods were implemented for purifying the enzymes necessary for this synthetic metabolism, including a first-case example on the isolation of a heterotetrameric acetate:coenzyme A transferase by affinity chromatography.

Optimizing microbial networks through metabolic bypasses

Metabolism has long been considered as a relatively stiff set of biochemical reactions. This somewhat outdated and dogmatic view has been challenged over the last years, as multiple studies exposed unprecedented plasticity of metabolism by exploring rational and evolutionary modifications within the metabolic network of cell factories. Of particular importance is the emergence of metabolic bypasses, which consist of enzymatic reaction(s) that support unnatural connections between metabolic nodes. Such novel topologies can be generated through the introduction of heterologous enzymes or by upregulating native enzymes (sometimes relying on promiscuous activities thereof). Altogether, the adoption of bypasses resulted in an expansion in the capacity of the host's metabolic network, which can be harnessed for bioproduction. In this review, we discuss modifications to the canonical architecture of central carbon metabolism derived from such bypasses towards six optimization purposes: stoichiometric gain, overcoming kinetic limitations, solving thermodynamic barriers, circumventing toxic intermediates, uncoupling product synthesis from biomass formation, and altering redox cofactor specificity. The metabolic costs associated with bypass-implementation are likewise discussed, including tailoring their design towards improving bioproduction.

A synthetic C2 auxotroph of Pseudomonas putida for evolutionary engineering of alternative sugar catabolic routes

Acetyl-coenzyme A (AcCoA) is a metabolic hub in virtually all living cells, serving as both a key precursor of essential biomass components and a metabolic sink for catabolic pathways for a large variety of substrates. Owing to this dual role, tight growth-production coupling schemes can be implemented around the AcCoA node. Building on this concept, a synthetic C2 auxotrophy was implemented in the platform bacterium Pseudomonas putida through an in silico-informed engineering approach. A growth-coupling strategy, driven by AcCoA demand, allowed for direct selection of an alternative sugar assimilation route-the phosphoketolase (PKT) shunt from bifidobacteria. Adaptive laboratory evolution forced the synthetic P. putida auxotroph to rewire its metabolic network to restore C2 prototrophy via the PKT shunt. Large-scale structural chromosome rearrangements were identified as possible mechanisms for adjusting the network-wide proteome profile, resulting in improved PKT-dependent growth phenotypes. ¹³C-based metabolic flux analysis revealed an even split between the native Entner-Doudoroff pathway and the synthetic PKT bypass for glucose processing, leading to enhanced carbon conservation. These results demonstrate that the P. putida metabolism can be radically rewired to incorporate a synthetic C2 metabolism, creating novel network connectivities and highlighting the importance of unconventional engineering strategies to support efficient microbial production.

On the flexibility of the cellular amination network in E coli

Ammonium (NH4+) is essential to generate the nitrogenous building blocks of life. It gets assimilated via the canonical biosynthetic routes to glutamate and is further distributed throughout metabolism via a network of transaminases. To study the flexibility of this network, we constructed an Escherichia coli glutamate auxotrophic strain. This strain allowed us to systematically study which amino acids serve as amine sources. We found that several amino acids complemented the auxotrophy either by producing glutamate via transamination reactions or by their conversion to glutamate. In this network, we identified aspartate transaminase AspC as a major connector between many amino acids and glutamate. Additionally, we extended the transaminase network by the amino acids β-alanine, alanine, glycine, and serine as new amine sources and identified d-amino acid dehydrogenase (DadA) as an intracellular amino acid sink removing substrates from transaminase reactions. Finally, ammonium assimilation routes producing aspartate or leucine were introduced. Our study reveals the high flexibility of the cellular amination network, both in terms of transaminase promiscuity and adaptability to new connections and ammonium entry points.

Development and characterization of a glycine biosensor system for fine-tuned metabolic regulation in Escherichia coli

Background

In vivo biosensors have a wide range of applications, ranging from the detection of metabolites to the regulation of metabolic networks, providing versatile tools for synthetic biology and metabolic engineering. However, in view of the vast array of metabolite molecules, the existing number and performance of biosensors is far from sufficient, limiting their potential applications in metabolic engineering. Therefore, we developed the synthetic glycine-ON and -OFF riboswitches for metabolic regulation and directed evolution of enzyme in Escherichia coli.

Results

The results showed that a synthetic glycine-OFF riboswitch (glyOFF6) and an increased-detection-range synthetic glycine-ON riboswitch (glyON14) were successfully screened from a library based on the Bacillus subtilis glycine riboswitch using fluorescence-activated cell sorting (FACS) and tetA-based dual genetic selection. The two synthetic glycine riboswitches were successfully used in tunable regulation of lactate synthesis, dynamic regulation of serine synthesis and directed evolution of alanine-glyoxylate aminotransferase in Escherichia coli, respectively. Mutants AGXT22 and AGXT26 of alanine-glyoxylate aminotransferase with an increase of 58% and 73% enzyme activity were obtained by using a high-throughput screening platform based on the synthetic glycine-OFF riboswitch, and successfully used to increase the 5-aminolevulinic acid yield of engineered Escherichia coli.

Conclusions

A synthetic glycine-OFF riboswitch and an increased-detection-range synthetic glycine-ON riboswitch were successfully designed and screened. The developed riboswitches showed broad application in tunable regulation, dynamic regulation and directed evolution of enzyme in E. coli.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01779-4.

Synthetic metabolism for biohalogenation

The pressing need for novel bioproduction approaches faces a limitation in the number and type of molecules accessed through synthetic biology. Halogenation is widely used for tuning physicochemical properties of molecules and polymers, but traditional halogenation chemistry often lacks specificity and generates harmful by-products. Here, we pose that deploying synthetic metabolism tailored for biohalogenation represents an unique opportunity towards economically attractive and environmentally friendly organohalide production. On this background, we discuss growth-coupled selection of functional metabolic modules that harness the rich repertoire of biosynthetic and biodegradation capabilities of environmental bacteria for in vivo biohalogenation. By rationally combining these approaches, the chemical landscape of living cells can accommodate bioproduction of added-value organohalides which, as of today, are obtained by traditional chemistry.