Synthetic Evolution of Metabolic Productivity Using Biosensors

The coming wave of confluent biosynthetic, bioinformational and bioengineering technologies

Information and energy flows form the basis of all economic activity, with advanced technologies underpinning both. Profound uncertainties caused by geostrategic forces have accelerated a trillion-dollar race for technological superiority. The result is an onrush of "technovation" at the nexus of synthetic biotechnologies, information technologies, nanotechnologies and engineering technologies. This article explores recent breakthroughs in integrating chip technologies and synthetic bioinformational engineering. It investigates prospects of biomolecules as carriers of stored digital data, synthetic cells-on-a-chip, and hybrid semiconductors and next-generation artificial intelligence processors. Consilience-unity of knowledge-redefines possibilities emerging from the living interface of biologically-inspired engineering and engineering-enabled biology.

Multidimensional Applications and Challenges of Riboswitches in Biosensing and Biotherapy

Riboswitches have received significant attention over the last two decades for their multiple functionalities and great potential for applications in various fields. This article highlights and reviews the recent advances in biosensing and biotherapy. These fields involve a wide range of applications, such as food safety detection, environmental monitoring, metabolic engineering, live cell imaging, wearable biosensors, antibacterial drug targets, and gene therapy. The discovery, origin, and optimization of riboswitches are summarized to help readers better understand their multidimensional applications. Finally, this review discusses the multidimensional challenges and development of riboswitches in order to further expand their potential for novel applications.

Graphene oxide aptasensor droplet assay for detection of metabolites secreted by single cells applied to synthetic biology

Synthetic biology harnesses the power of natural microbes by re-engineering metabolic pathways to manufacture desired compounds. Droplet technology has emerged as a high-throughput tool to screen single cells for synthetic biology, while the challenges in sensitive flexible single-cell secretion assay for bioproduction of high-value chemicals remained. Here, a novel droplet modifiable graphene oxide (GO) aptasensor was developed, enabling sensitive flexible detection of different target compounds secreted from single cells. Fluorophore-labeled aptamers were stably anchored on GO through π-π stacking interactions to minimize the non-specific interactions for low-background detection of target compounds with high signal-to-noise ratios. The assay's versatility was exhibited by adapting aptamer sequences to measure metabolic secretions like ATP and naringenin. To show the case, engineered E. coli were constructed for the bioproduction of naringenin. The high signal-to-noise ratio assay (∼2.72) was approached to precisely measure the naringenins secreted from single E. coli in the droplets. Consequently, secretory cells (Gib) were clearly distinguished from wild-type (WT) cells, with a low overlap in cell populations (∼0%) for bioproduction.

Have you tried turning it off and on again? Oscillating selection to enhance fitness-landscape traversal in adaptive laboratory evolution experiments

Adaptive Laboratory Evolution (ALE) is a powerful tool for engineering and understanding microbial physiology. ALE relies on the selection and enrichment of mutations that enable survival or faster growth under a selective condition imposed by the experimental setup. Phenotypic fitness landscapes are often underpinned by complex genotypes involving multiple genes, with combinatorial positive and negative effects on fitness. Such genotype relationships result in mutational fitness landscapes with multiple local fitness maxima and valleys. Traversing local maxima to find a global maximum often requires an individual or sub-population of cells to traverse fitness valleys. Traversing involves gaining mutations that are not adaptive for a given local maximum but are necessary to 'peak shift' to another local maximum, or eventually a global maximum. Despite these relatively well understood evolutionary principles, and the combinatorial genotypes that underlie most metabolic phenotypes, the majority of applied ALE experiments are conducted using constant selection pressures. The use of constant pressure can result in populations becoming trapped within local maxima, and often precludes the attainment of optimum phenotypes associated with global maxima. Here, we argue that oscillating selection pressures is an easily accessible mechanism for traversing fitness landscapes in ALE experiments, and provide theoretical and practical frameworks for implementation.

Characterization and designing of an SAM riboswitch to establish a high-throughput screening platform for SAM overproduction in Saccharomyces cerevisiae

S-adenosyl- l-methionine (SAM) is a high-value compound widely used in the treatment of various diseases. SAM can be produced through fermentation, but further enhancing the microbial production of SAM requires novel high-throughput screening methods for rapid detection and screening of mutant libraries. In this work, an SAM-OFF riboswitch capable of responding to the SAM concentration was obtained and a high-throughput platform for screening SAM overproducers was established. SAM synthase was engineered by semirational design and directed evolution, which resulted in the SAM2S203F,W164R,T251S,Y285F,S365R mutant with almost twice higher catalytic activity than the parental enzyme. The best mutant was then introduced into Saccharomyces cerevisiae BY4741, and the resulting strain BSM8 produced a sevenfold higher SAM titer in shake-flask fermentation, reaching 1.25 g L-1 . This work provides a reference for designing biosensors to dynamically detect metabolite concentrations for high-throughput screening and the construction of effective microbial cell factories.

Biosensor-guided discovery and engineering of metabolic enzymes

A variety of chemicals have been produced through metabolic engineering approaches, and enhancing biosynthesis performance can be achieved by using enzymes with high catalytic efficiency. Accordingly, a number of efforts have been made to discover enzymes in nature for various applications. In addition, enzyme engineering approaches have been attempted to suit specific industrial purposes. However, a significant challenge in enzyme discovery and engineering is the efficient screening of enzymes with the desired phenotype from extensive enzyme libraries. To overcome this bottleneck, genetically encoded biosensors have been developed to specifically detect target molecules produced by enzyme activity at the intracellular level. Especially, the biosensors facilitate high-throughput screening (HTS) of targeted enzymes, expanding enzyme discovery and engineering strategies with advances in systems and synthetic biology. This review examines biosensor-guided HTS systems and highlights studies that have utilized these tools to discover enzymes in diverse areas and engineer enzymes to enhance their properties, such as catalytic efficiency, specificity, and stability.

Parallel laboratory evolution and rational debugging reveal genomic plasticity to S. cerevisiae synthetic chromosome XIV defects

Synthetic chromosome engineering is a complex process due to the need to identify and repair growth defects and deal with combinatorial gene essentiality when rearranging chromosomes. To alleviate these issues, we have demonstrated novel approaches for repairing and rearranging synthetic Saccharomyces cerevisiae genomes. We have designed, constructed, and restored wild-type fitness to a synthetic 753,096-bp version of S. cerevisiae chromosome XIV as part of the Synthetic Yeast Genome project. In parallel to the use of rational engineering approaches to restore wild-type fitness, we used adaptive laboratory evolution to generate a general growth-defect-suppressor rearrangement in the form of increased TAR1 copy number. We also extended the utility of the synthetic chromosome recombination and modification by loxPsym-mediated evolution (SCRaMbLE) system by engineering synthetic-wild-type tetraploid hybrid strains that buffer against essential gene loss, highlighting the plasticity of the S. cerevisiae genome in the presence of rational and non-rational modifications.

Biosensors in microalgae: A roadmap for new opportunities in synthetic biology and biotechnology

Biosensors are powerful tools to investigate, phenotype, improve and prototype microbial strains, both in fundamental research and in industrial contexts. Genetic and biotechnological developments now allow the implementation of synthetic biology approaches to novel different classes of microbial hosts, for example photosynthetic microalgae, which offer unique opportunities. To date, biosensors have not yet been implemented in phototrophic eukaryotic microorganisms, leaving great potential for novel biological and technological advancements untapped. Here, starting from selected biosensor technologies that have successfully been implemented in heterotrophic organisms, we project and define a roadmap on how these could be applied to microalgae research. We highlight novel opportunities for the development of new biosensors, identify critical challenges, and finally provide a perspective on the impact of their eventual implementation to tackle research questions and bioengineering strategies. From studying metabolism at the single-cell level to genome-wide screen approaches, and assisted laboratory evolution experiments, biosensors will greatly impact the pace of progress in understanding and engineering microalgal metabolism. We envision how this could further advance the possibilities for unraveling their ecological role, evolutionary history and accelerate their domestication, to further drive them as resource-efficient production hosts.

Large-scale pathway reconstruction and colorimetric screening accelerate cellular metabolism engineering

The capability to manipulate and analyze hard-wired metabolic pathways sets the pace at which we can engineer cellular metabolism. Here, we present a framework to extensively rewrite the central metabolic pathway for malonyl-CoA biosynthesis in yeast and readily assess malonyl-CoA output based on pathway-scale DNA reconstruction in combination with colorimetric screening (Pracs). We applied Pracs to generate and test millions of enzyme variants by introducing genetic mutations into the whole set of genes encoding the malonyl-CoA biosynthetic pathway and identified hundreds of beneficial enzyme mutants with increased malonyl-CoA output. Furthermore, the synthetic pathways reconstructed by randomly integrating these beneficial enzyme variants generated vast phenotypic diversity, with some displaying higher production of malonyl-CoA as well as other metabolites, such as carotenoids and betaxanthin, thus demonstrating the generic utility of Pracs to efficiently orchestrate central metabolism to optimize the production of different chemicals in various metabolic pathways. Pracs will be broadly useful to advance our ability to understand and engineer cellular metabolism.

Visioning synthetic futures for yeast research within the context of current global techno-political trends

Yeast research is entering into a new period of scholarship, with new scientific tools, new questions to ask and new issues to consider. The politics of emerging and critical technology can no longer be separated from the pursuit of basic science in fields, such as synthetic biology and engineering biology. Given the intensifying race for technological leadership, yeast research is likely to attract significant investment from government, and that it offers huge opportunities to the curious minded from a basic research standpoint. This article provides an overview of new directions in yeast research with a focus on Saccharomyces cerevisiae, and places these trends in their geopolitical context. At the highest level, yeast research is situated within the ongoing convergence of the life sciences with the information sciences. This convergent effect is most strongly pronounced in areas of AI-enabled tools for the life sciences, and the creation of synthetic genomes, minimal genomes, pan-genomes, neochromosomes and metagenomes using computer-assisted design tools and methodologies. Synthetic yeast futures encompass basic and applied science questions that will be of intense interest to government and nongovernment funding sources. It is essential for the yeast research community to map and understand the context of their research to ensure their collaborations turn global challenges into research opportunities.

Beyond rational-biosensor-guided isolation of 100 independently evolved bacterial strain variants and comparative analysis of their genomes

BACKGROUND

In contrast to modern rational metabolic engineering, classical strain development strongly relies on random mutagenesis and screening for the desired production phenotype. Nowadays, with the availability of biosensor-based FACS screening strategies, these random approaches are coming back into fashion. In this study, we employ this technology in combination with comparative genome analyses to identify novel mutations contributing to product formation in the genome of a Corynebacterium glutamicum L-histidine producer. Since all known genetic targets contributing to L-histidine production have been already rationally engineered in this strain, identification of novel beneficial mutations can be regarded as challenging, as they might not be intuitively linkable to L-histidine biosynthesis.

RESULTS

In order to identify 100 improved strain variants that had each arisen independently, we performed > 600 chemical mutagenesis experiments, > 200 biosensor-based FACS screenings, isolated > 50,000 variants with increased fluorescence, and characterized > 4500 variants with regard to biomass formation and L-histidine production. Based on comparative genome analyses of these 100 variants accumulating 10-80% more L-histidine, we discovered several beneficial mutations. Combination of selected genetic modifications allowed for the construction of a strain variant characterized by a doubled L-histidine titer (29 mM) and product yield (0.13 C-mol C-mol-1) in comparison to the starting variant.

CONCLUSIONS

This study may serve as a blueprint for the identification of novel beneficial mutations in microbial producers in a more systematic manner. This way, also previously unexplored genes or genes with previously unknown contribution to the respective production phenotype can be identified. We believe that this technology has a great potential to push industrial production strains towards maximum performance.

Application of Adaptive Laboratory Evolution in Lipid and Terpenoid Production in Yeast and Microalgae

Due to the complexity of metabolic and regulatory networks in microorganisms, it is difficult to obtain robust phenotypes through artificial rational design and genetic perturbation. Adaptive laboratory evolution (ALE) engineering plays an important role in the construction of stable microbial cell factories by simulating the natural evolution process and rapidly obtaining strains with stable traits through screening. This review summarizes the application of ALE technology in microbial breeding, describes the commonly used methods for ALE, and highlights the important applications of ALE technology in the production of lipids and terpenoids in yeast and microalgae. Overall, ALE technology provides a powerful tool for the construction of microbial cell factories, and it has been widely used in improving the level of target product synthesis, expanding the range of substrate utilization, and enhancing the tolerance of chassis cells. In addition, in order to improve the production of target compounds, ALE also employs environmental or nutritional stress strategies corresponding to the characteristics of different terpenoids, lipids, and strains.

Synthetic biology tools for engineering Corynebacterium glutamicum

Corynebacterium glutamicum is a promising organism for the industrial production of amino acids, fuels, and various value-added chemicals. From the whole genome sequence release, C. glutamicum has been valuable in the field of industrial microbiology and biotechnology. Continuous discovery of genetic manipulations and regulation mechanisms has developed C. glutamicum as a synthetic biology platform chassis. This review summarized diverse genomic manipulation technologies and gene expression tools for static, dynamic, and multiplex control at transcription and translation levels. Moreover, we discussed the current challenges and applicable tools to C. glutamicum for future advancements.

Visualizing the next frontiers in wine yeast research

A range of game-changing biodigital and biodesign technologies are coming of age all around us, transforming our world in complex ways that are hard to predict. Not a day goes by without news of how data-centric engineering, algorithm-driven modelling, and biocyber technologies-including the convergence of artificial intelligence, machine learning, automated robotics, quantum computing, and genome editing-will change our world. If we are to be better at expecting the unexpected in the world of wine, we need to gain deeper insights into the potential and limitations of these technological developments and advances along with their promise and perils. This article anticipates how these fast-expanding bioinformational and biodesign toolkits might lead to the creation of synthetic organisms and model systems, and ultimately new understandings of biological complexities could be achieved. A total of four future frontiers in wine yeast research are discussed in this article: the construction of fully synthetic yeast genomes, including minimal genomes; supernumerary pan-genome neochromosomes; synthetic metagenomes; and synthetic yeast communities. These four concepts are at varying stages of development with plenty of technological pitfalls to overcome before such model chromosomes, genomes, strains, and yeast communities could illuminate some of the ill-understood aspects of yeast resilience, fermentation performance, flavour biosynthesis, and ecological interactions in vineyard and winery settings. From a winemaker's perspective, some of these ideas might be considered as far-fetched and, as such, tempting to ignore. However, synthetic biologists know that by exploring these futuristic concepts in the laboratory could well forge new research frontiers to deepen our understanding of the complexities of consistently producing fine wines with different fermentation processes from distinctive viticultural terroirs. As the saying goes in the disruptive technology industry, it take years to create an overnight success. The purpose of this article is neither to glorify any of these concepts as a panacea to all ills nor to crucify them as a danger to winemaking traditions. Rather, this article suggests that these proposed research endeavours deserve due consideration because they are likely to cast new light on the genetic blind spots of wine yeasts, and how they interact as communities in vineyards and wineries. Future-focussed research is, of course, designed to be subject to revision as new data and technologies become available. Successful dislodging of old paradigms with transformative innovations will require open-mindedness and pragmatism, not dogmatism-and this can make for a catch-22 situation in an archetypal traditional industry, such as the wine industry, with its rich territorial and socio-cultural connotations.

Biosensor-Coupled In Vivo Mutagenesis and Omics Analysis Reveals Reduced Lysine and Arginine Synthesis To Improve Malonyl-Coenzyme A Flux in Saccharomyces cerevisiae

Malonyl-coenzyme A (malonyl-CoA) is an important precursor for producing various chemicals, but its low availability limits the synthesis of downstream products in Saccharomyces cerevisiae. Owing to the complexity of metabolism, evolutionary engineering is required for developing strains with improved malonyl-CoA synthesis. Here, using the biosensor we constructed previously, a growth-based screening system that links the availability of malonyl-CoA with cell growth is developed. Coupling this system with in vivo continuous mutagenesis enabled rapid generation of genome-scale mutation library and screening strains with improved malonyl-CoA availability. The mutant strains are analyzed by whole-genome sequencing and transcriptome analysis. The omics analysis revealed that the carbon flux rearrangement to storage carbohydrate and amino acids synthesis affected malonyl-CoA metabolism. Through reverse engineering, new processes especially reduced lysine and arginine synthesis were found to improve malonyl-CoA synthesis. Our study provides a valuable complementary tool to other high-throughput screening method for mutant strains with improved metabolite synthesis and improves our understanding of the metabolic regulation of malonyl-CoA synthesis.

IMPORTANCE Malonyl-CoA is a key precursor for the production a variety of value-added chemicals. Although rational engineering has been performed to improve the synthesis of malonyl-CoA in S. cerevisiae, due to the complexity of the metabolism there is a need for evolving strains and analyzing new mechanism to improve malonyl-CoA flux. Here, we developed a growth-based screening system that linked the availability of malonyl-CoA with cell growth and manipulated DNA replication for rapid in vivo mutagenesis. The combination of growth-based screening with in vivo mutagenesis enabled quick evolution of strains with improved malonyl-CoA availability. The whole-genome sequencing, transcriptome analysis of the mutated strains, together with reverse engineering, demonstrated weakening carbon flux to lysine and arginine synthesis and storage carbohydrate can contribute to malonyl-CoA synthesis. Our work provides a guideline in simultaneous strain screening and continuous evolution for improved metabolic intermediates and identified new targets for improving malonyl-CoA downstream product synthesis.

Biosensor-based growth-coupling and spatial separation as an evolution strategy to improve small molecule production of Corynebacterium glutamicum

Evolutionary engineering is a powerful method to improve the performance of microbial cell factories, but can typically not be applied to enhance the production of chemicals due to the lack of an appropriate selection regime. We report here on a new strategy based on transcription factor-based biosensors, which directly couple production to growth. The growth of Corynebacterium glutamicum was coupled to the intracellular concentration of branched-chain amino acids, by integrating a synthetic circuit based on the Lrp biosensor upstream of two growth-regulating genes, pfkA and hisD. Modelling and experimental data highlight spatial separation as key strategy to limit the selection of 'cheater' strains that escaped the evolutionary pressure. This approach facilitated the isolation of strains featuring specific causal mutations enhancing amino acid production. We envision that this strategy can be applied with the plethora of known biosensors in various microbes, unlocking evolution as a feasible strategy to improve production of chemicals.

Bioinformational trends in grape and wine biotechnology

The creative destruction caused by the coronavirus pandemic is yielding immense opportunity for collaborative innovation networks. The confluence of biosciences, information sciences, and the engineering of biology, is unveiling promising bioinformational futures for a vibrant and sustainable bioeconomy. Bioinformational engineering, underpinned by DNA reading, writing, and editing technologies, has become a beacon of opportunity in a world paralysed by uncertainty. This article draws on lessons from the current pandemic and previous agricultural blights, and explores bioinformational research directions aimed at future-proofing the grape and wine industry against biological shocks from global blights and climate change.

Engineering Bacteria to Monitor the Bleeding of Animals Using Far-Red Fluorescence

Microorganisms living in animals can function as drug delivery systems or as detectors for some diseases. Here, we developed a biosensor constructed by the deletion of hemF and harboring ho1, chuA, and bdfp1.6 in Escherichia coli. HemF is an enzyme involved in heme synthesis in E. coli. ChuA and HO1 can transfer extracellular heme into cells and generate biliverdin (BV). BDFP1.6 can bind BV autocatalytically, and it emits a far-red fluorescence signal at 667 nm. Therefore, we named this biosensor as the far-red light for bleeding detector (FRLBD). Our results indicated that the FRLBD was highly efficient and specific for detecting heme or blood in vitro. Moreover, the FRLBD could be used to detect bleeding in the zebrafish induced by aspirin, and a convolutional neural network was an appropriate model to identify the fluorescence features in the images.

Seeding the idea of encapsulating a representative synthetic metagenome in a single yeast cell

Synthetic metagenomics could potentially unravel the complexities of microbial ecosystems by revealing the simplicity of microbial communities captured in a single cell. Conceptionally, a yeast cell carrying a representative synthetic metagenome could uncover the complexity of multi-species interactions, illustrated here with wine ferments.

Synthetic biology-driven microbial production of folates: Advances and perspectives

With the development and application of synthetic biology, significant progress has been made in the production of folate by microbial fermentation using cell factories, especially for using generally regarded as safe (GRAS) microorganism as production host. In this review, the physiological functions and applications of folates were firstly discussed. Second, the current advances of folate-producing GRAS strains development were summarized. Third, the applications of synthetic biology-based metabolic regulatory tools in GRAS strains were introduced, and the progress in the application of these tools for folate production were summarized. Finally, the challenges to folates efficient production and corresponding emerging strategies to overcome them by synthetic biology were discussed, including the construction of biosensors using tetrahydrofolate riboswitches to regulate metabolic pathways, adaptive evolution to overcome the flux limitations of the folate pathway. The combination of new strategies and tools of synthetic biology is expected to further improve the efficiency of microbial folate synthesis.

Transcription Factor-Based Biosensor for Dynamic Control in Yeast for Natural Product Synthesis

The synthesis of natural products in yeast has gained remarkable achievements with intensive metabolic engineering efforts. In particular, transcription factor (TF)-based biosensors for dynamic control of gene circuits could facilitate strain evaluation, high-throughput screening (HTS), and adaptive laboratory evolution (ALE) for natural product synthesis. In this review, we summarized recent developments of several TF-based biosensors for core intermediates in natural product synthesis through three important pathways, i.e., fatty acid synthesis pathway, shikimate pathway, and methylerythritol-4-phosphate (MEP)/mevalonate (MVA) pathway. Moreover, we have shown how these biosensors are implemented in synthetic circuits for dynamic control of natural product synthesis and also discussed the design/evaluation principles for improved biosensor performance.

Sensing the future of bio-informational engineering

The practices of synthetic biology are being integrated into 'multiscale' designs enabling two-way communication across organic and inorganic information substrates in biological, digital and cyber-physical system integrations. Novel applications of 'bio-informational' engineering will arise in environmental monitoring, precision agriculture, precision medicine and next-generation biomanufacturing. Potential developments include sentinel plants for environmental monitoring and autonomous bioreactors that respond to biosensor signaling. As bio-informational understanding progresses, both natural and engineered biological systems will need to be reimagined as cyber-physical architectures. We propose that a multiple length scale taxonomy will assist in rationalizing and enabling this transformative development in engineering biology.

Repositioning microbial biotechnology against COVID-19: the case of microbial production of flavonoids

Coronavirus-related disease 2019 (COVID-19) became a pandemic in February 2020, and worldwide researchers try to tackle the disease with approved drugs of all kinds, or to develop novel compounds inhibiting viral spreading. Flavonoids, already investigated as antivirals in general, also might bear activities specific for the viral agent causing COVID-19, SARS-CoV-2. Microbial biotechnology and especially synthetic biology may help to produce flavonoids, which are exclusive plant secondary metabolites, at a larger scale or indeed to find novel pharmaceutically active flavonoids. Here, we review the state of the art in (i) antiviral activity of flavonoids specific for coronaviruses and (ii) results derived from computational studies, mostly docking studies mainly inhibiting specific coronaviral proteins such as the 3CL (main) protease, the spike protein or the RNA-dependent RNA polymerase. In the end, we strive towards a synthetic biology pipeline making the fast and tailored production of valuable antiviral flavonoids possible by applying the last concepts of division of labour through co-cultivation/microbial community approaches to the DBTL (Design, Build, Test, Learn) principle.

Biosensor-enabled droplet microfluidic system for the rapid screening of 3-dehydroshikimic acid produced in Escherichia coli

Genetically encoded biosensors are powerful tools used to screen metabolite-producing microbial strains. Traditionally, biosensor-based screening approaches also use fluorescence-activated cell sorting (FACS). However, these approaches are limited by the measurement of intracellular fluorescence signals in single cells, rather than the signals associated with populations comprising multiple cells. This characteristic reduces the accuracy of screening because of the variability in signal levels among individual cells. To overcome this limitation, we introduced an approach that combined biosensors with droplet microfluidics (i.e., fluorescence-activated droplet sorting, FADS) to detect labeled cells at a multi-copy level and in an independent droplet microenvironment. We used our previously reported genetically encoded biosensor, 3-dehydroshikimic acid (3-DHS), as a model with which to establish the biosensor-based FADS screening method. We then characterized and compared the effects of the sorting method on the biosensor-based screening system by subjecting the same mutant library to FACS and FADS. Notably, our developed biosensor-enabled, droplet microfluidics-based FADS screening system yielded an improved positive mutant enrichment rate and increased productivity by the best mutant, compared with the single-cell FACS system. In conclusion, the combination of a biosensor and droplet microfluidics yielded a more efficient screening method that could be applied to the biosensor-based high-throughput screening of other metabolites.

ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation

Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover causal mutations that confer desired phenotypic functions. ALE not only represents a controllable experimental approach to systematically discover genotype-phenotype relationships, but also allows for the revelation of the series of genetic alterations required to acquire the new phenotype. Numerous ALE studies have been published, providing a strong impetus for developing databases to warehouse experimental evolution information and make it retrievable for large-scale analysis. Here, the first step towards establishing this resource is presented: ALEdb (http://aledb.org). This initial release contains over 11 000 mutations that have been discovered from eleven ALE publications. ALEdb (i) is a web-based platform that comprehensively reports on ALE acquired mutations and their conditions, (ii) reports key mutations using previously established trends, (iii) enables a search-driven workflow to enhance user mutation functional analysis through mutation cross-reference, (iv) allows exporting of mutation query results for custom analysis, (v) includes a bibliome describing the databased experiment publications and (vi) contains experimental evolution mutations from multiple model organisms. Thus, ALEdb is an informative platform which will become increasingly revealing as the number of reported ALE experiments and identified mutations continue to expand.

Acclimation of bacterial cell state for high-throughput enzyme engineering using a DmpR-dependent transcriptional activation system

Genetic circuit-based biosensors have emerged as an effective analytical tool in synthetic biology; these biosensors can be applied to high-throughput screening of new biocatalysts and metabolic pathways. Sigma 54 (σ⁵⁴)-dependent transcription factor (TF) can be a valuable component of these biosensors owing to its intrinsic silent property compared to most of the housekeeping sigma 70 (σ⁷⁰) TFs. Here, we show that these unique characteristics of σ⁵⁴-dependent TFs can be used to control the host cell state to be more appropriate for high-throughput screening. The acclimation of cell state was achieved by using guanosine (penta)tetraphosphate ((p)ppGpp)-related genes (relA, spoT) and nutrient conditions, to link the σ⁵⁴ TF-based reporter expression with the target enzyme activity. By controlling stringent programmed responses and optimizing assay conditions, catalytically improved tyrosine phenol lyase (TPL) enzymes were successfully obtained using a σ⁵⁴-dependent DmpR as the TF component, demonstrating the practical feasibility of this biosensor. This combinatorial strategy of biosensors using σ factor-dependent TFs will allow for more effective high-throughput enzyme engineering with broad applicability.

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.

Improvement of l-Valine Production by Atmospheric and Room Temperature Plasma Mutagenesis and High-Throughput Screening in Corynebacterium glutamicum

As one of the branched-chain amino acids, l-valine is an essential nutrient for most mammalian species. In this study, the l-valine producer Corynebacterium glutamicum ΔppcΔaceEΔalatΔpqo was first constructed. Additionally, an improved biosensor based on the Lrp-type transcriptional regulator and temperature-sensitive replication was built. Then, the C. glutamicum strain was mutagenized by atmospheric and room temperature plasma. A sequential three-step procedure was carried out to screen l-valine-producing strains, including the fluorescence-activated cell sorting (FACS), 96-well plate screening, and flask fermentation. The final mutant HL2-7 obtained by screening produced 3.20 g/L of l-valine, which was 21.47% higher than the titer produced by the starting strain. This study demonstrates that the l-valine-producing mutants can be successfully isolated based on the Lrp sensor system in combination with FACS screening after random mutagenesis.

Advanced Strategies for Production of Natural Products in Yeast

Natural products account for more than 50% of all small-molecule pharmaceutical agents currently in clinical use. However, low availability often becomes problematic when a bioactive natural product is promising to become a pharmaceutical or leading compound. Advances in synthetic biology and metabolic engineering provide a feasible solution for sustainable supply of these compounds. In this review, we have summarized current progress in engineering yeast cell factories for production of natural products, including terpenoids, alkaloids, and phenylpropanoids. We then discuss advanced strategies in metabolic engineering at three different dimensions, including point, line, and plane (corresponding to the individual enzymes and cofactors, metabolic pathways, and the global cellular network). In particular, we comprehensively discuss how to engineer cofactor biosynthesis for enhancing the biosynthesis efficiency, other than the enzyme activity. Finally, current challenges and perspective are also discussed for future engineering direction.

In vivo evolutionary engineering of riboswitch with high-threshold for N-acetylneuraminic acid production

Riboswitches with desired properties, such as sensitivity, threshold, dynamic range, is important for its application. However, the property change of a natural riboswitch is difficult due to the lack of the understanding of aptamer ligand binding properties and a proper screening method for both rational and irrational design. In this study, an effective method to change the threshold of riboswitch was established in vivo based on growth coupled screening by combining both positive and negative selections. The feasibility of the method was verified by the model library. Using this method, an N-acetylneuraminic acid (NeuAc) riboswitch was evolved and modified riboswitches with high threshold and large dynamic range were obtained. Then, using a new NeuAc riboswitch, both ribosome binding sites and key gene in NeuAc biosynthesis pathway were optimized. The highest NeuAc production of 14.32 g/l that has been reported using glucose as sole carbon source was obtained.

Tools and strategies of systems metabolic engineering for the development of microbial cell factories for chemical production

This tutorial review covers tools, strategies, and procedures of systems metabolic engineering facilitating the development of microbial cell factories efficiently producing chemicals and materials.

Model-Assisted Fine-Tuning of Central Carbon Metabolism in Yeast through dCas9-Based Regulation

Engineering Saccharomyces cerevisiae for industrial-scale production of valuable chemicals involves extensive modulation of its metabolism. Here, we identified novel gene expression fine-tuning set-ups to enhance endogenous metabolic fluxes toward increasing levels of acetyl-CoA and malonyl-CoA. dCas9-based transcriptional regulation was combined together with a malonyl-CoA responsive intracellular biosensor to select for beneficial set-ups. The candidate genes for screening were predicted using a genome-scale metabolic model, and a gRNA library targeting a total of 168 selected genes was designed. After multiple rounds of fluorescence-activated cell sorting and library sequencing, the gRNAs that were functional and increased flux toward malonyl-CoA were assessed for their efficiency to enhance 3-hydroxypropionic acid (3-HP) production. 3-HP production was significantly improved upon fine-tuning genes involved in providing malonyl-CoA precursors, cofactor supply, as well as chromatin remodeling.

Multimodal Microorganism Development: Integrating Top-Down Biological Engineering with Bottom-Up Rational Design

Biological engineering has unprecedented potential to solve society's most pressing challenges. Engineering approaches must consider complex technical, economic, and social factors. This requires methods that confer gene/pathway-level functionality and organism-level robustness in rapid and cost-effective ways. This article compares foundational engineering approaches - bottom-up, gene-targeted engineering, and top-down, whole-genome engineering - and identifies significant complementarity between them. Cases drawn from engineering Saccharomyces cerevisiae exemplify the synergy of a combined approach. Indeed, multimodal engineering streamlines strain development by leveraging the complementarity of whole-genome and gene-targeted engineering to overcome the gap in design knowledge that restricts rational design. As biological engineers target more complex systems, this dual-track approach is poised to become an increasingly important tool to realize the promise of synthetic biology.

Genetically encoded biosensors for lignocellulose valorization

Modern society is hugely dependent on finite oil reserves for the supply of fuels and chemicals. Moving our dependence away from these unsustainable oil-based feedstocks to renewable ones is, therefore, a critical factor towards the development of a low carbon bioeconomy. Lignin derived from biomass feedstocks offers great potential as a renewable source of aromatic compounds if methods for its effective valorization can be developed. Synthetic biology and metabolic engineering offer the potential to synergistically enable the development of cell factories with novel biosynthetic routes to valuable chemicals from these sustainable sources. Pathway design and optimization is, however, a major bottleneck due to the lack of high-throughput methods capable of screening large libraries of genetic variants and the metabolic burden associated with bioproduction. Genetically encoded biosensors can provide a solution by transducing the target metabolite concentration into detectable signals to provide high-throughput phenotypic read-outs and allow dynamic pathway regulation. The development and application of biosensors in the discovery and engineering of efficient biocatalytic processes for the degradation, conversion, and valorization of lignin are paving the way towards a sustainable and economically viable biorefinery.

Engineering Pseudomonas putida for isoprenoid production by manipulating endogenous and shunt pathways supplying precursors

BACKGROUND

The soil bacterium Pseudomonas putida is a promising platform for the production of industrially valuable natural compounds. In the case of isoprenoids, the availability of biosynthetic precursors is a major limiting factor. In P. putida and most other bacteria, these precursors are produced from pyruvate and glyceraldehyde 3-phosphate by the methylerythritol 4-phosphate (MEP) pathway, whereas other bacteria synthesize the same precursors from acetyl-CoA using the unrelated mevalonate (MVA) pathway.

RESULTS

Here we explored different strategies to increase the supply of isoprenoid precursors in P. putida cells using lycopene as a read-out. Because we were not aiming at producing high isoprenoid titers but were primarily interested in finding ways to enhance the metabolic flux to isoprenoids, we engineered the well-characterized P. putida strain KT2440 to produce low but detectable levels of lycopene under conditions in which MEP pathway steps were not saturated. Then, we compared lycopene production in cells expressing the Myxococcus xanthus MVA pathway genes or endogenous MEP pathway genes (dxs, dxr, idi) under the control of IPTG-induced and stress-regulated promoters. We also tested a shunt pathway producing isoprenoid precursors from ribulose 5-phosphate using a mutant version of the Escherichia coli ribB gene.

CONCLUSIONS

The most successful combination led to a 50-fold increase in lycopene levels, indicating that P. putida can be successfully engineered to substantially increase the supply of metabolic substrates for the production of industrially valuable isoprenoids.

Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range for glycerate

Microbial biosensors are used to detect the presence of compounds provided externally or produced internally. The latter case is commonly constrained by the need to screen a large library of enzyme or pathway variants to identify those that can efficiently generate the desired compound. To address this limitation, we suggest the use of metabolic sensor strains which can grow only if the relevant compound is present and thus replace screening with direct selection. We used a computational platform to design metabolic sensor strains with varying dependencies on a specific compound. Our method systematically explores combinations of gene deletions and identifies how the growth requirement for a compound changes with the media composition. We demonstrate this approach by constructing a set of E. coli glycerate sensor strains. In each of these strains a different set of enzymes is disrupted such that central metabolism is effectively dissected into multiple segments, each requiring a dedicated carbon source. We find an almost perfect match between the predicted and experimental dependence on glycerate and show that the strains can be used to accurately detect glycerate concentrations across two orders of magnitude. Apart from demonstrating the potential application of metabolic sensor strains, our work reveals key phenomena in central metabolism, including spontaneous degradation of central metabolites and the importance of metabolic sinks for balancing small metabolic networks.

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.

Evolutionary engineering in Saccharomyces cerevisiae reveals a TRK1-dependent potassium influx mechanism for propionic acid tolerance

BACKGROUND

Propionic acid (PA), a key platform chemical produced as a by-product during petroleum refining, has been widely used as a food preservative and an important chemical intermediate in many industries. Microbial PA production through engineering yeast as a cell factory is a potentially sustainable alternative to replace petroleum refining. However, PA inhibits yeast growth at concentrations well below the titers typically required for a commercial bioprocess.

RESULTS

Adaptive laboratory evolution (ALE) with PA concentrations ranging from 15 to 45 mM enabled the isolation of yeast strains with more than threefold improved tolerance to PA. Through whole genome sequencing and CRISPR-Cas9-mediated reverse engineering, unique mutations in TRK1, which encodes a high-affinity potassium transporter, were revealed as the cause of increased propionic acid tolerance. Potassium supplementation growth assays showed that mutated TRK1 alleles and extracellular potassium supplementation not only conferred tolerance to PA stress but also to multiple organic acids.

CONCLUSION

Our study has demonstrated the use of ALE as a powerful tool to improve yeast tolerance to PA. Potassium transport and maintenance is not only critical in yeast tolerance to PA but also boosts tolerance to multiple organic acids. These results demonstrate high-affinity potassium transport as a new principle for improving organic acid tolerance in strain engineering.

Scaling up genetic circuit design for cellular computing: advances and prospects

Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.

Blueprints for Biosensors: Design, Limitations, and Applications

Biosensors are enabling major advances in the field of analytics that are both facilitating and being facilitated by advances in synthetic biology. The ability of biosensors to rapidly and specifically detect a wide range of molecules makes them highly relevant to a range of industrial, medical, ecological, and scientific applications. Approaches to biosensor design are as diverse as their applications, with major biosensor classes including nucleic acids, proteins, and transcription factors. Each of these biosensor types has advantages and limitations based on the intended application, and the parameters that are required for optimal performance. Specifically, the choice of biosensor design must consider factors such as the ligand specificity, sensitivity, dynamic range, functional range, mode of output, time of activation, ease of use, and ease of engineering. This review discusses the rationale for designing the major classes of biosensor in the context of their limitations and assesses their suitability to different areas of biotechnological application.

Emerging Opportunities for Synthetic Biology in Agriculture

Rapid expansion in the emerging field of synthetic biology has to date mainly focused on the microbial sciences and human health. However, the zeitgeist is that synthetic biology will also shortly deliver major outcomes for agriculture. The primary industries of agriculture, fisheries and forestry, face significant and global challenges; addressing them will be assisted by the sector’s strong history of early adoption of transformative innovation, such as the genetic technologies that underlie synthetic biology. The implementation of synthetic biology within agriculture may, however, be hampered given the industry is dominated by higher plants and mammals, where large and often polyploid genomes and the lack of adequate tools challenge the ability to deliver outcomes in the short term. However, synthetic biology is a rapidly growing field, new techniques in genome design and synthesis, and more efficient molecular tools such as CRISPR/Cas9 may harbor opportunities more broadly than the development of new cultivars and breeds. In particular, the ability to use synthetic biology to engineer biosensors, synthetic speciation, microbial metabolic engineering, mammalian multiplexed CRISPR, novel anti microbials, and projects such as Yeast 2.0 all have significant potential to deliver transformative changes to agriculture in the short, medium and longer term. Specifically, synthetic biology promises to deliver benefits that increase productivity and sustainability across primary industries, underpinning the industry’s prosperity in the face of global challenges.

Yeast 2.0-connecting the dots in the construction of the world's first functional synthetic eukaryotic genome

Historians of the future may well describe 2018 as the year that the world's first functional synthetic eukaryotic genome became a reality. Without the benefit of hindsight, it might be hard to completely grasp the long-term significance of a breakthrough moment in the history of science like this. The role of synthetic biology in the imminent birth of a budding Saccharomyces cerevisiae yeast cell carrying 16 man-made chromosomes causes the world of science to teeter on the threshold of a future-defining scientific frontier. The genome-engineering tools and technologies currently being developed to produce the ultimate yeast genome will irreversibly connect the dots between our improved understanding of the fundamentals of a complex cell containing its DNA in a specialised nucleus and the application of bioengineered eukaryotes designed for advanced biomanufacturing of beneficial products. By joining up the dots between the findings and learnings from the international Synthetic Yeast Genome project (known as the Yeast 2.0 or Sc2.0 project) and concurrent advancements in biodesign tools and smart data-intensive technologies, a future world powered by a thriving bioeconomy seems realistic. This global project demonstrates how a collaborative network of dot connectors-driven by a tinkerer's indomitable curiosity to understand how things work inside a eukaryotic cell-are using cutting-edge biodesign concepts and synthetic biology tools to advance science and to positively frame human futures (i.e. improved quality of life) in a planetary context (i.e. a sustainable environment). Explorations such as this have a rich history of resulting in unexpected discoveries and unanticipated applications for the benefit of people and planet. However, we must learn from past explorations into controversial futuristic sciences and ensure that researchers at the forefront of an emerging science such as synthetic biology remain connected to all stakeholders' concerns about the biosafety, bioethics and regulatory aspects of their pioneering work. This article presents a shared vision of constructing a synthetic eukaryotic genome in a safe model organism by using novel concepts and advanced technologies. This multidisciplinary and collaborative project is conducted under a sound governance structure that does not only respect the scientific achievements and lessons from the past, but that is also focussed on leading the present and helping to secure a brighter future for all.

Modularization and Response Curve Engineering of a Naringenin-Responsive Transcriptional Biosensor

To monitor the intra- and extracellular environment of micro-organisms and to adapt their metabolic processes accordingly, scientists are reprogramming nature's myriad of transcriptional regulatory systems into transcriptional biosensors, which are able to detect small molecules and, in response, express specific output signals of choice. However, the naturally occurring response curve, the key characteristic of biosensor circuits, is typically not in line with the requirements for real-life biosensor applications. In this contribution, a natural LysR-type naringenin-responsive biosensor circuit is developed and characterized with Escherichia coli as host organism. Subsequently, this biosensor is dissected into a clearly defined detector and effector module without loss of functionality, and the influence of the expression levels of both modules on the biosensor response characteristics is investigated. Two collections of ten unique synthetic biosensors each are generated. Each collection demonstrates a unique diversity of response curve characteristics spanning a 128-fold change in dynamic and 2.5-fold change in operational ranges and 3-fold change in levels of Noise, fit for a wide range of applications, such as adaptive laboratory evolution, dynamic pathway control and high-throughput screening methods. The established biosensor engineering concepts, and the developed biosensor collections themselves, are of use for the future development and customization of biosensors in general, for the multitude of biosensor applications and as a compelling alternative for the commonly used LacI-, TetR- and AraC-based inducible circuits.