Functional optimization of gene clusters by combinatorial design and assembly

GOLDBAR: A Framework for Combinatorial Biological Design

With the rise of new DNA part libraries and technologies for assembling DNA, synthetic biologists are increasingly constructing and screening combinatorial libraries to optimize their biological designs. As combinatorial libraries are used to generate data on design performance, new rules for composing biological designs will emerge. Most formal frameworks for combinatorial design, however, do not yet support formal comparison of design composition, which is needed to facilitate automated analysis and machine learning in massive biological design spaces. To address this need, we introduce a combinatorial design framework called GOLDBAR. Compared with existing frameworks, GOLDBAR enables synthetic biologists to intersect and merge the rules for entire classes of biological designs to extract common design motifs and infer new ones. Here, we demonstrate the application of GOLDBAR to refine/validate design spaces for TetR-homologue transcriptional logic circuits, verify the assembly of a partial nif gene cluster, and infer novel gene clusters for the biosynthesis of rebeccamycin. We also discuss how GOLDBAR could be used to facilitate grammar-based machine learning in synthetic biology.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

Formation of NifA-PII complex represses ammonium-sensitive nitrogen fixation in diazotrophic proteobacteria lacking NifL

Biological nitrogen fixation catalyzed by nitrogenase contributes greatly to the global nitrogen cycle. Nitrogenase expression is subject to regulation in response to nitrogen availability. However, the mechanism through which the transcriptional activator NifA regulates nitrogenase expression by interacting with PII nitrogen regulatory proteins remains unclear in diazotrophic proteobacteria lacking NifL. Here, we demonstrate that in Rhodopseudomonas palustris grown with ammonium, NifA bound deuridylylated PII proteins to form an inactive NifA-PII complex, thereby inhibiting the expression of nitrogenase. Upon nitrogen limitation, the dissociation of uridylylated PII proteins from NifA resulted in the full restoration of NifA activity, and, simultaneously, uridylylation of the significantly up-regulated PII protein GlnK2 led to the increased expression of NifA in R. palustris. This insight into how NifA interacts with PII proteins and controls nitrogenase expression sets the stage for creating highly efficient diazotrophs, reducing the need for energy-intensive chemical fertilizers and helping to diminish carbon emissions.

A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation

The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.

Deciphering the Nitrogen Fixation Gene Cluster in Vibrio natriegens: A Study on Optimized Expression and Application of Nitrogenase

Microbial nitrogen fixation presents a viable alternative to chemical fertilizers, yet the limited colonization and specificity of naturally occurring nitrogen-fixing microorganisms present significant challenges to their widespread application. In this study, we identified a nitrogen fixation gene cluster (VNnif) in Vibrio natriegens (VN) and tested its nitrogenase activity through the acetylene reduction assay. We investigated the potential utilization of nitrogenase by incorporating the nitrogenase gene cluster from VN into plant growth-promoting rhizosphere bacteria Pseudomonas protegens CHA0 and enhancing its activity to 48.16 nmol C2H2/mg/h through promoter replacement and cluster rearrangement. The engineered strain CHA0-PVNnif was found to positively impact the growth of Arabidopsis thaliana col-0 and Triticum aestivum L. (wheat). This study expanded the role of plant growth-promoting rhizobacteria (PGPR) and provided a research foundation for enhancing nitrogenase activity.

Promoter engineering of natural product biosynthetic gene clusters in actinomycetes: concepts and applications

Covering 2011 to 2022Low titers of natural products in laboratory culture or fermentation conditions have been one of the challenging issues in natural products research. Many natural product biosynthetic gene clusters (BGCs) are also transcriptionally silent in laboratory culture conditions, making it challenging to characterize the structures and activities of their metabolites. Promoter engineering offers a potential solution to this problem by providing tools for transcriptional activation or optimization of biosynthetic genes. In this review, we summarize the 10 years of progress in promoter engineering approaches in natural products research focusing on the most metabolically talented group of bacteria actinomycetes.

A CRISPR interference system for engineering biological nitrogen fixation

A grand challenge for the next century is in facing a changing climate through bioengineering solutions. Biological nitrogen fixation, the globally consequential, nitrogenase-catalyzed reduction of atmospheric nitrogen to bioavailable ammonia, is a vital area of focus. Nitrogen fixation engineering relies upon extensive understanding of underlying genetics in microbial models, including the broadly utilized gammaproteobacterium, Azotobacter vinelandii (A. vinelandii). Here, we report the first CRISPR interference (CRISPRi) system for targeted gene silencing in A. vinelandii that integrates genomically via site-specific transposon insertion. We demonstrate that CRISPRi can repress transcription of an essential nitrogen fixation gene by ~60%. Further, we show that nitrogenase genes are suitably expressed from the transposon insertion site, indicating that CRISPRi and engineered nitrogen fixation genes can be co-integrated for combinatorial studies of gene expression and engineering. Our established CRISPRi system fills an important gap for engineering microbial nitrogen fixation for desired purposes.IMPORTANCEAll life on Earth requires nitrogen to survive. About 78% of the atmosphere alone is nitrogen, yet humans cannot use it directly. Instead, we obtain the nitrogen we need for our survival through the food we eat. For more than 100 years, a substantial portion of agricultural productivity has relied on industrial methods for nitrogen fertilizer synthesis, which consumes significant amounts of nonrenewable energy resources and exacerbates environmental degradation and human-induced climate change. Promising alternatives to these industrial methods rely on engineering the only biological pathway for generating bioaccessible nitrogen: microbial nitrogen fixation. Bioengineering strategies require an extensive understanding of underlying genetics in nitrogen-fixing microbes, but genetic tools for this critical goal remain lacking. The CRISPRi gene silencing system that we report, developed in the broadly utilized nitrogen-fixing bacterial model, Azotobacter vinelandii, is an important step toward elucidating the complexity of nitrogen fixation genetics and enabling their manipulation.

Choreographing root architecture and rhizosphere interactions through synthetic biology

Climate change is driving extreme changes to the environment, posing substantial threats to global food security and bioenergy. Given the direct role of plant roots in mediating plant-environment interactions, engineering the form and function of root systems and their associated microbiota may mitigate these effects. Synthetic genetic circuits have enabled sophisticated control of gene expression in microbial systems for years and a surge of advances has heralded the extension of this approach to multicellular plant species. Targeting these tools to affect root structure, exudation, and microbe activity on root surfaces provide multiple strategies for the advancement of climate-ready crops.

Combinatorial optimization of gene expression through recombinase-mediated promoter and terminator shuffling in yeast

Microbes are increasingly employed as cell factories to produce biomolecules. This often involves the expression of complex heterologous biosynthesis pathways in host strains. Achieving maximal product yields and avoiding build-up of (toxic) intermediates requires balanced expression of every pathway gene. However, despite progress in metabolic modeling, the optimization of gene expression still heavily relies on trial-and-error. Here, we report an approach for in vivo, multiplexed Gene Expression Modification by LoxPsym-Cre Recombination (GEMbLeR). GEMbLeR exploits orthogonal LoxPsym sites to independently shuffle promoter and terminator modules at distinct genomic loci. This approach facilitates creation of large strain libraries, in which expression of every pathway gene ranges over 120-fold and each strain harbors a unique expression profile. When applied to the biosynthetic pathway of astaxanthin, an industrially relevant antioxidant, a single round of GEMbLeR improved pathway flux and doubled production titers. Together, this shows that GEMbLeR allows rapid and efficient gene expression optimization in heterologous biosynthetic pathways, offering possibilities for enhancing the performance of microbial cell factories.

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

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

ONE-SENTENCE SUMMARY

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

Enabling pathway design by multiplex experimentation and machine learning

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

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Beneficial effect of optimizing the expression balance of the mevalonate pathway introduced into the mitochondria on terpenoid production in Saccharomyces cerevisiae

Terpenoids are used in various industries, and Saccharomyces cerevisiae is a promising microorganism for terpenoid production. Introducing the mevalonate (MVA) pathway into the mitochondria of a strain with an augmented inherent cytosolic MVA pathway increased terpenoid production but also led to the accumulation of toxic pyrophosphate intermediates that negatively affected terpenoid production. We first engineered the inherent MVA pathway in the cytosol and then introduced the MVA pathway into the mitochondria using several promoter combinations, considering the toxicity of pyrophosphate intermediates. However, the highest titer, 183 mg/L, tends to be only 5% higher than that of the strain that only augmented the inherent MVA pathway (SYCM1; 174 mg/L). Next, we hypothesized that, in addition to the toxicity of pyrophosphate, other compounds in the MVA pathway could affect the squalene titer. Thus, we constructed a combinatorial strain library expressing MVA pathway enzymes in the mitochondria with various promoter combinations. The highest squalene titer (230 mg/L) was 32% higher than that of SYCM1. The promoter set revealed that mitigation of mono- and pyrophosphate compound accumulation was important for mitochondrial usage. This study demonstrated that a combinatorial strain library is useful for discovering the optimal gene expression balance in engineering yeast.

Golden Standard: a complete standard, portable, and interoperative MoClo tool for model and non-model proteobacteria

Modular cloning has become a benchmark technology in synthetic biology. However, a notable disparity exists between its remarkable development and the need for standardization to facilitate seamless interoperability among systems. The field is thus impeded by an overwhelming proliferation of organism-specific systems that frequently lack compatibility. To overcome these issues, we present Golden Standard (GS), a Type IIS assembly method underpinned by the Standard European Vector Architecture. GS unlocks modular cloning applications for most bacteria, and delivers combinatorial multi-part assembly to create genetic circuits of up to twenty transcription units (TUs). Reliance on MoClo syntax renders GS fully compatible with many existing tools and it sets the path towards efficient reusability of available part libraries and assembled TUs. GS was validated in terms of DNA assembly, portability, interoperability and phenotype engineering in α-, β-, γ- and δ-proteobacteria. Furthermore, we provide a computational pipeline for parts characterization that was used to assess the performance of GS parts. To promote community-driven development of GS, we provide a dedicated web-portal including a repository of parts, vectors, and Wizard and Setup tools that guide users in designing constructs. Overall, GS establishes an open, standardized framework propelling the progress of synthetic biology as a whole.

Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.

Regulatory response to a hybrid ancestral nitrogenase in Azotobacter vinelandii

Biological nitrogen fixation, the microbial reduction of atmospheric nitrogen to bioavailable ammonia, represents both a major limitation on biological productivity and a highly desirable engineering target for synthetic biology. However, the engineering of nitrogen fixation requires an integrated understanding of how the gene regulatory dynamics of host diazotrophs respond across sequence-function space of its central catalytic metalloenzyme, nitrogenase. Here, we interrogate this relationship by analyzing the transcriptome of Azotobacter vinelandii engineered with a phylogenetically inferred ancestral nitrogenase protein variant. The engineered strain exhibits reduced cellular nitrogenase activity but recovers wild-type growth rates following an extended lag period. We find that expression of genes within the immediate nitrogen fixation network is resilient to the introduced nitrogenase sequence-level perturbations. Rather the sustained physiological compatibility with the ancestral nitrogenase variant is accompanied by reduced expression of genes that support trace metal and electron resource allocation to nitrogenase. Our results spotlight gene expression changes in cellular processes adjacent to nitrogen fixation as productive engineering considerations to improve compatibility between remodeled nitrogenase proteins and engineered host diazotrophs. IMPORTANCE Azotobacter vinelandii is a key model bacterium for the study of biological nitrogen fixation, an important metabolic process catalyzed by nitrogenase enzymes. Here, we demonstrate that compatibilities between engineered A. vinelandii strains and nitrogenase variants can be modulated at the regulatory level. The engineered strain studied here responds by adjusting the expression of proteins involved in cellular processes adjacent to nitrogen fixation, rather than that of nitrogenase proteins themselves. These insights can inform future strategies to transfer nitrogenase variants to non-native hosts.

Towards universal synthetic heterotrophy using a metabolic coordinator

Engineering the utilization of non-native substrates, or synthetic heterotrophy, in proven industrial microbes such as Saccharomyces cerevisiae represents an opportunity to valorize plentiful and renewable sources of carbon and energy as inputs to bioprocesses. We previously demonstrated that activation of the galactose (GAL) regulon, a regulatory structure used by this yeast to coordinate substrate utilization with biomass formation during growth on galactose, during growth on the non-native substrate xylose results in a vastly altered gene expression profile and faster growth compared with constitutive overexpression of the same heterologous catabolic pathway. However, this effort involved the creation of a xylose-inducible variant of Gal3p (Gal3pSyn4.1), the sensor protein of the GAL regulon, preventing this semi-synthetic regulon approach from being easily adapted to additional non-native substrates. Here, we report the construction of a variant Gal3pMC (metabolic coordinator) that exhibits robust GAL regulon activation in the presence of structurally diverse substrates and recapitulates the dynamics of the native system. Multiple molecular modeling studies suggest that Gal3pMC occupies conformational states corresponding to galactose-bound Gal3p in an inducer-independent manner. Using Gal3pMC to test a regulon approach to the assimilation of the non-native lignocellulosic sugars xylose, arabinose, and cellobiose yields higher growth rates and final cell densities when compared with a constitutive overexpression of the same set of catabolic genes. The subsequent demonstration of rapid and complete co-utilization of all three non-native substrates suggests that Gal3pMC-mediated dynamic global gene expression changes by GAL regulon activation may be universally beneficial for engineering synthetic heterotrophy.

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.

A blueprint for a synthetic genetic feedback optimizer

Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.

Autonomous Synthesis of Functional, Permanently Phosphorylated Proteins for Defining the Interactome of Monomeric 14-3-3ζ

14-3-3 proteins are dimeric hubs that bind hundreds of phosphorylated "clients" to regulate their function. Installing stable, functional mimics of phosphorylated amino acids into proteins offers a powerful strategy to study 14-3-3 function in cellular-like environments, but a previous genetic code expansion (GCE) system to translationally install nonhydrolyzable phosphoserine (nhpSer), with the γ-oxygen replaced with CH2, site-specifically into proteins has seen limited usage. Here, we achieve a 40-fold improvement in this system by engineering into Escherichia coli a six-step biosynthetic pathway that produces nhpSer from phosphoenolpyruvate. Using this autonomous "PermaPhos" expression system, we produce three biologically relevant proteins with nhpSer and confirm that nhpSer mimics the effects of phosphoserine for activating GSK3β phosphorylation of the SARS-CoV-2 nucleocapsid protein, promoting 14-3-3/client complexation, and monomerizing 14-3-3 dimers. Then, to understand the biological function of these phosphorylated 14-3-3ζ monomers (containing nhpSer at Ser58), we isolate its interactome from HEK293T lysates and compare it with that of wild-type 14-3-3ζ. These data identify two new subsets of 14-3-3 client proteins: (i) those that selectively bind dimeric 14-3-3ζ and (ii) those that selectively bind monomeric 14-3-3ζ. We discover that monomeric-but not dimeric-14-3-3ζ interacts with cereblon, an E3 ubiquitin-ligase adaptor protein of pharmacological interest.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

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

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

Nitrogenase resurrection and the evolution of a singular enzymatic mechanism

The planetary biosphere is powered by a suite of key metabolic innovations that emerged early in the history of life. However, it is unknown whether life has always followed the same set of strategies for performing these critical tasks. Today, microbes access atmospheric sources of bioessential nitrogen through the activities of just one family of enzymes, nitrogenases. Here, we show that the only dinitrogen reduction mechanism known to date is an ancient feature conserved from nitrogenase ancestors. We designed a paleomolecular engineering approach wherein ancestral nitrogenase genes were phylogenetically reconstructed and inserted into the genome of the diazotrophic bacterial model, Azotobacter vinelandii, enabling an integrated assessment of both in vivo functionality and purified nitrogenase biochemistry. Nitrogenase ancestors are active and robust to variable incorporation of one or more ancestral protein subunits. Further, we find that all ancestors exhibit the reversible enzymatic mechanism for dinitrogen reduction, specifically evidenced by hydrogen inhibition, which is also exhibited by extant A. vinelandii nitrogenase isozymes. Our results suggest that life may have been constrained in its sampling of protein sequence space to catalyze one of the most energetically challenging biochemical reactions in nature. The experimental framework established here is essential for probing how nitrogenase functionality has been shaped within a dynamic, cellular context to sustain a globally consequential metabolism.

An optimized electrotransformation protocol for Lactobacillus jensenii

Engineered bacteria are promising candidates for in situ detection and treatment of diseases. The female uro-genital tract presents several pathologies, such as sexually transmitted diseases or genital cancer, that could benefit from such technology. While bacteria from the gut microbiome are increasingly engineered, the use of chassis isolated from the female uro-genital resident flora has been limited. A major hurdle to implement the experimental throughput required for efficient engineering in these non-model bacteria is their low transformability. Here we report an optimized electrotransformation protocol for Lactobacillus jensenii, one the most widespread species across vaginal microflora. Starting from classical conditions, we optimized buffers, electric field parameters, cuvette type and DNA quantity to achieve an 80-fold improvement in transformation efficiency, with up to 3.5·103 CFUs/μg of DNA in L. jensenii ATCC 25258. We also identify several plasmids that are maintained and support reporter gene expression in L. jensenii. Finally, we demonstrate that our protocol provides increased transformability in three independent clinical isolates of L. jensenii. This work will facilitate the genetic engineering of L. jensenii and enable its use for addressing challenges in gynecological healthcare.

Engineering Nitrogenases for Synthetic Nitrogen Fixation: From Pathway Engineering to Directed Evolution

Globally, agriculture depends on industrial nitrogen fertilizer to improve crop growth. Fertilizer production consumes fossil fuels and contributes to environmental nitrogen pollution. A potential solution would be to harness nitrogenases-enzymes capable of converting atmospheric nitrogen N2 to NH3 in ambient conditions. It is therefore a major goal of synthetic biology to engineer functional nitrogenases into crop plants, or bacteria that form symbiotic relationships with crops, to support growth and reduce dependence on industrially produced fertilizer. This review paper highlights recent work toward understanding the functional requirements for nitrogenase expression and manipulating nitrogenase gene expression in heterologous hosts to improve activity and oxygen tolerance and potentially to engineer synthetic symbiotic relationships with plants.

High-throughput genetic engineering tools for regulating gene expression in a microbial cell factory

With the rapid advances in biotechnological tools and strategies, microbial cell factory-constructing strategies have been established for the production of value-added compounds. However, optimizing the tradeoff between the biomass, yield, and titer remains a challenge in microbial production. Gene regulation is necessary to optimize and control metabolic fluxes in microorganisms for high-production performance. Various high-throughput genetic engineering tools have been developed for achieving rational gene regulation and genetic perturbation, diversifying the cellular phenotype and enhancing bioproduction performance. In this paper, we review the current high-throughput genetic engineering tools for gene regulation. In particular, technological approaches used in a diverse range of genetic tools for constructing microbial cell factories are introduced, and representative applications of these tools are presented. Finally, the prospects for high-throughput genetic engineering tools for gene regulation are discussed.

Ammonia Production Using Bacteria and Yeast toward a Sustainable Society

Ammonia is an important chemical that is widely used in fertilizer applications as well as in the steel, chemical, textile, and pharmaceutical industries, which has attracted attention as a potential fuel. Thus, approaches to achieve sustainable ammonia production have attracted considerable attention. In particular, biological approaches are important for achieving a sustainable society because they can produce ammonia under mild conditions with minimal environmental impact compared with chemical methods. For example, nitrogen fixation by nitrogenase in heterogeneous hosts and ammonia production from food waste using microorganisms have been developed. In addition, crop production using nitrogen-fixing bacteria has been considered as a potential approach to achieving a sustainable ammonia economy. This review describes previous research on biological ammonia production and provides insights into achieving a sustainable society.

Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications

Metabolic engineering has evolved towards creating cell factories with increasingly complex pathways as economic criteria push biotechnology to higher value products to provide a sustainable source of speciality chemicals. Optimization of such pathways often requires high combinatory exploration of best pathway balance, and this has led to increasing use of high-throughput automated strain construction platforms or novel optimization techniques. In addition, the low catalytic efficiency of such pathways has shifted emphasis from gene expression strategies towards novel protein engineering to increase specific activity of the enzymes involved so as to limit the metabolic burden associated with excessively high pressure on ribosomal machinery when using massive overexpression systems. Metabolic burden is now generally recognized as a major hurdle to be overcome with consequences on genetic stability but also on the intensified performance needed industrially to attain the economic targets for successful product launch. Increasing awareness of the need to integrate novel genetic information into specific sites within the genome which not only enhance genetic stability (safe harbors) but also enable maximum expression profiles has led to genome-wide assessment of best integration sites, and bioinformatics will facilitate the identification of most probable landing pads within the genome.To facilitate the transfer of novel biotechnological potential to industrial-scale production, more attention, however, has to be paid to engineering metabolic fitness adapted to the specific stress conditions inherent to large-scale fermentation and the inevitable heterogeneity that will occur due to mass transfer limitations and the resulting deviation away from ideal conditions as seen in laboratory-scale validation of the engineered cells. To ensure smooth and rapid transfer of novel cell lines to industry with an accelerated passage through scale-up, better coordination is required form the onset between the biochemical engineers involved in process technology and the genetic engineers building the new strain so as to have an overall strategy able to maximize innovation at all levels. This should be one of our key objectives when building fermentation-friendly chassis organisms.

Genetic Circuit Design in Rhizobacteria

Genetically engineered plants hold enormous promise for tackling global food security and agricultural sustainability challenges. However, construction of plant-based genetic circuitry is constrained by a lack of well-characterized genetic parts and circuit design rules. In contrast, advances in bacterial synthetic biology have yielded a wealth of sensors, actuators, and other tools that can be used to build bacterial circuitry. As root-colonizing bacteria (rhizobacteria) exert substantial influence over plant health and growth, genetic circuit design in these microorganisms can be used to indirectly engineer plants and accelerate the design-build-test-learn cycle. Here, we outline genetic parts and best practices for designing rhizobacterial circuits, with an emphasis on sensors, actuators, and chassis species that can be used to monitor/control rhizosphere and plant processes.

A versatile active learning workflow for optimization of genetic and metabolic networks

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO₂-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 10²⁵ conditions with only 1,000 experiments to yield the most efficient CO₂-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, aimed at democratization and standardization, the authors describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets.

Biological nitrogen fixation and prospects for ecological intensification in cereal-based cropping systems

The demand for nitrogen (N) for crop production increased rapidly from the middle of the twentieth century and is predicted to at least double by 2050 to satisfy the on-going improvements in productivity of major food crops such as wheat, rice and maize that underpin the staple diet of most of the world's population. The increased demand will need to be fulfilled by the two main sources of N supply - biological nitrogen (gas) (N2) fixation (BNF) and fertilizer N supplied through the Haber-Bosch processes. BNF provides many functional benefits for agroecosystems. It is a vital mechanism for replenishing the reservoirs of soil organic N and improving the availability of soil N to support crop growth while also assisting in efforts to lower negative environmental externalities than fertilizer N. In cereal-based cropping systems, legumes in symbiosis with rhizobia contribute the largest BNF input; however, diazotrophs involved in non-symbiotic associations with plants or present as free-living N2-fixers are ubiquitous and also provide an additional source of fixed N. This review presents the current knowledge of BNF by free-living, non-symbiotic and symbiotic diazotrophs in the global N cycle, examines global and regional estimates of contributions of BNF, and discusses possible strategies to enhance BNF for the prospective benefit of cereal N nutrition. We conclude by considering the challenges of introducing in planta BNF into cereals and reflect on the potential for BNF in both conventional and alternative crop management systems to encourage the ecological intensification of cereal and legume production.

A miniaturized 3D printed pressure regulator (µPR) for microfluidic cell culture applications

Well-defined fluid flows are the hallmark feature of microfluidic culture systems and enable precise control over biophysical and biochemical cues at the cellular scale. Microfluidic flow control is generally achieved using displacement-based (e.g., syringe or peristaltic pumps) or pressure-controlled techniques that provide numerous perfusion options, including constant, ramped, and pulsed flows. However, it can be challenging to integrate these large form-factor devices and accompanying peripherals into incubators or other confined environments. In addition, microfluidic culture studies are primarily carried out under constant perfusion conditions and more complex flow capabilities are often unused. Thus, there is a need for a simplified flow control platform that provides standard perfusion capabilities and can be easily integrated into incubated environments. To this end, we introduce a tunable, 3D printed micro pressure regulator (µPR) and show that it can provide robust flow control capabilities when combined with a battery-powered miniature air pump to support microfluidic applications. We detail the design and fabrication of the µPR and: (i) demonstrate a tunable outlet pressure range relevant for microfluidic applications (1-10 kPa), (ii) highlight dynamic control capabilities in a microfluidic network, (iii) and maintain human umbilical vein endothelial cells (HUVECs) in a multi-compartment culture device under continuous perfusion conditions. We anticipate that our 3D printed fabrication approach and open-access designs will enable customized µPRs that can support a broad range of microfluidic applications.

A colorimetric method to measure in vitro nitrogenase functionality for engineering nitrogen fixation

Biological nitrogen fixation (BNF) is the reduction of N2 into NH3 in a group of prokaryotes by an extremely O2-sensitive protein complex called nitrogenase. Transfer of the BNF pathway directly into plants, rather than by association with microorganisms, could generate crops that are less dependent on synthetic nitrogen fertilizers and increase agricultural productivity and sustainability. In the laboratory, nitrogenase activity is commonly determined by measuring ethylene produced from the nitrogenase-dependent reduction of acetylene (ARA) using a gas chromatograph. The ARA is not well suited for analysis of large sample sets nor easily adapted to automated robotic determination of nitrogenase activities. Here, we show that a reduced sulfonated viologen derivative (S2Vred) assay can replace the ARA for simultaneous analysis of isolated nitrogenase proteins using a microplate reader. We used the S2Vred to screen a library of NifH nitrogenase components targeted to mitochondria in yeast. Two NifH proteins presented properties of great interest for engineering of nitrogen fixation in plants, namely NifM independency, to reduce the number of genes to be transferred to the eukaryotic host; and O2 resistance, to expand the half-life of NifH iron-sulfur cluster in a eukaryotic cell. This study established that NifH from Dehalococcoides ethenogenes did not require NifM for solubility, [Fe-S] cluster occupancy or functionality, and that NifH from Geobacter sulfurreducens was more resistant to O2 exposure than the other NifH proteins tested. It demonstrates that nitrogenase components with specific biochemical properties such as a wider range of O2 tolerance exist in Nature, and that their identification should be an area of focus for the engineering of nitrogen-fixing crops.

Freedom of expression: A synthetic route to metabolites

Microbial specialized metabolites play key roles in microbiome interactions, but their biosynthetic pathways are difficult to characterize. In this issue, Patel et al. (2022) describe new technologies for the computer-aided redesign of gene clusters to facilitate heterologous expression across diverse hosts and showcase their utility by identifying a new class of microbiome-derived nucleotide metabolites.

Cross-kingdom expression of synthetic genetic elements promotes discovery of metabolites in the human microbiome

Small molecules encoded by biosynthetic pathways mediate cross-species interactions and harbor untapped potential, which has provided valuable compounds for medicine and biotechnology. Since studying biosynthetic gene clusters in their native context is often difficult, alternative efforts rely on heterologous expression, which is limited by host-specific metabolic capacity and regulation. Here, we describe a computational-experimental technology to redesign genes and their regulatory regions with hybrid elements for cross-species expression in Gram-negative and -positive bacteria and eukaryotes, decoupling biosynthetic capacity from host-range constraints to activate silenced pathways. These synthetic genetic elements enabled the discovery of a class of microbiome-derived nucleotide metabolites-tyrocitabines-from Lactobacillus iners. Tyrocitabines feature a remarkable orthoester-phosphate, inhibit translational activity, and invoke unexpected biosynthetic machinery, including a class of "Amadori synthases" and "abortive" tRNA synthetases. Our approach establishes a general strategy for the redesign, expression, mobilization, and characterization of genetic elements in diverse organisms and communities.

RoboMoClo: A Robotics-Assisted Modular Cloning Framework for Multiple Gene Assembly in Biofoundry

Efficient and versatile DNA assembly frameworks have had an impact on promoting synthetic biology to build complex biological systems. To accelerate system development, laboratory automation (or biofoundry) provides an opportunity to construct organisms and DNA assemblies via computer-aided design. However, a modular cloning (MoClo) system for multiple DNA assemblies limits the biofoundry workflow in terms of simplicity and feasibility by preparing the number of cloning materials such as destination vectors prior to the automation process. Herein, we propose robot-assisted MoClo (RoboMoClo) to accelerate a synthetic biology project with multiple gene expressions at the biofoundry. The architecture of the RoboMoClo framework provides a hybrid strategy of hierarchical gene assembly and iterative gene assembly, and fewer destination vectors compared with other MoClo systems. An industrial bacterium, Corynebacterium glutamicum, was used as a model host for RoboMoClo. After building a biopart library (promoter and terminator; level 0) and evaluating its features (level 1), various transcriptional directions in multiple gene assemblies (level 2) were studied using the RoboMoClo vectors. Among the constructs, the convergent construct exhibited potential transcriptional interference through the collision of RNA polymerases. To study design of experiment-guided lycopene biosynthesis in C. glutamicum (levels 1, 2, and 3), the biofoundry-assisted multiple gene assembly was demonstrated as a proof-of-concept by constructing various sub-pathway units (level 2) and pathway units (level 3) for C. glutamicum. The RoboMoClo framework provides an improved MoClo toolkit for laboratory automation in a synthetic biology application.

Synthetic Biology Tool Development Advances Predictable Gene Expression in the Metabolically Versatile Soil Bacterium Rhodopseudomonas palustris

Harnessing the unique biochemical capabilities of non-model microorganisms would expand the array of biomanufacturing substrates, process conditions, and products. There are non-model microorganisms that fix nitrogen and carbon dioxide, derive energy from light, catabolize methane and lignin-derived aromatics, are tolerant to physiochemical stresses and harsh environmental conditions, store lipids in large quantities, and produce hydrogen. Model microorganisms often only break down simple sugars and require low stress conditions, but they have been engineered for the sustainable manufacture of numerous products, such as fragrances, pharmaceuticals, cosmetics, surfactants, and specialty chemicals, often by using tools from synthetic biology. Transferring complex pathways has proven to be exceedingly difficult, as the cofactors, cellular conditions, and energy sources necessary for this pathway to function may not be present in the host organism. Utilization of unique biochemical capabilities could also be achieved by engineering the host; although, synthetic biology tools developed for model microbes often do not perform as designed in other microorganisms. The metabolically versatile Rhodopseudomonas palustris CGA009, a purple non-sulfur bacterium, catabolizes aromatic compounds derived from lignin in both aerobic and anaerobic conditions and can use light, inorganic, and organic compounds for its source of energy. R. palustris utilizes three nitrogenase isozymes to fulfill its nitrogen requirements while also generating hydrogen. Furthermore, the bacterium produces two forms of RuBisCo in response to carbon dioxide/bicarbonate availability. While this potential chassis harbors many beneficial traits, stable heterologous gene expression has been problematic due to its intrinsic resistance to many antibiotics and the lack of synthetic biology parts investigated in this microbe. To address these problems, we have characterized gene expression and plasmid maintenance for different selection markers, started a synthetic biology toolbox specifically for the photosynthetic R. palustris, including origins of replication, fluorescent reporters, terminators, and 5′ untranslated regions, and employed the microbe’s endogenous plasmid for exogenous protein production. This work provides essential synthetic biology tools for engineering R. palustris’ many unique biochemical processes and has helped define the principles for expressing heterologous genes in this promising microbe through a methodology that could be applied to other non-model microorganisms.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast

Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR-fapO-based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the recognition sequence insertion scheme and the choice of UAS have a significant impact on the dynamic range. Taken together, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Codon-Restrained Method for Both Eliminating and Creating Intragenic Bacterial Promoters

Future applications of synthetic biology will require refactored genetic sequences devoid of internal regulatory elements within coding sequences. These regulatory elements include cryptic and intragenic promoters, which may constitute up to a third of the predicted Escherichia coli promoters. The promoter activity is dependent on the structural interaction of core bases with a σ factor. Rational engineering can be used to alter key promoter element nucleotides interacting with σ factors and eliminate downstream transcriptional activity. In this paper, we present codon-restrained promoter silencing (CORPSE), a system for removing intragenic promoters. CORPSE exploits the DNA-σ factor structural relationship to disrupt σ⁷⁰ promoters embedded within gene coding sequences with a minimum of synonymous codon changes. Additionally, we present an inverted CORPSE system, iCORPSE, which can create highly active promoters within a gene sequence while not perturbing the function of the modified gene.

BioCCP.jl: collecting coupons in combinatorial biotechnology

SUMMARY

In combinatorial biotechnology, it is crucial for screening experiments to sufficiently cover the design space. In the BioCCP.jl package (Julia), we provide functions for minimum sample size determination based on the mathematical framework coined the Coupon Collector Problem.

AVAILABILITY AND IMPLEMENTATION

BioCCP.jl, including source code, documentation and Pluto notebooks, is available at https://github.com/kirstvh/BioCCP.jl.

Toward improved terpenoids biosynthesis: strategies to enhance the capabilities of cell factories

Terpenoids form the most diversified class of natural products, which have gained application in the pharmaceutical, food, transportation, and fine and bulk chemical industries. Extraction from naturally occurring sources does not meet industrial demands, whereas chemical synthesis is often associated with poor enantio-selectivity, harsh working conditions, and environmental pollutions. Microbial cell factories come as a suitable replacement. However, designing efficient microbial platforms for isoprenoid synthesis is often a challenging task. This has to do with the cytotoxic effects of pathway intermediates and some end products, instability of expressed pathways, as well as high enzyme promiscuity. Also, the low enzymatic activity of some terpene synthases and prenyltransferases, and the lack of an efficient throughput system to screen improved high-performing strains are bottlenecks in strain development. Metabolic engineering and synthetic biology seek to overcome these issues through the provision of effective synthetic tools. This review sought to provide an in-depth description of novel strategies for improving cell factory performance. We focused on improving transcriptional and translational efficiencies through static and dynamic regulatory elements, enzyme engineering and high-throughput screening strategies, cellular function enhancement through chromosomal integration, metabolite tolerance, and modularization of pathways.

15 years of microbial biotechnology: the time has come to think big-and act soon

Our epoch is largely characterized by the growing realization and concern about the reality of climate change and environmental deterioration, the surge of global pandemics, the unacceptable inequalities between developed and underdeveloped countries and their unavoidable translation into messy immigration, overpopulation and food crises. While all of these issues have a fundamentally political core, they are not altogether removed from the fact that Earth is primarily a microbial planet and microorganisms are the key agents that make the biosphere (including ourselves) function as it does. It thus makes sense that we bring the microbial world-that is the environmental microbiome-to the necessary multi-tiered conversation (hopefully followed by action) on how to avoid future threats and how to make our globe a habitable common house. Beyond discussion on governance, such a dialogue has technical and scientific aspects that only frontline microbial biotechnology can help to tackle. Fortunately, the field has witnessed the onset of new conceptual and material tools that were missing when the journal started.

PermaPhos Ser : autonomous synthesis of functional, permanently phosphorylated proteins

Installing stable, functional mimics of phosphorylated amino acids into proteins offers a powerful strategy to study protein regulation. Previously, a genetic code expansion (GCE) system was developed to translationally install non-hydrolyzable phosphoserine (nhpSer), with the γ-oxygen replaced with carbon, but it has seen limited usage. Here, we achieve a 40-fold improvement in this system by engineering into Escherichia coli a biosynthetic pathway that produces nhpSer from the central metabolite phosphoenolpyruvate. Using this "PermaPhos Ser " system - an autonomous 21-amino acid E. coli expression system for incorporating nhpSer into target proteins - we show that nhpSer faithfully mimics the effects of phosphoserine in three stringent test cases: promoting 14-3-3/client complexation, disrupting 14-3-3 dimers, and activating GSK3β phosphorylation of the SARS-CoV-2 nucleocapsid protein. This facile access to nhpSer containing proteins should allow nhpSer to replace Asp and Glu as the go-to pSer phosphomimetic for proteins produced in E. coli .

Combinatorial assembly platform enabling engineering of genetically stable metabolic pathways in cyanobacteria

Cyanobacteria are simple, efficient, genetically-tractable photosynthetic microorganisms which in principle represent ideal biocatalysts for CO2 capture and conversion. However, in practice, genetic instability and low productivity are key, linked problems in engineered cyanobacteria. We took a massively parallel approach, generating and characterising libraries of synthetic promoters and RBSs for the cyanobacterium Synechocystis sp. PCC 6803, and assembling a sparse combinatorial library of millions of metabolic pathway-encoding construct variants. Genetic instability was observed for some variants, which is expected when variants cause metabolic burden. Surprisingly however, in a single combinatorial round without iterative optimisation, 80% of variants chosen at random and cultured photoautotrophically over many generations accumulated the target terpenoid lycopene from atmospheric CO2, apparently overcoming genetic instability. This large-scale parallel metabolic engineering of cyanobacteria provides a new platform for development of genetically stable cyanobacterial biocatalysts for sustainable light-driven production of valuable products directly from CO2, avoiding fossil carbon or competition with food production.

Top-down synthetic biology approach for titer improvement of clinically important antibiotic daptomycin in Streptomyces roseosporus

Secondary metabolites are produced at low titers by native producers due to tight regulations of their productions in response to environmental conditions. Synthetic biology provides a rational engineering principle for transcriptional optimization of secondary metabolite BGCs (biosynthetic gene clusters). Here, we demonstrate the use of synthetic biology principles for the development of a high-titer strain of the clinically important antibiotic daptomycin. Due to the presence of large NRPS (non-ribosomal peptide synthetase) genes with multiple direct repeats, we employed a top-down approach that allows transcriptional optimization of genes in daptomycin BGC with the minimum inputs of synthetic DNAs. The repeat-free daptomycin BGC was created through partial codon-reprogramming of a NRPS gene and cloned into a shuttle BAC vector, allowing BGC refactoring in a host with a powerful recombination system. Then, transcriptions of functionally divided operons were sequentially optimized through three rounds of DBTL (design-build-test-learn) cycles that resulted in up to ~2300% improvement in total lipopeptide titers compared to the wild-type strain. Upon decanoic acid feeding, daptomycin accounted for ∼ 40% of total lipopeptide production. To the best of our knowledge, this is the highest improvement of daptomycin titer ever achieved through genetic engineering of S. roseosporus. The top-down engineering approach we describe here could be used as a general strategy for the development of high-titer industrial strains of secondary metabolites produced by BGCs containing genes of large multi-modular NRPS and PKS enzymes.

Importance of the 5' regulatory region to bacterial synthetic biology applications

The field of synthetic biology is evolving at a fast pace. It is advancing beyond single-gene alterations in single hosts to the logical design of complex circuits and the development of integrated synthetic genomes. Recent breakthroughs in deep learning, which is increasingly used in de novo assembly of DNA components with predictable effects, are also aiding the discipline. Despite advances in computing, the field is still reliant on the availability of pre-characterized DNA parts, whether natural or synthetic, to regulate gene expression in bacteria and make valuable compounds. In this review, we discuss the different bacterial synthetic biology methodologies employed in the creation of 5' regulatory regions - promoters, untranslated regions and 5'-end of coding sequences. We summarize methodologies and discuss their significance for each of the functional DNA components, and highlight the key advances made in bacterial engineering by concentrating on their flaws and strengths. We end the review by outlining the issues that the discipline may face in the near future.

Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters

Multistable switches are ubiquitous building blocks in both systems and synthetic biology. Given their central role, it is thus imperative to understand how their fundamental properties depend not only on the tunable biophysical properties of the switches themselves, but also on their genetic context. To this end, we reveal in this article how these factors shape the essential characteristics of toggle switches implemented using leaky promoters such as their stability and robustness to noise, both at single-cell and population levels. In particular, our results expose the roles that competition for scarce transcriptional and translational resources, promoter leakiness, and cell-to-cell heterogeneity collectively play. For instance, the interplay between protein expression from leaky promoters and the associated cost of relying on shared cellular resources can give rise to tristable dynamics even in the absence of positive feedback. Similarly, we demonstrate that while promoter leakiness always acts against multistability, resource competition can be leveraged to counteract this undesirable phenomenon. Underpinned by a mechanistic model, our results thus enable the context-aware rational design of multistable genetic switches that are directly translatable to experimental considerations, and can be further leveraged during the synthesis of large-scale genetic systems using computer-aided biodesign automation platforms.

Nitrogen fixing cereal: A rising hero towards meeting food security

Nitrogen serves as one of the primary components of major biomolecules and thus extends a significant contribution to crop growth and yield. But the inability of plants to utilize freely available atmospheric N₂ makes the whole agricultural system dependent on chemical fertilizers, which incur significant input cost to supplement required quantities of nitrogen to crops. Only bacteria and archaea have been gifted with the power of drawing free N₂ from air to convert them into NH₃, which is one of the two utilizable forms of nitrogen taken up by plants. Legumes, the only family of crops, can engage themselves in symbiotic nitrogen fixation where they establish a mutualistic relationship with nitrogen-fixing bacteria and in turn, can waive off the necessity of adding nitrogen fertilizers. Sincere effort, therefore, has been undertaken to incorporate this capability of nitrogen-fixation into non-legume crops, especially cereals which make up a vital portion in the food basket. Biotechnological interventions have also played important role in providing nitrogen fixing trait to non-legumes. This review takes up an effort to look into and accumulate all the important updates to date regarding nitrogen-fixing non-legumes with a special focus on cereals, which is one of the most important future goals in the field of science in the present era.

Simultaneous Multiplex Genome Engineering via Accelerated Natural Transformation in Bacillus subtilis

Multiplex engineering at the scale of whole genomes has become increasingly important for synthetic biology and biotechnology applications. Although several methods have been reported for engineering microbe genomes, their use is limited by their complex procedures using multi-cycle transformations. Natural transformation, involving in species evolution by horizontal gene transfer in many organisms, indicates its potential as a genetic tool. Here, we aimed to develop simultaneous multiplex genome engineering (SMGE) for the simple, rapid, and efficient design of bacterial genomes via one-step of natural transformation in Bacillus subtilis. The transformed DNA, competency factors, and recombinases were adapted to improved co-editing frequencies above 27-fold. Single to octuplet variants with genetic diversity were simultaneously generated using all-in-one vectors harboring multi-gene cassettes. To demonstrate its potential application, the tyrosine biosynthesis pathway was further optimized for producing commercially important resveratrol by high-throughput screening of variant pool in B. subtilis. SMGE represents an accelerated evolution platform that generates diverse multiplex mutations for large-scale genetic engineering and synthetic biology in B. subtilis.