Strategies, Achievements, and Potential Challenges of Plant and Microbial Chassis in the Biosynthesis of Plant Secondary Metabolites.

Diverse secondary metabolites in plants, with their rich biological activities, have long been important sources for human medicine, food additives, pesticides, etc. However, the large-scale cultivation of host plants consumes land resources and is susceptible to pest and disease problems. Additionally, the multi-step and demanding nature of chemical synthesis adds to production costs, limiting their widespread application. In vitro cultivation and the metabolic engineering of plants have significantly enhanced the synthesis of secondary metabolites with successful industrial production cases. As synthetic biology advances, more research is focusing on heterologous synthesis using microorganisms. This review provides a comprehensive comparison between these two chassis, evaluating their performance in the synthesis of various types of secondary metabolites from the perspectives of yield and strategies. It also discusses the challenges they face and offers insights into future efforts and directions.

Advances and prospects in microbial production of biotin.

Biotin, serving as a coenzyme in carboxylation reactions, is a vital nutrient crucial for the natural growth, development, and overall well-being of both humans and animals. Consequently, biotin is widely utilized in various industries, including feed, food, and pharmaceuticals. Despite its potential advantages, the chemical synthesis of biotin for commercial production encounters environmental and safety challenges. The burgeoning field of synthetic biology now allows for the creation of microbial cell factories producing bio-based products, offering a cost-effective alternative to chemical synthesis for biotin production. This review outlines the pathway and regulatory mechanism involved in biotin biosynthesis. Then, the strategies to enhance biotin production through both traditional chemical mutagenesis and advanced metabolic engineering are discussed. Finally, the article explores the limitations and future prospects of microbial biotin production. This comprehensive review not only discusses strategies for biotin enhancement but also provides in-depth insights into systematic metabolic engineering approaches aimed at boosting biotin production.

Synthetic bacteria designed using ars operons: a promising solution for arsenic biosensing and bioremediation.

The global concern over arsenic contamination in water due to its natural occurrence and human activities has led to the development of innovative solutions for its detection and remediation. Microbial metabolism and mobilization play crucial roles in the global cycle of arsenic. Many microbial arsenic-resistance systems, especially the ars operons, prevalent in bacterial plasmids and genomes, play vital roles in arsenic resistance and are utilized as templates for designing synthetic bacteria. This review novelty focuses on the use of these tailored bacteria, engineered with ars operons, for arsenic biosensing and bioremediation. We discuss the advantages and disadvantages of using synthetic bacteria in arsenic pollution treatment. We highlight the importance of genetic circuit design, reporter development, and chassis cell optimization to improve biosensors' performance. Bacterial arsenic resistances involving several processes, such as uptake, transformation, and methylation, engineered in customized bacteria have been summarized for arsenic bioaccumulation, detoxification, and biosorption. In this review, we present recent insights on the use of synthetic bacteria designed with ars operons for developing tailored bacteria for controlling arsenic pollution, offering a promising avenue for future research and application in environmental protection.

Enzyme-assisted high throughput sequencing of an expanded genetic alphabet at single base resolution.

With just four building blocks, low sequence information density, few functional groups, poor control over folding, and difficulties in forming compact folds, natural DNA and RNA have been disappointing platforms from which to evolve receptors, ligands, and catalysts. Accordingly, synthetic biology has created "artificially expanded genetic information systems" (AEGIS) to add nucleotides, functionality, and information density. With the expected improvements seen in AegisBodies and AegisZymes, the task for synthetic biologists shifts to developing for expanded DNA the same analytical tools available to natural DNA. Here we report one of these, an enzyme-assisted sequencing of expanded genetic alphabet (ESEGA) method to sequence six-letter AEGIS DNA. We show how ESEGA analyses this DNA at single base resolution, and applies it to optimized conditions for six-nucleotide PCR, assessing the fidelity of various DNA polymerases, and extending this to AEGIS components with functional groups. This supports the renewed exploitation of expanded DNA alphabets in biotechnology.

Robust genetic codes enhance protein evolvability.

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability-the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

Red-Light-Induced Genetic System for Control of Extracellular Electron Transfer.

Optogenetics is a powerful tool for spatiotemporal control of gene expression. Several light-inducible gene regulators have been developed to function in bacteria, and these regulatory circuits have been ported to new host strains. Here, we developed and adapted a red-light-inducible transcription factor for Shewanella oneidensis. This regulatory circuit is based on the iLight optogenetic system, which controls gene expression using red light. A thermodynamic model and promoter engineering were used to adapt this system to achieve differential gene expression in light and dark conditions within a S. oneidensis host strain. We further improved the iLight optogenetic system by adding a repressor to invert the genetic circuit and activate gene expression under red light illumination. The inverted iLight genetic circuit was used to control extracellular electron transfer within S. oneidensis. The ability to use both red- and blue-light-induced optogenetic circuits simultaneously was also demonstrated. Our work expands the synthetic biology capabilities in S. oneidensis, which could facilitate future advances in applications with electrogenic bacteria.

Synthetic Genomics: Repurposing Biological Systems for Applications in Engineering Biology.

Substantial improvements in DNA sequencing and synthesis technologies and increased understanding of genome biology have empowered the development of synthetic genomics. The ability to design and construct engineered living cells boosted up by synthetic chromosomes provides opportunities to tackle enormous current and future challenges faced by humanity and the planet. Here we review the progresses, considerations, challenges, and future direction of the "design-build-test-learn" cycle used in synthetic genomics. We also discuss future applications enabled by synthetic genomics as this emerging field shapes and revolutionizes biomanufacturing and biomedicine.

Improving crop health by synthetic microbial communities: Progress and prospects.

Crop health directly affects yields and food security. At present, agrochemicals such as fertilizers and pesticides are mainly used in agricultural production to promote crop health. However, long-term excessive utilization of agrochemicals will damage the ecological environment of farmlands and increase the safety risk of agricultural products. It is urgent to explore efficient and environment-friendly agricultural products. Rhizosphere microbiome are considered as the second genome of plants, which are closely related to crop health. Understanding the key functional microbes, microbe-microbe interactions, and plant-microbe interactions are fundamental for exploring the potential of beneficial microbes in promoting crop health. However, due to the heterogeneity and complexity of the natural environment, stimulating the function of indigenous microorganisms remains uncertain. Synthetic microbial community (SynCom) is an artificial combination of two or more different strain isolates of microorganisms, with different taxonomic, genetic, or functional characteristic. Because of the advantages of maintaining species diversity and community stability, SynCom has been widely applied in the fields of human health, environmental governance and industrial production, and may also have great potential in promoting crop health. We summarized the concept and research status of SynCom, expounded the principles and methods of constructing SynCom, and analyzed the research on the promotion of crop health by exploring the mechanism of plant-microbe interactions, promoting plant growth and development, and improving stress resistance. Finally, we envisaged the future prospects to guide the using SynCom to improve crop health.

Chromosome Transplantation: Opportunities and Limitations.

There are thousands of rare genetic diseases that could be treated with classical gene therapy strategies such as the addition of the defective gene via viral or non-viral delivery or by direct gene editing. However, several genetic defects are too complex for these approaches. These "genomic mutations" include aneuploidies, intra and inter chromosomal rearrangements, large deletions, or inversion and copy number variations. Chromosome transplantation (CT) refers to the precise substitution of an endogenous chromosome with an exogenous one. By the addition of an exogenous chromosome and the concomitant elimination of the endogenous one, every genetic defect, irrespective of its nature, could be resolved. In the current review, we analyze the state of the art of this technique and discuss its possible application to human pathology. CT might not be limited to the treatment of human diseases. By working on sex chromosomes, we showed that female cells can be obtained from male cells, since chromosome-transplanted cells can lose either sex chromosome, giving rise to 46,XY or 46,XX diploid cells, a modification that could be exploited to obtain female gametes from male cells. Moreover, CT could be used in veterinary biology, since entire chromosomes containing an advantageous locus could be transferred to animals of zootechnical interest without altering their specific genetic background and the need for long and complex interbreeding. CT could also be useful to rescue extinct species if only male cells were available. Finally, the generation of "synthetic" cells could be achieved by repeated CT into a recipient cell. CT is an additional tool for genetic modification of mammalian cells.

Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro.

The ability to control cellular processes using optogenetics is inducer-limited, with most optogenetic systems responding to blue light. To address this limitation, we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems. Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive split transcription factors in the budding yeast, Saccharomyces cerevisiae. We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control. This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs, with broad implications for biotechnology and bioengineering.

CRISPR-Cas tools for simultaneous transcription & translation control in bacteria.

Robust control over gene translation at arbitrary mRNA targets is an outstanding challenge in microbial synthetic biology. The development of tools that can regulate translation will greatly expand our ability to precisely control genes across the genome. In Escherichia coli, most genes are contained in multi-gene operons, which are subject to polar effects where targeting one gene for repression leads to silencing of other genes in the same operon. These effects pose a challenge for independently regulating individual genes in multi-gene operons. Here, we use CRISPR-dCas13 to address this challenge. We find dCas13-mediated repression exhibits up to 6-fold lower polar effects compared to dCas9. We then show that we can selectively activate single genes in a synthetic multi-gene operon by coupling dCas9 transcriptional activation of an operon with dCas13 translational repression of individual genes within the operon. We also show that dCas13 and dCas9 can be multiplexed for improved biosynthesis of a medically-relevant human milk oligosaccharide. Taken together, our findings suggest that combining transcriptional and translational control can access effects that are difficult to achieve with either mode independently. These combined tools for gene regulation will expand our abilities to precisely engineer bacteria for biotechnology and perform systematic genetic screens.

Diffusion-Based Generative Network for de Novo Synthetic Promoter Design.

Computer-aided promoter design is a major development trend in synthetic promoter engineering. Various deep learning models have been used to evaluate or screen synthetic promoters, but there have been few works on de novo promoter design. To explore the potential ability of generative models in promoter design, we established a diffusion-based generative model for promoter design in Escherichia coli. The model was completely driven by sequence data and could study the essential characteristics of natural promoters, thus generating synthetic promoters similar to natural promoters in structure and component. We also improved the calculation method of FID indicator, using a convolution layer to extract the feature matrix of the promoter sequence instead. As a result, we got an FID equal to 1.37, which meant synthetic promoters have a distribution similar to that of natural ones. Our work provides a fresh approach to de novo promoter design, indicating that a completely data-driven generative model is feasible for promoter design.

Understanding and computational design of genetic circuits of metabolic networks.

The expression of metabolic proteins is controlled by genetic circuits, matching metabolic demands and changing environmental conditions. Ideally, this regulation brings about a competitive level of metabolic fitness. Understanding how cells can achieve a robust (close-to-optimal) functioning of metabolism by appropriate control of gene expression aids synthetic biology by providing design criteria of synthetic circuits for biotechnological purposes. It also extends our understanding of the designs of genetic circuitry found in nature such as metabolite control of transcription factor activity, promoter architectures and transcription factor dependencies, and operon composition (in bacteria). Here, we review, explain and illustrate an approach that allows for the inference and design of genetic circuitry that steers metabolic networks to achieve a maximal flux per unit invested protein across dynamic conditions. We discuss how this approach and its understanding can be used to rationalize Escherichia coli's strategy to regulate the expression of its ribosomes and infer the design of circuitry controlling gene expression of amino-acid biosynthesis enzymes. The inferred regulation indeed resembles E. coli's circuits, suggesting that these have evolved to maximize amino-acid production fluxes per unit invested protein. We end by an outlook of the use of this approach in metabolic engineering applications.

Advancements in the Application of Ribosomally Synthesized and Post-Translationally Modified Peptides (RiPPs).

Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent a significant potential for novel therapeutic applications because of their bioactive properties, stability, and specificity. RiPPs are synthesized on ribosomes, followed by intricate post-translational modifications (PTMs), crucial for their diverse structures and functions. PTMs, such as cyclization, methylation, and proteolysis, play crucial roles in enhancing RiPP stability and bioactivity. Advances in synthetic biology and bioinformatics have significantly advanced the field, introducing new methods for RiPP production and engineering. These methods encompass strategies for heterologous expression, genetic refactoring, and exploiting the substrate tolerance of tailoring enzymes to create novel RiPP analogs with improved or entirely new functions. Furthermore, the introduction and implementation of cutting-edge screening methods, including mRNA display, surface display, and two-hybrid systems, have expedited the identification of RiPPs with significant pharmaceutical potential. This comprehensive review not only discusses the current advancements in RiPP research but also the promising opportunities that leveraging these bioactive peptides for therapeutic applications presents, illustrating the synergy between traditional biochemistry and contemporary synthetic biology and genetic engineering approaches.

Methodological advances enabled by the construction of a synthetic yeast genome.

The international Synthetic Yeast Project (Sc2.0) aims to construct the first synthetic designer eukaryote genome. Over the past few years, the Sc2.0 consortium has achieved several significant milestones by synthesizing and characterizing all 16 nuclear chromosomes of the yeast Saccharomyces cerevisiae, as well as a 17thde novo neochromosome containing all nuclear tRNA genes. In this commentary, we discuss the recent technological advances achieved in this project and provide a perspective on how they will impact the emerging field of synthetic genomics in the future.

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems.

BACKGROUND: The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. RESULTS: BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. CONCLUSIONS: BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

Synthetic biology approaches for improving the specificity and efficacy of cancer immunotherapy.

Immunotherapy has shown robust efficacy in treating a broad spectrum of hematological and solid cancers. Despite the transformative impact of immunotherapy on cancer treatment, several outstanding challenges remain. These challenges include on-target off-tumor toxicity, systemic toxicity, and the complexity of achieving potent and sustainable therapeutic efficacy. Synthetic biology has emerged as a promising approach to overcome these obstacles, offering innovative tools for engineering living cells with customized functions. This review provides an overview of the current landscape and future prospects of cancer immunotherapy, particularly emphasizing the role of synthetic biology in augmenting its specificity, controllability, and efficacy. We delineate and discuss two principal synthetic biology strategies: those targeting tumor surface antigens with engineered immune cells and those detecting intratumoral disease signatures with engineered gene circuits. This review concludes with a forward-looking perspective on the enduring challenges in cancer immunotherapy and the potential breakthroughs that synthetic biology may contribute to the field.

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

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

Rational construction of synthetic consortia: Key considerations and model-based methods for guiding the development of a novel biosynthesis platform.

The rapid development of synthetic biology has significantly improved the capabilities of mono-culture systems in converting different substrates into various value-added bio-chemicals through metabolic engineering. However, overexpression of biosynthetic pathways in recombinant strains can impose a heavy metabolic burden on the host, resulting in imbalanced energy distribution and negatively affecting both cell growth and biosynthesis capacity. Synthetic consortia, consisting of two or more microbial species or strains with complementary functions, have emerged as a promising and efficient platform to alleviate the metabolic burden and increase product yield. However, research on synthetic consortia is still in its infancy, with numerous challenges regarding the design and construction of stable synthetic consortia. This review provides a comprehensive comparison of the advantages and disadvantages of mono-culture systems and synthetic consortia. Key considerations for engineering synthetic consortia based on recent advances are summarized, and simulation and computational tools for guiding the advancement of synthetic consortia are discussed. Moreover, further development of more efficient and cost-effective synthetic consortia with emerging technologies such as artificial intelligence and machine learning is highlighted.