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.

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.

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.

Edible mycelium bioengineered for enhanced nutritional value and sensory appeal using a modular synthetic biology toolkit.

Filamentous fungi are critical in the transition to a more sustainable food system. While genetic modification of these organisms has promise for enhancing the nutritional value, sensory appeal, and scalability of fungal foods, genetic tools and demonstrated use cases for bioengineered food production by edible strains are lacking. Here, we develop a modular synthetic biology toolkit for Aspergillus oryzae, an edible fungus used in fermented foods, protein production, and meat alternatives. Our toolkit includes a CRISPR-Cas9 method for gene integration, neutral loci, and tunable promoters. We use these tools to elevate intracellular levels of the nutraceutical ergothioneine and the flavor-and color molecule heme in the edible biomass. The strain overproducing heme is red in color and is readily formulated into imitation meat patties with minimal processing. These findings highlight the promise of synthetic biology to enhance fungal foods and provide useful genetic tools for applications in food production and beyond.

Cell-Free Protein Expression in Polymer Materials.

While synthetic biology has advanced complex capabilities such as sensing and molecular synthesis in aqueous solutions, important applications may also be pursued for biological systems in solid materials. Harsh processing conditions used to produce many synthetic materials such as plastics make the incorporation of biological functionality challenging. One technology that shows promise in circumventing these issues is cell-free protein synthesis (CFPS), where core cellular functionality is reconstituted outside the cell. CFPS enables genetic functions to be implemented without the complications of membrane transport or concerns over the cellular viability or release of genetically modified organisms. Here, we demonstrate that dried CFPS reactions have remarkable tolerance to heat and organic solvent exposure during the casting processes for polymer materials. We demonstrate the utility of this observation by creating plastics that have spatially patterned genetic functionality, produce antimicrobials in situ, and perform sensing reactions. The resulting materials unlock the potential to deliver DNA-programmable biofunctionality in a ubiquitous class of synthetic materials.

Enzymatic Preparation of DNA with an Expanded Genetic Alphabet Using Terminal Deoxynucleotidyl Transferase and Its Applications.

Efficient preparation of DNA oligonucleotides containing unnatural nucleobases (UBs) that can pair with their cognates to form unnatural base pairs (UBPs) is an essential prerequisite for the application of UBPs in vitro and in vivo. Traditional preparation of oligonucleotides containing unnatural nucleobases largely relies on solid-phase synthesis, which needs to use unstable nucleoside phosphoramidites and a DNA synthesizer, and is environmentally unfriendly and limited in product length. To overcome these limitations of solid-phase synthesis, we developed enzymatic methods for daily laboratory preparation of DNA oligonucleotides containing unnatural nucleobase dNaM, dTPT3, or one of the functionalized dTPT3 derivatives, which can be used for orthogonal DNA labeling or the preparation of DNAs containing UBP dNaM-dTPT3, one of the most successful UBPs to date, based on the template-independent polymerase terminal deoxynucleotidyl transferase (TdT). Here, we first provide a detailed procedure for the TdT-based preparation of DNA oligonucleotides containing 3'-nucleotides of dNaM, dTPT3, or one of dTPT3 derivatives. We then present the procedures for enzyme-linked oligonucleotide assay (ELONA) and imaging of bacterial cells using DNA oligonucleotides containing 3'-nucleotides of dTPT3 derivatives with different functional groups. The procedure for enzymatic synthesis of DNAs containing an internal UBP dNaM-dTPT3 is also described. Hopefully, these methods will greatly facilitate the application of UBPs and the construction of semi-synthetic organisms with an expanded genetic alphabet.

Genetic Network Design Automation with LOICA.

Genetic design automation (GDA) is the use of computer-aided design (CAD) in designing genetic networks. GDA tools are necessary to create more complex synthetic genetic networks in a high-throughput fashion. At the core of these tools is the abstraction of a hierarchy of standardized components. The components' input, output, and interactions must be captured and parametrized from relevant experimental data. Simulations of genetic networks should use those parameters and include the experimental context to be compared with the experimental results.This chapter introduces Logical Operators for Integrated Cell Algorithms (LOICA), a Python package used for designing, modeling, and characterizing genetic networks using a simple object-oriented design abstraction. LOICA represents different biological and experimental components as classes that interact to generate models. These models can be parametrized by direct connection to the Flapjack experimental data management platform to characterize abstracted components with experimental data. The models can be simulated using stochastic simulation algorithms or ordinary differential equations with varying noise levels. The simulated data can be managed and published using Flapjack alongside experimental data for comparison. LOICA genetic network designs can be represented as graphs and plotted as networks for visual inspection and serialized as Python objects or in the Synthetic Biology Open Language (SBOL) format for sharing and use in other designs.

In Vivo DNA Assembly Using the PEDA Method.

Simple and efficient DNA assembly methods have been widely used in synthetic biology. Here, we provide the protocol for the recently developed PEDA (phage enzyme-assisted in vivo DNA assembly) method for direct in vivo assembly of individual DNA parts in multiple microorganisms, such as Escherichia coli, Ralstonia eutropha, Pseudomonas putida, Lactobacillus plantarum, and Yarrowia lipolytica. PEDA allows in vivo assembly of DNA fragments with homologous sequences as short as 5 bp, and the efficiency is comparable to the prevailing in vitro DNA assembly, which will broadly boost the rapid progress of synthetic biology.

Enzymatic Assembly of Small Synthetic Genes with Repetitive Elements.

Gene synthesis efficiency has greatly improved in recent years but is limited when it comes to repetitive sequences, which results in synthesis failure or delays by DNA synthesis vendors. This represents a major obstacle for the development of synthetic biology since repetitive elements are increasingly being used in the design of genetic circuits and design of biomolecular nanostructures. Here, we describe a method for the assembly of small synthetic genes with repetitive elements: First, a gene of interest is split in silico into small synthons of up to 80 base pairs flanked by Golden-Gate-compatible overhangs. Then, synthons are made by oligo extension and finally assembled into a synthetic gene by Golden Gate Assembly. We demonstrate the method by constructing eight challenging genes with repetitive elements, e.g., multiple repeats of RNA aptamers and RNA origami scaffolds with multiple identical aptamers. The genes range in size from 133 to 456 base pairs and are assembled with fidelities of up to 87.5%. The method was developed to facilitate our own specific research but may be of general use for constructing challenging and repetitive genes and, thus, a valuable addition to the molecular cloning toolbox.

A synthetic differentiation circuit in Escherichia coli for suppressing mutant takeover.

Differentiation is crucial for multicellularity. However, it is inherently susceptible to mutant cells that fail to differentiate. These mutants outcompete normal cells by excessive self-renewal. It remains unclear what mechanisms can resist such mutant expansion. Here, we demonstrate a solution by engineering a synthetic differentiation circuit in Escherichia coli that selects against these mutants via a biphasic fitness strategy. The circuit provides tunable production of synthetic analogs of stem, progenitor, and differentiated cells. It resists mutations by coupling differentiation to the production of an essential enzyme, thereby disadvantaging non-differentiating mutants. The circuit selected for and maintained a positive differentiation rate in long-term evolution. Surprisingly, this rate remained constant across vast changes in growth conditions. We found that transit-amplifying cells (fast-growing progenitors) underlie this environmental robustness. Our results provide insight into the stability of differentiation and demonstrate a powerful method for engineering evolutionarily stable multicellular consortia.

Highly Parallelized Construction of DNA from Low-Cost Oligonucleotide Mixtures Using Data-Optimized Assembly Design and Golden Gate.

Commercially synthesized genes are typically made using variations of homology-based cloning techniques, including polymerase cycling assembly from chemically synthesized microarray-derived oligonucleotides. Here, we apply Data-optimized Assembly Design (DAD) to the synthesis of hundreds of codon-optimized genes in both constitutive and inducible vectors using Golden Gate Assembly. Starting from oligonucleotide pools, we synthesize genes in three simple steps: (1) amplification of parts belonging to individual assemblies in parallel from a single pool; (2) Golden Gate Assembly of parts for each construct; and (3) transformation. We construct genes from receiving DNA to sequence confirmed isolates in as little as 4 days. By leveraging the ligation fidelity afforded by T4 DNA ligase, we expect to be able to construct a larger breadth of sequences not currently supported by homology-based methods, which require stability of extensive single-stranded DNA overhangs.

Enhancing circuit stability under growth feedback with supplementary repressive regulation.

The field of synthetic biology and biosystems engineering increasingly acknowledges the need for a holistic design approach that incorporates circuit-host interactions into the design process. Engineered circuits are not isolated entities but inherently entwined with the dynamic host environment. One such circuit-host interaction, 'growth feedback', results when modifications in host growth patterns influence the operation of gene circuits. The growth-mediated effects can range from growth-dependent elevation in protein/mRNA dilution rate to changes in resource reallocation within the cell, which can lead to complete functional collapse in complex circuits. To achieve robust circuit performance, synthetic biologists employ a variety of control mechanisms to stabilize and insulate circuit behavior against growth changes. Here we propose a simple strategy by incorporating one repressive edge in a growth-sensitive bistable circuit. Through both simulation and in vitro experimentation, we demonstrate how this additional repressive node stabilizes protein levels and increases the robustness of a bistable circuit in response to growth feedback. We propose the incorporation of repressive links in gene circuits as a control strategy for desensitizing gene circuits against growth fluctuations.

A programmable reaction-diffusion system for spatiotemporal cell signaling circuit design.

Cells self-organize molecules in space and time to generate complex behaviors, but we lack synthetic strategies for engineering spatiotemporal signaling. We present a programmable reaction-diffusion platform for designing protein oscillations, patterns, and circuits in mammalian cells using two bacterial proteins, MinD and MinE (MinDE). MinDE circuits act like "single-cell radios," emitting frequency-barcoded fluorescence signals that can be spectrally isolated and analyzed using digital signal processing tools. We define how to genetically program these signals and connect their spatiotemporal dynamics to cell biology using engineerable protein-protein interactions. This enabled us to construct sensitive reporter circuits that broadcast endogenous cell signaling dynamics on a frequency-barcoded imaging channel and to build control signal circuits that synthetically pattern activities in the cell, such as protein condensate assembly and actin filamentation. Our work establishes a paradigm for visualizing, probing, and engineering cellular activities at length and timescales critical for biological function.

Standardizing cassette-based deep mutagenesis by Golden Gate assembly.

Protocols for the construction of large, deeply mutagenized protein encoding libraries via Golden Gate assembly of synthetic DNA cassettes employ disparate, system-specific methodology. Here we present a standardized Golden Gate method for building user-defined libraries. We demonstrate that a 25 µL reaction, using 40 fmol of input DNA, can generate a library on the order of 1 × 106 members and that reaction volume or input DNA concentration can be scaled up with no losses in transformation efficiency. Such libraries can be constructed from dsDNA cassettes generated either by degenerate oligonucleotides or oligo pools. We demonstrate its real-world effectiveness by building custom, user-defined libraries on the order of 104 -107 unique protein encoding variants for two orthogonal protein engineering systems. We include a detailed protocol and provide several general-use destination vectors.

Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo.

Enhancers control gene expression and have crucial roles in development and homeostasis1-3. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo: the central nervous system, epidermis, gut, muscle and brain. We first train convolutional neural networks using genome-wide single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) datasets and then fine-tune the convolutional neural networks with smaller-scale data from in vivo enhancer activity assays, yielding models with 13% to 76% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (8 per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for central nervous system and muscle). The strategy of combining genome-wide and small-scale functional datasets by transfer learning is generally applicable and should enable the design of tissue-, cell type- and cell state-specific enhancers in any system.

Efficient design of synthetic gene circuits under cell-to-cell variability.

BACKGROUND: Synthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. RESULTS: Here, we present a computational framework and an efficient algorithm to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a Non-linear Mixed-Effects (NLME) framework into a Markov Chain Monte-Carlo (MCMC) algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. CONCLUSION: We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.

Illuminating protein space with a programmable generative model.

Three billion years of evolution has produced a tremendous diversity of protein molecules1, but the full potential of proteins is likely to be much greater. Accessing this potential has been challenging for both computation and experiments because the space of possible protein molecules is much larger than the space of those likely to have functions. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences, and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems that enables long-range reasoning with sub-quadratic scaling, layers for efficiently synthesizing three-dimensional structures of proteins from predicted inter-residue geometries and a general low-temperature sampling algorithm for diffusion models. Chroma achieves protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics and even natural-language prompts. The experimental characterization of 310 proteins shows that sampling from Chroma results in proteins that are highly expressed, fold and have favourable biophysical properties. The crystal structures of two designed proteins exhibit atomistic agreement with Chroma samples (a backbone root-mean-square deviation of around 1.0 Å). With this unified approach to protein design, we hope to accelerate the programming of protein matter to benefit human health, materials science and synthetic biology.

Manipulating the 3D organization of the largest synthetic yeast chromosome.

Whether synthetic genomes can power life has attracted broad interest in the synthetic biology field. Here, we report de novo synthesis of the largest eukaryotic chromosome thus far, synIV, a 1,454,621-bp yeast chromosome resulting from extensive genome streamlining and modification. We developed megachunk assembly combined with a hierarchical integration strategy, which significantly increased the accuracy and flexibility of synthetic chromosome construction. Besides the drastic sequence changes, we further manipulated the 3D structure of synIV to explore spatial gene regulation. Surprisingly, we found few gene expression changes, suggesting that positioning inside the yeast nucleoplasm plays a minor role in gene regulation. Lastly, we tethered synIV to the inner nuclear membrane via its hundreds of loxPsym sites and observed transcriptional repression of the entire chromosome, demonstrating chromosome-wide transcription manipulation without changing the DNA sequences. Our manipulation of the spatial structure of synIV sheds light on higher-order architectural design of the synthetic genomes.

Harnessing synthetic biology for advancing RNA therapeutics and vaccine design.

Recent global events have drawn into focus the diversity of options for combatting disease across a spectrum of prophylactic and therapeutic approaches. The recent success of the mRNA-based COVID-19 vaccines has paved the way for RNA-based treatments to revolutionize the pharmaceutical industry. However, historical treatment options are continuously updated and reimagined in the context of novel technical developments, such as those facilitated through the application of synthetic biology. When it comes to the development of genetic forms of therapies and vaccines, synthetic biology offers diverse tools and approaches to influence the content, dosage, and breadth of treatment with the prospect of economic advantage provided in time and cost benefits. This can be achieved by utilizing the broad tools within this discipline to enhance the functionality and efficacy of pharmaceutical agent sequences. This review will describe how synthetic biology principles can augment RNA-based treatments through optimizing not only the vaccine antigen, therapeutic construct, therapeutic activity, and delivery vector. The enhancement of RNA vaccine technology through implementing synthetic biology has the potential to shape the next generation of vaccines and therapeutics.