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.

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.

Building biosecurity for synthetic biology.

The fast-paced field of synthetic biology is fundamentally changing the global biosecurity framework. Current biosecurity regulations and strategies are based on previous governance paradigms for pathogen-oriented security, recombinant DNA research, and broader concerns related to genetically modified organisms (GMOs). Many scholarly discussions and biosecurity practitioners are therefore concerned that synthetic biology outpaces established biosafety and biosecurity measures to prevent deliberate and malicious or inadvertent and accidental misuse of synthetic biology's processes or products. This commentary proposes three strategies to improve biosecurity: Security must be treated as an investment in the future applicability of the technology; social scientists and policy makers should be engaged early in technology development and forecasting; and coordination among global stakeholders is necessary to ensure acceptable levels of risk.

Microbes as Biosensors.

The ability to detect disease early and deliver precision therapy would be transformative for the treatment of human illnesses. To achieve these goals, biosensors that can pinpoint when and where diseases emerge are needed. Rapid advances in synthetic biology are enabling us to exploit the information-processing abilities of living cells to diagnose disease and then treat it in a controlled fashion. For example, living sensors could be designed to precisely sense disease biomarkers, such as by-products of inflammation, and to respond by delivering targeted therapeutics in situ. Here, we provide an overview of ongoing efforts in microbial biosensor design, highlight translational opportunities, and discuss challenges for enabling sense-and-respond precision medicines.

Efficient and Low-Cost Error Removal in DNA Synthesis by a High-Durability MutS.

Enzyme-based error correction is a key step in de novo DNA synthesis, yet the inherent instability of error-correction enzymes such as MutS has hindered the throughput and efficiency of DNA synthesis workflows. Here we introduce a process called Improved MICC (iMICC), in which all error-correction steps of oligos and fragments within a complete gene-synthesis cycle are completed in a simple, efficient, and low-cost manner via a MutS protein engineered for high durability. By establishing a disulfide bond of L157C-G233C, full-activity shelf life of E. coli MutS (eMutS) was prolonged from 7 to 49 days and was further extended to 63 days via cellulose-bound 4 °C storage. In synthesis of 10 Cas9 homologues in-solution and 10 xylose reductase (XR) homologues on-chip, iMICC reduced error frequency to 0.64/Kb and 0.41/Kb, respectively, with 72.1% and 86.4% of assembled fragments being error-free. By elevating base accuracy by 37.6-fold while avoiding repetitive preparation of fresh enzymes, iMICC is more efficient and robust than the wild-type eMutS, and it is 6.6-fold more accurate and 26.7-fold cheaper than CorrectASE. These advantages promise its broad applications in industrial DNA synthesis.

Reinforcement Learning for Bioretrosynthesis.

Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.

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

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

Cell-Free Prototyping of AND-Logic Gates Based on Heterogeneous RNA Activators.

RNA-based devices controlling gene expression bear great promise for synthetic biology, as they offer many advantages such as short response times and light metabolic burden compared to protein-circuits. However, little work has been done regarding their integration to multilevel regulated circuits. In this work, we combined a variety of small transcriptional activator RNAs (STARs) and toehold switches to build highly effective AND-gates. To characterize the components and their dynamic range, we used an Escherichia coli (E. coli) cell-free transcription-translation (TX-TL) system dispensed via nanoliter droplets. We analyzed a prototype gate in vitro as well as in silico, employing parametrized ordinary differential equations (ODEs), for which parameters were inferred via parallel tempering, a Markov chain Monte Carlo (MCMC) method. On the basis of this analysis, we created nine additional AND-gates and tested them in vitro. The functionality of the gates was found to be highly dependent on the concentration of the activating RNA for either the STAR or the toehold switch. All gates were successfully implemented in vivo, offering a dynamic range comparable to the level of protein circuits. This study shows the potential of a rapid prototyping approach for RNA circuit design, using cell-free systems in combination with a model prediction.

Biosynthetic strategies to produce xylitol: an economical venture.

Xylitol is a natural five-carbon sugar alcohol with potential for use in food and pharmaceutical industries owing to its insulin-independent metabolic regulation, tooth rehardening, anti-carcinogenic, and anti-inflammatory, as well as osteoporosis and ear infections preventing activities. Chemical and biosynthetic routes using D-xylose, glucose, or biomass hydrolysate as raw materials can produce xylitol. Among these methods, microbial production of xylitol has received significant attention due to its wide substrate availability, easy to operate, and eco-friendly nature, in contrast with high-energy consuming and environmental-polluting chemical method. Though great advances have been made in recent years for the biosynthesis of xylitol from xylose, glucose, and biomass hydrolysate, and the yield and productivity of xylitol are substantially improved by metabolic engineering and optimizing key metabolic pathway parameters, it is still far away from industrial-scale biosynthesis of xylitol. In contrary, the chemical synthesis of xylitol from xylose remains the dominant route. Economic and highly efficient xylitol biosynthetic strategies from an abundantly available raw material (i.e., glucose) by engineered microorganisms are on the hard way to forwarding. However, synthetic biology appears as a novel and promising approach to develop a super yeast strain for industrial production of xylitol from glucose. After a brief overview of chemical-based xylitol production, we critically analyzed and comprehensively summarized the major metabolic strategies used for the enhanced biosynthesis of xylitol in this review. Towards the end, the study is wrapped up with current challenges, concluding remarks, and future prospects for designing an industrial yeast strain for xylitol biosynthesis from glucose.

Microbial production of sialic acid and sialylated human milk oligosaccharides: Advances and perspectives.

Sialic acids (SAs) are important functional sugars, and monomers of sialylated human milk oligosaccharides (sialylated HMOs or sialyllactoses), which are crucial for improving infant development and can facilitate infant brain development, maintain brain health, and enhance immunity. The most common form of SA is N-acetylneuraminic acid (NeuAc), and the main forms of sialyllactoses are 6'-sialyllactose (6'-SL) and 3'-sialyllactose (3'-SL). As functional food additive, the demand for NeuAc and sialyllactoses will continuously increase due to their wide and important fields of application. However, NeuAc and sialyllactoses produced by traditional extraction methods are inefficient and may cause allergen contamination, and cannot keep up with the rapidly increasing market demand. Therefore, the production of NeuAc and sialyllactoses by sustainable biotechnological methods have attracted increasing attention. In particular, the development of metabolic engineering and synthetic biology techniques and strategies have promoted efficient biosynthesis of NeuAc and sialyllactoses. In this review, we first discussed the application of NeuAc and sialyllactoses. Secondly, metabolic engineering and protein engineering-fueled progress of whole-cell catalysis and de novo synthesis of NeuAc and sialyllactoses were systematically summarized and compared. Furthermore, challenges of efficient microbial production of NeuAc and sialyllactoses as well as strategies for overcoming the challenges were discussed, such as clustered regularly interspaced short palindromic repeats interference (CRISPRi)-aided identification of key precursor transport pathways, synergistically debottleneck of kinetic and thermodynamic limits in synthetic pathways, and dynamic regulation of metabolic pathways for balancing cell growth and production. We hope this review can further facilitate the understanding of limiting factors that hampered efficient production of sialic acid and sialyllactoses, as well as contribute to the development of strategies for the construction of efficient production hosts for high-level production of sialic acid and sialyllactose based on synthetic biology tools and strategies.

A Simple, Robust, and Low-Cost Method To Produce the PURE Cell-Free System.

We demonstrate a simple, robust, and low-cost method for producing the PURE cell-free transcription-translation system. Our OnePot PURE system achieved a protein synthesis yield of 156 µg/mL at a cost of 0.09 USD/µL, leading to a 14-fold improvement in cost normalized protein synthesis yield over existing PURE systems. The one-pot method makes the PURE system easy to generate and allows it to be readily optimized and modified.

Fast and Flexible Synthesis of Combinatorial Libraries for Directed Evolution.

Directed evolution (DE) is a powerful tool for optimizing an enzyme's properties toward a particular objective, such as broader substrate scope, greater thermostability, or increased kcat. A successful DE project requires the generation of genetic diversity and subsequent screening or selection to identify variants with improved fitness. In contrast to random methods (error-prone PCR or DNA shuffling), site-directed mutagenesis enables the rational design of variant libraries and provides control over the nature and frequency of the encoded mutations. Knowledge of protein structure, dynamics, enzyme mechanisms, and natural evolution demonstrates that multiple (combinatorial) mutations are required to discover the most improved variants. To this end, we describe an experimentally straightforward and low-cost method for the preparation of combinatorial variant libraries. Our approach employs a two-step PCR protocol, first producing mutagenic megaprimers, which can then be combined in a "mix-and-match" fashion to generate diverse sets of combinatorial variant libraries both quickly and accurately.

A low-cost paper-based synthetic biology platform for analyzing gut microbiota and host biomarkers.

There is a need for large-scale, longitudinal studies to determine the mechanisms by which the gut microbiome and its interactions with the host affect human health and disease. Current methods for profiling the microbiome typically utilize next-generation sequencing applications that are expensive, slow, and complex. Here, we present a synthetic biology platform for affordable, on-demand, and simple analysis of microbiome samples using RNA toehold switch sensors in paper-based, cell-free reactions. We demonstrate species-specific detection of mRNAs from 10 different bacteria that affect human health and four clinically relevant host biomarkers. We develop a method to quantify mRNA using our toehold sensors and validate our platform on clinical stool samples by comparison to RT-qPCR. We further highlight the potential clinical utility of the platform by showing that it can be used to rapidly and inexpensively detect toxin mRNA in the diagnosis of Clostridium difficile infections.

A framework for the identification of promising bio-based chemicals.

Recent progress in metabolic engineering and synthetic biology enables the use of microorganisms for the production of chemicals-"bio-based chemicals." However, it is still unclear which chemicals have the highest economic prospect. To this end, we develop a framework for the identification of such promising ones. Specifically, we first develop a genome-scale constraint-based metabolic modeling approach, which is used to identify a candidate pool of 209 chemicals (together with the estimated yield, productivity, and residence time for each) from the intersection of the high-production-volume chemicals and the KEGG and MetaCyc databases. Second, we design three screening criteria based on a chemical's profit margin, market volume, and market size. The total process cost, including the downstream separation cost, is systematically incorporated into the evaluation. Third, given the three aforementioned criteria, we identify 32 products as economically promising if the maximum yields can be achieved, and 22 products if the maximum productivities can be achieved. The breakeven titer that renders zero profit margin for each product is also presented. Comparisons between extracellular and intracellular production, as well as Escherichia coli and Saccharomyces cerevisiae systems are also discussed. The proposed framework provides important guidance for future studies in the production of bio-based chemicals. It is also flexible in that the databases, yield estimations, and criteria can be modified to customize the screening.

An Automated Induction Microfluidics System for Synthetic Biology.

The expression of a recombinant gene in a host organism through induction can be an extensively manual and labor-intensive procedure. Several methods have been developed to simplify the protocol, but none has fully replaced the traditional IPTG-based induction. To simplify this process, we describe the development of an autoinduction platform based on digital microfluidics. This system consists of a 600 nm LED and a light sensor to enable the real-time monitoring of  the optical density (OD) samples coordinated with the semicontinuous mixing of a bacterial culture. A hand-held device was designed as a microbioreactor to culture cells and to measure the OD of the bacterial culture. In addition, it serves as a platform for the analysis of regulated protein expression in E. coli without the requirement of standardized well-plates or pipetting-based platforms. Here, we report for the first time, a system that offers great convenience without the user to physically monitor the culture or to manually add inducer at specific times. We characterized our system by looking at several parameters (electrode designs, gap height, and growth rates) required for an autoinducible system. As a first step, we carried out an automated induction optimization assay using a RFP reporter gene to identify conditions suitable for our system. Next, we used our system to identify active thermophilic ß-glucosidase enzymes that may be suitable candidates for biomass hydrolysis. Overall, we believe that this platform may be useful for synthetic biology applications that require regulating and analyzing expression of heterologous genes for strain optimization.

Innovative technologies for point-of-care testing of viral hepatitis in low-resource and decentralized settings.

According to the Global Burden of Diseases, chronic viral hepatitis B and C are one of the most challenging global health conditions that rank among the first causes of morbidity and mortality worldwide. Low- and middle-income countries are particularly affected by the health burden associated with HBV or HCV infection. One major gap in efficiently addressing the issue of viral hepatitis is universal screening. However, the costs and chronic lack of human resources for using traditional screening strategies based on serology and molecular biology preclude any scaling-up. Point-of-care tests have been deemed a powerful potential solution to fill the current diagnostics gap in low-resource and decentralized settings. Despite high interest resulting from their development in recent years, very few point-of-care devices have reached the market. Scaling down and automating all testing steps in 1 single device (eg, sample preparation, detection and readout) is indeed challenging. But innovations in multiple disciplines such as nanotechnologies, microfluidics, biosensors and synthetic biology have led to the creation of chip-sized laboratory systems called "lab-on-a-chip" devices. This review aims to explain how these innovations can overcome technological barriers that usually arise for each testing step while developing integrated point-of-care tests. Point-of-care test prototypes rarely meet the requirements for mass production, which also hinders their large-scale production. In addition to logistical hurdles, legal and economic constraints specific to the commercialization of in vitro diagnostics, which have also participated in the low transfer of innovative point-of-care tests to the field, are discussed.

A Genetic Tool to Track Protein Aggregates and Control Prion Inheritance.

Protein aggregation is a hallmark of many diseases but also underlies a wide range of positive cellular functions. This phenomenon has been difficult to study because of a lack of quantitative and high-throughput cellular tools. Here, we develop a synthetic genetic tool to sense and control protein aggregation. We apply the technology to yeast prions, developing sensors to track their aggregation states and employing prion fusions to encode synthetic memories in yeast cells. Utilizing high-throughput screens, we identify prion-curing mutants and engineer "anti-prion drives" that reverse the non-Mendelian inheritance pattern of prions and eliminate them from yeast populations. We extend our technology to yeast RNA-binding proteins (RBPs) by tracking their propensity to aggregate, searching for co-occurring aggregates, and uncovering a group of coalescing RBPs through screens enabled by our platform. Our work establishes a quantitative, high-throughput, and generalizable technology to study and control diverse protein aggregation processes in cells.