Portable, On-Demand Biomolecular Manufacturing.

Synthetic biology uses living cells as molecular foundries for the biosynthesis of drugs, therapeutic proteins, and other commodities. However, the need for specialized equipment and refrigeration for production and distribution poses a challenge for the delivery of these technologies to the field and to low-resource areas. Here, we present a portable platform that provides the means for on-site, on-demand manufacturing of therapeutics and biomolecules. This flexible system is based on reaction pellets composed of freeze-dried, cell-free transcription and translation machinery, which can be easily hydrated and utilized for biosynthesis through the addition of DNA encoding the desired output. We demonstrate this approach with the manufacture and functional validation of antimicrobial peptides and vaccines and present combinatorial methods for the production of antibody conjugates and small molecules. This synthetic biology platform resolves important practical limitations in the production and distribution of therapeutics and molecular tools, both to the developed and developing world.

Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components.

The recent Zika virus outbreak highlights the need for low-cost diagnostics that can be rapidly developed for distribution and use in pandemic regions. Here, we report a pipeline for the rapid design, assembly, and validation of cell-free, paper-based sensors for the detection of the Zika virus RNA genome. By linking isothermal RNA amplification to toehold switch RNA sensors, we detect clinically relevant concentrations of Zika virus sequences and demonstrate specificity against closely related Dengue virus sequences. When coupled with a novel CRISPR/Cas9-based module, our sensors can discriminate between viral strains with single-base resolution. We successfully demonstrate a simple, field-ready sample-processing workflow and detect Zika virus from the plasma of a viremic macaque. Our freeze-dried biomolecular platform resolves important practical limitations to the deployment of molecular diagnostics in the field and demonstrates how synthetic biology can be used to develop diagnostic tools for confronting global health crises. PAPERCLIP.

Design of synthetic promoters for cyanobacteria with generative deep-learning model.

Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In particular, they can identify and unravel hidden traits encoded within a complicated nucleotide sequence, allowing us to design genetic parts with accuracy. Here, we provide a deep-learning based generic framework to design and evaluate synthetic promoters for cyanobacteria using generative models, which was in turn validated with cell-free transcription assay. We developed a deep generative model and a predictive model using a variational autoencoder and convolutional neural network, respectively. Using native promoter sequences of the model unicellular cyanobacterium Synechocystis sp. PCC 6803 as a training dataset, we generated 10 000 synthetic promoter sequences and predicted their strengths. By position weight matrix and k-mer analyses, we confirmed that our model captured a valid feature of cyanobacteria promoters from the dataset. Furthermore, critical subregion identification analysis consistently revealed the importance of the -10 box sequence motif in cyanobacteria promoters. Moreover, we validated that the generated promoter sequence can efficiently drive transcription via cell-free transcription assay. This approach, combining in silico and in vitro studies, will provide a foundation for the rapid design and validation of synthetic promoters, especially for non-model organisms.

Creating Single-Copy Genetic Circuits.

Synthetic biology is increasingly used to develop sophisticated living devices for basic and applied research. Many of these genetic devices are engineered using multi-copy plasmids, but as the field progresses from proof-of-principle demonstrations to practical applications, it is important to develop single-copy synthetic modules that minimize consumption of cellular resources and can be stably maintained as genomic integrants. Here we use empirical design, mathematical modeling, and iterative construction and testing to build single-copy, bistable toggle switches with improved performance and reduced metabolic load that can be stably integrated into the host genome. Deterministic and stochastic models led us to focus on basal transcription to optimize circuit performance and helped to explain the resulting circuit robustness across a large range of component expression levels. The design parameters developed here provide important guidance for future efforts to convert functional multi-copy gene circuits into optimized single-copy circuits for practical, real-world use.

Cooperative sequence clustering and decoding for DNA storage system with fountain codes.

MOTIVATION: In DNA storage systems, there are tradeoffs between writing and reading costs. Increasing the code rate of error-correcting codes may save writing cost, but it will need more sequence reads for data retrieval. There is potentially a way to improve sequencing and decoding processes in such a way that the reading cost induced by this tradeoff is reduced without increasing the writing cost. In past researches, clustering, alignment and decoding processes were considered as separate stages but we believe that using the information from all these processes together may improve decoding performance. Actual experiments of DNA synthesis and sequencing should be performed because simulations cannot be relied on to cover all error possibilities in practical circumstances. RESULTS: For DNA storage systems using fountain code and Reed-Solomon (RS) code, we introduce several techniques to improve the decoding performance. We designed the decoding process focusing on the cooperation of key components: Hamming-distance based clustering, discarding of abnormal sequence reads, RS error correction as well as detection and quality score-based ordering of sequences. We synthesized 513.6 KB data into DNA oligo pools and sequenced this data successfully with Illumina MiSeq instrument. Compared to Erlich's research, the proposed decoding method additionally incorporates sequence reads with minor errors which had been discarded before, and thus was able to make use of 10.6-11.9% more sequence reads from the same sequencing environment, this resulted in 6.5-8.9% reduction in the reading cost. Channel characteristics including sequence coverage and read-length distributions are provided as well. AVAILABILITY AND IMPLEMENTATION: The raw data files and the source codes of our experiments are available at: https://github.com/jhjeong0702/dna-storage.

Detection of Coronaviruses Using RNA Toehold Switch Sensors.

A rapid, sensitive and simple point-of-care (POC) nucleic acid diagnostic test is needed to prevent spread of infectious diseases. Paper-based toehold reaction, a recently emerged colorimetric POC nucleic acid diagnostic test, has been widely used for pathogen detection and microbiome profiling. Here, we introduce an amplification method called reverse transcription loop-mediated amplification (RT-LAMP) prior to the toehold reaction and modify it to enable more sensitive and faster colorimetric detection of RNA viruses. We show that incorporating the modified RT-LAMP to the toehold reaction detects as few as 120 copies of coronavirus RNA in 70 min. Cross-reactivity test against other coronaviruses indicates this toehold reaction with the modified RT-LAMP is highly specific to the target RNA. Overall, the paper-based toehold switch sensors with the modified RT-LAMP allow fast, sensitive, specific and colorimetric coronavirus detection.

Biosensor-Assisted Adaptive Laboratory Evolution for Violacein Production.

Violacein is a naturally occurring purple pigment, widely used in cosmetics and has potent antibacterial and antiviral properties. Violacein can be produced from tryptophan, consequently sufficient tryptophan biosynthesis is the key to violacein production. However, the complicated biosynthetic pathways and regulatory mechanisms often make the tryptophan overproduction challenging in Escherichia coli. In this study, we used the adaptive laboratory evolution (ALE) strategy to improve violacein production using galactose as a carbon source. During the ALE, a tryptophan-responsive biosensor was employed to provide selection pressure to enrich tryptophan-producing cells. From the biosensor-assisted ALE, we obtained an evolved population of cells capable of effectively catabolizing galactose to tryptophan and subsequently used the population to obtain the best violacein producer. In addition, whole-genome sequencing of the evolved strain identified point mutations beneficial to the overproduction. Overall, we demonstrated that the biosensor-assisted ALE strategy could be used to rapidly and selectively evolve the producers to yield high violacein production.

Next-generation biocontainment systems for engineered organisms.

The increasing use of engineered organisms for industrial, clinical, and environmental applications poses a growing risk of spreading hazardous biological entities into the environment. To address this biosafety issue, significant effort has been invested in creating ways to confine these organisms and transgenic materials. Emerging technologies in synthetic biology involving genetic circuit engineering, genome editing, and gene expression regulation have led to the development of novel biocontainment systems. In this perspective, we highlight recent advances in biocontainment and suggest a number of approaches for future development, which may be applied to overcome remaining challenges in safeguard implementation.

'Deadman' and 'Passcode' microbial kill switches for bacterial containment.

Biocontainment systems that couple environmental sensing with circuit-based control of cell viability could be used to prevent escape of genetically modified microbes into the environment. Here we present two engineered safeguard systems known as the 'Deadman' and 'Passcode' kill switches. The Deadman kill switch uses unbalanced reciprocal transcriptional repression to couple a specific input signal with cell survival. The Passcode kill switch uses a similar two-layered transcription design and incorporates hybrid LacI-GalR family transcription factors to provide diverse and complex environmental inputs to control circuit function. These synthetic gene circuits efficiently kill Escherichia coli and can be readily reprogrammed to change their environmental inputs, regulatory architecture and killing mechanism.

Homo-succinic acid production by metabolically engineered Mannheimia succiniciproducens.

Succinic acid (SA) is a four carbon dicarboxylic acid of great industrial interest that can be produced by microbial fermentation. Here we report development of a high-yield homo-SA producing Mannheimia succiniciproducens strain by metabolic engineering. The PALFK strain (ldhA-, pta-, ackA-, fruA-) was developed based on optimization of carbon flux towards SA production while minimizing byproducts formation through the integrated application of in silico genome-scale metabolic flux analysis, omics analyses, and reconstruction of central carbon metabolism. Based on in silico simulation, utilization of sucrose would enhance the SA production and cell growth rates, while consumption of glycerol would reduce the byproduct formation rates. Thus, sucrose and glycerol were selected as dual carbon sources to improve the SA yield and productivity, while deregulation of catabolite-repression was also performed in engineered M. succiniciproducens. Fed-batch fermentations of PALFK with low- and medium-density (OD600 of 0.4 and 9.0, respectively) inocula produced 69.2 and 78.4g/L of homo-SA with yields of 1.56 and 1.64mol/mol glucose equivalent and overall volumetric SA productivities of 2.50 and 6.02g/L/h, respectively, using sucrose and glycerol as dual carbon sources. The SA productivity could be further increased to 38.6g/L/h by employing a membrane cell recycle bioreactor system. The systems metabolic engineering strategies employed here for achieving homo-SA production with the highest overall performance indices reported to date will be generally applicable for developing superior industrial microorganisms and competitive processes for the bio-based production of other chemicals as well.

Highly selective production of succinic acid by metabolically engineered Mannheimia succiniciproducens and its efficient purification.

Succinic acid (SA) is one of the fermentative products of anaerobic metabolism, and an important industrial chemical that has been much studied for its bio-based production. The key to the economically viable bio-based SA production is to develop an SA producer capable of producing SA with high yield and productivity without byproducts. Mannheimia succiniciproducens is a capnophilic rumen bacterium capable of efficiently producing SA. In this study, in silico genome-scale metabolic simulations were performed to identify gene targets to be engineered, and the PALK strain (ΔldhA and Δpta-ackA) was constructed. Fed-batch culture of PALK on glucose and glycerol as carbon sources resulted in the production of 66.14 g/L of SA with the yield and overall productivity of 1.34 mol/mol glucose equivalent and 3.39 g/L/h, respectively. SA production could be further increased to 90.68 g/L with the yield and overall productivity of 1.15 mol/mol glucose equivalent and 3.49 g/L/h, respectively, by utilizing a mixture of magnesium hydroxide and ammonia solution as a pH controlling solution. Furthermore, formation of byproducts was drastically reduced, resulting in almost homo-fermentative SA production. This allowed the recovery and purification of SA to a high purity (99.997%) with a high recovery yield (74.65%) through simple downstream processes composed of decolorization, vacuum distillation, and crystallization. The SA producer and processes developed in this study will allow economical production of SA in an industrial-scale. Biotechnol. Bioeng. 2016;113: 2168-2177. © 2016 Wiley Periodicals, Inc.

Comparison of end-tidal CO2 measured by transportable capnometer (EMMA™ capnograph) and arterial pCO2 in general anesthesia.

An end-tidal CO2 monitor (capnometer) is used most often as a noninvasive substitute for PaCO2 in anesthesia, anesthetic recovery, and intensive care. Additionally, the wide spread on-site use of portable capnometers in emergency and trauma situations is now observed. This study was conducted to compare PaCO2 measurement between the EMMA™ portable-capnometer and sidestream capnometry. End-tidal CO2 (portable capnometer: EMMA™ capnograph, side stream capnometry module: Datex-Ohmeda S5 Anesthesia Monitor) levels were recorded at the time of arterial blood gas sampling of patients undergoing general anesthesia. Data were compared using the Bland and Altman method, and by evaluating the clinical significance performed by calculating the percent error (%). A total of 100 data were obtained from 35 patients. The bias of PaCO2 and portable capnometer was 6.0 mmHg, where the upper and lower limits of the agreement were 11.8 and 0.3 mmHg, respectively. The percent error was 18.0 %. The bias of side stream capnometry and portable capnometer was 2.2 mmHg, where the upper and the lower limits of the agreement were 6.0 and -1.6 mmHg, respectively. The percent error was 13.0 %. Significant differences between the PETCO2 and PaCO2 values of the EMMA™ portable-capnometer were not observed for patients undergoing general anesthesia. ClinicalTrials.gov identifier NCT02184728.

Systems metabolic engineering of microorganisms for natural and non-natural chemicals.

Growing concerns over limited fossil resources and associated environmental problems are motivating the development of sustainable processes for the production of chemicals, fuels and materials from renewable resources. Metabolic engineering is a key enabling technology for transforming microorganisms into efficient cell factories for these compounds. Systems metabolic engineering, which incorporates the concepts and techniques of systems biology, synthetic biology and evolutionary engineering at the systems level, offers a conceptual and technological framework to speed the creation of new metabolic enzymes and pathways or the modification of existing pathways for the optimal production of desired products. Here we discuss the general strategies of systems metabolic engineering and examples of its application and offer insights as to when and how each of the different strategies should be used. Finally, we highlight the limitations and challenges to be overcome for the systems metabolic engineering of microorganisms at more advanced levels.

Optimizing Nucleic Acid Delivery Systems through Barcode Technology.

Conventional biological experiments often focus on in vitro assays because of the inherent limitations when handling multiple variables in vivo, including labor-intensive and time-consuming procedures. Often only a subset of samples demonstrating significant efficacy in the in vitro assays can be evaluated in vivo. Nonetheless, because of the low correlation between the in vitro and in vivo tests, evaluation of the variables under examination in vivo and not solely in vitro is critical. An emerging approach to achieve high-throughput in vivo tests involves using a barcode system consisting of various nucleotide combinations. Unique barcodes for each variant enable the simultaneous testing of multiple entities, eliminating the need for separate individual tests. Subsequently, to identify crucial parameters, samples were collected and analyzed using barcode sequencing. This review explores the development of barcode design and its applications, including the evaluation of nucleic acid delivery systems and the optimization of gene expression in vivo.

Iterative Soft Decoding Algorithm for DNA Storage Using Quality Score and Redecoding.

Ever since deoxyribonucleic acid (DNA) was considered as a next-generation data-storage medium, lots of research efforts have been made to correct errors occurred during the synthesis, storage, and sequencing processes using error correcting codes (ECCs). Previous works on recovering the data from the sequenced DNA pool with errors have utilized hard decoding algorithms based on a majority decision rule. To improve the correction capability of ECCs and robustness of the DNA storage system, we propose a new iterative soft decoding algorithm, where soft information is obtained from FASTQ files and channel statistics. In particular, we propose a new formula for log-likelihood ratio (LLR) calculation using quality scores (Q-scores) and a redecoding method which may be suitable for the error correction and detection in the DNA sequencing area. Based on the widely adopted encoding scheme of the fountain code structure proposed by Erlich et al., we use three different sets of sequenced data to show consistency for the performance evaluation. The proposed soft decoding algorithm gives 2.3%  âˆ¼  7.0% improvement of the reading number reduction compared to the state-of-the-art decoding method and it is shown that it can deal with erroneous sequenced oligo reads with insertion and deletion errors.

Programmable Synthesis of Biobased Materials Using Cell-Free Systems.

Motivated by the intricate mechanisms underlying biomolecule syntheses in cells that chemistry is currently unable to mimic, researchers have harnessed biological systems for manufacturing novel materials. Cell-free systems (CFSs) utilizing the bioactivity of transcriptional and translational machineries in vitro are excellent tools that allow supplementation of exogenous materials for production of innovative materials beyond the capability of natural biological systems. Herein, recent studies that have advanced the ability to expand the scope of biobased materials using CFS are summarized and approaches enabling the production of high-value materials, prototyping of genetic parts and modules, and biofunctionalization are discussed. By extending the reach of chemical and enzymatic reactions complementary to cellular materials, CFSs provide new opportunities at the interface of materials science and synthetic biology.

Tailoring the Hydrophilicity for Delayed Condensation Frosting in Antifogging Coatings.

In the last few decades, numerous studies have focused on designing suitable hydrophilic materials to inhibit surface-induced fog or frost under extreme conditions. As fogging and condensation frosting on a film involves molecular interaction with water prior to forming discrete droplets on the surface, it is essential to control the extent of a film to strongly bind with water molecules for antifogging coatings. While the water contact angle measurement is commonly used to probe the hydrophilicity of a film, it oftentimes fails to predict the antifogging and antifrosting performance as this value only reflects the wettability of a given surface to water droplet. In this work, a polysaccharide-based film composed of chitosan (CHI) and carboxymethyl cellulose (CMC) is used as the model system and oligo(ethylene glycol) (OEG) moieties are additionally introduced to study the effect of OEG moieties on antifogging and condensation frosting. We show that the film containing OEG-grafted CHI exhibits excellent frost-resistant capability due to the OEG moieties in the film that serve as active sites for water molecules to strongly interact in a nonfreezable state.

Kinetic compartmentalization by unnatural reaction for itaconate production.

Physical compartmentalization of metabolism using membranous organelles in eukaryotes is helpful for chemical biosynthesis to ensure the availability of substrates from competitive metabolic reactions. Bacterial hosts lack such a membranous system, which is one of the major limitations for efficient metabolic engineering. Here, we employ kinetic compartmentalization with the introduction of an unnatural enzymatic reaction by an engineered enzyme as an alternative strategy to enable substrate availability from competitive reactions through kinetic isolation of metabolic pathways. As a proof of concept, we kinetically isolate the itaconate synthetic pathway from the tricarboxylic acid cycle in Escherichia coli, which is natively separated by mitochondrial membranes in Aspergillus terreus. Specifically, 2-methylcitrate dehydratase is engineered to alternatively catalyze citrate and kinetically secure cis-aconitate for efficient production using a high-throughput screening system. Itaconate production can be significantly improved with kinetic compartmentalization and its strategy has the potential to be widely applicable.

Understanding and engineering of microbial cells based on proteomics and its conjunction with other omics studies.

The abilities of microorganisms to produce a wide variety of products ranging from human therapeutics to chemicals and to tolerate or detoxify exogenous stresses such as toxic compounds and pollutants are of great importance in fundamental and applied research. Proteomics has become an indispensable tool for large-scale protein analyses and can be used to understand the resulting physiological changes and uncover the mechanisms responsible for the cellular processes under various genetic and environmental conditions. Recent development of a multi-omic approach that combines proteomics with one or more of other omics is allowing us to better understand cellular physiology and metabolism at the systems-wide level, and consequently paving a way toward more efficient metabolic engineering. In this review, we describe the use of proteomics and its combination with other omics to broaden our knowledge on microorganisms in the field of bioscience and biotechnology. With the increasing interest in practical applications, the strategies of employing proteomics for the successful metabolic engineering of microorganisms toward the enhanced production of desired products as well as the approaches taken to identify novel bacterial components are reviewed with corresponding examples.

Genome-wide identification of the subcellular localization of the Escherichia coli B proteome using experimental and computational methods.

Escherichia coli K-12 and B strains have most widely been employed for scientific studies as well as industrial applications. Recently, the complete genome sequences of two representative descendants of E. coli B strains, REL606 and BL21(DE3), have been determined. Here, we report the subproteome reference maps of E. coli B REL606 by analyzing cytoplasmic, periplasmic, inner and outer membrane, and extracellular proteomes based on the genome information using experimental and computational approaches. Among the total of 3487 spots, 651 proteins including 410 non-redundant proteins were identified and characterized by 2-DE and LC-MS/MS; they include 440 cytoplasmic, 45 periplasmic, 50 inner membrane, 61 outer membrane, and 55 extracellular proteins. In addition, subcellular localizations of all 4205 ORFs of E. coli B were predicted by combined computational prediction methods. The subcellular localizations of 1812 (43.09%) proteins of currently unknown function were newly assigned. The results of computational prediction were also compared with the experimental results, showing that overall precision and recall were 92.16 and 92.16%, respectively. This work represents the most comprehensive analyses of the subproteomes of E. coli B, and will be useful as a reference for proteome profiling studies under various conditions. The complete proteome data are available online (http://ecolib.kaist.ac.kr).