Synthetic Modules Research Articles

A Modular In Vitro Platform for the Production of Terpenes and Polyketides from CO2.

A long‐term goal in realizing a sustainable biocatalysis and organic synthesis is the direct use of the greenhouse gas CO2 as feedstock for the production of bulk and fine chemicals, such as pharmaceuticals, fragrances and food additives. Here we developed a modular in vitro platform for the continuous conversion of CO2 into complex multi‐carbon compounds, such as monoterpenes (C10), sesquiterpenes (C15) and polyketides. Combining natural and synthetic metabolic pathway modules, we established a route from CO2 into the key intermediates acetyl‐ and malonyl‐CoA, which can be subsequently diversified through the action of different terpene and polyketide synthases. Our proof‐of‐principle study demonstrates the simultaneous operation of different metabolic modules comprising of up to 29 enzymes in one pot, which paves the way for developing and optimizing synthesis routes for the generation of complex CO2‐based chemicals in the future.

Chemogenetic Manipulation: Caffeine‐Operated Synthetic Modules for Chemogenetic Control of Protein Activities by Life Style (Adv. Sci. 3/2021)

Chemogenetic Manipulation: Caffeine‐Operated Synthetic Modules for Chemogenetic Control of Protein Activities by Life Style (Adv. Sci. 3/2021)

A Global Approach to Estimating the Abundance and Duplication of Polyketide Synthase Domains in Dinoflagellates.

Many dinoflagellate species make toxins in a myriad of different molecular configurations but the underlying chemistry in all cases is presumably via modular synthases, primarily polyketide synthases. In many organisms modular synthases occur as discrete synthetic genes or domains within a gene that act in coordination thus forming a module that produces a particular fragment of a natural product. The modules usually occur in tandem as gene clusters with a syntenic arrangement that is often predictive of the resultant structure. Dinoflagellate genomes however are notoriously complex with individual genes present in many tandem repeats and very few synthetic modules occurring as gene clusters, unlike what has been seen in bacteria and fungi. However, modular synthesis in all organisms requires a free thiol group that acts as a carrier for sequential synthesis called a thiolation domain. We scanned 47 dinoflagellate transcriptomes for 23 modular synthase domain models and compared their abundance among 10 orders of dinoflagellates as well as their co-occurrence with thiolation domains. The total count of domain types was quite large with over thirty-thousand identified, 29 000 of which were in the core dinoflagellates. Although there were no specific trends in domain abundance associated with types of toxins, there were readily observable lineage specific differences. The Gymnodiniales, makers of long polyketide toxins such as brevetoxin and karlotoxin had a high relative abundance of thiolation domains as well as multiple thiolation domains within a single transcript. Orders such as the Gonyaulacales, makers of small polyketides such as spirolides, had fewer thiolation domains but a relative increase in the number of acyl transferases. Unique to the core dinoflagellates, however, were thiolation domains occurring alongside tetratricopeptide repeats that facilitate protein-protein interactions, especially hexa and hepta-repeats, that may explain the scaffolding required for synthetic complexes capable of making large toxins. Clustering analysis for each type of domain was also used to discern possible origins of duplication for the multitude of single domain transcripts. Single domain transcripts frequently clustered with synonymous domains from multi-domain transcripts such as the BurA and ZmaK like genes as well as the multi-ketosynthase genes, sometimes with a large degree of apparent gene duplication, while fatty acid synthesis genes formed distinct clusters. Surprisingly the acyl-transferases and ketoreductases involved in fatty acid synthesis (FabD and FabG, respectively) were found in very large clusters indicating an unprecedented degree of gene duplication for these genes. These results demonstrate a complex evolutionary history of core dinoflagellate modular synthases with domain specific duplications throughout the lineage as well as clues to how large protein complexes can be assembled to synthesize the largest natural products known.

Caffeine-Operated Synthetic Modules for Chemogenetic Control of Protein Activities by Life Style.

A genetically encoded caffeine‐operated synthetic module (COSMO) is introduced herein as a robust chemically induced dimerization (CID) system. COSMO enables chemogenetic manipulation of biological processes by caffeine and its metabolites, as well as caffeinated beverages, including coffee, tea, soda, and energy drinks. This CID tool, evolved from an anti‐caffeine nanobody via cell‐based high‐throughput screening, permits caffeine‐inducible gating of calcium channels, tumor killing via necroptosis, growth factors‐independent activation of tyrosine receptor kinase signaling, and enhancement of nanobody‐mediated antigen recognition for the severe acute respiratory distress coronavirus 2 (SARS‐CoV‐2) spike protein. Further rationalized engineering of COSMO leads to 34–217‐fold enhancement in caffeine sensitivity (EC50 = 16.9 nanomolar), which makes it among the most potent CID systems like the FK506 binding protein (FKBP)–FKBP rapamycin binding domain (FRB)–rapamycin complex. Furthermore, bivalent COSMO (biCOMSO) connected with a long linker favors intramolecular dimerization and acts as a versatile precision switch when inserted in host proteins to achieve tailored function. Given the modularity and high transferability of COMSO and biCOSMO, these chemical biology tools are anticipated to greatly accelerate the development of therapeutic cells and biologics that can be switched on and off by caffeinated beverages commonly consumed in the daily life.

Metabolic Engineering of Escherichia coli for De Novo Production of 1,5-Pentanediol from Glucose.

1,5-Pentanediol (1,5-PDO) is an important C5 building block for the synthesis of different value-added polyurethanes and polyesters. However, no natural metabolic pathway exists for the biosynthesis of 1,5-PDO. Herein we designed and constructed a promising nonnatural pathway for de novo production of 1,5-PDO from cheap carbohydrates. This biosynthesis route expands natural lysine pathways and employs two artificial metabolic modules to sequentially convert lysine into 5-hydroxyvalerate (5-HV) and 1,5-PDO via 5-hydroxyvaleryl-CoA. Theoretically, the 5-hydroxyvaleryl-CoA-based pathway is more energy-efficient than a recently published carboxylic acid reductase-based pathway for 1,5-PDO production. By combining strategies of systematic enzyme screening, pathway balancing, and transporter engineering, we successfully constructed a minimally engineered Escherichia coli strain capable of producing 3.19 g/L of 5-HV and 0.35 g/L of 1,5-PDO in a medium containing 20 g/L of glucose and 5 g/L lysine. Introducing the synthetic modules into a lysine producer and enhancing NADPH supply enabled the strain to accumulate 1.04 g/L of 5-HV and 0.12 g/L of 1,5-PDO using glucose as the main carbon source. This work lays the basis for the development of a biological route for 1,5-PDO production from renewable bioresources.

Flexible Adipose-Vascular Tissue Assembly Using Combinational 3D Printing for Volume-Stable Soft Tissue Reconstruction.

A new concept, assembling cell-laden tissue modules, is for the first time proposed for soft tissue engineering. Adipose-vascular tissue modules composed of a synthetic polymer-based substructure and customized bioinks using planar 3D cell printing are engineered. Such tissue modules are systematically assembled into a synthetic polymer-based module holder fabricated with rotational 3D printing, resulting in the development of a flexible and volumetric tissue assembly. Whereas most of the previous studies about the construction of adipose tissue are limited to hypoxia, poor vascularization, rapid resorption, and mismatch in mechanical properties, it is aimed to realize the construction of nonhypoxic, flexible, and volume-stable tissue assembly in this study. The significance of engineered tissue assembly is proven through various in vitro and in vivo evaluations. In particular, stable volume and remarkable neovascularization/adipogenesis are observed in the implanted assembly over four weeks. Interestingly, the size of newly formed lipid droplets and the remodeled morphology in the assembly are comparable to those in native adipose tissue. As far as it is known, this work is a first report suggesting a cell printing-based tissue assembly for functional reconstruction of soft tissue.

Microbial Production of Retinyl Palmitate and Its Application as a Cosmeceutical

Chemically synthesized retinyl palmitate has been widely used in the cosmetic and biotechnology industry. In this study, we aimed to demonstrate the microbial production of retinyl palmitate and the benefits of microbial retinyl palmitate in skin physiology. A heterologous retinyl palmitate biosynthesis pathway was reconstructed in metabolically engineered Escherichia coli using synthetic expression modules from Pantoea agglomerans, Salinibacter ruber, and Homo sapiens. High production of retinyl palmitate (69.96 ± 2.64 mg/L) was obtained using a fed-batch fermentation process. Moreover, application of purified microbial retinyl palmitate to human foreskin HS68 fibroblasts led to increased cellular retinoic acid-binding protein 2 (CRABP2) mRNA level [1.7-fold (p = 0.001) at 100 μg/mL], acceleration of cell proliferation, and enhancement of procollagen synthesis [111% (p < 0.05) at 100 μg/mL], strongly indicating an anti-ageing-related effect of this substance. These results would pave the way for large-scale production of retinyl palmitate in microbial systems and represent the first evidence for the application of microbial retinyl palmitate as a cosmeceutical.

Synthetic rewiring and boosting type I interferon responses for visualization and counteracting viral infections.

Mammalian first line of defense against viruses is accomplished by the interferon (IFN) system. Viruses have evolved numerous mechanisms to reduce the IFN action allowing them to invade the host and/or to establish latency. We generated an IFN responsive intracellular hub by integrating the synthetic transactivator tTA into the chromosomal Mx2 locus for IFN-based activation of tTA dependent expression modules. The additional implementation of a synthetic amplifier module with positive feedback even allowed for monitoring and reacting to infections of viruses that can antagonize the IFN system. Low and transient IFN amounts are sufficient to trigger these amplifier cells. This gives rise to higher and sustained—but optionally de-activatable—expression even when the initial stimulus has faded out. Amplification of the IFN response induced by IFN suppressing viruses is sufficient to protect cells from infection. Together, this interfaced sensor/actuator system provides a toolbox for robust sensing and counteracting viral infections.

Reconstitution of the Ornithine Cycle with Arginine:Glycine Amidinotransferase to Engineer Escherichia coli into an Efficient Whole-Cell Catalyst of Guanidinoacetate.

Guanidino compounds can be synthesized by transamidination reactions using arginine as a guanidine group donor. The efficiency of guanidino biosynthesis is often affected by the supply of arginine and the inhibition of the coproduct ornithine. To alleviate this shortcoming, we designed a reconstituted ornithine cycle in Escherichia coli to engineer an efficient whole-cell catalyst for guanidinoacetate (GAA) production by introducing a heterogeneous arginine:glycine amidinotransferase (AGAT). To alleviate the inhibition of ornithine, a citrulline synthetic module was constructed and optimized by introducing a glutamine self-sufficient system. Then, to improve the pathway from citrulline to arginine, an aspartate self-sufficient system was introduced into the arginine synthetic module. By combining these modules (GAA, citrulline, and arginine synthetic modules), a reconstituted ornithine cycle was developed, which significantly improved the biocatalyst efficiency (3.9-fold increase). In the system, arginine was regenerated efficiently through the reconstituted ornithine cycle, which converted arginine from a substrate to a cofactor for the transamidination reaction, thereby relieving the ornithine inhibition. Moreover, the amidino group of GAA in this system was mainly supplied by carbon and nitrogen assimilation. After the engineering process, 8.61 g/L GAA (73.56 mM) with a productivity of 0.39 g/L/h was achieved in a 22 h bioconversion. To the best of our knowledge, this is the first time that GAA has been produced in E. coli. This reconstructed ornithine cycle could be used as a transamidination platform for amidino group supply and has potential applications in the biosynthesis of other guanidino compounds.

Machine learning for synthetic biology: Methods and applications

Traditional synthetic biology takes a trial-and-error approach, suffering from inefficiency and local optima. Recent advances in high-throughput experimental techniques generate a huge amount of biological data, which enables the use of machine learning to close the “design-build-test-learn” loop. Machine learning, especially deep learning, is a data-driven modeling method, which extracts useful patterns from big data and then leverages learned knowledge to tackle specific tasks. In this review, we aim to provide a brief primer of machine learning to synthetic biologists. Starting with common taxonomy, we introduce representative methods, pipelines, and underlying principles of machine learning that can be applied in synthetic biology. We include typical methods such as support vector machine, deep neural networks, generative adversarial nets, transfer learning and reinforcement learning. In particular, discriminative models, including convolutional neural networks and support vector machine, are appropriate for predicting sequence-function relationship. Generative models, including generative adversarial nets (GANs) and deep generative models for graph generation, are suitable for sequence or network design. Next, we review the recent applications of machine learning in studying synthetic biology parts and modules, including promoters, bioactive peptides, enzymes, metabolic pathways, and genetic circuits. For example, DeePromoter combined a convolutional neural network and a long-short term memory to achieve an accuracy as high as 90% when predicting promoter sequences. For enzyme design, a Gauss Process model was proposed with Bayesian optimization by upper confidence bound method, which resulted in the engineering of thermostable P450 enzymes. For antimicrobial peptides, a generative GAN model enhanced with a feedback mechanism was trained to design peptide sequences with new functions. Finally, we conclude with future challenges and directions. Particularly, interpretable machine learning models are desirable to guide mechanistic investigation. Moreover, it is necessary to develop new machine learning methods that are more compatible with biological data, which are heterogeneous, multi-modal (such as sequence, network, image, and structure), and lack of proper labels. With the increasing availability of big biological data and development of machine learning methods tailored for synthetic biology, we envision a paradigm shift towards a closed cycle of “design-build-test-learn” in creating artificial life with predictable functions.

Holistic engineering of cell-free systems through proteome-reprogramming synthetic circuits

Synthetic biology has focused on engineering genetic modules that operate orthogonally from the host cells. A synthetic biological module, however, can be designed to reprogram the host proteome, which in turn enhances the function of the synthetic module. Here, we apply this holistic synthetic biology concept to the engineering of cell-free systems by exploiting the crosstalk between metabolic networks in cells, leading to a protein environment more favorable for protein synthesis. Specifically, we show that local modules expressing translation machinery can reprogram the bacterial proteome, changing the expression levels of more than 700 proteins. The resultant feedback generates a cell-free system that can synthesize fluorescent reporters, protein nanocages, and the gene-editing nuclease Cas9, with up to 5-fold higher expression level than classical cell-free systems. Our work demonstrates a holistic approach that integrates synthetic and systems biology concepts to achieve outcomes not possible by only local, orthogonal circuits.

Construction and function of a root-specific promoter SRSP

The responsibility of root is absorbing water and nutrients, it is an important plant tissue, but easily to be affected by biotic and abiotic stresses, affecting crop growth and yield. The design of a synthetic root-specific promoter provides candidate promoters for the functional analysis and efficient expression of stress-related genes in crop roots. In this study, a synthetic root-specific module (pro-SRS) was designed using tandem four-copies of root specific cis-acting elements (OSE1ROOTNODULE, OSE2ROOTNODULE, SP8BFIBSP8AIB, and ROOTMOTIFAPOX1), and fused with minimal promoter from the CaMV 35S promoter to synthesize an artificially synthetic SRSP promoter. The SRSP promoter was cloned in pCAMBIA2300.1 by replacing CaMV 35S promoter so as to drive GUS expression. The constructs with SRSP promoter were transformed in tobacco by Agrobacterium-mediated method. SRSP promoter conferred root-specific expression in transgenic tobacco plants through Real-time PCR (RT-PCR) analysis and GUS histochemical staining analysis. It is indicated that the repeated arrangement of cis-acting elements can realize the expected function of the promoter. This study laid a theoretical foundation for the rational design of tissue-specific promoters.

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of in vivo mouse xenograft breast cancer images.

Engineering T Cells to Treat Cancer: The Convergence of Immuno-Oncology and Synthetic Biology

T cells engineered to recognize and kill tumor cells have emerged as powerful agents for combating cancer. Nonetheless, our ability to engineer T cells remains relatively primitive. Aside from CAR T cells for treating B cell malignancies, most T cell therapies are risky, toxic, and often ineffective, especially those that target solid cancers. To fulfill the promise of cell-based therapies, we must transform cell engineering into a systematic and predictable science by applying the principles and tools of synthetic biology. Synthetic biology uses a hierarchical approach—assembling sets of modular molecular parts that can be combined into larger circuits and systems that perform defined target tasks. We outline the toolkit of synthetic modules that are needed to overcome the challenges of solid cancers, progress in building these components, and how these modules could be used to reliably engineer more effective and precise T cell therapies.

Reconstitution of Respiratory Enzymes in PDMS-g-PEO Polymer and Polymer/Lipid Hybrid Vesicles

Mechanical characteristics (e.g. rigidity, tightness) of cellular compartments are alterable by partial or complete replacement of natural building blocks with synthetic alternatives, but often at the expense of other properties (e.g. fluidity, thickness). Since the scope of bottom-up synthetic biology aims to reconstitute essential life processes such as selective transport and energy transduction, we seek to retain properties of the interfaces, necessary for interaction with membrane proteins. PDMS-g-PEO polymer appears to accommodate these demands. Thus, we co-reconstituted the chemically-driven proton pump bo3 oxidase and F1Fo-ATPase in PDMS-g-PEO polymer and hybrid nanocompartments (LUVs) in order to build a synthetic energy conversion module. Successful scale-up to the micron-scale plays a crucial role in the real-time visualization and characterization of this module. Via an optimized fusion/electroformation approach we integrated bo3 oxidase in giant vesicles (GUVs) and determined its influence on mechanical properties. The reconstituted enzymes had preserved activity and exhibited unidirectional orientation, which led to the acidification of the synthetic compartments. The most remarkable finding is that the characteristics of hybrid membranes are not always intermediate between pure lipid- and pure polymer ones. Blending led to increased permeability, but after inserting the proton pump the compartments were surprisingly re-sealed. The measurements of active proton pumping and passive proton permeability were done in a microfluidic setup, which enabled monitoring of individual GUVs as well as better control of the experimental conditions. Moreover, we observed an interesting phenomenon beneficial for crowded membranes: While insertion of bo3 oxidase in soy PC decreased the fluidity, it exercised the opposite effect on the polymer by loosening its structure. The F1Fo-GUVs were prepared by the aforementioned procedure and the enzymatic activity was analyzed via outward proton pumping. Finally, respiratory-driven energy-converting GUVs were constructed. Sponsored by BMBF and MPG.

Viewpoint: Homeostasis as Inspiration-Toward Interactive Materials.

Homeostatic systems combine an ability to maintain integrity over time with an incredible capacity for interactive behavior. Fundamental to such systems are building blocks of "mini-homeostasis": feedback loops in which one component responds to a stimulus and another opposes the response, pushing the module to restore its original configuration. Particularly when they cross time and length scales, perturbation of these loops by external changes can generate diverse and complex phenomena. Here, it is proposed that by recognizing and implementing mini-homeostatic modules-often composed of very different physical and chemical processes-into synthetic materials, numerous interactive behaviors can be obtained, opening avenues for designing multifunctional materials. How a variety of controlled, nontrivial material responses can be evoked from even simple versions of such synthetic feedback modules is illustrated. Moreover, random events causing seemingly random responses give insights into how one can further explore, understand and control the full interaction space. Ultimately, material fabrication and exploration of interactivity become inseparable in the rational design of such materials. Homeostasis provides a lens through which one can learn how to combine and perturb coupled processes across time and length scales to conjure up exciting behaviors for new materials that are both robust and interactive.

Part-Based Enhanced Super Resolution Network for Low-Resolution Person Re-Identification

Person re-identification (REID) is an important task in video surveillance and forensics applications. Many previous works often build models on the assumption that they have same resolution cross different camera views, while it is divorced from reality. To increase the adaptability of person REID models, this paper focuses on the low-resolution person REID task to relax the impractical assumption when traditional low-resolution person REID models are under pixel-to-pixel supervision in low and high resolution pedestrian image pairs. In addition, they are easily influenced by the global background, illumination or pose variations across camera views. Therefore, we propose a Part-based Enhanced Super Resolution (PESR) network by employing a part division strategy and an enhanced generative adversarial network to boost the unpaired pedestrian image super resolution process. Specifically, the part-based super resolution network transforms low resolution image in probe into high resolution without any pixel-to-pixel supervision and the part-based synthetic feature extractor module can learn discriminative pedestrian feature representation for the generated high resolution images, which employ a part feature connection loss as constraint to conduct matching for person re-identification. Furthermore, evaluations on four public person REID datasets demonstrate the advantages of our method over the state-of-the-art ones.

Controlled deprotection and release of a small molecule from a compartmented synthetic tissue module

Synthetic tissues built from communicating aqueous droplets offer potential applications in biotechnology, however, controlled release of their contents has not been achieved. Here we construct two-droplet synthetic tissue modules that function in an aqueous environment. One droplet contains a cell-free protein synthesis system and a prodrug-activating enzyme and the other a small-molecule prodrug analog. When a Zn2+-sensitive protein pore is made in the first droplet, it allows the prodrug to migrate from the second droplet and become activated by the enzyme. With Zn2+ in the external medium, the activated molecule is retained in the module until it is released on-demand by a divalent cation chelator. The module is constructed in such a manner that one or more, potentially with different properties, might be incorporated into extended synthetic tissues, including patterned materials generated by 3D-printing. Such modules will thereby increase the sophistication of synthetic tissues for applications including controlled multidrug delivery.

Synthesis of Uracil-Iodonium(III) Salts for Practical Utilization as Nucleobase Synthetic Modules.

Iodonium(III) salts bearing uracil moieties have recently appeared in the literature, but their structural scope and utilization are limited because of their hygroscopic characteristics. In this study, we describe our detailed investigations for synthesizing a series of uracil iodonium(III) salts derived with various structural motifs and counterions. These new compounds have been utilized as attractive synthetic modules in constructing functionalized nucleobase and nucleosides.

Hysteretic Genetic Circuit for Detection of Proteasomal Degradation in Mammalian Cells.

Synthetic hysteretic mammalian gene circuits generating sustained cellular responses to transient perturbations provide important tools to investigate complex cellular behaviors and reprogram cells for a variety of applications, ranging from protein production to cell fate decisions. The design rules of synthetic gene circuits with controlled hysteretic behaviors, however, remain uncharacterized. To identify the criteria for achieving predictable control of hysteresis, we built a genetic circuit for detection of proteasomal degradation (Hys-Deg). The Hys-Deg circuit is based on a tetracycline-controlled transactivator (tTA) variant engineered to interface with the ubiquitin proteasome system (UPS). The tTA variant activates its own expression, generating a positive feedback loop that is triggered by expression of another tTA gene that is constitutively regulated. Guided by predictive modeling, we characterized the hysteretic response of the Hys-Deg circuit. We demonstrated that control of the hysteretic response is achieved by modulating the ratio of expression of constitutive to inducible tTA. We also showed that the system can be finely tuned through dosage of the inducer tetracycline to calibrate the circuit for detection of the desired levels of UPS activation. This study establishes the design rules for building a hysteretic genetic circuit with an autoregulatory feedback loop and provides a synthetic memory module that could be easily integrated into regulatory gene networks to study and engineer complex cellular behaviors.