ARPA-H recently started its operation with a great vision and potential to revolutionize the biomedical field and research. To help them shape the future biomedical field and community and to make the biotechnology community aware of this new, exciting funding agency, I share my vision after collecting many comments and opinions from researchers, policymakers, journal editors, and funding agency directors. As DARPA has made huge impacts on science, engineering, and society, ARPA-H will do so by paying attention to and implementing stakeholders’ suggestions. I also suggest that the biotechnology community members, including academic researchers, people in the biotechnology industry, and policymakers, should nurture innovation and diversity.

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input–output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

The COVID-19 pandemic has stimulated the scientific world to intensify virus-related studies aimed at the development of quick and safe ways of detecting viruses in the human body, studying the virus–antibody and virus–cell interactions, and designing nanocarriers for targeted antiviral therapies. However, research on dangerous viruses can only be performed in certified laboratories that follow strict safety procedures. Thus, developing deactivated virus constructs or safe-to-use virus-like objects, which imitate real viruses and allow performing virus-related studies in any research laboratory, constitutes an important scientific challenge. Such species, called virus-like particles (VLPs), contain instead of capsids with viral DNA/RNA empty or synthetic cores with real virus proteins attached to them. We have developed a method for the preparation of VLPs imitating the virus responsible for the COVID-19 disease: the SARS-CoV-2. The particles have Au cores surrounded by “coronas” of S1 domains of the virus’s spike protein. Importantly, they are safe to use and specifically interact with SARS-CoV-2 antibodies. Moreover, Au cores exhibit localized surface plasmon resonance (LSPR), which makes the synthesized VLPs suitable for biosensing applications. During the studies, the effect allowed us to visualize the interaction between the VLPs and the antibodies and identify the characteristic vibrational signals. What is more, additional functionalization of the particles with a fluorescent label revealed their potential in studying specific virus-related interactions. Notably, the universal character of the developed synthesis method makes it potentially applicable for fabricating VLPs imitating other life-threatening viruses.

Glycosylation is an important method of modifying natural products and is usually catalyzed by uridine 5′-diphosphate (UDP)-glycosyltransferase. UDP-β-l-arabinose (UDP-Ara) confers specific functions to natural products such as pentacyclic triterpenoids. However, UDP-arabinosyltransferase with high regioselectivity toward pentacyclic triterpenoids has rarely been reported. In addition, UDP-Ara is mainly biosynthesized from UDP-α-d-glucose (UDP-Glc) through several reaction steps, resulting in the high cost of UDP-Ara. Herein, UGT99D1 was systematically characterized for specifically transferring one moiety of arabinose to the C-3 position of typical pentacyclic triterpenoids. Subsequently, 15 enzymes from plants, mammals, and microorganisms were characterized, and a four-enzyme cascade comprising sucrose synthase, UDP-Glc dehydrogenase, UDP-α-d-glucuronic acid decarboxylase, and UDP-Glc 4-epimerase was constructed to transform sucrose into UDP-Ara with UDP recycling. This system was demonstrated to efficiently produce the arabinosylated derivative (Ara-BA) of typical pentacyclic triterpenoid betulinic acid (BA). Finally, the in vitro cytotoxicity tests indicated that Ara-BA showed much higher anticancer activities than BA. The established arabinosylation platform shows the potential to enhance the pharmacological activity of natural products.

Saccharomyces cerevisiae has been extensively used as a convenient synthetic biology chassis to reconstitute fungal polyketide biosynthetic pathways. Despite progress in refactoring these pathways for expression and optimization of the yeast production host by metabolic engineering, product yields often remain unsatisfactory. Such problems are especially acute when synthetic biological production is used for bioprospecting via genome mining or when chimeric fungal polyketide synthases (PKSs) are employed to produce novel bioactive compounds. In this work, we demonstrate that empirically balancing the expression levels of the two collaborating PKS subunits that afford benzenediol lactone (BDL)-type fungal polyketides is a facile strategy to improve the product yields. This is accomplished by systematically and independently altering the copy numbers of the two plasmids that express these PKS subunits. We applied this plasmid copy number engineering strategy to two orphan PKSs from genome mining where the yields of the presumed BDL products in S. cerevisiae were far too low for product isolation. This optimization resulted in product yield improvements of up to 10-fold, allowing for the successful isolation and structure elucidation of new BDL analogues. Heterocombinations of these PKS subunits from genome mining with those from previously identified BDL pathways led to the combinatorial biosynthesis of several additional novel BDL-type polyketides.

Transcriptional factors-based biosensors are commonly used in metabolic engineering for inducible control of gene expression and related applications such as high-throughput screening and dynamic pathway regulations. Mining for novel transcriptional factors is essential for expanding the usability of these toolsets. Here, we report the identification, characterization, and engineering of a phenolic acid responsive regulator PadR from Bacillus amyloliquefaciens (BaPadR). This BaPadR-based biosensor system showed a unique ligand preference and exhibited a high output strength comparable to that of commonly used inducible expression systems. Through engineering the DNA binding region of BaPadR, we further enhanced the dynamic range of the biosensor system. The DNA sequences that are responsible for BaPadR recognition were located by promoter truncation and hybrid promoter building. To further explore the tunability of the sensor system, base substitutions were performed on the BaPadR binding region of the phenolic acid decarboxylase promoter (PpadC) and the hybrid promoter. This novel biosensor system can serve as a valuable tool in future synthetic biology applications.

Osthole is a coumarin compound found in the traditional Chinese medicine Cnidium monnieri. Extensive studies have shown that osthole exhibits many medicinal properties, and recently, researchers have found that it possesses potent airway-relaxation activity by inhibiting phosphodiesterase 4D activity, making it a potential novel bronchodilator that does not target β2-adrenoceptors for asthma treatment. Here, we report the complete biosynthesis of osthole in engineered yeast. We created an umbelliferone (UMB)-producing strain by reconstituting the complete UMB pathway in yeast. We found that coumarin synthase (COSY) is essential for the conversion of 2′,4′-dihydroxycinnamoyl-CoA into UMB in yeast; this conversion has been treated as a spontaneous step in previously reported UMB-producing microbials. By introducing downstream prenyltransferase and methyltransferase genes and addressing problems such as protein expression and cofactor supply to fulfill the downstream steps, complete biosynthesis of osthole was achieved. Finally, through metabolic engineering, to ensure precursor supply, and the debugging of rate-limited steps, the osthole titer reached 108.10 mg/L in shake flasks and 255.1 mg/L in fed-batch fermentation. Our study is the first to produce osthole using engineered microbes, providing a blueprint for the supply of plant-derived osthole via microbial fermentation, which will remove the barriers of resource limitations for osthole-based drug development.

As the impacts of engineering biology grow, it is important to introduce the field early and in an accessible way. However, teaching engineering biology poses challenges, such as limited representation of the field in widely used scientific textbooks or curricula, and the interdisciplinary nature of the subject. We have created an adaptable curriculum module that can be used by anyone to teach the basic principles and applications of engineering biology. The module consists of a versatile, concept-based slide deck designed by experts across engineering biology to cover key topic areas. Starting with the design, build, test, and learn cycle, the slide deck covers the framework, core tools, and applications of the field at an undergraduate level. The module is available for free on a public website and can be used in a stand-alone fashion or incorporated into existing curricular materials. Our aim is that this modular, accessible slide deck will improve the ease of teaching current engineering biology topics and increase public engagement with the field.

Marine red algal biomass is a promising feedstock for sustainable production of value-added chemicals. However, the major constituents of red algal biomass, such as agar and carrageenan, are not easily assimilated by most industrial metabolic chassis developed to date. Synthetic biology offers a solution by utilizing nonmodel organisms as metabolic chassis for consolidated biological processes. In this study, the marine heterotrophic bacterium Pseudoalteromonas atlantica T6c was harnessed as a metabolic chassis to produce value-added chemicals from the affordable red algal galactans or agaropectin, a byproduct of industrial agarose production. To construct a heterologous gene expression device in P. atlantica T6c, promoters related to agar metabolism were screened from the differentially expressed genes using RNA-Seq analysis. The expression device was built and tested with selected promoters fused to a reporter gene and tuned by incorporation of a cognate repressor predicted from the agar-specific polysaccharide utilization locus. The feasibility of the marine bacterial metabolic chassis was examined by introducing the biosynthetic gene clusters of β-carotene and violacein. Our results demonstrate that the metabolic chassis platform enables direct conversion of low-cost red algal galactans or industrial waste agaropectin into valuable bioactive pigments without any pretreatment of biomass. The developed marine bacterial chassis could potentially be used in a biorefinery framework to produce value-added chemicals from marine algal galactans.

Biomolecular condensates are a promising platform for synthetic cell formation and constitute a potential missing link between the chemical and cellular stage of the origins of life. However, it has proven challenging to integrate complex reaction networks into biomolecular condensates, such as a cell-free in vitro transcription–translation (IVTT) system. Integrating IVTT into biomolecular condensates successfully is one precondition for condensation-based synthetic cell formation. Moreover, it would provide a proof of concept that biomolecular condensates are in principle compatible with the central dogma, one of the hallmarks of cellular life. Here, we have systemically investigated the compatibility of eight different (bio)molecular condensates with IVTT incorporation. Of these eight candidates, we have found that a green fluorescent protein-labeled, intrinsically disordered cationic protein (GFP-K72) and single-stranded DNA (ssDNA) can form biomolecular condensates that are compatible with up to μM fluorescent protein expression. This shows that biomolecular condensates can indeed integrate complex reaction networks, confirming their use as synthetic cell platforms and hinting at a possible role in the origin of life.

Genetically encoded optical sensors and advancements in microscopy instrumentation and techniques have revolutionized the scientific toolbox available for probing complex biological processes such as release of specific neurotransmitters. Most genetically encoded optical sensors currently used are based on fluorescence and have been highly successful tools for single-cell imaging in superficial brain regions. However, there remains a need to develop new tools for reporting neuronal activity in vivo within deeper structures without the need for hardware such as lenses or fibers to be implanted within the brain. Our approach to this problem is to replace the fluorescent elements of the existing biosensors with bioluminescent elements. This eliminates the need of external light sources to illuminate the sensor, thus allowing deeper brain regions to be imaged noninvasively. Here, we report the development of the first genetically encoded neurotransmitter indicators based on bioluminescent light emission. These probes were optimized by high-throughput screening of linker libraries. The selected probes exhibit robust changes in light output in response to the extracellular presence of the excitatory neurotransmitter glutamate. We expect this new approach to neurotransmitter indicator design to enable the engineering of specific bioluminescent probes for multiple additional neurotransmitters in the future, ultimately allowing neuroscientists to monitor activity associated with a specific neurotransmitter as it relates to behavior in a variety of neuronal and psychiatric disorders, among many other applications.

Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.

One strategy to decrease both the consumption of crude oil and environmental damage is through the production of bioethanol from biomass. Cellulolytic enzyme stability and enzymatic hydrolysis play important roles in the bioethanol process. However, the gradually increased ethanol concentration often reduces enzyme activity and leads to inactivation, thereby limiting the final ethanol yield. Herein, we employed an optimized Two-Gene Recombination Process (2GenReP) approach to evolve the exemplary cellulase CBHI for practical bioethanol fermentation. Two all-round CBHI variants (named as R2 and R4) were obtained with simultaneously improved ethanol resistance, organic solvent inhibitor tolerance, and enzymolysis stability in simultaneous saccharification and fermentation (SSF). Notably, CBHI R4 had a 7.0- to 34.5-fold enhanced catalytic efficiency (kcat/KM) in the presence/absence of ethanol. Employing the evolved CBHI R2 and R4 in the 1G bioethanol process resulted in up to 10.27% (6.7 g/L) improved ethanol yield (ethanol concentration) than non-cellulase, which was far more beyond than other optimization strategies. Besides bioenergy fields, this transferable protein engineering routine holds the potential to generate all-round enzymes that meet the requirement in biotransformation and bioenergy fields.

The N-terminal modification of nascent proteins, such as acetylation and myristoylation, is one of the most abundant post-translational modifications. To analyze the function of the modification, it is important to compare the modified and unmodified proteins under defined conditions. However, it is technically difficult to prepare unmodified proteins because cell-based systems contain endogenous modification systems. In this study, we developed a cell-free method to conduct N-terminal acetylation and myristoylation of nascent proteins in vitro using a reconstituted cell-free protein synthesis system (PURE system). Proteins synthesized using the PURE system were successfully acetylated or myristoylated in a single-cell-free mixture in the presence of modifying enzymes. Furthermore, we performed protein myristoylation in giant vesicles, which resulted in their partial localization to the membrane. Our PURE-system-based strategy is useful for the controlled synthesis of post-translationally modified proteins.

It is impractical to develop a new parts collection for every potential host organism. It is well-established that gene expression parts, like genes, are qualitatively transferable, but there is little quantitative information defining transferability. Here, we systematically quantified the behavior of a parts set across multiple hosts. To do this, we developed a broad host range (BHR) plasmid system compatible with the large, modular CIDAR parts collection for E. coli, which we named openCIDAR. This enabled testing of a library of DNA constructs across the Pseudomonadota─Escherichia coli, Pseudomonas putida, Cupriavidus necator, and Komagataeibacter nataicola. Part performance was evaluated with a standardized characterization procedure that quantified expression in terms of molecules of equivalent fluorescein (MEFL), an objective unit of measure. The results showed that the CIDAR parts enable graded gene expression across all organisms─meaning that the same parts can be used to program E. coli, P. putida, C. necator, and K. nataicola. Most parts had a similar expression trend across hosts, although each organism had a different average gene expression level. The variability is enough that to achieve the same MEFL in a different organism, a lookup table is required to translate a design from one host to another. To identify truly divergent parts, we applied linear regression to a combinatorial set of promoters and ribosome binding sites, finding that the promoter J23100 behaves very differently in K. nataicola than in the other hosts. Thus, it is now possible to evaluate any CIDAR compatible part in three other hosts of interest, and the diversity of these hosts implies that the collection will also be compatible with many other Proteobacteria (Pseudomonadota). Furthermore, this work defines an approach to generalize modular synthetic biology parts sets beyond a single host, implying that only a few parts sets may be needed to span the tree of life. This will accelerate current efforts to engineer diverse species for environmental, biotechnological, and health applications.

Optogenetic systems use genetically encoded light-sensitive proteins to control cellular processes. This provides the potential to orthogonally control cells with light; however, these systems require many design–build–test cycles to achieve a functional design and multiple illumination variables need to be laboriously tuned for optimal stimulation. We combine laboratory automation and a modular cloning scheme to enable high-throughput construction and characterization of optogenetic split transcription factors in Saccharomyces cerevisiae. We expand the yeast optogenetic toolkit to include variants of the cryptochromes and enhanced Magnets, incorporate these light-sensitive dimerizers into split transcription factors, and automate illumination and measurement of cultures in a 96-well microplate format for high-throughput characterization. We use this approach to rationally design and test an optimized enhanced Magnet transcription factor with improved light-sensitive gene expression. This approach is generalizable to the high-throughput characterization of optogenetic systems across a range of biological systems and applications.

Burkholderia β-Proteobacteria are emerging sources of natural products. We are interested in developing Burkholderia sp. FERM BP-3421 into a synthetic biology chassis to facilitate natural product discovery. FERM BP-3421 produces autologous spliceostatins on gram per liter scale. We reasoned that transcription factors and promoters that regulate spliceostatin biosynthesis would provide valuable parts for heterologous expression. Herein we demonstrate that fr9A encodes a pathway-specific transcriptional activator of spliceostatin biosynthesis. In-frame deletion of fr9A abolished spliceostatin production, which was restored by genetic complementation. Using transcriptomics and green fluorescent protein (GFP) reporter assays, we identified four fr9 promoters, three of which are activated by LuxR-type regulator Fr9A. We then constructed an Fr9A-regulated promoter system that was compared to benchmarks and effectively applied for GFP and capistruin lasso peptide expression in an optimized host background. Our findings enrich the genetic toolbox for optimizing heterologous expression and promoting the discovery and development of natural products from Burkholderia bacteria.

Geraniol is a class of natural products that are widely used in the aroma industry due to their unique aroma. Here, to achieve the synthesis of geraniol and alleviate the intense competition from the yeast ergosterol pathway, a transcription factor-mediated ergosterol feedback system was developed in this study to autonomously regulate ergosterol metabolism and redirect carbon flux to geraniol synthesis. In addition, the modification of ergosterol-responsive promoters, the optimization of transcription factor expression intensity, and stepwise metabolic engineering resulted in a geraniol titer of 531.7 mg L–1. For sustainable production of geraniol, we constructed a xylose assimilation pathway in Candida glycerinogenes (C. glycerinogenes). Then, the xylose metabolic capacity was ameliorated and the growth of the engineered strain was rescued by activating the pentose phosphate (PP) pathway. Finally, we obtained 1091.6, 862.4, and 921.8 mg L–1 of geraniol in a 5 L bioreactor by using pure glucose, simulated wheat straw hydrolysates, and simulated sugarcane bagasse hydrolysates, with yields of 47.5, 57.9, and 59.1 mg g–1 DCW, respectively. Our study demonstrated that C. glycerinogenes has the potential to produce geraniol from lignocellulosic biomass, providing a powerful tool for the sustainable synthesis of other valuable monoterpenes.

1,3-Propanediol (1,3-PDO), an important dihydric alcohol, is widely used in textiles, resins, and pharmaceuticals. More importantly, it can be used as a monomer in the synthesis of polytrimethylene terephthalate (PTT). In this study, a new biosynthetic pathway is proposed to produce 1,3-PDO using glucose as a substrate and l-aspartate as a precursor without the addition of expensive vitamin B12. We introduced a 3-HP synthesis module derived from l-aspartate and a 1,3-PDO synthesis module to achieve the de novo biosynthesis. The following strategies were then pursued that included screening key enzymes, optimizing the transcription and translation levels, enhancing the precursor supply of l-aspartate and oxaloacetate, weakening the tricarboxylic acid (TCA) cycle, and blocking competitive pathways. We also used transcriptomic methods to analyze the different gene expression levels. Finally, an engineered Escherichia coli strain produced 6.41 g/L 1,3-PDO with a yield of 0.51 mol/mol glucose in a shake flask and 11.21 g/L in fed-batch fermentation. This study provides a new pathway for production of 1,3-PDO.

Cell-free expression (CFE) systems are fundamental to reconstituting metabolic pathways in vitro toward the construction of a synthetic cell. Although an Escherichia coli-based CFE system is well-established, simpler model organisms are necessary to understand the principles behind life-like behavior. Here, we report the successful creation of a CFE system derived from JCVI-syn3A (Syn3A), the minimal synthetic bacterium. Previously, high ribonuclease activity in Syn3A lysates impeded the establishment of functional CFE systems. Now, we describe how an unusual cell lysis method (nitrogen decompression) yielded Syn3A lysates with reduced ribonuclease activity that supported in vitro expression. To improve the protein yields in the Syn3A CFE system, we optimized the Syn3A CFE reaction mixture using an active machine learning tool. The optimized reaction mixture improved the CFE 3.2-fold compared to the preoptimized condition. This is the first report of a functional CFE system derived from a minimal synthetic bacterium, enabling further advances in bottom-up synthetic biology.