Synthetic biology conceptualizes biological complexity as a network of biological parts, devices, and systems with predetermined functionalities and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesize and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms, can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multispecies communities. We argue for a shift in perspective from single organism-centred approaches to emphasizing the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities. Graphical Abstract.
Synthetic biology and advanced genetic engineering applications rely heavily on the efficient construction of large and complex DNA sequences. Current DNA synthesis technologies have limited capacity to efficiently generate ultralong oligonucleotides for complex gene construction, particularly those with extensive repetitive motifs and uneven base distribution. Here, we report a novel platform named UCOS (Ultralong Complex Oligonucleotides Synthesis) that enables the efficient synthesis of long, complex, and challenging DNA fragments. This platform employs nonporous silica microspheres as the solid support instead of the traditional controlled pore glass solid support, full-length enrichment based on 5' flank sequence hybridization and an error-removing enzyme for correct sequence selection, substantially enhancing the fidelity of intricate, ultralong oligonucleotides. Using this approach, we successfully synthesized challenging sequences ≤600 nt in length, encompassing tandem repeats and uneven base distributions. Overall, this novel platform demonstrates exceptional efficiency and reliability in handling ultralong DNA fragments with highly repetitive and complex features. This novel platform provides a strong foundation for advancing synthetic biology and metabolic engineering, showing great potential as a powerful tool for constructing challenging genes and enabling the customized synthesis of functional genetic elements for complex genetic programmes and synthetic genomics.
Course-based Undergraduate Research Experiences (CUREs) have emerged as a transformative approach to science education, expanding access to authentic research opportunities beyond the traditional undergraduate research assistant (URA) training. By embedding research into a curriculum, CUREs engage a broad and diverse population of students in a classroom environment that emphasizes experimental design, data analysis, and scientific communication. However, this has been difficult to develop for fields such as plant synthetic biology due to the long timescales of plant transformation. One avenue around this problem is to utilize a recent innovation that enables high throughput and rapid screening of guide RNA (gRNA) efficacy by leveraging viral-based delivery of gRNAs. In this work, we develop and validate a CURE with undergraduate students at Colorado State University (CSU). Students worked in teams to design and test efficacy of gRNAs targeting a Cas9-based transcriptional repressor to different regions of the promoters of the three GIBBERELLIN INSENSITIVE 1 genes (GID1a, GID1b, and GID1c) in Arabidopsis thaliana. Over the semester, students generated and analysed gene expression data to understand the efficiency of twelve new gRNAs. We further validated CURE student-identified gRNAs with an URA that assessed target gene expression and phenotypic outcomes in stable transgenic lines expressing synthetic transcription factor (SynTF) constructs with the strongest gRNAs from the class. We further describe the curriculum structure to facilitate adoption at other institutions and present student-generated datasets demonstrating the utility of VipariNama-based screening for identifying effective SynTF gRNAs for plant functional genomics and engineering.
Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.
Heparosan is a natural polymer with unique chemical and biological properties, that holds great promise for biomedical applications. The molecular weight (Mw) and polydispersion index (PDI) are critical factors influencing the performance of heparosan-based materials. Achieving precise control over the synthesis process to consistently produce heparosan with low Mw and low PDI can be challenging, as it requires tight regulation of reaction conditions, enzyme activity, and precursor concentrations. We propose a novel approach utilizing synthetic biology principles to precisely control heparosan biosynthesis in bacteria. Our strategy involves designing a biomolecular controller that can regulate the expression of genes involved in heparosan biosynthesis. This controller is activated by biosensors that detect heparosan precursors, allowing for fine-tuned control of the polymerization process. Through this approach, we foresee the implementation of this synthetic device, demonstrating the potential to produce low Mw and low PDI heparosan in the probiotic E. coli Nissle 1917 as a biosafe and biosecure biofactory. This study represents a significant advancement in the field of heparosan production, offering new opportunities for the development and manufacturing of biomaterials with tailored properties for diverse biomedical applications.
Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.
Synthetic biology has immense potential to ameliorate widespread environmental damage. The promise of such technology could, however, be argued to potentially risk the public, industry or governments not curtailing their environmentally damaging behavior or even worse exploit the possibility of this technology to do further damage. In such cases, there is the risk of a worse outcome than if the technology was not deployed. This risk is often couched as an objection to new technologies, that the technology produces a moral hazard. This paper describes how to navigate a moral hazard argument and mitigate the possibility of a moral hazard. Navigating moral hazard arguments and mitigating the possibility of a moral hazard will improve the public and environmental impact of synthetic biology.
Synthetic cells are a novel class of cell-like bioreactors, offering the potential for unique advancements in synthetic biology and biomedicine. To realize the potential of those technologies, synthetic cell-based drugs need to go through the drug approval pipeline. Here, we discussed several regulatory challenges, both unique to synthetic cells, as well as challenges typical for any new biomedical technology. Overcoming those difficulties could bring transformative therapies to the market and will create a path to the development and approval of cutting-edge synthetic biology therapies. Graphical Abstract.
The protocol for Extraction and Analysis of Small Yeast-Chromosomes (EASY-C) is a transformative, rapid, cost-effective, and user-friendly method designed for the efficient isolation of artificial synthetic mini-chromosomes (~42-52 kb) and large plasmids (~12 kb) from Saccharomyces cerevisiae wild-type and synthetic yeast strains (Sc2.0), and a range of nonconventional yeast (NCY) species. In this two-step workflow, the DNA from yeast is first extracted and transferred into bacteria, and then the circular DNA is recovered from the bacteria and subjected to downstream analysis, including long-read sequencing. The EASY-C protocol operates at small volumes (~1 mL) and requires less than 2.5 hours, allowing the use of standard commercial plasmid purification kits for bacterial plasmids. Under the tested conditions, the EASY-C methodology yielded clean DNA that could be digested and linearized prior to sequencing, resulting in a higher number of high-quality reads (~2000). The EASY-C protocol worked successfully for the extraction of a variety of constructs, including low-copy centromeric vectors (CEN/ARS), high-copy plasmids (pan/ARS), and artificial mini-chromosomes harbouring (CEN/ARS). It is also applicable to a variety of yeast species, including NCY such as Starmerella sp., Maudiozyma sp., and Kazachstania sp. Thanks to its precision, robustness, and simplicity, EASY-C equips researchers with a powerful, time-saving tool and cost-effective approach to accelerate the validation of a wide array of synthetic genetic and metabolic constructs engineered in vivo across diverse yeast species.
Scaffolds are powerful tools in synthetic biology used for various applications, from increasing yield to optimizing signalling specificity. Protein scaffolds can be built by fusing peptide binding domains (PBD) and attaching the peptide they bind to enzymes, inducing spatial proximity. Only a few PBD-peptide combinations have been tested in this context, and no combination produced a high yield in yeast, an important chassis in biotechnology. Therefore, there is a need for more exploration of PBD-peptide pairs to be used in this model. Scaffold characterization is challenging because it is often dependent on a model pathway with an output that is difficult to measure quantitatively. Here, we use the dihydrofolate reductase protein-fragment complementation assay (DHFR PCA) to study scaffolding efficiency in yeast, which allows to couple scaffolding efficiency with growth rate. First, we characterize the strength of PBD-peptide interactions (PPI) and the binding availability of the PBDs and peptides. Then, we test different scaffold architectures and expression levels to quantify the simultaneous binding of peptide pairs to the scaffold. We show that PPI strength of the weakest binding PBD-peptide pair is critical for scaffolding efficiency and that PPI strength is limited by low binding availability of some domains and peptides in vivo. Also, we find that slight architectural variations and expression levels have a significant impact on scaffolding efficiency detected by DHFR PCA. Finally, we used DHFR PCA approaches to characterize novel PBD-peptide pairs and we identified pairs to expand the sequence toolbox for scaffold design in yeast through DHFR PCA easy-to-read signal.
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Giant unilamellar vesicles (GUVs) provide a powerful model compartment for synthetic cells. However, a key challenge is the incorporation of membrane proteins that allow for transport, energy transduction, compartment growth and division. Here, we have successfully incorporated the membrane protein complex SecYEG-the key bacterial translocase that is essential for the incorporation of newly synthesized membrane proteins-in GUVs. Our method consists of fusion of small unilamellar vesicles containing reconstituted SecYEG into GUVs, thereby forming SecGUVs. These are suitable for large-scale experiments while maintaining a high protein:lipid ratio. We demonstrate that incorporation of SecYEG into GUVs does not inhibit its translocation efficiency. Robust membrane protein functionalized proteo-GUVs are promising and flexible compartments for use in the formation and growth of synthetic cells.
The paper addresses the application of engineering biology strategies and techniques to the automation of laboratory workflow-primarily in the context of biofoundries and biodesign applications based on the Design, Build, Test and Learn paradigm. The trend toward greater automation comes with its own set of challenges. On the one hand, automation is associated with higher throughput and higher replicability. On the other hand, the implementation of an automated workflow requires an instruction set that is far more extensive than that required for a manual workflow. Automated tasks must also be conducted in the order specified in the workflow, with the right logic, utilizing suitable biofoundry resources, and at scale-while simultaneously collecting measurements and associated data. The paper describes an approach to an automated workflow that is being trialed at the London Biofoundry at SynbiCITE. The solution represents workflows with directed graphs, uses orchestrators for their execution, and relies on existing standards. The approach is highly flexible and applies to not only workflow automation in single locations but also distributed workflows (e.g. for biomanufacturing). The final section presents an overview of the implementation-using the simple example of an assay based on a dilution, measurement, and data analysis workflow.
Recent advances in bottom-up synthetic biology have made it possible to reconstitute cellular systems from non-living components, yielding artificial cells with potential applications in industry, medicine and basic research. Although a variety of cellular functions and components have been reconstituted in previous studies, sustained biological energy production remains a challenge. ATP synthesis via ribulose-1,5-diphosphate carboxylase/oxygenase (RuBisCO), a central enzyme in biological CO2 fixation, holds potential as an energy production system, but its feasibility in a cell-free expression system has not yet been tested. In this study, we test RuBisCO expression and its activity-mediated ATP synthesis in a reconstituted Escherichia coli-based cell-free translation system. We then construct a system in which ATP is synthesized by RuBisCO activity in giant vesicles and used as energy for translation reactions. These results represent an advance toward independent energy production in artificial cells. Graphical Abstract.
γδ T cells are unconventional lymphocytes that bridge innate and adaptive immunity by combining recognition of stress-induced ligands independently of classical major histocompatibility complex molecules with the capacity to undergo clonal expansion and long-term adaptation. Their unusual ability to detect malignant transformation using semi-invariant T-cell receptors, butyrophilin recognition and natural killer-like receptors positions them as powerful effector cells in tumors that evade classical immune escape mechanisms. Furthermore, distinct γδ subsets have distinct phenotyping and specific tissue-residencies, which could be leveraged to modulate immunological responses. We evaluate engineered therapies and different experimental platforms for studying γδ T cell biology. We conclude that next-generation cancer treatments should strategically integrate γδ T cells into synthetic immunology, individualized modeling, and combinatorial regimes.
Sophisticated genetic engineering tasks such as protein domain grafting and multi-gene fusions are hampered by the lack of suitable vector backbones. In particular, many restriction sites are in the backbone outside the polylinker region (multiple cloning site; MCS) and thus unavailable for use, and the overall length of a plasmid correlates with poorer ligation efficiency. To address this need, we describe the design and validation of a collection of six minimal integrating or centromeric shuttle vectors for Saccharomyces cerevisiae, a widely used model organism in synthetic biology. We constructed the plasmids using de novo gene synthesis and consisting only of a yeast selection marker (HIS3, LEU2, TRP1, URA3, KanMX, or natMX6), a bacterial selection marker (ampicillin resistance), an origin of replication, and the MCS flanked by M13 forward and reverse sequences. We used truncated variants of these elements where available and eliminated all other sequences typically found in plasmids. The MCS consists of ten unique restriction sites. To our knowledge, at sizes ranging from ~2.6 to 3.5 kb, these are the smallest shuttle vectors described for yeast. Further, we removed common restriction sites in the open reading frames and terminators, freeing up ~30 cut sites in each plasmid. We named our pLS series in accordance with the well-known pRS vectors, which are on average 63% larger: pLS400, pLS410 (KanMX); pLS403, pLS413 (HIS3); pLS404, pLS414 (TRP1); pLS405, pLS415 (LEU2); pLS406, pLS416 (URA3); and pLS408, pLS418 (natMX6). This resource substantially simplifies advanced synthetic biology engineering in S. cerevisiae.
The ability to precisely insert DNA payloads into a genome enables the comprehensive engineering of cellular phenotypes and the creation of new biotechnologies. To achieve such modifications, the most widely used techniques rely on a host cell's native DNA repair mechanisms like homologous recombination, which hampers their broader use in organisms lacking these capabilities. Here, we explore the current landscape of genome integration systems with a particular focus on those that function in bacteria and are precise, self-contained, and portable, placing minimal requirements on the host cell. Through a historical analysis, we observe long-term use of recombineering technologies, a recent rise in the use of CRISPR-guided systems that consist of associated integrase machinery, and growing efforts to modify non-model organisms. Looking forward, we highlight some of the remaining challenges and how synthetic genomics may offer a way to create bacterial strains optimized for extensive long-term modification. As the field of synthetic biology sets its sights on real-world impact, the effective engineering of genomes will be critical to shaping the robust phenotypes that applications demand.
Synthetic microbial consortia can leverage their expanded enzymatic reach to tackle biotechnological challenges too complex for single strains, such as biosynthesis of complex secondary metabolites or waste plant biomass degradation and valorisation. The benefit of metabolic cooperation comes with a catch-installing stable interactions between consortium members. Here, we established a mutualistic relationship in the synthetic consortium of Pseudomonas putida strains through reciprocal processing of two disaccharides-cellobiose and xylobiose-obtainable from lignocellulosic residues. Two strains were engineered to hydrolyse and metabolize these sugars: one grows on xylose and hydrolyses cellobiose to produce glucose, while the other grows on glucose and cleaves xylobiose to produce xylose. This specialization allows each strain to provide essential growth substrate to its partner, establishing a mutualistic interaction, which can be termed reciprocal substrate processing. Key enzymes from Escherichia coli (xylose isomerase pathway) and Thermobifida fusca (glycoside hydrolases) were introduced into P. putida to broaden its carbohydrate utilization capabilities and arranged in a way to instal the strain cross-dependency. A mathematical model of the consortium assisted in predicting the effects of substrate composition, strain ratios, and protein expression levels on population dynamics. Our results demonstrate that modulating extrinsic factors such as substrate concentration can help in balancing fitness disparities between the strains, but achieving this by altering intrinsic factors such as glycoside hydrolase expression levels is much more challenging. This study presents reciprocal substrate processing as a strategy for establishing an obligate dependency between strains in the engineered consortium and offers valuable insights into overcoming the challenges of fostering synthetic microbial cooperation.
Glycolaldehyde (GA), the smallest sugar, has significant potential as a biomass-derived platform chemical and is a key metabolite in several synthetic pathways for one-carbon metabolism and new-to-nature photorespiration. This study introduces two metabolic schemes for engineering Escherichia coli into GA biosensors. Through creating GA-dependent auxotrophies, we link growth of these strains to GA-dependent biosynthesis of the essential vitamin pyridoxal-5-phosphate, and 2-ketoglutarate, respectively. We characterized and optimized these strains for the quantification of externally added GA from 2 µM to 1.5 mM. We also demonstrate the capability of these strains to detect GA that is produced intracellularly through different metabolic routes and from different substrates such as xylose, ethylene glycol, and glycolate. Our biosensors offer complementary sensitivities and features, opening up different applications in metabolic engineering and synthetic biology, which we demonstrate in a proof-of-principle by providing the first in vivo demonstration of the reduction of glycolate to GA by a new-to-nature route using engineered enzymes.
Continuous directed evolution is a powerful Synthetic Biology tool to engineer proteins with desired functions in vivo. Mimicking natural evolution, it involves repeated cycles of high-frequency mutagenesis, selection, and replication within platform cells, where the function of the target gene is tightly linked to the host cell's fitness. However, cells might escape the selection pressure due to the inherent flexibility of their metabolism, which allows for adaptation. Whole-proteome analysis as well as targeted proteomics offer valuable insights into global and specific cellular changes. They can identify modifications in the target protein and its interactors to help understand its evolution and network integration. Using the continuous evolution of the Arabidopsis thaliana methionine synthases AtMS1 and AtMS2 as an example, we show how mass spectrometry-based proteomics was able to assess the abundance of target enzymes, identify flaws in population construction, measure methionine metabolic adaptation, and allow informed decision-making in the evolution campaign.