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.
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.
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.
Cell-free biosensor systems offer a promising platform for portable diagnostics. However, most employ fluorescent reporter proteins that require complex instrumentation and can be affected by photo-bleaching and auto-fluorescence, limiting translatability. Electrochemical reporters do not suffer from these drawbacks. Here, we evaluate horseradish peroxidase (HRP) as a redox enzyme reporter for cell-free biosensor systems. HRP was synthesized in an E. coli cell-free transcription-translation system supplemented with hemin, calcium acetate, and commercial disulfide bond enhancers. The electrochemical detection of its activity was established by chronoamperometry, with H 2 O 2 as a substrate and tetramethylbenzidine as a redox mediator. Cell-free expressed HRP produced a strong steady state current compared to a catalytically inactive mutant and a no-template control. Kinetic analysis showed a K m for the cell-free expressed HRP close to that of the native enzyme. To explore the potential of HRP as an electrochemical reporter, we placed it under the control of a tetracycline-responsive regulatory promoter and demonstrated a 3-fold current increase in the presence of anhydrotetracycline. These results support HRP as an electrochemical reporter for cell-free biosensors, offering a practical alternative to optical reporters for future use in handheld analytical devices.
In biotechnological applications, it is often necessary to introduce genes or entire pathways into a host cell, which can create a significant metabolic burden on the host, limiting productivity. In this study, we systematically investigated the physiological stress responses of Pseudomonas putida during heterologous protein production using a modular monitoring system consisting of a plasmid encoding a heterologous protein fused to eGFP and a chromosomally integrated capacity reporter. Our findings reveal that translation is the main bottleneck, with translational capacity becoming saturated under high expression loads. While increasing the strength of the ribosome binding site improved protein production for non-burdensome proteins, this effect was not observed for larger fusion proteins. Variations in fusion protein size suggested that translational demand, rather than the overall mass of protein produced, determines metabolic burden. We further evaluated how resource availability affects protein expression by modifying the metabolic regime or supplementing with amino acids. While the carbon source affected cellular capacity, it did not significantly alter heterologous protein production. Amino acid supplementation alleviated the growth defects of MBPeGFP-producing cells and modestly improved protein production rates. Together, these findings emphasize that metabolic burden is influenced not only by the size of the produced protein but also by transcript architecture, resource allocation, and the physiological state of the host. Therefore, successful optimization of heterologous protein production requires a holistic approach integrating construct design with host physiology and cultivation strategies.
Whole-cell biosensors detecting the heavy metal arsenic have been widely studied for their potential in environmental monitoring. And while inducible biosensors have been shown to be an effective tool to tune the operational range, a thoroughly characterized inducible biosensor is currently lacking. Here, we present an Escherichia coli biosensor for arsenic in which the transcription factor (TF) gene arsR is inducible by naringenin, a plant-derived secondary metabolite. Increasing the naringenin concentration reduced the basal output while increasing both the dynamic range and sensing threshold of the biosensor dose-response curve, but the operational range appeared constrained by a fixed upper limit. Comparison with a previously published phenomenological model revealed good overall agreement between experimental data and model predictions, except for the behaviour of the maximum output and threshold. This work expands the biosensor toolbox with a profoundly characterized arsenic biosensor and raises a potential practical limit to dose-response curve engineering by tuning TF expression alone.
Guide RNA (gRNA) arrays can enable targeting multiple genomic loci simultaneously using CRISPR-Cas9. In this study, we present a streamlined and efficient method to rapidly construct gRNA arrays with up to 10 gRNA units in a single day. We demonstrate that gRNA arrays maintain robust functional activity across all positions, and can incorporate libraries of gRNAs, combining scalability and multiplexing. Our approach will streamline combinatorial perturbation research by enabling the economical and rapid construction, testing, and iteration of gRNA arrays. To facilitate the adoption of this approach, we have made a web tool to design oligo sequences necessary to assemble gRNA arrays.
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.
Programmable CRISPR-Cas9 nucleases have become invaluable tools for genome editing. However, off-target cleavage by these nucleases can lead to unintended changes in the edited genome. Detection of off-target sites is critical to make genome editing technology safe and predictable. Although current in vitro methods for off-target detection can identify these sites, they are time-consuming and complex. Here, we present CROFT-Seq (CRISPR nuclease off-target detection by sequencing), a sensitive, rapid, and affordable assay for the genome-wide detection of Cas9 off-target sites in vitro. CROFT-Seq performs comparably to the commonly used in vitro methods and serves as a valuable and efficient tool for the rapid assessment of genome-editing nuclease specificity. Notably, a high proportion of the top-ranked off-target sites identified by CROFT-Seq were validated in cells, highlighting its strong predictive performance.
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.
Fine-tuning of gene expression is often required to achieve competitive production levels in microbial cell factories. Several orthogonal expression systems based on heterologous regulatory parts have been developed for Saccharomyces cerevisiae. In laboratory conditions the systems demonstrate predictable results, but few expression systems have been tested in industrial conditions. Here, a new expression system based on the bacterial gene cusR was developed for S. cerevisiae, and two previous developed systems, the strong Bm3R1-based system and the quinic acid inducible Q-system, were adapted for compatibility with the Yeast MoClo Toolkit. The bacterial transcription factors CusR and Bm3R1 acted as DNA binding domains, and fused to a viral activation domain, they functioned as transcriptional activators. The Q-system is originally from Neurospora crassa and consists of a transcriptional repressor, QS, which in the absence of quinic acid blocks the activity of a transcriptional activator, QF2. Quinic acid binds to QS, inhibiting QS from blocking the activity of QF2 in a dose-dependent manner. The gene expression systems were assessed in industrially relevant conditions, proving a predictable performance at low pH. The performance of the constitutive systems was predictable also at high temperature and in a synthetic lignocellulosic hydrolysate medium. Altogether, the MoClo-compatible expression systems enable fast construction of fine-tuned production pathways for S. cerevisiae cell factories used for industrial applications.
Modular cloning systems streamline laboratory workflows by consolidating genetic 'parts' into reusable and modular collections, enabling researchers to fast-track strain construction. The GoldenBraid 2.0 modular cloning system utilizes the cutting property of type IIS restriction enzymes to create defined genetic 'grammars', which facilitate the reuse of standardized genetic parts and assembly of genetic parts in the right order. Here, we present a GoldenBraid 2.0 toolkit of genetic parts designed to accelerate cloning in the model bacterium Escherichia coli. This toolkit features 478 pre-made parts for gene expression and protein tagging as well as strains to expedite cloning and strain construction, enabling researchers to quickly generate functional plasmid-borne or chromosome-integrated expression constructs. In addition, we provide a complete laboratory manual with overviews of common reagent recipes, E. coli protocols, and community resources to promote toolkit utilization. By streamlining the assembly process, this resource will reduce the financial and temporal burdens of cloning and strain building in many laboratory settings.
Cell-free expression (CFE) systems are emerging as a powerful tool in synthetic biology, with diverse applications from prototyping genetic circuits to serving as a platform for point-of-care biosensors. When multiple genes need to be expressed in the same CFE reaction, their DNA templates (often added as plasmids) are generally assumed to behave independently of each other, with neither affecting the other's expression. However, recent work in Escherichia coli CFE systems shows that multiple aspects of these templates can lead to antagonistic or synergistic interactions in expression levels of individual genes, a phenomenon referred to as plasmid crosstalk. Plasmid crosstalk can confound efforts for precise engineering of genetic circuits and even give rise to misleading observations about circuit function. Unfortunately, current mathematical and computational models are incapable of reproducing critical aspects of plasmid crosstalk. To address this gap, we created an ordinary differential equation model incorporating mechanisms to account for competition for transcription, translation, and degradation resources, as well as toxic molecule build-up. This model can recapitulate the predominant observed phenomena of plasmid crosstalk. Simulation results and subsequent experimental validation provided insights into the different sources of burden and interactions in CFE systems, including that translation is negatively impacted by macromolecular crowding caused by possibly both transcription and translation. This model thus enables deeper understanding of CFE systems and could serve as a useful tool for future CFE application design.
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.
Directed evolution is a technique that allows for protein engineering without prior knowledge. Continuous directed evolution employs gene-specific hypermutation tied to functional selection within a single cell, enabling a broad search of sequence space for gene variants with improved or novel functions. However, currently available techniques for continuous directed evolution can be inflexible or laborious to establish. To address this issue, we present a modular toolkit for deaminase-fused viral RNA polymerase continuous directed evolution, based on Golden Gate assembly. We include an alternative RNA polymerase from phage SP6 and show that it can introduce gene-specific mutations. This work builds on the available repertoire of synthetic biology techniques, brings accessibility and versatility to directed evolution, and enables researchers to build custom and complex plasmids for their own evolutionary campaigns.
The utilization of biocatalysts in biotechnological applications often necessitates their heterologous expression in suitable host organisms. However, the range of standardized microbial hosts for recombinant protein production remains limited, with most being mesophilic and suboptimal for certain protein types. Although the thermophilic bacterium Thermus thermophilus has long been established as a valuable extremophile host, thanks to its high-temperature tolerance, robust growth, and extensively characterized proteome, its genetic toolkit has predominantly depended on a limited set of native promoters. To overcome this bottleneck, we have expanded the available regulatory repertoire in T. thermophilus by developing novel artificial 5$<^>\prime $ regulatory sequences (ARESs). In this study, we applied our Gene Expression Engineering platform to engineer 53 artificial ARES in T. thermophilus. These ARES, which comprise both promoter and 5$<^>\prime $ untranslated regions, were functionally characterized in both T. thermophilus and Escherichia coli, revealing distinct host-specific expression patterns. Furthermore, we demonstrated the utility of these ARES by achieving high-level expression of thermostable proteins, including $\beta $-galactosidase, a superfolder citrine fluorescent protein, and phytoene synthase. A bioinformatic analysis of the novel sequences has also been carried out indicating that the ARES possess markedly lower Guanine (G) and Cytosine (GC) content compared to native promoters. This study contributes to expanding the genetic toolkit for recombinant protein production by providing a set of functionally validated ARES, enhancing the versatility of T. thermophilus as a synthetic biology chassis for thermostable protein expression.
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.
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.
DNA has emerged as a promising material to address growing data storage demands. We recently demonstrated a structure-based DNA data storage approach where DNA probes are spatially oriented on the surface of DNA origami and decoded using DNA-PAINT. In this approach, larger origami structures could improve the efficiency of reading and writing data. However, larger origami require long single-stranded DNA scaffolds that are not commonly available. Here, we report the engineering of a novel longer DNA scaffold designed to produce a larger rectangle origami needed to expand the origami-based digital nucleic acid memory (dNAM) approach. We confirmed that this scaffold self-assembled into the correct origami platform and correctly positioned DNA data strands using atomic force microscopy and DNA-PAINT super-resolution microscopy. This larger structure enables a 67% increase in the number of data points per origami and will support efforts to efficiently scale up origami-based dNAM.
